Proceedings of the Joint
Conference on Language
Evolution (JCoLE)
Kanazawa, Japan 2022

Editors
Andrea Ravignani
Rie Asano
Daria Valente
Francesco Ferretti
Stefan Hartmann
Misato Hayashi
Yannick Jadoul
Mauricio Martins

Yoshei Oseki
Evelina Daniela Rodrigues
Olga Vasileva
Slawomir Wacewicz

The Evolution of Language
Proceedings of the Joint Conference on Language
Evolution (JCoLE)

Editors:

Andrea Ravignani

Yannick Jadoul

Rie Asano

Mauricio Martins

Daria Valente

Yohei Oseki

Francesco Ferretti

Evelina Daniela Rodrigues

Stefan Hartmann

Olga Vasileva

Misato Hayashi

Sªawomir Wacewicz

JCoLE Scientic Committee
September 5, 2022

Published by:
Joint Conference on Language Evolution (JCoLE)
Max Planck Institute for Psycholinguistics
Wundtlaan 16525 XD Nijmegen
The Netherlands

Proceedings of the Joint Conference on Language Evolution (JCoLE)
Copyright status of individual papers is indicated on their rst page. Allother sections are licensed under a Creative Commons BY-SA 4.0 license.
Cover photography and gures by Juan Olvido Perea García, Theresa
Matzinger, Hironori Nakatani, Serena Nicchiarelli, Marie-Therese Pekny,
Florian Römer, Genta Toya, and Daria Valente, licensed under a Creative
Commons BY-ND 4.0 license.

ISSN 2666-917X
doi:10.17617/2.3398549

A digital copy of this volume will remain available for download at:

http://www.evolang.org/jcole2022_proceedings/jcole2022_proceedings.pdf

iiPrefaces
No one thought that September 2022 would be the best time to host aninternational conference. But we were proud to have hosted it. Remember, when we hosted Evolng in Kyoto, Japan in 2012, it was only one
year since the devastating earthquake and nuclear accident in Fukushima.
Nevertheless, researchers from all over the world contributed greatly to theprogress of the eld by engaging in lively discussions about the origin andevolution of language. All participants must have keenly felt that sciencecan only advance when scientists gather face-to-face for discussions.
And this year. For those of us interested in the origin and evolution of
language, the last two and a half years have been particularly challenging.
Coronaviruses have threatened our way of life and severely interfered with
face-to-face communication. This is due to our language that developed
technologies for telecommunication and international travels. This is sopity. The war that began in Eastern Europe cruelly shattered the assumption that scientic and technological progress should make us peaceful beings. This is also due to our language. This also is so pity. That is why wedecided that this is the year we should hold an international conference
face-to-face.
It was during the corona disaster that this plan emerged. But frankly,
we did not expect the corona disaster to last this long. Nor did we expect
a war to break out in Eastern Europe. And the situation itself was getting
more and more dicult. Nevertheless, we chose to hold this international
conference.
It is precisely because of this situation that researchers interested in
the origin and evolution of language should get together.

I wanted tothink about what exactly language can do for us in this situation. Does
language make us happy or unhappy?

What kind of communication ispossible for us? Can we have productive discussions with people face toface or electronically?

This international conference provided a unique

opportunity to test these questions in action. Everyone will have his or
her own impressions.

What we gained will be dierent for each of us.

Nevertheless, we scientists are happy to have had this conference. We will
never give up discussing.
I would like to thank people in Evolang, Protolang, and Evolinguistics
for their support of this idea. I thank all my colleagues in Evolinguistics
that made this possible. I thank Professor Takashi Hashimoto for making
this happen in the beautiful city of Kanazawa. I am particularly thankfuliii

to Dr.

Rie Asano for her incredible power and skill in getting people

together to make this happen. As long as someone like her exists, we cankeep science going on.

September 2022
Joint Conference on Language Evolution
Kazuo Okanoya, Director

iv

The Joint Conference on Language Evolution was held in Kanazawa,
Japan on September 5th  8th 2022, organised by Evolang, Protolang, andEvolinguistics.
This conference was special in 3 respects. It included in-person attendance, after more than 2 years of online only language evolution events.
It pioneered the hybrid modality, with all the benets - but also logistic
issues - it entails. It was the rst time in the history of language evolution
research that three of the main societies/bodies (Evolang, Protolang andEvolinguistics) came together to organize a joint conference.
As always, the success of the event was, among other things, facilitatedby the team eort and synergy of ve bodies: the permanent committees, the local organizers, the hybrid conference design and online support
team, the scientic committee, and the panel of reviewers.

The perma-

nent committees of Evolang (headed by Erica Cartmill and Simon Kirby)

and Protolang, and Evolinguistics project leaders, have always been keen
to provide advice and support when needed. Many members of the permanent committees are our mentors or peers, and they keep making theworld of language evolution a better place. The local organizers and the
hybrid conference design and online support team have worked hard to
make the conference possible.

Kudos to this cohesive team for turninga `Joint Conference on Language Evolution' idea into reality.

The sci-

entic committee was in charge of editing and reviewing all contributed

abstracts and papers. The JCoLE scientic committee aimed at a strong
involvement of early career researchers in the language evolution researchcommunity. The committee this year spanned several countries and institutions, featuring members at various stages including PhD, postdoc andearly PI. By building on the expertise of existing members and recruiting new ones, we really tried to achieve diversity of scientic backgrounds.
The elds and perspective covered by the scientic committee span, among
other things: developmental psychology, scientic communication, classical linguistics, speech sciences, eld research (both in humans and otherspecies), cognitive neuroscience, gesture, articial intelligence, computational modelling, genetics, anthropology, acoustics, and music cognition.
The conference received many high quality submissions, making the hard
work of our reviewers especially important. This volume contains many
contributions from various disciplines: syntax, semantics, speech sciences,(developmental) psychology, genetics, bioacoustics, anthropology, animal
behaviour, neuroscience, and historical linguistics. In selecting contributions we have adopted two main guidelines. First, reviews of submissions
were double-blind, avoiding potential biases. Second, contributions werevselected not only based on reviewers' scores, but also on the reviewers' condence in the topic and the dispersion of their scores in terms of statisticalmeasures. The local organising committee, the hybrid conference designand online support team, the reviewers, the scientic committee and thepermanent committees all voluntarily contribute their time. Thanks to all
for acting in coordination to ensure we did the best possible job we could,
considering our other commitments.
Members from Evolang, Protolang and Evolinguistics will surely ndnovelties and dierences in JCoLE 2022 when compared to previous editions of individual conferences.

We hope you all enjoy many aspects of

those, and let us kindly know about possible improvements.

The programme committee members
Andrea Ravignani, Rie Asano, Daria Valente, Francesco
Ferretti,

Stefan,Hartmann,

MisatoHayashi,

Yannick

Jadoul, Mauricio Martins, Yohei Oseki, Evelina Daniela
Rodrigues, Olga Vasileva, Sªawomir WacewiczviPermanent Organizing Committees
Evolang Luke McCrohon Kazuo Okanoya
 Thom Scott-Phillips
 Andrea Ravignani
 Nikolaus Ritt
 Kenny Smith Maggie Tallerman Natalie Uomini
 Sªawomir Wacewicz
 Przemysªaw ywiczy«ski

 Andreas Baumann
 Rudolf Botha Christine Cuskley
 Erica Cartmill
 Jean-Louis Dessalles Ramon Ferrer i Cancho Tecumseh Fitch
 Jim Hurford
 Simon Kirby
 Chris Knight Heidi Lyn

Protolang Ines Adornetti
 Cedric Boeckx
 Piotr Chruszczewski
 Evelina Daniela Rodrigues
 Francesco Ferretti Nathalie Gontier
 Stefan Hartmann

 Sverker Johansson
 Luke McCrohon
 Sylwester Orzechowski
 Natalie Uomini Olga Vasileva
 Sªawomir Wacewicz
 Przemysªaw ywiczy«ski

Evolinguistics
 Kazuo Okanoya
 Koji Fujita
 Yasuo Ihara
 Harumi Kobayashi
 Takashi Hashimoto Kazumi Taniguchi
 Hiroki Koda
 Michio Nakamura
 Yuki Hirose Reiji SuzukiviiConference organization
Conference director
 Kazuo Okanoya

Coordinator Rie Asano

Local organizing committee
 Masaharu Mizumoto Shun Kurokawa
 Matoba Ryuichi
 Misato Hayashi
 Miwa Shichiri Yulri Nonaka

 Takashi Hashimoto (Chair) Koji Fujita (Co-chair) Chikako Saeki Kazumi Taniguchi Yuko Yoshimura Tojo Satoshi
 Shohei Hidaka

Hybrid conference design
 Rie Asano Tzu-Yin Chen

 Takashi Hashimoto

Online conference organization team Runa Ya (Team leader) Elif Yildiz (Team leader) Rie Asano Tab Bellmann

 Pedro Tiago Martins Hironori Nakatani
 Sina Sachse
 Genta Toya

viii

Panel of Reviewers
Natasha Abner
Ines Adornetti
Daniela Altavilla
Takaya Arita
Mark Arono
Rie Asano
Jaume Baixeries
Lluis Barcelo-Coblijn
Andreas Baumann
Ted Bayne
Veronika Beeck
Christina Behme
Teresa Bejarano
Antonio Benítez-Burraco
Aleksandrs Berdicevskis
Balthasar Bickel
Ermenegildo Bidese
Richard Blythe
Cedric Boeckx
Monika Boruta
Hannah Brown
Claudio Brozzoli
Federica Cavicchio
Alexandre Celma-Miralles
Alessandra Chiera
Hélène Cochet
Chris Code
Chris Cummins
Christine Cuskley
Robert Damper
Emily Davis
Bart de Boer
Chiara De Gregorio
Marianne de Heer Kloots
Koen de Reus
Connie de Vos
Dan Dediu
Tania Delgado
Valentina Deriu
Jean-Louis Dessalles
Mark Dingemanse
Ellie Donnelly
Nataliia Drozhashchikh
Shelece Easterday
Axel G. Ekström
Livio Favaro
Nicolas Fay
Andrew Feeney
Francesco Ferretti
Molly Flaherty
Michael Franke
Koji Fujita
Marco Gamba
Mchael Gasser
Chris Golston
Tao Gong
Nathalie Gontier
Michaª Gªuszkowski
Harald Hammarström
Stefan Hartmann
Takashi Hashimoto
Misato Hayashi
Yuki Hirose

Michio Hosaka
Koji Hoshi
Carla Hudson Kam
Julia Hyland Bruno
Yasuo Ihara
Aritz Irurtzun
Toshiki Iwabuchi
Yannick Jadoul
Gerhard Jaeger
Niklas Johansson
Sverker Johansson
Dariusz Kaloci«ski
Andres Karjus
Noriko Katsu
Henri Kauhanen
Koji Kawahara
Risa Kikuchi
Say Young Kim
Yukinori Kimoto
Simon Kirby
Mirjam Knörnschild
Harumi Kobayashi
Hiroki Koda
Osamu Kondo
Takeshi Konno
Marion Laporte
Kieu Van Le Thi
Shiri Lev-Ari
Simon Levy
Hannah Little
Gary Lupyan
Hannah Lutzenberger
Heidi Lyn
Melanie Malzahn
Mauricio Martins
Pedro Tiago Martins
Hiromi Matsumae
Daiki Matsumoto
Theresa Matzinger
Marco Mazzeo
Alexander Mehler
Gregory Mills
Alexandre Miralles
Steven Moran
Emily Morgan
Olivier Morin
Masahito Morita
Tetsuharu Moriya
Katie Mudd
Elliot Murphy
Thomas Müller
Tomoya Nakai
Michio Nakamura
Hironori Nakatani
Savithry Namboodiripad
Kuniya Nasukawa
Bill Noble
Jonas Nölle
Shinri Ohta
Kazuo Okanoya
Ayaka Onohara
Sylwester Orzechowski
Yohei Oseki

ix

Joel Parthemore
Gerald Penn
Irene Pepperberg
Marcus Perlman
Simone Pika
Marek Placi«ski
Michael Pleyer
Ljiljana Progovac
Monika Pukli
Sonia Ragir
Andrea Ravignani
Limor Raviv
Gareth Roberts
Sean Roberts
Evelina Daniela Rodrigues
Kazutoshi Sasahara
Sabrina Schalz
Niels Schiller
Robert Scholz
Thom Scott-Phillips
Jose Segovia Martin
Yoshimasa Seki
Arturs Semenuks
Sharita Sharma
Edward Ruoyang Shi
Marta Sibierska
Andrew Smith
Kenny Smith
Göran Sonesson
Rose Stamp
Shane Steinert-Threlkeld
Yushi Sugimoto
Reiji Suzuki
Takuya Takahashi
Miki Takahasi
Jun Takakura
Masanori Takezawa
Monica Tamariz
Shin-ichi Tanaka
Valeria Torti
Genta Toya
Patrick C. Trettenbrein
Ryoko Uno
Martti Vainio
Daria Valente
Freek Van de Velde
Olga Vasileva
Laura Verga
Tessa Verhoef
Marilyn Vihman
Sªawomir Wacewicz
Mikihiko Wada
Stuart Watson
Søren WichmannJames Winters
Marieke Woensdregt
Hiroto Yonenoh
Shoko Yuki
Dietmar Zaeerer
Anna Zanoli
Elizabeth Qing Zhang
Przemysªaw ywiczy«ski

xContents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Plenary Presentationsiii

1

Hunter-Gatherers of Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2Cedric Boeckx

What Can We Learn From Bird Songs and Rat Tweets? . . . . . .

3Kazuo Okanoya

A World of Sign Languages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6Carol PaddenInteraction in Animal Communication . . . . . . . . . . . . . . . . . . . . . .

8Simone Pika

Invited Presentations

10

Beyond Common Descent: Cooperative Breeding and Language
Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11

Judith Maria Burkart

Language Evolution  Updating the Lens of Primate Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

13

Zanna ClayMultiple Attention Underpins the Co-Evolution of Thought andCommunication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

16

Koji Fujita

Emergent Constructive Approach to Evolinguistics . . . . . . . . . . .
Takashi Hashimotoxi

19

Historical and Ecological Aspects of the Evolution of Human
Language Faculty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

20

Yasuo Ihara

Inferential Nature of Gesture and its Role in Evolution of Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

22

Harumi Kobayashi

Vocal Learning and the Evolution of Beat-Based Rhythmic Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

23Aniruddh Patel

Linking Language Evolution, Language Acquisition, and Language Diversity: How Social and Cognitive Pressures ShapeLearning and Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . .

25

Limor Raviv

Refereed Contributions

27

Quality Not Quantity in Synonymy Avoidance . . . . . . . . . . . . . . .

28

Aja Altenhof, Gareth Roberts

R You Sure That Your /r/ Is Trilled? A Methodological Caveat

31

Rémi Anselme, François Pellegrino, Dan Dediu

A Cognitive Bias for Zipan Distributions? Uniform Distributions Become More Skewed via Cultural Transmission . . . . . . .

34

Inbal Arnon, Amir Shufaniya

Noise Resistance at Multiple Levels of Linguistic Structure . . . .

36

Rachael Bailes, Joel Wallenberg, Christine Cuskley

Exploring the Concept of Phenotypic Accommodation:

The

Case of the Loss of Air Sacs . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

39

Lluís Barceló-Coblijn

Prevalence Based Quantitative Analysis of Innovations in Lexical Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

47

Andreas Baumann, Klaus HofmannLess of the Same: Modeling Horror Aequi and Extravagance as
Mechanisms of Negative Frequency Dependence in Linguistic
Diversication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Andreas Baumann, Roland Mühlenbernd

xii

50

The Role of Trust in Language Emergence . . . . . . . . . . . . . . . . . .

58

Ted Bayne

Early Language Homolog Brain Asymmetry in Newborn Monkeys Paves the Way for the Development of Gestural Communication's Lateralization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Yannick
JulienBecker,Sein,

RomaneLionel

Velly,

Phelipon,
Luc

Siham

Renaud,69

Bouziane,

EmilieRapha,

Adrien Meguerditchian

Visualization of Vocal Flexibility in Asian Elephants . . . . . . . . .

72

Veronika C. Beeck, Gunnar Heilmann, Angela S. Stoeger

Timbre in Music and Prosody: Comparative and Evolutionary
Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

75

Oliver Tab Bellmann

Cognitive and Genetic Correlates of a Single Macro-Parameterof Crosslinguistic Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

78

Antonio Benítez-Burraco, Candy Cahuana, Sihan Chen,David Gil, Ljiljana Progovac, Jana Reifegerste, Tatiana
Tatarinova

Continuity of Emotion and Aggression in Language Evolution .

81

Antonio Benítez-Burraco, Ljiljana Progovac

Complex Sound Sequences in Iterated Learning: Clusters Can
Become More Stable When They Signal Morphological Boundaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

84

Irene Boehm, Nikolaus Ritt

Eects of Meaning Negotiation on Correctness, Gesture Countand Timespan in a Game of Charades . . . . . . . . . . . . . . . . . . . .

87

Monika Boruta, Marek Placi«ski, Marta Sibierska, Przemysªaw ywiczy«ski

Sequential Dynamics in the Vocal Communication System ofCommon Marmosets (Callithrix jacchus ) . . . . . . . . . . . . . . . . . .

91

Alexandra Bosshard, Paola Merlo, Balthasar Bickel, Judith
Maria Burkart, Simon W. Townsend

Does a Communicative Context Produce a Preference for Early
Negation? An Articial Language Learning Study . . . . . . . . . .
Danielle Burgess

xiii

95

Prosociality in Swarm Robotics: A Model to Study Self-Domesticationand Language Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
NicolasCambier,

Roman

Miletitch,

Antonio98

Benítez-

Burraco, Limor Raviv

Decoupling Speed of Change and Long-Term Preference in Language Evolution: Insights From Romance Verb Stem Alternations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

101

Chundra Cathcart, Borja Herce, Balthasar Bickel

Simulation of Emergent Communication With Large Scale Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

109

Rahma Chaabouni, Florian Strub, Florent Altché, Corentin
Tallec, Eugene Tarassov, Elnaz Davoodi, Kory Mathewson,
Olivier Tieleman, Angeliki Lazaridou, Bilal Piot

Linguistic Density in a Global Scale Is Determined by Social,
Political and Economic Factors . . . . . . . . . . . . . . . . . . . . . . . . . . .

112

Thiago Chacon, Marc Allassonnière-Tang

A Computational Simulation of the Genesis and Spread of Lexical Items in Situations of Abrupt Language Contact . . . . . . . .

115

Ching Yat Cheung, Ko Yakpo, Christophe Coupé

Using Eye Tracking to Better Understand Children's Processing of Events During Verb Learning: Is the Focus on People(Faces) or Their Actions (Hands)? . . . . . . . . . . . . . . . . . . . . . . . .
Jane Childers,

Emily Warkentin,

Marissa Young,

123

Sneh

Lalani, Akila Gopalkrishnan

Evidence for the Cognitive Origins of Syntactic Harmony . . . . .

131

Jennifer Culbertson, Simon Kirby

Non-Arbitrariness Across Modalities: Bootstrapping the Emergence and Development of Questions . . . . . . . . . . . . . . . . . . . . . .

134

Christine Cuskley, Johannes Heim

Learnability and Emergence of Dependency Structures in an
Articial Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

143

Emily Davis, Kenny Smith

Hysteresis in Language Emergence and Evolution . . . . . . . . . . . .
Bart de Boer

xiv

151

Body Size Predicts Vocal Tract Size in a Mammalian Vocal
Learner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Koen de Reus,

Daryll Carlson,

Alice Lowry,

154

Stephanie

Gross, Maxime Garcia, Ana Rubio-García, Anna SalazarCasals, Andrea Ravignani

Language-Specic and Universal Factors Behind Morphological
Simplication: An Agent-Based Modelling Study of Alorese .

157

Peter Dekker, Marian Klamer, Bart de Boer

Convergent Cultural Evolution of Continuers (mhmm ) . . . . . . . .

160Mark Dingemanse, Andreas Liesenfeld, Marieke Woensdregt

Amplifying the Bias to Order Nouns Before Adjectives: Studies
of Silent Gesture and Emerging Sign Language . . . . . . . . . . . . .

168

Monica Do, Simon Kirby, Susan Goldin-Meadow, Natasha
Abner, Molly Flaherty, Ann Senghas

The Role of Structural Priming, Semantics and Population Structure in Word Order Conventionalization: A Computational
Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

171Loïs Dona, Marieke Schouwstra

Syllable Sequence Perception in Songbirds and Parrots . . . . . . .

174

Robert Dooling, Vicky Tu, Adam Fishbein, Katie Stennette

Neuronal Recycling as Speech Origins . . . . . . . . . . . . . . . . . . . . . .

177

Axel G. Ekström

The Form and Function of Chimpanzee Buttress Drumming . . .

185

Vesta Eleuteri, Matthew Henderson, Adrian Soldati, Gal
Badihi, Klaus Zuberbühler, Catherine Hobaiter

Lectal Contact as a Path to Language Speciation . . . . . . . . . . . .

189

T. Mark Ellison, Luisa Miceli, John Manseld

Vocal Iconicity in Nominal Classication . . . . . . . . . . . . . . . . . . . .

192

Niklas Erben Johansson, Sandra Cronhamn

Tone and Loanword Adaptation in Ixpantepec Mixtec:

Hori-zontal Transfer in the Evolution of Linguistic Diversity . . . . . .

194

Deandre Espree-Conaway

Exploring the Role of Speech in the Transmission of Airborne
Viruses via a Novel Method for Combining Aerosol and Phonetic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Caleb

Everett,

Marva

Seifert,Jonathan Slade, Ryan Tumminello

xv

ChantalDarquenne,

196

Signal Embodiment Enhances Gesture's Communication Success203

Nicolas Fay, Bradley Walker, T. Mark Ellison, Zachary
Blundell, Naomi De Kleine, Murray Garde, Casey Lister,Susan Goldin-Meadow

Communication or Cognition? The Locus of Recursion in the
Evolution of Human Language . . . . . . . . . . . . . . . . . . . . . . . . . . .

206

Andrew Feeney

A General Solution to the Combined Eects of Selection andBiased Mutation in Cultural Evolution . . . . . . . . . . . . . . . . . . . .

209

Vanessa Ferdinand

Babbling Behavior: Shared Vocal Ontogenetic Features in Bat
Pups and Human Infants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

217

Ahana Aurora Fernandez, Mirjam Knörnschild, Martina
Nagy, Lara Sophie Burchardt

From Proto-Conversation to Modern Conversation: The Roleof Human Self-Domestication . . . . . . . . . . . . . . . . . . . . . . . . . . . .

220

Francesco Ferretti, Ines Adornetti, Ljiljana Progovac, Antonio Benítez-Burraco

Language Contact Is Part of Language Emergence: Noun Phrase
Ordering in Nicaraguan Sign Language . . . . . . . . . . . . . . . . . . .
Molly Flaherty,

Savithry Namboodiripad,

223

Susan Goldin-

Meadow

Social Tolerance and Interactional Opportunities as Drivers ofGestural Redoings in Orang-Utans . . . . . . . . . . . . . . . . . . . . . . .

226

Marlen Fröhlich, Carel P. van Schaik

Primate Rhythmic Categories Analyzed on an Individual Basis

229

Marco Gamba, Chiara De Gregorio, Daria Valente, Teresa
Raimondi, Valeria Torti, Longondraza Miaretsoa, Filippo
Carugati, Olivier Friard, Cristina Giacoma, Andrea Ravignani

Constraining Free Merge: Labeling and the Theta-Criterion

..

237

Great Ape Pragmatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

245

Jason Ginsburg

Kirsty Graham, Catherine Hobaiter

Iconic Words May Be Common in Early Child Interactions Because They Are More Engaging . . . . . . . . . . . . . . . . . . . . . . . . . .
Kirsty Green, Marcus Perlman

xvi

248

Selective Forces in Language Evolution . . . . . . . . . . . . . . . . . . . . .

256

Juan Antonio Guerrero Montero, Andres Karjus, Kenny
Smith, Richard Blythe

Understanding Design Features of Music and Language: The
Choric/Dialogic Distinction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

259

Felix Haiduk, W. Tecumseh Fitch

Pressure for and against Non-Arbitrariness:

Evidence From

Phonesthemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

262

David Haslett, Zhenguang Cai

The Study of Onomatopoeia From the Comparative Developmental Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

270

Misato Hayashi, Takemi Yamada, Yusuke Umeda, Keiko
Tanabe, Maki Takamura, Kensuke Nakajima

Exploring Our Inner-Ape:Shared Understanding of Gesture

Between Humans and Chimpanzees . . . . . . . . . . . . . . . . . . . . . . .

272

Matthew Henderson, Catherine Hobaiter, Kirsty Graham,
Patrick Grosz, Pritty Patel-Grosz

Horizontal Transfer of Linguistic Features as an Integral Part of
the Evolutionary History of Language Families  Phylogenetic
Reconstruction of Linguistic Tree-Based Networks . . . . . . . . . .

275

Lina Herbst, Volker Gast, Denise Kühnert, Mary Walworth

Semantic Similarity Shapes How Agreement Markers Split Over
Positions in Verb Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . .

278

Borja Herce, Carmen Saldana, Balthasar Bickel

Beyond Broca: Neural Architecture and Evolution of a Dual
Motor Speech Coordination System . . . . . . . . . . . . . . . . . . . . . . .

281

Gregory Hickok, Jonathan Venezia, Alex Teghipco

Incremental Evolution of Syntax: From 'Bigrams' to 'Branching' 285
Dieter Hillert

Piecing Together the Building Blocks of the Vocal Learning Bat
Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

294

Nienke Hoeksema, Peter Hagoort, Sonja Vernes

The Eect of Improvisation and Learning on Word Order Preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

297

Annie Holtz, Simon Kirby, Jennifer Culbertson

Word Order Convergence in the History of English . . . . . . . . . . .
Michio Hosaka

xvii

300

To What Extent Can the Human Agent-First Bias Be Captured
by Linguistic Experience? Evidence From Machine-Learningof Corpora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

302

Eva Huber, Paola Merlo, Balthasar Bickel

Phylogenetic Reconstruction for Japonic: New Data Types andMethodological Insights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

306

John Huisman, Bonnie McLean, Chieh-Hsi Wu

Non-Linearity in Human Language Evolution: A General Ecology Perspective Grounded in Pleistocene Archaeology . . . . . . .

309

Shumon T. Hussain, Alexandra R. Kratschmer

Generation of Structured Spatial Patterns Through Adversarial
Imitation Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

317

Hiroyuki Iizuka, Wataru Noguchi, Taiki Sasaki, Masahito
Yamamoto

Deaf and Hard-of-Hearing People Can Detect Sound Symbolism: Implications for the Articulatory Origin of Word Meaning 325
Mutsumi Imai,

Kimi Akita,

Sotaro Kita,

Noburo Saji,

Masato Ohba, Miki Namatame

Male Northern Gray Gibbon Songs Have a Phrase-Inserting
Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Yoichi

Inoue,

Waidi

Sinun,

ShigetoYosida,

333

Kazuo

OkanoyaElements for a Cultural Evolution of Supernatural Ritual Languages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

340

Aritz Irurtzun

Analyzing Property Predication From the Perspective of Evolutionary Linguistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

348

Masayuki Ishizuka, Ryoko Uno

Nominal Plurality in Sino-Tibetan: A Diachronic Account . . . . .

351

Jessica K Ivani, Taras Zakharko, Chundra Cathcart

Letters and Their Sounds Are Not Perfectly Arbitrary: Exploring Grapho-Phonemic Systematicity in Multiple Orthography
Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

354

Hana Jee, Fiona Du, Monica Tamariz, Richard Shillcock

Phylogenetic Multilevel Models Reveal a Simplicity Bias in Uralic 357
Yingqi Jing, Miina Norvik, Outi Vesakoski, Michael Dunn

xviii

Pointer Evolution Points to Gradual Evolution of HierarchicalComplexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

360

Sverker Johansson

The Colour Lexicon Is Shaped by Environment and Biology:
Comparing Himba and French Colour Perception . . . . . . . . . . .

368

Mathilde Josserand, Serge Caparos, François Pellegrino,
Dan Dediu

Language Without Shared Meaning:

Preliminary Considera-

tions on the Evolutionary Benets of Subjectivity . . . . . . . . . .

371

Alexandre Kabbach, Aurelie Herbelot

Language Change in Hindi From a Paninian Perspective . . . . . .

379

Aniket Kali, Gerald Penn

Emergence of Negation in a Turkish Homesign System: Insights
From the Family Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

387

U§ur Kan, Kadir Gökgöz, Beyza Sümer, Elvan Tamyürek,Asl Özyürek

Evolving Linguistic Divergence in Socio-Political Polarities . . . .

390

Andres Karjus, Christine Cuskley

Behavioral Evidence for Representations of the Other's Action
During a Joint Task in Rats . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

393

Noriko Katsu, Kazuo Okanoya

A Critical Population Threshold for Contact-Induced Simplication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

396

Henri Kauhanen

Pots Versus Clocks: Integrating Archaeological Evidence With
Bayesian Estimates of Divergence Times in Oceanic Languages 399
Benedict King, Mary Walworth, Aymeric Hermann, Simon
J. Greenhill, Russell D. Gray

Compositionality Arises From Iterated Learning Despite a Preference for Holistic Signals: An Experimental Model of Sign
Language Emergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

402

Simon Kirby, Yasamin Motamedi, Lucie Wolters, Kenny
Smith

The Recursion That Those Who Study Linguistics Seek Is Everywhere if You Know Where to Look for It . . . . . . . . . . . . . . .
Robert Kluender, Emily Davis, Marc Rolnik

xix

405

Ecological and Socio-Cultural Constraints on Linguistic Diversication: New Insights From the Bantu Expansion . . . . . . . . .
Ezequiel

Koile,

Simon

J.

Greenhill,

DamiánE.

413

Blasi,

Remco Bouckaert, Russell D. Gray

The Evolutionary Roots of Human Communicative Repair: The
Case of Chimpanzee Grooming . . . . . . . . . . . . . . . . . . . . . . . . . . .

416

Kayla Kol, Simone Pika

Mechanisms Underlying the Hermeneutic Circle Between Connotation and Denotation in the Construction of Symbolic Communication Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

424

Takeshi Konno, Ryusei Ota, Ryosuke Nakano

Frequency and Character Complexity in 27 Writing Systems . . .

427

Alexey Koshevoy, Helena Miton, Olivier Morin

Modelling the Emergence of Vocal Grooming . . . . . . . . . . . . . . . .

434

Tom Kouwenhoven, Ross J. Towns, Tessa Verhoef

Semiotic Archaeology: Correlated Evolutionary Stages of Language, Art and Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

437

Alexandra R. Kratschmer, Jacob Wamberg

Neural Correlates of Speech and Non-Speech Production in Childhood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

445

Monica Lancheros, Tanja Atanasova, Marina Laganaro

The Importance of the Fundamental Frequency in Chimpanzees
Vocal Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

447

Marion Laporte, Louis-Jean Boë, Nicolas AudebertWord Segmentation Is Facilitated in Zipan Distributions . . . . .

450

Ori Lavi-Rotbain, Inbal Arnon

How the Structures of Umwelts and Signs Are Related . . . . . . . .

453

Eric Leonardis, Arturs Semenuks, Greyson Sims, Seana
CoulsonCall Combinations in Chimpanzees . . . . . . . . . . . . . . . . . . . . . . . . .

456Maël Leroux, Anne Schel, Claudia Wilke, Bosco Chandia,
Klaus Zuberbühler, Katie Slocombe, Simon W. Townsend

Community Size Inuences Word Order . . . . . . . . . . . . . . . . . . . . .

458

Shiri Lev-Ari

Language Acquisition and Language Evolution: Lessons From
Dual Inheritance Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Josh Linden

xx

461

Synonyms and Homonyms in Signaling Game  Which Are Stable and When? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

465

Dorota Lipowska

How Much Do We Agree on What Words Mean? . . . . . . . . . . . . .
Gary Lupyan,

Steven Piantadosi,Louis Martí,

469

CelesteKidd, Ella Liu, Jeroen van Paridon, Yanchao Bi

Phonetic Representation in Bonobos (Pan paniscus ) as Revealed
Through Vocabulary Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

472

Heidi Lyn, Mark Yates

Testing the 'Roundness' and 'Sharpness' of Nonwords and Shapes
in Great Apes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

475

Konstantina Margiotoudi, Friedemann Pulvermüller

Vocal Learning Is at Odds With Non-Vocal Dexterity . . . . . . . . .

478

Pedro Tiago Martins

Evolution of Topicalization in Human Language . . . . . . . . . . . . .

481

Daiki Matsumoto

Co-Evolutionary Links Between Linguistic Alignment and Cooperation  An Experimental Study . . . . . . . . . . . . . . . . . . . . . .
TheresaMatzinger,

Marek

Placi«ski,

Adam

489

Gutowski,Mariusz Lewandowski, Przemysªaw ywiczy«ski, Sªawomir
Wacewicz

Aesthetic Perception of Prosodic Patterns as a Potential Factor
in the Cultural Evolution of Language . . . . . . . . . . . . . . . . . . . .

492

Theresa Matzinger, Eva Specker, Nikolaus Ritt, W. Tecumseh Fitch

Disentangling the Statistical Properties That Drive Language
Acquisition: Evidence From Maximally Diverse Languages . . .
Jekaterina

Mazara,Giuachin

Kreiliger,

Sabine495

Stoll,

Balthasar Bickel

The Emergence of Gaze Conventions in Interpersonal Coordination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

498

Gregory Mills, Remko Boschker

Self-Repair Increases Abstraction of Referring Expressions . . . .

500

Gregory Mills, Gisela RedekerEect of Environmental Richness on Responsiveness of Rats toSpecies-Specic Emotional Calls . . . . . . . . . . . . . . . . . . . . . . . . . .
Marika Minamii, Yumi Saito, Kazuo Okanoya

xxi

503

Linguistic Laws in Primate Vocal Communication . . . . . . . . . . . .

506

Steven Moran, Quentin Gallot, Marco Maiolini, Auriane
LeFloch, Adrian Soldati, Klaus Zuberbühler

Great Ape Vocal Repertoires Are All Similar in Size: Now What? 514
Steven Moran, Marco Maiolini, Adriano LameiraOn the Relation Between Context Dependency and Vocabularyin Human Language and Birdsong . . . . . . . . . . . . . . . . . . . . . . . .

517

Takashi Morita, Ryosuke O. Tachibana, Kazuo Okanoya,
Hiroki Koda

The Eect of Network Structure on Lexical Variability in Sign
Language Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

525

Katie Mudd, Marnix Van Soom, Connie de Vos, Bart de
Boer

Modeling the Origins of Communication: Alignment of Interests
and Social Cues Thereof . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

528

Roland Mühlenbernd, Andreas Baumann

Machine Impostors Impede Human Detection and the Successof Stable Conventions by Imitating Past Interactions . . . . . . . .

536

Thomas Müller, Levin Brinkmann, James Winters, Niccolò
Pescetelli

The Role of Cross-Cultural Dierences in Referential Strategy
Emergence: Data From Articial Sign Language Learning . . .

539

Danielle Naegeli, David Peeters, Emiel Krahmer, Marieke
Schouwstra, Yasamin Motamedi, Connie de Vos

Evolution of the Word Order Rules Through Communication
and Cultural Transmission: An Iterated Learning Experiment

542

Seiya Nakata, Akari Ohira, Masanori Takezawa

Challenging a Widely-Accepted Account of Vowel Metathesis in
Nagoya Japanese With No Reference to Precedence . . . . . . . . .

550

Kuniya Nasukawa, Shin-ichi Tanaka

The Complexity of a Language Is Shaped by the Communicative
Needs of Its Users: An Iterated Hierarchical Bayesian Model .

553

Vlad Nedelcu, Kenny Smith

Integration and Segregation of Auditory Streams During Rhythm
Perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Jun Nitta, Sotaro Kondoh, Kazuo Okanoya, Ryosuke O.
Tachibana

xxii

556

More Than Aect: Human Facial Expressions Provide Iconic
and Pragmatic Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

558

Jonas Nölle, Yichen Wu, Pablo Arias, Oliver G. B. Garrod,
Philippe G. Schyns, Rachael E. Jack

Neural Signatures of Prosodic Processing . . . . . . . . . . . . . . . . . . .

562

Chantal Oderbolz, Elisabeth Stark, Martin Meyer

Neurofeedback Training as a Method for Evaluation of Brain
Systems for Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

566

Jiro Okuda, Hiroki Kataoka, Masayuki Fujiwara, Takashi
Hashimoto

Similarities and Dierences in a Cross-Linguistic Sample of Songand Speech Recordings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

569

Yuto Ozaki, Jiei Kuroyanagi, John McBride, Polina Proutskouva,

Adam

Tierney,Peter

Pfordresher,

Emmanouil

Benetos, Fang Liu, Patrick Savage

The Adaptive Signicance of Human Scleral Brightness.

An

Experimental Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

573

Juan Perea Garcia, Dariusz Danel, Zdzisªaw Lewandowski,
Sªawomir Wacewicz

Bodily Rotation in the Gesturing, Play, and Display of Great
Apes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

576

Marcus Perlman

Theta Rhythm Is Widespread in Vocal Production Across theAnimal Realm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

579

Theophane Piette, Chundra Cathcart, Chiara Barbieri, Didier Grandjean, Éloïse Déaux, Anne-Lise Giraud

How Senses Are Grouped Within Word Forms Over Time: An
Analysis of Diachronic Colexication Patterns . . . . . . . . . . . . .

583

Lucía Pitarch, Thomas Brochhagen

Re-Evaluating Hockett's Design Features From a Cognitive and
Neuroscience Perspective: The Case of Displacement . . . . . . . .

586

Michael Pleyer, Elizabeth Qing Zhang

An Evolutionary Perspective on Impoliteness and the Pragmatic Concept of Face . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

590

Monika Pleyer, Michael Pleyer

The 'Musical' Origin of Language Grammar as a Milestone forthe Development of a Complex Conceptual Mind . . . . . . . . . . .
Piotr Podlipniak

xxiii

598

Evolang Database: A Comprehensive Source of Information on
the Science of Language Evolution Community . . . . . . . . . . . . .
Aleksandra
Ng,

Poniewierska,

Marta Sibierska,

Aleksandra

Szczepa«ska,

Marek Placi«ski,

601

Yen

Przemysªaw y-wiczy«ski, Sªawomir Wacewicz

Elephants as a New Animal Model for Studying the Evolutionof Language as a Result of Self-Domestication . . . . . . . . . . . . .

606

Limor Raviv, Sarah L. Jacobson, Joshua M. Plotnik, Jacob
Bowman, Vincent J. Lynch, Antonio Benítez-Burraco

The Power of Information Theory Ideas for Studying Animals'
Natural Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

609

Zhanna Reznikova

Investigation of Octave Similarity in Pitch Perception in Rats:
In Search of Common Auditory Properties Between Humans
and Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

613

Koshiishi Riseru, Okanoya Kazuo

The Common Task Framework: Using Causal Theories From
Linguistic Anthropology to Explore the Emergence of Symbolic Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

616

Sean Roberts, Elizabeth Irvine, Fiona Jordan

The Evolutionary Trends of Noun Class Systems in Atlantic
Languages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

624

Neige Rochant, Marc Allassonnière-Tang, Chundra Cathcart

The Landscape of Gestural Research in Language Evolution: A
Systematic Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

632

Evelina Daniela Rodrigues, António José Santos, Flávia
Veppo, Joana Pereira, Catherine Hobaiter

Cognition and the Stability of Evolving Complex Morphology:
An Agent-Based Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

635Erich Round, Stephen Francis Mann, Sacha Beniamine,
Emily Lindsay-Smith, Louise Esher, Matt Spike

Group Binding as the Main Driver of Language Evolution . . . . .

643

Daniil Ryabko, Alvaro Mbeju Moreno

Human Language Laws in Chimpanzee Sexual Solicitation Gestures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Alexandra

Safryghin,

Catharine

Cross,

BrittanyFal-lon, Raphaela Heesen, Ramon Ferrer-i-Cancho, Catherine
Hobaiter

xxiv

650

The Function and Evolution of Child-Directed Speech . . . . . . . .

653

Johanna Schick, Caroline Fryns, Franziska Wegdell, Marion Laporte, Klaus Zuberbühler, Carel P. van Schaik, Simon W. Townsend, Sabine Stoll

The Puzzle of Great Ape Gesture & a Solution . . . . . . . . . . . . . .

656

Thom Scott-Phillips, Christophe Heintz

Music Production by Cockatiels . . . . . . . . . . . . . . . . . . . . . . . . . .

659

Yoshimasa Seki

Investigating Cross-Linguistic Variability in Psycholinguistic Difculty Through Information Density Variability Measures . . .

661

Arturs Semenuks

Investigating the Relationship Between Number of Speakers, IComplexity, and E-Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . .

665

Arturs Semenuks

Grammatical Complexity Is Only Weakly Inuenced by the Sociolinguistic Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

669

Olena Shcherbakova, Susanne Maria Michaelis, Hannah J.
Haynie, Simon J. Greenhill, Damián E. Blasi, Russell D.
Gray, Volker Gast, Skirgård Hedvig

Constraints on Communicating Order of Events in Pantomime .

672

Marta Sibierska, Przemysªaw ywiczy«ski, Jordan Zlatev,
Monika Boruta,

Joost van de Weijer,

Adam Gutowski,

Mariusz Lewandowski

Shall I Point It Out? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

675

Rebecca Slagle Luenser, Heidi Lyn

Simultaneity as an Emergent Property of Sign Languages . . . . .

678

Anita Slonimska, Asli Ozyurek, Olga Capirci

The Ontogeny of Behavioural Responses to Pant Hoots in Wild
Chimpanzees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
AdrianSoldati,

Pawel

Fedurek,

Guillaume

681

Dezecache,

Geresomu Muhumuza, Klaus Zuberbühler, Josep Call

How Self-Domestication and Prosociality May Shape Cross-Modal
Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

685

Kees Sommer, Christine Cuskley, Simon Kirby, Tessa Verhoef

Combining Pointing and Language During the Early Stages ofDevelopment: A Case Study of Russian and Chintang . . . . . .
Sabine Stoll, Jekaterina Mazara, Elena Lieven

xxv

688

Verbal Labels Increase Conceptual Alignment . . . . . . . . . . . . . . .

691

Ellise Sull, Jeroen van Paridon, Gary Lupyan

A Computational Framework for Studying the Evolution of Cultural Traits Among Chatting Agents Using a Generative Model 699
Reiji Suzuki, Soichiro Hirata, Takaya Arita

Audio-Vocal Mirror Neuron in the Songbird Basal Ganglia . . . .

702

Yuka Suzuki, Shin Yanagihara, Kazuo Okanoya

How Much Language Use Is Actually on Social Topics? . . . . . . .

705

Anna Szala, Marek Placi«ski, Aleksandra Poniewierska,
Aleksandra Szczepa«ska, Sªawomir Wacewicz

What Linguistics Textbooks Teach About Language Evolution .

708

Aleksandra Szczepa«ska, Aleksandra Poniewierska, Michael
Pleyer, Przemysªaw ywiczy«ski, Sªawomir Wacewicz

Inferring the Temporal and Spatial Evolution of Accent Systems
in Japanese Dialects: A Phylogeographic Approach . . . . . . . . .

711

Takuya Takahashi, Ayaka Onohara, Yasuo Ihara

Cumulative Cultural Evolution, Population Structure and theOrigin of Combinatoriality in Human Language...........

714

The Prevalence of Systematicity in Indo-European Languages .

717

Monica Tamariz, Simon Kirby

Nicole Tamer, Paul Widmer

Integrative Brain Activity in Process of Visual and Audial Recognition of Imitative Words on Dierent De-Iconization Stages .

721

Liubov Tkacheva, Maria Flaksman, Yulia Sedelkina, Yulia
Lavitskaya, Andrey Nasledov, Elizaveta Korotaevskaya

Embedding Parallelepiped in Co-Occurrence Matrix:

Simula-tion and Empirical Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

725

Takuma Torii, Akihiro Maeda, Shohei Hidaka

Group Size and Linguistic Structure in Silent Gesture . . . . . . . .

733

Ross J. Towns, Tessa Verhoef

The Condition of Recursive Combination in the Evolution of
Reinforcement Learning Agents.........................

736

Genta Toya, Rie Asano, Takashi Hashimoto

Can Phonological Structure Emerge Through Iterated Imitations of Environmental Sounds? . . . . . . . . . . . . . . . . . . . . . . . . . .
Julia

Trzeciakowska,

Elizabeth

Wacewicz

xxvi

Qing

Zhang,

Sªawomir739

Environmental and Social Factors in the Emergence of Spatial
Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

742

Kristian Tylén, Zahra Alinam, Cordula Vesper

Initiating Learned Vocalization Is Preceded by Long Ramping
in Neural Activity in Basal Ganglia in a Species of Songbird .

745

Sachio Umemoto, Shin Yanagihara, Kazuo Okanoya

Implicit Communication of Word Meaning Through Co-Occurrence 748
Jeroen van Paridon, Gary Lupyan

On Scaling and Models of Language Evolution . . . . . . . . . . . . . . .

756

Remi van Trijp

Evo-Devo Framework: Contextualizing Language Ontogeny in
Language Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

764

Olga Vasileva, Antonio Benítez-Burraco

Language Development and Cognitive Ontogeny: An Important
Link . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

767

Olga Vasileva, Michael Pleyer

Language Evolution Without Trust . . . . . . . . . . . . . . . . . . . . . . . . .
Till

NikolausvonHeiseler,

Christopher Droppa,

Anne

Aerts,

Kai

Shaman,Daphne Preston-Kendal,

Franziska

770

Eberhardt, Maurice Pug, Anna Maaser, Claudio Fabbroni,
Victoria Charlott Uhlich

Unpacking the Proximate Functions of Functionally Referential
Signals: A Test of 3 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . .

773

Stuart Watson, Andrea Müller, Marta Manser

Using Machine Learning to Show Chimpanzee (Pan troglodytes
schweinfurthii ) Lip Smack Rhythm Variation:

Implications

for the Study of Human Speech Evolution . . . . . . . . . . . . . . . . .

776Charlotte Wiltshire, Gal Badihi, André Pereira, Adrian
Soldati, Klaus Zuberbühler, Adriano R. Lameira, Katie Slocombe, Catherine Hobaiter

Evolutionary Dynamics of Multilingual Competition Under Intervention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

780

Zhijun Wu

Role of Midbrain Dopaminergic System in Social Enhancementof Vocal Learning in Songbird . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Shin Yanagihara, Maki Ikebuchi, Chihiro Mori, Ryosuke O.
Tachibana, Kazuo Okanoya

xxvii

788

Discovering Hierarchy in Ambiguous Communications During
an Experimental Semiotics Task . . . . . . . . . . . . . . . . . . . . . . . . . .

791

Soma Yano, Naoki Inoue, Junya Morita

Homo Regularis: Language as a Cultural Invention of the RuleObsessed Species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

799

Masato Yoshikawa

Your Theory of Language Evolution Also Depends on Your
View of Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

802

Thiago Macek Gonçalves Zahn

Is Redundancy Useful in Language?

Agent-Recipient Disam-

biguation in English and Dutch . . . . . . . . . . . . . . . . . . . . . . . . . .

806

Eva Zehentner, Dirk Pijpops

Categorical Perception and Discreteness: An Evolutionary and
Neurocognitive Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

809

Elizabeth Qing Zhang, Edward Ruoyang Shi

Cross-Linguistic Evidence for Dependence Between the Perceptual Dispersion and Salience of Vowel Systems . . . . . . . . . . . . .

817

Menghan Zhang, Tao Gong

Author Index

821

xxviiiPlenary Presentations

1

2

HUNTER-GATHERERS OF WORDS
Cedric Boeckx*1,2,3*

Corresponding Author: cedric.boeckx@ub.edu
ICREA (Catalan Institute for Advanced Studies and Research), Spain
2
Universitat de Barcelona, Section of General Linguistics, Barcelona, Spain
3
Universitat de Barcelona Institute for Complex Systems (UBICS), Barcelona,Spain
1

In this talk I’ll be asking what, if anything, the past decade of progress in ancientgenomics has been able to tell us about the evolution of human language andcognition. While it has clearly been telling us that a sudden emergence/cognitive
revolution scenario (Berwick and Chomsky 2016) is (at best) highly unlikely, it
seems to me to also call for a more nuanced scenario than the currently favoredalternative (“recognizably modern language is likely an ancient feature of ourgenus pre-dating at least the common ancestor of modern humans and Neandertals
about half a million years ago”, as argued in Dediu and Levinson 2013).
The case I will make for this more nuanced narrative builds on an examinationof regions of modern human genes associated with signals of positive selection
and/or depleted from signatures of introgression from other hominins.
Collectively, data from these regions point to neural changes that impacted theway we came to share information and learn from others. If correct, this suggeststhat the languages/grammars that came to be must have had a distinct profile fromprevious communicative systems.
References
Berwick, R. C., & Chomsky, N. (2016). Why only us: Language and evolution.
MIT press.
Dediu, D., & Levinson, S. C. (2013). On the antiquity of language: thereinterpretation of Neandertal linguistic capacities and its consequences.
Frontiers in psychology, 4, 397.

This paper is distributed under a Creative Commons BY-ND 4.0 license.

3

WHAT CAN WE LEARN FROM BIRD SONGSAND RAT TWEETS?
KAZUO OKANOYA*1,2*

Corresponding Author: kazuookanoya@gmail.com
Advanced Comprehensive Research Institute, Teikyo University, Tokyo, Japan
2
Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan1

1. Comparative Approach
Just like our physical body is a product of evolution, so does our behavior. Thenit follows that our speech is also a product of evolution. Speech utilizes the
respiration system, vocal apparatus, and the brain, all of which are directly
descended from our ancestors. Here I introduce two lines of research, one is on
birdsong and the other on rat tweets. These studies should provide with preadaptations to speech in animal models (Okanoya, 2007).
2. Domestication and cultural evolution of complex songs in finches
Juvenile birds learn their courtship songs from their conspecifics through the
motor-auditory feedback. Bengalese finches (BFs) are a domesticated strain of
wild white-rumped munias (WRMs) imported from China to Japan 270 years ago.
BF songs are composed of multiple chunks and each chunk is a combination of
2-4 song notes. Furthermore, chunks are arranged in a finite-state probabilistic
automaton. We studied how and why BFs sing such complex songs. We found
the following facts. 1) The ancestral strain sings simpler songs. 2) There is highlearning specificity in WRMs but not in BFs. 3) BFs have larger song control
nuclei and higher level of glutamate receptor gene expressions than WRMs (. 4)Both BF and WRM females prefer complex songs as measured by the nest string
assay and males with complex songs are physically fitted than the males withsimpler songs. These results promoted sexual selection scenario of songcomplexity in BFs (Okanoya, 2004).
We further examined factors related with domestication. We examined songsof WRMs in subpopulations of Taiwan. Where there is a sympatric species toThis paper is distributed under a Creative Commons BY-ND 4.0 license.

4

WRMs, songs were simpler. This leads to a hypothesis that in the wild songs
needed to be simple to secure species identification, but under domestication thisconstrains was set free. We also examined socio-emotional indexes including
neophobic tendency, tameness, behavioral stress reactions, and corticosterone
levels. All indexes suggested that WRMs have higher level of stress and social
shyness, which should be adaptive under natural environment, but could be
limiting opportunities for learning complex songs (Okanoya, 2017).
To strengthen our hypothesis, we conducted a cultural evolution study. In zebra
finches, isolated songs converge into wild type songs after a few generations (Fehret al, 2009). We isolated juveniles of both strains and reared only by mothers. We
used these birds that did not listen to the species-specific songs as founders. After
5 generations, BF songs diverged even more than the founders and neverconverged. After 3 generations, WRM songs converged into simple patterns, butthese were not similar to the wild songs. Results are consistent with the hypothesis
on innate constraints of learning in these two strains.
Thus, evolution of song complexity involves not only factors related withstrengthen of sexual selection and relaxation of species identification, but alsosocio- emotional factors due to domestication. Furthermore, recent suggestion of‘neural crest’ hypothesis that might account for the domestication syndrome fits
well with the properties of Bengalese finches. These results on Bengalese finches
must be useful in discussing possible biological origin of human speech in terms
of proximate and ultimate factors.
2. Auditory, cognitive, and brain processing of emotional calls in rats
Rats emit distinct emotional calls under specific situations. When they are in apositive state, such as while copulating or being tickled, they emit 50 kHz pleasurecalls. When they are in a distress state, such as when being bitten or received anelectric shock, they emit 22 kHz calls. Playing back 50 kHz calls attract rats while
playing back 20 kHz calls freeze them.
We studied how these calls were perceived, changed cognitive states, andprocessed in the brain. We first examined which acoustical parameters weresalient in making discrimination of the two categories of the calls. Rats weretrained by an operant go-left, go-right discrimination task. They were then tested
with various synthetic calls to find out the salient parameters. We found that thefrequency (pitch) is the most salient, followed by duration, and then modulation(Saito et al, 2019).
Next, we trained rats to discriminate between two pure tones by the same
operant procedure. When they learned the discrimination, they were tested with5

pure tones of the ambiguous frequencies. Rats responded to the negative leverafter exposed to the 22 kHz call while they responded to the positive lever after
exposed to the 50 kHz call. Thus, their cognitive states were also influenced bythese emotion calls (Saito et al, 2017).
Finally, we tested how these calls were analyzed in the brain. We recorded
ERPs from the rat anterior cingulate cortex. ERP amplitudes were stronger tonaturally modulated calls than artificial pure tones, and the 50 kHz call emitted
stronger response than the 22 kHz call. Taken together, rat emotional calls trigger
cognitive bias in the hearer and processed categorically in the brain.
2. Putting these together
The birdsong study examined how sequential expertise evolved under sexual
selection and domestication. The rat tweet study examined how emotions areconveyed in tweets and affect receivers. These two lines of studies point toimportant aspects of human speech, namely sequential nature and prosodic
modulation.
Acknowledgements
Work supported by JST, JSPS, and MEXT to KO. I thank all co-workers andstudents who made these projects possible.
References
Fehér, O., Wang, H., Saar, S., Mitra, P. P., & Tchernichovski, O. (2009). De novo
establishment of wild-type song culture in the zebra finch. Nature, 459(7246),564-568.
Okanoya, K. (2004). Song syntax in Bengalese finches: proximate and ultimate
analyses. Advances in the Study of Behavior, 34, 297-346.
Okanoya, K. (2007). Language evolution and an emergent property. Current
opinion in neurobiology, 17(2), 271-276.
Okanoya, K. (2017). Sexual communication and domestication may give rise to
the signal complexity necessary for the emergence of language: An indicationfrom songbird studies. Psychonomic bulletin & review, 24(1), 106-110.
Saito, Y., Yuki, S., Seki, Y., Kagawa, H., & Okanoya, K. (2016). Cognitive bias
in rats evoked by ultrasonic vocalizations suggests emotional contagion.
Behavioural processes, 132, 5-11.
Saito, Y., Tachibana, R. O., & Okanoya, K. (2019). Acoustical cues for perceptionof emotional vocalizations in rats. Scientific reports, 9(1), 1-9.

6

A WORLD OF SIGN LANGUAGESCAROL PADDEN
Department of Communication, University of California San Diego, USA

The project of sign language research since its start in the middle of the lastcentury has been to document and describe sign languages of different
populations around the world. More recently, this body of research has expanded
to include sign languages in small community populations: a village, a town, or afamily. Even smaller is the sign language of a single deaf signer, called “homesign” where family and sometimes community members participate in signcommunication with this individual. Home signers have been reported in nearly
every habitable continent in the world. This project has identified as many as 215
different sign languages, large and small, descriptions of which can be found on
glottolog.org. The work on smaller sign languages has been especially fruitful inobserving emergence of linguistic structures across as brief as one to three
generations of signers. Using our work on a small sign language, Al-Sayyid
Bedouin Sign Language, my colleagues and I have joined others in describing
how linguistic structure unfolds over time (Meir et al., 2017). Faced with thisdiversity of languages, our field has debated issues of nomenclature for sign
languages and how to describe social and cultural factors that play a role inshaping linguistic features of urban and rural or national and village sign
languages. Most of these investigations of community properties focus on sign
languages where the presence of deaf people – a single signer, a family, a village,or a new school for deaf children – is the impetus leading to the appearance of asign language. The world language databases, ethnologue.com and glottolog.org,
have categorized primary sign languages as those shared in a community of deaf
people, distinct from “auxiliary” or “alternative” sign languages, such as thoseused by Australian Indigenous communities (Ellis et al., 2019) which exist
alongside and on occasion, in place of the spoken language. Their existence (andpersistence) despite no significant presence of deaf people in the community calls
for an explanation. Lauren Reed (2021) has described the case of a home signer
in Papua Guinea who enjoys a network of willing hearing communicative partners
in and beyond his family. They find that hearing signers are not simply bystanders
but are contributing to a shared lexicon used by deaf home signers in different
villages who have little contact with one another. Because some have more
This paper is distributed under a Creative Commons BY-ND 4.0 license.

7

mobility, hearing signers are transporting signs across geographic space. Thesesign languages, which they call “network sign languages” (because there is no
deaf community as such), exist at the outer bounds of the more deeply researcheddeaf community sign languages. But they may yet yield unexpected insights into
the vast human capacity to build nascent languages when faced with
communicative need. If sign languages have helped us to understand propertiesof spoken languages, small and nascent sign languages – of all types - may be
opportunities for us to track the interweaving of social pattern with language use
and transmission, as it unfolds in human time.
References
Ellis, E. M., Green, J., Kral, I., & Reed, L. W. (2019). 'Mara yurriku': Western
Desert sign languages. Australian Aboriginal Studies, 2, 89-111.
Meir, I., Aronoff, M., Börstell, C., Hwang, S., Ilkbasaran, D., Kastner, I., Lepic,
R., Lifshitz, A., Padden, C. & W. Sandler (2017) The effect of being humanand the basis of grammatical word order: Insights from novel communication
systems and young sign languages. Cognition, 158:189-207.
Reed, L. W. (2021). Sign networks: Nucleated network sign languages and rural
homesign in Papua New Guinea. Language in Society, 1-35.

8

INTERACTION IN ANIMAL COMMUNICATIONSIMONE PIKA *1*Corresponding Author: simone.pika@uni-osnabrueck.de
1 Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany

Language, one of humans’ most distinctive traits, still remains a ‘mystery’ forevolutionary theory. Recently, Levinson (2006) and Levinson and Holler (2014)
proposed that it is not language that makes human communication possible, but a
special capacity for social interaction. This capacity is a layered assemblage ofdifferent social cognitive skills including joint attention, common ground,collaboration and reasoning about communicative intent (Clark 1996). It involves
the specific characteristics of face-to-face interaction, frequent employment ofmutual gaze, and the exchange of rapid communicative turns.
Surprisingly, this hypothesis has received relatively little research attention
in the fields of Animal Behavior and Comparative Psychology (but see Logue andStivers 2012; Rossano 2013; Genty et al. 2020; Heesen et al. 2022; Pika et al.
2018). The present paper will draw attention to this promising research avenue by(i) providing an overview of the state of the art, and (ii) introducing usefulcandidates of human social interaction which may be shared across species andtaxa.
References
Clark, Herbert H. 1996. Using Language. Cambridge: Cambridge University
Press.
Genty, Emilie, Raphaela Heesen, Jean-Pascal Guéry, Federico Rossano, Klaus
Zuberbühler, and Adrian Bangerter. 2020. How apes get into and out of joint
actions. Shared intentionality as an interactional achievement. Interaction
Studies 21 (353-386).
Heesen, Raphaela, Marlen Fröhlich, Christine Sievers, Marieke Woensdregt, andMark Dingemanse. 2022. Coordinating social action: a primer for the cross-This paper is distributed under a Creative Commons BY-ND 4.0 license.

9

species investigation of communicative repair. Philosophical Transactions of
the Royal Society B 377 (1859):20210110.
Levinson, Stephen C. 2006. On the human “interaction engine”. In Roots of
Human Sociality: Culture, Cognition and Interaction, edited by N. J. Enfieldand S. C. Levinson. Oxford: Berg.
Levinson, Stephen C., and Judith Holler. 2014. The origin of human multi-modal
communication. Philosophical Transactions of the Royal Society B Biological
Sciences 369 (20130302):9.
Logue, David M., and Tanya Stivers. 2012. Squawk in interaction: A primer ofconversation analysis for students of animal communication. Behaviour
149:1283-1298.
Pika, Simone, Ray Wilkinson, Kobin H. Kendrick, and Sonja C. Vernes. 2018.
Taking turns: Bridging the gap between human and animal communication.
Proceedings of the Royal Society B Biological Sciences 285 (20180598):9.
Rossano, Federico. 2013. Sequence organization and timing of bonobo motherinfant interactions. Interaction Studies 14 (2):160-189.

Invited Presentations

10

11

BEYOND COMMON DESCENT: COOPERATIVE BREEDING
AND LANGUAGE EVOLUTION

Judith M. Burkart*1,2*judith.burkart@aim.uzh.ch
1Department of Anthropology, University of Zurich, Switzerland
2Center for the Interdisciplinary Study of Language Evolution ISLE, Universityof ZurichGreat apes are skillful communicators, and enculturation studies have shownthat they have an impressive latent potential to acquire human-likecommunication systems. Nevertheless, only humans have evolved language, andthis requires an explanation. Here we argue that a broader phylogenetic
perspective that not only focuses on great apes can provide complementary
insights by identifying cases of convergent evolution.
We focus on callitrichid monkeys, who like humans are cooperative breeders,i.e. individuals others than the mothers are significantly involved in raising thegroup’s offspring. Group members thus frequently have to closely coordinate
their activities and actions, such as handing over infants from one carrier toanother, or deciding who engages in infant care or group defense. Callitrichidmonkeys are equipped with a set of socio-cognitive skills and abilities thatfacilitate this coordination and that show striking similarities to elements of theso-called human interaction engine (Levinson & Holler 2014): reliance on mutual
gaze as coordination smoother, turn-taking, intentional prosociality, and tuning inwith others at the physiological and behavioral level. Thus, the “callitrichid
interaction engine” may have catalyzed the emergence of flexible communicationin callitrichid monkeys (Burkart et al. 2022), like in humans.
A short review of vocal communication in callitrichids indeed reveals highplasticity which appears rare in non-human primates. They are highly voluble,
have large repertoires, and frequently produce combinations of two or more calls.
Some calls are functionally referential and callitrichids use the information about

This paper is distributed under a Creative Commons BY-ND 4.0 license.

12

identity, group and sex encoded in the calls. They also have some voluntary
control over their vocalizations, engage in vocal turn-taking (ontogeneticallylearned, supported by adult scaffolding), and show some vocal learning even as
adults (accommodation). Infants engage in extended bouts of babbling, which
predicts the quality of vocal production at the end of infancy but also elicits social
interactions from adults, reflecting the special challenges that growing up in sucha cooperative breeding system imposes on immatures (Hrdy & Burkart 2020).
It has been argued that to understand the origin of human language, it is
crucial to understand the origin of the sociality that enabled its evolution, thehuman interaction engine. The work on callitrichid monkeys suggests thatcooperative breeding contributed to the emergence of several of the elements
typically attributed to the human interaction engine, most importantly perhaps
high levels of prosociality that become evident in the human eagerness to share
information. Since even enculturated, language trained apes rarely use their skills
in declarative ways to inform others, and thus clearly lack a strong and intrinsic
Mitteilungsbedürfnis, this may well have been crucial for language to evolve.
Other elements of the human interaction engine may well be unique to humans,or shared with primates generally. Based on currently available primate data, agood working hypothesis may be that our uniquely human language system could
emerge because of our double legacy. On the one hand, as great apes, we haveinherited from our closest relatives many of the cognitive prerequisites; on theother hand, because our ancestors, unlike all other great apes, started to engage incooperative breeding and additional forms of intensive cooperation that led tostrong interdependence, prosocial motivational dispositions were convergently
added and together paved the way for language to emerge.
References
Burkart, J. M., Adriaense, J., Brügger, R. K., Miss, F. M., Wierucka, K. & van
Schaik, C. P. (2022). A convergent interaction engine: Vocal communication
among marmoset monkeys. Phil Trans B, 377, 20210098.
Hrdy, S. B. & Burkart, J. M. (2020) The emergence of emotionally modern
humans: Implications for language and learning. Phil Trans B, 375, 20190499.
Levinson, S. C., & Holler, J. (2014). The origin of human multi-modal
communication. Phil Trans B, 369, 20130302.

13

LANGUAGE EVOLUTION - UPDATING THE LENS OFPRIMATE COMMUNICATIONZANNA CLAY**

1

Corresponding Author: zanna.e.clay@durham.ac.uk
Department of Psychology, Durham University, Durham, UK

As our closest living relatives, non-human primates (henceforth primates) provide
a unique lens through which we can explore the evolutionary basis for our
behavioural and cognitive capacities, including for language. Over the past four
decades, there has been intense amount of interest in the communicative
capacities of primates and in particular, what they tell us about the evolutionary
origins of language. Inspired by seminal work of Seyfarth, Cheney and colleagues(e.g. Seyfarth et al., 1980), the initial focus of interest was mostly on primate
vocalisations, particularly monkey alarm calls, and what they could inform about
the evolution of semantics, syntax and relatedly social cognition (e.g.
Zuberbuhler, 2005). A large body of research has, in particular, been dedicated tothe study of functional reference, considered an evolutionary precursor to
linguistic reference (Clay et al, 2010; Townsend & Manser, 2013). Referential
signals must fulfill both the production criteria of being tightly associated with
their eliciting stimulus as well as the perception criteria of eliciting the same
response in a receiver as the putative referent would do (in its absence)(Macedonia & Evans, 2003). While evidence of functionally referential signals
has provided important insights into the evolutionary building blocks of semantics
and other linguistic properties, empirical focus on such signals, which bydefinition are functionally fixed, may have led to a biased view of primate
communication which fails to take into account its apparent flexibility (Taylor etal., 2022) and multimodal nature (Liebal et al., 2014). In addition, by focusing soheavily on referential primate calls, the comparative literature has dedicatedrelatively scant attention towards the affective nature of primate signals, whichhave long been deemed out of the realms of scientific investigation and evenscientifically uninteresting.
This paper is distributed under a Creative Commons BY-ND 4.0 license.

14

Nevertheless, comparative research is now starting to address these
imbalances, and as a consequence, offers new and exciting insights into primate
communication that enable a richer picture of the complex mosaic of traits which
underlie language evolution. In this presentation, I will discuss three key
empirical shifts that are starting to occur in the primate communication literatureand their relevance for understanding language evolution.
The first is a growing appreciation of the functionally flexible nature ofprimate vocalisations. While certain linguistic signals are tightly associated with
their referents, many are actually highly context-dependent and loosely associatedwith their referents, enabling them to fulfill a range of functions. We have argued
that the same may also be true for primate signals and that very notion offunctional reference needs revision in order to account for the important role ofcontext (Scarantino & Clay, 2015) as well as the functionally flexible nature ofmost primate vocalisations (Taylor et al., 2022).
The second is a greater focus on the multi-modal nature of primate
communication and its relevance to language. Until recently, most primate
communication research has focused on unimodal signals with often a strong
discontinuity in both theoretical and methodological approaches between
modalities, particularly vocalisations and gestures (Liebal et al., 2014). Focusing
on one modality alone can result in biased views about the evolutionary basis oflanguage. For instance, many theories have focused on a unimodal origin of
language, despite the fact that both primate communication and human language
are multi-modal. Thus, although such work on multi-modality is still in its
infancy, addressing this imbalance in primate research is starting to enable moreaccurate and balanced models of language origins (Slocombe et al., 2011; Liebal
et al., 2014, 2022).
The third shift is a steadily growing interest in the affective basis of primate
communication and its relation to intentionality (Heesen et al., 2022). Althoughintentional communication about affective states is a central part of humancommunication, there has long been a perceived discontinuity between intentionalversus affective signaling in primate communication. In particular, primate
vocalisations and facial expressions have been traditionally viewed as involuntary
expressions of internal states, whereas gestures are by definition intentional.
Nevertheless, such a view is increasingly contentious given evidence ofintentional vocal and facial expression production, as well as the apparent void ofarousal-based explanations in gesture research. By showing that primate signals
can be both affective and intentional, regardless of signal modality, a moredimensional approach can contribute to a richer picture of primate communication

15

and how its affective and cognitive processes have jointly shaped the evolution of
language.
References
Heesen R., Sievers C, Gruber T., Clay, Z. (2022). Primate communication:
Affective, intentional, or both? In B. L. Schwartz & M. J. Beran (Eds.),
Primate Cognitive Studies. Cambridge University Press.
Liebal, K., Waller, B. M., Slocombe, K. E., & Burrows, A. M. (2014). Primate
communication: a multimodal approach. Cambridge University Presss
Liebal, K., Slocombe, K. E., & Waller, B. M. (2022). The language void 10 years
on:multimodal
primatecommunication
researchisstilluncommon. Ethology Ecology & Evolution, 34(3), 274-287.
Macedonia, J. M., & Evans, C. S. (1993). Essay on contemporary issues in
ethology: Variation among mammalian alarm call systems and the problem
of meaning in animal signals. Ethology, 93(3), 177-197.
Taylor, D., Clay, Z., Dahl, C. D., Zuberbühler, K., Davila-Ross, M., & Dezecache,G. (2022). Vocal functional flexibility: what it is and why it matters. Animal
Behaviour, 186, 93-100.
Townsend, S. W., & Manser, M. B. (2013). Functionally referential
communication
inmammals:thepast,present
andthefuture. Ethology, 119(1), 1-11.
Scarantino, A., & Clay, Z. (2015). Contextually variable signals can befunctionally referential. Animal Behaviour, 100(100), e1-e8.
Seyfarth, R. M., Cheney, D. L., & Marler, P. (1980). Monkey responses to threedifferent alarm calls: evidence of predator classification and semantic
communication. Science, 210(4471), 801-803.
Slocombe, K. E., Waller, B. M., & Liebal, K. (2011). The language void: the needfor multimodality in primate communication research. Animal
Behaviour, 81(5), 919-924.
Zuberbühler, K. (2005). The phylogenetic roots of language: evidence fromprimate communication and cognition. Current Directions in Psychological
Science, 14(3), 126-130.

16

MULTIPLE ATTENTION UNDERPINS THE CO-EVOLUTION
OF THOUGHT AND COMMUNICATION
KOJI FUJITA
fujita.koji.3x@kyoto-u.ac.jp
Graduate School of Human and Environmental Studies,
Kyoto University, Kyoto, Japan

Human language sharply differs from animal communication systems in that it isadaptive not only for communication but also for creative, complex thinking(internal mental calculations such as inference, planning, imagination, etc.), but it
is a totally different issue, still remaining open, whether language initially evolved
as a tool of communication or as a tool of thought. While many researchers,
linguists and biologists alike, opt for the communication view, the opposite view,that language was originally an internal tool of private thought and only laterreused for communicative purposes when coupled with some means ofexternalizing such thoughts (gestures, signs, vocalizations, etc.), is also favoredby linguists of a certain theoretical persuasion (Berwick & Chomsky, 2016). Bothviews have their own merits and demerits, and it is worth considering thepossibility of unifying them into an integrative co-evolutionary theory of thought
and thought. I will argue that “multiple attention,” here defined as a cognitive
capacity for paying and maintaining attention to more than one object, provides a
key to such an integration.
It is important to note that language is a complex trait consisting of several
subfunctions each of which may have evolved in other species independently
before they were linked together to form the human language faculty. In otherwords, there is no single precursor to human language, hence no single originalfunction. Different views stem from which of these subfunctions one focuses on.
While gestures and vocalization, as well as cooperativeness, prosociality andmind reading, are obviously linked to communicative functions, the kind of
hierarchical syntax that is sometimes taken to be the hallmark of human language
This paper is distributed under a Creative Commons BY-ND 4.0 license.

17(structure dependence) may not be so. Rather, its contribution to complex,
hierarchical thought seems more likely, given in particular that structure
dependence often gives rise to structural ambiguity such that the same linear
strings of words have more than one semantic interpretation depending on whichhierarchical structure they derive from, which hinders efficient communication.
Thus there is an apparent tension or dilemma between complex thought andcommunication.
A close examination of linguistic hierarchical structure, in particular of how
complex structures are dynamically built up from scratch, reveals that there is acommon cognitive underpinning for structure dependence and social
communicative behavior; namely, one needs to pay attention to multiple objects
as a target of internal mental manipulation, freely switching between them. Thisis illustrated by comparing a basic linguistic structure like [[ the dog ][ saw [ thecat ]]] (where the dog and the cat are derived separately, to be later integrated intoa single structure) and intention sharing (or shared intentionality; Tomasello,2008) between a speaker and a hearer (each takes note of the other’s intent andtries to conform). I suggest that this kind of multiple attention allowed the coevolution of complex thought (by hierarchical linguistic structure) andcommunication (by mind-reading for intention sharing).
Previously, the thought-first view was advanced on certain debatable
assumptions about language evolution, in particular that there was no
protolanguage, and that human language came into being abruptly when thecapacity for hierarchical structure building (sometimes known as “Merge”)
emerged by some mutation, without any precursor. There are other more natural
assumptions we should take into serious account which reconcile the thought view
with the communication view. It can be argued, for instance, that there was a
protolanguage stage where it already served for the purpose of both thought andcommunication, and that the shift from linear protolanguage (Jackendoff &Wittenberg, 2016) to hierarchical human language changed human thought more
drastically than human communication. More pertinent to the present discussion,
however, is the idea that hierarchical syntax is supported by the same cognitive
trait that underlies human communication.
In such a co-evolutionary scenario, the role of externalization cannot be
overemphasized. Rather than being a subsidiary process added later, it was thecore feature that made it possible to manipulate abstract linguistic concepts on apar with concrete objects to create more and more complex thoughts. Language
is more than communication, but it could not have evolved without
communication via externalization.

18

References
Berwick, R.C., & Chomsky, N. (2016). Why only us: Language and evolution.
Cambridge, MA: MIT Press.
Fujita, H., & Fujita, K. (2021). Human language evolution: a view fromtheoretical linguistics on how syntax and the lexicon first came into being.
Primates, online. DOI:10.1007/s10329-021-00891-0
Fujita, K. (2017). On the parallel evolution of syntax and lexicon: a Merge-onlyview. Journal of Neurolinguistics, 43, 178-192.
Greenfield, P.M. (1991). Language, tools, and brain: the ontogeny and phylogenyof hierarchically organized sequential behavior. Behavioral and Brain
Sciences, 14, 531-595.
Hauser, M.D., Chomsky, N., & Fitch, W.T. (2002). The faculty of language: what
is it, who has it, and how did it evolve? Science, 298, 1569-1578.
Hauser, M.D., & Watumull, J. (2017). The universal generative faculty: thesource of our expressive power in language, mathematics, morality, andmusic. Journal of Neurolinguistics, 43, 78-94.
Jackendoff, R., & Wittenberg, E. (2016). Linear grammar as a possible steppingstone in the evolution of language. Psychonomic Bulletin & Review, 24, 219224.
Martins, P.T., & Boeckx, C. (2019). Language evolution and complexity
considerations: the no half-Merge fallacy. PLoS Biology, 17(11), e3000389.
Tomasello, M. (2008). Origins of human communication. Cambridge, MA: MIT
Press.

19

EMERGENT CONSTRUCTIVE APPROACH
TO EVOLINGUISTICS
TAKASHI HASHIMOTO
hash@jaist.ac.jp
Japan Advanced Institute of Science and Technology, Japan

Evolinguitics aims to deepen our understanding of humans from an evolutionary
perspective by clarifying the origins and evolution of language andcommunication, emphasizing two idiosyncratic natures of human linguistic
communication. They are to use hierarchically organized symbol sequences inlanguage and to share intentions in communication. We believe that integratingthese two characteristics makes humans co-creative and intelligent. In this talk, I
introduce two studies taking an emergent complex approach. It is a methodology
to analyze complex systems by constructing and operating the evolutionary andemergent process of complex phenomena and plays a vital role in the attempt of
Evolinguistics. One research I introduce is an evolutionary simulation of recursive
combination, which is thought of as the essential ability to form hierarchicalstructures in language and many human cultural products. The other is anexperimental semiotic study to consider the process, mechanisms, and neuralbasis of symbolic communication systems. Finally, I propose a hypothesis that
understanding others’ intentions is realized by abduction, which consists ofhypothesis generation and their selection, and that the former is brought byrecursive combination and the latter by embodiment. This hypothesis is a sort ofintegration of hierarchy and intention sharing.

This paper is distributed under a Creative Commons BY-ND 4.0 license.

20

HISTORIAL AND ECOLOGICAL ASPECTS OF THE
EVOLUTION OF HUMAN LANGUAGE FACULTYYASUO IHARA
iharay@bs.s.u-tokyo.ac.jp
Department of Biological Sciences, University of Tokyo, Tokyo, JapanAny meaningful hypothesis to account for the emergence of language facultyneeds to be congruent with our knowledge about the history of human evolution,
which is continuously updated based on paleoanthropological researches. It also
should consider the ecology of human ancestors, given that natural selection has
shaped functional traits as adaptation to their physical as well as social
environment. During the last five years, I have participated in the Evolinguistics
Project as a member of the Human Evolution Team. The key role that our teamhas played in the project has been to bring historical and ecological perspectives
to the study of language evolution. I will talk about some of the issues that wehave worked on in this research program. As a basic premise, I consider theevolution of language faculty as a long-term, step-by-step process through theseven-million-year history of human evolution, rather than a single major event.
I also assume that sub-functions composing the language faculty emerged
sequentially during the process, some of which may have emerged, perhaps long
before language itself, as preadaptations.
For the purpose of summarizing the relevant historical framework as simply
as possible, without being overwhelmed by the vast detailed information, I
propose a highly simplified three-phase scheme for the history of human
evolution (Ihara, forthcoming). Phase 1 of the scheme corresponds to the first
several million years since the human-chimpanzee divergence. Among theAfrican apes at that time, the early hominins were unique in that they were
bipedal and had reduced canine teeth. The transition to Phase 2 is marked by the
beginning of the oldest stone-tool industry. During Phase 2 there was a dramaticincrease of brain size, which chronologically parallels increasing sophisticationof tool-making technologies. This is also when early Homo diffused out ofThis paper is distributed under a Creative Commons BY-ND 4.0 license.

21

Africa for the first time in the human history. A second phase transition isequated with the advent of novel types of artifacts, such as personal ornaments
and arts, so-called modern human behavior. For example, one of the oldest signsof modern human behavior is the 75,000-year-old shell beads from South
Africa.
I further argue that each of these phases may be characterized by a distinct
dominant evolutionary force, and that they were not just temporally consecutive,
but also causally connected (Ihara, forthcoming). To be specific, I speculate thatsocial and life-history evolution that occurred in Phase 1 set the stage forcognitive evolution in Phase 2, which enable cultural evolution in Phase 3. I amaware that this is all too simplistic, and I do not intend to suggest, for example,
that social and life-history evolution took place only in Phase 1. Nevertheless,this kind of schematization may hopefully help to draw attention to the possible
role of niche construction in human evolution and a sort of evolutionary cascadethat may have resulted to drive phenotypic diversification between modern
humans and non-human apes.
I will discuss some specific research topics that have been studied in the
Human Evolution Team, including hominin confrontational scavenging(Nakamura et al., 2019), manufacture of composite tools (Sano et al., 2019),
social selection to favor reduced aggression (Ihara, 2020), and hierarchicalobject manipulation (Hayashi & Takeshita, 2022).

Acknowledgement
This work was supported by KAKENHI JP17H06381.
References
Hayashi, M., & Takeshita, H. (2022). Hierarchical object combination and tool
use in the great apes and human children. Primates.
Ihara, Y. (2020). A mathematical model of social selection favoring reduced
aggression. Behavioral Ecology and Sociobiology 74, 91.
Ihara, Y. (forthcoming). Early adaptations to human language: Historical andecological aspects. In Y. Ihara et al. (Eds.), Human Evolution and Language:
A View from Hierarchy and Intention Sharing. Springer.
Nakamura M. et al. (2019). Wild chimpanzees deprived a leopard of its kill:
Implications for the origin of hominin confrontational scavenging. Journal of
Human Evolution 131, 129-138.
Sano K. et al. (2019). The earliest evidence for mechanically delivered projectile
weapons in Europe. Nature Ecology & Evolution 3, 1409-1414.

22

INFERENTIAL NATURE OF GESTURE AND ITS ROLE IN
EVOLUTION OF LANGUAGE
HARUMI KOBAYASHI *1*Corresponding Author: h-koba@mail.dendai.ac.jp
1 Tokyo Denki University, Japan

One important characteristic of human gesture is its inferential nature, that is, themeaning of gesture must be inferred by the recipient. This inferential nature canbe observed in almost every aspect of human communication, therefore,
examining this inferential nature of gesture must contribute to the exploration of
the origins and the evolution of language. Based on experimental evidence fromour laboratory observations, I discuss the inferential nature of pointing gestures.
To interpret pointing gesture, it is necessary to recognize the accompanying
characteristics of pointing gestures and make relevant inferences in the given
context. The present results show that both children and adults are sensitive toinferential nature of pointing gesture and appropriately use the information for
word learning. These results suggest that inferential nature of humancommunication played an important role in evolution of language.

This paper is distributed under a Creative Commons BY-ND 4.0 license.

23

VOCAL LEARNING AND THE EVOLUTION OF BEAT-BASEDRHYTHMIC PROCESSING
ANIRUDDH D. PATEL*1*Corresponding Author: a.patel@tufts.edu
1Dept. of Psychology, Tufts University, Medford, MA, USA

Rhythms with a periodic beat are widespread in human music and are not part ofordinary language. Yet beat-based rhythmic processing may have deep
evolutionary and neurological links to spoken language. About 15 years ago I
hypothesized that human beat processing built on the neural circuitry for complex
vocal learning, an ability foundational to the evolution of speech (Patel, 2006). In
this talk I review and update this hypothesis in light of cross-species, neural, andgenetic research since that time, including research on nonhuman primates, birds,
pinnipeds, humans, and rodents (Patel, 2021). Based on this research I maintainthat there are important evolutionary and neural relations between vocal learningand beat-based rhythmic processing. I also hypothesize that gene-culture
coevolution elaborated on these links to produce neural specializations for beatprocessing in the dorsal auditory stream of the human brain. I suggest that beatbased rhythmic processing provides a model system for studying cognitive geneculture coevolution, in which neuroscientific, cross-species, and genetic research
can be meaningfully integrated (Patel, in press).
References
Patel, A.D. (2006). Musical rhythm, linguistic rhythm, and human evolution.
Music Perception, 24:99-104.
Patel, A.D. (2021). Vocal learning as a preadaptation for the evolution of musicalbeat perception and synchronization. Philosophical Transactions of the Royal
Society B. 376 (1835), 20200326
Patel, A.D. (in press). Musicality and gene-culture coevolution: Ten concepts toguide productive exploration. In: E.H. Margulis, P. Loui, & D. Loughridge

This paper is distributed under a Creative Commons BY-ND 4.0 license.

24(Eds.) The Science Music Borderlands: Reckoning with the Past andImagining the Future. MIT Press. https://psyarxiv.com/qp6jx/

25

LINKING LANGUAGE EVOLUTION, LANGUAGEACQUISITION, AND LANGUAGE DIVERSITY: HOW SOCIALAND COGNITIVE PRESSURES SHAPE LEARNING AND
COMMUNICATIONLIMOR RAVIV *1,2
*Corresponding Author: limor.raviv@mail.huji.ac.il
1 Language Evolution and Adaptation in Diverse Situations (LEADS) group, Language &Genetics Department, Max Planck Institute for Psycholinguistics, The Netherlands
2 Centre for Social, Cognitive and Affective Neuroscience (cSCAN), University ofGlasgow, UK

What are the social, environmental, and cognitive pressures that shape theevolution of language in our species? Why are there so many different languages
in the world? And how did this astonishing linguistic diversity come about? These
are some of the most interesting questions in the fields of cognitive science andlinguistics, and represent the range of topics discussed in my research so far.
My work focuses on linking core aspects of language acquisition, language
evolution, and language diversity using a range of novel behavioral paradigms
and computational models. My goals are to (1) shed light on the communicative
pressures and cognitive constraints (e.g., memory limitations, input variability,
generalization, multimodality) that shape language use and social interaction in
our species, and (2) to identify the social, environmental, and cross-cultural
factors (e.g., population size, network structure, gender/age distribution, noisy
physical conditions) that lead to language diversity and to cross-linguistic
variation.
In this talk, I will provide an overview of my research (including methods
and results from selected projects), as well as present future directions andongoing work. Specifically, I will outline previous studies that used group
communication experiments and artificial language learning experiments to test
the live formation and acquisition of new languages in the lab. I will focus on theeffect of population size, namely, the differences between languages that evolved
in big vs. small mini-communities in terms of their compositional structure, theirrate of change, and their ease of learning. I will then discuss the role of input
variability in underlying group size effects and shaping this evolutionary process,as well as shaping humans’ learning and generalization more broadly. Finally, I
will link these results to the human self-domestication hypothesis for language
evolution, highlighting recent work that tests key components of selfdomestication (e.g., prosociality, interaction with strangers) in new animal models
and in AIs.

This paper is distributed under a Creative Commons BY-ND 4.0 license.

26

Refereed Contributions

27

28

QUALITY NOT QUANTITY IN SYNONYMY AVOIDANCEAja Altenhof1,2 and Gareth Roberts*2* Corresponding Author: gareth.roberts@ling.upenn.edu

1 Centre for Language Evolution, University of Edinburgh, Edinburgh, Scotland
2 Department of Linguistics, University of Pennsylvania, Philadelphia, USA

Words often compete for meaning and synonymy is extremely common. But
absolute synonymy is exceedingly rare (Murphy, 2003). How does this situationarise? One possibility is that it results from a cognitive bias against synonymy,
such as the mutual exclusivity bias (Markman & Wachtel, 1988; Hurford, 2003).
Another possibility is that it is caused by differences in the perceived contextual
distribution of candidate synonyms, which become amplified in learning (Hudson
Kam & Newport, 2009).
To investigate this we conducted an experiment in which 362 participants wereasked to learn three new slang words—presented in example sentences—and (asa cover task) to guess what the words meant. Two of the words (snater and fincur) were verbs, and participants were told they referred to the same action. The
third word (murp) was a noun and functioned purely as a distractor. In the initial
Exposure phase, 36 sentences were presented on screen, one by one, with 10 s
between each sentence. Sentences were presented in a random order, with 12
per word. Verbs could appear in negative contexts (e.g., “Don’t fincur in front ofme!”), positive contexts (e.g., “My friends and family love my snatering skills.”),or neutral contexts (e.g., “Dogs can’t snater”). The noun murp appeared only inneutral contexts.1
After the Exposure phase came the Generalization phase in which participants
were presented with 36 unseen sentences, each with a gap for participants to insert
one of the three words. Of these sentences, 24 required verbs (12 positive and 12
negative) and the remaining 12 required nouns.
We manipulated the distribution of contexts in the Exposure phase. In the
Neutral condition, all sentences were neutral. In the Random condition, both verbs
were evenly distributed across positive and negative contexts. In the Consistentcondition, one verb appeared only in positive sentences and the other appeared
only in negative sentences. In the Overlapping condition, one verb occurred 75%
1 Sentences were chosen following extensive piloting to ensure that the intended difference in valence existed.

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

29of the time in positive sentences and 25% in negative sentences; the reverse wastrue for the other verb. For the 75%-positive condition, both verbs occurred 75%of the time in positive sentences and 25% in negative sentences; this distribution
was reversed for the 75%-negative condition.
To identify how much participants differentiated the verbs in the Generalization phase we calculated, for each verb, a word context score by dividing its
frequency in its higher-frequency context by the sum of its frequencies for bothcontexts. We then multiplied the context scores for the two verbs to get a Differentiation score. A Differentiation score of 0 meant that one verb had not been used
at all. A score of 1 meant that the two verbs were 100% differentiated. 0.25 meantthat both verbs were used equally often in both contexts (equivalent to random
assignment). A score around 0.667 indicated “partial differentiation”, where one
verb was entirely specialized to one context and the other played a more defaultrole and occurred in both (as with, e.g., English thin : skinny).

Figure 1.

Differentiation score distributions for all conditions. Red dots indicate mean values.

Results are displayed in Figure 1. In the Consistent condition, full (or closeto-full) differentiation occurred. In all other conditions, regardless of Exposurephase distribution, we found partial differentiation. A replication in which neutral
sentences were included in the Generalization phase found the same result.
We consider these results to be consistent with an account based on a cognitive
bias against synonymy (cf. Hurford, 2003). Participants differentiated potential
synonyms at a greater than chance level and to an extent inconsistent with theirdistribution. This is not to say that distribution was irrelevant; there was significantly greater differentiation in the Consistent condition. However, it seems thatthe quality of differentiation in Exposure was more important than quantity.

30

References
Hudson Kam, C. L., & Newport, E. L. (2009). Getting it right by getting it wrong:
When learners change languages. Cognitive Psychology, 59(1), 30–66.
Hurford, J. R. (2003). Why synonymy is rare: Fitness is in the speaker. In
W. Banzhaf, J. Ziegler, T. Christaller, P. Dittrich, & J. T. Kim (Eds.), European conference on artificial life (pp. 442–451). Dortmund.
Markman, E. M., & Wachtel, G. F. (1988). Children’s use of mutual exclusivity
to constrain the meanings of words. Cognitive Psychology, 20(2), 121–157.
Murphy, M. L. (2003). Semantic relations and the lexicon: Antonymy, synonymy
and other paradigms. Cambridge: Cambridge University Press.

31

R YOU SURE THAT YOUR /R/ IS TRILLED? A METHODOLOGICALCAVEAT
RÉMI ANSELME*1 , FRANÇOIS PELLEGRINO1 , and DAN DEDIU2* Corresponding Author: remi.anselme@univ-lyon2.fr

1 Laboratoire Dynamique du Langage, CNRS/University of Lyon, Lyon, France

2 University of Barcelona & Catalan Institute for Research and Advanced Studies (ICREA),

Barcelona, Spain

Databases are great tools for typological studies and for understanding theevolution of language, but they can rarely be taken at face value. Many (if notall) databases have various limitations and usage constraints (see, for example,
for WALS (Cysouw, Dediu, & Moran, 2012; Hunley, Bowern, & Healy, 2012)
and UPSID (Simpson, 1999)), which can ”trickle down” to (or even be amplifiedby) the studies based on them. We focus here on phonemic databases, which
can be used, among others, to compare phonemic inventories across languages
and to make claims about language evolution through perceptual or articulatory
mechanisms for instance. However, such databases often suffer from an intrinsic
ambiguity between phoneme/allophone/phone, which often percolates down toresearch that aims to study the phonetic systems of languages from (supposedly)
phoneme inventories (for a recent example, see (Winter, Sóskuthy, Perlman, &Dingemanse, 2022)). One common hurdle is that sometimes the phoneme is seen
as a monolithic object without variation in its phonetic realizations, and without
contextual dependencies. Moreover, written symbols can be misleading because
the same symbol may represent different phonetic realities (Anderson et al., 2018).
To drive these points home, we will focus here on r (Barry, 1997).
Not all segments are directly comparable, and the rhotics class is a perfect
example because of their great phonetic variability (Chabot, 2019; Lindau, 1985).
In particular, by default the rhotic r is often considered as an alveolar trill: froma phonemic point of view, UPSID (Maddieson, 1984) reports that among the languages that have a rhotic, at least half have one alveolar trill or more. But to saythat a language has an alveolar trill phoneme does not inform about its potential
phonetic realizations. Several studies show that the trill alveolar phonemes have
few of their allophones that are actually trilled (Sebregts, 2014; Blecua, 2002;
Rafat, 2010), potentially leading to a distorted picture. As an example, we consider here a recent article (Winter et al., 2022) which correlates the presence ofthe trilled /r/ sound with “roughness” cross-modally. Their result is supported byseveral approaches, one being a cross-linguistic statistical study of 332 languages,
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

32

where, in order to determine whether a language has a trilled /r/ or not, the authors
rely on (a) their phonetic judgments based on the literature, and (b) PHOIBLE(Moran & McCloy, 2019). Although their phonetic judgments took precedence
over PHOIBLE, both methods tend to force the interpretation of the phoneme ashaving a single realization and may introduce some residual subjectivity.
As a sort of proof of concept, we decided to reproduce their process, by focusing on a random sub-sample of the languages they considered (34 of 332; ≈10%)for which they mention a trilled /r/. Our rerun is based on the authors’ own comments in the data files used for data preparation, which we used as a starting point
for searching grammars and reports on the languages of interest. When the authors
based their judgment on PHOIBLE, we accessed the primary sources in there toassess whether /r/ was indeed trilled (however, this is still problematic because of
the limitations mentioned in the previous paragraph). We systematically tried touse several resources and avoid making decisions based on only one source.
The findings on this sub-sample are that, first, in 16 of the languages (47% of
the sub-sample), we fail to reach the same conclusions as the authors. There arethree languages that should not have been included in the original analysis as theyshow a contrast between a trilled /r/ and a non-trilled /r/ (exclusion criterion in theoriginal study). Eight other languages should not have been considered as having
a trilled /r/ because the rhotic is described as not trilled in the primary sources even
if the symbol r is used in the grammars (leading to confusion). And in another six
languages, the articulation of the rhotic is not defined in the grammars, making
it is impossible to tell if it is a trilled /r/ (sonorant, resonant, liquid, or rhotic) ornot, other than by implicitly assuming an interpretation of the symbol r as a trilled/r/ (which is not warranted). For the remaining 18 languages (53% of the subsample), we may consider them as having a trilled /r/, since we did find grammars
containing phonetic information. Still, this is not always the case, with somedecisions made solely on the basis of the mention ”trill” in the phoneme inventoryof the languages’ reference grammars.
Should our preliminary results generalize to the whole sample of 332 languages, it may question the validity of the statistical cross-linguistic findings of across-modal association between the alveolar trill and “roughness”. However, ourpoint here is not to criticize this particular study, but to highlight the dangers ofassuming phonetic meaning from written symbols allegedly representing phonological systems. In fact, we want to underline that, while large databases (and theirassociated statistical methods) are essential for the modern language sciences andthe study of language change and evolution, they cannot be used without considerable care and expertise. The good news is that such expertise is available andachievable, as well as incorporable in large-scale databases (see, for example, theinclusion of multiple inventories and of allophones in PHOIBLE), and that new
statistical approaches, capable of dealing with the remaining uncertainty, can be
developed (probably in a Bayesian framework).

33

References
Anderson, C., Tresoldi, T., Chacon, T., Fehn, A.-M., Walworth, M., Forkel, R.,& List, J.-M. (2018). A cross-linguistic database of phonetic transcription
systems. Yearbook of the Poznan Linguistic Meeting, 4(1), 21–53.
Barry, W. J. (1997). Another r-tickle. Journal of the International Phonetic
Association, 27(1-2), 35–45.
Blecua, B. (2002). Las vibrantes del español: manifestaciones acústicas y procesos fonéticos. Unpublished doctoral dissertation, Universitat Autònoma deBarcelona.
Chabot, A. (2019). What’s wrong with being a rhotic? Glossa: a journal ofgeneral linguistics, 4(1), 38.
Cysouw, M., Dediu, D., & Moran, S. (2012). Comment on “Phonemic Diversity Supports a Serial Founder Effect Model of Language Expansion fromAfrica”. Science, 335(6069), 657–657.
Hunley, K., Bowern, C., & Healy, M. (2012). Rejection of a serial founder effects model of genetic and linguistic coevolution. Proceedings of the Royal
Society B: Biological Sciences, 279(1736), 2281–2288.
Lindau, M. (1985). The Story of /r/. In V. A. Fromkin (Ed.), Phonetic Linguistics:
Essays in Honor of Peter Ladefoged (p. 260). Academic Press Inc.
Maddieson, I. (1984). Patterns of sounds. Cambridge [Cambridgeshire] ; New
York: Cambridge University Press.
Moran, S., & McCloy, D. (Eds.). (2019). PHOIBLE 2.0. Jena: Max Planck
Institute for the Science of Human History.
Rafat, Y. (2010). A Socio-Phonetic Investigation of Rhotics in Persian. IranianStudies, 43(5), 667–682.
Sebregts, K. D. C. J. (2014). The Sociophonetics and Phonology of Dutch r.
Unpublished doctoral dissertation, LOT, Netherlands Graduate School of
Linguistics, Utrecht.
Simpson, A. P. (1999). Fundamental problems in comparative phonetics andphonology: does upsid help to solve them. In Proceedings of the 14th international congress of phonetic sciences (Vol. 1, pp. 349–352). San Francisco.
Winter, B., Sóskuthy, M., Perlman, M., & Dingemanse, M. (2022). Trilled /r/is associated with roughness, linking sound and touch across spoken languages. Scientific Reports, 12(1), 1035.

34

A COGNITIVE BIAS FOR ZIPFIAN DISTRIBUTIONS? UNIFORM
DISTRIBUTIONS BECOME MORE SKEWED VIA CULTURAL TRANSMISSION
Shufaniya, Amir1, Arnon, Inbal 2*1*Corresponding Author: inbal.arnon@mail.huji.ac.il
1Psychology Department, Hebrew University, Jerusalem, ISRAEL

Despite the many differences between them, human languages share certain
similarities. One of them is the Zipfian distribution of word frequencies (Zipf,
1949). Across languages, word frequency follows a power law distribution withmany low frequency words, few highly frequent words and a non-linear decreasein frequency (e.g., Piantadossi, 2014). The presence of such skewed distributions
is a recurring feature of natural language, with ongoing debate about their source
andwhethertheyreflect
foundational
propertiesofhuman
language/cognition/communication (e.g., Ferrer i Cancho & Sole, 2003). Here,
we use an iterated learning paradigm – which shows how weak individual biases
impact language structure over time (e.g., Kirby et al. 2008) - to ask whether
learners have a cognitive bias for Zipfian distributions, reflected in a tendency toshift uniform distributions into skewed ones during cultural transmission.
Recent work suggests the recurrence of Zpifian distributions in language
may be driven (at least in part) by learnability pressures (e.g., Bentz et al., 2017;
Lavi-Rotbain & Arnon, 2020, 2022). In particular, the lower unigram entropy ofsuch distributions (compared to uniform and less skewed ones) may benefit
learning (Ferrer i Cancho, 2018; Lavi-Rotbain & Arnon, 2022). Indeed, growing
evidence suggests Zipfian distributions facilitate word segmentation and learning(Kurumada, Meylan & Frank, 2013; Hendrickson & Perfors, 2019), and that they
do so because of their lower unigram entropy (Lavi-Rotbain & Arnon, 2022).
Importantly, existing findings do not tell us whether there is a cognitive
preference for skewed distributions: the facilitation may reflect learners’ priorexperience with such distributions rather than a cognitive preference for them.
Here, we explore the existence of such a preference by asking if learners willchange their input to make it more skewed. In the first study, we show thatlearners are biased to produce skewed word distributions in telling a novel story:
participants were given a short prompt including six nonce words introduced asnames, nouns or verbs (Noun-prompt: “John has a store with six objects in it: aplizet, a nilbo, a skiger, a vamey, a chila, and a fengle. Write a short story about
John’s day”). Aggregated over participants, the nonce words followed a ZipfianThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

35

distribution for all three parts of speech (high fit of summed frequency by rank toa power law distribution for nouns (R2=.98), verbs (R2=.98), and names (R2=.97).
In the second study, we ask if this bias leads to a shift from uniform
distributions towards more skewed ones using an iterated learning paradigm. We
exposed the first learner to a story where the six nonce words appeared equallyoften, and asked them to re-tell it. Their output served as input for the next learner,and so on for a chain of ten learners (or "generations"). We looked at two
conditions: (1) where the number of unique words could decrease (five chains)and (2) where it couldn’t (participant had to use all words, five chains). Overtime, word distributions became more skewed (as measured by lower levels ofentropy), even when the number of word types remained constant (significant inmodel comparisons of mixed-effect regression models, also when excluding
differently shaped chain 4). These findings suggest speakers have a cognitive bias
for skewed distributions that gets amplified over time, lending support to the ideathat their recurrence in language may be driven in part by learnability pressures.

Figure 3. The decrease in entropy levels over generations in each of the five diffusion chains in
condition 1 (left) and 2 (right)

References
Ferrer i Cancho, R., & Sole, R. V. (2003). Least effort and the origins of scaling inhuman language. Proceedings of the National Academy of Sciences, 100(3), 788–
791.
Kirby, S., Cornish, H., & Smith, K. (2008). Cumulative cultural evolution in thelaboratory: An experimental approach to the origins of structure in humanlanguage. Proceedings of the National Academy of Sciences, 105(31), 1068110686.
Kurumada, C., Meylan, S. C., Frank, M. C. (2013). Zipfian frequency distributions
facilitate word segmentation in context. Cognition. 127, 439–453.
Lavi-Rotbain, O. & Arnon, I. (2022). The learnability consequences of Zipfian
distributions, Cognition, 223, xx-x
Piantadosi, S. T. (2014). Zipf’s word frequency law in natural language: A critical review
and future directions. Psychonomic Bulletin & Review, 21(5), 1112–1130.
Retrieved from
Zipf, G. K. (1949). Human behavior and the principle of least effort. In Human behavior
and the principle of least effort. Oxford, England

36

NOISE RESISTANCE AT MULTIPLE LEVELS OF LINGUISTICSTRUCTURE
Rachael Bailes*1 , Joel C. Wallenberg2 , and Christine Cuskley1* Corresponding Author: rachael.bailes@ncl.ac.uk

1 School of English Literature, Language, and Linguistics, Newcastle University, UK
2 Department of Language and Linguistic Science, University of York, UK

Design for noise resistance is a fundamental requirement of successful communication systems both in technology and the natural world (Shannon, 1948;
Brumm & Slabbekoorn, 2005). Redundancy, one way of resisting noise, has been
observed at multiple levels of linguistic structure (Fenk-Oczlon, 2001; Aylett &Turk, 2004; Pluymaekers, Ernestus, & Baayen, 2005; Jaeger & Levy, 2007). The
ultimate function of noise resistance features is to minimise the loss of information, which is quantified in bits for each signal unit in terms of its probability(log2 p1i ), from Shannon (1948)’s well-known formula for the average informationof a set of signals.
We demonstrate that the ordering of high and low information units can yield
more or less uniform distributions of information, and that more uniform distributions are functional for noise resistance. We also show that speakers arebiased toward producing utterances with more uniform distributions (Cuskley,
Bailes, & Wallenberg, 2021). Sentences of 10 words were extracted from the
The Penn-York Computer-annotated Corpus of a Large amount of English (PYCCLE; 2015), from which we derived four versions of each sentence: Optimised(where the information distribution was made maximally uniform by reordering
units with a dedicated algorithm); Asymmetric (where the units were ordered byinformation content values such that the distribution was entirely asymmetric);
Original (the original order of units in the natural sentence), and; Random (where
the ordering of units was randomly shuffled). Each set of versions was subjected
to two noise conditions: Single Unit Noise (where 3 single units were deleted fromthe 10 unit string in each version) and Clustered Noise (where 3 sequential units
were deleted). The results show that (i) natural sentences were more uniform thanrandom and asymmetric orders, (ii) more uniform distributions lost less information overall, and (iii) uniform distributions of information are 100% successful at
preventing catastrophic information loss (defined as the loss of ≥50% of a distribution’s information content). These findings indicate that linguistic units are
dynamically ordered across the sentence so as to optimise resistance to noise.
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

37

We expand on the theoretical ramifications of an information-loss threshold
and the effect of clustered noise as they apply to linguistic communication. Noise(anything that prevents the receiver from observing a complete signal, and only
the intended signal) is particularly problematic for language when it comes in alarge chunk, due to language’s famous “duality of patterning” (Hockett, 1958).
Linguistic structure is multi-layered, and the structure at each layer must be inferred from the information a previous layer provides.
An information theoretic approach suggests that each layer of structure serves
as a signal for another layer. Each layer of structure is a message for which anotherlayer is the signal, and so on. While noise might take a form that affects particularunits in isolation (e.g., omitting or altering one particular word in a sentence;(Ryskin, Futrell, Kiran, & Gibson, 2018)), chunks of noise that have the potential
to obscure multiple linguistic units are a larger danger from the perspective ofcatastrophic signalling failures. Even a small amount of noise that spans multiple
units - e.g. obscuring the last few phonological segments of one word and the first
few of the following word - may well result in a failure to reconstruct both words.
Because linguistic structure at levels other than the strictly phonetic consists of
categorical units, analogue types of noise have the potential to obscure multiple
categorical linguistic symbols at once, and so result in knock-on effects disrupting
multiple units at higher levels. This may also predict different distributions of
information at different levels. Phonemic categories are the first to be signalled,
and terminal fading of phonetic material presents a particular noise constraint on
this level of signalling. Since information loss compounds, conservation at thisfirst level is crucial. The risk of compounded information loss combined withphonetic processing constraints therefore predict the frontloading of phonemic
information within a word (King & Wedel, 2020). That phonetic articulation isparticularly amenable to redundancy through lengthening (Aylett & Turk, 2004)
may serve to mitigate the asymmetry this introduces.
We thus explicate a model of how different levels of linguistic structure signal
one another in information theoretic terms. This information-theoretic paradigm
for linguistic processing specifies that (i) linguistic communication (i.e. ‘message’
reconstruction) is a multilayered process of sub-setting the space of possible outcomes based on signal information, and (ii) signals at lower linguistic levels subset the space of possibilities at higher linguistic levels in a serial ‘daisy-chain’of inter-level communication, such that (iii) information loss at lower levels of
linguistic structure compounds information loss at higher, propositional levels.
From this perspective, it follows that linguistic communication is constrained
not only by the absolute amount of noise, but by the potential for analogue noise tocause catastrophic signalling failure between linguistic levels. Linguistic planningis biased toward more uniform distributions of information, suggesting adaptation
for the prevention of such catastrophic communication failure.

38

References
Aylett, M., & Turk, A. (2004). The smooth signal redundancy hypothesis: A functional explanation for relationships between redundancy, prosodic prominence, and duration in spontaneous speech. Language and speech, 47(1),31–56.
Brumm, H., & Slabbekoorn, H. (2005). Acoustic communication in noise. Advances in the Study of Behavior, 35, 151–209.
Cuskley, C., Bailes, R., & Wallenberg, J. (2021). Noise resistance in communication: Quantifying uniformity and optimality. Cognition, 214, 104754.
Ecay, A. (2015). The Penn-York Computer-annotated Corpus of a Largeamount of English based on the TCP (PYCCLE-TCP). Public release 1.
https://github.com/uoy-linguistics/pyccle.
Fenk-Oczlon, G. (2001). Familiarity, information flow, and linguistic form. Typological Studies in Language, 45, 431–448.
Hockett, C. F. (1958). A course in modern linguistics.
Jaeger, T. F., & Levy, R. P. (2007). Speakers optimize information density throughsyntactic reduction. In Advances in neural information processing systems(pp. 849–856).
King, A., & Wedel, A. (2020). Greater early disambiguating information for lessprobable words: The lexicon is shaped by incremental processing. Open
Mind, 4, 1–12.
Pluymaekers, M., Ernestus, M., & Baayen, R. H. (2005). Lexical frequency andacoustic reduction in spoken dutch. The Journal of the Acoustical Societyof America, 118(4), 2561–2569.
Ryskin, R., Futrell, R., Kiran, S., & Gibson, E. (2018). Comprehenders model
the nature of noise in the environment. Cognition, 181, 141–150.
Shannon, C. E. (1948). A mathematical theory of communication. Bell systemtechnical journal, 27(3), 379–423.

39

EXPLORING THE CONCEPT OF PHENOTYPICACCOMMODATION: THE CASE OF THE LOSS OF AIR SACS
LLUÍS BARCELÓ-COBLIJN*1*
Corresponding Author: lluis.barcelo@uib.cat
Laboratori d’Investigació en Complexitat i de Lingüística Experimental (LICLE),
Universitat de les Illes Balears, Palma, Spain

1

During human evolution our ancestors developed a new phenotype that excluded anorgan present in all extant great apes: laryngeal air sacs. This change has been
acknowledged as an important step towards modern human phonetics. However, to datethere is little reflection about how to conceive such a drastic evolutionary change within
the subtribe Hominina. Here we propose the theoretical integration of air sacs loss as acase of phenotypic accommodation, a notion that helps to understand how our ancestors
managed to survive, accommodating and consolidating the phenotype without air sacs,
paving the way for a new phonetic system.

1. Laryngeal air sacs and Hominoidea
Laryngeal air sacs is a primitive trait present in many mammals: cetaceans likedolphins and whales, some ungulates (e.g. takins, from the Hymalayas) and also
in many primates. Hewitt et al. (2002) showed that, within 128 primate species,
up to 72 still have this anatomical feature. Schön Ybarra (1995) attested up to
four different kinds of laryngeal air sacs within primates: (1) lateral ventricular,(2) subhyoid, (3) infraglottal and (4) dorsal. The first one is present in all great
apes with the exception of H. sapiens. Air sacs are present in juvenile apes, evenin fetuses and neonates (Stark & Schneider 1960). Steele et al. (2013) used 3-D
reconstructions and observed that air sacs in chimpanzees are lateral ventricular,
extending in a position behind the clavicle bone.
1.2. Possible functions of air sacs
The relation of the vocal tract and air sacs and their potential functions iscontroversial, since their potential functions could be several and not all species
use air sacs for the same functions: for saving exhaled air (Negus, 1949); for theThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

40

reduction of hyper-ventilation (Hewitt et al., 2002); for generating a new soundsource and stronger and longer lasting calls (Fitch & Hauser, 2003). Lieberman(2011) notes that air sacs are probably related to vocalization since inspiring
carbon dioxide-rich air is not as useful as controlling speech. Falk (1975)
pointed out that the vertical movement of the hyoid bone also compresses theorifice of the laryngeal air sacs and hence, participating in the mechanics when
air sacs are emptied out and filled up. De Boer (2008) and Riede et al. (2009)
created several models showing the influence of the sound produced by air sacson the sound produced by vocal folds, while experimental research shows that,when both kind of sounds become superimposed, modern humans havedifficulties in order to distinguish vowels properly (de Boer, 2012). Although
Harrison (1995) dismissed the idea that air sacs are not necessary for
vocalizations, several scholars do not agree. Lieberman (2010: 333) recalls aswell that another function of air sacs in gorillas is acting “like a resonatingdrum” when gorillas thump their chest. More recently, Perlman & Salmi (2017)
analyzed the vocalizations of gorillas and suggest that this species could use
them for male display.
1.3. Abrupt change and the view of complexity
During the evolution of the subtribe Hominina, laryngeal air sacs disappeared atsome point. The key feature to discern the presence or absence of air sacs in thefossil record seems to be the morphology of hyoid bone. The australopithecine(Au. afarensis) hyoid bone found, by Alemseged et al. (2006) in Dikika(Ethiopia), shows a primitive morphology that would suggest the connection of
air sacs to the respiratory system and, hence, to the whole apparatus in charge ofspeech. The shape of the Dikika hyoid shows a deep bulla, while the shape ofthe H. sapiens hyoid describes an arch, without that deep cavity on the bodysection. The deep bulla would be a result of the development of the individual,
from the direct contact of the tissues of air sacs to the hyoid bone.
The next fossilized hyoid bone from the fossil record seemed to belong to a
H. erectus (Capasso et al., 2008). That particular hyoid bone showed a modern
morphology, i.e. there is no cavity or bulla shape to which air sacs could be incontact with. However, a second examination by Capasso et al. (2016)
confirmed that that bone was “too thick and short to be the body of humanhyoid”. Finally, the neanderthal hyoid bone is quite similar to the H. sapiens’hyoid bone (Arensburg et al., 1989; Bar-Yosef et al., 1992).
Thus, at some temporary point of the early stages of genus Homo –somewhere between the Australopithecus and H. erectus clades–, an early
hominin started making steps into modern speech. A plausible, falsifiable41

hypothesis is that H. habilis could be that hominin. Suggestions about H. habilis
capability for speech are not new at all (Tobias, 1987), but information about
this hominin is still too scarce and contentious (Wood & Collard, 1999) and,hence, this hypothesis is, for the moment, still more grounded upon plausible
ideas than upon solid data from the fossil record.
1.4. Modularity and complexity
The enigma of air sacs loss in our lineage needs an explanation integrating
the fact that the tissue of an air sac is always in physical contact with othertissues: hard tissues (the hyoid bone) and soft tissues (muscles and ligaments).
This fact establishes a direct relationship between them, since all tissues interact
with each other, not only due to contact, but also through the movements of the
body (e.g. movements of the laryngeal muscles when producing sounds or
during deglutition; or while turning the head). In fact, this can be considered as acollection of interacting elements or, in other words, a complex system. In such
systems, the deletion of some well-connected elements leads to a reorganizationof the system.
The structure of organisms seem to present a modular building schema(Wagner, 1996; Schlosser & Wagner, 2004; Callebaut & Rasskin-Gutman,2005). Heads have been analyzed as a complex system which integrates several
modules and submodules, which include different sets of bones (more than 20),
teeth (up to 32), a brain, sensory organs, muscles, ligaments, cartilages, veins,nerves, etc., showing altogether an evident modular organization (Lieberman,
2011: 8-12). Heads include the neck and all its hard and soft tissues. Inside thehuman neck two passages can be detected, one for the air and another for the
food. Anatomical network analyses have revealed musculoskeletal modularity inprimates (Esteve-Altava et al., 2015a; Esteve-Altava et al., 2015b; Powell et al.,2018).
In a recent network analysis of musculoskeletal organization, Powell et al.
(2018) show that, in spite of having more musculoskeletal elements (up to 157),
humans do not seem to be more complex than other great apes. These authors
argue that “even major changes in function […] can occur without profoundchanges to the network organization and modularity of the whole system”.
Although Powell and collaborators include only direct musculoskeletal
connections and not organs like air sacs, this reflection is still valid for the
discussion on the evolution of air sacs: air sacs can disappear, as it has occurredin many taxa, without compromising the network organization, but affecting thefunction of the module. Following Powell et al. (2018), developmental plasticity

42

can facilitate the accommodation of functional and anatomical modifications
without conditioning severely the network configuration.
Being it as it may, when an individual develops following a divergent andatypical developmental path, for example lacking (part of) an organ, theunexpected phenotype can provoke that other tissues of the system develop
abnormally as well, due to the absence of physical opposition, and due to the“departure” –metaphorically speaking– from what was “planned” by theoriginal, species-specific, body schema. Hence, soft tissues interact with hardtissues through physical contact. Lieberman recalls a good example of it inhumans: an infant born lacking eyes will probably develop “tiny orbits withabnormally small upper faces because the eyeball normally pushes out the bones
that form the orbital cavity walls” (2011: 53). Similarly, the air sacs loss had tohave consequences for the surrounding tissues.
Other forces, like persistent muscular movements, if lasted throughgenerations, could also exert an influence on the phenotypes, which could havebeen finally selected. Far from simply resorting to Lamarck’s (1809) Zoological
Philosophy, we advocate a hypothesis in which phenotypes, not mutations, arethe selected ones (West-Eberhard, 2003). For example, recent analyses of dry
skulls of both humans and chimpanzees suggest a link between the large numberof mandibular movements of muscles involved in speech (much larger thanthose used in chewing) and the morphology of the mandibular space in relation
to the tongue (Bermejo-Fenoll et al. 2019). In spite of the difficulty for showing
a cause-effect relationship, Bermejo-Fenoll and colleagues could show that H.
sapiens’ ancestors followed an evolutionary trend which, progressively andincreasingly, included more and more movements that today are related tomodern speech, like for example lip smacking and oscillatory movements of thejaw (Ghanzafar et al., 2013; Ghanzafar et al., 2012; MacNeilage & Davis, 2005).
Anyway, it seems out of question that, at some point between australopithecinesand modern humans and –we suggest– after the disappearance of air sacs, thephysical context of speech and the laryngeal structure changed, paving the way
to new motor routines which could involve more (and perhaps new) movementsthat today are related to modern speech.
2. Air sacs loss as a case of phenotypic accommodation
West-Eberhard (2005a: 6547) recalls that “frequency of expression does notdepend on the frequency of the inducer (mutation or environmental factor)
alone”. Thus, according to West-Eberhard’s (2003, 2005b) proposal,
evolutionary selection combines both natural and sexual or social selection, andhence, genes would be followers, not leaders in adaptive evolution. We think

43

this is a good theoretical framework for a change like the loss of air sacs. This issomething that has not been yet approached and, thus, there are somepossibilities open to inquiry. One possibility is that the first individuals lacking
air sacs represented atypical phenotypes within their group of conspecifics.
Hence, it is expectable that the disappearance of air sacs from the body schema
altered, even if slightly, some developmental patterns during ontogeny, and thus
yielding new, different phenotypes in adult individuals. Some available evidence
from comparative studies do support such a view: works on the ontogeny of thedescent of the hyolaryngeal complex and the root of the tongue takes place at
about 3 months of age (Lieberman et al., 2001). By contrast, at 4 months of age
the initial pouch that will become the air sac is already visible in chimps(Nishimura et al., 2007).
The observable fact is that this new phenotype somehow reachedstabilization, and one of the reasons could be that it was not incompatible withsurvival. This can be interpreted as an indication (though still not confirmed
evidence) of a fitness effect. According to West-Eberhard (2005a), if the newphenotype has a fitness effect, then selection occurs. Were this the case of air
sacs loss, it could be conceived as a phenotype that has been consolidated,becoming the typical phenotype of current H. sapiens. Observed through thislens, thus, the air sacs loss could be classified a case of phenotypicaccommodation (West-Eberhard, 2003; 2005b). Phenotypic accommodation
takes place when an individual develops plastically, adapting the organism to
new environmental inputs (here “environmental” refers to all levels in biology,
from cells to ecological niches). If the new inputs persist and other conspecifics
can develop the same adaptive phenotype as well, this new phenotype could
become stabilized by, for example, new mutations promoting and reinforcing it.
Arguments supporting this hypothesis are, for example, that air sac loss is a
consolidated phenotype in many primate species, some of them phylogeneticallydistant from one another (cf. Hewitt et al., 2002). Thus, it seems to be arecurrent homoplasy within the Primates order. Recurrent phenotypes withdiscontinuous phylogenetic distributions have been detected in many taxa (cf.
West-Eberhard, 2003, for a highly detailed revision). Moreover, the existence ofboth phenotypes within Primates suggests ancestral developmental plasticity for
producing both forms (West-Eberhard, 2005b: 6546). Were the air sacs loss
phenotype dependent of a mutation (or methylation) only, it would be amutation/methylation that has appeared many times and hence, less plausible.
Thus, phenotypic plasticity seems to be a good candidate for the origins of air
sac loss, given the fact that it can account for the repetition of homoplasies indifferent primates and in different stages of phylogeny. Moreover, phenotypic

44

accommodation could give time to this developmental variant for becoming
stabilized within populations and species. The absence of air sacs would have
led the possibility for some individuals to develop towards new phenotypes thatwere compatible with life (here we follow Pere Alberch’s (1989) “logic ofmonsters” and assume the idea of a phenotypic space of biological possibilities).
Let us remark that this hypothesis is conceived as a phenotypic possibility forsome ancestors of H. sapiens, and not for all species that have evolved towards
phenotypes lacking air sacs. The reason is that, usually, there is not a uniquefactor affecting the several environmental levels by which an organism isaffected, and each species is affected by a different set of environmental factors(admittedly, partially coincidental with other species). The first individuals whodeveloped like that, were at first unusual and atypical. However, some of theirdescendants managed to survive, consolidating (accommodating) a phenotype
without air sacs, and thus paving the way for a new phonetic system.
Conclusion
Laryngeal air sacs is an ancestral trait in Primates, while its absence is a
derived characteristic. Both possibilities exist in monkeys, lesser apes
(Hylobatidae) and great apes (Hominidae). It is well-known that the descent oflarynx does not guarantee vocalization (Fitch 2009). Similarly, the lack of air
sacs does not guarantee neither the descent of larynx nor modern speech. But it
was a necessary step for evolving towards what is known today as modern
human speech. Nonetheless, both phenotypes are recurrent and it is puzzling
how to integrate this into evolutionary theory. We propose that this could be acase of phenotypic plasticity and, more concretely, a case of phenotypicaccommodation. This concept satisfies the recurrence of both phenotypes andthe differences in ontogeny in juvenile chimps and humans. Finally, the conceptof phenotypic accommodation solves the difficulty of linking this evolutionarychange to mutation only, and gives the time a species needs until the new variant
becomes stabilized.
Acknowledgements
This research was partially supported by grant number PID2021-128404NA-I00
from the Ministerio de Ciencia e Innovación (Spain).

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47

PREVALENCE BASED QUANTITATIVE ANALYSIS OFINNOVATIONS IN LEXICAL EVOLUTION
ANDREAS BAUMANN*1 and KLAUS HOFMANN1*Corresponding Author: andreas.baumann@univie.ac.at
1Faculty of Philological and Cultural Studies, University of Vienna, Vienna, Austria

The spread of linguistic innovations has been modeled by means of logistic
growth (Altmann, 1985; Labov, 1994; Ghanbarnejad et al., 2014). The
epidemiological argument underlying this type of growth is that (a) innovations
are transmitted from one individual to the next and that (b) growth rate depends
on both the number of individuals that already know the innovation and thenumber of individuals that do not yet know it (Figure 1b, left; Cavalli-Sforza &Feldman, 1981). The resulting dynamics are S-shaped (as in Figure 1c; cf. Blythe& Croft, 2012). Interestingly, although this argument is inherently population
dynamic and involves interacting individuals, empirical accounts typically assess
S-shaped linguistic spread by means of token frequency (Denison, 2003).
In this contribution, we promote the use of prevalence for studying lexicalchange, i.e., the fraction of individuals that know and use a word. Lexicalprevalence was employed in mathematical accounts of lexical spread (Nowak,2000) and, more recently, considered in psycholinguistic research whereprevalence was argued to function as relevant (control-)variable in experimental
setups (Brysbaert et al., 2019). Here, we show (a) that lexical prevalence isrelevant for studying the spread of lexical innovations in that prevalence
trajectories typically precede frequency trajectories, and we propose (b) the useof coupled dynamics of prevalence and frequency. Our study unfolds in two steps.
In the first part, we use a diachronic corpus of German newspapers spanninga period of two decades (Ransmayr et al., 2017) and derive trajectories of token
frequency and prevalence (by using the fraction of authors using a word as a proxyfor prevalence; cf. Johns et al., 2020) for a set of about 700 words that have beenstrongly increasing in this period (e.g., googeln, ‘to google’). By fitting logistic
models to each trajectory, we determine (i) the intrinsic growth rate, (ii) the
inflection point, and (iii) goodness-of-fit for each word. We show that prevalenceThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

48

curves have (i) a higher intrinsic growth rate (cf. Figure 1a), (ii) earlier inflection
points, and (iii) a better fit that frequency curves. We take this to show that logistic
prevalence curves detect lexical innovations earlier and more reliably than this is
the case for logistic frequency curves.

Figure 1. (a) Empirical comparison of prevalence and frequency trajectories; (b) studied dynamicalsystems; (c) solution for coupled dynamics; comparison of goodness-of-fit of the systems in (b).

In the second part of our study, we test if goodness-of-fit improves if we couple
the dynamics of prevalence and frequency rather than keeping them separate as
before. This is done by letting the growth of frequency at a certain time not only
depend on token frequency but also on prevalence (Figure 1b, left). This is
motivated by the assumption that the production of words depends on whether anindividual knows a word and on how entrenched it is (Bybee, 2006). Given that
intrinsic growth is higher for prevalence than for frequency, the frequency
trajectory is shown to be preceded by the prevalence trajectory in this model(Figure 1c). Subsequently, we use the empirical trajectories analyzed before andcompute goodness-of-fit for the respective solutions of both systems, coupled anddecoupled. We show that although coupled dynamics are in some cases
uninformative (goodness-of-fit close to zero), they generally show a slightly
better fit than decoupled systems (Figure 1d; our study is complemented with asensitivity analysis of goodness-of-fit differences). We conclude that coupled
dynamics of prevalence and frequency represent a reasonable model for studying
lexical evolution that, in addition, help to reconstruct diachronic prevalence
trajectories from frequency data.

49

References
Altmann, G. (1985). On the dynamic approach to language. In Linguistic
Dynamics (pp. 181-189). de Gruyter.
Blythe, R. A., & Croft, W. (2012). S-curves and the mechanisms of propagationin language change. Language, 269-304.
Brysbaert, M., Mandera, P., McCormick, S. F., & Keuleers, E. (2019). Word
prevalence norms for 62,000 English lemmas. Behavior researchmethods, 51(2), 467-479.
Bybee, J. (2006). Frequency of use and the organization of language. Oxford
University Press.
Cavalli-Sforza, L. L., & Feldman, M. W. (1981). Cultural transmission andevolution: A quantitative approach. Princeton University Press.
Denison, D. (2003). Log (ist) ic and simplistic S-curves. Motives for languagechange, 54, 70.
Ghanbarnejad, F., Gerlach, M., Miotto, J. M., & Altmann, E. G. (2014).
Extracting information from S-curves of language change. Journal of The
Royal Society Interface, 11(101), 20141044.
Johns, B. T., Dye, M., & Jones, M. N. (2020). Estimating the prevalence anddiversity of words in written language. Quarterly Journal of Experimental
Psychology, 73(6), 841-855.
Nowak, M. A. (2000). The basic reproductive ratio of a word, the maximum sizeof a lexicon. Journal of theoretical biology, 204(2), 179-189.
Ransmayr, J., Mörth, K., & Ďurčo, M. (2017). AMC (Austrian Media Corpus) –Korpusbasierte Forschungen zum österreichischen Deutsch. In Claudia Reschand Wolfgang U. Dressler (eds.), Digitale Methoden der Korpusforschung inÖsterreich. 27-38. Vienna: Verlag der Österreichischen Akademie derWissenschaften.

50

LESS OF THE SAME: MODELING HORROR AEQUI AND
EXTRAVAGANCE AS MECHANISMS OF NEGATIVE FREQUENCYDEPENDENCE IN LINGUISTIC DIVERSIFICATIONANDREAS BAUMANN*1 and ROLAND MÜHLENBERND2* Corresponding Author: andreas.baumann@univie.ac.at

1 Faculty of Philological and Cultural Studies, University of Vienna, Vienna, Austria
2 Leibniz-Centre General Linguistics, Berlin, Germany

Two mechanisms involving negative frequency dependence, i.e., pressures for less of thesame, have been suggested to affect linguistic evolution: (i) horror aequi, a cognitivepsychophysiological effect that leads to avoidance of repetitions, and (ii) extravagance, which
is a pragmatic strategy to signal non-conformity and emphasis by avoiding predominant constituents in favor of innovative ones. In this paper, we explore how avoidance biases like thisinfluence the long-term evolution of linguistic constituents. We do so by means of evolutionary
analysis of population dynamic models of linguistic diffusion. We show that if individuals are
sufficiently sensitive with respect to variation then avoidance biases can yield linguistic diversification, i.e., stable coexistence of constituent variants.

1. Introduction
Linguistic systems exhibit diversity on various levels. Phonological systems consist of diverse sounds, some of which are closely related to each other (e.g., moreor less leniated variants of a consonant like /b/). Vocabularies usually consistof thousands of words, some of which may be formally similar but semanticallydifferent (e.g., freak denoting an abnormal individual or, in fact, an expert or aficionado). Other words are formally different but denote the same concept (e.g.,
session and its slang variant sesh). And on the syntactic level, languages feature a
multitude of constructions, although some of them are formally slightly different
but might fulfill the same function (e.g., [going to + V] and [gonna + V] for expressing future events), or indeed formally similar but semantically different (e.g.,[going to + V] and [going to + N]). Where does this diversity come from?
One of the potential causes of the successful implementation of variants likethe ones above is the tendency to avoid more of the same. We will refer to thisas avoidance bias. Two mechanisms have been discussed in this matter. On the
cognitive-psychophysiological level, the horror aequi effect was suggested to promote variation in that it provides a bias against repetitions. On the pragmaticlevel, extravagance was discussed as a mechanism that enhances innovations tomake their users stand out of the crowd (Haspelmath, 2000; Walter, 2007; Petré,This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

51

2017; Ungerer & Hartmann, 2020). In this paper, we study the effect of such biases on linguistic diversity by means of population-dynamic models. For this, we
implement frequency dependent avoidance biases into a dynamical system thatcharacterizes the spread of a linguistic constituent (phoneme, word, construction)
through a population of speakers and analyze the long-term evolution of this constituent (Doebeli & Ispolatov, 2010; Dercole & Rinaldi, 2008). We show thatdiversification of the constituent, i.e., the stable establishment of two variants ofitself, depends on the scope of the mechanism and how easy it is for speakers todifferentiate between variants.1
The subsequent section provides a brief review of the two mechanisms, horror aequi and extravagance. After that, we outline our model together with its
evolutionary analysis and finally discuss its implications and limitations.
2. Horror aequi and extravagance in linguistic evolution
Avoidance biases can operate on different levels. The horror aequi effect (‘fearof the same’) was suggested as a mechanism that lets speakers avoid repetitionsof similar (or even identical) linguistic structures operating on various levels of
linguistic organization. The effect has been suggested to be motivated by psychological and physiological constraints as well as constraints on cognitive planning(Walter, 2007). In the phonological domain, it was proposed as a mechanism foravoiding repetitions by Brugmann (1917) already in the beginning of the 20th century (e.g., German Zauberin rather than Zaubererin, where Zauberer is a ‘male
magician’ and -in is the German feminine suffix). In phonetics and phonology,
avoidance biases have been studied under the term obligatory contour principle.
For instance, Walter (2008) has shown that repetition of the consonants /b, d, g/leads to the production of lenited variants. In the morpho-syntactic domain, theeffect was studied by Rohdenburg and others to account for the avoidance of repeating -ing forms in English in sequences like without bothering to tell him vs.
without bothering telling him (Szmrecsanyi, 2008; Rohdenburg, 2011). The effecttypically applies within utterances but it can also apply across utterance boundaries, as long as they are temporally close. Indeed, horror aequi was suggested
to be a potential counterpart of (asymmetric) priming (Jäger & Rosenbach, 2008)
and attested experimentally (Hilpert & Saavedra, 2018) (although horror aequi
was argued by Szmrecsanyi (2008) to be weaker than supportive effects).
Extravagance, in contrast, is a pragmatic phenomenon that by definition applies to interactions among individuals. In an excellent recent review, Ungererand Hartmann (2020) elaborate on the different aspects of extravagance covered
1 In biological evolution, negative frequency-dependent selection is a well-studied mechanism potentially accounting for diversification and stable polymorphism (Brisson, 2018; Chesson, 2000). It isless well studied in cultural evolution; but see, e.g., Doebeli and Ispolatov (2010), who use a model
similar to that in this paper to model coexistence of religions.

52

in the literature. An important aspect of extravagant language seems to be thatit lets their users stand out of the crowd. While this may have the reason to signal (out-)group membership (Fajardo, 2019) in a non-conformative fashion (e.g.,
back-clipping session to sesh in youth slang), this is not necessarily always thecase. Extravagant expressions may be as well used to gain attention or as emphasis
device. So, the [going to + V] future construction or the progressive construction[be V-ing] were argued to be motivated by extravagance, as was the lengthened an
more explicit expression by means of in place of with (Haspelmath, 1999, 2000;
Vosberg, 2003; Petré, 2017). Length seems to be a particularly relevant property
related to extravagance, as seen in phrasal compounds like make-your-stomachhurt difficult (Günther, Kotowski, & Plag, 2020).
What both mechanisms have in common is (i) the tendency to avoid a predominant variant in favor of another variant and (ii) frequency dependence. That is,both horror aequi and extravagance only apply if certain structures are used andencountered relatively often. Horror aequi requires nearby repetitions of linguistic structures and extravagant expressions require a background of predominantly
used forms against which they are perceived as extravagant in the first place. Frequency dependence is negative because avoidance biases impede the usage of the
predominant variant. In what follows, we model avoidance biases and their effecton linguistic diffusion through speaker populations.
3. Modeling linguistic evolution under avoidance biases
Let us consider a linguistic constituent that is characterized by a certain property
x. Such a constituent could be, e.g., a phoneme, a phoneme sequence, a word, ora construction, and its property x could be some formal aspect (such as the degreeof lenition of the consonant /b/) or some semantic aspect (like the sentiment of
the word freak, which could be positive, negative, or something in between). We
assume that x can be measured on a continuous scale, i.e., x ∈ X ⊆ R, where X
is an interval defining the range of possible values. By learning the constituent,
it is transmitted from one individual to the next, either horizontally, or verticallyin first-language acquisition. Whenever a user of the constituent and a learner
who does not yet know it meet, successful learning takes place at an intrinsic rate
λ depending on x. We also assume that learning is optimal for some value x0(i.e., f is locally concave around x0 ). When an individual learns the constituent,
they switch from the learner to the user class. Let L and U denote the respective
number of individuals.
Individuals can also cease to use a constituent so that they switch from the userclass back to the learner class. In our model, switching back to the learner classis motivated by avoidance biases outlined before. Thus, users cease to use a constituent whenever they interact with another user of that constituent at an avoidance rate α. That is, growth is subject to negative frequency dependence (Brisson,2018). Oftentimes, individuals using slightly different variants will interact. Sup-53

pose that the values x and y define two different variants of a constituent (twodifferent degrees of lenition; two different sentiment values). We assume that α isa decreasing function of the distance ∆ = |x − y| between both variants obtaining its maximum α0 at ∆ = 0. Avoidance rate α is highest if both variants are
identical and decreases the more different they are. A function that models such abehavior isα(∆) = α0 exp(−1/2 · ∆2 /σ 2 ).
(1)This is a bell-shaped curve depending on ∆. The steepness of this curve is determined by the parameter σ, which stands for the scope of the avoidance mechanism. The scope defines the range of values that are affected by that mechanism in a fuzzy manner (Figure 1a, top panel). If σ is high (flat curve) then evensubstantially different variants lead to high mutual avoidance rates. If σ is low(steep curve) then only relatively similar constituents will lead to high avoidance
rates. The reciprocal 1/σ can be interpreted as a measure of the sensitivity withrespect to variation. High sensitivity (low σ) means that only nearby variants
are perceived as identical. Low sensitivity (high σ) means that most variants areperceived as identical, which promotes avoidance. Note that, for simplicity, ourmodel only captures mechanisms of negative frequency dependence although language clearly shows positive frequency dependence as well; see Doebeli (2011)
for a model juxtaposing conformity and non-conformity biases. We will comeback to this limitation in the final section.
Given the above considerations, the population dynamics of a constituent
specified by x, in the absence of other variants, is given by the differential equationU̇ = λ(x)U L − α0 U U,

(2)where we assume that population size is normalized so that L + U = 1. If λ(x) >0 (positive learning rate) the population dynamic equilibrium is given by Û (x) =λ(x)/(λ(x) + α0 ).
What can we say about the long-term evolution of the constituent’s property
x? We can model the evolutionary trajectory of x with the help of the canonical equation of adaptive dynamics (Meszena, Kisdi, Dieckmann, Geritz, & Metz,
2002; Dercole & Rinaldi, 2008), which relates to the Price equation (Page &Nowak, 2002) and defines the rate of change of x asẋ = M∂f (x, y)Σ2Û (x)
.
2
∂y
y=x

(3)

Here, the constants M and Σ2 define the rate and variance of linguistic innovations, respectively. More importantly, f (x, y) denotes invasion fitness, i.e., theexponential growth rate of a rare variant characterized by y in a population inwhich x is the predominant variant. That is, for every x, f (x, y) defines a fitness

54

landscape that rare variants y have to cope with (Figure 1a, mid panel). Given the
population dynamics in (2), invasion fitness can be derived asf (x, y) = λ(y)(1 − Û (x)) − α(y − x)Û (x).

(4)

One can show that x0 (where learning is optimal) defines an equilibrium of theevolutionary dynamics (3) (Doebeli & Ispolatov, 2010). Moreover, x0 is an evolutionary attractor, so that values close to x0 are driven towards this equilibrium.
What is more interesting is this: if σ is large (low sensitivity with respect tovariation) then x0 is a local maximum of the fitness landscape given by f (x0 , y)(Figure 1a, bottom panel). This can be seen by looking at the curvature of f
around x0 (because α′′ (0) is small for large σ):∂ 2 f (y, x0 )= λ′′ (x0 )(1 − Û (x0 )) − α′′ (0)Û (x0 ).
∂y 2
y=x0

(5)

This means that x0 cannot be invaded by nearby variants. If, however, σ is sufficiently small then x0 is a local minimum of the fitness landscape f (x0 , y), sothat x0 can be invaded by nearby variants. Thus, x0 is a branching point at which
the population is split into two variants that stably coexist (Geritz, Metz, Kisdi, &Meszéna, 1997). This is shown in Figure 1b. High sensitivity with respect to variation combined with avoidance biases leads to diversification of the constituent
into two variants (e.g., more/less lenited; positive/negative).
4. Discussion and conclusion
We have seen that mechanisms accounting for negative frequency dependence candrive linguistic diversification. Two such mechanisms have been discussed: (i) the
horror aequi effect operating on the cognitive-psychophysiological level and (ii)extravagance, i.e., the tendency to behave differently in order to stand out as apragmatic phenomenon. In the literature, both mechanisms have been suggested
to promote linguistic innovations and hence linguistic evolution. What we haveshown in our contribution, though, is that stable coexistence of two variants (i.e.,
diversity) that result from one of these mechanisms depends on how sensitive individuals are with respect to variation. If individuals do not differentiate between
different variants and put all of them into one basket then neither horror aequi
nor extravagance will lead to diversification. Both mechanisms require a certain
ability to differentiate between variants. Without this ability, linguistic evolution
simply optimizes learnability (so that, say, only lenited /b/ and freak with a positive connotation would remain).
Why is this plausible? If (i) individuals treat almost all variants as identicalthen horror aequi is very likely to apply. If, however, horror aequi only applies tovery similar variants, the mechanism will lead to deletion much more rarely, hence
enforcing coexistence. Likewise, if (ii) even distant variants are considered the55

Figure 1. (a) Evolutionary dynamics for wide (left, σ = 0.4) and narrow (right, σ = 0.2) scopes.
Top panel: avoidance rates α are modulated by the scope of the mechanism. Mid panel: changing fitness landscape showing invasion fitness f (x, y) for all combinations of a predominant variant(‘resident’) characterized by x and a rare variant (‘mutant’) characterized by y. Dark regions denotepositive, and light regions negative invasion fitness. In both cases, x0 is an evolutionary attractor.
This is because below/above x0 , mutants closer to x0 than the resident have positive invasion fitness,so that x is driven towards x0 . Bottom panel: Fitness landscapes of the mutant variant y in an environment set by the resident x0 = 0.5 at the evolutionary equilibrium. In the right case, x0 is aminimum of the fitness landscape, can be invaded by nearby variants and is hence a branching point.
(b) Diversification into two variants if the underlying avoidance mechanism has a narrow scope (smallσ). NB: the symmetry in (b) is a direct consequence of the symmetry of λ(x) in the present model
which we assume for simplicity (the shape of λ(x) can be more complex, of course). The same holds
true for the location of x0 which we simply assume to be 0.5 in this simulation.

56

same, they will be dropped in order to behave in an extravagant way. But if individuals have a very fine-grained perception of different variants and can tease themapart easily then they will stick to their own behavior because, after all, it is thenmore likely to be judged as extravagant by others. Thus, fine-grained perception offormal and semantic differences between linguistic constituents which—given themechanisms discussed here—can ensure the diversity of linguistic (phonological,
lexical, constructional) inventories.2
The model proposed in this paper clearly is simplistic. It builds on homogeneous mixing, simple population structure, and the technical assumption that
linguistic properties evolve gradually (in other words, that changes are small andthat innovations spread fast). Most importantly, it only explicitly features mechanisms of negative but no mechanisms of positive frequency dependence, respectively, such as conformity biases (on the pragmatic level), priming effects (onthe cognitive-psychophysiological level), or simplicity preferences inducing positive frequency dependence. Obviously, such mechanisms exist as well and shape
linguistic evolution (Jäger & Rosenbach, 2008; Enfield, 2008; Baumann & Sommerer, 2018). Our point is not that avoidance biases are necessary for explaining
linguistic diversity. The point is that, everything else being equal, mechanismsof negative frequency dependence can yield interesting evolutionary dynamics inthat they represent sufficient conditions for linguistic diversification.
References
Baumann, A., & Sommerer, L. (2018). Linguistic diversification as a long-term
effect of asymmetric priming: an adaptive-dynamics approach. Language
Dynamics and Change, 8(2), 253–296.
Brisson, D. (2018). Negative frequency-dependent selection is frequently confounding. Frontiers in Ecology and Evolution, 6.
Brugmann, K. (1917). Gleichklangvermeidung in der lautgesetzlichen Entwicklung und in der Wortbildung. Indogermanische Forschungen (1917),
38(1917), 117–128.
Chesson, P. (2000). Mechanisms of maintenance of species diversity. Annual
review of Ecology and Systematics, 31(1), 343–366.
Dercole, F., & Rinaldi, S. (2008). Analysis of evolutionary processes. PUP.
Doebeli, M. (2011). Adaptive diversification. PUP.
Doebeli, M., & Ispolatov, I. (2010). A model for the evolutionary diversificationof religions. Journal of theoretical biology, 267(4), 676–684.
Enfield, N. (2008). Transmission biases in linguistic epidemiology. Journal of
Language Contact, 2(1), 299–310.
2 As a corollary, populations with impeded perceptive sensitivity (for whatever reason, be it environmental, technological, or, thinking of language evolution on the biological time-scale, physiologicalor cognitive) should accommodate less diverse inventories.

57

Fajardo, J. A. S. (2019). Exploring the ‘shashification’of teenage slang. English
Today, 35(3), 49–54.
Geritz, S. A., Metz, J. A., Kisdi, É., & Meszéna, G. (1997). Dynamics of adaptation and evolutionary branching. Physical Review Letters, 78(10), 2024.
Günther, C., Kotowski, S., & Plag, I. (2020). Phrasal compounds can have adjectival heads: evidence from English. English Language & Linguistics, 24(1),75–95.
Haspelmath, M. (1999). Why is grammaticalization irreversible? Linguistics,
37(6), 1043–1068.
Haspelmath, M. (2000). The relevance of extravagance: A reply to Bart Geurts.
Linguistics.
Hilpert, M., & Saavedra, D. C. (2018). The unidirectionality of semantic changes
in grammaticalization: an experimental approach to the asymmetric priming hypothesis. English Language & Linguistics, 22(3), 357–380.
Jäger, G., & Rosenbach, A. (2008). Priming as a testing ground for historicallinguists?–A reply to Chang, Eckardt and Traugott. Theoretical Linguistics,
34(2), 143–156.
Meszena, G., Kisdi, E., Dieckmann, U., Geritz, S. A., & Metz, J. A. (2002). Evolutionary optimisation models and matrix games in the unified perspectiveof adaptive dynamics. Selection, 2(1-2), 193–220.
Page, K. M., & Nowak, M. A. (2002). Unifying evolutionary dynamics. Journalof Theoretical Biology, 219, 93–98.
Petré, P. (2017). The extravagant progressive: an experimental corpus study on
the history of emphatic [be ving] 1. English Language & Linguistics, 21(2),
227–250.
Rohdenburg, G. (2011). Cognitive complexity and horror aequi as factors determining the use of interrogative clause linkers in english. In Determinantsof grammatical variation in english (pp. 205–250). De Gruyter Mouton.
Szmrecsanyi, B. (2008). Morphosyntactic persistence in spoken English: A corpus study at the intersection of variationist sociolinguistics, psycholinguistics, and discourse analysis. De Gruyter Mouton.
Ungerer, T., & Hartmann, S. (2020). Delineating extravagance: Assessing speakers’ perceptions of imaginative constructional patterns. Belgian Journal of
Linguistics, 34(1), 345–356.
Vosberg, U. (2003). The role of extractions and horror aequi in the evolutionof -ing complements in modern english. In Determinants of grammaticalvariation in english (pp. 305–328). De Gruyter Mouton.
Walter, M. A. (2007). Repetition avoidance in human language. Unpublisheddoctoral dissertation, Massachusetts Institute of Technology.
Walter, M. A. (2008). Repetition leads to lenition in consonant sequences. 8thInternational Seminar on Speech Production, 209–212.

58

THE ROLE OF TRUST IN LANGUAGE EMERGENCE
Ted Bayne
Independent Researcher
tedbayne@comcast.net
Concord, MA 01742 USA
Over the past 25 years Chris Knight, Jerome Lewis, James Hurford, and others haveproposed community-wide trust and social egalitarianism as the critical conditions for the
innovation and enculturation of spoken language (gender political balance is tied into
egalitarianism). This paper evaluates the underpinnings and predispositions for thesedevelopments. We contend that inter-subjective trust heightens the efficacy of cognition insemiotic inference. Human trust has particularly deep evolutionary antecedents in theentanglement of social and personal domains within cultural and biological factors.
Millions of years of primate and hominin mind-reading (ToM) laid down tracks for an
eventual transition to human speech using the same inferential socio-cognitive channels (atvery different levels).

1. Introduction.
The cultural achievement of social trust ultimately leverages bio-genetic
evolution preceding and underpinning it but, as cultural, it has a complex set of
dependencies in which the primary factor seems to be a socio-politicalegalitarianism (Knight & Lewis, 2017; Boehm, 2009). Moreover, this egalitarian
thread has dependencies that beg for explanation. At some point, H. sapiens
launched into a “culture only” evolutionary phase (Welsch, 2016). There isdebate about when this happened. Evidence suggests that it happened slowly but
was in place before the migration out of Africa (~60 kya; Domínguez-Andrés &Netea, 2019).
The history and field work forming the stimulus for this paper primarily havestudied sub-Saharan and south African immediate-return hunter-gatherers.
2. A Protracted Process on the way to Trust: Preconditions
The hypothesis of a combination of community-wide trust, social egalitarianism,and gender political balance has been developing a growing appeal. H. sapiens
shows in early traces the capacity to accumulate culture over generations. Trust
eased and supported the innovation of shared spoken language. But these times oftrust were the last act in a play with a hundred acts: key underpinnings andprecursors were needed. As Deacon asserts (2017), no new brain structures
evolved to support language or its correlates (Christiansen, 2013). So, from an
evolutionary point of view, a culture-only evolutionary history was called for toachieve these outcomes but this evolutionary process was highly complex: “a very

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

59

wide spectrum of entangled conditions is required” (Dor, Knight, & Lewis 2014).
This spectrum includes social, political, cognitive, emotional as well as cultural
factors and there are implicit sub-dependencies between those factors. This paper
aims to expose these underpinnings.
3. Subcortical & Autonomic Predispositions to Sociality and Proximity
Critical to the later emergence of Trust is a multifaceted set of evolutionary
developments, some in primate development and some later in the hominin treethat relate to the toleration of closeness and then an expansion of cooperative
closeness into inter-subjectivity.
3.1. Social engagement’s autonomic capacities: an autonomic socialengagement system (Porges & Carter, 2013) developed in the primate line overmillions of years. This system developed long after the much earlier
parasympathetic and sympathetic autonomic systems. Among primates, bonobos
outperform chimps in social intelligence showing this emergent set of capacities.
(Krupenye et al, 2017; Gruber et al, 2016). In a provocative example of this,bonobo sexual beckoning gestures combine deixis and iconicity (Clay & Genty,
2017). Porges summarizes this autonomic system in this passage [key is thevisceral sensing of safety - required for trust]:
This synergism of neural mechanisms in mammals down-regulated
defensive systems and promoted proximity by providing social cues (e.g.,intonation of vocalization, facial expressivity, posture, and head gesture)that the organism was not in a physiological state that promoted
aggressive and dangerous behaviors. Detection of these social cues
allowed for symbiotic regulation of behavior and the elaboration ofreciprocal care-giving. (Porges & Carter, 2013)
The role of social hormones (e.g. oxytocin) in this area has often been pushed tocenter stage. But reducing anxiety about social proximity cannot by itself claim
so much of the credit for such a broad and creative spectrum of behavioral andcommunicative social invention. Human neuroplasticity arising in a social setting
without a straight-jacket of “fixed behaviors” builds these forms of trust. (arelevant discussion is in Hurford, 2007)
3.2. Interaction engine (Levinson, 2006): The main thrust of Levinson’s“interaction engine” is that humans engaged in enhanced communication
exercises before formal language emerged. H. sapiens developed this semiotic
interactivity on top of the autonomic social engagement capacities mentioned
above. Attention to the Other occurred as an early precursor to verbal exchanges(Hrdy, 2011). Elementary mind-reading (ToM) developed in the primate line

60

reinforcing this. (Vogeley, 2017; de Waal, 2022). As an example, Brinck stresses
that, from a very early age, infants are poised to respond to communicative
engagements, shifting attention and gaze vis-à-vis objects in terms of the
caregiver (Brinck et al, 2017). Also, interactive play is a critical modality withdeep roots and is linked to prosocial intelligence (Kuczaj et al, 2008).
3.3. Intersubjectivity: (de Waal, 2019) The claim of Trust in a theory oflanguage-emergence is congruent with accounts of early ToM and forms ofcommunication that leverage inferential sharing of intent-signals (non-verbal).
The cognitive substrates for these faculties are critical. Deep in the primate line
are forms of proto-inter-subjectivity in genus Pan (Krupenye et al, 2017; Kano etal, 2017). Interpreting the intent of the Other exercises faculties of domaingeneral cognitive inference that work well within a wide spectrum ofcommunicativeness. What evolved was not discretely coded signals “one at atime”. What evolved was a multi-purpose meta-representational capacity that
could work with any signal system because it evolved cognitive strengths thatgrew increasingly adept at “getting” what the Other intended across multiple
modalities. Only humans use ostensive communication while nonhuman primates
are mainly intentional (Scott-Phillips, 2015). With trust, humans cooperate insharing an inferential common ground that can draw on a vast data store. Trust
endows human participants with a critical measure of efficiency in both innovationand early usage phases of this communications adventure (Knight, 2018).
3.4. Attachment theory (Hrdy, 2009) reveals the relational/cognitive strengthsof human children that support their ability to securely attach in alloparental
settings (Kramer et al, 2018; Gopnik, 2016a). The child’s autonomous
contribution to this hugely advantageous evolutionary pathway is often
overlooked. In their summary of this subject, the authors assert “Theseobservations clearly support Hrdy’s emphasis on the extensiveness andimportance of alloparental behavior among hunter-gatherer groups…” (Hewlett& Lamb, 2005). The ToM strengths of the young child (<6 yrs.) (Nonnenmacher
et al, 2021) presage the same capacities emerging in adults (Dörrenberg et al,2018). The child can appraise the safety and emotional signals of the caregiver
towards herself. Infant attachment styles later become a psychological advantage
for the adult (Fraley, 2019). Trust between adults is affected here as well.
Children achieve security with non-maternal caregivers using perceptive faculties
that assess the Other’s emotional intent.
3.5. Spoken Language (evolving later) grew to leverage a semiotic-semanticmetaphoric common ground which scaffolded an early ritual and multi-modal

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expressiveness – dance/song, oro-facial and body gesture (Lewis, 2014: §7.5;
Prieur et al, 2019). This semiotic scaffolding was embedded in the cultural
practices that sought to protect egalitarianism and its roots in the economy,
reproductive politics, the hunt, and the spirits of the forest. For ages, preverbal
inter-subjectivity (ToM) used domain-general inferential faculties (multisensory) to grasp communicative intent: taking in contextual cues from multiple
channels. Cultural life was already rich in semiotic/semantic meanings.
…no new brain structures evolved to support the distinctive cognitive
capacities of the human species such as language. Instead, language
functions have effectively recruited older neural systems, previously
adapted to serve other functions. (Deacon, 2017)
As H. sapiens evolved along a culture-only path, social communication
increasingly enjoyed a plasticity – a semiotic continuum (Lewis in Arbib, 2013;
Malafouris, 2016: §4). Modalities other than verbal speech can be ostensive andhave communicative intent. The pre-verbal inferential faculty is congruent withand segues into the later verbal ostensive-inferential faculty (Scott-Phillips,2015). The sound-stream of words becomes yet another modality to blend withavailable (usual or new) cues and scaffolding. The “digital” aspects of verbal
speech don’t deny what is called the “invariance problem”. The interpretation ofthis sound-stream relies on the cues and contexts that disambiguate the stimulus– to leverage this scaffolding is essential.
Jerome Lewis in his study of the Mbendjele goes right to the heart of this semiotic
plasticity and the prominence of their multi-faceted musicality:“Mbendjele have developed specific styles of communication fordifferent audiences and situations. They mix words with sung sounds,
ideophones, expletives, whistles, signs, hand signals, gestures,vocabulary from other people’s languages, animal sounds, and otherenvironmental sounds, sometimes in a single speech act. In the context
of forest hunting and gathering, the role of different language styles andcommunicative strategies suggests that diverse styles of communicating
could have been crucial to the survival of early humans…” (Lewis in
Botha & Knight, 2009)[For the Mbenjele] …it is not what people are singing but the polyphonicyodelling singing style, not which dances they dance or which spirits theycall but the ritual structures they follow, not the language they speak but
how it is spoken. The perception of what it means to be Yaka is basedon an aesthetic quality and lifestyle as much as on genealogical accident,a distinctive sense of style in which music is more central to culture thanlanguage. (Lewis, 2002)

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4. Relaxed Selection and Self-Domestication
Important protracted developments resulted in less over-determination of Homoby hardwired genes and more by environmental responses coming from neuroplasticity and epigenetics (Deacon, 2017). “Self-domestication” and neoteny (deWaal, 2022; Bednarik, 2011) form two major themes in this long term process(Hare, 2017). This evolutionary process goes back to forms of our genus roughly
2 mya. Deacon places “relaxed selection” in that time frame as well as growthspurts in stone tool use and inter-continental migration (Deacon, 2016).
Although traditional assumptions about the role of genetic change inevolution have tended to focus on mutations that augment some function,evidence is growing that gene duplication and gene loss – including
especially loss of non-coding regulatory sequences – has contributed tosignificant evolutionary change… Human-specific loss of over 500
otherwise highly conserved non-coding regulatory sequences has recently
been reported (McLean et al 2011). This extensive loss of genetic
regulation may be a signal of human-specific relaxation of selection andan increased sensitivity to epigenetic and environmental influences.”(Deacon, 2016)
The net effect of self-domestication is the release of the organism from pre-set
genetic constraints or fixed behavioral plans (Gómez-Robles et al, 2015). Thereare connections between the license to innovate, pretend-play, and ritually sing &dance just as examples (Hare, 2017). The relaxation of genetic constraints meansthat Play can bridge to multiple cultural, political, and interpersonal domains in
multi-modal ways. For instance, rather than have an aggressive single-mindedrigidity about issues of sexual politics, humorful imagination can enter into theavailable responses and bring the female cohort together making reverse
dominance an exhausting and exhilarating exercise.
5. Plasticity, Play, Egalitarianism, and Trust
Trust is tacitly built into play. Playmates “play along” honoring unspoken rules.
If the rules get extended, playmates might pick up on this extension andaccommodate the new “twist”. Play draws on a prior and very long developmentof cooperation in the genus Homo (Tomasello, 2014) along with a reduction ofboth the proactive and reactive types of aggression in H. sapiens (Wrangham,
2018).
Particularly among immediate-return hunter-gatherers, the ethos of sharing andcooperating is very strong. There is an implicit element of trust in the cooperationin an immediate-return economy. The presence of trust in Play would be as

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palpable. Baka children’s play exhibits values like egalitarianism that are strongly
embraced in the adult culture (Kamei, 2005).
Play in children and adults is an opportunity stemming from the loss of genetic
regulation: invented vocalizations versus fixed calls, for instance. While the selfdomestication and relaxed selection mentioned earlier took place significantly
before the period of culture-only or proto-culture-only evolution (Welsch, 2016),
play in child-development or adult ritual would need cultural supports to become
stable or habitual parts of human life.
“… in the safe and well-provisioned context of play, characteristic of a
species with an extended juvenile period, individuals place themselves into
unconventional and often disorienting positions or orientations. Thesenovel situations afford opportunities to experiment with a variety of
behavioral and cognitive routines and generate novel, and possibly
adaptive, responses, or modules.” (Pellegrini & Pellegrini, 2013)
Play draws on intuitive and creative cognitive and affective resources. It
perpetuates an egalitarian setting and asserts it anew.
“Social play – play involving two or more playmates – is necessarily egalitarian.
It always requires a suspension of aggression and dominance and heightened
sensitivity to the needs and desires of the other individual involved.” (Gray, 2013)
Closely related to the suspension of aggression is the matter of self-control.
“[The] same cultures that allowed their children the greatest freedom to play also
produced people who seemed to have the greatest capacity for self-control. …
[The] … amounts of play engaged in by hunter-gatherer children helped topromote their extraordinary capacities to regulate their emotions in ways that wereadaptive to their conditions of life.” (Gray, 2013)
5.1. Play Liberates Ritual Content
Rituals still practiced in extant groups (Congo basin) are created with animagination driven by Play. Shared aesthetic rules allow autonomy and humorful
coordination. Play allows the moral themes of reverse dominance and female
solidarity to be explicitly danced and sung, together with impersonations thatbring on wild laughter. Play allows performance to be a virtual space where
creativity is free and enthusiastic. Even an origins story is playful and whimsicalyet evocative of “real life”. Named rituals (e.g. for the Mbendjele: “Ngoku” andtheir origins story) are embodied metaphorical figures (without prescribed lyrics)
that evoke the moral and spiritual narratives lived by the band.
6. Female Strength and Role in the Hunt

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Women are not excluded. They are often active in the hunt and then they are the“arms” of the nets when they are thrown to catch game. They have a spiritualtelepathic channel to the creatures. This channel is why the very antipathy
between menstrual blood and the blood of the hunted represents the potency of
their communicative relationship with those creatures (Finnegan, 2016). “it isoverwhelmingly women who locate forest animals. It is through a privileged
relationship with spirits that women participate in the hunt.” (Finnegan, 2016)
When women perform the bobanda ritual to recover the men’s hunting luck orreset social homeostasis in the community, they are revered for their strength.
Their role in these works is a positive and assertive one.
Cooperative childcare combined with women’s rightful claim to a share of themeat forms the sexual core of the egalitarianism kept alive by ritual vigilance.
Evidence shows that in early kinship systems, childbearing women mostly raised
their young collectively in a matrilocal setting (Knight, 2011; Hawkes et al, 1998)
and were better poised there to obtain services from interested males. Climatic
adversity and population pressures often increased the incentive to “go home”(Kim et al, 2014). Vigilance of the female cohort began with an early campaign
by women to get control over sexual matters. The history and timeline of
bodypainting is catalogued and analyzed thoroughly in Watts’ “The Red Thread”(2014). The sex-strike boldly dissimulating the sexual status quo is a “moral
strategy” (Finnegan, 2016).
The species of trust that is possible at this level of political counterpoint does not
emerge from sentimentalism. It emerges from women’s playful confidence andself-esteem as well as their sense of reproductive potency in the community. They
know well the virulent danger of the alternative: they’re always on the lookout.
Pretend-play is an alternative to alpha-male power with its competition and fear.
Importantly, Play in the Ngoku ritual dance-song shows an affirmation ofplasticity, possible when personal autonomy is embraced along with its culture(Lewis, 2013). Given the female cohort’s continual vigilance coupled with its
cooperative bonds (Finnegan, 2016), the resulting egalitarianism yields an
ecology of safety producing the “good sounds” that keep the people and the forest
in peace. (Lewis, 2014; Pellegrini & Pellegrini, 2013). This safety is needed for
language innovation.
7. Overview
The several threads discussed above are mutually tied up in the emergence of afunctioning human trust. Certain long term overarching developments were
essential: relaxed selection, self-domestication, neoteny, autonomic support forsocial engagement (hormonal and neurological), plasticity and epigenetics (vs.

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genetically “hardwired”). The growth and inheritance of cultural forms becamea follow-on protracted “evolution” with its own genetic consequences. Ultimately
an evolving culture-only social process took center stage. Socio-cultural legacies
underpinning trust included gender balance and egalitarianism, play, attachment,
forms of inter-subjectivity as well as cooperation in multiple domains. Trust opens
up human cognitive pores to a broad range of commonly held realities andfantasies. Inferential grasp of the Other’s gist is emboldened by trust (Gweon,2021). Proto-language (and beyond “proto”) draws on an intimate and patient
sharing of ostensive behaviors and resulting inferences – all having deep roots in
the history of ToM in our genus. “In an evolving hominin species, language will
not even begin to evolve unless and until intensified levels of community-wide
trust and a shared virtual domain have been put in place.” (Knight & Lewis, 2017)
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EARLY ‘LANGUAGE HOMOLOG’ BRAIN ASYMMETRY IN
NEWBORN MONKEYS PAVES THE WAY FOR THE
DEVELOPMENT OF GESTURAL COMMUNICATION’SLATERALIZATIONYANNICK BECKER*1, ROMANE PHELIPON2, SIHAM BOUZIANE1, JULIEN
SEIN3 , LIONEL VELLY3, LUC RENAUD3, EMILIE RAPHA3, ADRIENMEGUERDITCHIAN1,4*
Corresponding Author: yannick.becker@gmx.net
1

Laboratoire de Psychologie Cognitive, Aix-Marseille Univ, CNRS UMR 7290,
Marseille, France
2
Centre de Recherche en Neurosciences de Lyon, Université Lyon 1, INSERM U1028
CNRS UMR5292, Lyon, France
3
Institut de Neurosciences de la Timone, Aix-Marseille Univ, CNRS UMR 7289,
Marseille, France
1,4
UAR846 Station de Primatologie CNRS, Rousset, France

The Planum Temporale (PT) is a key language area and is structurally andfunctionally left-lateralized in pre-linguistic infants (Dehaene-Lambertz et al.,
2002; Dubois et al., 2010). Therefore, the PT was considered a marker of theuniquely human pre-wired brain for language acquisition. This latter hypothesis
has been questioned by recent studies reporting similar structural asymmetries innewborn baboons (Becker et al., 2021; 2022). Nevertheless, its functional
implication in monkeys, and their potential shared properties with any language
features related to PT asymmetry in humans remains unknown.
In the present followed-up study in the same baboons (Papio anubis) living insocial groups at the Station de Primatologie CNRS, we found a clear link between
this early brain feature and communicative gesturing. In fact, we show that
direction of early PT grey matter asymmetry in newborn baboons predicts thedevelopment of later communicative gesture’s manual lateralization once
juveniles (but not of handedness for non-communicative manipulative action).

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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Specifically, we investigated intra-individual brain/behaviour correlates within 26
healthy infant baboons (10 females, 16 males). Behavioral measurements
included both handedness for communicative gestures versus handedness for noncommunicative bimanual object manipulation from data collected from 9 months
to 3 years old. Brain measurements included previously published MRI data on
PT grey matter volume interhemispheric asymmetry at the earliest “newborn" ageclass (i.e., 4 days to 2 months old before the full maturation of myelin, synapses
and cell bodies + an outlier of 165 days of age, see Becker et al., 2021 for the full
MRI acquisitions and region delineation procedures). Handedness for
manipulative actions was assessed using the bimanual coordinated “Tube task”(Hopkins et al., 1996). Communicative gesture was defined as a movement of thehand directed to a specific partner or audience in order to affect its behavior(Molesti et al., 2019). The “Hand slapping” gesture was previously found optimalfor measuring such gestural communication’s lateralization in the baboon(Meguerditchian et al., 2013). For each subject and both behaviors, a handedness
index of the left (L) and the right (R) hand was computed HI = (R – L) /(R + L) with the sign indicating the direction of asymmetry (negative: left side,
positive: right side) and the value, the strength of asymmetry. Subjects wereclassified into two groups, according to direction of PT asymmetry at birth (19typical left-lateralized, 8 atypical non- or right-lateralized). One sample t-test
highlighted a significant positive handedness for communication for the typicalPT biased group (p < .006), which was not found for the atypical PT biased group(p > .3). Two-sided t-test demonstrated a significant difference between groups(p < .042). No effect was found for non-communicative actions (p > .5). Logistic
regression significantly modeled the effect of the asymmetry quotient of thePlanum Temporale at birth on the likelihood of becoming left or right-handed incommunicative gesture (p < .028). In other words, newborn monkeys with an
early larger left-than-right PT were more likely to developed right-handed
gestural communication once juvenile.
This finding suggests that early PT asymmetry in both monkeys and humans
might be a pre-wired brain marker of a shared functional feature between humanlanguage and monkey’s gestural communication.

Acknowledgements
The project has received funding from the European Research Council under theEuropean Union’s Horizon 2020 research and innovation program grant

71

agreement No 716931 - GESTIMAGE - ERC-2016-STG, as well as from grants
ANR-16-CONV- 0002 (ILCB), ANR-17-EURE-0029 (NeuroSchool) and the
Excellence Initiative of Aix-Marseille University via A*Midex funding (AMX19- IET-004).

References
Becker, Y., Sein, J., Velly, L., Giacomino, L., Renaud, L., Lacoste, R., Anton, J.L., Nazarian, B., Berne, C., & Meguerditchian, A. (2021). Early Left-Planum
Temporale Asymmetry in Newborn Monkeys (Papio anubis): A longitudinal
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Becker, Y., Phelipon, R., Sein, J., Velly, L., Renaud, L., & Meguerditchian, A.
(2022). Planum temporale grey matter volume asymmetries in newbornmonkeys (Papio anubis). Brain Structure and Function.
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Cognition.

72

VISUALIZATION OF VOCAL FLEXIBILITY IN ASIAN
ELEPHANTS
Veronika C. Beeck *1, Gunnar Heilmann 2, and Angela S. Stoeger 1*Corresponding Author: veronika.beeck@univie.ac.at
1Department of Behavioural & Cognitive Biology, University of Vienna, Vienna, Austria
2MeCalc Technologies GmbH, Berlin, Germany

Background
Elephants demonstrate both biomechanical and cognitive flexibility in soundproduction. They are even among the few mammals capable of vocal production
learning (Poole et al., 2005; Stoeger et al., 2012). Functions and mechanisms ofthis flexibility remain largely unknown. The small vocal repertoire of 8-13 calltypes spans from infrasonic rumbles (F0 8-35 Hz) to high pitched trumpets (F0300-600 Hz) and, in Asian elephants, species-specific squeaks (F0 300-2300
Hz) (Stoeger & de Silva, 2014). In this project, we studied sound production
and how it relates to potential information coding and social learning in Asianelephants, which have been studied much less than African elephants.
Methods
We used an acoustic camera (gfai tech, Berlin) to visualize
sound emission in addition to audio and video data to analyse
acoustic structures and body movements in captive Asianelephants. The acoustic camera uses an array of microphones
to localize sound sources based on a sum- and delaybeamforming algorithm (Stoeger et al., 2012) and depicts soundpressure levels (SPL) by colour coding (Figure 1).
Results

Figure 1. Nasal rumble.

The acoustic camera showed that squeaks were emitted orally in three females(Ncalls = 90) and nasally in one (Ncalls = 14). Our data suggest oral squeaks areproduced by vibration of the tensely closed lips, a mechanism unique in theThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

73

animal kingdom except for humans (Beeck et al., 2021). We recorded squeaks
only from 19 out of 56 subjects. One female squeaked through her narrowed
nostril, revealing productive flexibility even within one call type. Together, thisindicates the involvement of learning in squeak production. Trumpets (utteredthrough the trunk) and squeaks occurred in contexts of arousal and encoded
individual identity. Rumbles in 9 females (Ncalls= 203) were emitted either
through the mouth or trunk (Figure 1) or both simultaneously, demonstratingvelo-pharyngeal coupling and complex vocal tract resonances that increase theparameter space for potential information coding (Beeck et al., submitted).
Conclusion
Our results demonstrate how Asian elephants increase acoustic variability throughanatomical and cognitive adaptations that overcome morpho-mechanicallimitations of laryngeal sound production, where across taxa body size and therelated vocal apparatus dimensions determine source and filter parameters. This
supports the hypothesis that vocal complexity can be expressed in graded
repertoires rather than only repertoire size. We set an important framework forelephants to be included in comparative analyses of the multiple dimensions of
vocal complexity and their evolution across socially dynamic species, which
include flexibility in sound production, vocal learning, and information coding.
Acknowledgements
This research was funded by the Austrian Science Fund (FWF) project number
W1262-B29, Angela S. Stoeger project number P31034-B29, and a VDS CoBeNe
final fellowship to Veronika C. Beeck.
References
Beeck, V. C., Heilmann, G., Kerscher, M., & Stoeger, A. S. (2021). A novel
theory of Asian elephant high-frequency squeak production. BMC
biology, 19(1), 121. doi:10.1186/s12915-021-01026-z
Beeck, V. C., Heilmann, G., Kerscher, M., & Stoeger, A. S. (submitted). Soundimaging demontsrates velopharyngeal coupling and complex spectral
variability in Asian elephants.
Poole, J. H., Tyack, P. L., Stoeger-Horwath, A. S., & Watwood, S. (2005).
Elephants are capable of vocal learning. Nature, 434(7032), 455-456.
Stoeger, A. S., & de Silva, S. (2014). African and Asian Elephant Vocal
Communication: A Cross-Species Comparison. In Biocommunication ofAnimals (pp. 21-39).
Stoeger, A. S., Heilmann, G., Zeppelzauer, M., Ganswindt, A., Hensman, S., &Charlton, B. D. (2012). Visualizing sound emission of elephant74

vocalizations: evidence for two rumble production types. PLoS One,
7(11), e48907. doi:10.1371/journal.pone.0048907
Stoeger, A. S., Mietchen, D., Oh, S., de Silva, S., Herbst, C. T., Kwon, S., & Fitch,
W. T. (2012). An Asian elephant imitates human speech. Current
Biology, 22(22), 2144-2148.

75

TIMBRE IN MUSIC AND PROSODY: COMPARATIVE AND
EVOLUTIONARY CONSIDERATIONS
OLIVER TAB BELLMANN*1*Corresponding Author: obellman@smail.uni-koeln.de
1

Systematic Musicology, Institute of Musicology, University of Cologne, Germany

It has been proposed that music and language share a common prosodic origin(e.g., Brown, 2017). Music and language have different components linked to
specific acoustical properties, each of which can be associated with evolutionary
functions. If these components are present in both domains as well as non-humanvocal communication and share functions, they are likely to be inherited fromphylogenetically older communication systems (Masataka, 2009).
In the current paper, I study the hypothesis on a common prosodic origin ofmusic and language by focusing on the domain of timbre through a literature
review. As shared components of prosody and music, so far, rhythm and pitch
gained much attention in comparative language and music research (e.g.,Temperley, 2022; Scharinger & Wiese, forthcoming). However, timbre, anothercomponent of prosody, received far less attention in comparative research.
Timbre has para-linguistic functions and plays an important role in speechprosody (Thompson et al., 2012). Its functions include encoding of emotion,
based on, e.g., spectral energy distribution and signal-to-noise ratio (Briefer,2012), and information about the signalers’ identity based on individual
characteristics of the signalers’ formant structure (Taylor & Reby, 2010). Those
two functions are shared with those of non-human vocal communication (Taylor& Reby, 2010).
In vocally produced – sung – music, too, timbral cues encode emotion andidentity (Scherer, 1995; Erickson, 2018). Communication of emotion throughtimbre in instrumental music was reported in several studies (e.g., Paquette et al.,2018; Bowman & Yamauchi, 2016). Communication of increased emotionalarousal and emotional intensity relies on spectral distribution (Eerola et al., 2012;
Wu et al., 2014). Encoding of emotion in instrumental music has been related toperceptual features associated with timbre such as “brightness” and “softness”(Eerola & Vuoskoski, 2012; Juslin & Laukka, 2004). Relevant spectral features
for this, such as Spectral Centroid and HF-LF Ratio, are also important acoustical

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76

features for encoding arousal in emotional prosody and mammalian vocal
communication (Briefer, 2012).
Timbre also plays a role in the identification of music and allows theidentification of instrumental music through the individual timbral properties ofmusical stimuli on various levels - of genres (Casey et al., 2014), whole pieces(Schellenberg et al., 1999), excerpts of pieces (Poulin-Charronnat et al., 2004),
melodies (e.g., Schellenberg & Habashi, 2015; Peretz et al., 1998) and single
musical sounds (e.g., Suied et al., 2014). Thus, identity is communicated in
instrumental music through the individual timbral properties of the music. This issimilar to the communication of signalers’ identity through individual timbral
characteristics seen in prosody and mammalian vocal communication (Handel,
1995; Patil et al., 2012; Taylor & Reby, 2010).
In summary, timbre conveys emotional and identity information in speechprosody, and vocal and instrumental music through the same acoustic cues as inmammalian vocal communication. Thus, I claim that music and language share acommon prosodic origin, with communication of identity and emotion as apreserved function found today in para-linguistic aspects of prosody and in music.
Acknowledgements
I would like to thank Dr. Rie Asano for her helpful comments on earlier versionsof this abstract.
References
Bowman, C., & Yamauchi, T. (2016). Perceiving categorical emotion in sound:
The role of timbre. Psychomusicology: Music, mind, and brain, 26(1), 15-25.
Briefer, E. F. (2012). Vocal expression of emotions in mammals: mechanisms of
production and evidence. Journal of Zoology, 288(1), 1-20.
Brown, S. (2017). A joint prosodic origin of language and music. Frontiers in
psychology, 8, 1894.
Casey, M., Thompson, J., Kang, O., Raizada, R., & Wheatley, T. (2012).
Population Codes Representing Musical Timbre for High-Level fMRI
Categorization of Music Genres. In G. Langs, I. Rish, M. Grosse-Wentrup,
and B. Murphy (Eds.), Machine Learning and Interpretation in Neuroimaging(pp.34-41). Berlin, Heidelberg: Springer
Eerola, T., & Vuoskoski, J. K. (2012). A review of music and emotion studies:
Approaches, emotion models, and stimuli. Music Perception: An
Interdisciplinary Journal, 30(3), 307-340.
Eerola, T., Ferrer, R., & Alluri, V. (2012). Timbre and affect dimensions:
Evidence from affect and similarity ratings and acoustic correlates of isolated
instrument sounds. Music Perception: An Interdisciplinary Journal, 30(1),49-70.

77

Erickson, M. L., Burchard, J., & Phillips, P. (2018). Can listeners hear who issinging? The development of voice category perception. Journal of
Voice, 32(4), 459-465.
Handel, S. (1995). Timbre perception and auditory object identification. In B. C.
J. Moore (Ed.), Hearing, 2 (pp. 425-461). San Diego: Academic.
Juslin, P. N., & Laukka, P. (2004). Expression, perception, and induction ofmusical emotions: A review and a questionnaire study of everyday listening.
Journal of new music research, 33(3), 217-238.
Masataka, N. (2009). The origins of language and the evolution of music: A
comparative perspective. Physics of Life Reviews, 6(1), 11-22.
Paquette, S., Takerkart, S., Saget, S., Peretz, I., & Belin, P. (2018). Cross‐classification of musical and vocal emotions in the auditory cortex. Annals of
the New York Academy of Sciences, 1423(1), 329-337.
Peretz, I., Gaudreau, D., & Bonnel, A. M. (1998). Exposure effects on music
preference and recognition. Memory & cognition, 26(5), 884-902.
Poulin-Charronnat, B., Bigand, E., Lalitte, P., Madurell, F., Vieillard, S., &McAdams, S. (2004). Effects of a change in instrumentation on therecognition of musical materials. Music Perception, 22(2), 239-263.
Scharinger, M. & Wiese, R. (forthcoming). How Language Speaks to Music:
Prosody from a Cross-domain Perspective. De Gruyter.
Schellenberg, E. G., & Habashi, P. (2015). Remembering the melody and timbre,
forgetting the key and tempo. Memory & Cognition, 43(7), 1021-1031.
Schellenberg, E. G., Iverson, P., & McKinnon, M. C. (1999). Name that tune:
Identifying popular recordings from brief excerpts. Psychonomic bulletin &review, 6(4), 641-646.
Scherer, K. R. (1995). Expression of emotion in voice and music. Journal of voice,9(3), 235-248.
Taylor, A. M., & Reby, D. (2010). The contribution of source–filter theory tomammal vocal communication research. Journal of Zoology, 280(3), 221-236.
Temperley, D. (2022). Music and Language. Annual Review of Linguistics, 8,
153-170.
Thompson, W. F., Marin, M. M., & Stewart, L. (2012). Reduced sensitivity toemotional prosody in congenital amusia rekindles the musical protolanguage
hypothesis. Proceedings of the National Academy of Sciences, 109(46),19027-19032.
Wu, B., Horner, A., & Lee, C. (2014). The correspondence of music emotion andtimbre in sustained musical instrument sounds. Journal of the AudioEngineering Society, 62(10), 663-675.

78

COGNITIVE AND GENETIC CORRELATES OF A SINGLE
MACRO-PARAMETER OF CROSSLINGUISTIC VARIATIONANTONIO BENÍTEZ-BURRACO*1, CANDY CAHUANA1, SIHAN CHEN2,DAVID GIL3, LJILJANA PROGOVAC4, JANA REIFEGERSTE5
and TATIANA TATARINOVA6*Corresponding Author: abenitez8@us.es
1Department of Spanish, Linguistics & Theory of Literature, University of Seville, Spain
2Department of Brain and Cognitive Science, Massachusetts Institute ofTechnology, Cambridge, USA
3Department of Linguistic and Cultural Evolution, Max Planck Institute for

Evolutionary Anthropology, Leipzig, Germany
4Linguistics Program, Wayne State University, Detroit, USA
5Potsdam Research Institute for Multilingualism, University of Potsdam,
Germany, and Brain and Language Lab, Georgetown University, Washington,
D.C., USA
6Department of Biology and Computational Biology, University of LaVerne,
USA

Evidence exists for a correlation, and perhaps also causation, between
specific linguistic and societal features, in particular those relating to exoteric(open) vs. esoteric (close-knit) society types, characterizable in terms of
population size, mobility, communication across distances, etc. Broadly speaking,languages associated with exoteric societies, or Type A languages, have beenreported to exhibit less complex phonologies and morphologies, but more
complex and more layered syntaxes, with more specialized and obligatory
grammaticalized distinctions, while languages associated with esoteric societies,
or Type B languages, exhibit a complementary clustering of features, including
simpler and less layered syntaxes, but more complex phonologies andmorphologies, with more irregularity, and more formulaic/memorized language
chunks (e.g. Wray & Grace, 2007). We conducted an exhaustive quantitative
analysis drawing upon WALS, D-Place, Ethnologue and Glottolog. Our
preliminary results find partial support for the above correlations. In general,

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79

albeit with some exceptions, Type A languages tend towards more complex
morphosyntax and greater expressive power in certain domains, although also
towards more complex phonological inventories, while Type B languages tend
towards more complex morphology.
Next, we hypothesize that this crosslinguistic variation entails differential
involvement of declarative versus procedural memories. Procedural memory
subserves the acquisition of compositional, automated, rule-governed(grammatical) aspects of language, while declarative memory typically subserves
vocabulary learning and irregular phenomena across domains, including
memorized, opaque, formulaic language (e.g. idioms and proverbs) (Ullman,
2004; 2015; Heyselaar et al., 2017; Elyoseph et al., 2020, for impairments). Whileboth memory systems are essential for language (with partly
overlapping/redundant functions), and while both language types certainly rely
on both memories, our hypothesis is that predominantly Type A languages rely
more on procedural memory, while predominantly Type B languages, incomparison, rely more on declarative memory. For testing this, we are conducting
standard cognitive experiments measuring the relative strengths of the twomemory types with speakers of Type A vs. Type B languages. Also, because these
two types of memories depend on brain regions whose emergence is geneticallyguided during development, another way of testing our hypothesis is by seeking
correlations between the Type A/Type B linguistic distinction, and the frequency
in the population of the candidate gene alleles supporting different memory types.
Various genes have been found to play a role in declarative memory, e.g., BDNF
and APOE (Ullman, 2015; Henke, 2010; Squire & Wixted, 2011; Eichenbaum,2012), as opposed to procedural memory, e.g. FOXP2, PPP1R1B and DRD2
(Packard, 2008; Doyon et al., 2009; Ashby et al., 2010; Eichenbaum, 2012). Since
cognitive biases can be linked to (epi)genetic modifications, any differential
reliance with respect to the two types of memories is expected to be detectable indifferences in the allele frequencies of specific genes. At present, we have founddifferences between speakers of Type A and Type B languages with regards togenes involved in synapse organization subserving relevant brain functions.
This approach provides a tangible way to engage the neurobiological andgenetic underpinnings of language variation, identifying specific and testable
implications for the role of both cultural and genetic factors in language evolution,
where they are seen as engaged in a feedback loop, with each reinforcing theother. As a bonus, our proposal sheds novel light on the long-standing linguist’s
puzzle (and controversy), where researchers often report trade-offs in complexity
among different linguistic domains (e.g. Sampson, Gil & Trudgill, 2009).

80

References
Ashby, F. G., Turner, B. O., & Horvitz, J. C. (2010). Cortical and basal ganglia
contributions to habit learning and automaticity. Trends in Cognitive Sciences,
14 (5), 208–215.
Doyon, J., Bellec, P., Amsel, R., Penhune, V. B., Monchi, O., Carrier, J.,
Lehéricy, S., Benali, H. (2009). Contributions of the basal ganglia andfunctionally related brain structures to motor learning. Behavioural Brain
Research, 199, 61–75.
Eichenbaum, H. (2012). The Cognitive Neuroscience of Memory: An Introduction(2nd ed.). Oxford: Oxford University Press.
Elyoseph, Z., Mintz, M., Vakil, E., Zaltzman, R., Gordon, C.R. (2020). Selective
procedural memory impairment but preserved declarative memory in
Spinocerebellar Ataxia Type 3. The Cerebellum 19:226–234.
https://doi.org/10.1007/s12311-019-01101-w
Henke, K. (2010). A model for memory systems based on processing modes
rather than consciousness. Nature Reviews Neuroscience, 11, 523–532.
Heyselaar, E., Segaert, K., Walvoort, S. J.W., Kessels, R. P.C., Hagoort, P.
(2017). The role of nondeclarative memory in the skill for language: Evidence
from syntactic priming in patients with amnesia. Neuropsychologia. 101, 97105. doi:10.1016/j.neuropsychologia.2017.04.033
Packard, M. G. (2008). Neurobiology of procedural learning in animals. In J. H.
Byrne (Ed.), Concise learning and memory: The editor’s selection (pp. 341–
356). London: Elsevier Science and Technology.
Sampson, G., Gil, D., and Trudgill, P. (Eds.) (2009). Language Complexity as an
Evolving Variable. Oxford: Oxford University Press.
Squire, L. R., and Wixted, J. T. (2011). The cognitive neuroscience of humanmemory since H.M. Annual Review of Neuroscience, 34, 259–288.
Ullman, M. T. (2004) Contributions of memory circuits to language: The
declarative/procedural model. Cognition 92 (1–2), 231–270.
Ullman, M. T. (2015). The declarative/procedural model: a neurobiologicallymotivated theory of first and second language. In Van Patten, B., andWilliams, J. (Eds.), Theories in Second Language Acquisition: An
Introduction (2nd ed.) (pp. 135-158). London and New York: Routledge.
Wray, A., Grace, G.W. (2007). The consequences of talking to strangers:
Evolutionary corollaries of socio-cultural influences on linguistic form.
Lingua 117, 543–578.

81

CONTINUITY OF EMOTION AND AGGRESSION IN
LANGUAGE EVOLUTIONANTONIO BENÍTEZ-BURRACO*1, LJILJANA PROGOVAC2*Corresponding Author: abenitez8@us.es
1Department of Spanish, Linguistics & Theory of Literature, University of Seville, Spain
2 Department of English (Linguistics), Wayne State University, Detroit, USA

While propositional language has been at the center of language evolution
theorizing, it faces a problem of discontinuity when it comes to other primates’communication. Our main claim is that continuity should not be abandoned, as it
reveals itself in a gradualist approach that ties together the evolution of
language/syntax with the management of emotion/aggression, where the two are
engaged in a co-evolutionary feedback loop. We rely on the finding that the samemechanism of dense neuronal connectivity between cortical and subcorticalstructures, implicated in the suppression of reactive aggression, is also responsible
for cross-modality (metaphoricity) and for syntactic processing (Benítez-Burraco
and Progovac 2021).
In this respect, early forms of language, with simplest grammars and highly
concrete, imageable vocabulary have been proposed to be more visceral (andlinked to older processing strategies) than typical constructions of modern
languages, in which even imageable, emotive words get buried under the layersof abstract syntactic categories (Progovac et al. 2018). Even today, metaphorical,
imageable vocabulary has a higher emotional impact than literal language, relying
more on the limbic system (e.g. Bohrn et al. 2012). These earliest stages oflanguage would have shown more continuity with animal communication (i.e.
vocalizations), which are highly emotional (Darwin 1872; Code 2011).
We tie our proposal to the history of genetic and biological changes associatedwith changes in the management of aggression, which resulted in physical,
behavioral and cognitive changes in our species, and which are sometimes
referred to as human self-domestication (HSD) (Hare et al., 2012). The period
before 200 kya saw high levels of reactive aggression and hominins at that stage
probably relied on brief (even single word) emotion-driven outbursts, such as
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82

commands, warnings, and threats (Watch out! Run! Move!) The fossil recordsuggests that features of HSD, and implicitly of reactive aggression, were
gradually decreasing between 200-100 kya (Leach, 2003; Zollikofer and Poncede León, 2010), which arguably coincided with the emergence of simple (twoslot) grammars, capable of merging only one verb and one noun (e.g. scatterbrain; hunch-back; turn-skin; cry-baby; Progovac, 2015). While they would havebeen truly beneficial in general, these highly metaphorical compositions also turnout to be especially well-suited for insult/verbal aggression, and as such theywould have been highly adaptive at this stage in evolution, significantly
contributing to replacing physical aggression/contest with verbal
aggression/cognitive contest. This further reinforced the trend in selection
towards increased control of subcortical networks by cortical devices,
contributing further to suppression/inhibition of reactive aggression, andultimately, to HSD features. Based on the fossil record, HSD reached its peakroughly between 100-50 kya and 10kya (Cieri et al., 2014), at the time when fullyfledged grammars emerged and referential uses of language started to become
more important.
Neurobiological and neuropsychiatric findings provide support for our
proposal. Reactive (physical) aggression typically implicates the limbic system(Rolls 2015 for review), the striatal regions, and parts of the cortex (Dolan et al.
2002; Yang et al. 2009; Boccardi et al. 2011). The processing of aggressive
language, such as swear words and profanity, shows a clear overlap/continuity
with physical aggression, implicating the basal ganglia, limbic structures,thalamus, and the right hemisphere (e.g. Code 2011). Disorders which sometimes
result in uncontrolled swearing/profanity (e.g. Tourette’s Syndrome) also involve
enhanced physical aggression, attributable to a basal-limbic connection
dysfunction (e.g. Ganos et al. 2013).
Overall, our proposal supports the view that human evolution saw a gradualshift from raw, emotional language (with rudimentary syntax), to structurally
highly complex forms of language, which can be quite detached from emotion,
and which arguably correlated with the relatively recent rise inproactive/premeditated aggression (Wrangham 2018). Less visceral, morerational uses of language of course open new possibilities for dishonest signaling.
The gradual progression in human evolution from reactive aggression (driven by
raw emotion), to proactive/premeditated aggression (driven by more detachedreasoning), is itself a transition from more emotional to more rational behavior.

83

Selected References
Benítez-Burraco A. and Progovac L. (2021). Language evolution: examining thelink between cross-modality and aggression through the lens of disorders.
Philos Trans R Soc Lond B Biol Sci. 376(1824):20200188
Boccardi, M., Frisoni, G. B., Hare, R. D., Cavedo, E., Najt, P., Pievani, M., et al.
(2011). Cortex and amygdala morphology in psychopathy. Psychiatry Res.
193, 85–92. doi: 10.1016/j.pscychresns.2010.12.013
Bohrn, I., Altmann, U. and Jacobs, A. M. (2012). Looking at the brains behindfigurative language - A quantitative meta-analysis of neuroimaging studies
on metaphor, idiom, and irony processing. Neuropsychologia 50, 26692683. doi: 10.1016/j.neuropsychologia.2012.07.021
Cieri, R. L., Churchill, S. E., Franciscus, R. G., Tan, J., and Hare, B. (2014).
Craniofacial feminization, social tolerance, and the origins of behavioral
modernity. Curr. Anthropol. 55:419–43.
Code, C. (2011). Nonfluent aphasia and the evolution of proto-language. J.
Neurolinguistics 24, 136–144. doi: 10.1016/j.jneuroling.2009.12.007
Darwin, C.R. (1872). The Expression of the Emotions in Man and Animals.
London: John Murray. 1st edition.
Dolan, M., Deakin, W. J., Roberts, N., and Anderson, I. (2002). Serotonergic andcognitive impairment in impulsive aggressive personality disordered
offenders: are there implications for treatment? Psychol. Med. 32, 105–
117. doi: 10.1017/S0033291701004688
Ganos C, Münchau A, Bhatia K. (2014). The semiology of tics, Tourette’s, andtheir associations. Mov. Disord. Clin. Pract. 1, 145–153.
doi:10.1002/mdc3.12043
Hare, B., Wobber. V., Wrangham, R. (2012). The self-domestication hypothesis:
Evolution of bonobo psychology is due to selection against aggression.
Animal Behavior 83.3: 573-585.
Leach, H.M. (2003). Human domestication reconsidered. Curr. Anthropol., 44,
349–368.
Progovac L. (2015). Evolutionary Syntax. Oxford, UK: Oxford University Press.
Progovac, L., Rakhlin, N., Angell, W., Liddane, R., Tang, L., and Ofen, N. (2018).
Neural correlates of syntax and proto-syntax: an fMRI study. Front.
Psychol. 9:2415. doi: 10.3389/fpsyg.2018.02415
Rolls, E. T. (2015). Limbic systems for emotion and for memory, but no single
limbic system. Cortex 62, 119–157. doi: 10.1016/j.cortex.2013.12.005
Yang, Y., Raine, A., Colletti, P., Toga, A. W., and Narr, K. L. (2009). Abnormal
temporal and prefrontal cortical gray matter thinning in psychopaths. Mol.
Psychiatry 14, 561–562. doi: 10.1038/mp.2009.12
Wrangham R. W. (2018). Two types of aggression in human evolution. PNAS,
115(2), 245–253. https://doi.org/10.1073/pnas.1713611115
Zollikofer, C. P. and Ponce de León, M. S. (2010). The evolution of hominin
ontogenies. Semin. Cell Dev. Biol. 21(4), 441–452.

84

COMPLEX SOUND SEQUENCES IN ITERATED LEARNING:
CLUSTERS CAN BECOME MORE STABLE WHEN THEY
SIGNAL MORPHOLOGICAL BOUNDARIESIRENE BÖHM*1 and NIKOLAUS RITT1*Corresponding Author: irene.boehm@univie.ac.at
1Department of English & American Studies, University of Vienna, Vienna, Austria

We report a spoken artificial iterated language learning experiment in which weinvestigated the transmission of final consonant clusters. Our main researchquestion was how well clusters that are exclusively produced through suffixation(such as the /gz/ in egg+s) are transmitted in comparison to (a) exclusively steminternal clusters (such as the /lk/ in hulk), and (b) clusters that occur both withinstems and across boundaries (such as /ks/ in fix and kick+s).
In comparison to consonant-vowel sequences, consonant clusters count as
highly ‘marked’ or ‘dispreferred’ in many linguistic theories (cf. Clements &Keyser, 1983; Prince & Smolensky, 2004; Dziubalska-Kołaczyk, 2002, 2019).
They are difficult both to perceive and to produce, cross-linguistically rare
(Maddieson, 1999, 2003), and historically unstable (e.g. Lutz, 1988). This implies
that they are selected against in cultural evolution (Ritt, 2004; Christiansen &Chater, 2008). However, consonant clusters appear to be more frequent and morestable when they are ‘morphotactic’, i.e. when they span morpheme boundaries,
as in English /gz/ in egg+s. It has been hypothesized that this is because they
indicate morphological complexity and facilitate the processing of complex wordforms (Dressler & Dziubalska-Kołaczyk, 2006).
From this reasoning an interesting question arises about final clusters that
occur both across morpheme boundaries and within word stems, such as English/nd/ in simple find and complex dine+d, for example. In contrast to
unambiguously morphotactic clusters like the /gz/ in egg+s, they do not reliably
signal complexity and may even impede processing (Post et al., 2008). Thissuggests that they should be difficult to transmit and be unstable in cultural
language evolution. On the other hand, however, it has also been argued that
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85

frequent morphotactic clusters may support the emergence and the stability ofstem-internal homophones through analogy (Hogg & McCully, 1987). Evidence
for both hypotheses has been found in processing studies (e.g. Celata et al., 2015)
and diachronic research (e.g. Baumann et al., 2016, 2019). We investigatetransmission of morphotactic, stem-internal, and ambiguous clusters by means ofa spoken iterated artificial language learning experiment.
Following the basic design of such experiments (Kirby, Cornish & Smith,2008), we asked participants to learn words from an artificial miniature language
and to reproduce them from memory. The outputs of ‘first-generation’participants served as learning input for a second ‘generation’. This procedure
was then repeated over ten participant-generations. In our specific experiment,
thirty Austrian German native speakers learned a set of singular and plural nouns,some of which contained a highly marked plosive+/k/ cluster (e.g. /zʊtk/). They
were trained on eleven nouns, each of which comprised an abstract image and a
spoken label, and subsequently asked to reproduce the sound labels corresponding
to each image. To address our research question, we set up three conditions. In
condition one, all clusters occurred within singular stems (e.g. SG /zʊtk/ – PL/zʊtk+ɒ/). In condition two, all clusters were produced by adding the plural suffix{-/k/} to stems (e.g. SG /zʊt/ – PL /zʊt+k/). Finally, in condition three, the clusters
occurred both within singular stems and across stem-suffix boundaries.
In our analysis, we compared the transmission of lexical, morphotactic, andambiguous clusters. Our results showed that /tk/ clusters remained stable only
when they signaled plural suffixation unambiguously, i.e. in condition (ii), where
they were successfully transmitted across all ten participant generations. In
contrast, both exclusively stem-internal clusters (condition i) and ambiguous
clusters (condition iii) were reduced and/or lost early in transmission.
Quantitatively, the significant interaction between cluster transmission andmorphotactic condition was substantiated in multivariate regression models and a
Pearson's correlation test.
We take our data as support for the position that morphotactic cross-boundary
clusters are selected in cultural evolution, but only when they signal complexity
unambiguously. If they have stem-internal homophones, on the other hand, thisdoes not seem to be the case. Instead, ambiguous clusters seem to be as unstable,
as clusters that occur only within stems and have no morphological signaling
function at all. However, the possibility that cluster homophones may also support
each other cannot be ruled out, since the relevant processes may occur in earlystages of first language acquisition, while our experiment involved only adultparticipants.

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References
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English phonotactics provide evidence for analogy effects among lexical andmorphonotactic consonant clusters. Papers in Historical Phonology, 1, 50–
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the syllable. Cambridge, MA: MIT Press.
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Main: Peter Lang.
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Cambridge: Cambridge University Press.
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G. Bauer (Eds.), Luick revisited: Papers read at the Luick-Symposium at
Schloß Liechtenstein (pp. 221-239). Tübigen: Narr.
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Maddison, I. (2013). Syllable structure. In M. S. Dryer and M. Haspelmath (Eds.),
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processing of English regular inflections: Phonological cues tomorphological structure. Cognition, 109, 1–17.
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Generative Grammar. Malden, MA: Wiley Blackwell.
Ritt, N. (2004). Selfish sounds and linguistic evolution: A Darwinian approach tolanguage change. Cambridge: Cambridge University Press.

87

EFFECTS OF MEANING NEGOTIATION ON CORRECTNESS,GESTURE COUNT AND TIMESPAN IN A GAME OF
CHARADES
Monika Boruta*, Marek Placiński*, Marta Sibierska*, and Przemysław Żywiczyński*Corresponding Author: monika.feba@vp.pl*Nicolaus Copernicus University in Torun, Poland*

1oper*NC
iclausMeaning negotiation, most commonly described in language learning andacquisition research (Long 1981; Pica, Young & Doughty 1987), facilitates
mutual understanding of interlocutors and influences efficiency ofcommunication (Rees 1998). Negotiation of meaning is based on repetition ofwords, usage of synonyms and restructuring phrases in a communicative
situation in order to enable the parties to work out common means of expression(Rees 1998; Dobao & Martinez 2007, SL: Zeshan 2015). If speech fails, one canresort to the use of gesture and pantomime (Dobao & Martinez 2007, SL:
Zeshan 2015) – which are often said to be a communicative universal, that canbe produced „on the fly” (Arbib 2018; Zlatev et al. 2020, Silva et al. 2020). We
analysed the process of meaning negotiation with respect to three factors: (i)communicative success, (ii) gesture count/pantomime, and (iii) timespan, in amute game of charades. Following insights from gesture and sign language
research, we assumed that meaning negotiation causes not only greater
understanding over time, but – what we focus our analysis on – a reduction of(pantomimic) gestures or signs and their conventionalisation and the time of
message presentation (Earley 1999; Dachkovsky et al. 2018, Mineiro et al. 2021;
Namboodiripad et al. 2016). To test this assumption, we used a dataset of video
recordings of pantomime, collected in a study based on the referential task
paradigm known from Experimental Semiotics (see e.g. Fay et al. 2010).
The recordings include interactions in 26 pairs of participants, who played amute game of charades consisting of 4 rounds and based on written input. We
analysed the following factors in the interactions: (I) communicative success,operationalised as the number of correct responses in each round; (II) meaning
unit count, operationalised as the number of lexical items from the input reenacted by means of gesture and pantomime in each round; (III) timespan ofThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

88each round, given in seconds. We focused on the differences in the three factors
in round 1, before the negotiation, and in round 4, after the negotiation.
In order to verify hypotheses, we performed three separate regression
analyses for each outcome variable (Correctness – Fig. 1, Gesture Count – Fig. 2,
Time – Fig. 2). Gesture count and Time were standardised before being enteredinto their respective models in R. In line with our assumptions, the participants
negotiated the meaning throughout the game and were making significantly
more mistakes in round 1 than they did in round 4; the time they spent on
presenting their re-enactments in round 1 compared to round 4 was alsosignificantly shorter; however, the number of gestures remained at the samelevel.

Figure 1. Box plots for correctness for rounds 1 v. 4.

Figure 2. Box plots: time span for rounds 1 v. 4 and gesture count for rounds 1 v. 4.

In the discussion, we propose explanations for such a result - we refer toresearch in sign/language change over time, language evolution, communicative
pressures, and meaning negotiation processes. We also discuss the results inlight of a more detailed description of the participants’ gestural choices andstrategies between the rounds.

89

Acknowledgements
This research was funded by the Polish National Science Centre (NCN) under
the agreement UMO-2017/27/B/HS2/00642.
References
Arbib, M. A. (2018). In support of the role of pantomime in language evolution.
Journal of Language Evolution, 3(1), 41-44.
Dachkovsky, S., Stamp, R., & Sandler, W. (2018). Constructing complexity in ayoung sign language. Frontiers in Psychology, 9, 2202.
Dobao, A. M. F., & Martínez, I. M. P. (2007). Negotiating meaning ininteraction between English and Spanish speakers via communicative
strategies. Atlantis, 87-105.
Earley, K. (1999). Historical Changes in Sign Language-a Comparison of
Spoken and Sign Language Change.[Thesis, online access 14.02.2022].
Fay, N. et al. (2010). The interactive evolution of human communication
systems. Cognitive Science, 34(3), 351–386.
Long, M. H. 1981. “Input, interaction and second language acquisition”. In
Winitz, H. (ed.), Annals of the New York Academy of Sciences: Native
Language and Foreign Language Acquisition 379, 259-278
Mineiro, A., Báez-Montero, I. C., Moita, M., Galhano-Rodrigues, I., & CastroCaldas, A. (2021). Disentangling pantomime from early sign in a new sign
language: window into language evolution research. Frontiers in
Psychology, 12, 1130.
Namboodiripad, S., Lenzen, D., Lepic, R., & Verhoef, T. (2016). Measuring
conventionalization in the manual modality. Journal of Language
Evolution, 1(2), 109-118.
Rees, D. (1998). The negotiation of meaning in EFL learning in the language
laboratory. ASp. la revue du GERAS, (19-22), 283-309.
Pica, T., R. Young & C. Doughty. (1987). “The impact of interaction oncomprehension”. TESOL Quarterly 21/4, 737-758.

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Macuch Silva, V., Holler, J., Ozyurek, A., & Roberts, S. G. (2020).
Multimodality and the origin of a novel communication system in face-toface interaction. Royal Society open science, 7(1), 182056.
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5(2), 156–174, doi: 10.1093/jole/lzaa006.

91

SEQUENTIAL DYNAMICS IN THE VOCAL SYSTEM OFCOMMON MARMOSETS (CALLITHRIX JACCHUS)
Alexandra B. Bosshard1,2,*, Balthasar Bickel1,2, Paola Merlo3, Judith Burkart2,4 and Simon
W. Townsend1,2,5*
Corresponding Author: alexandra.bosshard@uzh.ch
Department of Comparative Language Science, University of Zurich, Zurich,
2
Center for the Interdisciplinary Study of Language Evolution (ISLE),
University of Zurich, Zurich, Switzerland
3
Department of Linguistics University of Geneva, Geneva, Switzerland
4
Department of Anthropology, University of Zurich, Zurich, Switzerland
5
Department of Psychology, University of Warwick, Coventry, UK

1Over the last 20 years there has been growing interest in the combinatorial
abilities of animals, namely the propensity to sequence context-specific calls (seee.g., Arnold & Zuberbühler, 2006; Berthet et al., 2019; Coye, Ouattara,
Zuberbühler, & Lemasson, 2015; Coye, Zuberbühler, & Lemasson, 2016), given
the light such data can shed on the evolutionary progression of our own
combinatorial communication system (Leroux & Townsend, 2020; Townsend,
Engesser, Stoll, Zuberbühler, & Bickel, 2018).
However, most studies to date have concentrated on the concatenation of twocall units (bigrams) (see e.g., Arnold & Zuberbühler, 2006; Coye, Ouattara,
Zuberbühler, & Lemasson, 2015; Coye, Zuberbühler, & Lemasson, 2016; Collier
et al., 2020) with such structures even being argued to represent the limit of animal
combinatorial abilities (see e.g., Miyagawa & Clarke, 2019). Whether animal
combinatorics lie beyond the two call level remains less explored, yet is criticalfor understanding how similar human and animal vocal systems are in theirsequential dynamics.
We revisit this question through investigating the formation and internal
structuring of call sequences in the highly social and cooperatively breeding
common marmoset (Callithrix Jacchus). We recorded the vocal output of eightindividuals (four pairs housed at the University of Zurich primate station,
Switzerland, resulting in four female and four male subjects) via focal sampling.

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

92This led to a corpus of 8600 calls, equalling 5800 call sequences (from unigram
to 9-gram sequences), generated by the eight different call types present in thecommon marmoset vocal system. Through borrowing methods traditionallyapplied in computational linguistics, including simple frequency values, finite
state automata (FSA), superordinate Markov models (MMs) (for an application
in song systems see Honda and Okanoya, 1999; Hosino and Okanoya, 2000;
Kershenbaum et al., 2014; Sainburg et al., 2019; Suzuki, Buck and Tyack, 2006;
Kershenbaum et al., 2014) and collocation analysis, we show that common
marmosets reliably concatenate vocalisations into larger structures of between
two and nine calls (trigrams being the most common length of combinations).
Furthermore, using FSA and MMs, we identified sequential relationships
between calls in combinations that point towards potential internal structuring.
For example, trigram MMs highlighted a non-adjacent-like edge structure (adependency between the first and last call element of a sequence (see Endress etal., 2009; Endress et al., 2010 and Grama, Wijnen & Kerkhoff, 2013)), wherebya trigrammic sequence starting with a food call always terminates with anotherfood call in the last position. Multiple distinctive collocation analysis confirmed
the food-call edge structure for three-call combinations and also four-callcombinations. In addition, joint probability showed that the non-adjacent-likefood-edge structure was present in 86% of all trigrams and 66% of all 4-grams.
While previous comparative research has identified non-adjacent structuring(structures that cannot be captured by more simple Markovian dynamics) inanimal song systems (e.g., Sainburg et al. 2019; Suzuki, Buck and Tyack, 2006;
Kershenbaum et al., 2014), this has not yet been shown in a non-human animal
context-specific call system as is the case for the common marmosets’ vocal
communication system.
MMs and collocation analyses additionally provided some evidence forsimple nestedness in common marmosets’ call structures. For example, thebigram Tsk-Ek, which was identified by joint probability values and by thecollocation analyses as one of the most common two-call units, also shows up in
trigrammic combinations and when it does, it is reliably followed by a contact callgiving rise to a “bigram-in-bigram” structuring.
We argue that call combinations in animals may be more sophisticated thanpreviously thought and that the current dearth of evidence for larger call
combinations in non-human animals could well be due to a lack of application of
objective measurements to capture the sequential dynamics of structures at thecall combination level.

93

References
Arnold, K., & Zuberbühler, K. (2006). Semantic combinations in primate calls.
Nature, 441(7091), 303–303.
Berthet, M., Mesbahi, G., Pajot, A., Cäsar, C., Neumann, C., & Zuberbühler, K.
(2019). Titi monkeys combine alarm calls to create probabilistic meaning.
Science Advances, 5(5), eaav3991.
Collier, K., Radford, A. N., Stoll, S., Watson, S. K., Manser, M. B., Bickel,
B., & Townsend, S. W. (2020). Dwarf mongoose alarm calls:
investigating a complex non-human animal call. Proceedings of theRoyal Society B, 287(1935), 20192514.
Coye, C., Ouattara, K., Zuberbühler, K., & Lemasson, A. (2015). Suffixationinfluences receivers’ behaviour in non-human primates. Proceedings of theRoyal Society B: Biological Sciences, 282(1807), 20150265.
Coye, C., Zuberbühler, K., & Lemasson, A. (2016). Morphologically structured
vocalizations in female Diana monkeys. Animal Behaviour, 115, 97–105.
Endress A.D., Carden S., Versace E. & Hauser M.D. (2010). The apes’ edge:
positional learning in chimpanzees and humans. Animal cognition, 13(3),483-495.
Grama I., Wijnen F. & Kerkhoff A. (2013). Constraints on non-adjacentdependency-learning: Distance matters an artificial grammar learning study
with adults. In Boston University Conference on Language Development 37
online proceedings supplement, Boston, MA.
Honda, E., & Okanoya, K. (1999). Acoustical and syntactical comparisons
between songs of the white-backed munia (Lonchura striata) and its
domesticated strain, the Bengalese finch (Lonchura striata var. domestica).
Zoological Science, 16(2), 319-326.
Hosino, T., & Okanoya, K. (2000). Lesion of a higher-order song nucleus
disrupts phrase level complexity in Bengalese finches. Neuroreport, 11(10),2091-2095.
Hurford, J. R. (2012). The origins of grammar: Language in the light ofevolution II (Vol. 2). Oxford University Press.
Kershenbaum, A., Bowles, A. E., Freeberg, T. M., Jin, D. Z., Lameira, A. R., &Bohn, K. (2014). Animal vocal sequences: Not the Markov chains wethought they were. Proceedings of the Royal Society B: Biological
Sciences, 281(1792), 20141370.
Leroux, M., & Townsend, S. W. (2020). Call combinations in great apes and theevolution of syntax. Animal Behavior and Cognition, 7(2), 131–139.
Miyagawa, S., & Clarke, E. (2019). Systems underlying human and old worldmonkey communication: One, two, or infinite. Frontiers in psychology,
1911.

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Sainburg, T., Theilmann, B., Thielk, M., & Gentner, T. Q. (2019). Parallels
in the sequential organization of birdsong and human speech. Nature
Communications, 10(1), 1-11
Suzuki, R., Buck, J. R., & Tyack, P. L. (2006). Information entropy of
humpback whale songs. The Journal of the Acoustical Society ofAmerica, 119(3), 1849–1866.
Townsend, S. W., Engesser, S., Stoll, S., Zuberbühler, K., & Bickel, B. (2018).
Compositionality in animals and humans. PLoS Biology, 16(8), e2006425.

95

DOES A COMMUNICATIVE CONTEXT PRODUCE A
PREFERENCE FOR EARLY NEGATION? AN ARTIFICIALLANGUAGE LEARNING STUDY
Danielle Burgess*1*Corresponding Author: dlburge@umich.edu
1Linguistics Department, University of Michigan, Ann Arbor, USA

Tendencies for negation to appear early in the sentence have been observed byscholars working in typology (Dryer, 1988), creole language emergence(Schneider, 2000), and language acquisition (Kim & Yun, 2013; Dimroth, 2010).
Some scholars have proposed that a tendency for early negation marking may bemotivated by considerations of audience design in communication, becausedelaying the negator could increase misunderstanding or comprehensiondifficulty when the sentence up to but not including the negative is the oppositeof the intended meaning (Dryer, 1988: 102; Horn, 1989: 449). However, suchproposals have not yet been widely investigated. Furthermore, evidence for an
early negation preference in L1 and L2 acquisition is complicated by confoundsof difficulty, complexity, or frequency of negative constructions and co-occurring
elements such as, for example, verb-raising (Dimroth 2010).
The present study utilizes an artificial language learning paradigm to verify
the presence of a preference for earlier negation in a language that allows for freevariation between preverbal and postverbal negation. Participants were also
paired to take part in a director-matcher paradigm to investigate whether apreference for early negation could be plausibly driven by the specific pressuresof communication, as opposed to learning and production in isolation.
54 English-speaking participants were exposed to a miniature language that
contained a mixture of preverbal and postverbal negation (NegVSO andVNegSO). Participants were divided equally among three experimental
conditions corresponding to different proportions of preverbal negation ordering
they were exposed to during training (25%, 50%, and 75%). After training,participants first produced sentences in a non-communicative context byThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

96

describing images withheld from the training set. Then, participants were pairedwith another learner (trained on the same input proportions) to perform a directormatcher task, followed by a final solo retest.
Across conditions and production phases, participants produced morepreverbal negation on average compared to the proportion of preverbal negationin the input (see Figure 1). Statistical analysis using mixed effects logistic
regression revealed that the use of the majority order in training was significantly
lower in the Majority VNeg condition than the Majority NegV condition (β = –0.97, SE = 0.35, p = 0.006). There was also a significant interaction indicating an
additional effect on the difference in use of the majority order between the
Majority NegV and Majority VNeg conditions in the interaction stage, compared
to pre-interaction production (β = –0.36, SE = 0.09, p < 0.001). Subsequent
pairwise comparisons revealed that within each condition, the only significant
differences in use of NegV order between production stages were within the
Majority NegV training condition, where there was significantly more NegV use
in the interaction stage (β = –1.22, SE = 0.29 p = 0.001) and post-interaction stage(β = –1.10, SE = 0.28 p = 0.003) compared to pre-interaction production.

Figure 1. Plot showing participants' average proportion of NegV production. The dashed lines indicate
the proportion of NegV order seen in training. Error bars represent standard error.

These results verify the existence of a production bias consistent withpreverbal negation preferences in adult English speakers learning and producing
a language which contains both preverbal and postverbal negation, consistent withpreferences observed in, for example, naturalistic L2 learning of Korean (Kim &Yun, 2013). Further research is necessary to test whether this preference extends
to speakers of postverbal negation languages like Japanese, though note that instudies of naturalistic L2 acquisition, even learners with L1s that use postverbal
negation have also shown some preference for preverbal negation patterns (Kim
& Yun, 2013; Hyltenstam 1977). Looking at the role of interaction, the directormatcher task failed to reliably boost NegV use compared to production in isolation
prior to interaction, which does not support the idea that a communicative context
induces a greater preference for preverbal negation in general.

97

Acknowledgements
This research was supported by the NSF Graduate Research Fellowship (grant
number DGE-1841052) and a Rackham Graduate School research grant.
References
Dimroth, C. (2010). The acquisition of negation. In L. R. Horn (Ed.), The
Expression of Negation. Berlin, New York: De Gruyter Mouton.
Dryer, M. S. (1988). Universals of negative position. M. Hammond, E.
Moravcsik, & J. Wirth (Eds.), Studies in Syntactic Typology. Amsterdam: John
Benjamins.
Horn, Laurence R. (1989). A natural history of negation. Chicago: University of
Chicago Press.
Hyltenstam, K. (1977). Implicational patterns in interlanguage syntax variation.
Language Learning 27, 383–410.
Kim, H. K. & Yun H. (2013). Examining the Influence of L1 on Learning Korean
Negation Forms. Foreign Languages Education, 20. 415–434.
Schneider, E. W. (2000). Feature diffusion vs. contact effects in the evolution of
new Englishes: A typological case study of negation patterns. English WorldWide 21, 201–230.

98

PROSOCIALITY IN SWARM ROBOTICS: A MODEL TO STUDY
SELF-DOMESTICATION AND LANGUAGE EVOLUTION
Nicolas Cambier*1 , Roman Miletitch2 , Antonio Benítez Burraco3 , and Limor Raviv4,5,6* Corresponding Author: n.p.a.cambier@vu.nl

1 Computational Intelligence Group, Vrije Universiteit Amsterdam, The Netherlands
2 IRIDIA, Université Libre de Bruxelles, Belgium
3 University of Seville, Spain

4 LEADS group, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
5 Centre for Social, Cognitive and Affective Neuroscience, University of Glasgow, UK

6 Artificial Intelligence Lab, Department of Computer Science, Vrije Universiteit, Brussels, Belgium

1. Introduction
Swarm Robotics, which studies collective behaviors of large populations of interacting robots with simple embodied cognition, is an ideal testbed for studying
the cultural evolution of language. Beyond simple communication processes, pastexperiments in swarm robotics have explored the potential of language games in an
embodied agent context (Trianni, De Simone, Reina, & Baronchelli, 2016; Cambier, Frémont, & Ferrante, 2017; Cambier, Albani, Frémont, Trianni, & Ferrante,
2021; Miletitch, Reina, Dorigo, & Trianni, 2019).
The current study expands on existing simulations in order to explore a dominant theory in evolutionary linguistics, namely, that the evolution of present-day
languages might have resulted in part from the self-domestication of the humanspecies (Thomas & Kirby, 2018; Benítez-Burraco & Progovac, 2020). According
to this human self domestication (HSD) hypothesis, humans’ distinctiveness is, toa large extent, the outcome of an evolutionary process similar to animal domestication (Hare, 2017). At the heart of this view lies the idea that HSD resulted
in less aggressive individuals, who are more prone to interact with others (andparticularly, with their kin, but also with strangers). This increased prosociality
and reduced aggressiveness would have in turn promoted more social contacts
within a community, and supported the emergence of more sophisticated forms oflanguage (Tamariz & Kirby, 2016; Steels, 2017). Our goal is to create an embodied
model of the effects of prosociality on the formation of language using swarm
robots, in order to investigate the self-domestication hypothesis and the process oflanguage evolution in general.
Current swarm robotics models, however, lack several crucial features that areconsidered prerequisites for this process. Swarm robots are typically collaborative,
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

99

but homogeneous, and have little to no memory, not to mention social memory(who did what to whom). In order to mimic the effects of self-domestication
properly, an evolutionary advantage for prosociality first needs to be introduced,
and robots need to be treated as distinct individuals. To this end, we designed anovel version of the naming game with swarm robots.
2. The current modelIn our model, robots in multiple nests are engaged in a foraging task (i.e., gatheringresources in their environment) while playing a naming game (Steels & Loetzsch,2012). Foraging and resource collection are typical animal activities, which has
been widely studied in the field of swarm robotics (e.g. (Miletitch, Dorigo, &Trianni, 2018)) and constitutes an obvious marker of evolutionary fitness, which
is highly relevant to an investigation of the HSD. Crucially, we include two novel
features in the model: (1) robot individuation: robots have a partner-specificmemory, keeping track of the outcomes of past interactions with specific robots; (2)parametrizable prosociality: robots’ tendency to interact is based on (a) an innate
factor and (b) experience: successful communication between robots reduces theiraggression toward each other and increases their chances of interacting again.
We examine the evolution of communicative alignment and foraging behavior
within and between nests as a function of prosociality and geographic distance,
which are two key factors accounting for language diversity and impacting on
language complexity (Padilla-Iglesias, Gjesfjeld, & Vinicius, 2020; Bickel &Nichols, 2009). First, we ask whether increased prosociality leads to more efficient
foraging, and whether this pattern is affected by the geographical proximity of nests(seeing as closer nests result in more competition over the same resources). Second,
we ask whether our manipulation of initial prosociality affects classic convergence
patterns (i.e., where all robots end up aligning on the same word variant), leading
to differential divergence depending on nest and past history. That is, does initialprosociality affect the degree and/or speed of convergence within and between
nests, and does it result in a distinction between in-group robots (belonging to thesame nest) and out-group robots (belonging to different nests)?
3. Results
First, we show that these manipulations lead to the formation of a classic “in-groupbias” where robots favor interaction with some robots over others - a bias which ishighly common in social animals in nature but that was so far absent from swarm
robotics models. Second, we observe that higher prosociality values result in thecollection of more resources, potentially indicating an evolutionary advantage.
Finally, we show that prosociality modulates the effect of physical distance on
lexical convergence, such that low values of innate prosocially lead to more stablesub-swarm divergence, even in relative proximity (i.e., different nests robustly
converge on different lexical variants despite being close to each other).

100

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DECOUPLING SPEED OF CHANGE AND LONG-TERM PREFERENCEIN LANGUAGE EVOLUTION: INSIGHTS FROM ROMANCE VERBSTEM ALTERNATIONS
Chundra A. Cathcart*1,2 , Borja Herce1,2 , and Balthasar Bickel1,2* Corresponding Author: chundra.cathcart@uzh.ch

1 Department of Comparative Language Science, University of Zurich, Switzerland

2 Center for the Interdisciplinary Study of Language Evolution, University of Zurich, Switzerland

Romance verb stem alternations (e.g., Spanish tengo ‘I have’ vs. tienes ‘you have’) constituteseemingly unnecessary but highly inheritable morphological traits. Using novel phylogenetic
methods, we assess the impact of frequency and alternation patterns on properties of their evolution, specifically on the speed of change and the long term preference for pattern types withinlemmata. We find credible differences in long-term trends between alternation patterns, andconfirm the notion that frequency drives the maintenance of irregular patterns. However, ourmodel reveals no or only weak effects of either predictor on the speed of change. Our findings
call for modeling the multiple dimensions of language change jointly but with distinct parameters for speed (or rates) of change and long-term (i.e., stationary) preferences.

1. Introduction
Stem alternations in Romance verb paradigms are well studied (Esher, 2016;
Maiden, 2018; Herce, 2019, etc.). Their importance lies in that they constitutea prime example of unmotivated yet systematic patterns (Aronoff, 1994; Bickel,
1994). Unlike morphological alternations that express differences in meaning(e.g. English drive PRESENT vs. drove PAST) or are phonologically conditioned(e.g., cat[s] vs. dog[z]), Romance stem alternations do not mark a specific semantic value and are phonologically unpredictable. Despite this lack of motivation,these patterns exhibit remarkable diachronic stability, frequently spreading to newverbs. Here we seek to quantify the evolutionary dynamics of these patterns in an
explicit phylogenetic framework, testing effects of frequency and alternation type.
The method we develop allows us to assess the effect of these two predictors on different components of language change, namely speed and stationary
probability, interpretable as the long-term preference for a feature. Past researchhas either focused on speed of change (Pagel, Atkinson, & Meade, 2007; Greenhill et al., 2017) or on biases in change and preferred configurations (Maslova,
2000; Cysouw, 2011; Bickel, 2015; Jäger & Wahle, 2021). Little work to date has
considered these properties of change jointly. We provide a principled means ofThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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teasing apart the dynamics of these two facets of language evolution, providing amore flexible understanding of the role of different factors in change.
2. Background
The philological literature identifies three main types of stem alternations in
Romance verbs, labeled N, L, and P(YTA); these can co-occur within verbal
paradigms. The emergence of the N and L patterns (see Table 1) occurred as aresult of sound changes after Classical Latin but (largely) before the break-up of
Romance into different languages. Thus, the alternation between e and ie in thestem of Spanish perder is due to different trajectories of change affecting stressed(leading to ie) and unstressed (leading to e) syllables. The alternation between gand c (phonetically [g] and [T], respectively) in the stem of Spanish decir resultsfrom the palatalization of Latin c ([k]) before the vowels e and i.
Table 1.

1SG
2SG
3SG
1PL
2PL
3PL

Pres. ind.
pierd-opierd-espierd-eperd-emos
perd-éispierd-enPartial paradigms of Spanish perder ’lose’ and decir ’say’
NPres. sbjv.
pierd-apierd-aspierd-aperd-amos
perd-áispierd-an

Ipf. ind.
perd-ı́aperd-ı́asperd-ı́aperd-ı́amos
perd-ı́ais
perd-ı́anPres. ind.
dig-o
dic-esdice
dec-imosdec-ı́sdic-enL
Pres. sbjv.
dig-a
dig-asdig-a
dig-amosdig-áisdig-anIpf. ind.
dec-ı́adec-ı́asdec-ı́adec-ı́amosdec-ı́aisdec-ı́an

While the alternations seen in these lexemes are expected developments due
to regular sound change (e.g. digo, di[T]es < dı̄[k]ō, dı̄[k]is), other lexemes havelost their alternations (e.g. cue[T]o, cue[T]es ← coquō, coquis ‘cook’ vs. expected†cuego, cue[T]es), or have acquired the alternations in analogy with other verbs(e.g. caigo, caes ← cadō, cadis ‘fall’ vs. expected †cao, caes).
Unlike the other two patterns, the P pattern is inherited from Latin (see Table2). In Latin, fēc- vs fac- marked a semantic distinction that is no longer present
in modern Romance languages, leading to alternations that are arbitrary from theperspective of meaning. Like the other patterns, however, the P pattern has also
been lost in some cases or expanded by analogy in others.
Irregular alternations like these have increasingly been studied in quantitative
frameworks and cross-linguistically, exploring the roles of different predictors(e.g., frequency, word length, uniqueness points) in their maintenance (Marzi,
Ferro, & Pirrelli, 2019; Sims-Williams, 2021). However, data-driven studies todate have not made use of a phylogenetic framework, which has the potential toshed light on evolutionary pressures that foster and militate against irregularity. In
response, we develop a novel phylogenetic model to obtain a detailed understanding of the dynamics of change of these irregular patterns.

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Table 2.

1SG
2SG
3SG
1PL
2PL
3PL

Partial paradigms of Spanish hacer, and Latin faciō ’do’ illustrating P

Ipf. ind.
hac-ı́ahac-ı́ashac-ı́ahac-ı́amos
hac-ı́aishac-ı́an

SpanishPret. ind.
hic-e
hic-istehi[T]-ohic-imos
hic-isteis
hic-ieronIpf. sbjv.
hic-iese
hic-ieses
hic-iese
hic-iesemos
hic-ieseis
hic-iesen

Ipf. ind.
fac-iēbam
fac-iēbās
fac-iēbat
fac-iēbāmusfac-iēbātis
fac-iēbant

Latin
Perf. ind.
fēc-ı̄fēc-istı̄
fēc-itfēc-imusfēc-istisfēc-ērunt

Plup. sbjv.
fēc-issemfēc-issēsfēc-issetfēc-issēmusfēc-issētis
fēc-issent

3. DataIn the Oxford Online Database of Romance Verb Morphology (Maiden, Smith,
Cruschina, Hinzelin, & Goldbach, 2010; Beniamine, Maiden, & Round, 2020)
each lemma is represented with its paradigm and is coded for its Latin etymon.
For each lemma in each variety, we manually assessed whether it contained a reflex of the stem alternation patterns (N, L, and P) presented in Section 2. We
gloss over language-specific idiosyncrasies in the inherited distribution of alternants in the paradigm for the purposes of this study (e.g., the L pattern affects the
3PL present indicative of Italian dire ‘say’ but is absent from the 1–2PL present
subjunctive, in contrast to its Spanish cognate in Table 1), coding patterns as
PRESENT / ABSENT / MISSING DATA . Among Latin-descended lemmata, 231 cognate sets are found in the database. We target items with wide coverage, excluding
cognate sets that are limited to dialects of a single language or attested in fewer
than five varieties. Additionally, we exclude uninformative lemma-pattern pairsthat are completely present or absent across all languages with attested data. In
total, we analyze 171 lemma-pattern pairs involving 66 lemmas from 67 varieties.
4. Method1
We investigate the historical dynamics of stem alternation using a phylogenetic
comparative model. Methods of this sort require a timed phylogenetic representation of the languages in our data set in the form of a Bayesian tree sample, which
we infer using RevBayes (Höhna et al., 2016) on the basis of both automaticallygenerated lexical cognacy data (Jäger, 2018) and sound class data indicating whichspeech sounds are present in each variety (Heggarty et al., 2019). We impose uncontroversial clade constraints on the tree topology along with lower and upperbounds for each clade’s date calibration drawn from the literature on Romance
languages (Hall, 1974). We use a a Birth-Death tree prior (Yang & Rannala,1997) and a General Time-Reversible model of character evolution (Tavaré, 1986),
along with a relaxed clock with log-normally distributed branch-level rate multipliers and gamma-distributed variation across 4 rate classes. We run 1,000,000
1 Code available at https://github.com/chundrac/JCoLE2022-morphomes

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iterations of Markov chain Monte Carlo over 4 chains, thinning the sample to 100
trees after discarding the first half as burn-in. We scale branch lengths so that one
unit represents a millennium of change.
Under our model, each binary lemma-pattern pair evolves independently overthe phylogeny of Romance according to a continuous-time Markov process. This
process is parameterized by a gain and loss rate, or alternatively, the speed ofchange (irrespective of direction) and the stationary probability of feature presence, interpretable as the long-term preference for a given feature over a phylogeny (irrespective of the speed of change). The gain and loss rate of a lemmapattern pair with index d ∈ {1, ..., D} are πd sd ρ and (1 − πd )sd ρ. Here, sd ρrepresents the speed of change for feature d, sd being a multiplier of the global
speed ρ. We place a Uniform(0, 10) prior over ρ, preventing changes from happening more frequently than ten times per millennium. The parameter πd is thestationary probability of presence for the lemma-pattern pair in question.
While evolutionary parameters in phylogenetic comparative methods are usually estimated without predictors, we model them in a hierarchical distributional
regression framework (Bürkner, 2018), allowing both the speed of change and stationary probability for each lemma-pattern pair to vary as a function of multiple
predictors. We assume speed multipliers s and stationary probabilities π to benormally distributed, with a logit link to keep values within (0, 1):s,LEMMA
s,PATTERNslogit sd ∼ Normal(αs + βLEMMA
IDd + βPATTERN IDd , σ )(1)π,LEMMA
π,PATTERN
πlogit πd ∼ Normal(απ + βLEMMA
IDd + βPATTERN IDd , σ )(2)In each sampling statement, α denotes the intercept, β LEMMA represents the contribution of each lemma type, and β PATTERN represents the contribution of eachalternation type. The standard deviations of these distributions represent the variance in speed and stationary probability that are not explained by the predictors
included. We model the contribution of lemma type as a monotonic function ofeach lemma’s frequency in Latin texts (Tombeur, 1998); this involves a combination of a parameter representing the effect of moving from the lowest to thehighest frequency and a simplex parameter representing the effect of moving
along the cline of frequency (Bürkner & Charpentier, 2020). We treat pattern
type as two dummy-coded factors, comparing the levels L and P to N, respectively. We place Normal(0, 1) priors over all model parameters in statements (1–2) with the exception of simplex parameters and standard deviations σ, which receive Dirichlet(1, ..., 1) and HalfNormal(0, 1) priors, respectively. Posterior distributions for parameters are inferred using the R package CmdStanR (Gabry &Češnovar, 2021). In line with the most conservative criteria for hypothesis evaluation, we infer decisive evidence for the effect of a predictor if the 95% credibleinterval (CI) of the corresponding parameter excludes zero, and strong evidence
in cases where the 95% CI overlaps with zero but the 85% CI does not.

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β s,LEMMA

60% > 0β π,LEMMAs,PATTERN
βL−N

95% > 0π,PATTERN
βL−N

99% < 0s,PATTERN
βP−N

73% > 0π,PATTERN
βP−N

100% < 0

-1

0

1

2

3

100% > 0

-1

0

1

2

Figure 1. Medians and posterior 95% and 85% (shaded) credible intervals for model parameters ofinterest on the logit scale, along with percentage of samples above or below 0. PATTERN refers to thedifference between N, L and P alternations; LEMMA refers to Latin lemma frequency.

5. Results
Posterior distributions for model parameters are given in Figure 1. In general, thepredictors in our model do not appear to have a decisive effect on features’ speedof change (s; left panel), though there is strong but not decisive evidence that
the L pattern shows a higher speed of change than the N pattern. In contrast, we
see decisive evidence for effects of all predictors on long-term pattern preference,
i.e. the stationary probabilities π (right panel). The 95% credible interval for theparameter β π,LEMMA is positive, indicating that more frequent lemmata are more
likely to display a stem alternation pattern and less frequent lemmata are likelier
to regularize. Additionally, we see decisive evidence that the long-term preference for N is greater than that of L and P, though post-hoc inspection of model
parameters showed no evidence for a L vs. P contrast.
6. Discussion
Our results show that the N pattern outranks L and P in long-term preference (stationary probability) but not in stability (speed of change). A possible explanation
might be that that the N pattern affects the 3SG present indicative, which tends tobe highly frequent, whereas the L and P patterns do not.
More generally, we confirm the role of lexical frequency in the maintenanceof irregular patterns. Arbitrary stem alternation patterns fly in the face of communicative and acquisitional efficiency in that they introduce more than one form permeaning. At the same time, irregularity enhances discriminability between forms
in a paradigm, especially in more frequent lexemes (Nübling, 2011; Blevins,
Milin, & Ramscar, 2017). Our results support the idea that more frequent lexemes are more likely to preserve and acquire irregular morphology over time.
The frequency effect is limited to stationary probabilties (long-term preferences), however, and does not affect how fast languages move to the preferredstate (cf. β π,LEMMA vs β s,LEMMA in Fig. 1). This finding is consistent with the notion that cognitive and communicative pressures (such as frequency effects) —or indeed “language universals” more generally — bear primarily on stationary
probabilities, and not on stability (Maslova, 2000; Cysouw, 2011; Bickel, 2015).
Our approach decouples these two aspects of evolution and facilitates explicit

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assessment and comparison of them in a single model. This flexibility is not generally found in recent work in evolutionary linguistics which tends to focus on
variation in speed. We suspect that this emphasis stems from two sources: first,
most prominent biological models of rate variation focus solely on speed variation (Huelsenbeck & Suchard, 2007; Heath, Holder, & Huelsenbeck, 2011), withfew exceptions (e.g., Lartillot & Poujol, 2011). Second, a large body of workin language evolution revolves around change in basic vocabulary, where the replacement of one word by another cannot necessarily be construed as incurring acommunicative cost in the same way that the development of unpredictable allomorphy might (though see Martin, 2007).
7. Conclusion
We investigated the development of irregular patterns in Romance verbal morphology using a novel method that unites phylogenetic modeling with hierarchical distributional regression modeling. Our results in some ways confirm received
wisdom regarding the role of frequency in the maintenance of irregularity, butalso shed light on poorly understood issues in language change, showing that frequency largely does not explain variation in speed of change but only in longterm preferences (stationary probabilities). One possible explanation for this isthat speed of change responds to social pressure for differentiation such as schismogenesis, which is most strongly associated with vocabulary (Greenhill et al.,2017). Stem alternation and irregularity might be less accessible as markers ofsocial differentiation and are therefore relatively immune to differences in speedof change. By contrast, they have direct implications for efficiency in processing,learning and communication, with effects on long-term preferences.
The model presented here can be expanded in a number of ways. It is straightforward to build in branch-level variation for speed and stationary probability,
which can help to identify events of drastic change coinciding with language contact, schismogenesis, or other changes in the linguistic system. Additionally, ourmodel used Latin lemma frequency as a proxy for an etymon’s frequency throughout Romance history, a simplification that does not fully capture the dynamics ofvocabulary change. In theory, it is possible to treat relative frequency as a continuous trait that varies over the tree (see Ringen, Martin, & Jaeggi, 2021 for flexible,
complex models of the co-evolution of continuous and discrete cultural traits).
A next step will involve building additional predictors and interaction terms into
the model to investigate among other things whether phonologically and semantically similar lemmata undergo similar patterns of change, and whether or notother variables, such as conjugation class, play an interpretable role.
Acknowledgements
This work was supported by the NCCR Evolving Language, Swiss National Science Foundation Agreement Nr. 51NF40 180888.

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SIMULATION OF EMERGENT COMMUNICATION WITH LARGESCALE MACHINE LEARNING
Rahma Chaabouni*1 , Florian Strub*1 , Florent Altché1 , Corentin Tallec1 , Eugene Trassov1 , Elnaz
Davoodi1 , Kory Mathewson1 , Olivier Tieleman1 , Angeliki Lazaridou1 , and Bilal Piot1** Corresponding Authors: {rahmac,fstrub,piot}@deepmind.com
1 DeepMind

The origins of human languages are unarguably a complex research problem (Christiansen & Kirby, 2003). Over the last fifty years, this inherent complexity has led to various research approaches from many fields ranging from biology,
linguistics, anthropology, computer science etc. (Harnad et al., 1976; Bickerton,
2007; Nölle et al., 2020). Here, we walk in the footprint of the computational
community (Steels, 1997; Kirby, 2002; Myers-Scotton, 2002). Roughly, the computational approach aims to simulate the prerequisite or processes that could trigger the emergence of a structured language within a controlled environment. In
practice, the classic modeling paradigm simulates two agents that must develop
a communication protocol to solve the Lewis game; the first agent describes anobject such as the second agent can locate it among a set of candidate objects.
Therefore, the two agents must settle on a communication protocol to solve thegame. In this paper, we discuss how new computational resources and recent
machine learning algorithms, namely deep learning (LeCun et al., 2015) and reinforcement learning (Sutton & Barto, 2018), allow scaling language simulation
emergence. We then discuss how this more realistic model can foster new research
directions in language evolution.
Despite the recent astonishing successes of deep neural networks in solving
complex tasks, computational emergent language has made little profit from theseadvances. Most works still consider small disentangled input spaces (Lazaridou& Baroni, 2020), where the expected language can often be reduced to a basic
identity operator. In fact, modeling scenarios barely evolved over twenty years,
e.g. Kirby (2002) and Ren et al. (2019) both use the same binary input vector ofsize eight, and only a few papers went beyond artificial input spaces (Havrylov& Titov, 2017; Lu et al., 2020). In this paper, we endorse Bickerton (2015)’s
view about the necessity of complex tasks and stimuli to model human language
communication. Furthermore, such challenging settings have been proven to
be paramount to emulate complex distributions in the machine learning literature (Krizhevsky et al., 2012; Brown et al., 2020). We argue for enhancing theThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

110

Lewis Game to more realistic settings to gain novel and more conclusive insights.
We cast the language emergence modeling into a deep reinforcement learning framework similar to Lazaridou, Peysakhovich, and Baroni (2017). Then, wesummarize the modeling tools and algorithms, and provide guidance towards goodpractices from the machine learning field1 . As a core contribution, we focus on
three independent aspects to challenge language emergence modeling: increasing task complexity, using complex visual inputs, generating large populations ofagents. We then assess the different properties of emergent languages through; (1)Generalization by computing the agents’ communication success at test time, (2)Topographic similarity as a proxy for compositionality (Brighton & Kirby, 2006),(3) Ease of learning by using the emergent protocol for transfer tasks.
First, we consider more complex tasks by increasing the number of candidates among which the listener must retrieve the target input. Specifically, agentstypically discriminate between less than 20 candidates in the emergent language
field (Mu & Goodman, 2021); we experiment with up to 1024 candidates in thisstudy. We found that scaling up the task complexity entails unstable optimization.
We propose to smooth language learning by using classic mathematical regularization. Furthermore, we observe that complexifying the task has two positive
aspects: it better discriminates the different models and improves the generalization of the learned communication protocol.
Second, most emergent language studies situated agents in a simple one-hot
vector environment (Ren et al., 2019; Rita et al., 2020), we here scale-up the input
space with continuous and ambiguous visual cues by using pretrained representations of natural images (Grill et al., 2020). We note no correlation between
generalization and the widely used topographic similarity metric in this set of experiments. We hence question if this metric is adequate to assess compositionality
in complex setups. Inspired by the computer vision community (Grill et al., 2020),
we then discuss how ease of learning may be surrogate for protocol evaluation.
Finally, we investigate the impact of population size. In particular, we scale
up the Lewis game from 2 to up to 100 agents. Here, unlike what was observedin human communication (Gary Lupyan, 2010; Raviv et al., 2019), we find little
to no systematic benefit on emergent languages’ properties when increasing the
population size. We propose alternative methods to leverage populations, namely
voting and imitation among speakers (Hester et al., 2018). Our results show thatsuch population dynamics lead to more robust, productive, and in some cases
easy-to-learn languages, opening up new research opportunities.
In the end, we expect that these observations, baselines, and good practices
would allow the language emergence community to benefit further from deep RL
advances. We believe that such a more realistic and challenging framework is aprerequisite to moving the field closer to its goals of modeling language evolution.
1 Code: https://github.com/deepmind/emergent_communication_at_scale

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Lu, Y., Singhal, S., Strub, F., Courville, A., & Pietquin, O. (2020). Countering language
drift with seeded iterated learning. In Procof ICML.
Mu, J., & Goodman, N. (2021). Emergent communication of generalizations. In Proc. of
NeurIPS.
Myers-Scotton, C. (2002). Contact linguistics: Bilingual encounters and grammaticaloutcomes. Oxford, UK: Oxford University Press.
Nölle, J., Hartmann, S., & Tinits, P. (2020). Language evolution research in the year 2020:
A survey of new directions. Language Dynamics and Change.
Raviv, L., Meyer, A., & Lev-Ari, S. (2019). Larger communities create more systematic
languages. Proceedings of the Royal Society B, 286(1907), 20191262.
Ren, Y., Guo, S., Havrylov, S., Cohen, S., & Kirby, S. (2019). Enhance the compositionality of emergent language by iterated learning. In Proc. of EmeCom - Neurips).
Rita, M., Chaabouni, R., & Dupoux, E. (2020). “LazImpa”: Lazy and impatient neuralagents learn to communicate efficiently. In Proc. of ACL.
Steels, L. (1997). The synthetic modeling of language origins. Evolution of communication, 1(1), 1–34.
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.

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LINGUISTIC DENSITY IN A GLOBAL SCALE IS DETERMINEDBY SOCIAL, POLITICAL AND ECONOMIC FACTORSTHIAGO CHACON*1, MARC ALLASSONNIERE-TANG2*Corresponding Author: name@domain.com
University of Brasilia / IEA-Paris, Brazil / France
2
Muséum National d'Histoire Naturelle, Paris, France
1

The worldwide distribution of languages and linguistic families is uneven, withsome areas presenting higher language and phylogenetic densities than others.
Previous research has explored the role of environmental, social and economic
factors in explaining global patterns of language and phylogenetic densities. For
example, Nichols (1997: 383) claims that “linguistic density is highest in areas
where small societies can be more or less autonomous on small territories”,
noticing a correlation of environmental and political-economic factors in a waythat “densities are higher in coastal regions, at lower latitudes, and in wetter andless seasonal climates” (op.cit.). Nettle (1998) attributes a causal effect fromclimate (year round mean growing season) on social networks and its direct
impact on language areas: “where the climate allows continuous food production
throughout the year, small groups of people can be reliably self-sufficient and so
populations fragment into many small languages”. Hua et al. (2019) build on
Nettle’s hypothesis and show that temperature and precipitation seasonality are
specific climatic factors associated with language densities, while showing thatclimate has no direct impact on the average population size of social groups.
Currie and Mace (2009) have shown that high political centralization of a society
is a factor determining the size of language areas, in a way that languages whose
societies have more centralized political organization will be spoken over larger
areas, which ultimately diminish the linguistic density within the areas wherethese languages are spoken.
Our study confirms and makes further advancements from the latter onesby investigating the effects of social, political, economic and environmental
variables in determining the global distribution of areas with high concentrationof languages and linguistic families. For social variables, we use D-Place (Kirby

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

113

et al. 2016) to test parameters related to marriage organization (EA015),
dependence on agriculture (EA05), political complexity (EA033) and ethnic
descent (EA043), which are found across 818 societies worldwide. Population
size is also included. The information of language richness (at the language andat the family level) and the information of ecological factors (precipitation rate inwettest quarter and roughness of terrain) were extracted based on the method of
Derungs et al (2018), where the world map is cut into grids (we tested resolutions
from 295 to 3267 point-grids) and each society found within a given grid isannotated with the value of that grid for the environmental variables.
Our results based on linear mixed models controlling for language family
and geographic area as random effects (and considering interaction between thevariables) show that political autonomy, high population densities, the existenceof ethnic boundaries such as clans and greater reliance on agriculture arepositively associated with areas with high linguistic densities. As theconcentration of distinct languages and linguistic families in an area is ultimately
dependent on migration, diversification, and the long-term sustainability of local
diversity, these results suggest that social, political and economic factors are
relevant in creating greater autonomy and boundaries that act as triggers oflanguage splits and/or as buffers to language shift.
As the high concentration of distinct languages and linguistic families inan area is ultimately dependent on migration, in-situ diversification events, andthe long-term sustainability of local diversity, these results suggest that social,political and economic factors are relevant in creating greater autonomy andboundaries that act as triggers of language splits and as buffers to language shift.
Politically independent societies can avoid dominance by external groups, canmore often cause splits within a social group as well as use language boundaries
to express differences in identity from neighboring groups. Endogamous societies
can reproduce the ethnolinguistic group with less dependence on their neighbors,which gives them greater autonomy to migrate and split-off from related groups,
as well as diminishes the effects of demographic pressure from speakers of
distinct languages, which could cause the linguistic homogenization of an area.
Agriculture can be seen not as a factor causing language spread and shift, but asan economic strategy that ensures greater autonomy of local groups. As we facean increasing threat to linguistic diversity on a global scale, this study suggeststhat greater autonomy for indigenous self-governance strategies can be the key tothe sustainability of the world’s linguistic and cultural diversity.
References
Bromham, L., Dinnage, R., Skirgård, H., Ritchie, A., Cardillo, M., Meakins,F., Greenhill, S., & Hua, X. (2021). Global predictors of language

114

endangerment and the future of linguistic diversity. Nature Ecology &Evolution. https://doi.org/10.1038/s41559-021-01604-y
Currie, T. E., & Mace, R. (2009). Political complexity predicts the spread of
ethnolinguistic groups. Proceedings of the National Academy of Sciences,106(18), 7339–7344. https://doi.org/10.1073/pnas.0804698106
Derungs, C., Köhl, M., Weibel, R., & Bickel, B. (2018). Environmental factors
drive language density more in food-producing than in hunter–gatherer
populations. Proceedings of the Royal Society B: Biological Sciences.
https://doi.org/10.1098/rspb.2017.2851
Hua, X., Greenhill, S. J., Cardillo, M., Schneemann, H., & Bromham, L.
(2019). The ecological drivers of variation in global language diversity.
Nature Communications, 10(1), 2047. https://doi.org/10.1038/s41467-01909842-2
Kirby, K. R., Gray, R. D., Greenhill, S. J., Jordan, F. M., Gomes-Ng, S.,
Bibiko, H.-J., Blasi, D. E., Botero, C. A., Bowern, C., Ember, C. R., Leehr,
D., Low, B. S., McCarter, J., Divale, W., & Gavin, M. C. (2016). D-PLACE:
A Global Database of Cultural, Linguistic and Environmental Diversity. PLOS
ONE, 11(7), e0158391. https://doi.org/10.1371/journal.pone.0158391
Nettle, D. (1998). Explaining Global Patterns of Language Diversity. JournalofAnthropological
Archaeology,
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https://doi.org/10.1146/annurev.anthro.26.1.359

115

A COMPUTATIONAL SIMULATION OF THE GENESIS ANDSPREAD OF LEXICAL ITEMS IN SITUATIONS OF ABRUPTLANGUAGE CONTACT
Ching-Yat Cheung *1,2, Kofi Yakpo 1, Christophe Coupé 1,3*

Corresponding Author: chingyat@connect.hku.hk

1Department of Linguistics, The University of Hong Kong, Hong Kong SAR
2 LEADS Group, Max Planck Institute for Psycholinguistics, The Netherlands
3 Laboratoire Dynamique Du Langage, Centre National de la Recherche

Scientifique, France

The current study presents an agent-based model which simulates the innovation andcompetition among lexical items in cases of language contact. It is inspired by relatively
recent historical cases in which the linguistic ecology and sociohistorical context are highly
complex. Pidgin and creole genesis offers an opportunity to obtain linguistic facts, social
dynamics, and historical demography in a highly segregated society. This provides a solid
ground for researching the interaction of populations with different pre-existing language
systems, and how different factors contribute to the genesis of the lexicon of a newly
generated mixed language. We take into consideration the population dynamics andstructures, as well as a distribution of word frequencies related to language use, in order tostudy how social factors may affect the developmental trajectory of languages. Focusing
on the case of Sranan in Suriname, our study shows that it is possible to account for thecomposition of its core lexicon in relation to different social groups, contact patterns, andlarge population movements.

1. Introduction
A consequence of colonialism and population movements is the genesis of amultilingual and sociologically complex ecology, in which people of diverse
language backgrounds are brought into contact, creating the need for a new sharedmeans for communication. In some of these cases, a pidgin or a creole, which ischaracterized by the mixture of lexical and grammatical features from different
languages in the language ecology, emerges to answer communicative needs.
Such recent emergence of languages may shed light on the overall evolutionary
process of human languages (Mufwene, 2008). Specifically, population
movements have been argued as one of the factors that motivate the evolution ofcontact languages (Mufwene, 2007).
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

116

The present study presents a computational model which serves as a complement
to empirical studies when investigating how population movements and social
structures affect the emergence of a language and specifically the developmental
trajectory of its lexical inventory. The paper expands on previous studies byconsidering social structures, population movements, communicative needs, andthe functional frequency of the lexical items. Our model is specifically inspiredby the historical case of Suriname, a former colony in South America that was
ruled by English and Dutch speakers. The Dutch took over the colony from theEnglish, which led to an exodus of English native speakers and influenced thedevelopment of Sranan, an English-lexified creole spoken in the area (Arends,
2017). Like other contact languages, it is characterized by a mixture of various
languages spoken in the language ecology. We thus constructed a model toinvestigate how the sudden departure of a dominant social group may have an
influence on the collective convergence towards a linguistic form when there is alanguage competition. Essentially, the agent-based model allows us to experimentwith several sociolinguistic variables and observe different population
movements can affect language evolution. As reported below, the model
demonstrates the possibility for agents to develop a multilingual - rather thanmonolingual - lexical inventory. The predictions also align quantitatively with thelexical data of Sranan.
2. Previous Studies
There have been recent attempts to construct computational models to better
understand contact languages. Jansson et al. (2015), inspired by the Naming
Game (Baronchelli et al., 2006), simulated the evolution of the lexicon,
phonology and syntax of Mauritian Creole using data on the demography andpopulation movements in Mauritius in the 18th century. Specifically for thelexicon, their model showed how agents can converge to one single lexifier (i.e.,
French) solely based on communicative needs. Tria et al., (2015) also employed
a computational model to study the possible emergence of creoles. Their model– a modified version of the Naming Game with non-trivial interaction rules basedon the observation of communicative pressures in highly segregated societies –revealed that such an emergence can be accurately predicted on the sole basis ofhistorical demographic data, more precisely the proportion of different
social/ethnic groups in the multilingual ecology. Additionally, Furman &Nitschke (2020) investigated the role of external factors which affect theevolution of creole languages, such as the population size of different interacting
groups and the lexical similarity between these groups, in the convergence oflexical items with an iterative agent-based naming game.
Recently, Cheung (2022) further showed that computational social networks withdifferent density and connectivity patterns within the same social group and117

across different social groups – which can differ due to their economic structures(such as ‘sparse’ cotton plantations and dense urban areas/ports) – could affectthe likelihood and rate of creole emergence. These results, which are specific toscenarios of recent abrupt language contact, can be related to more generic studies
that attempt to model language competition, change and death in a multilingual
society (e.g. Abrams & Strogatz, 2003; Minett & Wang, 2008), especially whendifferent social structures (Ke et al., 2008; Loureiro-Porto & Miguel, 2017) arefound to be relevant.
The previous attempts have all inspired the present study.
3. Modelling the lexical propagation of historical creoles
Our model is inspired by the historical case of Suriname, a former colony in SouthAmerica. Historical data (Migge, 1998, based on Postma, 1990 and Voorhoeve &Lichtveld, 1975) show the demographic evolution of each social group/ethnicityof the region through time. There was a significant drop of the English-speaking
population from 1668 due to the colony being ceded by the British Empire to theNetherlands under the Treaty of Breda in 1667 (Arends, 2017). There wereapproximately 2000 English speakers in 1666, but the number dropped to 820 in
1668, and there were only 38 still remained in 1680 (Voorhoeve & Lichtveld,1975). The number of other non-English Europeans, mainly Dutch, increasedslightly, while the number of Blacks also increased. The exodus of English native
speakers reportedly influenced the development of Sranan, an English-lexified
creole spoken in the area (Arends, 2017).
Our computational model employs an agent-based naming game that simulates
repeated interactions between agents. Each agent initially has their own lexiconof 300 basic words in their own language. Each lexical referent is assigned witha probability drawn from the Zipfian distribution, which indicates the chance ofbeing picked out and used during an interaction, such that we can partially accountfor language use.
Agents can be assigned with one of three fixed roles: Blacks, European group 1(E1) (the English) or European group 2 (E2) (the Dutch). The population ofBlacks is further divided into five groups of rural plantation workers (R1-R5) andan urban group (U). Members of each rural group are sparsely distributed andrelated according to a scale-free social network structure (the average degree of
each node is ~1.93), while each of the European and Black Urban groups is adense small-world network (rewiring probability of 0.05 with an average degreeof 8). Random links across the different groups are added in proportion to the total
population of these groups. This hypothetical network setup is based on otherstudies of models and empirical cases (c.f. Cheung, 2022).

118

Given the situation, an agent may know up to four words for a given referent: one
in English (E1), one in Dutch (E2), one in an African language (A), and one in an
emerging creole (C). We modify the interaction rules presented in Tria et al.
(2015), where all the agents could interact with each other. In our model, for eachinteraction, an existing edge between two agents is randomly picked and theseagents play a naming game with a random assignation of the roles of speaker andlistener. If the communication is successful (i.e., the listener knows the speaker’s
word , in the language they chose, for the chosen referent), any other word – inanother language – for the referent is discarded. If the communication fails, thelistener picks up the word from the speaker. While this widely used paradigm may
not be the most accurate reflection of the underlying cognitive mechanisms ofword learning, it offers a plausible model for the switch of belief between different
lexical representations for a particular referent in order to choose which one ismost suitable to foster communication success.
The interaction is governed by several constraints reflecting the segregated social
dynamics. Even if both speaker and listener have a word in an African language
for a particular referent, they will only be able to communicate successfully witha probability δ. This parameter reflects the actual diversity of African languages
spoken by Blacks. The Europeans (E1, E2) only have a probability β to learn aword in an African language successfully (this includes European-to-European
communication), and they further only have a probability ε to learn from theBlacks (R/U), due to differences in socioeconomic status. The chance of creoleinnovation, if the listener possesses both a European and an African language fora lexical referent, is given by γ, and the chance of a listener adopting this creoleis given by α, an adjustment made to represent the failure of learning due topotential difficulties in interpreting the mixing and the preference to adapt items
from the superstratum. Given the case of Suriname and our focus on the effect of
population movements, we assumed a reasonable fixed value for each of theparameters across all conditions: α = 0.8, β = 0.2, δ = 0.25, ε = 0.25 and γ = 0.8(the parameters could of course be modified to explore significantly different
language ecologies)
In order to investigate the consequences of the sudden exit of the English
speakers, a large proportion (over 60%) of the members of group 1 (E1) isremoved after a number of interactions/steps – with two possible departure times(Conditions 1 and 2). Figure 1 illustrates how such an exodus transforms theoverall social network. Furthermore, we consider the condition in which the graphremains unchanged throughout the whole simulation as a baseline (Condition 3).
We finally model a condition (Condition 4), a setup based on condition 1, in which
both groups of Europeans are removed after a number of interactions to simulate
the development of a creole among the Blacks themselves – a situation typical of
creole development. Table 1 illustrates the 4 conditions of the simulations.

119

Table 1: the different conditions that were used in the simulations
1
E1 departs
E2 increases
2
E1 departs
E2 increases
3
4

E1 departs
E2 increases

Steps

100,000

End
End
End

Both E1 &E2 depart…

1,000,000………

2,000,000

End…….…………….

5,000,000

Figure 1: an illustration of the simulation of an exodus of English-speaking populations (group E1)
Before theexodusAfter theexodus3. Results
The resulting lexicons are assessed for the different social groups. We find thatthe departure of the native speakers of European languages affects the final stageof the process of linguistic convergence. The speakers remain undercommunicative pressure due to multilingualism after the exit of the speakers of
the superstrate languages, and they still need to develop a lingua franca, causing
more creole innovation and language diversification. This is in alignment withprevious observations in the literature (Mufwene, 1996, 2007).
Figure 2 shows the results after 5 million steps of interaction in our different
conditions. Across all conditions, the agents are under communicative pressure
and must converge to communicate with each other. An observation that can bemade from the simulation under Condition 3 is that if the native speakers of themain lexifier, English, remain throughout the simulation, all agents tend toconverge to this single language, due to sustained linguistic input and sufficient
exposure. Furthermore, in condition 2, in which English speakers depart relatively
late (at the 1-millionth step), it can be observed that although a majority of thewords still stem from their language, some mid-frequency words are adopted from120

the other European language, Dutch. For both conditions 2 and 3, no significant
amount of creole innovation occurs (i.e., no convergence to creole words). In
condition 1, the native speakers of the main language exit early, and while theirlanguage still occupies a large proportion of the final lexicon, Dutch is also used
to denote some words that are of higher frequency.
In figure 3, a comparison of Conditions 1 and 4 shows that creole words are more
prominent in the latter condition. Condition 4, in particular, appears to be theclosest approximation of the etymological distribution of Sranan, in which it isestimated that there are roughly 18% English words, 21.5% Dutch words, and
36% innovations (Romaine, 2001).
Figure 2: Etymological composition of the lexicon for different conditions after 5 million steps
Condition1: E1 departs early at the 100kth step

Condition 2: E1 departs late at the 1mth step

Condition3: No group departs throughout

Condition 4: E1 departs at the 100kth step, E1 & E2
depart at the 2-millionth step

The x-axis corresponds to the lexical items ranked by decreasing frequency from left to right, and the y-axis
reports the average proportion of each language across all agents (Blue: E1; Purple: E2; Green: C; Red: A). Eachplot represents the situation of a social group.

121

Figure 3: cumulative composition of the lexicon across different groups (Purple: A, Blue: C, Green:
E1, Yellow: E2)
Condition 1: E1 departs early at the 100kth step Condition 4: E1 departs at the 100kth step, E1 &E2 departs at the 2-millionth step

In addition, due to the high segregation of the society, there is a delay in the ruralarea in terms of linguistic exposure. This suggests a possible variation continuum
between the urban and the rural areas, which is also observed in Suriname(Arends, 2017). The study of the interaction between language variety and social
stratification could be an interesting direction for further exploration.
4. Conclusion
The present model offers a plausible way to computationally investigate sociohistorically complicated and empirically specific scenarios of language contact. It
offers reasonable predictions for a given social dynamics and segregatedrelationships between different social groups, and the group selection of lexical
items appears to be highly sensitive to the interactions and language use between
agents.
The current model could be extended to other (socio)linguistic phenomena, suchas the social dynamics during second language learning. The observation of thedifferent intermediate stages of language development could also explain how avariation continuum could appear between various social groups. Meanwhile, themodel mostly focuses on horizontal transmission of language, and the role ofvertical transmission is underplayed. It would be worthwhile to model the birthof children with initially blank-slate agents (in terms of linguistic repertoire andexposure), and observe how this affects the linguistic dynamic.
References
Abrams, D. M., & Strogatz, S. H. (2003). Modelling the dynamics of language
death. Nature, 424(6951), 900–900. https://doi.org/10.1038/424900a
Arends, J. (2017). Language and Slavery: A social and linguistic history of theSuriname creoles (Vol. 52). John Benjamins Publishing Company.
https://doi.org/10.1075/cll.52

122

Baronchelli, A., Felici, M., Caglioti, E., Loreto, V., & Steels, L. (2006). Sharp
transition towards shared vocabularies in multi-agent systems. Journal of
Statistical Mechanics: Theory and Experiment, 2006(06), P06014–P06014.
https://doi.org/10.1088/1742-5468/2006/06/P06014
Cheung, C. Y. (2022). Investigating contact languages with agent-basedmodelling. The University of Hong Kong.
Furman, G., & Nitschke, G. (2020). Evolving an Artificial Creole. Proceedingsof The Genetic and Evolutionary Computation Conference 2020
(GECCO ’20)., 2.
Jansson, F., Parkvall, M., & Strimling, P. (2015). Modeling the Evolution ofCreoles.
Language
Dynamics
andChange,5(1),1–51.
https://doi.org/10.1163/22105832-00501005
Ke, J., Gong, T., & Wang, W. S.-Y. (2008). Language Change and Social
Networks. Commun. Comput. Phys., 15.
Loureiro-Porto, L., & Miguel, M. S. (2017). Language Choice in a MultilingualSociety: A View from Complexity Science. In S. S. Mufwene, C. Coupe, &F. Pellegrino (Eds.), Complexity in Language (pp. 187–217). Cambridge
University Press. https://doi.org/10.1017/9781107294264.008
Migge, B. (1998). Substrate influence in creole formation: The origin of givetype serial verb constructions in the Surinamese Plantation Creole. Journal of
PidginandCreoleLanguages,
13(2),
215–266.
https://doi.org/10.1075/jpcl.13.2.02mig
Minett, J. W., & Wang, W. S.-Y. (2008). Modelling endangered languages: The
effects of bilingualism and social structure. Lingua, 118(1), 19–45.
https://doi.org/10.1016/j.lingua.2007.04.001
Mufwene, S. S. (1996). The Founder Principle in Creole Genesis. Diachronica,
13(1), 83–134. https://doi.org/10.1075/dia.13.1.05muf
Mufwene, S. S. (2007). Population Movements and Contacts in Language
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Romaine, S. (2001). Lexical structure in Pidgins and Creoles. In D. A. Cruse, F.
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123

USING EYE TRACKING TO BETTER UNDERSTAND
CHILDREN’S PROCESSING OF EVENTS DURING
VERB LEARNING: IS THE FOCUS ON PEOPLE(FACES) OR THEIR ACTIONS (HANDS)?
JANE B. CHILDERS*1, EMILY WARKENTIN1, MARISSA YOUNG1, SNEH
LALANI1, and AKILA GOPALKRISHNAN1*Corresponding Author: jchilder@trinity.edu
1Psychology Department, Trinity University, San Antonio, Texas USA

Verbs are central to the syntactic structure of sentences, and thus important for learning
one’s native language. This study examines how children are visually inspecting events asthey hear, and do not hear, a new verb. Specifically, there is evidence that children may
focus on the agent of the action (Maguire et al., 2008; Childers et al., 2017) or mayprioritize attention to the action being performed (e.g., Behrend, 1990); to date, little
evidence is available. This study uses an eye tracker to track participants’ looking to theagent (i.e., face) vs. action (i.e., hands) when viewing events linked to a new verb as well
as distractor events to better understand children’s visual attention patterns. Two 1/2, 3
1/2- and 4 1/2-year-olds saw dynamic target and distractor events and heard new verbs
while a Tobii x30 eye tracker recorded fixations to AOIs (head/face, hands). Results showthat children in all age groups were able to learn and extend the new verbs to new eventsat test. Additionally, across age groups, when viewing target events, children increased
their looking to the hands (where the action is taking place) as those trials progressed anddecreased their looking to the agents’ face, which is less informative for learning a newverb’s meaning. In contrast, when viewing distractor events, children decreased theirlooking to hands over trials and maintained their attention to the face. In sum, children’svisual attention to agents’ faces and hands differed depending on whether the events
cooccurred with the new verb. These results are important as this is the first study to show
this pattern of visual attention during verb learning, and thus these results help reveal
underlying attentional strategies children may use when learning verbs. Additionally, thestudy of children’s acquisition of verbs, which vary across languages, could provide clues
to how languages may have changed in response to the learnability of emerging predicate
structures.

1. Introduction
Verbs are central to the syntactic structure of sentences. A controversy in thisarea is whether children focus on the agent of the action or the action being
performed, and to what extent they focus on each one. Some studies suggestthat young children learn verbs best when a single agent is seen, which could
mean that they are attending too much to agents rather than the actions whilelearning verbs (Maguire, Hirsh-Pasek, Golinkoff & Brandone, 2008; Childers,
Paik, Flores, Lai & Dolan, 2017: complex events). In other studies, children
seem to be attending to actions (e.g., Scott & Fisher, 2012) or results (e.g.,
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

124Childers, 2011; Behrend, 1990). Additionally, in everyday contexts, children
often see events linked to a new verb that are interspersed with distracting
events. Yet most laboratory studies of verb learning show children relevant
events as they hear the new verb. Thus, the present study includes distracting
interleaved events to examine whether participants focus more on agents (faces)or their actions (hands) when learning verbs, and whether this varies dependingon event relevancy.
To accurately acquire a verb, learners must solve what Gleitman and
Gleitman (1992) described as the “packaging problem”, wherein learners must
package together only the relevant aspects of a dynamic event and disregard any
irrelevant information. Given the difficulty of this problem, learners often use
information across events and sentences or engage in cross-situational learning(e.g., Imai & Childers, 2020). Research has shown that the comparison of
events can help children learn and generalize verbs (e.g., Childers & Paik, 2009;
Haryu, Imai & Okada, 2011; Waxman, Lidz, Braun & Lavin, 2009).
Additionally, children can benefit from seeing similar or varied events,
depending on the nature of the task and test conditions (Haryu et al., 2011; Imai,
Haryu, Okada, Hirsh-Pasek, Golinkoff, Shigematsu, 2008; Childers, Parrish,
Olson, Burch, Fung & McIntyre, 2016).
In light of this prior research, it seems clear that young children can glean
information across a set of events as they learn verbs, but theories differ in themechanisms they posit that underlie this cross-situational processing. In
statistical learning, observers compare events by noting co-occurrences between
specific words and referents (e.g., Yu & Smith, 2007). In structural alignment,
observers compare events linked to a new verb over instances by aligningelements across the instances based on their common relational structure (e.g.,
Gentner & Namy, 2006). Our study tests whether children’s looking at targetand distracting events during the learning phase differs in terms of their focus on
agents (faces) or actions (hands). By including both types of trials, we can ask
whether there are general looking preferences (e.g., for faces or hands) orwhether children attend to events differently when viewing events linked toverbs vs. distracting events. To our knowledge, no prior study verb has used eye
tracking in this way.
The present research is related to a set of studies with 2 ½- to 4 ½-year-old
children shown the same events as in the present study (without an eye tracker)(Howard et al., 2019). Children as young as 2 ½ years were able to extend the
new verb, demonstrating an ability to distinguish between target and distractor
events. However, without an eye tracker, we only have indirect evidence ofchildren’s ability to ignore distracting events.

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We hypothesized that children would look more to the hands as the hands
AOI (area of interest) is larger (in our stimuli and in everyday life) than the face
AOI, and the hands are moving. However, two prior studies demonstrate thatchildren can also be overly attentive to the agent in an event, performing fewer
verb extensions in events with multiple agents (Maguire et al., 2008),
particularly when events were more complex (Childers et al., 2016). Thus, we
asked whether children focused more on the face/head or the hands while seeing
events in the learning phase, whether this differed depending on whether the
event was linked to a new verb (target) or was not (distractor), and whether
children could extend the new verbs at test.
1.2. Method
1. Participants
Twenty-four 2 1⁄2 -year-olds (Mage= 2;8; range: 2;0-2;11), thirty-one 3 1⁄2 year-olds (Mage= 3;4; range: 3;0-3;11) and twenty-one 4 ½-year-olds(Mage= 4;4; range: 4;0-4;10) participated in this study; 40 girls, 36 boys.
Children were excluded if exposure to English was <80%, if teachersreported a speech delay, for equipment failure (17) or child refusal (1).
2. Materials and Design
Video stimuli were created with 3 target events showing a single causative
action and 2 distinct distractor actions for each of two novel verbs. Test
scenes showed a new target scene and a new distractor action. A three second
black screen appeared between each event.
For example, in one set, an actor picks up a natural object so that it sticks
to an open hand (picking up a leaf, a stick and a rock in the target events, seeFig. 1, left), and waves a leaf around and twirls a stick on the table using her
finger in the two distractor events (see Fig. 1, right). At test, children saw her
pick up an object using an open hand (correct) or move an object from thecenter to the sides of the table (incorrect) (see Fig. 2).
Four sets of events were constructed and two of these were shown toeach participant so that they could learn two verbs. Different children saw
different sets to minimize the influence of a single set of events on the results.
We also created three orders of the events: Target first (TTDDT), Distractor
first (DTTDT) and Alternating (TDTDT), and children were assigned
randomly to one of these three orders (T=Target, D=Distractor).
3. Experimental Set-up
Children sat in front of a 21-inch flat screen video monitor; a Tobii X30 eye
tracker device was at the bottom of the monitor connected to a laptop. A
webcam recorded the children’s pointing responses. The distance between

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the table holding the monitor and participant was 16 inches. The eye tracker
used a corneal reflectance tracking technique to measure eye movements.
The experimenter calibrated the Tobii x30 eye-tracker using the Tobii 5point calibration stimuli for infants; the software used was Tobii Studio.

Fig 1: Learning phase example Target (L) and Distractor event (R).

Fig 2: Test trial example.

4. Procedure
Two experimenters were present: one produced sentences from a script while
controlling the eye tracker, the other coded children’s pointing. Participants
saw two warm-up trials and were asked to point to a familiar object out of apair (e.g. “Can you point to the grapes?”). Then each child was shown two
sets of events, one at a time and heard two novel verbs: gorp and snarf (oneverb for each set). In each set, children saw 3 target events and 2 distractor
events in one of three orders while hearing the new verbs. During the target
events, they heard the novel verb three times (“She is going to ___ it. She is___ing it. She ___ed it.”). During the distractor events, they heard nonlabelling speech (“Oh, look what she’s doing.”). At test, they saw a split
screen with two different events while hearing “Now it’s your turn to find<verb>ing”, and were asked to point, “Point to <verb>ing. Can you point to
the one who’s <verb>ing?”(see Fig. 1). In a second test trial, the same videos
were shown on the opposite sides of the screen “You get one more turn toplay the game. Can you point to <verb>ing? Which one is <verb>ing?”).

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The correct side of the screen was counterbalanced. This process was
repeated for a second verb. Interrater reliability calculated between thesecond and a third coder, both from video, showed 94% agreement with
Cohen’s kappa = .88 (almost perfect agreement).
1.3 Results
1. Pointing results
A univariate ANOVA with Age group (3: 2s, 3s, 4s) and Order (3: Target
first, Distractor first, Alternating) as BS factors, dv= proportion trials
correct (number correct/total trials with responses), showed a main effect ofAge group, F(2, 75) = 3.94, p= .024, np2= .11, and an Age group by Orderinteraction, F(4, 75)= 3.04, p=.023, np2= .15. Given the significant
interaction, we split the data by age group and repeated the univariate
ANOVA within each age group, following up with one sample t-tests tocompare responses to chance.
No significant effects of Order emerged in the 2 ½-year-olds and 4 ½year-olds data. There was a significant effect of Order only in the 3 ½-yearold group, with Order, F(2, 30) = 4.25, p< .05, np2= .23. Because thisresult was only found in one age group, it suggests that order of the events
did not exert a major effect on children’s responses.
One sample t-tests showed that children in all three age groups exceeded
chance. Specifically, 2 ½-year-old children’s responses exceeded chance,t(23) = 3.40, p= .002, as did 3 ½-year-old children’s, t(30) = 4.60, p< .001,
and 4 ½-year-old children’s, t(20) = 12.21, p< .001. An independent
samples t-test comparing 4½–year-olds’ to 3½-year-olds’ responses was
significant, t(50)= -2.25, p=.029 (see Fig. 2).

Proportion correct

1
0.8

*

*

2 1/2 years

3 1/2 years
Age group

*

*

0.6
0.4
0.2
0

4 1/2 years

Fig. 2: Pointing results, *ps< .05, error bars show SEM, blue line represents chance.

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2.

Eye tracking results: Looking to the face vs. the hands

Mean Fixation Duration(seconds)

A repeated measures ANOVA was computed with Age group (3: 2 ½, 3 ½,
4 ½ years) as BS factor and Trial type (2: target, distractor), Trial number(2: first, last) and AOI (2: face, hands) as WS factors; dv= total fixation
duration (with zeros). There was a significant main effect of Trial type,
Trial number, and AOI. There were also 3 two-way interactions, and a 3
way interaction of Trial type x Trial number x AOI, F(1, 67)=27.36,
p< .001.

Target Events

5

*

4
3
2
1
0

1st
event
Last
event

*
head/face

AOI type

hands

Fig. 3. Target Events: Graph shows mean total fixation duration by Trial (first, last) and AOItype (face, hands), *ps< .05.

Distractor Events

Mean Fixation
Duration (seconds)

4

*

3

1st
event
Last
event

2
1
0

head/face

AOI type

hands

Fig. 4. Distractor Events: Graph shows mean total fixation duration by Trial (first, last) and AOItype (face, hands), *p< .05.

Target Trials: A repeated measures ANOVA examining looking during
the Target trials revealed a main effect of AOI, F(1, 69)= 580.62, p< .001,
and a Trial number x AOI interaction, F(1, 69)= 30.14, p< .001. Pairwise

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comparisons with Sidak corrections showed that, in both the First and Last
Target events, children looked longer at the Hands than the Face, ps< .05.
Importantly though, across trials, looking to the Hands increased, p= .003,
while looking to the Face significantly decreased, p< .001 (see Fig. 3).
Distractor trials: A repeated measures ANOVA revealed a main effect of
Trial number, F(1, 69)= 26.96, p< .001, AOI, F(1, 69)= 53.91, p< .001, and a
Trial number x AOI interaction, F(1, 69)= 14.10, p< .001. Pairwise
comparisons with Sidak corrections showed that children looked longer atthe Hands than the Face in the first distractor event, p< .001, and in thesecond distractor event, p=.004. However, across trials, a different pattern
was seen with children’s looking to the Hands decreasing, p< .001, whilelooking to the Face was maintained (see Fig. 4).
1.4 Discussion
Children learning verbs often see other intervening events as they arelearning verbs (e.g., seeing a stirring event while learning the verb ‘chop’ in the
kitchen), which need to be processed differently than events linked to the target
verb. Our results provide experimental evidence for differential visual
processing of relevant vs. distractor events as events are experienced.
Specifically, across age groups, when viewing relevant events, children increase
their looking to the hand region (actions) over trials and decrease their looking
to the agents’ face, which is less informative for learning a verb’s meaning. In
contrast, when viewing distracting events, children decrease their looking tohands over trials and maintain their attention to the head. These results add tothe body of research showing that children can compare events during verb
learning, and that comparisons help them extend new verbs (e.g., Childers et al.,2016; Scott & Fisher, 2012; Waxman et al., 2009). Specifically, they provide
evidence that children are adjusting their visual fixations differently as they see
relevant events linked to a new verb as opposed to irrelevant events, suggestingthat they are strategic in how they visually inspect events.
Learning new verbs is important to learning one’s native language. Our
study suggests that by 2 ½ years, children have developed visual strategies forinspecting events that should help them attend to events appropriately whenseeing relevant events and hearing verbs (attending more to what the hands are
doing than the face) and perhaps help them ignore distracting information (asthey focus on faces and not hands) when they see irrelevant events. These are
exciting new findings that reveal what mental mechanisms could underlie earlyverb learning. The study of children’s acquisition of verbs, which vary acrosslanguages, could suggest ways languages may have changed in response to thelearnability of emerging predicate structures.

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Acknowledgements
Funding for this research was provided by the National Institute of Health (2R15
HD044447), Murchison Scholars Fund, and Trinity University. We thankVictoria Bell, Sophia Arriazola, Aidan Burke, Priscilla Tovar-Perez, Hannah
Bortz, Katy Capps, and Sophia Rodriguez for data collection and coding.
References
Behrend, D. A. (1990). The development of verb concepts: Children's use of verbs to label
familiar and novel events. Child Development, 61, 681-696.
Childers, J. B. (2011). Attention to multiple events helps two-and-a-half-year-olds extend
new verbs. First Language, 31(1), 3-22.
Childers, J. B., & Paik, J. H. (2009). Korean- and English-speaking children use crosssituational information to learn novel predicate terms. Journal of Child Language,36(1), 201–224.
Childers, J. B., Parrish, R., Olson, C. V., Burch, C., Fung, G., & McIntyre, K. P. (2016).
Early verb learning: How do children learn how to compare events? Journal of
Cognition and Development, 17(1), 41–66.
Childers, J. B., Paik, J. H., Flores, M., Lai, G., & Dolan, M. (2017). Does variability acrossevents affect verb learning in English, Mandarin, and Korean? Cognitive Science, 41,808-830.
Gentner, D., & Namy, L. L. (2006). Analogical processes in language learning. Current
Directions in Psychological Science, 15(6), 297–301.
Gleitman, L. R., & Gleitman, H. (1992). A picture is worth a thousand words, but that's the
problem: The role of syntax in vocabulary acquisition. Current Directions in
Psychological Science, 1(1), 31-35.
Haryu, E., Imai, M., & Okada, H. (2011). Object similarity bootstraps young children toaction based verb extension. Child Development, 82(2), 674-686.
Howard, T. J., Porter, B. M., Childers, J. B. (2019). Can young children ignore irrelevant
events, or subevents, during verb learning? Journal of Cognition and Development,
20(3), 411-432.
Imai, M., & Childers, J. B. (2020). Learning individual verbs and the verb system: Whenare multiple examples helpful? In J. B. Childers (Ed.), Language and Concept
Acquisition from Infancy Through Childhood (pp. 131-158). Springer, Cham.
Imai, M., Li, L., Haryu, E., Okada, H., Hirsh‐Pasek, K., Golinkoff, R. M., & Shigematsu,
J. (2008). Novel noun and verb learning in Chinese‐, English‐, and Japanese‐speaking
children. Child Development, 79(4), 979-1000.
Maguire, M. J., Hirsh-Pasek, K., Golinkoff, R. M., & Brandone, A. C. (2008). Focusing
on the relation: Fewer exemplars facilitate children's initial verb learning andextension. Developmental Science, 11(4), 628–634.
Scott, R. M., & Fisher, C. (2012). 2.5-year-olds use cross-situational consistency to learn
verbs under referential uncertainty. Cognition, 122(2), 163–180.
Waxman, S. R., Lidz, J. L., Braun, I. E., & Lavin, T. (2009). Twenty four-month-old
infants’ interpretations of novel verbs and nouns in dynamic scenes. Cognitive
Psychology, 59(1), 67–95.
Yu, C., & Smith, L. B. (2007). Rapid word learning under uncertainty via cross-situational
statistics. Psychological Science, 18(5), 414–420.

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EVIDENCE FOR THE COGNITIVE ORIGINS OF SYNTACTIC
HARMONYJENNIFER CULBERTSON*1 and SIMON KIRBY1* Corresponding Author: jennifer.culbertson@ed.ac.uk

1 Centre for Language Evolution, University of Edinburgh, United Kingdom

1. Background
There is a consensus that cognition-general mechanisms and biases shape syntactic typology. However there is very little direct behavioral evidence linking suchmechanisms to specific typological patterns in syntax. Here we test a recent proposal by Culbertson and Kirby (2016) that syntactic harmony–alignment between
syntactic heads and dependents (Greenberg, 1963)–reflects a domain-general bias
for simplicity (Chater & Vitányi, 2003), acting on linearized, language-specificcategories (i.e., heads and dependents). Previous research suggests that humanlearners do indeed prefer syntactic harmony (e.g., Culbertson, Franck, Braquet,
Barrera Navarro, & Arnon, 2020). However, if harmony is the result of a domaingeneral bias for simplicity, then a similar preference should hold when sequencesof meaningless and/or non-linguistic categories are linearized.
2. Experiment 1: meaningless letter sequences
In Experiment 1 we take one step away from syntactic harmony by using sequences of meaningless letter strings rather than combinations of meaningful
words. Stimuli consisted of two categories: ‘head’ elements and ‘dependent’ elements. In natural language these categories are presumably learned based on similarities in structure and/or meaning. Here, the categories are distinguished basedon length and structure. Heads are comprised of contrasting CVCVC strings, anddependents of CVC strings. Dependents paired with each type of head have C’s
that match the heads in terms of voicing, and V’s chosen from the same set inorder to help learner identify the relevant head-dependent combinations (akin todifferent phrase types in natural language).
Participants (N=74) were randomly assigned to one of three conditions: harmonic (either Head-Dep or Dep-Head), non-harmonic across heads (Head-Dep
order for one head type, Dep-Head for the other), and non-harmonic within heads(one Dep type for each Head type was Dep-Head, the other Head-Dep). They

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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were exposed to grammatical sequences in the language, and then tested using 2AFC. Accuracy was highest in the harmonic condition (vs. non-harmonic
across heads β = −3.18 ± 0.81, p < 0.001; vs. non-harmonic within heads
β = −5.70 ± 0.83, p < 0.001) and lowest in the non-harmonic within heads
condition (vs. non-harmonic across heads β = −2.38 ± 0.55, p < 0.001).
3. Experiment 2: paired abstract shapes

Experiment 2 was identical to Experiment 1 in terms of the structure of the“languages” and the experimental conditions participants (N=76) were assigned
to. The only difference was in how head and dependent elements were constructed: heads were single larger shapes, dependents were two smaller stacked
shapes, positioned either to the left or right of the larger shape. As in Experiment 1, dependents paired with each head matched, here in the roundness ofthe shapes in order to help learners identify the relevant head-dependent combinations. Accuracy was highest in the harmonic condition (vs. non-harmonic
across heads β = −1.88 ± 0.72, p < 0.001; vs. non-harmonic within heads
β = −4.02 ± 0.71, p < 0.001) and lowest in the non-harmonic within heads
condition (vs. non-harmonic across heads β = −1.92 ± 0.51, p < 0.001).
Figure 1. Head and dependent stimuli.
Figure 2.
Heads
H1: nageng, negang,
genang, ganeng
H2: shukoth,shokuth,
koshuth, kushothExperiment 1.
Dependents
Dep1a: bav, baz, dav, daz
Dep1b: veb, ved, zeb, zed
Dep2a: puf, pus, tuf, tus
Dep2b: fop, fot, sop, sot

Figure 3.
Heads
H1:
H2:

Experiment 2.
Dependents
Dep1a:
Dep1b:
Dep2a:
Dep2b:

4. Discussion
In language, we see a preference for harmony in the linearization of grammatical categories—heads and dependents comprising syntactic phrases. Here wehave shown that a linearization preference akin to harmony will emerge in similarly structured stimuli when no linguistic meaning is present, and even when thestimuli are non-linguistic in nature. Our findings suggest that rather than being
language-specific, the origins of harmony lie in a cognition-general bias for simpler representations, active in individual learners, likely amplified over time via
cultural transmission.
References
Chater, N., & Vitányi, P. (2003). Simplicity: A unifying principle in cognitive
science? Trends in Cognitive Sciences, 7(1), 19–22.

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Culbertson, J., Franck, J., Braquet, G., Barrera Navarro, M., & Arnon, I. (2020).
A learning bias for word order harmony: evidence from speakers of nonharmonic languages. Cognition, 204.
Culbertson, J., & Kirby, S. (2016). Simplicity and specificity in language: Domain general biases have domain specific effects. Frontiers in Psychology,6(1964).
Greenberg, J. (1963). Some universals of grammar with particular reference
to the order of meaningful elements. In J. Greenberg (Ed.), Universals of
language (p. 73-113). Cambridge, MA: MIT Press.

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NON-ARBITRARINESS ACROSS MODALITIES:BOOTSTRAPPING THE EMERGENCE AND DEVELOPMENTOF QUESTIONSCHRISTINE CUSKLEY1, JOHANNES M. HEIM*1,2*Corresponding Author: johannes.heim@abdn.ac.uk
1Language Evolution, Acquisition, and Development Group, Newcastle University,

Newcastle upon Tyne, UK
2Language and Linguistics, University of Aberdeen, Aberdeen, UK

Over the last decade, language evolution research has been the driving force behind arenewed interest in non-arbitrary mappings (Cuskley & Kirby, 2013), as well as
multimodal theories of language development (Vigliocco et al., 2014). These theories havefocused largely on lexical iconicity in language systems. Here, we begin to extend this lineof research by considering how non-arbitrariness plays a role in language development andemergence beyond the lexicon, moving from lexical to pragmatic meaning across different
modalities. In other words, we focus on non-arbitrariness in situated language (Murgiano
et al., 2021; Özyürek, 2021). Specifically, we consider the role of indices of dominance inscaffolding the development of questions. By surveying the state of the art in research on
prosody, gesture, and signing, we outline evidence for cross-linguistically attested, iconic
gestural scaffolding of questions across signed and spoken languages. This supports
development of indexical use of pitch in question prosody, which has analogies in nonarbitrary dominance signalling. We conclude by synthesizing this evidence, presenting anovel non-arbitrary bootstrapping theory for the emergence of questions.

1. Introduction
Making our desires known through linguistic directives, such as questions, liesat the very heart of human communication. Questions play a key role in driving
conversations by engaging in a collaborative effort in building common ground(Roberts, 2012) and have a prominent place in accounts of first language
acquisition (Thornton, 2016). While morphosyntactic cues to questions arearbitrarily aligned and exhibit considerable cross-linguistic variation, different
modalities – gesture and intonation - are aligned across many languages. For
example, while both English and Mandarin use rising intonation to encode
questions, English combines rising intonation with a sentence-initial auxiliary‘do’ and Mandarin with a question particle. Moreover, questions in bothlanguages will be accompanied by raised eyebrows, hands, and shoulders.

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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Intonation and by extension, gesture (Bolinger, 1983) can mirror featuresassociated with dominance asymmetries (Ohala, 1983). We argue that questions
inherently involve a temporary dominance asymmetry within a conversationaldyad, where the speaker uses upward movement to encode that they needsomething from the addressee (materially or informationally).
We propose that a rich inventory of developmentally robust and crossculturally attested multimodal, that is: non-morphosyntactic cues scaffolds the
emergence of complex linguistic questions, both in development and language
evolution. This paper will draw on previous literature for synthesising three
distinct lines of evidence for this novel theory: i) the cross-linguistic ubiquity of
non-morphosyntactic cues to questionhood ii) the importance of such cues in early
pragmatic development, iii) the parallels of grammaticalized facial cues in spoken
and signed languages. On this basis, we show that non-arbitrary cues are likely tohave played a key role in the emergence of questions given them a role as acatalyst in language development, the key features occurring consistently acrosslanguages and dominant signalling modalities.
2. Non-arbitrariness across modalities and languages
Questions of whatever morphosyntactic form engage the addressee torespond to the speaker’s epistemic stance (Heim, 2019). Questions are widely
considered the marked form of human conversation as opposed to assertions,which occur more frequently (Huddleston, 1994). Correspondingly, an auxiliaryinitial question word order, which make up the majority of questions in the
caregiver input of an English-speaking child (Estigarribia 2009), occurs less often
than an auxiliary-medial word order (Newport et al., 1977). Morphosyntactic
marking of questions is present in 80% of the world’s languages (Dryer, 2008),
and comes in the shape of word order changes, interrogative clitics and particles,
and/or verbal inflection (Siemund, 2001). Content questions have an additionalcue form of a variable corresponding to the element unknown (e.g., wh-pronouns
in English) and sometimes omit other cues present in yes-no questions. Whilemorphosyntactic marking varies significantly across languages, other modalities,
such as intonation and gesture, show considerable cross-linguistic consistency.
By far the most common cue to mark questions is rising intonation (Ultan,
1978), although prosodic details vary alongside how pitch is employed at thelexical level. Pitch plays a key role in other kinds of non-arbitrariness, particularly
linked to size sound symbolism via the frequency code hypothesis (Ohala, 1994).
The mechanism of motivatedness behind the frequency code is that a larger
resonant chamber is an index for a larger producer, but motivatedness can havemultiple mechanisms (Ahlner & Zlatev, 2010). For example, while high vowels
are generally considered “smaller” than low vowels, Diffloth (1994) describes
Bahnar, a language where high vowels are large and low vowels are small,mediated by the size of the tongue within the mouth (which occupies a larger
volume when producing high vowels like /i/ relative to low vowels like /a/).

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Despite some cross-linguistic variation in how question intonation is
deployed, it is nonetheless a reliable indexical cue, motivated by dominanceasymmetries in pragmatic context across languages. For example, Gussenhoven& Chen (2000) show that peak alignment, peak height, and end pitch areassociated with questions by speakers of Dutch, Chinese, and Hungarian;
although these languages vary in their specific encoding of questions. In general,animals and humans exhibit a strong association between pitch and dominance(Puts et al., 2007), with lower pitch signalling larger size and greater dominance.
The manual-gestural modality shows similar non-arbitrariness in marking
questions, arguably tied more straightforwardly to dominance: hands andeyebrows are raised (Krahmer & Swerts, 2005). This contrasts markedly withnegation, where facial articulators show the opposite direction: eyebrows arelowered, and lips are pressed against each other (Benitez-Quiroz et al., 2016).
Hence, just like with intonation, a downward movement expresses dominance, an
upward movement a lack thereof. One other prevalent gesture associated with theabsence of knowledge is the palm-up (epistemic) gesture, which is also associatedwith submissiveness (Cooperrider et al., 2018) and echoes the upward pitch
contours often associated with questions in speech (Ultan, 1978). In contrast to
co-speech manual gestures, House (2002) argues that facial cues are less reliable
than manual ones for encoding questions, at least in spoken languages. Sign
languages, however, are more reliable in this context: They generally markquestions with raised eyebrows (Cecchetto, 2012) including ASL, Sign Languageof the Netherlands, and Kazakh-Russian Sign Language (Kimmelman et al.,2020). This facial gesture is also a widespread marker of submissiveness inprimates (Keating, 1985), lending further support to the idea that dominanceindices played a catalyst role in language emergence.
Areas that require further research are cues such as breathiness, duration
effects, and speech rate (Dehé et al., 2022), which have all been found to encode
questions in several languages. Moreover, in Yélî Dnye (Levinson, 2010),
questions are marked by mutual gaze making rather than intonation. This
nonetheless arises from systematic dominance asymmetries within a conversation(with the speaker asserting dominance, rather than telegraphing submission).
3. Non-arbitrariness in pragmatic development
Non-arbitrary cues to questionhood are not only attested widely acrosslanguages and modalities, but also in language development. Here, too, nonarbitrary cues tend to be non-morphosyntactic. Intonation and gesture provide anavenue into pragmatic development, and questions in child directed speech which
use non-morphosyntactic cues are ubiquitous (Kania, 2016). Morphosyntactic
encoding of questions, on the other hand, has a long developmental trajectory,
with the first questions marked by word order occurring from around 20 months
in Italian, German and Swedish, but only from 30 months in English. It takes years
for children to master the morphosyntactic variation in questions, especially when

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they include negation (Guasti, 2017). With non-morphosyntactic cues appearing
early, the acquisition of morphosyntactic cues can benefit from multimodal
scaffolding in development, building on motivated manual and facial gestures,
which then bootstrap indexical use of pitch.
We assume manual and facial gestures to be the starting point of thisdevelopmental process, because they are the first means available forcommunication (Iverson & Goldin-Meadow 2005). Gestures frequently serve asprecursors for what is later expressed using more complex linguistic structure(Capirci et al., 2005). Children then combine vocalizations and gestures for
relating non-arbitrary signs to previous experience with a rapid increase of
gestures during the first year (Burkhardt-Reed et al., 2021). Pointing, too, serves
as a cue to questions when the addressee is knowledgeable (Begus & Southgate,2012). From 14 months, infants use prelinguistic vocalizations to differentiate
conversational strategies (Grünloh & Liszkowski, 2015). Combining thesefindings with the those of intonational cues, we find that non-arbitrary forms are
well-suited for bootstrapping linguistic development (Bohn et al., 2019).
Intonational cues follow early on. Sensitivity to different sentence melodies
is evident form as early as 5 months (Frota et al., 2014). Intonational distinctionsof questions from other speech acts occur as early as 7 months, and adult-likeintonation is produced by English-speaking children in their second year of life
(Prieto Vives & Esteve Gibert, 2013). Yet, infants show notable variation as towhen this command equals adult performance. Correspondingly, children use
different strategies for asking questions up to age 11 (Patel & Grigos, 2006).
German children aged 2;8-2;10 (age; month) show striking variability inproducing question intonation and appear not to reach prosodic targets before age
3;0 (Lleó & Rakow, 2011). Hübscher and colleagues (2019) show that facial andprosodic cues for expressing knowledge states are employed at the age of three,while lexical items are only employed later. This strongly suggests that nonarbitrary, non-morphosyntactic cues play a central role in advancing pragmaticdevelopment and can act as a platform for later integrating morphosyntactic cues.
4. Non-arbitrariness in signed and spoken languages
Parallels in the non-arbitrariness of encoding questions across spoken andsigned languages add a third piece of evidence supporting our hypothesis of thecatalytic function of these cues. Broadly speaking, non-arbitrariness has beenshown to play an important role across both infant-directed speech and signing(Brand et al., 2002) across different modalities. Perniss and colleagues (2015)
demonstrate that non-arbitrariness plays a greater role in referring to non-visiblereferents in child-directed signing compared to adult-directed speech. Laing andcolleagues (2017) show that prosody in onomatopoetic words (e.g., quack) is
more marked in infant-directed speech than in equivalent conventional words(e.g., duck). In both studies, however, the focus was very much on the nonarbitrary signalling of lexical meaning, where the benefits for language

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development are well-established (Ota et al., 2018; although see Nielsen &Dingemanse, 2021). This suggests that children acquiring language – regardlessof the dominant modality (speech or sign) – exploit non-arbitrariness. We argue
this extends to cues which aid in acquiring pragmatic concepts, such as questions.
As in section 2, we focus here on the directionality of these cues, whichshows consistent trends across spoken and signed languages. Zeshan (2004)
reports that most signed languages use a combination of eyebrow raise, eyes wide
open, eye contact, a head forward position or forward body posture to ask
questions. These non-manual interrogative signs accompany manual/lexical
question markers, such as content question words or dedicated morphemes. The
non-manual signs are key components of marking questions across many sign
languages (Kimmelman et al., 2020). Their frequent presence in questions remind
us that these are the marked speech acts compared to assertions. Remarkably, thedirectionality of these upward and forward oriented non-manual signs in signed
languages mirrors that of the intonational patterns observed in many spoken
languages (Dixon, 2012). The same applies for the upward movement of cospeech gestures in spoken languages.
Even more remarkable is the fact that directionality changes exhibit the same
patterns across modalities. Content and polarity questions in English, for instance,
have a fall and rise as their standard intonational contours, respectively (Bartels,
1997). Reversing the directionality for these types is meaningful, too, as theirfunction changes from and open to a closed question (Hedberg et al., 2017), where‘closedness’ evokes a set expectation about the answer. We find the analogous
pattern for non-manual signs in sign-languages. Italian Sign Language has raised
eyebrows in polar questions and lowered eyebrows in content questions (Zeshan,2004) just as spoken Italian has a falling-rising contour for polar questions and afalling contour for content questions (D’Imperio, 2002). The paradigm iscomplete with lowered eyebrows reported for closed or loaded questions, asreported for Israeli Sign Language (Meir, 2004). The similarity between spoken
and signed questions also has some equivalence in morphosyntax where bothsigned and spoken languages use the words for indefinite pronouns and question
markers (Dixon, 2012). Studies that address the role of non-arbitrariness acrossinfant-directed signing and speaking with respect to interactional strategies arestill in their infancy and researchers have just begun the explore the richness ofmultimodal cues in the learner’s input. (Murgiano et al., 2021)
5. Conclusions
By comparing non-morphosyntactic cues across languages, ages, andmodalities, we have argued that their non-arbitrariness presents an opportunity
for bootstrapping morphosyntactic development of questions. By drawing on awide range of findings from production and perception data, we establish that
non-morphosyntactic cues are likely to be non-arbitrary, transcend cultural andlinguistic borders, and precede morphosyntactic competence in infancy. These139

properties make non-arbitrary cues to questionhood a strong candidate for playing
a central role in pragmatic development, and by extension in language emergence
due to their prevalence and early availability. Moreover, this theory opens specifichypothesis spaces which can be probed with more specific experimental andtypological work. For example, using iterated artificial language learning, wemight expect that readily available indexical cues might outcompete complex
morphosyntactic cues for questions in a dyadic face to face context. We outline a
possible experimental approach wherein participants are trained on an artificial
language with complex morphosyntactic cues for questionhood used irregularly(i.e., in 60% of questions) in the first generation. In a face-to-face condition, we
predict that these cues will either stagnate or fade out of the system over iterated“generations”, in favor of indexical cues. In a virtual condition without theavailability of indexical cues, we predict the cues will regularize and come to be
more reliable cues to questionhood. In addition, typologically, we might expectthat the 20% of languages without complex morphosyntactic cues (Dryer, 2008)
are likely to have richer non-morphosyntactic cues. Assuming non-arbitrary, nonmorphosyntactic cues are to some extent present even in the many languages withcomplex morphosyntactic marking, this points to the former type of cues as anevolutionarily older strategy. In summary, we have established (i) the consistentcross-linguistic availability of non-arbitrary question cues, (ii) the use of thesecues in bootstrapping the acquisition of question, and (iii) evidence that similar
non-arbitrary cues play an important role in both acquisition and evolution.
Together, these lines of evidence point to a novel theory for the emergence ofquestions, where non-arbitrary cues from intonation, gesture, and signing play acatalyst role in the language emergence and development.
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LEARNABILITY AND EMERGENCE OF DEPENDENCYSTRUCTURES IN AN ARTIFICIAL LANGUAGE
Emily Davis*1 and Kenny Smith2*

Corresponding Author: e4davis@ucsd.edu
Department of Linguistics, UC San Diego, La Jolla, CA, USA
2
Centre for Language Evolution, School of Philosophy, Psychology andLanguage Sciences, University of Edinburgh, UK
1

In a pair of artificial language experiments, we investigated the learnability andemergence of different dependency structures: branching, center-embedding, and crossed.
In natural languages, branching is the most common dependency structure;
center-embedding occurs but is often disfavored, and crossed dependencies are very rare.
Experiment 1 addressed learnability, testing comprehension and production on small
artificial languages exemplifying each dependency type in noun phrases. As expected,
branching dependency grammars were the easiest to learn, but crossed grammars were no
harder to learn than center-embedding. Experiment 2 employed iterated learning to
examine the emergence and stabilization of consistent grammar using the same type ofstimuli as Experiment 1. The initial participant in each chain of transmission was trainedon phrases generated by a random grammar, with the language produced by thatparticipant passed to the next participant through an iterated learning process. Branchingdependency grammar appeared in most chains within a few generations and remained
stable once it appeared, although one chain stabilized on output consistent with a crossed
grammar; no chains converged on center-embedding grammars. These findings, alongwith some previous results, call into question the assumption that crossed dependencies
are more cognitively complex than center-embedding, while confirming the role oflearnability in the typology of dependency structures.

1. Introduction
In a pair of artificial language experiments, we investigated the learnability ofdifferent dependency structures: branching, center-embedding, and crossed.
Long-distance dependencies between words (e.g. nouns and verbs, or nouns andadpositions) are an essential aspect of human language, and can be arranged indifferent ways. In (1), the dependency between subject and verb is indicated by
the subscript (examples based on Vosse and Kempen 1991):
1.
a. (Right-) branching dependencies in English:… when John1 saw1 Peter2 walk2

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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b.

Center-embedded dependencies in German:… als Johan1 Peter2 laufen2 sah1c.

Crossed dependencies in Dutch:… toen Jan1 Peter2 sag1 lopen2

Most natural languages predominantly use some combination of
branching and center-embedding dependency structures; crossed dependencies
are rare, but some examples are attested, e.g. in Dutch subordinate clauses(Bresnan et al. 1982). Center-embedding tends to be less prevalent thanbranching both within and across languages, likely because of the cognitivedifficulties it presents (see e.g. Kuno 1974, Hawkins 2004). Some experimental
evidence from natural language processing (Bach et al. 1986) and nonlinguistic
sequence learning (Öttl et al. 2015) also suggests that crossed dependencies areeasier to process and learn than center-embedded dependencies, which issurprising in light of the marginal status of crossed dependencies in natural
languages.
Artificial language learning experiments can shed light on the cognitive
factors shaping natural language structures (e.g. Culbertson 2012, Fedzechkina2018), and the regularization and stabilization of irregular input (e.g. Hudson
Kam & Newport 2005). In the present study we applied these methods todependencies. In two experiments, we investigated the learnability of different
dependency types within the same paradigm, using meaningful sequences(descriptions for visual scenes) that constituted a small artificial language withseveral nouns and spatial adpositions.
2. Experiment 1: Learnability
Experiment 1 concerned the learnability of the three types of dependency
structures, as well as head-dependent order (head-initial or head-final), using
noun phrases with locative adpositional phrases for configurations of novel
objects.
2.1. Methodology
We created simple artificial languages representing the six possiblecombinations of dependency type and order (Figure 1). The associated visual
stimuli were composed of images from the NOUN database as components(Horst 2015). Both experiments were conducted online through Mechanical
Turk. Participants (n = 120, 20 per condition, all self-reported English speakers)
were trained on one of these languages by a mix of passive training trials(viewing individual objects or configurations of objects with an appropriate
description; Figure 2a) and comprehension trials (being presented with adescription and attempting to identify the corresponding scene from an array ofpossibilities, with corrective feedback provided; Figure 2b-d).

145Over the course of the training procedure participants progressed from scenes
involving single objects to three-object configurations. In the final stage of theexperiment, participants were asked to produce descriptions of scenes byclicking on labeled buttons (Figure 2e); these final test items included new
configurations not seen in training (including some scenes featuring four
objects), in order to assess generalization.

Figure 1. Sample array with description in each of the six syntactic orders in Experiment 1. Objects
are labeled with letters for clarity here and in other example arrays; no such labels were used on
stimuli for the actual experiment.

Figure 2: Passive training (a), comprehension (b), feedback on correct (c) and incorrect responses tocomprehension (d) trials, and production trial with partially entered description (e) from Experiment
1.

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Figure 3: Learning accuracy (1 - string edit distance between trained and produced descriptions) bylevel (number of objects in the scene) and grammar type. Each point is one participant’s performanceat that level; large diamonds represent mean performance across all participants. “Seen” indicates
items seen in training; “new” are novel test items. Note that participants were not tested on
single-item scenes, and were not trained on 4-item scenes.

2.2. Analysis and results
Participant-created labels were analyzed for consistency with the training data
(evaluated by calculating string edit distance between the trained and produced
label for each scene) and for adherence to one of many possible grammars (bygenerating grammars with all possible combinations of dependency order andheadedness, and finding the grammar which generated the description which
best matched the participant’s description, as assessed by edit distance). Figure 3
shows accuracy by condition. As expected, branching grammars were the easiest
to learn: learning accuracy on the mean of center-embedded and crossed
dependencies was significantly lower than that for branching structures asshown by a linear mixed effects model (B = -0.02, SE = 0.001, p = .04), andparticipants trained on branching grammars generally produced output
consistent with a branching grammar. Learning accuracy for crossed grammars
was not significantly different from center-embedding grammars (B = -0.01, SE= 0.02, p = .51). However, 6 participants of the 40 trained on center-embedded
grammar produced labels consistent with a crossed grammar, suggesting thatthey had reanalyzed the input in conformity with a new grammar. The converse(participants trained on crossed grammar generating center-embedded strings)
did not occur. There was also a significantly greater decline in accuracy withlevel in head-final than in head-initial conditions (B = -0.03, SE = 0.01, p <.001).

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3. Experiment 2: Emergence of grammar
Experiment 2 set out to examine the emergence and stabilization of a consistentgrammar in noun phrases, and furthermore to observe whether the results
reflected real language typology. We hypothesized that branching syntax would
be predominant in the results, due to its greater learnability andcomprehensibility, whereas center-embedding would be uncommon and crossed
grammar might appear rarely, in keeping with previous experimental results (e.g.
Öttl et al. 2015) and with the learnability results of Experiment 1. To this end
we employed the method of iterated learning (Kirby et al. 2008), using stimuliof the same type as Experiment 1. Iterated learning is a process where theresponse of one participant (in our case, the descriptions produced for scenes)
becomes the training input for the next participant in a chain of transmission.
The first participants are trained on experimenter-designed items (termed
generation 0); each participant’s response to the input that he or she is exposed
to is referred to as a generation, and the “genealogy” of all participant responses
in the same line of descent constitutes a chain.
3.1. Methodology
This study was conducted online with English-speaking participants. Generation
0 participants were trained on an artificial language with no consistent structure;each description contained the nouns and adpositions of the artificial language ina randomized order (for example, an array with object B atop object A could
bear the description (in English) “A B atop,” “atop B A,” “B atop A” and so on).
Participants were trained on these descriptions using the same procedure as inExperiment 1, and as in Experiment 1, in the final production stage, participants
were asked to label further scenes, including both trained and novel items. The
labels for trained items created in this stage were passed on as input to the next
participant in the chain, using training and comprehension trials in the sameformat as in Experiment 1. Consequently, at each generation, participants weretrained on description for the same scenes, and then asked to label novel scenes;
these novel labels made it possible to observe how the grammar was being
generalized and whether it was stabilizing. We ran twelve chains, each of fivegenerations (n = 60).
3.2. Analysis and results
Each string generated by a participant was compared to corresponding strings(i.e. referring to the same configuration of objects) generated by all 6 possible
grammars (branching, crossed, center-embedding; head-initial or head-final),
and the best match determined in the same manner as with the Experiment 1
results. We then calculated the entropy of the resulting grammar distribution (thedistribution of best fit grammars for all strings produced by a given participant).

148

Grammar entropy of 0 bits would indicate a participant whose productions best
matched those of a single grammar; entropy of 1 bit would indicate that a
participant’s productions were split over 2 grammars.
Grammars clearly stabilized over generations: a significant decrease inentropy occurred from 0 to 5 as measured by linear regression (B = -.207, SE =.033, p < .001), consistent with an increase in uniformity as irregularities were
eliminated and a consistent grammar came to predominate. Figure 4 shows thebest fit grammar for every production for every participant. While head-initialbranching grammars (i.e. English-like grammars) dominate, one chain converges
on a head-final branching grammar (chain A) and one chain (chain K) appears toconverge on a crossed-dependency grammar. Center-embedding patterns did not
emerge.
4. General discussion and conclusion
The results of these experiments are consistent with long-standing
psycholinguistic findings (e.g. Bach et al. 1982) concerning the difficulty ofcenter-embedded dependencies compared to branching structures. The status ofcrossed dependencies, however, remains somewhat anomalous: this form ofdependency appears to be at least as easy to learn as center-embedding andpossibly easier, but is very rare in natural languages. In our experiments, (strings
consistent with) crossed dependency grammar emerged both as reanalyses ofcenter-embedded input, and as a result of iterated learning (in one chain) frominitially unstructured input.
In sum, these results provide additional evidence for the somewhat
unexpected observation that the crossed dependency structure rarely seen innatural languages is as learnable as, or even more learnable than, thewidely-foundcenter-embedded
structure.
Theemergenceofacrossed-dependency grammar in one iterated learning chain is also interesting,especially in light of the fact that center-embedding grammar was not observed
to emerge in any chain. This strikingly conflicts with the observed patterns innatural languages. These results are also significant in light of the fact that thisexperiment featured meaningful semantic strings, demonstrating that theanomaly of crossed and center-embedded dependencies can arise in ameaningful language-like system as well as in non-semantic symbol sequences(e.g. DeVries et al 2008, Uddén et al 2012). Why natural language typology,
where grammars allowing center-embedding are common, seems to beinconsistent with learnability remains to be explained.
Acknowledgments
This research received funding from the European Research Council under the European
Union’s Horizon 2020 research and innovation program (Grant 681942, held by K.
Smith).

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Figure 4: grammar by string, chain, and generation, Experiment 2. Each facet (A to L) is one chain;
generations (participants) are columns and rows are individual scenes; the color of a given cell
represents dependency type and O/X symbols represent head order, as listed in the sidebar.

150

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HYSTERESIS IN LANGUAGE EMERGENCE AND EVOLUTION
BART DE BOER*1*Corresponding Author: bart@ai.vub.ac.be
1AI-lab, Vrije Universiteit Brussel, Brussels, Belgium

Correlational studies have shown that the social structure can predict propertiesof language – first established for population size and morphological complexity(Lupyan & Dale 2010). In emerging sign languages, different social factors appear to result in different linguistic properties. For instance, Israeli Sign language(a Deaf community sign language) shows more conventionalization and less variability than Al Sayyid Bedouin Sign Language (an equally old shared sign language) (Meir et al. 2012). Several explanations have been proposed (and supported with computer models) based on, for instance, differences between adultand infant learning1 (Dale & Lupyan 2012) memory limitations (Thompson, Raviv & Kirby 2020) or shared cultural knowledge (Mudd, de Vos & de Boer 2022).
None of these proposed explanations are mutually exclusive, and all of them
may play a role in explaining the observed phenomena. However, they all provide
a one-to-one mapping between the social property (e.g. group size or social network structure) and the linguistic property (e.g. morphological complexity or lexical variability). In other words, they propose that for any setting of the social
property, there is one optimal value of the linguistic property. This abstract presents a first exploration of the consequences of abandoning this assumption, andan argument that this may help explain real linguistic phenomena.
The existence of multiple linguistic optima for some values of the social
property creates the possibility of hysteresis (see left panel of Fig. 1). When thesocial property (for instance group size) increases for a particular population (inparticular during language emergence) the (linguistic) property of its language
stays stuck in the lower branch of the curve. When at a later stage the social property returns to an earlier value, the language may stay stuck on the upper branch,and thus appear quite different. This may explain differences between emergent
languages and established languages with similar social properties.
1

Dale & Lupyan's model focuses on morphology; the other models focus more on lexical variability.
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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A simple model of this phenomenon can be based on statistical physics: it isassumed that the distribution of the linguistic property (e.g. complexity of morphemes) follows a Maximum Entropy Distribution (MED). Properies of systems
undergoing random fluctuations will tend to have MEDs. A MED can be characterized by setting its statistical moments to have fixed values2. In order to allow
for hysteresis, the MED must have two peaks. A possible simple model constrains
the mean of the distribution and the moment given by ∫ 𝑑𝑥 ∙ 𝑝(𝑥)(2𝑥 2 − 𝑥 4 ). The
precise shape of this second constraint is unimportant, only that it has two peaks,
indicating that a language makes a compromise between two pressures. The distribution (with a scaling factor Z to ensure a total probability of 1) then becomes:
24𝑝(𝑥) = 𝑒 −𝛼𝑥+𝛽(2𝑥 −𝑥 ) ⁄𝑍
(1)
where α represents the social property, and β determines how high the peaks are.
Simulating a varying value of the social property with a Markov Chain Monte
Carlo approach (details in the supplementary material) shows hysteresis (rightpanel of Fig. 1). When the property increases, transition between the optimal
states happens at a higher value than when it decreases. The transition is gradual:
during the transition, the language is a mixture of the two linguistic optima.
This preliminary investigation shows that hysteresis can emerge from a simple model. The challenge is now to interpret the constraints on the moments interms of cognitive and social factors and to define empirical tests to establish
whether a model like this describes the linguistic reality accurately.

Figure 1. The left panel illustrates hysteresis qualitatively; solid black lines are trajectories of gradualchange, dotted black lines jumps, and arrows indicate the direction of change. Green lines show the
distribution of the linguistic property. The right panel shows hysteresis in a simulated language with
100 "morphemes" when a social property first slowly increases (red dots) and then decreases (bluedots). The transition does not occur in the same place when going up as when going down.

Acknowledgements
This work was funded by the FWO project number G034720N and by the Flemish AI plan.
2

E.g. fixing the mean gives Boltzmann's distribution; fixing variance gives the normal distribution.

153

References
Dale, R., & Lupyan, G. (2012). Understanding the origins of morphological diversity: The linguistic niche hypothesis. Advances in Complex Systems,
15(03n04), 1150017.
Lupyan, G., & Dale, R. (2010). Language structure is partly determined by social
structure. PloS One, 5(1), e8559.
Meir, I., Israel, A., Sandler, W., Padden, C. A., & Aronoff, M. (2012). The influence of community on language structure: Evidence from two young sign languages. Linguistic Variation, 12(2), 247–291. doi: 10.1075/lv.12.2.04mei
Mudd, K., de Vos, C., & de Boer, B. (2022). Shared Context Facilitates Lexical
Variationin
Sign
Language
Emergence.
Languages,
7(1).
https://doi.org/10.3390/languages7010031
Thompson, B., Raviv, L., & Kirby, S. (2020). Complexity can be maintained insmall populations: A model of lexical variability in emerging sign languages.
In Ravignani, A.; Barbieri, C.; Flaherty, M.(ed.), The Evolution of Language:
Proceedings of the 13th International Conference (Evolang13) (pp. 440-442).

154

BODY SIZE PREDICTS VOCAL TRACT SIZE IN A
MAMMALIAN VOCAL LEARNER
KOEN DE REUS*1,2,3, DARYLL CARLSON3,4, ALICE LOWRY3,5, STEPHANIEGROSS6, MAXIME GARCIA7,8, ANA RUBIO-GARCIA3, ANNA SALAZARCASALS3, and ANDREA RAVIGNANI1,3,9*Corresponding Author: koen.dereus@mpi.nl
1Comparative Bioacoustics Group, Max Planck Institute for Psycholinguistics, Nijmegen,

Netherlands
2Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium
3

Research Department, Sealcentre Pieterburen, Pieterburen, Netherlands

4Department of Earth System Science, Stanford University, Stanford, USA
5School of Environmental Science, University of Liverpool, Liverpool, UK
6Institute for Terrestrial and Aquatic Wildlife Research (ITAW), University of

Veterinary Medicine Hannover, Foundation, Büsum, Germany
7Animal Behaviour, Department of Evolutionary Biology and Environmental Studies,

University of Zurich, Zürich, Switzerland
8Center for the Interdisciplinary Study of Language Evolution, University of

Zurich, Zürich, Switzerland
9Center for Music in the Brain, Department of Clinical Medicine, Aarhus

University & The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark

1. Mechanisms for escaping acoustic allometry
Animals whose call features do not scale with their body size are said to escapeacoustic allometry. These animals may thus sound smaller or larger than they are.
Recent work (Garcia & Ravignani, 2020; Ravignani & Garcia 2021) found thatthey can achieve this by evolving vocal tract modifications (e.g., laryngeal
descent; Reby & McComb, 2003) or by learning to better control their vocal
organs (i.e., vocal learning, Janik & Slater, 1997). To identify which mechanism
is used by species to escape acoustic allometry, one could perform an anatomical
study to test if vocal tract size scales with body size. Acoustic allometry is escapedby anatomical adaptations if vocal tract size does not scale with body size, andthrough vocal learning if there is scaling between vocal tract size and body size.

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

155

2. Anatomical studies can help find more vocal learners
Studies that test whether animal vocal tracts scale with their body size offer asimple way of testing the hypothesis pitting anatomical adaptations vs. vocal
learning (Garcia & Ravignani, 2020; Ravignani & Garcia 2021). Moreover, they
can help to identify new species capable of vocal learning, a prerequisite forhuman speech. Adopting a comparative approach which considers an increasing
number of vocal learning species could offer promising insights into the
biological underpinnings of communication systems such as spoken language.
3. Harbor seal vocal tracts scale with their body size
Harbor seals’ (Phoca vitulina) large vocal plasticity allows them to modulate thecall frequencies they produce (Ralls et al., 1985; Torres Borda et al., 2021),
enabling them to produce sounds with different frequencies than predicted fromtheir body size. The current study tests if the vocal tract of the harbor seal, aknown mammalian vocal learner, scales with its body size (de Reus et al., 2022).
Vocal tracts, including larynges, of 68 young harbor seals (pups and weaners)
were measured using a caliper and tested for allometry with body size using
generalized linear mixed models. We find that both body length and body mass
predict vocal tract length, vocal fold length, and tracheal dimensions (Table 1).
Interestingly, allometry between body size and vocal fold length emerges after
puppyhood, suggesting that ontogeny may modulate the anatomy vs. learning
distinction. We conclude that the vocal tracts of harbor seals do indeed scale withbody size. Consequently, by exclusion, vocal learning is the likely mechanism
used in young harbor seals to escape acoustic allometry.
Table 1. Selected models for each vocal tract measurement.
Measurement

Model

Deviance
explained

Vocal tract length

BL+BM+A+S+BL*S

59.30%

Vocal fold length

BL+BM+A+S+A*BM+A*S

74.89%

Subglottic-tracheal dorsoventral distance 1

BL+BM+A+S

69.99%

Subglottic-tracheal dorsoventral distance 2

BL+BM+A+S

58.38%

Note. BL = body length, BM = body mass, A = age class, S = sex. Predictor terms joined by anasterisk denote an interaction. Significant predictor terms are underlined.

156

Acknowledgements
We would like to thank all members of Sealcentre Pieterburen for their continued
support in this research. In particular, we are grateful to Letty Stupers, Rebecca
Andreini, and Maria Jose Robles for help in data collection. We thank Aline
Joustra for her illustrations of harbor seal vocal tracts. We also greatly appreciate
the extensive comments provided by Taylor Hersh, Laura Verga, Yannick Jadoul,
and Limor Raviv. This work was supported by a Max-Planck-Gesellschaft (Max
Planck Research Group, MPRG) and a Fonds Wetenschappelijk Onderzoek(FWO) Pegasus Marie Curie fellowship 12N5517N awarded to A.R. K.d.R. was
supported by the Fonds Wetenschappelijk Onderzoek project ‘Interactive vocal
rhythms’ G034720N awarded to Bart de Boer. The German Ministry of Energy,
Agriculture, the Environment, Nature and Digitalization (MELUND) funded themarine mammal stranding scheme and the necropsies in Schleswig-Holstein. The
Sealcentre Pieterburen funded the seal stranding scheme and the necropsies at the
Sealcentre Pieterburen. M.G. was supported by a research grant from theEthologische Gesellschaft e.V., as well as a postdoctoral research grant from the
Forschungskredit of the University of Zurich [grant no. FK-21-121].

References
de Reus, K., Carlson, D., Lowry, A., Gross, S., Garcia, M., Rubio-Garcia, A., ...
& Ravignani, A. (2022). Vocal tract allometry in a mammalian vocal
learner. Journal of Experimental Biology, 225(8), jeb243766.
Garcia, M., & Ravignani, A. (2020). Acoustic allometry and vocal learning in
mammals. Biology Letters, 16(7), 20200081.
Janik, V. M., & Slater, P. J. (1997). Vocal learning in mammals. In: P. Slater, C.
Snowdon, J. Rosenblatt, & M. Milinski (Eds.), Advances in the Study ofBehaviour, 26, 59-100.
Ralls, K., Fiorelli, P., & Gish, S. (1985). Vocalizations and vocal mimicry incaptive harbor seals, Phoca vitulina. Canadian Journal of Zoology, 63(5),1050–1056.
Ravignani, A., & Garcia, M. (2021). A cross-species framework to identify
vocal learning abilities in mammals. Philosophical Transactions of the Royal
Society B, 377(1841), 20200394.
Reby, D., & McComb, K. (2003). Anatomical constraints generate honesty:
Acoustic cues to age and weight in the roars of red deer stags. Animal
Behaviour, 65(3), 519–530.
Torres Borda, L., Jadoul, Y., Rasilo, H., Casals, A. S., & Ravignani, A. (2021).
Vocal plasticity in harbour seal pups. Philosophical Transactions of theRoyal Society B: Biological Sciences. 376, 20200456.

157

LANGUAGE-SPECIFIC AND UNIVERSAL FACTORS BEHIND
MORPHOLOGICAL SIMPLIFICATION: AN AGENT-BASEDMODELLING STUDY OF ALORESE
Peter Dekker*1 , Marian Klamer2 , and Bart de Boer1* Corresponding Author: peter.dekker@ai.vub.ac.be

1 AI Lab, Vrije Universiteit Brussel, Brussels, Belgium
2 LUCL, Leiden University, Leiden, The Netherlands

Large-scale correlational investigations (Lupyan & Dale, 2010) indicate that
population size and the degree of contact with other groups correlate negatively
with the morphological complexity of the language spoken. To study what mechanisms give rise to morphological simplification and how these surface in theworld’s languages, we apply agent-based models (Smith, 2014) to a case study
with real-world data.
Alorese, an Austronesian language, is spoken on the coasts of the Alor andPantar islands in Eastern Indonesia, while inward, Papuan Alor-Pantar languages
are spoken. Many L1 users of Alor-Pantar languages have learned Alorese as asecond language. While Alorese lost most of its morphology, its sister languages,
which have not been in contact with Alor-Pantar languages, retained it (Klamer,
2012, 2020). The island geography of this case study presents us with a relatively
isolated contact situation between two groups of speakers. We use agent-basedmodels to study the hypothesis that adult language contact caused morphological simplification, focusing on inflectional verb morphology. In our model, a
population of agents play a language game (Steels, 1998), in which they try tocommunicate concepts (verb+person) using inflected verb forms. The model isinitialized with phonetic representations of verb forms in Lewoingu Lamaholot(1) (cf. Nishiyama & Kelen, 2007, p. 32), a sister language of Alorese (2) (cf.
Klamer, 2011, p. 65), which lost the inflection:(1) lodo-na
Lamaholot(2) lodo-Ø
Aloresego.down-3SG
go.down-3SG‘he goes down’‘he goes down’Model outcomes are compared to Alorese, where simplification has occurred.
We evaluate two cognitive mechanisms that we think could give rise to morphological simplification: the procedural-declarative model of L1/L2 language processing (Ullman, 2001) and reduction of word forms based on language-specificphonotactics. According to the procedural/declarative model, in L1 users, grammar is produced by a procedural cognitive system, while the lexicon is memorizedThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

158

in a declarative system. In L2 learners, linguistic forms which are normally produced in the procedural system by L1 users, such as morphology, are memorized
in the declarative system. We chose to represent the procedural system in L1 users
by, with a certain probability, generalizing during update: the received signal isadded not only to the inferred concept, but to all concepts. In our model withphonotactic reduction, a producing agent drops the affix if adding it would leadto violation of the default CV structure of Alorese. The chosen data representation, staying close to the real word forms in Alorese, allows for this modelling of
language-specific phonotactics. It should be noted that the influence of the phonotactic mechanism is more local than that of the procedural/declarative model. Our
results show that the standard model (Fig 1a) does not show a substantive decreasein morphological complexity, when the proportion of L2 users increases. The
phonotactic reduction model (Fig 1b), which does allow an extra possibility forsimplification, does not show this decrease either. The model with generalization
for L1 (Fig 1c) shows a decrease in morphological complexity for higher proportions of L2, although the complexity of suffixes stays relatively high compared to
the real situation in Alorese. Based on our results, it appears that generalizationin the context of the procedural/declarative model may be an important mechanism in explaining the relation between language contact and simplification. In
the future, similar data-based agent-based models could be applied to case studies
from other languages in the world, to identify which mechanisms are universal
and which are dependent on language-specific context.

proportion utterances non-empty

proportion utterances non-empty

0.8
0.6
0.4
0.2
0.0

statistic
prop_communicated_prefix_l1
prop_communicated_suffix_l1
prop_communicated_prefix_l2
prop_communicated_suffix_l2
0.0

0.2

0.4
proportion_l2

0.6(a) Basic model.

0.8

statistic
prop_communicated_prefix_l1
prop_communicated_suffix_l1
prop_communicated_prefix_l2
prop_communicated_suffix_l2

0.8
0.6
0.4
0.2
0.0

0.0

0.2

0.4
proportion_l2

0.6(b) Basic + phonotactic.

0.8

1.0
proportion utterances non-empty

1.0

1.0

statistic
prop_communicated_prefix_l1
prop_communicated_suffix_l1
prop_communicated_prefix_l2
prop_communicated_suffix_l2

0.8
0.6
0.4
0.2
0.0

0.0

0.2

0.4
proportion_l2

0.6

0.8

(c) Basic + generalization L1

Figure 1.: Morphological complexity (measured by the proportion of utterances for which the affix is
not empty) for different proportions of L2 users. Morphological complexity for prefix and suffix forL1 (blue/orange) and L2 (green/red).

Acknowledgements
This work was supported by funding from the Flemish Government under theOnderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen programme. PD
was supported by a PhD Fellowship fundamental research (11A2821N) of the
Research Foundation – Flanders (FWO).

159

References
Klamer, M. (2011). A short grammar of Alorese (Austronesian). Muenchen:
LINCOM Europa. (OCLC: ocn760922643)
Klamer, M. (2012). Papuan-Austronesian language contact: Alorese from an
areal perspective. Language Documentation & Conservation, Melanesian
Languages on the Edge of Asia: Challenges for the 21st Century. Special
Publication No. 5, 72–108.
Klamer, M. (2020). From Lamaholot to Alorese: Morphological loss in adultlanguage contact. In D. Gil & A. Schapper (Eds.), Austronesian Undressed:
How and why languages become isolating (pp. 339–367). Amsterdam: John
Benjamins Publishing Company.
Lupyan, G., & Dale, R. (2010). Language Structure Is Partly Determined bySocial Structure. PLoS ONE, 5(1), e8559.
Nishiyama, K., & Kelen, H. (2007). A grammar of Lamaholot, Eastern Indonesia.
Lincom Europa.
Reali, F., Chater, N., & Christiansen, M. H. (2018). Simpler grammar, larger vocabulary: How population size affects language. Proceedings of the Royal
Society B: Biological Sciences, 285(1871), 20172586.
Smith, A. D. (2014). Models of language evolution and change. Wiley Interdisciplinary Reviews: Cognitive Science, 5(3), 281–293. (Publisher: Wiley
Online Library)
Steels, L. (1998). Synthesising the origins of language and meaning using coevolution, self-organisation and level formation. In Approaches to the Evolution of Language (pp. 384–404). Cambridge University Press.
Ullman, M. T. (2001). The neural basis of lexicon and grammar in first and second
language: the declarative/procedural model. Bilingualism: Language andCognition, 4(2), 105–122.

160

CONVERGENT CULTURAL EVOLUTION OF CONTINUERS (mmhm)
MARK DINGEMANSE*1,2 , ANDREAS LIESENFELD1 , and MARIEKE WOENSDREGT2* Corresponding Author: mark.dingemanse@ru.nl

1 Center for Language Studies, Radboud University
2 Max Planck Institute for Psycholinguistics

Continuers —words like mm, mmhm, uhum and the like— are among the most frequent types of
responses in conversation. They play a key role in joint action coordination by showing positive
evidence of understanding and scaffolding narrative delivery. Here we investigate the hypothesis that their functional importance along with their conversational ecology places selective
pressures on their form and may lead to cross-linguistic similarities through convergent cultural
evolution. We compare continuer tokens in linguistically diverse conversational corpora andfind languages make available highly similar forms. We then approach the causal mechanismof convergent cultural evolution using exemplar modelling, simulating the process by which
a combination of effort minimization and functional specialization may push continuers to aparticular region of phonological possibility space. By combining comparative linguistics andcomputational modelling we shed new light on the question of how language structure is shapedby and for social interaction.

1. Introduction
Social interaction is characterised by people exchanging short bursts of articulatory activity organised in turns (Sacks, Schegloff, & Jefferson, 1974). Evidence
that linguistic resources are organized in response to this fact is the existence of aclass of responsive items variously called response tokens, backchannels, or continuers (Yngve, 1970; Jefferson, 1985). CONTINUERS are items that occur at theboundaries of turn-constructional units and that “demonstrate both that one unit
has been received and that another is now awaited” (Goodwin, 1986). Common
examples of continuers so defined are forms like mm, uh huh, yeah in English(Indo-European) or umm, mm, ing in Anal Naga (Tibeto-Burman) (Fig. 1).

Figure 1.: Conversation in Anal Naga (Tibeto-Burman, India), showing how continuers like mm

support another participant’s production of multiple longer turns and often occur in streaks of 3+
items, a fact we use to identify candidate tokens in a language-agnostic way (data from Ozerov, 2018).

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

161

Continuers have been studied in conversation analysis and psycholinguistics(Yngve, 1970; Jefferson, 1985; Ward, 2006; Bavelas, Coates, & Johnson, 2000),
but work on their cultural evolution is scarce. Yet if interaction is the primary
ecology of language in use (Schegloff, 2006) and if utterances are the main targetof selection in cultural evolution (Croft, 2000), then continuers, as stand-alone utterances directly exposed to the exigencies of conversation, provide a crucial locus
for studying how language is shaped by interaction. We propose that key properties of items like this can be explained by thinking of them as a distinctive stratumof vocabulary (Ward, 2006) adapted to the ecology of turn-by-turn interaction.
Continuers are highly frequent words whose conversational ecology is characterized by frequent overlap and a strong skew in the amount of talk contributed byeach participant (Fig. 1; cf. Rühlemann, 2018). This places special requirementson their shape (Gardner, 1997). Optimal continuers are (i) easy to plan and produce, (ii) unobtrusive, and (iii) sufficiently distinct from regular words to be seen
as ceding the conversational floor. While the first criterion may be ascribed tofrequency-driven effort minimization, the other two are likely to push continuers
into a particular part of the possibility space. In cultural evolutionary terms, theseare selective pressures that over diachronic time are likely to result in convergent
solutions across unrelated languages (Dingemanse, Torreira, & Enfield, 2013).
Here we test this proposal by combining two methods. Cross-linguistic comparison provides us with primary data on the interactional ecology of continuers
across languages, and can show whether indeed continuers, more than some otherwords, display tell-tale signs of being adapted to this ecology. Computationalmodelling provides us with ways to formalize and test the causal account, enabling us to see whether the proposed convergence of form can arise from cultural
evolution given selective pressures of effort minimization and conversational ecology. While we focus on continuers, the results have wider relevance for language
evolution in at least two ways. They point to interactional infrastructure as a key
causal locus affecting the cultural evolution of linguistic items (Enfield, 2014);
and they put the spotlight on metacommunication, one of the major advances inthe evolution of communication (Bateson, 1972).
2. Methods
Observational. We curate transcribed corpora of natural conversation in 67 spoken languages of 28 phyla around the world (Fig. 2). Crucially, we do not searchfor continuers by form, but define their conversational ecology as a sequential pattern, making it possible to identify candidate tokens in a language-agnostic, noncircular way. We look for streaks of non-unique conversational turns that occurin frequent alternation with unique turns by other participants. Using a minimum
streak length of 3, we identify the top 5 candidate continuer tokens per language.
We also collect two contrasting sets of linguistic items for comparison. First a set
we call discontinuers, defined as the top 5 highest frequency stand-alone turn for-

162

Figure 2.: The sample incorporates conversational corpora of 67 spoken languages from 28 distinct
phyla, with 32 languages from 12 phyla studied in more detail.

mats that do not occur in streaks like continuers do. The interactional work doneby these items is almost the converse of continuers: they do not cede the floor
but take it as they redirect, resume, or repair a conversation. This means they are
not subject to the same selective pressures. Second a set of top tokens, defined as
the top 5 highest frequency tokenised words in the language (this provides a baseline comparison for frequency-driven reduction effects). Since audio processing
is ongoing, most observations reported here are based on written annotations.
Computational. To formalise our causal account of how continuers are shapedby social interaction, we use exemplar models. Such models allow us to simulatebiases affecting the shape of culturally evolving items over repeated production
and perception cycles (Winter & Wedel, 2016), and have been used to study how
language adapts to interaction (Roberts & Levinson, 2017). We start by reproducing a model from Wedel (2012) that aims to capture the cultural evolution ofwords in an abstract phonological space under various pressures. To simulate thecase of continuers, we adjust the model in three ways. First, we add an overarching category distinction between a set of words that together form the ‘regularvocabulary’, and a set (here consisting of a single word) that constitutes the ‘continuer’ category. This allows us to put continuers under different pressures from
regular vocabulary. Second, we increase the pressure for minimising production
effort for continuers relative to regular words. Third, we remove the pressure forreuse of phonetic features for continuers. For regular words, this pressure pushes
the system to make use of a limited set of phonetic features, leading to a restricted
phoneme inventory, rather than a proliferation of somewhat distinct sounds. For
continuers, we remove this pressure in order to allow a distinct stratum to emerge.
Data and code for observational and computational methods are available throughOSF: https://osf.io/v6kfn.

163

3. Results
3.1. Observational
Our language-agnostic sequential search method allows us to identify candidate
continuers in all 67 languages in the sample. Figure 3 shows candidate continuers
in their natural conversational ecology in 10 unrelated languages. Already theseexamples suggest the close intertwining of conversational habitat and continuer
form. In line with their function of displaying recipiency in minimally obtrusive
ways, continuers are shorter than the turns they are interspersed with, often occur
in full or partial overlap, and appear to be phonologically quite minimal.

Figure 3.: Candidate continuers in 10 unrelated languages (A) shown in their natural ecology (B,annotations as in the original data), with spectrograms and pitch traces of representative tokens madeusing the Parselmouth interface to Praat (Jadoul et al., 2018; Boersma & Weenink, 2013) (C).

Out of the full sample there are 32 languages (of 12 phyla) with large enoughcorpora to yield sufficient examples of all three groups of items to be compared(we find a total of 118 continuers, 104 discontinuers, and 160 top tokens, whichworks out to per-language averages of 4, 3, and 5 respectively). The observations
below relate to this subset of data. Because the work is ongoing and we aim
for more direct comparisons of acoustic and phonetic features, we do not run
inferential statistics on the preliminary observations presented here.
Top tokens are an order of magnitude more frequent than either continuers or
discontinuers. They are short (2.28 characters) and relatively phonemically simple (1.8 distinct characters per token). This provides us with baseline expectations

164

Figure 4.: A. Length of tokens for continuers, discontinuers and top tokens. Dotted lines markgroup averages; items marked △ are vowelless. B. Frequencies of major sound classes across types.
Vowel nuclei occur across types, but continuers stand out for their preference for nasals. C. Random
forest analysis for 118 continuer forms in 32 spoken languages showing the top 10 most predictive
phonemes (out of 29 attested). Vertical black line: absolute value of the least predictive phoneme.

for the other groups. Discontinuers are twice as long (4.1 characters) and more
phonemically diverse (3.0 distinct characters per token), in line with expectations
if length is affected by frequency and phonology is arbitrary. Continuers appear tobe phonemically shorter than discontinuers yet longer than top tokens (2.8 characters), and as phonemically simple as top tokens (1.8 distinct characters per token).
Continuers appear to stand out in several ways. On average, one continuer
token per language (32/118) is transcribed as a vowelless token like hm, mhm,
mm (Fig. 3 and Fig. 4A), against only 1/104 discontinuers and only 3/160 top
tokens. Further, 47% of continuer tokens (56/118) feature a nasal, against 32% of
discontinuers (33/104)) and 24% of top tokens (39/160). Both trends are seen in atally of phoneme classes across word types (Fig. 4B). A random forest analysis of
all continuer forms together shows that ‘m’ is highly predictive of continuer statusacross languages (Fig. 4C). Further, 25% of continuer tokens (29/118) feature fullor partial reduplication as in mhm, hm hm, uhum, un un (see also Fig. 3A), against
only 10% of discontinuers (11/106 tokens) and 1% of top tokens (1/160).
While our main focus here has been on the 32 languages for which a comparison of the three sets is possible, the prevalence of nasals, vowelless tokens andreduplication in continuers is also seen in the remaining 35 languages, and also if
we only look at 1 language per phylum (as reported in the online materials).

165

3.2. Computational
Figure 5 shows that whereas regular words (blue, yellow, green and orange) end
up in arbitrarily different positions in the possibility space across different runsof the simulation, continuers (purple triangles) consistently end up in the same
region: the centre of the graph which is the region of least effort. We also seea mild effect of this on regular words, which across independent simulation runs
tend to avoid the very centre of the space as a result of the anti-ambiguity bias.
A) End state of selection of 6 separate runs

100

100
75

B) End state across 20 simulation runs, overlaid

80

Dimension 2

Dimension 2

50
25
0
100

60
40

75
20

50
25
0

0
0 25 50 75 100 0 25 50 75 100 0 25 50 75 100

Dimension 1

0

20

40

60

Dimension 1

80

100

Figure 5.: Plots of where in a phonetic possibility space different words end up after 10,000 rounds

of interaction, across 20 independent simulation runs (each cloud of 100 exemplar dots/triangles represents a single word at round 10,000 of a single simulation run). Blue, yellow, green and orange areregular words; purple is the continuer word. On each independent simulation run, all words are initialised at randomly selected positions in the space. A shows a selection of 6 separate simulation runs
chosen for illustrative purposes (showing how regular words end up in different positions); B showsthe end-state of all 20 simulation runs overlaid. Parameter settings: (i) minimal effort bias 3 times asstrong for continuer word (G=1250) than for regular vocabulary words (G=5000), and (ii) the bias forreuse of features (i.e. segment-similarity bias) is not applied to the continuer category.

The simulation results show that while most words in most languages can differ arbitrarily, a distinct and cross-linguistically similar stratum of vocabulary canemerge under the cumulative effect of relatively small biases over many communicative events. They also show that commonality does not exclude diversity:
while continuers are probabilistically pulled towards a similar part of the space,individual languages can still be organized in language-specific ways.
4. Conclusions
We have presented a first investigation into the cultural evolution of continuers.
Cross-linguistic evidence suggests that spoken languages make available highly
similar forms for this function. While frequency-driven reduction may partly account for the minimal shape of continuers, this is not the full story: more frequentwords are still shorter on average, yet they are more phonemically diverse and lessskewed in terms of word forms. Horizontal and vertical diffusion may be respon-

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sible for some similarities, but are unlikely to explain the prevalence of similar
forms in over 20 independent language families. The crosslinguistic evidence isin line with the proposal that conversational infrastructure can drive convergent
cultural evolution. Computational modelling supports this account by showing
how a combination of effort minimization and a relaxation of the pressure for thereuse of phonetic features can push continuer-like words to the same low-effort
yet distinctive part of the possibility space across languages.
The work reported here is ongoing and we are aware of a number of limitations. While written annotations are already telling, they reduce fluid signals todiscrete categories, so the next step is to derive more fine-grained measures ofsimilarity and dissimilarity from richer phonemic representations and audio signals (Fig. 3C). Further, continuers are often realized multimodally with blinks andnods; however, the subset of conversational corpora for which video is available isstill limited. Finally, we have focused on spoken language, not because sign languages do not have continuers (Mesch, 2016), but because the few sign language
corpora available do not systematically annotate items like this.
We have combined linguistic evidence with computational modelling to formalise and test a proposed process of conversational ecology driving convergent
cultural evolution. The observational evidence suggests that continuers are pushed
to a particular part of the possibility space of linguistic forms in similar ways
across unrelated languages. The computational modelling shows that selective
pressures enacted over cumulative cultural evolution can produce such a pattern.
While modelling does not deliver definitive answers about the history of natural
languages, it does shed light on the probability of explanations, and has the added
virtue of requiring clarity in formulating assumptions and predictions. The findings have implications for models of cultural evolution and for our understandingof the fundamentals of human interactional infrastructure.
Acknowledgements
We thank Andy Wedel for providing insight into his original models and Jacqueline van Arkel for help in implementing the computational model. Funding comesfrom Dutch Research Council (NWO) Vidi grant 016.vidi.185.205 to MD.
References
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Bavelas, J. B., Coates, L., & Johnson, T. (2000). Listeners as co-narrators. Journalof Personality and Social Psychology, 79(6), 941–952.
Boersma, P., & Weenink, D. (2013). Praat: Doing phonetics by computer.
Croft, W. (2000). Explaining Language Change: An Evolutionary Approach.
Harlow: Pearson Education Limited.

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Dingemanse, M., Torreira, F., & Enfield, N. J. (2013). Is ”Huh?” a universal
word? Conversational infrastructure and the convergent evolution of linguistic items. PLOS ONE, 8(11), e78273.
Enfield, N. J. (2014). Natural Causes of Language: Frames, biases and culturaltransmission. Berlin: Language Science Press.
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6(3), 283–316.
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python interface to praat. Journal of Phonetics, 71, 1–15.
Jefferson, G. (1985). Notes on a systematic Deployment of the Acknowledgement
tokens ’Yeah’ and ’Mmhm’. Papers in Linguistics, 17(2), 197–216.
Mesch, J. (2016). Manual backchannel responses in signers’ conversations in
Swedish Sign Language. Language & Communication, 50, 22–41.
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Languages Archive.
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for the Organization of Turn-Taking for Conversation. Language, 50(4),696–735.
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Regional Meeting, Chicago Linguistic Society (pp. 567–578).

168

AMPLIFYING THE BIAS TO ORDER NOUNS BEFOREADJECTIVES: STUDIES OF SILENT GESTURE AND
EMERGING SIGN LANGUAGE
Monica L. Do*1, Simon Kirby2, Susan Goldin-Meadow3, Natasha Abner4, Molly
Flaherty5, Ann Senghas6*

Corresponding Author: monicado@uchicago.edu
Linguistics, University of Chicago, Chicago, IL, USA
2
Centre for Language Evolution, University of Edinburgh, Edinburgh, UK
3
Psychology, University of Chicago, Chicago, IL, USA
4
Linguistics, University of Michigan, Ann Arbor, MI, USA
5
Psychology, Davidson College, Davidson, NC, USA
6
Psychology, Barnard College, New York, NY, USA
1

Cross-linguistically, roughly 64% of the world’s languages order Nouns
before Adjectives, compared to the roughly 27% with the reverse order (Dryer,2013). Yet, the factors driving this cross-linguistic tendency towards the NounAdj(i.e., post-nominal) order remains largely unknown. To investigate thisasymmetry, we analyze production corpora elicited from three cohorts ofNicaraguan Sign Language (NSL1, NSL2, NSL3; Exp1a) and Silent Gesturers(Exp1b). Exp2 then examined the contribution of learning biases such as the bias
towards regularization (Hudson Kam & Newport, 2005, 2009; Singleton &Newport, 2004) to the prominence of the NounAdj order cross-linguistically.
In Exp1a (NSL 1-3), we extracted 223 manual utterances containing a Nounand at least one Adjective and coded for order (NounAdj or AdjNoun) within each
Noun Phrase;utteranceswhere orderwasambiguous(e.g.,
AdjNounAdj/NounAdjNoun) were excluded. Binomial tests showed a reliable
preference for the NounAdj order for each NSL cohort (Table 1). Linear mixed
models using forward difference contrasts revealed no difference between cohorts(p’s > .3), suggesting that the bias towards NounAdj did not strengthen overconsecutive generations. 1 In Exp1b, we analyzed 276 Noun Phrases from a
production corpus of English-speakers asked to gesture without talking (Silent
Gesturers). Although NounAdj preference was weaker for Gesturers compared to
NSL cohorts (p < .01), we nonetheless found a preference for NounAdj among
Gesturers (Table 1). Thus, manual productions from signers of an emerging
1

In Exp1, we also collected utterances from Nicaraguan and Guatemalan Homesigners. However,data from these groups were too sparse to submit for analysis.
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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language and from native speakers of an AdjNoun language both point to a“natural” (Goldin-Meadow et al., 2008) way of ordering Nouns versus Adjs.
Table 1: Proportion of NounAdj versus AdjNoun utterances for each participant group. Raw counts
are given in parentheses.
Language Group

NounAdj

AdjNounSDp

95% CINSL1 (n=8).84 (62).16 (12).37< .001(.73, .91)

NSL2 (n=6).89 (62).11 (8).32< .001(.79, .94)

NSL3 (n=8).87 (69).13 (10).33< .001(.78, .94)

Silent Gesturers(n=20).61 (168).39 (108).49< .001(.55, .67)

In addition to the cognitive bias towards NounAdj, we also wanted to see
how other biases in language – such as the well-known bias toward regularizing
variation in an input language (e.g., Hudson Kam & Newport, 2005, 2009;
Singleton & Newport, 2004) – might simultaneously contribute to the prevalenceof the NounAdj order cross-linguistically. Exp2, therefore, recruited 160 Englishspeakers to a web-based silent gesture regularization study. Comprehenders saw
an event (e.g., someone waving a spotted spoon) and then were trained on two
gesture vignettes describing that event. Vignettes differed only in the order of theNoun versus Adj gestures. Critically, the frequency that participants saw
NounAdj versus AdjNoun vignettes varied by condition. In Majority NounAdj
Conditions, they saw NounAdj and AdjNoun orders in 75% versus 25% of trials,
respectively. Frequencies were reversed in the Majority AdjNoun Conditions.
We analyzed Entropy Change scores (Ferdinand et al., 2019; Shannon, 1948)
and proportion of Majority Order selections using mixed models. In line withpredictions, Entropy Change scores showed evidence of regularization in
Majority NounAdj (p’s < .01) but not Majority AdjNoun conditions. Participantsalso selected Majority Order vignettes more frequently when vignettes were in
the NounAdj configuration (p < .01). Thus, participants were more willing toregularize towards the NounAdj order than to the AdjNoun order.
Our results point to two factors driving the cross-linguistic prominence of
NounAdj word orders. The first is a cognitive bias for NounAdj orders stemmingfrom a “natural” way of representing objects and their attributes. The second
factor is a regularization bias amplifying those underlying preferences. Ongoingwork investigates the NounAdj order in silent gesture communication and in
iterated learning paradigms.

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Acknowledgements
This work was funded by the NIDCD of the NIH, R01 DC005407 awarded to A.
Senghas and NSF Grant #1654154 awarded to S. Goldin-Meadow.
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Haspelmath (Eds.), The World Atlas of Language Structures Online. Max
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Ferdinand, V., Kirby, S., & Smith, K. (2019). The cognitive roots ofregularizationinlanguage.
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https://doi.org/10.1016/j.cogpsych.2004.05.001

171

THE ROLE OF STRUCTURAL PRIMING, SEMANTICS AND
POPULATION STRUCTURE IN WORD ORDERCONVENTIONALIZATION: A COMPUTATIONAL MODEL
Loı̈s Dona*1 and Marieke Schouwstra2* Corresponding Author: l.m.dona@students.uu.nl

1 Information and computing sciences, Utrecht University, Utrecht, The Netherlands
2 Humanities, University of Amsterdam, Amsterdam, The Netherlands

1. Population structure and word order regularization
There is an ongoing debate about the influence of population structure on theemergence of linguistic rules. Some stress the importance of population size, or
factors often confounded with it (Raviv, Meyer, & Lev-Ari, 2019; Thompson, Raviv, & Kirby, 2020; Lupyan & Dale, 2010; Wray & Grace, 2007; Lou-Magnuson& Onnis, 2018), while others argue for the importance of network connectivity (thedegree of diversity in agent encounters) (Segovia-Martı́n, Walker, Fay, & Tamariz,
2020; Richie, Yang, & Coppola, 2014). In a series of simulations, we investigate
the influence of population structure on the conventionalization of basic word order. Of the languages in the world, many use a dominant order of subject, object
and verb for conveying who did what to whom (Dryer, 2013). Previous research
has suggested that in the early stages of language emergence, people use word
orders variably, depending on event semantics: SOV order for extensional events(in which the direct object is specific and concrete; e.g. pirate throws ball), andSVO for intensional events (in which the direct object is more abstract, and possibly dependent on the verb; e.g. pirate thinks of ball) (Schouwstra & Swart, 2014).
Structural priming is a potential mechanism driving increased regularity in word
order (Christensen, Fusaroli, & Tylén, 2016; Schouwstra, Smith, & Kirby, 2020).
A key question is how properties of the population influence regularization.
2. Local regularization: interaction in pairs of agents
Our first model investigates the relative contributions of semantics and structural priming over time. We compare three different simulations of dyadic interaction, one where the influence of structural priming increases over time andthat of semantics decreases (increasing-priming), one where it is the other way
around (decreasing-priming), and one where their influences are equal and constant (constant-priming). The increasing and decreasing influence of priming areThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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modelled by an exponential function with time as the exponent. The influenceof semantics is conversely modelled as 1 − priming. The influence shifts wereaccomplished by treating the probability of choosing SOV or SVO word order asa linear combination of previously observed and produced word orders that were
SOV or SVO (structural priming), modelled as 1 and 0 respectively, and event
semantics. Regularization was measured by calculating the change in mutual information over time. A comparison of the simulations shows that only when theinfluence of priming increases relative to the influence of event semantics, word
order becomes more regular. By contrast, when structural priming is constant overtime, this does not lead to increased regularity.
3. Simulating population properties
To assess the effects of population properties on word order regularization, we
simulated populations with different sizes and densities, copying the agent properties of the increasing priming simulation above. We measured network connectivity of these populations using the distribution of the number of observations
made by every agent from every other agent after every thirty two interactions. We
used the slope parameter of a curve fitted to mutual information data over time asa measure for regularization speed.
The results show a positive correlation between population density andconnectivity, and a negative correlation between population size and connectivity (using Spearman’s rank correlation Myers & Sirois, 2004). Bothsize and density correlate significantlywith regularization speed (see figure1), but density correlates significantly Figure 1. Population size and density plottedagainst regularization speed (k).
stronger (Fisher Z-transform.; z =−16.74, p < 0.001).
The relatively modest effect of population size on regularization speed indicates that population size alone is not an explanatory factor for the differences inconvergence rates and linguistic simplicity between communities, but often correlates with these effects due to it being confounded with network connectivity.
These findings contribute to the unraveling of the forces at play during word
order conventionalization on the cognitive and population level. Moreover, thecomputational structure of the model in this paper allows for easy incorporationof other potential influences (e.g. effects of language contact). Recent work has
shown that viewing word order as continuous rather than discrete might be more
realistic (Levshina et al., 2021), and that word order regularization is also reflectedin learning (Motamedi, Wolters, Naegeli, Schouwstra, & Kirby, 2021). The current framework makes it possible to explore these new insights further.

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References
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shaping constituent order in emerging communication systems: Structural
iconicity, interactive alignment and conventionalization. Cognition, 146,
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et al.. (2021). Why we need a gradient approach to word order.
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Lupyan, G., & Dale, R. (2010). Language structure is partly determined by social
structure. PloS one, 5(1), e8559.
Motamedi, Y., Wolters, L., Naegeli, D., Schouwstra, M., & Kirby, S. (2021).
Regularisation, systematicity and naturalness in a silent gesture learning
task. In Proceedings of the annual meeting of the cognitive science society(Vol. 43).
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between. Encyclopedia of statistical sciences, 12.
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in homesign systems. Topics in cognitive science, 6(1), 183–195.
Schouwstra, M., Smith, K., & Kirby, S. (2020). The emergence of word orderconventions: improvisation, interaction and transmission.
Schouwstra, M., & Swart, H. de. (2014). The semantic origins of word order.
Cognition, 131(3), 431–436.
Segovia-Martı́n, J., Walker, B., Fay, N., & Tamariz, M. (2020). Network connectivity dynamics, cognitive biases, and the evolution of cultural diversity inround-robin interactive micro-societies. Cognitive science, 44(7), e12852.
Thompson, B., Raviv, L., & Kirby, S. (2020). Complexity can be maintainedin small populations: a model of lexical variability in emerging sign languages.
Wray, A., & Grace, G. W. (2007). The consequences of talking to strangers: Evolutionary corollaries of socio-cultural influences on linguistic form. Lingua,117(3), 543–578.

174

SYLLABLE SEQUENCE PERCEPTION IN SONGBIRDS AND
PARROTSROBERT J. DOOLING*1, HSIAO-WEI TU1, ADAM R. FISHBEIN1, KATHERINE A.
STENNETTE1.

*Corresponding Author: Rdooling@umd.edu
1Psychology Department, University of Maryland, College Park MD USA

The capacity to use recursive syntax is considered a fundamental difference
between human and animal vocal communication (Hauser and Fitch, 2003). Even
more fundamental, however, is the capacity to produce and perceive acoustic
sequences. The sequentially complex vocalizations of songbirds have long served
as a potential bridge between human language and animal vocalizations withmuch of the historical evidence coming from field studies (e.g., Kroodsma andMiller, 1996). Laboratory perceptual studies, on the other hand, have provided
less compelling evidence that songbirds have the perceptual machinery to processcomplex sound sequences (Fishbein, et al, 2019; Geberzahn and Deregnaucourt,
2020; Lawson, et al, 2018; Seki, et al, 2013). By contrast, laboratory studies on
one psittacine, the budgerigar (Melopsittacus undulatus), has revealed that thisspecies can both produce a sequentially complex vocal repertoire consisting ofmultiple syllable categories and perceive changes to syllable sequence in its song
(Fishbein, et al, 2019; Tu and Dooling, 2012; Tu, et al, 2011).
A major difficulty in studying sequence production and perception in birds isthat cross species comparisons, in which each species is tested under identicalconditions, are rarely done. Here we first showed that the complex sequential
structure of the budgerigar warble song can be modeled as a 5th order Markov
sequence. Then we successfully trained budgerigars and various songbirds,
including canaries (Serinus canaria), zebra finches (Taeniopygia guttata),
Bengalese finches (Lonchura striata domestica), and Cordon Bleus (Uraeginthus
bengalus) by operant conditioning to discriminate changes in the sequential orderThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

175

of various natural vocalizations as well as neutral sounds including tones, words,
and syllables within a word.
Results show that budgerigars, a psittacine, can discriminate changes in thesequence of syllables in their warble and in other sound sequences. This ability
matches well with the sequential complexity of their warble. Several species of
songbirds tested on these sequences of sounds, and their own species-specificvocalizations, cannot discriminate these perturbations in syllable or soundsequences. Even reducing the task to its simplest level, discriminating a change
in the order of two syllables, budgerigars well outperform songbirds even whenboth budgerigars and songbirds are tested on songbird species-specificvocalizations.
While there is a long history of efforts aimed at understanding the differencesin acoustic communication between humans and birds, the focus of much of thiscomparative work has been on the production side (e.g., Kroodsma and Miller,
1996). Our results suggest that the limits to acoustic communication using soundsequences in birds could lie more on perceptual side. Indeed, the differences
observed here between songbirds and parrots may offer hints as to how theperception of sequences evolved in the primate lineage to support the emergenceof human language. It might be fruitful to focus on anatomical differences
between songbird and parrot brains to identify the neural candidates that mightunderly these significant differences in the perception of complex acoustic
sequences.
Acknowledgements
This work has been supported by both National Institutes of Health and the
National Science Foundation.
References
Hauser, M. D., & Fitch, W. T. (2003). What are the uniquely human componentsof the language faculty? In M. H. Christiansen and S. Kirby (Eds.), Language
evolution (pp. 158-181). Oxford: Oxford University Press.
Fishbein, A. R., Idsardi, W. J., Ball, G. F., Dooling, R. J. (2019) Sound sequencesin birdsong: how much do birds really care? Philosophical Transactions of
the Royal Society B. 2019.
Geberzahn, N. and Deregnaucourt, S. (2020). Individual vocal recognition inzebra finches relies on song syllable structure rather than song syllable order.
Journal of Experimental Biology, 223, 1-9.

176

Lawson, S. L., Fishbein, A. R., Prior, N. H., Ball, G. F., and Dooling, R. J. (2018)“Relative salience of syllable structure and syllable order in zebra finch song,”
Animal Cognition 21, 467-480.
Mol, C., Bolhuis, J.J., and Moorman, S. (2021). “Vocal learning in songbirds: therole of syllable order in song recognition”. Philosophical Transactions B.
Biological Science, 376.
Seki, Y., Suzuki, K., Osawa, A. M., and Okanoya, K. (2013) “Songbirds andhumans apply different strategies in a sound sequence discrimination task,”
Frontiers in Psychology 4, 1-9.
Tu, H.-W. and Dooling, R.J. (2012) Perception of warble song in budgerigars(Melopsittacus undulatus): evidence for special processing. Animal
Cognition, 15(6), 1151-1159.
Tu, H.-W., Smith, E.W., & Dooling, R.J. (2011) Acoustic and Perceptual
Categories of Vocal Elements in the Warble Song of Budgerigars(Melopsittacus undulates). Journal of Comparative Psychology, 125(4), 42030.

177

NEURONAL RECYCLING AS SPEECH ORIGINSAXEL G. EKSTRÖM*1*Corresponding Author: axeleks@kth.se
1KTH Speech, Music & Hearing, Stockholm, Sweden

The neuronal recycling hypothesis posits that cultural inventions invade evolutionarily
adapted neural circuitry and facilitate predictable neurological development in humans. I
present an account of phonological development as derived from that same set of proposed
principles. So applied, the hypothesis predicts (1) observable neural biases and abilities ofcommunicative learning continuous across primates, (2) patterns of cross–culturally
expectable phonological development in human infants, and (3) principled manifestationof phonologies across human cultures. I argue that all three are supported by empirical
work in primatology, neurolinguistics, developmental psychology, and phonetics. Speech
is invented anew in every speaking child, but representations of speech-centric behavior
are consistently allocated to the same neural architecture across individuals – structuresthat house related functionality in non–human primates. Development of speech
production and perception also follow predictable trajectories across cultures. Relevantscientific findings thus support the view that human infants are equipped with neuralarchitecture evolutionarily prepared for the development of vocal–communicative
behavior, which skew their ontogenetic development and constrain their ultimate
expression. Implications for speech ontogenesis are discussed. The present work adds tothe understanding of systems of speech as products of cultural evolution.

1. Introduction
All humans who have ever held a conversation have solved the same problem in
ontogeny. They have learned to speak. Yet, despite their importance, themechanisms by which speech is learned by the growing child remain poorly
understood, and in urgent need of hypothesizing. In this work, I address the
ontogenetic development of speech, and the evolutionary roots from whichlearned experience drives emergent speech behavior. I am not here concernedwith aspects of social cognition (e.g., intentionality), but the biologicalarchitecture that underlies spoken language in particular. A view of speech fromevolution must base its assumptions on principles of the natural world, without
resorting to abstract theorizing. I address the fundamental question of how

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178

abilities of speech may develop in the individual, without taking out significant
loans that theories of evolution cannot hope to cash. The present stated goal,
rather, is an account of speech ontogenesis that derives speech, and its neural, andperceptual features, from more basic elements of non–speech behavior.
The process by which phylogenetically adaptive neural structures are coopted
for new uses has been described by various researchers (see Andersson, 2010).
For one, Dehaene (2009) has argued that preexisting evolutionarily adaptivearchitectures in the human brain enact constraints upon the development ofwriting systems – cultural inventions too recent to have driven any significant
evolutionary selection pressure – such that they exhibit principally emergent
features across cultures. Accordingly, reading in the brain predictably occupies
the same neural regions across individuals (i.e., the visual word form area). Thisset of theoretical assumptions is referred to as the neuronal recycling hypothesis(hereafter NR). By analogy with reading, I posit that speech development issimilarly non-arbitrary but follows a predictable protocol. It is not claimed thatspeech as such is solely a learned behavior; rather, speech capacities likely
represent a relatively recent evolutionary heritage in Homo (see Ghazanfar et al.,2012; MacNeilage, 1998; Lieberman, 2017; Lieberman et al., 2019). However,speech is also invented anew by every developing child, and the processing andproduction of speech is localized to the same neural architecture acrossindividuals, languages, and cultures, but selectively sensitive to language-specificphonologies and linguistic cues. As cultural mutually agreed–upon conventions,human spoken languages possess multiple distinct common features, facilitating
formalization of linguistic codes and usage. The NR framework helps explain
several puzzling findings in speech-centric science, including observed
constraints on individual linguistic development in ontogeny, and the apparently
limited size of phonological repertoires inherent to particular languages. The text
thus also places human phonology in a cultural evolution framework, such thatsystems of speech, themselves products of culture, exhibit influence over human
linguistic development. The present account argues that the fallout of suchorganization in turn tunes and organizes the neural circuitry of the infant and child
exposed to it.
2. Neuronal recycling for speech
Applied to speech, NR can be postulated as a set of three hypotheses (Dehaene,
2009; Christiansen & Müller, 2015). In the follow sections, support for eachhypothesis is discussed at length. First, NR predicts (1) phylogenetic continuity
across related species. For speech, such a supposition touches on a rich and179growing research tradition on the linguistic capacities of non–human primates(hereafter primates). Further, NR anticipates (2) predictable linguistic
development in infants across cultures. By now, the trajectory of children’s
acquisition of speech capacities has been described in significant detail, allowing
for a composite illustration. Finally, NR posits (3) limited variability in
expression, resulting from the predispositions of adapted neurological structures
toward learning prelusive behavior. Systems of speech are indeed characterized
by a range of cross-cultural commonalities. Regardless of language, culture, andgeography, speech-centric behavior is allocated to the same neural architecture
but develops selective sensitivities to language-specific phonological elements.
2.1 Primate speechlike behavior
Articulate speech is a novel invention in the hominid lineage, its biomechanicalcomponents having evolved gradually. Because humans are the only (extant)species capable of generative speech, a proper cross–species continuity may
initially appear impossible, or even undesirable. However, recent research on thecommunicative abilities and vocal capacities of primates allows for careful
inference with relevance for the evolution of speech. Because language does not
fossilize, bioanthropological research has instead focused on identifying
biological precursors to speech. While the anatomy of the vocal tract itself ishighly conserved in mammalian species (Negus, 1949) and thus unlikely to havecontributed to the evolution of speech, much discussion has centered on uniquely
human adult anatomy likely to enable speech (e.g., Lieberman et al., 2001;
Lieberman, 2017). Of potentially greater relevance to understanding ontogenetic
emergence of phonology, however, are studies of vocalizations themselves.
While primate communicative repertoires often include either vocalization or
jaw–driven speech–like motor behavior, such as lip–smacking (e.g., Ghazanfar etal., 2012), the ability to couple the two is typically absent. Further, the acoustic
structure of primate calls appears largely fixed, such that call behaviors develop
reliably regardless of social experience (e.g., Cheney et al., 1992). A proper
discussion of anatomical and neural limitations precluding primates from humanor humanlike speech is beyond the scope of the present text and remains the
subject of some controversy (see e.g., Lieberman, 2017). As a behavioral
composite of voluntary mandibular and laryngeal control, speech is contingent on
cortical coupling (i.e., overlap) between jaw and larynx muscle primary motor
somatotopic representations (MacNeilage, 1998; Brown et al., 2021). In humans,
the laryngeal motor cortex – the primary cortical center for control of vocalization– is located in the ventral primary motor cortex; in primates, the same region is180

located in the ventral premotor cortex. Thus, the primary neural region of
vocalization control has undergone a dorsal shift with the evolution of the Homogenus, likely contributing to the evolution of speech (Simonyan & Horwitz,2011). Further, comparative neuroscience suggests that neural architecturecommonly associated with human speech have correlates in the brains of otherhominids, with species such as Chimpanzees (Pan troglodytes) possessing
neuroanatomical homologues to Broca’s area (Brodmann areas 44, 45), whichappears to activate during vocal signaling (see e.g., Taglialatela et al., 2011).
Finally, there is substantial continuity in vocal perception across the primate
lineage. Research on the call repertoires of primates suggests that modification of
produced calls remains relatively limited across the lifespan, with more flexibility
in perception than production (e.g., Cheney et al., 1992). Such findings are echoedby observations of human infants, who develop the ability to perceive anddifferentiate ingroup language-specific phonemes around the age of six months –long before they begin producing it reliably and willingly. Neurologically, speech
perception and language–specific perception in the Broca’s area also develop
during the first year of life (Imada et al., 2006), whereas the articulatory vocal
production organs take several months yet to bring under voluntary control.
2.2. A view from ontogeny
In human infants, mandibular oscillatory motor movements later coopted forsyllable production are exclusively utilized for voiceless sucking (i.e., feeding)behavior, and are not coupled with motor movement of the vocal tract.
Vocalizations observed during this stage include crying and are – unlike speechproper – innate, involuntary, and likely contingent on the same neural circuitrythat underlies innate call repertoires observed in other hominids (Jürgens, 2009).
Acoustically, infants’ crying aligns more closely with primate vocalizations, thanwith adult human speech (Lieberman et al., 2019). Indeed, stemming fromanatomical limitations, the infant vocal apparatus is likely incapable of producing
anything resembling the range of speech sounds available to the adult speaker.
Only around the age of six months does this begin to change, with the pre–verbal
infant’s invention of babbling, a form of emergent protospeech characterized byarticulate non–word speech sounds (i.e., ‘vocal play’; see Vihman, 2013). While
there has been significant debate over the relationship of babbling to speechproper, researchers have long since mapped its developmental trajectory.
Human infants typically begin to babble by no later than six months of age.
Around the same time, infants also begin to display an ability to discriminate
perceptually between different vowel and consonant sounds (see Oller, 2000). At

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this point in development, infants exhibit the emergent ability to voluntarily open
and close the vocal tracts. NR further predicts the observed development ofspeech–centric behavior in the growing infant (for an overview, see Vihman,2013). The innate mandibular–oscillatory motor behavior utilized for sucklingprovide a scaffold for the earliest occurring vowel-like “cooing” sounds, which
are predictably produced by human infants in the first few months (Oller, 2000).
While languages exhibit significant variability in vowel systems (Maddieson,1984), such early pseudo vowels roughly correspond to the adult /a/ and /u/, whichappear almost universally across languages. Similarly, phonemes composed frombilabial plosives such as /maː/ or /baː/ are more readily produced and acquired by
the individual infant early in development, around the age of 10 months. It is thus
not surprising that even unrelated languages have similar–sounding words for‘mother’ – tellingly, some variation of /maː/ or /məˈmɑː/. This stands in contrast
to, for example, phonemes composed from fricative alveolars such as /taː/ or /kaː/,which require the use of the tongue tip and tongue body, respectively; substantial
articulatory maneuverability and muscle dexterity need develop before theproduction of such sounds becomes possible. That is, a developmental thresholdmust be passed before relatively complex speech sounds can be achieved.
Neurologically, then, at this stage of development, the biomechanicalequipment necessary for organizing speech into consonant ‘frames’, andconsonant ‘content’ (MacNeilage, 1998) has likely begun to take place in theinfant; however, the process of adapting to the ambient soundscape and local
phonology (i.e., available phonemes inherent to the language spoken by present
ingroup members; Goldstein et al., 2003) is not yet completed. Rather, the infant
has reached a stage of orosensory exploration, where possible speech sounds –made available by combinations of laryngeal and mandibular motor activity – canbe mapped onto perceptual phonemes (Kuhl, 2000). The resulting composite
emphasizes their relationship in perceptual memory, enabling successful
encoding across time. Thus, while a set of phonemes may be ‘given’, infants areborn with a great range of possible developments, which are subsequently pruned
through cultural learning and ingroup interaction. Such plasticity is greater inchildren than adult humans, facilitating acquisition of speech-centric behavior.
Thus, in adult language learners, foreign speech sounds. For example, theJapanese language lacks the phoneme /l/ as observed in e.g., English, and as aresult, adult Japanese speakers readily experience difficulty perceiving andproducing it (see Yamada & Tohkura, 1992). Crucially, while being able toreliably discriminate /l/ from /r/ at six months of age, at eight months, Japanese
infants have apparently lost this ability (Tsuhima et al., 1994). That is, cultural

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learning via attention to, and interaction with, adult caregivers have formed thebasis for later-in-life language-specific phonological speech production(Goldstein et al., 2003). That is, much of the necessary recycling takes place inthe early years of development, after which much of the plasticity required forachieving articulatory fluency with regard to a given phonology is exhausted.
2.3. Principled manifestation
NR predicts constraints on cultural–environmental factors, such that culture
influences the development of evolutionarily adaptive brain circuits – but notunlimitedly so. All languages are indeed organized through similar constraints oncomposition (e.g., grammar; but for a nuanced discussion, see Christiansen &Chater, 2008) regulating their combinatorial capacities; and content (i.e., words
as units of transmission). Additionally, in another homologue to writing systems,
representations of speech–centric behavior and processing is also predictably
allocated to the same neural architectures (e.g., Brodmann areas 22, 44, and 45)
in all human brains. Yet, human infants are born without language, and need todevelop substantially before language acquisition becomes cognitively feasible.
As culturally agreed-upon systems of communication, languages exhibit
significant cross-cultural similarities. Whereas all (spoken) languages constitutephonologically combinatorial systems, attempts have been made to formalize
their structure according to phonological universals. The most successful of these
posits that spoken language is typically organized in consonant/vowel cycles(MacNeilage, 1998). Further, spoken languages contain a limited pool of speechsounds. While phonemic libraries vary significantly between languages(Maddieson, 1984), the organization and character of those libraries are naturally
constrained by the mechanical apparatus of speech. Namely, the humanarticulators – the tongue, upper and lower lips, alveolar ridge, hard palate, velum,
uvula, pharyngeal wall, and glottis – allow for a large though ultimately limited
set of articulate speech sounds. The resulting phonemic repertoire, averaging ~40
phonemes in a given language (Maddieson, 1984), however, is constrained in not
only the dimension of production – resulting from biological and anatomical
limitations of the vocal cords, vocal tract, and phonemic articulators – but alsothat of perception, operationalized as limitations on cognitive processing andmemory, with classic work on optimal maximization of vowel space by
Liljencrants and Lindblom (1972) indicating a crucial role of perceptual contrast
in the emergent organization of phonetic structure of language vowel systems.
Relatedly, Christianson and Chater (2008) have argued for a set of cognitive
constraints on language evolution, shaped by limitations of the human brain. The

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particular repertoire of any given spoken language should thus be seen as resulting
from a combination of economic-organizational and articulatory limiting factors.
Whereas the most prominent limitations on phonemic expression are biologicaland anatomical, principles of economy of memory impose further limitations on
the emergent phonemic repertoire recruited into systems of speech.
3. Conclusions
I have presented an account of speech ontogenesis, based on principles ofneurobiology, phonetics, and observations of phonological and linguistic
development. It is the view presented here that systems of speech, as learned
cultural practices enact significant pressure on phonological and neurologicaldevelopment. While the text is primarily concerned with evolutionary,developmental, and neurological aspects of phonological activity, it aligns with acultural evolution framework. Exposure to language-specific phonology prunes
the infant listener for continued interaction and future speech sound production
and perception. Speech systems – motor as well as orosensory – are subject toextensive such pruning throughout infancy and early childhood in preparation andaccommodation for native-language speech sounds. Language-specific speech
behavior can thus be seen as a product of recycling by cultural systems of speech.
References
Anderson, M. L. (2010). Neural reuse: A fundamental organizational principle ofthe brain. Behavioral and brain sciences, 33(4), 245-266.
Brown, S., Yuan, Y., & Belyk, M. (2021). Evolution of the speech‐ready brain:
The voice/jaw connection in the human motor cortex. Journal of Comparative
Neurology, 529(5), 1018–1028.
Cheney, D. L., Owren, M. J., Dieter, J. A., & Seyfarth, R. M. (1992). 'Food'calls
produced by adult female rhesus (Macaca mulatta) and Japanese (M. fuscata)
macaques, their normally–raised offspring, and offspring cross–fostered
between species. Behaviour, 120(3–4), 218–231.
Christiansen, M. H., & Chater, N. (2008). Language as shaped by thebrain. Behavioral and brain sciences, 31(5), 489-509.
Christiansen, M. H., & Müller, R. A. (2015). Cultural recycling of neuralsubstrates during language evolution and development. The cognitive
neurosciences V, 675–682.
Dehaene, S. (2009). Reading in the brain. New York.
Ghazanfar, A. A., Takahashi, D. Y., Mathur, N., & Fitch, W. T. (2012).
Cineradiography of monkey lip–smacking reveals putative precursors ofspeech dynamics. Current Biology, 22(13), 1176–1182.

184

Goldstein, M. H., King, A. P., & West, M. J. (2003). Social interaction shapes
babbling: Testing parallels between birdsong and speech. Proceedings of the
National Academy of Sciences, 100(13), 8030–8035.
Imada, T., Zhang, Y., Cheour, M., Taulu, S., Ahonen, A., & Kuhl, P. K. (2006).
Infant speech perception activates Broca's area: a developmental
magnetoencephalography study. Neuroreport, 17(10), 957–962.
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review. Journal of Voice, 23(1), 1-10.
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National Academy of Sciences, 97(22), 11850-11857.
Lieberman, D. E., McCarthy, R. C., Hiiemae, K. M., & Palmer, J. B. (2001).
Ontogeny of postnatal hyoid and larynx descent in humans. Archives of oral
biology, 46(2), 117–128.
Lieberman, P. (2017). Comment on “Monkey vocal tracts are speechready”. Science Advances, 3(7), e1700442.
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cry and nonhuman–primate vocalizations (pp. 41–53). De Gruyter Mouton.
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systems: The role of perceptual contrast. Language, 839-862.
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production. Behavioral and brain sciences, 21(4), 499–511.
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Press.
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Oller, D. K. (2000). The emergence of the speech capacity. Psychology Press.
Simonyan, K., & Horwitz, B. (2011). Laryngeal motor cortex and control ofspeech in humans. The Neuroscientist, 17(2), 197–208.
Taglialatela, J. P., Russell, J. L., Schaeffer, J. A., & Hopkins, W. D. (2011).
Chimpanzee vocal signaling points to a multimodal origin of humanlanguage. PloS one, 6(4), e18852.
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(1994). Discrimination of English /r-l/ and /w-y/ by Japanese infants at 6-12
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the perception of American English/r/and/l/by Japanese listeners. Perception& psychophysics, 52(4), 376-392.

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THE FORM AND FUNCTION OF CHIMPANZEE BUTTRESSDRUMMING
ELEUTERI VESTA1,3, HENDERSON MATTHEW1, SOLDATI ADRIAN1,2, BADIHI
GAL1, ZUBERBÜHLER KLAUS1,2,4, HOBAITER CATHERINE1,4*
Corresponding Author: vesta.eleuteri@univie.ac.at
School of Psychology and Neuroscience, University of St Andrews, St Andrews KY16
9JP, Scotland, United Kingdom
2
Department of Comparative Cognition, University of Neuchâtel, Neuchâtel,Switzerland
3
Department of Behavioral and Cognitive Biology, University of Vienna,
Vienna, Austria
4
Budongo Conservation Field Station, Masindi, Uganda

1

1.

Introduction

Human language is flexible. Speakers combine signal types (e.g., speech,
gestures, facial expressions) to adjust the meaning of their messages. Signal
combination is also present in non-human apes, but the functions of multi-signal
combinations are still unclear (see Genty et al., 2014; Hobaiter et al., 2017;
Wilke et al., 2017). Buttress drumming is a non-vocal acoustic signal often
combined with pant-hoots, the species-typical long-distance vocalisation(Arcadi et al., 1998; Babiszewska et al., 2015). Chimpanzees produce drummingbouts by hitting the buttress roots of trees, generating low-frequency sounds thatcan be heard at over one kilometre (Arcadi et al., 1998). To understand why
chimpanzees drum during pant-hoots, we investigated whether individual
differences exist in the acoustic structure and in the timing within the pant-hootof drumming bouts produced by adult male chimpanzees in different contexts
and whether individual, contextual, and social factors impact their use of
drumming.
2.

Methods

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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Data were collected June-July 2019 and January-March 2020 in the Waibira
chimpanzee community of the Budongo Forest Reserve, Uganda. During focal
follows we noted the behavioural context of the focal individual andcommunication events produced while they were on the ground including apant-hoot and/or drumming. We marked the identity of other individuals near
the focal, representing the “party composition”, and calculated the preferredsocial partners of each focal (following Babiszewska et al., 2015). Drumming
events by any adult male were filmed and recorded ad libitum.
A ‘Drumming bout’ was defined as a series of beats produced by hitting the
buttress roots of one tree with hands and/or feet (Arcadi & Wallauer, 2013). We
coded five measures of acoustic structure for analysis (number of beats; boutduration; mean inter-beat duration; shortest inter-beat interval; longest inter-beatinterval) and marked the start and end point of the drumming bout during thepant-hoot (Arcadi et al., 1998; Fedurek et al., 2016).
3. Results
We found individual differences in the acoustic structure of drumming bouts
produced during traveling (n=105; DFA1: p<0.001) and in their timing during
the pant-hoot. In contrast, we found no individual differences in the acoustic
structure of drumming bouts produced by the same individuals during displays(n=36; DFA2: p=0.589). Pant-hoot drumming combinations were mainlypresent during traveling while virtually absent during displays. Chimpanzees
drummed most frequently in the context of traveling and drummed lessfrequently as their party size increased. We found no effect of the social
composition of the party on the use of drumming.
4. Discussion
Encoding of individual identity and increased use while traveling and when insmaller parties suggest that drumming may be incorporated in pant-hoots during
traveling to facilitate chimpanzee fission-fusion spatial dynamics, specifically tojoin other individuals. In addition, absence of individual differences in display
drumming suggests that chimpanzees can choose when to encode signaller
identity in drumming “signatures”, altering the function of this signal acrosssocial and behavioural contexts. By exploring the use of pant-hoot drumming
this study helps elucidate the functions of multi-signal combinations in nonhuman apes, contributing to our understanding of the evolutionary origins of theflexibility central to human language.

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Acknowledgements
We thank the Uganda National Council for Science and Technology, thePresident’s Office, the Uganda Wildlife Authority, and the National Forestry
Authority for permission to conduct research, and the Royal Zoological Societyof Scotland for their support of the field site. This research received funding
from the European Union’s 8th Framework Programme, Horizon 2020, under
grant agreement no 802719.
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chimpanzees: Temporal patterning, phrase integration into loud calls,
and preliminary evidence for individual distinctiveness. Primates,
39(4), 505–518. https://doi.org/10.1007/BF02557572
Arcadi, A. C., & Wallauer, W. (2013). They wallop like they gallop:
Audiovisual analysis reveals the influence of gait on buttress drummingby wild chimpanzees (Pan troglodytes). International Journal ofPrimatology, 34, 194–215. https://doi.org/DOI 10.1007/s10764-0139656-1
Babiszewska, M., Schel, A. M., Wilke, C., & Slocombe, K. E. (2015). Social,
contextual, and individual factors affecting the occurrence and acoustic
structure of drumming bouts in wild chimpanzees (Pan troglodytes).
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189

LECTAL CONTACT AS A PATH TO LANGUAGE SPECIATION
T. MARK ELLISON*1, LUISA MICELI2, and JOHN MANSFIELD3*Corresponding Author: t.m.ellison@uni-koeln.de
1Universität zu Köln, Cologne, Germany
2University of Western Australia, Perth, Australia
3University of Melbourne, Melbourne, Australia

Language speciation has been attributed to a loss of contact between parts of aspeech community, or to contact with other languages (Thomason and Kaufman1988). In this talk, we offer a psycholinguistically-grounded account for
language speciation that does not presume a pre-existing fracturing of thelinguistic community, or external contact.
Speciation begins with lectal variation being associated with an areal, social orsituational identity. Three stages of differentiation follow. Form differentiation
is an accelerated (compared to drift) loss of shared vocabulary in the lects due tobilectalism. Structural stability is maintained by the same bilectalism, evenwhen lexical forms have differentiated beyond mutual intelligibility. If
bilectalism breaks down, or is restricted in context, there is then rapid
grammatical and semantic structural divergence.
This account explains why linguistic diversity in places with egalitarian
multilingualism is apparent in lexical forms rather than structural features.
1. Lectal Variation
Lectal variation arises when social groups undergo fission, with an earlier
shared lect dividing into distinct varieties. Early stages of social fission may
involve social or geographic separation (Trudgill 1986: 39); but distinct lects
may also arise while the social group is in close contact (Labov 1963; Nettle &Dunbar 1997; Stanford 2009; Roberts 2010; Mansfield et al 2021). A shared
Norman French variety on the isle of Jersey split when in the 1500s, forty
families moved to nearby Sark. This social fission and the new local identities
lead to lectal variation between Sark and Jersey dialects (Liddicoat 1994: 6).
2. Form Differentiation
Ellison & Miceli (2017) describe the anti-doppel bias (ADB) as a
psycholinguistic process in which bilinguals prefer vocabulary not shared
between their languages (i.e. non-doppels) where available. In contexts allowing
both words, Dutch/English bilinguals prefer the English-only picture to theThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

190

doppel photo in comparison to monolingual English speakers. The ADB seems
likely to also apply in bilectals generally, including those who use multiple
dialects. For example, Kapović (2005) notes that Croatian prescriptivists
recommend ponovo rather than the common ponovno as it is (incorrectly) felt toparticularly belong to the mutually-intelligible lect Serbian.
New lectal identities may develop slowly. Once, however, lects are associatedwith particular synonyms, the ADB can amplify differentiation, at least in the
speech of bilectals. So long as many are bilectal, lexicon differentiation will
increase. Mutual intelligibility will eventually break down.
3. Structural Stability
The next stage occurs while much of the population remain bilectal and theshared vocabulary has been substantially reduced (by Form Differentiation). The
lects are no longer mutually intelligible and individuals identifying with one
lect, learn the other as a foreign language. The cognitive load using two
lects,and monitoring for intrusive forms, facilitates cross-lectal structural
priming, maintaining and stabilising structures shared between the lects. At thisstage the lects essentially share one grammar but have distinct pools of lexical
forms.
4. Structural Divergence
Where the social conditions promoting bilectalism weaken, or are lostcompletely (e.g. trade networks or marriage patterns change), contact between
the lectal communities will be reduced or end. Without contact, there is no brakeon differentiation of linguistic structures.
Because the lects by this stage have substantially distinct vocabularies,
accidental similarities between lexical forms and semantics occur between
different sememes. Analogical extension of constructions thus takes different
paths in the two lects, leading to distinct morphological generalisations, and thus
grammatical constructions peculiar to the lects, which are now different species.
5. Discussion
The above account of language speciation offers a number of advantages. It
explains the shift from lectal differentiation to speciation in a known effect ofhuman language processing, the ADB. It sees the structural uniformity but
lexical form diversity seen in places like Vanuatu (François 2011) and Australia(Miceli 2019), as a natural consequence of egalitarian multilingualism neverbreaking down - the final stage of structural divergence is therefore neverreached. Although we do not explore it here, it is possible to incorporate contact
with distantly related or unrelated languages as part of this speciation model.

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References
Croft, W. 2000. Explaining language change: an evolutionary approach.
London: Longman.
Ellison, T. M., & Miceli, L. (2017). Language monitoring in bilinguals as amechanism for rapid lexical divergence. Language, 93(2), 255-287.
François, A. (2011). Social ecology and language history in the northern
Vanuatu linkage: a tale of divergence and convergence. Journal of Historical
Linguistics 1(2), 175-246.
Kapović, M. (2011). Language, ideology and politics in Croatia. Slavia
centralis, 4(2), 45-56.
Labov, W. (1963). The social motivation of a sound change. Word Journal of theInternational Linguistic Association 19, 273–309.
Liddicoat, A. (1994). A grammar of the Norman French of the Channel Islands.
Berlin: Mouton De Gruyter
Mansfield, J., Leslie-O’Neil, H. & Li, H. (2021). Grammatical Variation and
Dialect Divergence. New Ways of Analyzing Variation 49. Austin, TX:
University of Texas.
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Nettle, D., and Dunbar, R. I. M. Social Markers and the Evolution of Reciprocal
Exchange. Current Anthropology 38(1): 93-99.
Roberts, G. (2010). An Experimental Study of Social Selection and Frequency
of Interaction in Linguistic Diversity. In B. Galantucci and S. Garrod (Eds),
Experimental Semiotics: A New Approach for Studying the Emergence andthe Evolution of Human Communication (pp. 138–59). Amsterdam: John
Benjamins.
Stanford, J. N. (2009). Eating the Food of Our Place’: Sociolinguistic Loyalties
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Trudgill, P. (1986). Dialects in Contact. Oxford: Blackwell.

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VOCAL ICONICITY IN NOMINAL CLASSIFICATIONNIKLAS ERBEN JOHANSSON*1 and SANDRA CRONHAMN1*Corresponding Author: niklas.erben_johansson@ling.lu.se
1 Centre for Languages and Literature, Lund University, Lund, Sweden

While vocal iconicity in lexemes is increasingly well-studied (Blasi et al., 2016;
Erben Johansson et al., 2020; Joo, 2020), its presence in more grammaticalized
meaningful linguistic units are yet rather poorly understood. Aside fromphonesthemes, vocal iconicity has been investigated in diminutives andaugmentatives, but the results have been inconclusive (Körtvélyessy, 2011).
Across languages, nominal classification involves fundamental semantic
categories, such as sex (feminine/masculine), humanness (human/non-human),animacy (animate/inanimate), physical properties (size, shape) and functional
properties (container, tool) (Aikhenvald, 2000). Thus, many of the categories
found to be iconic in previous studies are also grammatically encoded in nominal
classification systems.
The present study investigates the presence of vocal iconicity in nominal
classification systems by using a genetically diverse dataset of languages, which
was distributed evenly across the two main types of nominal classification
systems (non-agreeing, such as more flexible classifier systems, and agreeing,such as more rigid gender or noun class systems). By collecting the nominal
classification devices (NCDs) in 210 non-agreeing languages (126 language
families) and 151 agreeing languages (123 language families), transcribing theNCDs using a coherent and comparable phonetic system, grouping themaccording to comparable semantic categories and analyzing them through
Bayesian generalized linear models, we were able to assess whether certain typesof sounds were overrepresented in certain class meanings.
The results revealed that the strongest overrepresentations of sounds were foundin NCDs that pertained to shape and size (low, front, unrounded vowels in FLAT,
high, back, rounded vowels in ROUND and high, front, unrounded vowels in
SMALL) which aligned with previous cross-linguistic findings. However, theiconic effects were restricted to non-agreeing systems, and the non-agreeing
systems included in the dataset contained more than three times as many nominal
classes and almost twice as many segments as the agreeing systems.
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These differences were attributed to more substantial phonetic erosion andsemantic bleaching of agreeing NCDs through grammaticalization as nominal
classification systems become increasingly formalized. This means that if a nonagreeing system changes into a more agreement-based system, it loses semantictransparency and gains formal predictability which dismantles one of the two key
components of vocal iconicity and causes sound-meaning mappings to break
down. While it is possible that these sound overrepresentations in non-agreeing
systems exist solely because of inherited iconic effects present in the lexemes that
the NCDs are derived from, iconicity has been shown to aid language acquisition(Imai & Kita, 2014; Massaro & Perlman, 2017; Nielsen & Dingemanse, 2021)
which suggests that vocal iconicity could also be functional grammaticalconstructions. This would mean that the grammatical section of human language
is affected by vocal iconicity and that the retrieval of noun semantics could beaccelerated if primed with iconically congruent NCDs.

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H. (2016). Sound–meaning association biases evidenced across thousands oflanguages. Proceedings of the National Academy of Sciences, 113(39),10818–10823. https://doi.org/10.1073/pnas.1605782113
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of sound symbolism: Defining macro-concepts via their semantic andphonetic
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194

TONE AND LOANWORD ADAPTATION IN IXPANTEPECMIXTEC: HORIZONTAL TRANSFER IN THE EVOLUTION OF
LINGUISTIC DIVERSITY
DEANDRÉ A. ESPREE-CONAWAY *1,2*

Corresponding Author: despreec@uoregon.edu
Department of Linguistic, University of Oregon, Eugene, Oregon,
United States of America
2
Bloomfield Language Institute, Houston, Texas, United States of America
1This poster describes phonological adaptation in Ixpantepec Mixtec [mks]—an OtoManguean language of southern Mexico. My main claim is that Ixpantepec Mixtec isincorporating Mexican Spanish loanwords into the language, adapting those loanwords
into its phonology. This is a complex adaptive strategy for maintaining the verticallytransmitted system while quickly and efficiently accepting new ‘packages of information’useful for survival (of both the language and its speakers). The process is akin to‘conjugation’ in horizontal gene transfer as it is a fitness- increasing strategy, found acrossthe animal kingdom, although this specific configuration, predicated on combinatorial andphonotactic phonology is (human) language-specific. Special attention will be given to theplacement of tones on loanwords. A corpus of over 200 loanwords is analyzed as part ofthis study.

References
Chung, Y., & Ritchart, A. (2014a). Intonation patterns of interrogative
constructions in Ixpantepec Nieves Tu’un Savi (Mixtec). Journal of the
Acoustical Society of America, 135(4), 2291-2291.
Chung, Y., & Ritchart, A. (2014b). Question prosody in Ixpantepec Nieves
Mixtec. (Oral presentation presented at SSILA 2014 (joint session with LSA),
January 2014.
Crawford, C. (2008). An evolutionary account of loanword–induced soundchange in Japanese. University of Pennsylvania Working Paper in Linguistics,
14(1), https://repository.upenn.edu/pwpl/vol14/iss1/5/ (6 December, 2021.)
Kager, R. (1999). Optimality theory. Cambridge: Cambridge University Press.
Kenstowicz, M., & Suchato, A. (2006). Issues in loanword adaptation: A casestudy from Thai. Lingua, 116, 921-949.

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195

Liang, B., & Du, Y. (2018). The functional neuroanatomy of lexical tone
perception: An activation likelihood estimation meta-analysis. Frontiers in
Neuroscience, 12(495), 1-17.
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evolution: Psycholinguistic predictors of loan word borrowing. Cognition,
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and Shinichiro Ishihara (Eds.). Oxford handbook of information structure (pp.
463-482). Oxford: Oxford University Press.
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Journal of Phonetics, 37, 125-144.

196

EXPLORING THE ROLE OF SPEECH IN THE TRANSMISSION
OF AIRBORNE VIRUSES VIA A NOVEL METHOD FOR
COMBINING AEROSOL AND PHONETIC ANALYSISCALEB EVERETT*1, MARVA SEIFERT2, CHANTAL DARQUENNE2, RYANTUMMINELLO3 and JONATHAN SLADE3*

Corresponding Author: caleb@miami.edu

1Department of Anthropology, University of Miami, Coral Gables, USA
2School of Medicine, University of California San Diego, La Jolla, USA
3Department of Chemistry and Biochemistry, University of California San Diego, La

Jolla, USA
Aerosol particle emissions from speech are thought to play a key role in airborne pathogentransmission, though relatively little is known about the mechanisms through which articulatory
gestures produce aerosol particles. We describe a novel method that allows for the more precise
isolation of the production of aerosol particles during speech. Some of the particles are smaller thanthose detected with previous approaches. Our approach allows for the isolation of aerosol bursts thatare associated with specific articulatory gestures. Combined aerosol and phonetic analysis illustrates
how this approach can shed light on the mechanisms through which aerosols are generated during
speech. We briefly discuss ongoing work that has substantially refined this method. In relationship tolanguage evolution, this work highlights the possibility that certain articulatory gestures could have“maladaptive” characteristics, in that they might generate inordinate numbers of aerosols. We stressthat more experimental research is required to address this possibility.

1. Introduction
The role of speech in the production of airborne particles and associated pathogentransmission has recently been highlighted as epidemiological evidence
implicates speaking in SARS-CoV-2 transmission. (Meselson 2020, vanDoremalen et al. 2020). Previous studies have utilized several methods to examine
how speech produces such particles, including laser-based detection, airflow
testing and aerosol analysis. (Stadnytskyi et al. 2020, Asadi et al. 2020) The latteris essential as growing consensus exists that particles smaller than 10 μm in
diameter, often referred to as aerosols, can transmit a variety of pathogens.
(Fennelly 2020) The SARS-CoV-2 virus appears to remain viable in aerosols,
which can remain airborne for hours, further increasing the odds of transmission.
(Fennelly 2020) Research with an aerodynamic particle sizer (APS) has allowed
scholars to show that some speakers are “super-emitters” of aerosols and that
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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some sound types correlate with higher emission rates. (Asadi et al. 2019, 2020)
However, APS instrumentation only samples once per second, limiting the extent
to which it can isolate the role of specific articulatory gestures in producing
aerosol particles. Other methods that have been used face related limitations. In
English, words typically last 150-500 ms, while the most common syllable types
last about 120-260 ms and individual sounds 60-150 ms. (Greenberg et al. 2003)
Given the durations of such units in English and other languages, a more complete
understanding of aerosol generation via speech could benefit from an approachwith higher temporal resolution in aerosol measurements.
Here we utilize a method for detecting aerosols, one not previously
applied to speech in the manner we illustrate, to address two lacunae in our current
understanding of particles generated during talking: i) the size distribution ofaerosols from 0.07 μm in aerodynamic diameter (i.e., at a size resolution of single
viruses) to 10 µm and ii) the mechanics of the production of such fine aerosol
particles on timescales relevant to individual sound types and utterances.
Addressing these lacunae is critical to elucidating the full range of emission
variations across speakers and sound categories and could potentially help refine
the modeling of speech-based pathogen transmission. We outline a new approachthat allows us to isolate with relative precision the moments at which aerosols
emerge from speakers’ mouths and the concentrations of aerosols speakers
produce at various size bands. Phonetic analysis in tandem with aerosol detection
reveals relationships between aerosol emissions and certain sound types notaccessible by previous approaches. In this work we detail initial results based on
three speakers, given pandemic-related restrictions to the number of participants
we could test. This general method is being applied to dozens of participants inthe coming months, though in current work participants breathe in particle-free
air, in contrast to the results described here. The results discussed here simply
serve as an illustration of the overall method, demonstrating how the heightened
temporal and physical resolution of the approach offers potential gains to ourunderstanding of the aerosol emissions associated with speech.

2. Results and discussion
For this study, three participants read various stimuli and breathed (nasally) at anatural rate into an aluminum funnel (opening diameter of 20 cm and length of 25cm), connected to an electronic particle impactor that could measure the size andvolume of aerosols produced ten times per second. Each individual’s nose andmouth were within the funnel opening. The funnel was attached to a Dekati
electrical low-pressure impactor (ELPI) via flexible conductive tubing with an
inner diameter of 1.2 cm and a length of 25 cm. The short residence time in the
tube and use of conductive tubing led to no distinguishable particle losses. Thismethod detected aerosols at 10 Hz across 14 size bands, between 0.006 μm and198

10 μm, though we focus only on those bands greater than 0.07 μm since they are
relevant to viral transmission. Experiments were conducted in the absence ofsynthetic particle-free air to mimic a real-world setting. Background aerosol
concentrations in the room air were measured before and after speaking or
breathing. The mean background was then subtracted from the data measured
while speaking or breathing. Although this method controlled for the backgroundroom aerosol by subtraction, in ongoing work we are using an approach withparticle-free air. (Participants breathe in particle-free air and their exhaled air ismixed with particle free-air as well, rather than room air.) The key difference
between the background-subtraction results is that the latter yield a much greaternumber of measured particles. Nevertheless, the results discussed here illustratesome of the gains of the new method, particularly the high physical and temporal
resolution of the approach. Further, some of the results discussed here are also
evident in our ongoing work employing particle-free air. For instance, across both
approaches whispered sounds produce a higher number of particles than sounds
produced with normal voicing at low amplitude.
In addition to the aerosol analysis, we also analyzed the utterances of the
speakers acoustically. An InnoGear Condenser Professional Cardioid
Microphone was placed about five cm to the right side of the funnel. (In our
ongoing work, this is also combined with airflow analysis.) Speakers were given
instructions and stimuli displayed on a desktop monitor placed immediately
behind the funnel, so that they could read without moving their heads. Recordings
were made onto a notebook computer at a sampling rate of 44.1 kHz. Relevant
wav files were analyzed with PRAAT. A few dozen words were recorded for all
speakers, in addition to the same paragraph. For this paper, speakers were asked
to read at a “normal” volume and cross-speaker amplitude was not found to vary
substantially. We did not have speakers read at intentionally varied volumes, asin previous work that uncovered an association between increased amplitude andincreased aerosol emissions. (Asadi et al. 2019) Across all three speakers weobserved greater aerosol production during speech than during normal breathing.
Likely, greater differences in the concentrations of aerosols emitted between
speaking and breathing occur when speaking at a higher amplitude or whensinging, and minor amplitude differences across speakers may contribute to someof the inter-speaker variability we observed. (Asadi et al. 2019)
With the high temporal resolution of the method, we were able to assess
aerosol production from specific sound types as highlighted in Fig. 1 for one
speaker. In Fig. 1A we observe that, after mask removal, there are aerosol bursts
immediately following each named letter of the recited alphabet. In this case thebursts of aerosols (diameter-weighted, D, μm cm-3) occur primarily across two
size bands of roughly 1-2 μm. Figure 1B illustrates how the temporal resolutionof the method allows us to match aerosol bursts with individual syllables and evenspecific sound types when words are produced several seconds apart. Previous
research suggests certain sound types, most notably the [i] high-front vowel, are199

associated with increases in aerosols, as is increased amplitude. (Asadi et al. 2020)
Here we focused on consonants, including two consonant types that havepreviously been suggested to be relevant to particle emission. Given the temporal
resolution of the method, we were able to directly observe aerosol bursts
following some consonants in isolated words, including word-final consonants
that have not previously been investigated. As illustrated in Figure 1B, at least insome words there was an increase in aerosols immediately following affricate
consonants such as the last sounds in “catch” and “h” and the first sound in “g”.
Figure 1C illustrates the variation in aerosol emissions across size bands during
the articulation of one sentence previously used in research on the airflow
produced during speech. (Abkarian et al. 2020) In this case there is an increase inaerosols shortly following the production of the [ph], an aspirated bilabial plosive.
This bilabial plosive has previously been shown to create an intense horizontal
stream of air. (Abkarian et al. 2020) Some scholars have speculated that aspirated
consonants like [ph] may be relevant to airborne pathogen transmission via
increased particle emission, but this is the first experimental evidence of any
increase in aerosols associated with such aspiration. (Inouye 2003) We stress that
it is very preliminary evidence, however, and must be replicated across many
more participants with the new approach involving particle-free air. Initial resultswith the new approach are broadly consistent with those in Figure 1, suggestingthat the association between aspiration and increased aerosols may be robust.
Figure 1D further demonstrates the extreme concentration of aerosols that can beproduced during some segments of speech for some speakers, in this case “shhh!”,a lengthened voiceless postalveolar fricative.

200

Figure 1. Temporal and physical resolution of method. Note that these values are much higher
than the values obtained when speakers breathe in particle-free air, as demonstrated in our
ongoing work. Panel A: Normalized heatmap of diameter-weighted aerosol particle
concentration. A1. The speaker repeated the word “spar” at a normal volume, while wearing an
N95 mask. A2. The speaker recited the alphabet at a normal volume, after removing his
facemask. A3. Portion for f, g, and h, each said about eight seconds apart. Panel B: Section A3
enlarged, alongside phonetic details. B1. Aerosol heatmap. B2. Transcription of the threeutterances with the International Phonetic Alphabet. B3. Waveform of [ejt͡ʃ]. Aperiodic soundwaves associated with the voiceless affricate are highlighted. B4. Spectrogram of [ejt͡ʃ].
Aperiodic elements in high frequency range are highlighted. C. Aerosol visualization for one
speaker’s articulation of “Sing a song of six pence”. D. Aerosols produced when the speaker was
asked to say “shhh” (a voiceless postalveolar fricative), as though telling someone to be quiet.
This was the most intense burst of aerosols for that speaker.

201

For more fluid segments of speech, the method still allows us to detect
surges in aerosols associated with sounds or sequences of sounds though theaerosols do not occur in readily segmented bursts in such cases. It was observedthat in fluid speech words with [st] sequences were weakly associated withincreased emissions, an association that requires further exploration with a greaternumber of speakers and the particle-free approach. Such sequences, like theaforementioned affricates in “catch” and “shh” are of course voiceless. Previous
work suggests that vocal cord vibration is also a key mechanism that dislodges
and emits aerosol particles during speech (Asadi et al. 2019, 2020) but the hightemporal resolution used here and in the laser-based detection in Stadnytskyi etal. (2020) suggests that at least some of the intense bursts of aerosols produced
during speech are not produced in the larynx during vocal cord vibration. (InStadnytskyi et al. (2020) it is observed that the voiceless interdental fricative in“healthy” emits many particles, though in a larger size range than that examined
here.) It is possible that some sound types emit aerosols generated deeper in the
respiratory tract, perhaps via the fluid-film burst mechanism in the terminal
bronchioles. (Graham & Morawska 2009, Almstrand et al. 2010)
In short, the high resolution of this approach could be used to explore thedetailed mechanisms through which aerosols are produced during speech,complementing other approaches. Ongoing refinement of this method could help
to shed light not just on the temporal and physical dynamics of particle emission,
but also on the mechanics through which aerosols are generated at the vocal cords
and at other locations in the vocal tract during speech. Consistent with a growing
literature using related methods that have more modest physical and temporal
resolution, our results suggest that speaking does yield a high total number andvolume of airborne particles that are potentially relevant to the transmission ofsome pathogens. Much work remains, however, to better understand how aerosols
are produced during speech, along with the role of particularly articulatory
gestures and associated sound types. In the context of language evolution, thepreliminary results discussed here raise an interesting question: Do somearticulatory gestures present a greater likelihood of intense aerosol bursts that canpotentially transmit pathogens during an airborne pandemic? These results
underscore this possibility, though we stress that much more experimental work
is required, with a greater number of participants, to more fully understandwhether some sound types have inaudible “maladaptive” features.

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References
Abkarian, M., S. Mendez, N. Xue, F. Yang, H. Stone. (2020). Speech can produce
jet-like transport relevant to asymptomatic spreading of virus. Proc. Natl.
Acad. Sci. U. S. A. 117, 25237-25245
Almstrand, A., B. Baker, E. Ljungstrom, P. Larsson, A. Bredberg, E.
Mirgorodskaya and A-C Olin. (2010). Effect of airway opening on
production of exhaled particles. J. Appl. Physiol. 108, 584–8.
Asadi, S., A. Wexler, C. Cappa, S. Barreda, N. Bouvier, W. Ristenpart. (2019).
Aerosol emission and superemission during human speech increase withvoice loudness. Sci. Rep. 9, 2348.
Asadi, S., A. Wexler, C.D. Cappa, S. Barreda, N. Bouvier, W. Ristenpart. (2020).
Effect of voicing and articulation manner on aerosol particle emission during
human speech. PloS One 15, e0227699.
Fennelly, K. (2020). Particle sizes of infectious aerosols: Implications for
infection control. Lancet Respir. Med. 8, 914-924.
Graham, R., L. Morawska. (2009). The mechanism of breath aerosol formation.
Journal of Aerosol Medicine and Pulmonary Drug Delivery 22, 229-237.
Greenberg, S., H. Carvey, L. Hitchcock, S. Chang. (2003).Temporal properties of
spontaneous speech – A syllable-centric perspective. Journal of Phonetics
31, 465-485 (2003).
Inouye, S. (2003). SARS transmission: Language and droplet production. The
Lancet 362, 170.
Järvinen, A., M. Aitomaa, A. Rostedt, J.Keskinen, J. Yli-Ojanperä. (2014).
Calibration of the new electrical low pressure impactor (ELPI+). J. Aerosol
Sci. 69, 150-159.
Meselson, M. (2020). Droplets and aerosols in the transmission of SARS-CoV-2.
The New England Journal of Medicine 382, 2063.
Stadnytskyi, V., C. Bax, A. Bax, P. Anfinrud. (2020). The airborne lifetime ofsmall speech droplets and their potential importance in SARS-CoV-2
transmission. Proc. Natl. Acad. Sci. U. S. A. 117, 11875-11877.
van Doremalen, N., D. Morris, M. Holbrook, A. Gamble, B. Williamson, A.
Tamin, J. Harcourt, N. Thornburg, S. Gerber, J. Lloyd-Smith, E. de Wit, V.
Munster. (2020). Aerosol and surface stability of SARS-CoV-2 as comparedwith SARS-CoV-1. The New England Journal of Medicine 382, 1564-1567.

203

SIGNAL EMBODIMENT ENHANCES GESTURE’SCOMMUNICATION SUCCESS
Nicolas Fay*1, Bradley Walker1, T. Mark Ellison2, Zachary Blundell1, Naomi De Kleine1,
Murray Garde3, Casey J. Lister1 and Susan Goldin-Meadow4*Corresponding Author: nicolas.fay@gmail.com
1School of Psychological Science, University of Western Australia, Perth, Australia
2Collaborative Research Centre for Linguistic Prominence, University of Cologne, Co-

logne, Germany
3School of Culture, History and Language, Australian National University, Canberra,

Australia
4Department of Psychology, University of Chicago, Chicago, USA

Introduction
The origin of language of one of the oldest question in science, but vocalfirst and gesture-first theories remain speculative due to a lack of direct evidence. Here we present empirical evidence to inform this debate.
Philosophers and early explorers recommended using ‘the universal language of the hands’ to communicate with indigenous people (Quintilian 95CE;
Cooperrider, 2019). Evidence for the universality of gesture was found in a recent paper that compared the communication success of gesture to (nonlinguistic) vocalization cross-culturally (Australian and Ni-Vanuatu producers)
and cross-experientially (sighted and severely vision-impaired Australian producers) (Fay et al., 2022). Like other referential communication studies (e.g.,Ćwiek et al., 2021; Fay et al., 2013), communication success was measured byhaving a group of interpreters guess the meaning of each of the gesture/vocal
signals produced (using a multiple choice format). Communication success was
twice as high for the gestured signals than for vocal signals across the two experiments reported (61.17% versus 29.04%), and gesture’s success was driven(in part) by its greater universality; the gestured signals produced by different
participants were more similar in form than their vocal signals (i.e., they weremore universal), and the degree of signal similarity was positively correlatedwith communication success.
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

204

Fay et al (2022) appealed to embodied theories of language and cognition to
explain the success and universality of the gestured signals. Embodied theories
highlight the importance of the body, and the body's interactions with the environment, to cognition (e.g., Clark, 1999; Hostetter & Alibali, 2008). Fay et al(2022) argued that whereas gesture lends itself to signal embodiment (e.g.,communicating the word ‘drink’ via manually simulating raising a container to
one’s mouth), the opportunity for signal embodiment is absent in the vocal modality.
Here, we re-analyzed the data collected by Fay et al (2022) to test an embodied account of the communication success of gestured signals. Producers
communicated a large number of words (997 distinct words), an equal numberof which were verbs, nouns and adjectives. In their analysis Fay et al (2022) did
not distinguish between the different word categories. In the gesture modality,
an embodied account predicts that verbs provide greater scope for signal embodiment than nouns, and nouns provide greater scope for embodiment that adjectives. If correct, communication success will be higher for verbs than nouns,
and communication success will be higher for nouns than adjectives. In the vocal modality there is little scope signal embodiment, so no specific prediction ismade with regard to the communication success of the different word categories.
This is tested in the current study.
MethodRe-analysis of the data collected by Fay et al (2022) and made openly available
on the Open Science Framework: https://osf.io/36jpy/
Results & Discussion
As predicted by an embodied account, in the gesture modality verbs were communicated more successfully than nouns and nouns were communicated more
successfully than adjectives: 78%, 74%, 60% for Australian Producers, 64%,
55%, 46% for Ni-Vanuatu Producers and 60%, 48% and 38% for Severely Vision-Impaired Producers. No such pattern was evident in vocal modality: 33%,
31%, 34% for Australian Producers, 16%, 14%, 13% for Ni-Vanuatu Producers
and 30%, 35% and 41% for Severely Vision-Impaired Producers. Our findings
indicate that signal embodiment is important to gesture’s communication success. Similarities in the way people use gesture to embody meaning may also
explain why gestured signals were found to be more universal than vocal signals.

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References
Clark, A. (1999). An embodied cognitive science? Trends in Cognitive Sciences,3(9), 345–351. https://doi.org/10.1016/S1364-6613(99)01361-3
Cooperrider, K. (2019). Universals and diversity in gesture: Research past, present, and future. Gesture, 18(2–3), 209–238.
https://doi.org/10.1075/gest.19011.cooĆwiek, A., Fuchs, S., Draxler, C., Asu, E. L., Dediu, D., Hiovain, K., Kawahara,S., Koutalidis, S., Krifka, M., Lippus, P., Lupyan, G., Oh, G. E., Paul, J.,
Petrone, C., Ridouane, R., Reiter, S., Schümchen, N., Szalontai, Á., ÜnalLogacev, Ö., … Perlman, M. (2021). Novel vocalizations are understoodacross cultures. Scientific Reports, 11(1), 10108.
https://doi.org/10.1038/s41598-021-89445-4
Fay, N., Arbib, M., & Garrod, S. (2013). How to Bootstrap a Human Communication System. Cognitive Science, 37(7), 1356–1367.
https://doi.org/10.1111/cogs.12048
Fay, N., Walker, B., Ellison, T. M., Blundell, Z., De Kleine, N., Garde, M., Lister, C. J., & Goldin-Meadow, S. (2022). Gesture is the Primary Modality for
Language Creation. Proceedings of the Royal Society B: Biological Sciences.
Hostetter, A. B., & Alibali, M. W. (2008). Visible embodiment: Gestures assimulated action. Psychonomic Bulletin & Review, 15(3), 495–514.

206

COMMUNICATION OR COGNITION? THE LOCUS OF
RECURSION IN THE EVOLUTION OF HUMAN LANGUAGE
Andrew Feeney1*
1Department of Humanities, Northumbria University, Newcastle upon Tyne, United

Kingdom*andrew.feeney@northumbria.ac.uk

It is now more than 25 years since the publication of the ‘Minimalist Program’(Chomsky: 1995) and this latest configuration of generative linguistics continues
to exert considerable influence within the discipline and beyond. However, amajor explanatory constraint against which such theories must now be evaluatedhas arisen in the form of the question of language evolution. This paper seeks to
identify and resolve tensions in the notion of ‘recursion’ in relation to generativegrammar and its interfaces with the conceptual-intentional (CI) and articulatoryperceptual (AP) or sensory-motor (SM) systems.
Writers working in different models of linguistics adhere to various accounts of
how language evolved. In particular there is a distinction between those who
propose a classic neo-Darwinian gradual narrative, and those, including
Chomsky, who claim that the core aspect of language had a more or lessinstantaneous emergence in early Homo sapiens. I agree that positing an
incremental evolution of such a mind internal capacity is deeply problematical.
However, I argue that a saltationist account of the emergence of language,
which seeks to identify a single key evolutionary step, is also flawed. The
essential depiction of the evolutionary basis of a Minimalist theory of language
appeared in Hauser, Chomsky and Fitch (2002) and was later clarified (Fitch,
Hauser and Chomsky, 2005; Chomsky, 2005). In this account the uniquely
human aspect of language was defined as the narrow recursive operation‘merge’ by which lexical items were combined in hierarchical derivations.
The use of ‘recursion’ is not always well-defined and has resulted inconsiderable debate in the literature (see e.g. Jackendoff & Pinker, 2005; andpapers in Lowenthal & Lefebvre, 2014). Nevertheless, two defining qualities
can be identified. Firstly, there is the condition that the output of an earlier stage
must constitute the input to a subsequent one, as in Fibonacci series. Secondly,
often highlighted in relation to language, is the capacity for one element of type
X to be embedded in an element of the same type. However, while hierarchicalrecursion certainly appears to be inherent in linguistic ‘structure’, it is also
clearly evident in other aspects of modern human cognition, most clearly in theThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

207

capacity by which I am able to have a thought about your belief about someone
else’s belief, and so on. While primates, like all higher animals, have a richconceptual system and a basic ability to reason based on observable cause andeffect relations, there is undoubtedly, to a greater or lesser degree, a ‘mental
gap’ between humans and other hominins and non-humans, often described as‘intentionality’ or ‘theory of mind’ (e.g. Baron-Cohen, 1995). Furthermore,
within cognitive science there are many compatible perspectives on humanthought (CI system) that envisage individuals having a conceptual array and asystem for structuring concepts independent of (externalised i.e. articulated)language, including ‘language of thought’ (Fodor, 1975, 2008) and similar
hypotheses (e.g. Carey, 2011; Harnard, 2010; Wyn et al, 2009).
The question then arises of the locus of recursion and its evolutionary origin in
relation to human language. While it has been suggested that this recursive
property in cognition may be the source for the apparent recursion in language(‘an optimal solution to expressing recursive thought’, Kinsella, 2009: 152),
there are no empirical or theoretical grounds for positing the existence of anautonomous level of linguistic syntactic structure in any sense. If there is awholly internal system for the recursive hierarchical structuring of conceptual
material (i.e. into thoughts), that constitutes the human CI system, then all that isadditionally required is a separate system for the communication of thosethoughts to others, the AP/SM system. This, I argue here, is effected by meansof a wholly external system of semiotic representation as conceived in the
Representational Hypothesis (RH) (e.g. Burton-Roberts, 2011). A central tenetof the RH is that there are essentially only two elements – sound (sign) andthought (signified) – in what is traditionally understood as language. In
interpreting the word ‘cat’ the phonological string /kæt/ leads directly to amental representation of the concept [CAT]; there is no need for intermediate
categories such as a noun, a ‘syntactic’ object, nor any reason why such a levelof representation would have evolved (either gradually or abruptly). The same istrue of composite multi-conceptual entities including full propositions. Devitt
attributes this thesis that ‘representations are not to be multiplied beyond
necessity (2006: 51) to Pylyshyn and, borrowing from Occam, terms it‘Pylyshyn’s Razor’. There is simply no evolutionary rationale for positing a
syntactic architecture, neither cognitive nor communicative; and certainly not(contra widespread assumption) for the purpose of disambiguation, which isentirely mental.
Pursuit of the origins of language in the style of the MP, the quest for the sourceof linguistic recursion, can only hinder further insight into language evolution.

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References
Baron-Cohen, Simon. (1995) Mindblindness: an essay on autism and theory of
mind. Cambridge, Mass.: MIT Press
Burton-Roberts, N. (2011) On the grounding of syntax and the role of phonology
in human cognition. Lingua. 121, 2089-2102.
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Sciences.
Chomsky, Noam. (1995) The Minimalist Program. Cambridge, MA.: MIT Press
Chomsky, N. (2010) Some simple evo devo theses: How true might they be for
language? In: Larson, R. K., Deprez, V. & Yamakido, H. (eds.) The evolutionof human language: Biolinguistic perspectives. Cambridge: Cambridge
University Press.
Devitt, M. (2006) Ignorance of language. Oxford: Oxford University Press
Fitch, W. T., Hauser, M. D. & Chomsky, N. (2005) The evolution of the language
faculty: Clarifications and implications. Cognition, 97, 179-210.
Fodor, J. (1975) The language of thought. New York: Crowell
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Hauser, M. D., Chomsky, N. & W. Fitch, T. (2002) The faculty of language: Whatis it, who has it, and how did it evolve? Science. 298, 1569-1579.
Jackendoff, R. & Pinker, S. (2005) The nature of the language faculty and its
implications for evolution of language. Cognition. 97, 211-225
Kinsella, A. (2009) Language Evolution and Syntactic Theory. Cambridge:
Cambridge University Press
Lowenthal, F. & Lefebvre, L. (eds) (2014) Language and Recursion. London:
SpringerWynn, T., Coolidge, F. & Bright, M. (2009) Hohlenstein-Stadel and the Evolutionof Human Conceptual Thought. Cambridge Archaeological Journal. 19, 7383

209

A GENERAL SOLUTION TO THE COMBINED EFFECTS OF
SELECTION AND BIASED MUTATION IN CULTURAL EVOLUTION
Vanessa Ferdinand
Melbourne School of Psychological Sciences, University of Melbourne, Australia
Santa Fe Institute, New Mexico, USA

The evolution of culture is shaped by two primary forces of directional change: biased transmission, which is similar to natural selection, and biased reconstruction, which is similar to
non-random recurrent mutation. The relative strength of these two forces has been the subjectof much debate and formal models to date provide conflicting results with no definitive resolution. In this paper, I derive an analytical solution to the combined effects of selection and biased
mutation when each are formalized as stochastic processes (e.g. Griffiths & Kalish, 2007). An
important observation that immediately appears in the solution is that the temporal order inwhich these forces operate causally affects the evolutionary dynamics of the system. This ordereffect explains conflicting results in the literature to date: models in which selection appears to
be stronger allow selection to operate last in the generational structure (e.g. Henrich & Boyd,
2002) and models in which biased mutation appears to be stronger allow mutation to operate last(e.g. Griffiths, Kalish, & Lewandowsky, 2008). In cultural evolution, various combinations ofbiasing forces are possible and these combinations are likely to vary across empirical domains.
The solutions in this paper offer a shortcut to calculating the combined effects of any arbitrary
number and ordering of selection and biased mutation processes, which should facilitate a more
complete characterization of the space of dynamics that cultural evolution is capable of andimprove our understanding of the relative effects of selection and biased mutation processes inculturally evolving systems.

1. Introduction
The evolution of culture is shaped by two primary forces of directional change.
The first type is selective in nature; it determines which kinds of cultural entities will be copied by people (winning these variants fecundity and “offspring”)
or ignored (effectively pronouncing these variants “dead”). A variety of selective
forces operate on culture, such as natural selection, social learning strategies, andbiased transmission processes (Boyd & Richerson, 1985). The second type is mutational in nature; it transforms cultural entities from one type to another whilethese entities are being copied and is capable of injecting novel variation into culture, often in biased ways. A variety of mutation-like processes operate on culture
and fall under the umbrella of biased reconstruction processes (Sperber, 1996).
Both of these forces are capable of creating directional changes that increase thefrequency of certain cultural variants relative to others.
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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There has been a great deal of debate over the relative importance of eachof these forces in shaping culturally evolving systems (e.g. Acerbi & Mesoudi,2015). Although much of the discussion focusses on the fidelity of the cultural
copying process, such as high vs low rates of random mutation, researchers arealso interested in pitting biased mutation against selective forces and observing
which force is more causally potent. This latter endeavour has produced several
idiosyncratic models with conflicting results, leading Acerbi and Mesoudi (2015)
to suggest the debate be moved to the empirical realm.
However, there is still conceptual ground to be gained in the modelling realm.
What is lacking is a sufficiently general framework that allows a wide range of fitness functions to be combined with a wide range of biased mutation functions andstraightforward analytical solutions to each of their combinations. In this paper,
I adopt a general stochastic process modeling framework for cultural evolution
and derive an analytical solution for the combined effects of any arbitrary fitness
values and any arbitrary biased recurrent mutation process.
2. Modelling framework
This section proposes a stochastic process model (e.g. Lawler, 2018) for biased
cultural mutation. Stochastic process modelling is commonly used in complex
systems science and was first applied to cultural evolution in the domain of language evolution (Niyogi & Berwick, 1997; Nowak, Komarova, & Niyogi, 2001,
2002) and expanded with the development of the Bayesian iterated learning model(Griffiths & Kalish, 2007). The main representational device in this framework is
a stochastic matrix called a transition matrix which contains the probability, pij ,that a variable in state vi transitions to state vj :v
 1
v1 p1,1v2  p2,1
M= .  .
..  ..
vn

pi,1

v2
p1,2
p2,2
..
.

...
···
···
..
.

pi,2

···

vn

p1,j
p2,j 

.. 
. 

(1)

pi,j

For the case of biased mutation, pij is be the probability that a cultural variantof type vi mutates into type vj and we will refer to the transition matrix as a mutation matrix, M. Transition matrices are also used in the field of mathematical
population genetics to represent non-random recurrent mutation processes (e.g.
Ewens, 2004, p. 174), but these are generally only 2-allele models with 2 × 2 matrices. Cultural entities, however, typically exhibit several variants. For example,
if you go to the hardware store to decide which cultural variant of “door handle”
you’d like your kitchen cabinets to have, there won’t be only two options, but211

dozens of variants to choose from. Equation 1 accommodates this by generalizing
to an arbitrarily large number of variants, V = {v1 , v2 , ...vn }.
Matrices are useful organizational devices for visualizing different patterns ofmutation flow. The off-diagonal values on the ith row show vi ’s losses under the
mutation process and the off-diagonal values on the jth column show vj ’s gains.
The probabilities on each row sum to one, indicating that all of the mutational
events are accounted for, but the columns may or may not sum to one. When thecolumn sums do not all sum to one, the mutation process is biased, with certain
variants receiving higher gains under the mutation process than others.
Representing biased mutation by a stochastic matrix is also convenient because it puts the tools of linear algebra at our disposal for calculating things suchas the evolutionary trajectories of populations and their equilibrium frequencies.
To make use of these tools, we represent the population as a stochastic vector,~qt , where ~qt (vi ) is the relative frequency of vi in the population at time t. For
example, ~qt = (.3, .5, .2) means that v1 constitutes 30% of the population at time
t whereas v2 constitutes 50% and v3 constitutes 20%. We will refer to ~qt as the
population vector. The expected frequency of each variant in two successive generations is ~qt+1 = ~qt M and for any arbitrary number of generations into the future,g, it is ~qt+g = ~qt Mg . The equilibrium frequencies are found by solving for thestationary distribution(s) of M, as in π = πM, or by solving π = ~qt M∞ , using asufficiently large numeric stand-in for ∞. Example solutions are shown in Figure
1 (leftmost panel) for the example mutation matrix
.8 .1 .1
(2)
M = .2 .6 .2
.3 .3 .4
where each black line represents the expected frequency of vi over the course of 50
generations and they converge to the unique stationary distribution of M, whichis π = (.55, .27, .18). Here we see that M is a biased process that causes v1 to be
the most plentiful variant, constituting 55% of the population at equilibrium.
The stochastic process model of cultural mutation described here is more general than the Bayesian iterated learning model because it does not require cultural
mutation to be implemented by Bayesian rational learners, and it is more restricted
than the Evolutionary Causal Matrix developed by Claidière, Scott-Phillips, andSperber (2014) because 1) the entries in the matrix are probabilities and 2) it ismeant to model a non-random recurrent mutation process, rather than model cultural attraction theory more broadly.
3. Combined effects solution
One approach to deriving an analytical solution to the combined effects of selection and biased mutation entails finding a way to incorporate fitness values into
the mutation matrix, creating a new combined effect matrix, C, and then applying

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our linear algebra solutions to C. In this section, I show how this can be done for ageneral model of natural selection, the replicator dynamics (Nowak, 2006).1 The
discrete-time replicator equation can be written as qt+1 (vi ) ∝ qt (vi )f (vi ), wheref (vi ) is the fitness of the ith variant. Fitness values can be represented in several
ways: as each type’s expected number of children, e.g. f (v1 , v2 ) = (10, 5), as
each type’s fitness relative to one a reference type, e.g. f (v1 , v2 ) = (1, 21 ), or as
each type’s fitness relative to the population’s total fitness, e.g. f (v1 , v2 ) = ( 23 , 13 ),but the choice of representation does not affect the solution for the relative frequency qt+1 (vi ). We will work with the latter representation where fitness is astochastic vector and refer to it as the fitness vector. Figure 1 (rightmost panel)shows the dynamics for example fitness values f (vi ) = (.5, .4, .1). Here we seethat the variant with the highest relative fitness, v1 , goes to fixation, meaning it
takes over the population and drives all other variants to extinction.
The combined effects matrix, C, can be obtained by converting the fitness
vector into a diagonal matrix, F , where


f1


..
(3)
F = diag(f1 , ..., fn ) := .
fn

and then multiplying F by the mutation matrix, M. It is important to note thatmatrix multiplication is not commutative, meaning F × M 6= M × F. Therefore,
there are clearly two solutions to C: Cf m = F × M, where selection acts before
mutation in the generational structure of the cultural system, and Cmf = M × F,
where mutation acts before selection.
When selection occurs first, F × M implements element-wise multiplicationof the fitness vector by each column in M and can be understood as incorporating
the fitness vector into the gains or input values of the mutation matrix, allowing
selection to act before the mutation process occurs:
f1 p1,1 f1 p1,2 · · · f1 p1,j
f2 p2,1 f2 p2,2 · · · f2 p2,j Cf m =  .
(4)
..
.. 
.
.
.
 .
.
.
. 
fn pi,1 fn pi,2 · · · fn pi,jWhen mutation occurs first, M × F implements element-wise multiplicationof the fitness vector by each row in M and can be understood as incorporating
1 Even though it contains the word “replicator”, the replicator dynamics equation itself neither

implies nor requires that the variants in question be high-fidelity replicators. The equation simply
assigns fitness values to variants based on their type at the moment the selection process acts and canbe combined with any model of mutation.

213

mutation only

selection, mutation

mutation, selection

selection onlyv1
q(vi )
v2
v3
generation

Figure 1. Evolutionary dynamics of four example combinations of selection and biased mutation,using fitness values f (vi ) = (.5, .4, .1), mutation process M from Equation 2, and initial population
q(vi ) = ( 13 , 13 , 13 ). Each panel (from left to right) shows the result when: mutation acts alone,selection occurs before mutation in the generational structure, mutation occurs before selection, andselection acts alone. Black lines show the expected frequency of each variant in the population at
the end of each generation: v1 (top), v2 (middle), v3 (bottom). Grey lines show the result of asimulated trajectory for a finite population size of 300 variants. The expected trajectories converge tothe stationary frequency of each variant, π(vi ).

the fitness vector into the losses or output values of the mutation matrix, allowing
selection to act after the mutation process occurs:
f1 p1,1 f2 p1,2 · · · fn p1,j
f1 p2,1 f2 p2,2 · · · fn p2,j 
Cmf =  .
(5)
..
.. 
..
 ..
.
.
. 
f1 pi,1f2 pi,2···fn pi,jFigure 1 shows the results for Cf m (second panel) and Cmf (third panel)for identical F and M. Here we see that the dynamics of these two processes
differ. Process Cf m more closely resembles the dynamics of mutation acting on
its own, whereas Cmf seems to be more influenced by selection. The analyticalsolutions πCf m = (.67, .20, .13) and πCmf = (.79, .18, .03) confirm that theseare, indeed, identifiably different stochastic processes.
4. Discussion of order effects
In the solution above, we saw that order matters because matrix multiplication is
not commutative. To understand why order matters, let’s consider the following
thought experiment. Imagine that you and I are standing in front of a ball of clay.
What we’re going to do is take turns punching the ball of clay: first I throw apunch, then you throw a punch, and we repeat this a few more times. Now let’sboth inspect the clay and ask, whose handprint can we see most clearly? The
one of us whose punch is the strongest? Perhaps. Or what about the one of us
who punched the clay most recently? If we introduce a consistent bias into ourmeasurement system by only inspecting the clay after I punch it, then we are214

selection last (original)

mutation last (reversed)

selection last (reversed)
mutation last (original)π

qt+1 (v1 )qt (v1 )qt (v1 )f

Figure 2. Replications of Henrich and Boyd (2002) (left, middle) and Griffiths et al. (2008) (right)
alongside results that reverse the order of selection and mutation in their original models.

probably going to walk away thinking I packed the stronger punch. But if we only
inspect it after you punch it, then we’ll make the opposite inference.
Many formal models of evolution assume discrete generations and those thatdeal with multi-process evolutionary systems normally organize the processes into
a set linear order within the generational structure. Furthermore, population frequencies are typically measured at the end of the generation, rather than after
each force acts. This particular set of modeling choices is common, but it is alsoa recipe for over-representing the effects of each generation’s final force.
Two examples of this ordering effect bias from the cultural evolution literature
are found in models by Henrich and Boyd (2002) and Griffiths et al. (2008). Bothof these papers address the relative potency of selection vs mutation in cultural
evolution, but their models yield opposite results. Henrich and Boyd (2002) combine a model of conformity copying (a type of selection) with high mutation rates
and find that the resulting evolutionary dynamics are barely affected by mutation.
Figure 4 (left) replicates their result for a mutation rate of 30% and a population
size of 20 (see their Figure 5). The overall shape of this curve resembles the typical s-shaped curve of conformity copying, which would have stable equilibria at
coordinates q(v1 ) = 1 and 0. When combined with high mutation rates, the stable equilibria remain near q(v1 ) = 1 and 0 and do not display the 30% mutationalload that would be expected from a 30% mutation rate. On the basis of this model,
the authors conclude that error-prone cultural evolution is “corrected” by conformity biases. However, in this model, mutation operates first in the generational
structure, conformity copying operates last, and frequencies are measured at theend of each generation. Figure 4 (middle) shows the results when the order ofthe selection and mutation processes are reversed. Here, the effects of mutation
are much more pronounced and we observe the expected mutational load of 30%,
pushing the equilibria to q(v1 ) = 0.7 and 0.3. This new perspective suggests a
win for mutation over conformity copying.
Griffiths et al. (2008) combine a model of cultural mutation, resulting from a
Bayesian learner with a prior bias favoring one cultural variant (v1 ) over another215

q(v1 )

measurement

Cmf

Cmf

Cfm

Cfm

measurement

Figure 3. Replot of the trajectories defined by process Cmf (blue) and Cfm (orange) showing thefrequency of v1 over 15 generations when measured at the end of each generation (left) and when measured twice per generation after each force acts (right). Both processes begin with the same uniform
initial population composition, q~t=0 = ( 13 , 13 , 13 ).

(v2 ), with a selection pressure causing differential fitness among the variants. Thismodel also assumes a fixed generational structure, but one where selection operates first and mutation operates last. Figure 4 (right, solid line) replicates their result for a 20% prior bias on v1 and a 10% noise rate (corresponding to parameters
π = 0.2 and  = 0.1 in their notation, see their Figure 5). Here, the equilibrium
frequency of v1 (x-axis) goes up as the fitness of v1 increases (y-axis), but plateaus
around 78% due to a mutational load of about 22%. This result was interpreted
as a win for mutation over selection, because even infinitely strong selection isincapable of pushing v1 to fixation. The dashed line, however, reverses the orderof these two processes. In this case, v1 goes to fixation as fitness goes to infinity
and this scenario could be interpreted as a win for selection over mutation.
How should we deal with these ordering effects when evaluating the relative
effects of selection and biased mutation in cultural evolution? One solution is tosimply measure the population after every process acts. Referring back to Figure 1, the data in these plots show the population frequencies at the end of eachgeneration only. Figure 4 replots the evolutionary trajectories of v1 for process
Cfm and Cmf , where the system is measured once at the end of each generation(left) and once after each force acts (right). The right panel clearly shows thateach force affects the frequency of v1 when it acts and that the system reaches
an oscillating attractor where the two forces kick the same population frequencies
back and forth between the equilibrium frequency solutions for Cfm and Cmf .
A second solution is to give a concrete or theoretically-justified interpretation
to the ordering of selection and mutation processes in our models. This is morechallenging as it requires some understanding about the “life-cycle” of a culturalvariant, including when specific selection and mutation forces typically act upon

216

it. For example, biases in attention, memory, and the pragmatic usage of language
may occur in a set order that is causally relevant to the evolution of language.
Future research into the relative importance of selection and biased mutation in
cultural evolution should either control for order effects or explicitly address thecausal role they play in determining population frequencies over time.
Acknowledgements
This work was completed with support from the University of Melbourne’s Research Fellowship in Computational Cognitive Science, under the supervision ofAndrew Perfors and Yoshihisa Kashima, and the Omidyar Fellowship at the Santa
Fe Institute. Thanks to three anonymous reviewers for their helpful comments.
References
Acerbi, A., & Mesoudi, A. (2015). If we are all cultural darwinians what’s thefuss about? clarifying recent disagreements in the field of cultural evolution.
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Claidière, N., Scott-Phillips, T. C., & Sperber, D. (2014). How darwinian iscultural evolution? Philosophical Transactions of the Royal Society B:
Biological Sciences, 369(20130368).
Ewens, W. J. (2004). Mathematical population genetics: theoretical introduction(Vol. 1). Springer.
Griffiths, T. L., & Kalish, M. L. (2007). Language evolution by iterated learningwith bayesian agents. Cognitive science, 31(3), 441–480.
Griffiths, T. L., Kalish, M. L., & Lewandowsky, S. (2008). Theoretical and empirical evidence for the impact of inductive biases on cultural evolution.
Philosophical Transactions of the Royal Society B: Biological Sciences,363(1509), 3503–3514.
Henrich, J., & Boyd, R. (2002). On modeling cognition and culture. Journal of
Cognition and Culture, 2(2), 87–112.
Lawler, G. F. (2018). Introduction to stochastic processes. Chapman and Hall.
Niyogi, P., & Berwick, R. C. (1997). Evolutionary consequences of language
learning. Linguistics and Philosophy, 20(6), 697–719.
Nowak, M. A. (2006). Evolutionary dynamics: exploring the equations of life.
Harvard university press.
Nowak, M. A., Komarova, N. L., & Niyogi, P. (2001). Evolution of universal
grammar. Science, 291(5501), 114–118.
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Sperber, D. (1996). Explaining culture: A naturalistic approach. Cambridge, MA:
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217

BABBLING BEHAVIOR: SHARED VOCAL ONTOGENETICFEATURES IN BAT PUPS AND HUMAN INFANTS
AHANA A. FERNANDEZ*1, LARA S. BURCHARDT1, MARTINA NAGY1 ANDMIRJAM KNÖRNSCHILD1,2,3*Corresponding Author: fernandez.aurora.ahana@gmail.com
1Natural History Museum – Leibniz Institute for Evolution and Biodiversity Science,

Berlin, Germany
2Institute of Biology, Freie Universität Berlin, Berlin, Germany
3Smithsonian Tropical Research Institute, Panama City, Panama

Speech is the vocal motor output aspect of language and requires precise control
over a complex muscle system (e.g. laryngeal and orofacial muscles) steered byneuronal mechanisms. During ontogeny, every child is challenged with theacquisition of speech sounds. The first utterances resembling speech sounds areobserved during babbling, a distinctive vocal behavior in human infants (Oller,
2000). Babbling is a production milestone in infant speech development because
it enables infants to practice speech sounds by gaining control over their speech
articulators (Oller, 2000; ter Haar et al., 2021; Vihman, 2014).
With our study, we introduce a new and promising mammalian candidate forcomparative biolinguistic research on vocal ontogenetic processes, the greater
sac-winged bat Saccopteryx bilineata. The bat S. bilineata is a vocal production
learner (Knörnschild, Nagy, Metz, Mayer, & von Helversen, 2010) and exhibitsa conspicuous vocal practice phase during ontogeny (Fernandez, Burchardt,
Nagy, & Knörnschild, 2021; Knörnschild, Behr, & von Helversen, 2006). Thispup vocal practice is strongly reminiscent of infant babbling: it is organized inbouts composed of multisyllabic repetitive sequences that are interspersed byshort silent intervals. Babbling bouts can last up to 43 minutes and throughout thebabbling phase (i.e. the period between first and last day of babbling), pups spend
about 30% of their active time with babbling. During babbling pups learn toproduce song by imitating adult tutors; pups’ renditions of adult song are often
found in babbling bouts (Fernandez et al., 2021; Knörnschild et al., 2006;
Knörnschild et al., 2010).
In human infants, irrespective of the language to be learned, babbling ischaracterized by several common features, e.g. repetitiveness, rhythmicity, earlyThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

218

onset in infancy (Oller, 2000; Vihman, 2014). Our study investigated whether
babbling behavior in S. bilineata, pups is characterized by the same features thatdefine infant babbling. The aim was to provide the first formal comparison of
babbling features across vocal learning mammals.
For this study, we investigated the entire vocal ontogeny of 20 pups from twowild populations in Costa Rica and Panama. Pup babbling was organized in threehierarchical levels: the syllables (level 1) were concatenated to sequences calledsyllable trains (level 2), which – interspaced by silent intervals – constituted thebabbling bouts (level 3). On the syllable level, we investigated the following
features: age at babbling onset (A), babbling bout composition (B), syllable typeacquisition (C), syllable type emergence during the babbling phase (D),reduplication (E), social context and function of babbling (G) and universality(H). We analyzed at least one babbling bout per week and pup (N=216 babblingbouts) for investigating all parameters, except for reduplication (E). To
investigate (E) we classified 55´056 syllables of 10 pups into syllable types andcalculated a correlation matrix showing the observed repetition rates of thesyllable types present in babbling. Rhythmicity (F) was investigated on thesyllable train level (N=30 trains, 712 syllables) by measuring syllable inter-onset
intervals and further assessed with a normalized pairwise variability index. For(A-D) and (H) we visually classified syllable types in babbling bouts based on
their high spectro-temporal similarity with the adult vocal repertoire (which isentirely delineated). Our visual classification was statistically verified bymeasuring a subset of our data and performing discriminant function analyses(acoustic analysis: Avisoft SasLab Pro, DFA: SPSS).
Our results revealed that the features that define human infant babbling are alsocharacteristic of pup babbling: (A) babbling onset occurred early, at one third of
the entire vocal ontogeny, (B) babbling bouts were composed of adult-likesyllable types (comparable to infant canonical syllables) and undifferentiatedproto-syllables (comparable to infant speech precursors). Pups only acquired asubset of the adult syllable type repertoire (C) and adult syllable type acquisition
followed a non-linear pattern (D). Bouts were dominated by reduplication ofsyllable types (E) and included syllable trains with regular beats (F). Babbling did
not require a social context for production and the adult-like syllable types did not
elicit the same reactions that they would when produced by adult bats (G). All
pups – irrespective of sex and regional origin – engaged in babbling behavior (andshowed no significant differences concerning the babbling features).
In conclusion, our study demonstrated that the babbling behavior of S. bilineata
pups is characterized by the same features that define infant babbling (Fernandez
et al., 2021).
The similarities in babbling features between two species with common traits such
as VPL, laryngeal sound production and similar brain architecture, are apromising basis for comparative investigations of neuronal substrates inmammalian VPL (Jarvis, 2019).

219

References
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Babbling in a vocal learning bat resembles human infant babbling.
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Knörnschild, M., Behr, O., & von Helversen, O. (2006). Babbling behavior in thesac-winged bat (Saccopteryx bilineata). Naturwissenschaften, 93(9),451-454. doi:10.1007/s00114-006-0127-9
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Vihman, M. M. (2014). Phonological development: The first two years (2 ed.):John Wiley and Sons, Inc.

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FROM PROTO-CONVERSATION TO MODERNCONVERSATION: THE ROLE OF HUMAN SELFDOMESTICATIONFRANCESCO FERRETTI*1, INES ADORNETTI1, LJILJANA PROGOVAC2,ANTONIO BENÍTEZ BURRACO3*Corresponding Author: francesco.ferretti@uniroma3.it
1Department of Philosophy, Communication and Performing Arts, Roma Tre University,

Rome, Italy
2 College of Liberal Arts & Science, Wayne State University, Detroit, USA
3 Department of Spanish, Linguistics, and Theory of Literature (Linguistics),

University of Seville, Seville, Spain

We propose that the evolution of one of the hallmarks of human communication,i.e., conversational abilities, is strongly linked to the reduction of reactive
aggression, one of the triggering factors in human self-domestication (HSD), thehypothesis that humans evolved through changes that parallel those found indomesticated animals (Hare, 2017; Shilton et al., 2020). Within this framework,
we focus on proto-conversation (PC) and proto-syntax at the basis of thecommunicative strategies of early anatomically-modern humans (AMH), viewing
them as an evolutionary platform for evolving fully fledged forms of conversationand syntax.
Most influential models of the evolution of conversation in humans sharply
distinguish proto-conversation (PC) and full-fledged conversation. For instance,
Tomasello (2003; 2019) advocates an abrupt transition from animal PC to humanconversation, that largely depends on the evolutionary emergence of cognitive
strategies capable of ensuring the “triangulation” between interlocutors andobjects of the outside world. This hypothesis builds on his analysis of the
ontogenesis of human conversational abilities, and particularly, his view that anabrupt change takes place in the child at the 9th month, when face-to-face (dyadic)
communication between mother and child is replaced by symbolic (triadic)
communication. We instead argue in favor of a stronger continuity of humanconversation and animal PC, as well as a smoother transition from PC toThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

221

sophisticated conversational skills in infants. Arguing for a continuity between
dyadic and triadic forms of intersubjectivity and ultimately, of PC andconversation, is in line with the view that higher-order competences are preparedand supported by a series of evolutionary steps (e.g. Trevarthen 1974; 1979;Bråten and Trevarthen, 2007), but also with the finding of turn-taking andassociated rules of conversation in non-human animal vocal interactions (e.g.,
Pika et al., 2018), and more generally, with claims of an evolutionary continuity
between human and animal communication (Levinson, 2016).
Our specific proposal is that the transition to full-fledged syntax and to fullfledged conversation is paralleled, and in fact facilitated by an increasing controlof reactive aggression, which is a hallmark of the human behavioral phenotype
and the ultimate reason of HSD. Importantly for our argument here, PCs arestrongly connected to the emotional level (Delafield-Butt and Trevarthen, 2015).
Accordingly, we contend that early stages of the dyadic mother-child relationshipdo not only represent the platform for the ontogenetic transition to triadic
communication, but also a basic strategy for reassurance of the child's tension anddistress, and ultimately, for controlling and reducing reactive aggression. Thisdevelopmental aspect has implications for our model of language evolution underthe effects of HSD. We propose that during phylogeny, the reduction of reactive
aggression under the effects of HSD prompted the emergence of face-to-facecommunication that is a necessary prerequisite for conversational communicationin humans. In turn, conversation triggered more complex forms of grammar thatfurther contributed to transform physical competition into verbal competition. In
this view, sophisticated uses of language, complexification of grammar, andreduction of aggression are involved in a process of coevolution - a feedback loop
in which HSD is both the cause and the result of the enhancement of grammar
and conversational pragmatics (Benítez-Burraco, Progovac, 2020; 2021; BenítezBurraco et al., 2021).Within a framework of this kind, PC becomes a precondition
for fully developed (symbolic) interactions, thus stressing the dialogic-persuasive
character of communication rather than its symbolic-descriptive nature.
References
Benítez-Burraco, A., & Progovac, L. (2021). Language evolution: examining thelink between cross-modality and aggression through the lens of disorders.
Philosophical Transactions of the Royal Society B, 376(1824), 20200188.
Benítez-Burraco, A., & Progovac, L. (2020). A four-stage model for language
evolution under the effects of human self-domestication. Language &Communication, 73, 1–17.

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Benítez‐Burraco, A., Ferretti, F., & Progovac, L. (2021). Human Self‐
Domestication and the Evolution of Pragmatics. Cognitive Science, 45(6),e12987.
Braten, S., & Trevarthen, C. (2007). From infant intersubjectivity and participant
movements to simulation and conversation in cultural common sense. In: On
being moved: From mirror neurons to empathy, 68, 21.
Delafield-Butt, J. T., & Trevarthen, C. (2015). The ontogenesis of narrative: frommoving to meaning. Frontiers in psychology, 6, 1157.
Hare B. (2017). Survival of the friendliest: Homo sapiens evolved via selection
for prosociality. Annual Review of Psychology, 68, 155–186.
Levinson, S. C. (2016). Turn-taking in human communication–origins andimplications for language processing. Trends in cognitive sciences, 20(1), 614.
Pika, S., Wilkinson, R., Kendrick, K. H., & Vernes, S. C. (2018). Taking turns:
bridging the gap between human and animal communication. Proceedings of
the Royal Society B, 285(1880), 20180598.
Shilton, D., Breski, M., Dor, D., & Jablonka, E. (2020). Human social evolution:
self-domestication or self-control?. Frontiers in Psychology, 11, 134.
Tomasello, M. (2003). Constructing a language: A usage-based theory oflanguage acquisition. Cambridge, MA: Harvard University Press.
Tomasello, M. (2019). Becoming human. Cambridge, MA: Harvard University
Press.
Trevarthen, C. (1974). Conversation with a two-month-old. New Scientist, 2, 230–
235.
Trevarthen, C. (1979). Communication and cooperation in early infancy: A
description of primary intersubjectivity. In: M. Bullowa (Ed.), Before Speech(pp. 227–270). Cambridge: Cambridge University Press.

223

LANGUAGE CONTACT IS PART OF LANGUAGE
EMERGENCE: NOUN PHRASE ORDERING IN NICARAGUAN
SIGN LANGUAGE
MOLLY FLAHERTY*1, SAVITHRY NAMBOODIRIPAD2, AND SUSAN GOLDINMEADOW3*

Corresponding Author: moflaherty@davidson.edu
Department of Psychology, Davidson College, Davidson, USA
2
Department of Linguistics, University of Michigan, Ann Arbor, USA
3
Department of Psychology, The University of Chicago, Chicago, USA1

Research on language emergence typically conceptualizes the birth ofgrammatical structure in a vacuum, i.e., an environment free of other languages.
New languages, however, are not born in petri dishes. Here we consider a case of
potential language contact between a new sign language in Nicaragua and the
spoken language with which it cohabitates: Spanish.
We investigated noun phrase (NP) ordering in three successive cohorts in
Nicaraguan Sign Language (NSL): Cohort 1, who came together in the 1970s andfirst formed NSL; Cohort 2 and Cohort 3, who were exposed to NSL upon school
entry 1986-1998. We showed participants a series of cards depicting sets ofobjects (e.g., dogs, cars). Set size varied 1-4, and objects were either large orsmall. Participants described each card, and we classified their descriptions by theordering of NP elements. Data was collected in 2009 (N=6) and 2015 (N=24,
including the original 6).
Both lab-based artificial language learning studies (e.g., Culbertson et al., 2012)
and cross linguistic data (see Dryer, 2008) indicate that adults prefer harmonic
noun phrase ordering patterns, where adjective and number are expressed either
both prenominally or both postnominally. In agreement with these findings, in
2009, we found that Cohort 1 and Cohort 2 signers produced harmonic orders(either noun-adjective-number or noun-number-adjective), with no significant
difference between cohorts. In 2015, the pattern was the same for Cohort 1, who
still preferred harmonic orders. Unexpectedly however, Cohort 2 signers
increased their use of the non-harmonic number-noun-adjective order, which was
also the preferred order for Cohort 3 signers. The preference for non-harmonic
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224

order increased significantly with cohort (β=-5.24, p<.02). This means in 2015,individual signers in the second cohort moved away from the harmonic patternthey displayed in 2009.
We propose language contact as an explanation for this unanticipated shift. Thissituation meets Thomason (2001)’s criteria for establishing contact-inducedchange: there is no language-internal motivation for this shift, word order is ahighly borrowable linguistic feature (Bickel & Hickey, 2017; Thomason &Kaufman, 1988), and the order of nouns, numerals, and adjectives in NSL in 2015
matches Spanish order. The shift’s rapidity has precedent in other dynamic
contact contexts; Fortescue (1993) found Native Alaskan languages underpressure from English went from being flexible SOV to rigidly SVO within two
generations.
We are currently in the preliminary stages of further investigating potential
language contact between NSL and Spanish during the period 2009-2015. Byinterviewing community members about their experiences with written Spanish(in the form of text messaging and social media use) over this period, we willprobe the degree to which Spanish use may have changed at the time of the shift.
Should we find that signers’ use of written Spanish increased during this period,
this will strengthen our case for language contact. In addition, we hypothesize thatdifferences in patterns of Spanish use could explain why Cohort 1 did not shift
toward the Spanish order as Cohort 2 did.
By combining interviews about language use and ideology with experimental
data, we draw on the approach advocated by Hou & de Vos (2021), whoemphasize a need for situating longitudinal data on language structure with details
about sociolinguistic contexts of learning and use. In this view, centering variationand dynamics is a way to move away from essentialist claims about particularlanguages. Further, contact between spoken and signed languages is an
ideologically charged area. As communities fight for recognition and access tosigned languages, there is substantial pressure to establish that signed languages
are not just “Spanish on the hands” (e.g., Rhodes 2020). We argue this is due to
deficit models towards multilingualism and language contact, which are present
in society at large and also reflected in linguistics and language emergence
research. Ignoring the multilingualism of signers, as well as the range of semiotic
resources available to signers as they “language” across contexts (e.g., Moriarty
Harrelson, 2019) does not serve to give an accurate picture of how conventions
emerge and change in languages such as NSL (cf. Ansaldo, 2017). As language
contact is the norm around the world, we seek to open a discussion of what we
can gain by bringing language contact to the center of our models of language
evolution.

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References
Ansaldo, U. (2017). Creole complexity in sociolinguistic perspective. Language
sciences, 60, 26-35.
Bickel, B., & Hickey, R. (2017). Areas and universals. Cambridge Handbooks
in Language and Linguistics, 40-55.
Culbertson, J., Smolensky, P., & Legendre, G. (2012). Learning biases predict aword order universal. Cognition, 122(3), 306-329.
Dryer, M. S. (2008). Order of numeral and noun. The world atlas of language
structures online, 362-365.
Fortescue, M. (1993). Eskimo word order variation and its contact-induced
perturbation. Journal of Linguistics, 29(2), 267-289.
Hou, L. & de Vos, C. (2021). Classifications and typologies: Labeling sign
languages and signing communities. Journal of Sociolinguistics. 00:18. https://doi.org/10.1111/josl.12490
Moriarty Harrelson, E. (2019). Deaf people with “no language”: Mobility andflexible accumulation in languaging practices of deaf people in
Cambodia . Applied Linguistics Review, 10(1), 55-72.
Rhodes, C. R. (2020). Dually authenticated and doubly modern:Institutionalizing jach maaya in the Yucatan today. Journal of
Linguistic Anthropology, 30(3), 326-352.
Thomason, S. G. (2001). Language contact. Edinburgh University Press.
Thomason, S. G., & Kaufman, T. (1988). Language contact, creolization, andgenetic linguistics. Univ of California Press.

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SOCIAL TOLERANCE AND INTERACTIONALOPPORTUNITIES AS DRIVERS OF GESTURAL REDOINGS IN
ORANG-UTANSMARLEN FRÖHLICH *1,2, and CAREL VAN SCHAIK 2*Corresponding Author: marlen.froehlich@uni-tuebingen.de
1Paleoanthropology lab, Institute for Archaeological Sciences, University of Tübingen,

Tübingen, Germany
2Department of Anthropology, University of Zurich, Zurich, Switzerland

Communicative repair is a fundamental and universal element of interactive
language use. It has been suggested that the persistence and elaboration after
communicative breakdown, as frequently observed in non-human primates,
constitute evolutionary building blocks of this capacity [1], but the conditions
favouring it are poorly understood. Social tolerance and interaction opportunities
beyond the mother-offspring dyad may be central drivers of gestural redoings (i.e.
repetition and/or modification of an initial signal after communicative failure) asa crucial element of social action coordination. Because zoo-housed individualsof some species are more sociable and terrestrial than in the wild, they should be
more likely to produce and respond to gestural redoings during the coordinationof joint activities.
We here examine this question by comparing the same species living in the wild
and in artificial, man-made habitats in captivity. The wild-captive contrast allows
us to directly test the prediction that captive individuals more readily exhibit
persistence and elaboration during face-to-face interactions, because captivity’s
more social and terrestrial settings may foster the extensive use of redoings in thecoordination of joint activities. We tested this prediction in orang-utans, a great
ape genus which is in our view ideal for this avenue of research. First, the orangutan populations of Borneo (Pongo pygmaeus wurmbii) and Northwest-Sumatra(i.e. Suaq and Ketambe, Pongo abelii) differ considerably in sociability [2]) andsocial tolerance (Bornean orang-utans become more stressed in group settings
than Sumatrans [3]). Second, in contrast to natural environments, captive orangutans are always in close proximity and more on the ground, clearing sight andfreeing hands for visual and tactile communication. Third, in addition to thesesetting and taxonomic contrasts, the pairing of social partners (i.e. the interactiondyad) also affects features of social interactions, e.g. due to differences in social
tolerance and familiarity. Our recent work demonstrated remarkable behaviouralplasticity in orang-utans, with regard to communicative repertoires [4] andmultimodal use of communicative acts contingent on social context and partner[5]. There are no systematic wild-captive comparisons of apes’ communicative
behaviour to date, but we assume that contrasts must be larger for orang-utans
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227

than any other great ape taxon in light of the profound contrasts in sociality andarboreality between captivity and the wild.
We studied a large comparative sample (N = 3869 signal instances) of wild andzoo-housed orang-utans of two different species (Pongo abelii, P. pygmaeus),
focusing on the gestural solicitation of three distinct joint activities: social play,allo-grooming and joint travel. Specifically, we tested how research setting,species and interaction dyad affected the probability of repetition and elaborationin signal use after communicative failure, as well as the success in obtaining theoriginal goal (the “apparently satisfactory outcome”; [6]), while controlling forcritical individual variables and context. Our predictions were confirmed only forelaboration, the more flexible form of redoings. Specifically, results showed thatgestural redoings in general were best predicted by the specific social context (i.e.
social play) and interaction dyad (i.e. beyond mother-offspring), although wereleast frequent in captive Bornean orang-utans. For gestural elaboration, we foundthe expected differences between captive and wild research settings in Borneans,
but not in Sumatrans (the more socially tolerant species). Moreover, we foundthat the effectiveness of elaboration in eliciting responses was higher in
Sumatrans, especially the captive ones, whereas effectiveness of mere repetition
was influenced by neither species nor setting.
In sum, this study demonstrated that Bornean and Sumatran orang-utans living indifferent socio-ecological environments (i.e. wild versus captive settings)
frequently deploy gestural redoings in the face of communicative failure within
and beyond mother-offspring dyads. Both repetition and elaboration have beeninterpreted as simple forms of repair [1], a fundamental layer of the humaninteraction engine [7]. Our findings also showed that social tolerance, as afoundation for extended social interactions (both higher in Sumatrans and in
captivity), plays a central role in the emergence of complex exchanges in great
apes. These findings support the notion that the human version of the interactionengine evolved when contexts with limited conflicts of interests and involving
coordination for joint action became more widespread [8]. The Interdependence
Hypothesis further posits that as individuals in hominin societies became morecohesive and interdependent, pressure for shared intentionality, and thus effective
coordination via communication increased [8]. As parts of the interactional basefor joint action coordination, this includes the repair of communicative “glitches”
in case of misunderstandings as well as negotiation in case of diverging interests[7]. For future empirical research on nonhuman primates, it would be important
to distinguish communicative sequences resulting from prior communicative
failure alone (and thus primarily driven by the signaller) from more interactional
communicative exchanges involving signalling by both parties, as common inhuman conversation.

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References
1.Heesen R., Fröhlich M., Sievers C., Woensdregt M., Dingemanse M. (in press)
Coordinating social action: A primer for the cross-species investigation ofcommunicative repair Philosophical Transactions of the Royal Society B:
Biological Sciences.
2.van Schaik C.P. (1999) The socioecology of fission-fusion sociality inorangutans. Primates 40(1), 69-86.
3.Weingrill T., Willems E.P., Zimmermann N., Steinmetz H., Heistermann M.
(2011) Species-specific patterns in fecal glucocorticoid and androgen levels inzoo-living orangutans (Pongo spp.). General and Comparative Endocrinology
172(3), 446-457.
4.Fröhlich M., Bartolotta N., Fryns C., Wagner C., Momon L., Jaffrezic M., Mitra
Setia T., Schuppli C., Noordwijk M.A., van Schaik Carel P. (2021) Orangutans
have larger gestural repertoires in captivity than in the wild – a case of weak
innovation? iScience 24(11), 103304.
5.Fröhlich M., Bartolotta N., Fryns C., Wagner C., Momon L., Jaffrezic M.,
Noordwijk M.A., van Schaik C.P. (2021) Multicomponent and multisensory
communicative acts in orang-utans may serve different functions.
Communications Biology 4, 917.
6.Hobaiter C., Byrne R. (2014) The meanings of chimpanzee gestures. Current
Biology 24(14), 1596-1600.
7.Levinson S.C. 2019 Interactional foundations of language: the interactionengine hypothesis. In Human language: From genes and brain to behavior (ed.
Hagoort P.), pp. 189-200. Cambridge, US, MIT Press.
8.Tomasello M., Melis A.P., Tennie C., Wyman E., Herrmann E. (2012) Two Key
Steps in the Evolution of Human Cooperation: The Interdependence Hypothesis.
Current Anthropology 53(6), 673-692.

229

PRIMATE RHYTHMIC CATEGORIES ANALYZED ON AN
INDIVIDUAL BASISMARCO GAMBA1, CHIARA DE GREGORIO1, DARIA VALENTE1, TERESARAIMONDI1, VALERIA TORTI1, LONGONDRAZA MIARETSOA1, FILIPPOCARUGATI1, OLIVIER FRIARD1, CRISTINA GIACOMA1, ANDREARAVIGNANI2,3*

Corresponding Author: marco.gamba@unito.it
Department of Life Sciences and Systems Biology, University of Torino, Torino, Italy
2
Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
3
Aarhus University, Aarhus, Denmark

1

Rhythm is a fundamental feature characterizing communicative displays, and recent studies
showed that primate songs encompass categorical rhythms falling on small integer ratios
observed in humans. We individually assessed the presence and sexual dimorphism ofrhythmic categories, analyzing songs emitted by 39 wild indris. Considering the intervals
between the units given during each song, we extracted 13556 interval ratios and foundthree peaks (at around 0.33, 0.47, and 0.70). Two peaks indicated rhythmic categories
corresponding to small integer ratios (1:1, 2:1). All individuals showed a peak at 0.70, andmost showed those at 0.47 and 0.33. In addition, we found sex differences in the peak at
0.47 only, with males showing lower values than females. This work investigates thepresence of individual rhythmic categories in a non-human species; further research may
highlight the significance of rhythmicity and untie selective pressures that guided its
evolution across species, including humans.

1. Introduction
Rhythm is a fundamental feature of human communicative displays andcharacteristically permeates the musical performances of our species (Hausen etal. 2013, Savage et al. 2015). When we think of a typical human communicative
display, such as conversation, we realize that numerous factors play a critical rolein the temporality of exchanges between interlocutors (Pouw & Holler 2022).
Among these features is rhythm, which is often contextually adapted to alignsemantic and syntactic processes (Himberg et al. 2015).
Moreover, we know that complex rhythms characterize communication in humanand non-human animals (Pouw et al. 2021; Ravignani et al. 2019). Recent
research also indicated comparable features in the temporal structureThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

230

characterizing human speech, music, and non-human animal vocalizations (Kelloet al. 2017; Pouw et al. 2021). Complex rhythms are typical of interactions
between two or more conspecifics, emitting a series of signals and taking turns(Gamba et al. 2011; 2016). It is difficult to imagine that we will be able to discern
the cognitive processes involved in turn-taking for a large number of species, butfinding similarities across extant primates could help us to understand theselective pressures that may have played a role during the evolution of humanlanguage (Levinson 2016).
Indeed, rhythmic displays can provide important information about thecommunicative features of other species, in particular concerning the contextual
variation and temporal characteristics that permeate long sequences of emissions(Clink et al. 2017). Specifically, recent research has shown that the study ofprimate songs may indicate the presence of rhythmic categories that fall on small
integer ratios which at least partially overlap with those we can observe in humanmusic (De Gregorio et al. 2021).
A question remains unanswered: to what extent rhythmic patterns are stable
across the individuals of a particular species and whether all individuals share acertain rhythmic category more often than others. In order to shed light on this
aspect, we investigated the acoustic emissions of a singing primate that has
already been the subject of studies on rhythmic categories, Indri indri. Indri is a
primate species with an evolutionary history parted from humans by around 74
million years (Hedges et al. 2015). Our first hypothesis is that adult individualsof this species present at least two rhythmic categories in their songs. We,
therefore, expect that the density distributions of all studied indris show two or
three peaks around human typical small integer ratios of 1:2 (0.33), 1:1 (0.50),
and 2:1 (0.67), following the results of previous work (De Gregorio et al. 2021).
The second hypothesis, alternative to the previous one, is that rhythmic categories
appear at least consistently within one of the sexes, in line with the fact that, in
the indri, many of the vocal features are distinctly dimorphic between males andfemales (Giacoma et al. 2010; Gamba et al. 2016; De Gregorio et al. 2019; Zanoli
et al. 2020, Valente et al. 2021).
2. Materials and Methods
2.1. Study subjects and recordings
We recorded songs produced by 39 reproductive indris of 20 different
groups. As indri groups usually comprise a reproductive pair and theiroffspring, by doing so we included in the analyses a number of individuals
comparable for each of the sampled group. These indris lived in various
forest sites in Madagascar: Analamazaotra Reserve (Andasibe-Mantadia

231

National Park), Mantadia (Andasibe-Mantadia National Park), Mitsinjo
Station Forestière, Maromizaha Forest.
We collected data between 2005 and 2016 using solid-state recorders(SoundDevices 702, Olympus S100 and LS05, and Tascam DR-100, DR40, and DR-05) equipped with Sennheiser (ME 66 and ME 67) or AKG(CK 98) microphones. We set a sampling rate of 44.1 kHz and 16-bit
amplitude resolution for all recording sessions. The distance ranged from 2
to 20 meters when recording the animals, with the microphone aimed at the
focal singing animal.
2.2. Acoustic and statistical analyses
We tracked inter onset intervals between the units given during each songusing Praat TextGrids, where we assigned each singer to a particular
TextGrid tier. After extracting interval duration to a text file (Gamba et al.
2016), we obtained 13556 interval ratios using the Roeske et al. (2020)
formula. Interval ratios rk were calculated by dividing a particular interonset interval tk for itself + the following inter-onset interval (rk = tk/(tk+tk+1)).
3. Results
The distribution of the peaks in the rk density graphs showed three different
patterns (Fig. 1A). A first pattern, occurring in 56% of the individuals, showed
the presence of three peaks means at 0.33 + 0.02, 0.47 + 0.02, 0.70 + 0.02. All
individuals had a peak at around 0.70 (Fig. 1B). In 26% of the indris, the peak ataround 0.33 was missing, while 18% did not show the peak at around 0.47 (Fig.
1B).

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Figure 1. Variation of the rhythm ratios (rk) across individual indris for each peak of theirdensity plot (A, see also Fig. 2). Grey points denote individual rhythm ratios for a particularpeak, black points denote outliers. Percentage distribution of rhythm ratios across individuals
for the three peaks (B, see also Fig. 2).

The coefficient of variation of the peak around 0.33 was 6.1%, while the othertwo showed lower values, 3.5% and 3.4%, respectively (Fig. 1A, Fig. 2). We
analysed the occurrence of peaks across the sexes and found no statisticaldifference for the peak at 0.70 (Mann-Whitney test, W = 213, p-value = 0.5315).
The peak at 0.33 showed marginal significance, but small effect size (W = 57, pvalue = 0.0476, r = -0.44), whereas the peak at around 0.47 showed significant
difference (W = 188, p-value = 0.02195, r = 0.47). Males showed an average peakof 0.47+0.02, while females showed values at 0.48+0.01.

233

Figure 2. Probability density function of rhythm ratios (rk), which we calculated across 39 adultindris and 636 individual contributions to songs. Solid lines denote peaks of each individual
indri for each group of peaks. Dotted lines denote 1:2 (0.33), 1:1 (0.50), and 2:1 (0.67) ratios.

4. Discussion
In agreement with the De Gregorio and colleagues (2021) study, we observedthat most indri part of our sample showed three distinct peaks representing as
many rhythmic categories. The location of the peaks is in agreement with thetwo rhythmic categories, significantly corresponding to small integer ratios
in De Gregorio and colleagues (2021). The peak at 2:1 (0.67) was notstatistically significant in the previously cited work because points around0.67 were not dense enough. Although the methodology used here is slightly
different, we see the same trend, showing a well-identified and consistentpeak at 0.70+0.02 in most animals studied. In most individuals, we observe
three peaks; however, the 44% of indris shows only two peaks, distributed
over two of the three categories mentioned above. Indeed, the presence of
two and not three rhythmic categories is not easily attributable to the sex of
the individuals. Interestingly, the absence of the third peak does not alter the234

consistency of the remainder concerning the species-specific characteristics
that emerged in this study and De Gregorio and colleagues (2021).
De Gregorio and colleagues' (2021) findings concerning male and female
absolute interval lengths are different, but the 1:1 ratios are the same andappear consistent with our overall results. However, by analyzing individual
peaks, a slight difference emerges for values below 1:1 (i.e., isochrony).
Indri groups are often small, include a pair and their offspring, andcommunicate in a very extensive network of other conspecific groups. Unlike
what has been suggested for birdsong, which undergoes a vocal learning
process, we do not know what role learning plays in primate songs.
Historically, it has been assumed that singing has a strong genetic
component, but recent studies point to plasticity that is difficult to explain byfactors that do not involve some degree of learning.
This work is the first to analyze rhythmic categories at the individual level
because previous investigations have focused on a species-specific pattern
rather than an individual pattern (Roeske et al. 2020; De Gregorio et al.,2021). Given the hierarchical organization of natural systems, expanding thelevels of analyses can provide insight regarding the evolutionary levels on
which selection mechanisms may act. Our results suggest new insights for
subsequent research, which may be aimed at understanding whether thepresence of rhythmic categories varies with the age of individuals, or is a trait
that becomes fixed early and then does not change during ontogeny, or
whether it varies dynamically over time. Our study also shows that mostindris studied exhibit rhythmic categories that correspond to small integer
ratios. Unfortunately, our findings concerning the production of categoricalrhythmic patterns in the indris cannot be paralleled by pieces of evidence
about the perception of these lemurs. However, it appears challenging tostudy whether the indris' perceptual abilities are biased towards the same
categories in the wild.
Acknowledgements
This research was supported by Università degli Studi di Torino and the African,
Caribbean, and Pacific (ACP) Science and Technology Programme of the ACPGroup of States, with the financial assistance of the European Union and by grants
from the Parco Natura Viva—Centro Tutela Specie Minacciate. We are grateful235

to GERP (Groupe d'Etude et des Recherche sur les Primates) and Dr. Jonah
Ratsimbazafy. We thank Dr. Cesare Avesani Zaborra and Dr. Caterina Spiezio
for helping us with the organization of the field station in Maromizaha. We are
grateful to the researchers and the research guides, to Mamatin and all our
Malagasy collaborators in the forest, for their help and logistical support. The
contents of this document are the sole responsibility of the authors and can underno circumstances be regarded as reflecting the position of the European Union.
We have received permits for all the research activities from “Direction des Eauxet Forêts” and “Madagascar National Parks” (formerly ANGAP).
References
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individual vocal signatures and small-scale patterns of geographic variationin female Bornean gibbon (Hylobates muelleri) great calls. International
Journal of Primatology, 38(4), 656-671.
De Gregorio, C., Zanoli, A., Valente, D., Torti, V., Bonadonna, G.,
Randrianarison, R. M., Giacoma, C. & Gamba, M. (2019). Female indris
determine the rhythmic structure of the song and sustain a higher cost when
the chorus size increases. Current Zoology, 65(1), 89-97.
Gamba, M., Favaro, L., Torti, V., Sorrentino, V., & Giacoma, C. (2011). Vocal
tract flexibility and variation in the vocal output in wild indris. Bioacoustics,
20(3), 251-265.
Gamba, M., Torti, V., Estienne, V., Randrianarison, R. M., Valente, D., Rovara,P., Bonadonna, G., Friard, O. & Giacoma, C. (2016). The indris have got
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sexes and age classes. Frontiers in Neuroscience, 10, 249.
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differences in the song of Indri indri. International Journal of Primatology,
31(4), 539-551.
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entrainment of speech rhythm during joint story building. Frontiers in
Psychology, 6, 797.
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Society Interface 14

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237

CONSTRAINING FREE MERGE: LABELING AND THE
THETA-CRITERIONJASON GINSBURG*1*

Corresponding Author: jginsbur@cc.osaka-kyoiku.ac.jp
Department of Educational Collaboration, Osaka Kyoiku University, Japan

1

Merge, the ability to recursively combine two elements together, is a core property ofhuman language, and thus it is reasonable to assume that language evolved to make use of
Merge. If Merge is essentially a free operation that the language module makes use of, it
must be constrained in some ways. I created a computer model of language that implements
Free Merge, and I explain how two properties of language, the need for syntactic structures
to be labeled (Labeling), and constraints regarding theta assignment (the theta-criterion)
can constrain Free Merge. This work suggests that labeling and the theta-criterion play animportant role in language generation.

1.

Introduction

Chomsky (2005, p. 11) writes that “the human language faculty is … a system of
discrete infinity” that is “based on a primitive operation that takes n objects
already constructed, and constructs from them a new object.” This process of
combining together linguistic objects is referred to as Merge. Chomsky (2011, p.
52) writes that “unbounded Merge is the sole recursive operation within UG” andthat it is “part of the genetic component of the language faculty.” If this is correct,
human language evolved to make use of recursive Merge.1
Chomsky (2001, 2013, 2015) takes the position that Merge is free. Chomsky(2015, p. 14) writes that “[t]he simplest conclusion … would be that Merge
applies freely” and “[o]perations can be free, with the outcome evaluated at thephase level for transfer and interpretation at the interfaces.” Free Merge would
result in an infinite number of possible structures generated for every possible
utterance. Thus, Free Merge must be constrained by the language faculty.
1
Berwick (2011) suggests that non-human primates have lexical items but no Merge, whereas birds
have something like Merge (used in songs) but no lexical items. Human language, crucially, makes
use of lexical items and Merge.

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

238

I utilized a computer model that automatically generates complete derivationsof sentences to test the limits of Free Merge. With this model, I was able toconstrain Free Merge with two language-related principles utilized in Minimalist
work, Labeling and the theta-criterion.
2.

Merge

Merge involves combining together syntactic objects (SOs). External Merge
refers to selecting a lexical item from a workspace and Merging it with anotherlexical item or SO, which is an already formed syntactic structure. Internal Merge
refers to selecting an element from within an SO and Merging it again with theroot of the SO.
There are two types of Merge: set-Merge and pair-Merge. Figure 1a shows
set-Merge of the two SOs X and Y. In this case, X is the label. Figure 1b shows
Z pair-Merged with an XP; pair-Merge is represented as a dotted arc. Crucially,
Z could be considered an adjunct on a separate plane.
(a)

(b)

Figure 1. Set-Merge and Pair-Merge

Given Free Merge, it is possible to internally set-Merge an argument freely.
In the derivation of Tom read a book (Chomsky, 2015), assume that LIs are
selected from an input list in (1)a, and that at each stage of a derivation, an LI canbe selected from the Input List and externally set-Merged with an already formed
SO, or an SO can be internally set-Merged. The desired output is (1)b, following
Chomsky’s (2015) view that an object internally set-Merges with the verbal root
and the verbal root read raises to v*. The subject also raises from the v*P to the
TP. Note that Free Merge would also produce the ill-formed structures in (1)c-d,
in which a book is internally set-Merged multiple times, and ends up in positions
in which it is not found in actual language, so these types of structures must crash.
(1) (a) Input list: [C, T, Tom, v*, read, a, book](b) C Tom T Tom v*+read a book read a book. à Tom read a book.
(c) C a book T Tom v*+read a book read a book. à *A book Tom read.
(d) C Tom T a book Tom v*+read a book read a book. à *Tom a book read.

239

The verb think, as in (2), differs from a transitive verb such as read in that it
lacks case and doesn’t agree with an object. To deal with this, I assume that the
intransitive v (cf. Chomsky 2001) Merges with think and that v is able to label byitself.2(2) He v thinks that John read the book.
3. Computer Model Output and Free Merge
I tested Free Merge using a computer model (which I constructed primarily in the
Python programming language) that selects and Merges together lexical items
from an input list, and automatically constructs detailed step-by-step derivationsof phrases and sentences. This model incorporates Free Merge so that an argument
can be freely internally set-Merged. This model also incorporates other core
notions of Labeling Theory, such as feature transmission; a phase head (C or v*)
passes unchecked phi-features to a complement and these unchecked phi-featuresneed to form an Agree relation and be checked.
3.2 Labeling
In Labeling Theory (Chomsky, 2013, 2015), a label refers to the ability to interpreta phrase, e.g., as a VP, NP, etc. If a phrase cannot be labeled, then it cannot beproperly interpreted. Murphy (2015) writes that “Label constitutes theevolutionary novelty which distinguishes the human cognome from non-humancognomes (7)”, where the human cognome is “the finite set of operations
available to the human brain (2).” Whether or not labeling is the core element that
distinguishes human language from other animal communication systems remains
an open question, but labeling appears to be crucial for human language.
The basic assumptions of Labeling Theory are that in an {H, XP}
configuration in which H is a head and XP is a phrase, H can label (if it is strong
enough to label). In an {XP, YP} configuration, if the heads of the XP and YP
share prominent features, the shared feature(s) can label, or if one of the phrases
moves out (undergoes internal Merge), the head of the remaining phrase can label

2
Epstein, Kitahara, and Seely (2016) propose that think and v* are externally pair-Merged together,resulting in <think, v*>, where v* is the pair-Merged element. External pair-Merge of v* with think
results in the uPhi, as well as the phasehood, of v* disappearing. This external pair-Merge analysis is
one way to account for verbs such as think. However, permitting external pair-Merge greatly increases
the possibilities for Free Merge. It also isn’t entirely clear how phasehood and uPhi of a phase headcan disappear under pair-Merge.

240(if that head is strong enough to label).3 In addition, Chomsky proposes that the
English T and verbal roots are inherently too weak to label by themselves.
However, when they Agree with an XP in an {XP, YP} structure, shared features
strengthen them so that they can label.4
Consider the derivation of Tom will read a book, shown in Figure 2, which is
successfully generated in my model. The object a book5 internally set-Merges
with the SO headed by read. This results in an {XP, YP} structure that is labeled
via shared phi-features, indicated as <ɸ, ɸ>, resulting from Agree between readand a book. Similarly, the subject Tom internally set-Merges with the TP,
resulting in a labelable {XP, YP} structure, resulting from the shared phi-featuresof the subject and T. Note that I follow Chomsky’s (2015) view that the verbal
root read moves to v*, producing <v*,read>, with v* the pair-Merged element.
In addition, will moves to T forming <Tpres, will>. This “head-movement” isimplemented as internal pair-Merge.
{XP,YP} structure• Labeled <ɸ,ɸ>{XP,YP} structure• Labeled <ɸ,ɸ>Tom Tpres+will
Tom willread aread abook
book

Figure 2. Successful derivation of Tom will read a book3
An anonymous reviewer asks about cases in which both elements in an {XP, YP} structure moveout, such as in a French v*P where the subject and verb supposedly move. Movement of the verb ishead movement. How exactly to deal with head movement is an open question. Chomsky (2001)
suggests that head movement is a phonological operation. If head movement occurs at PF(phonological form), then head movement of the verb does not influence the syntax and will not
influence labeling. In my model, the head is visible for labeling in its base position even after it has
undergone movement, which I have implemented as internal pair-Merge.
4Murphy & Shim (2020) argue that T can label by itself. If uPhi are responsible for blocking labeling(cf. Mizuguchi 2017 for a proposal that heads without unvalued features can label), then it is possiblethat finite T (which has uPhi) cannot label by itself, whereas non-finite T (which lacks uPhi) is able tolabel. Murphy & Shim also note potential problems for permitting shared features to label, and they
propose that only categorial heads label. Although these issues are worthy of further analysis, I assume
the standard Labeling view that shared prominent features can label.
5Note that I assume that a book is an NP, with the determiner being a pair-Merged adjunct, a viewthat has been suggested by Chomsky (2007), as well as Oishi (2015). See Bruening (2020), among
others, for support for the view that arguments are NPs and not DPs. Whether or not an argument istreated as an NP or a DP is not crucial for the examples discussed in this paper.

241

My model implements Free Merge of arguments, which can result inderivations that crash. In Figure 3, the subject Tom remains within the v*P anddoes not move to the specifier of TP. Since Merge is free, nothing forces the
subject to move. However, this derivation crashes because of labeling failures,shown in Figure 3b. In the v*P, there is an {XP,YP} structure in which the subject
and the v*P do not share phi-features, so labeling is not possible. In addition,
assuming that English T is too weak to label by itself (following Chomsky 2015),
T, represented as <Tpres, will>, is unable to label since it has no subject withwhich it shares phi-features. In another unsuccessful derivation shown in Figure
4, the object, a book, undergoes internal set-Merge with the TP, and theunderlying subject Tom remains in-situ. This results in the labeling failures shownin Figure 4b. Although a book and <Tpres, will> are able to form a labelable{XP, YP} structure due to phi-feature agreement, the v*P-internal subject and thev*P form an unlabelable {XP, YP} structure, due to a lack of shared phi-features.
Furthermore, the root read is also unlableable, since the object the book has
moved out and the root read cannot be strengthened by shared phi-features.
(a)

(b)

Labeling failure

Labeling failure

Figure 3. Labeling failure: unsuccessful derivation of Tom will read a book
(a)

(b)

Labeling failure
Labeling
failureFigure 4. Labeling failure: another unsuccessful derivation of Tom will read a book

The sentence He thinks that John read the book has the successful derivation
shown in Figure 5, which contains v that Merges with think, and v is able to label.
Free Merge also permits derivations such as that in Figure 6, in which thesubject remains in-situ. This will crash because the matrix subject is in an242

unlabeleable {XP, YP} configuration with the vP and because T is notstrengthened. Thus, this ill-formed derivation is ruled out by Labeling.

heheTpres+think
thinks

that
that

John
John

Tpast+read
read

the
the

book
book

Figure 5. Successful derivation of He thinks that John read the book

(a)

(b)
Labeling failures

Figure 6. Labeling failure in He thinks that John read the book

3.3 Theta-Criterion
The theta-criterion is the notion that “[e]ach argument bears one and only one θrole, and each θ-role is assigned to one and only one argument (Chomsky, 1981,
p. 36).” The theta-criterion plays an important part in constraining Free Merge.
Figure 7 below shows an unsuccessful portion of a derivation, automaticallygenerated by my model, of Tom will read a book (compare with the successful

243

Figure 2 above). The object a book is externally set-Merged with read, where it
obtains a theta-role. The object then internally set-Merges with read and again
with <v*, read> - this is the traditional specifier of v*P subject position. Since
Merge is free, this operation is permitted. However, the transitive <v*, read> hasa subject theta-role to assign. Thus, a book will get a second theta-role. Thisviolates the theta-criterion, so this derivation crashes.
a book gets a second
theta-role from<v*,read>

Figure 7. Theta-criterion violation: Unsuccessful derivation of Tom will read a book

4. Conclusion
I have discussed the derivations of 2 simple sentences and attempted to show
how Labeling and the theta-criterion are sufficient for constraining Free Merge.
The number of crashed and successful derivations 6 for these 2 sentences are
summarized in Table 1. All of the crashes are caused by labeling failures orviolations of the theta-criterion. Thus, labeling and the theta-criterion (in someform or other) seem to be core properties of language, and accounts of language
evolution likely need to explain these properties.
Table 1. Crashes and successful derivations
Sentence(a)

Tom will read a book.

# of
Crashes
14(b)

He thinks that John read the book.

22# of Successful Derivations

1

2

5. Supplementary Materials
The complete derivations for the target examples discussed in this paper can befound at: http://www.osaka-kyoiku.ac.jp/~jginsbur/JCole22Appendix
6
In some cases, my model predicts that there can be more than 1 convergent derivation for a single
sentence, as with Tom will read a book (which differ with respect to the number of times that the
subject Tom undergoes internal set-Merge). This raises the question of whether a single well-formed
sentence can have multiple possible structures resulting in the same interpretation.

244

Acknowledgements
This research was supported by Japan Society for the Promotion of Science Grantin-Aid for Scientific Research #20K00664. I would like to thank the anonymous
reviewers and Hiroshi Terada for helpful comments. All errors are my own.
References
Berwick, R. (2011). All you need is Merge: Biology, computationa, and language
from the bottom up. In A. M. Di Sciullo and C. Boeckx (Eds.), The
Biolinguistic Enterprise: New perspectives on the evolution and nature of
the human language faculty (pp. 461-491). Oxford, Oxford University Press.
Bruening, B. (2020). The head of the nominal is N, not D: N-to-D Movement,
Hybrid Agreement, and conventionalized expressions. Glossa: a journal ofgeneral linguistics, 5(1) 15, 1–19.
Chomsky, Noam. (1981). Lectures on government and binding: the Pisa lectures.
Berlin: Mouton de Gruyter.
Chomsky, N. (2001). Derivation by phase. In M. Kenstowicz (Ed.), Ken Hale: alife in language (pp. 1-52). Cambridge, MA: MIT Press.
Chomsky, N. (2005). Three factors in language design. Linguistic Inquiry, 36, 122.
Chomsky, N. (2007). Approaching UG from below. In U. Sauerland and H.-M.
Gärtner (eds.), Interfaces + Recursion = Language? Chomsky’s
Minimalism and the view from syntax-semantics (pp. 1-30). The Hague:
Mouton de Gruyter.
Chomsky, Noam. (2010). Some simple evo devo theses: How true might they be
for language? In R. K. Larson, V. Déprez, and H. Yamakido (Eds.), Theevolution of human language (pp. 45-62). Cambridge, UK: Cambridge
University Press.
Chomsky, N. (2013). Problems of projection. Lingua, 130, 33-49.
Chomsky, N. (2015). Problems of projections: Extensions. In E. Di Domenico, C.
Hamann, and S. Matteini (Eds.), Structures, strategies and beyond: Studies
in honour of Adriana Belletti (pp. 1-16). Amsterdam: John Benjamins.
Epstein, S. D., Hisatusugu K., & Seely, T. D. (2016). Phase cancellation by
external pair-merge of heads. The Linguistic Review, 33, 87-102.
Mizuguchi, M. (2017). Labelability and interpretability. Studies in Generative
Grammar 27. 327-365.
Murphy, E. (2015). Labels, cognomes, and cyclic computation: An ethologicalperspective. Frontiers in Psychology, 6, 715.
Murphy, E. & Shim, J.-Y. (2020). Copy invisibility and (non)-categorial labeling.
Linguistic Research, 37, 187-215.
Oishi, Masayuki. 2015. The hunt for a label. In H. Egashira et al. (Eds.), Untiring
pursuit of better alternatives (pp. 222-334). Tokyo: Kaitakusha.

245

GREAT APE PRAGMATICSKIRSTY E. GRAHAM*1, and CATHERINE HOBAITER 21*Corresponding Author: keg4@st-andrews.ac.uk
1School of Psychology & Neuroscience, University of St Andrews, UK

Increasingly, animal behaviour researchers are applying a linguistic framework to
studying nonhuman communication (Berthet et al., 2021; Fishbein et al., 2020).
While animal communication systems alone likely do not provide the key tohuman language evolution (Fitch, 2020), applying linguistic tools tocommunication systems beyond language can be revealing. Ethologists have long
been aware of the impact of context on behaviour (e.g. Smith, 1965), and so it issomewhat surprising that pragmatics (the role of context in communication) has
not been more widely applied to research on primate communication. Part of the
problem has been that historically researchers have methodologically muddled
semantics and pragmatics, allowing context to stand in place of meaning without
considering the relationship between them. To disentangle the two, it is necessary
to look separately at (1) the behavioural outcome of the signal that seems to satisfy
the signaller, and (2) the context in which the signal was deployed.
Recent research in ape gestural communication has made great strides insemantics, determining the meanings of gestures independently of the immediate
behavioural context or social relationship between signaler and recipient (Cartmill& Byrne, 2010; Graham et al., 2018; Hobaiter & Byrne, 2014). Pragmatics hasreceived more attention in primate vocal research (Seyfarth & Cheney, 2018),
although here the underlying intended meanings are not always clear. Given its
large, diverse repertoires, used by all apes to achieve their day-to-day goals acrosscontexts and with flexible meanings, gestural communication is a promisingsystem for studying pragmatics in nonhuman species. And indeed it seems thatbonobo gestures have different meanings in different contexts (Graham et al.,2020). We expect context to similarly impact meanings for other ape gesturalcommunication systems, raising intriguing questions about the underlying
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

246

cognitive mechanisms involved, and their shared similarities with thoseunderpinning human language.
Here, we make a theoretical case for expanding the study of pragmatics acrossanimal communication research. We sift through existing research on nonhumancommunication to extract pragmatics where possible, and use this as a jumping
off point from which to offer suggestions on ways of studying pragmatics innonhuman species. We make specific recommendations in regards to recordingand extracting data on different aspects of context that may be relevant tononhuman interlocutors, such as behavioural (e.g. prior behaviour of signaller,
recipient, or audience), social (e.g. kin relationships, rank), and environmental(e.g. vegetation density, weather) contexts, including how we may operationalise
these. We draw on linguistic approaches and incorporate these with our own
expertise in great ape gestural communication. We propose that broadening ourview to include the context in which signals are deployed will provide new insight
into understanding nonhuman communication from their perspectives.

References
Berthet, M., Coye, C., Dezecache, G., & Kuhn, J. (2021). Animal Linguistics: A
Primer. https://doi.org/10.31234/osf.io/ezmxq
Cartmill, E. A., & Byrne, R. W. (2010). Semantics of primate gestures: intentional
meanings of orangutan gestures. Animal cognition, 13(6), 793-804.
Fishbein, A. R., Fritz, J. B., Idsardi William J. & Wilkinson Gerald S. 2020 Whatcan animal communication teach us about human language? Phil. Trans. R.
Soc. B, 375, 20190042
Fitch, W. T. (2020). Animal cognition and the evolution of human language: why
we cannot focus solely on communication. Phil. Trans. R. Soc. B 375,
20190046
Graham, K. E., Hobaiter, C., Ounsley, J., Furuichi, T., & Byrne, R. W. (2018).
Bonobo and chimpanzee gestures overlap extensively in meaning. PLoS
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Graham, K. E., Furuichi, T., & Byrne, R. W. (2020). Context, not sequence order,
affects the meaning of bonobo (Pan paniscus) gestures. Gesture, 19(2-3),
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Hobaiter, C., & Byrne, R. W. (2014). The meanings of chimpanzee gestures.
Current Biology, 24(14), 1596-1600.

247

Seyfarth, R., & Cheney, D. (2018). Pragmatic flexibility in primate vocal
production. Current Opinion in Behavioral Sciences, 21, 56-61.
Smith, W. J. (1965). Message, meaning and context in ethology. The American
Naturalist, 99, 405-409.

248

ICONIC WORDS MAY BE COMMON IN EARLY CHILD
INTERACTIONS BECAUSE THEY ARE MORE ENGAGINGKIRSTY GREEN*1 and MARCUS PERLMAN*2*Kirsty Green: kirsty.green@warwick.ac.uk*Marcus Perlman: m.perlman@bham.ac.uk
1Department of Psychology, University of Warwick, Coventry, UK
2Department of English Language and Linguistics, University of Birmingham,

Birmingham, UK
Spoken vocabularies contain a substantial number of iconic words, and a complete accountof language evolution must explain how and why this is the case. Iconicity is especiallyprevalent in early communication, with one hypothesis being that this is because it
scaffolds word learning by helping infants to establish referentiality, and another being thatthe phonological simplicity of iconic words makes them more pronounceable for infants.
This study develops methods to examine whether another function of iconicity could bethat it increases infants’ engagement in interactions because there is something inherently
fun about iconic communication. Iconicity ratings were assigned to transcribed words in amother’s utterances in 41 sessions with her infant between the ages of 12 and 24 months(from the Providence corpus (Demuth et al., 2006; MacWhinney 2000)). High and low
iconicity bouts were identified by calculating the rolling average of iconicity per 5 words
and focusing on those utterances with the highest and lowest averages. Key features from58 high and 58 low iconicity bouts were then coded from corresponding video. Results
showed that high iconicity interactions contained more behaviors considered to be
indicative of engagement: eye contact, additional paralinguistic features, joint attention,
smiling/laughing, gesture, child-directed utterances, and less displacement. These
preliminary findings speak to the multi-functional nature of iconicity in parent-childinteractions. They suggest that iconic words might be prevalent in current spoken
vocabularies because people, especially young language learners and their caregivers, findthese words to be intrinsically fun and engaging.

1. Background
Despite the longstanding doctrine of the ‘arbitrariness of the sign’, thevocabularies of spoken (as well as signed and protactile) languages are now
understood to be characterized by iconicity, as well as arbitrariness (Dingemanse
et al. 2015; Perniss et al. 2010; see Edwards 2017 for protactile). That is, the formsof many words are perceived to bear some amount of resemblance to what theymean, such as words like woof and beep that convey aspects of sound, and also
words in other semantic domains like wiggle expressing manner of movement,
and teeny expressing small size. Such words are especially prevalent in the speechThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

249

of young children and their caregivers (Laing, 2020; Perry et al. 2018). In light ofaccumulating evidence of iconicity in spoken vocabularies, it is clear that acomplete account of language evolution must explain, not just how iconicity
could have grounded the first spoken words, but also why current vocabularies
continue to be iconic, and why some kinds of words are more iconic than others.
One hypothesis is that iconicity is maintained in languages because it helps
young children learn their first words by “bootstrapping” the connection between
the form of a word and its referent (Imai & Kita, 2014; Perniss & Vigliocco 2014).
If languages evolve to be more learnable by children (Christiansen & Chater,2008), then iconic form-meaning pairings that are more learnable may be
preferred in the lexicon over time. There is considerable evidence for thebootstrapping hypothesis. Children acquire iconic forms early and both they andtheir caregivers use them disproportionately often (Motamedi et al., 2020;
Perlman et al., 2017; Perry et al., 2018). And critically, experiments show that
iconicity can help children learn new words (e.g., Miyazaki et al., 2013). For
example, 3-year-old English speaking children better learned Japanese verbs
when they were iconic (Kantarzis et al 2011).
Another reason that iconic words – specifically, sound-imitative words oronomatopoeia – might be especially prevalent in early child vocabularies isbecause they tend to be phonological simple and easy to produce (Laing, 2014).
An analysis of diary and video data from six languages showed that infants’ earlylexicons were dominated by a set of easily pronounceable prosodic structures –most often consonant harmony and consonant vowel – and that onomatopoeia fit
these structures more often than non-onomatopoeic forms (Laing, 2019).
Here, we explore an alternative to these two hypotheses for why early childcommunication is so iconic. We consider whether iconic words might occur
frequently in child communication because they are especially fun and engaging(whee!). More than prosaic words, iconic words tend to activate the senses andemotions (Nielsen & Dingemanse, 2020). Japanese speakers exposed tomimetics perceive meanings more vividly, describing experiencing an ‘at-thescene’ feeling when hearing or reading these words, as though they contain, insome sense, a degree of ‘sensory input’ (Kita, 1997, p. 381). Moreover,Dingemanse and Thompson (2020) found that words rated high for iconicity
also tended to be rated high for ‘funniness’. They suggest that iconic words are‘valued as much for their performative character as their informative content’ (p.
205).
Thus, iconic words could play a role in early word learning because they
are more engaging for infants and their caregivers. There may be somethinginherently more fun about iconic communication that could enrich languagelearning and facilitate the acquisition of early words. We explored thispossibility by comparing infant-caregiver interactions with iconic words to250

those with arbitrary words, asking whether high iconicity interactions show
more engagement by child and mother. To find out, we employed a novel
method of using lexical iconicity ratings to continuously track the level of
iconicity throughout an interaction, identifying bouts of high/low iconicity, andthen coding these for different indicators of engagement by child and caregiver.
2. Methods
The data were taken from the Providence corpus (Demuth et al., 2006), partof the CHILDES database (MacWhinney 2000), a corpus of spontaneousvideo-recorded and transcribed interactions from six pairs of American
English-speaking parents and children in their homes. As a proof ofconcept, we focused on a sub-corpus of 41 sessions between the infant‘Naima’ and her mother (203201 total words). The recordings were madefortnightly while Naima was between the ages of 0;11.26 – soon after the
onset of her first words – and 1;11.26.
From the mother’s speech (155004 words), we identified bouts of highand low iconicity by using the mean iconicity ratings of her words used in
the interactions (see Figure 1). These were taken from Winter, et al. (2022),
which collected ratings for over 14,000 English words by asking adultnative speakers to rate words on a scale of 1-7, with 1 being ‘not iconic atall’ and 7 being ‘very iconic’. The average iconicity rating of each word
was used to calculate a running average of iconicity per five words acrossthe interactions. Iconicity ratings were assigned to the lemmatized forms of
all words in the mother’s utterances. Any words for which no iconicity
rating existed were excluded (10.3% of words in the corpus). We set a
threshold of a running average of 5 or above for bouts of high iconicity and
2 or below for bouts of low iconicity, which resulted in 58 bouts of each(high iconicity bouts = 2586 words; low iconicity bouts = 2676 words).
We next looked at the video of these instances to identify the featuresthat characterized these different types of interactions. To assess the level ofengagement, we coded these bouts for: eye contact (mother and childlooking directly at each other during an utterance), joint attention (motherand child both directing their gaze towards the same object during anutterance), smiles and/or laughter, gestures and/or actions performed on an
object by the mother and by the child, paralinguistic features (distinct
moderations to tone, pitch, emphasis or extra-phonetic features such as
panting like a dog), displacement (mother mentioning referents not present
in the immediate context during an utterance) and child-directed speech

251(directly addressed to the child, as opposed to another adult, the camera or
herself).

MOT:

3.7

4.06

3.62

3.7

3.3

3.8

2.1

5.7

savoring

applesandblueberries

4.12
5.7

MOT:

MOT:

NA

oh

mm

3.944

4.1688

4.86

4.3

3.75

3.6

3.88

4

3.4

5.5

2.9

4.3

1.9

4.8

have

another

bite

it

is

so

good

3.58
4

MOT:

MOT:

MOT:

4.1
NA

5.5

have

a

bite

4.75

5.3

5.64

6.14

5.7

6.5

6.5

6.5

yum

yum

oh

yum

6.18

5.08

5.7

6.3

oh

yummy!

Figure 1: An excerpt of transcribed speech from Naima’s mother, showing the calculation of a rolling
average of iconicity. The bottom number in red shows the average iconicity of the word below it, andthe top number in blue shows the rolling average of iconicity. The high iconicity portion of theutterance is in bold.

3. Results
Table 1 summarizes the coded features observed within the high and low iconicity
bouts. High iconicity bouts showed greater evidence of engagement than low
iconicity bouts across all features. High iconicity interactions were characterizedby more eye contact between mother and child, more joint attention on objects,
more smiles and laughs by the child, and more gestures and actions on objects by
the child and by the mother. In addition, the mother’s speech contained moreparalinguistic features, was more focused on the ‘here-and-now’ (lessdisplacement), and was almost always child-directed.

252

Table 1. Features of utterances of high and low iconicity.

High Iconicity

Low Iconicity

Eye contact

38%

12%

Joint attention on an object

45%

36%

Number of smiles/ laughs by CHI

16

6

Number of gestures/ actions on objects by CHI

29

10

Number of gestures/ actions on objects by MOT

47

35

Number of interesting paralinguistic features by MOT

49

9

MOT’s utterances that contain displacement

4%

38%

MOT’s utterances that are child-directed

99%

92%

4. Discussion

Iconic words are now known to be prevalent across spoken vocabularies,
being especially common in interactions between young children andcaregivers. One reason for this may be that iconic words are easier forchildren to learn, with iconicity providing an intuitive link between form
and meaning. Additionally, onomatopoeic words in particular might beespecially common because they are adapted to be some of the easiest
words for young children to produce. However, it may also be that iconic
words occur more frequently in this context because they are fun and moreengaging for young children and their caregivers.
To explore this possibility, we developed a novel approach toinvestigate the role of iconicity in early child interactions, using iconicity
ratings of words to identify high and low iconicity bouts during the natural
flow of discourse. The findings showed consistently that high iconicity
interactions were indeed more engaging. Compared to low iconicity

253

interactions, high iconicity interactions typically involved caregiver andchild as more active participants, more focused on each other and theirshared environment, and openly enjoying the communicative situation.
These results raise questions for future research into engagement as afunction of iconicity and how it figures into early vocabulary development.
One notable finding – pointing to a question for future study – was thathigh iconicity interactions tended to be focused on the here-and-now, rather
than discussing referents and topics outside of the present context. Thisfinding differs from Perniss et al. (2017), who found British Sign
Language-signing caregivers modified iconic signs more in non-ostensive
contexts than ostensive contexts. While these contrasting results could
reflect a difference between the modalities of speaking and signing, theymight also be explained by other differences between the studies. Perniss etal. used an experimental design where they asked caregivers to imagine
talking to their child about certain toys, both with the toys present andabsent. They therefore controlled the topic and ensured that caregivers’
signing would be specifically directed to the (imagined) child. In
comparison, in our more naturalistic data, not all the mother’s utteranceswere directed at her child – for example, sometimes another adult waspresent, or the mother spoke introspectively. This suggests that iconicity’srole can be nuanced depending on context. While, as in Perniss et al.’s(2017) study of BSL, iconic modification may help a child identify areferent in a non-ostensive context, iconicity can serve other functions inostensive contexts, such as increasing a child’s enjoyment of acommunication that focuses on the here-and-now.
Our proof-of-concept study has only analyzed a set of interactions
between a single (English-speaking) parent and child. Future studies –expanding not just to more infant-caregiver dyads, but to interactions fromwidely diverse languages and cultures – are needed to understand thefunctions of iconicity in early language learning and development.
However, even in this narrow demonstration, we think there is value to thenovel method tested here, using word ratings to track the running level of
iconicity over the course of conversation. This opens a new approach to
studying iconicity in infant and caregiver interactions, and in many otherdiscourses, that operationalizes iconicity in a more continuous fashion.
Such an approach views iconicity, not just in terms of individual words and254gestures, but as a broader quality of discourse in which the level of iconicity
fluctuates over time.
5. Conclusion
We have suggested that iconic words may be especially common in early childinteractions because they create an overall more engaging experience that
motivates infants to communicate more. To the extent this is true, it implies thata different sort of selection pressure – not just learnability and ease ofarticulation – may operate on iconic words to maintain them in spoken lexicons.
These words might continue to populate spoken languages because people,
especially young language learners and their caregivers, find it to be intrinsicallyfun and engaging to use iconicity. Perhaps then the emergence of spoken
language was partly rooted in an evolving sense of enjoyment that our hominin
ancestors derived from the use of iconic vocalizations and what eventuallybecame the first iconic words.
References
Christiansen, M. H., & Chater, N. (2008). Language as shaped by the brain.
Behavioral and Brain Sciences, 31, 489-509.
Demuth, K., Culbertson, J., and Alter, J., (2006). Word-minimality, Epenthesis,
and Coda Licensing in the Acquisition of English. Language & Speech. 49.
pp. 137-174.
Dingemanse, M., Blasi, D. E., Lupyan, G., Christiansen, M. H., & Monaghan, P.
(2015). Arbitrariness, iconicity, and systematicity in language. Trends in
Cognitive Science, 19, 603-615.
Dingemanse, M. and Thompson, B., (2020). Playful iconicity: structural
markedness underlies the relation between funniness and iconicity. Language
and Cognition. 12(1). pp. 203-224. doi:10.1017/langcog.2019.49.
Edwards, T. (2017). Sign-creation in the Seattle DeafBlind community: A
triumphant story about the regeneration of obviousness. Gesture, 16(2), 305–
328.
Imai, M. and Kita, S. (2014). The sound symbolism bootstrapping hypothesis forlanguage acquisition and language evolution. Philosophical Transactions of
the Royal Society B. 369. doi: http://dx.doi.org/10.1098/rstb.2013.0298.
Kantartzis, K., Imai, M., & Kita, S. (2011). Japanese sound- symbolism facilitates
word learning in English-speaking children. Cognitive Science, 35, 575–586.
Kita, S. (1997). Two-dimensional semantic analysis of Japanese mimetics.
Linguistics. 35. pp. 379-415.
Laing, C.E. (2014). A phonological analysis of onomatopoeia in early word
production. First Language, 34(5), 387-405.

255

Laing, C.E. (2019). A role for onomatopoeia in early language: evidence fromphonological development. Language and Cognition, 11, 173-187.
MacWhinney, B. (2000). The CHILDES project: tools for analyzing talk (3rded.). Mahwah, NJ: Lawrence Erlbaum Associates
Motamedi, Y., Murgiano, M., Perniss, P., Wonnacott, E., Marshall, C., GoldinMeadow, S. and Vigliocco, G. (2020). Linking language to sensory
experience: Onomatopoeia in early language development. Developmental
Science.
Miyazaki, M., Hidaka, S., Imai, M., Yeung, H.H., Kantartzis, K., Okada, H., andKita, S. (2013). The facilitatory role of sound symbolism in infant wordlearning. in M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Eds.). CogSci
2013: the 35th Annual Conference of the Cognitive Science Society (pp.
3080–3085). Cognitive Science Society.
Nielsen, A. and Dingemanse, M (2020). Iconicity in Word Learning and Beyond:
A Critical Review. Language and Speech. pp.1–21.
Perlman, M., Fusaroli, R., Fein, D., & Naigles, L. (2017). The Use of Iconic
Words in Early Child-Parent Interactions. Proceedings of the 39th Annual
Meeting of the Cognitive Science Society, 6.
Perniss P, Thompson T, Vigliocco G. (2010). Iconicity as a general property oflanguage: evidence from spoken and signed languages. Front. Psychol. 1, 1–
15. (doi:10.3389/fpsyg.2010.00227).
Perniss, P., Lu, J. C., Morgan, G., and Vigliocco, G. (2017). Mapping language
to the world: The role of iconicity in the sign language input. Developmental
Science.. 21. e12551.
Perniss, P. and Vigliocco, G. (2014). The bridge of iconicity: from a world ofexperience to the experience of language. Phil. Trans. R. Soc. B. 369:
20130300.
Perry, L.K., Perlman, M., Winter, B., Massaro, D.W., Lupyan, G. (2018).
Iconicity in the speech of children and adults. Developmental Science. 201805. Vol.21(3).
Winter, B., Lupyan, G., Perry, L.K., Dingemanse, M., and Perlman, M. Iconicity
ratings for 14,000+ English words. OSF. https://osf.io/y3wtx/

256

SELECTIVE FORCES IN LANGUAGE EVOLUTION
Juan A. Guerrero Montero*1 , Andres Karjus2 , Kenny Smith1 , and Richard A. Blythe1* Corresponding Author: j.a.guerrero-montero@sms.ed.ac.uk

1 Centre for Language Evolution, University of Edinburgh, Edinburgh, United Kingdom
2 School of Humanities, Tallinn University, Tallinn, Estonia

The quantitative study of language evolution and change has seen great development, with models like Iterated Bayesian Learning (Griffiths & Kalish, 2007)
and the Utterance Selection Model (Baxter et al., 2006) admitting analytical andcomputational explorations of fundamental questions in the field (Kirby, 2000;
Thompson et al., 2016; Nowak et al., 2001). With the advent of massive digital
historical corpora, data-driven methods add a new empirical perspective on thefundamental forces that shape human language over long timescales.
A key question in evolutionary linguistics is how competition between linguistic variants shapes linguistic structure. The Wright-Fisher model from population
genetics provides a simple but powerful description of competition, accounting forboth stochasticity in inter-generational transmission and selective forces (Crow &Kimura, 1970). This model is justified for its use in linguistic scenarios throughits derivation from Iterated Bayesian Learning (Reali & Griffiths, 2010).
Nevertheless, the Wright-Fisher model is not easy to apply to corpus data
since approximations are necessary for computational efficiency. These typically assume a normal distribution, which is inaccurate in certain scenarios (Karjus et al., 2020). Here, we introduce a robust algorithm based on the Betawith-Spikes (BwS) approximation to transition probabilities in the Wright-Fisher
model, whose analytical form is given by Tataru et al. (2015). In figure 1.A, we
illustrate the nuance and robustness of this method by comparing to other algorithms in the analysis of a reference data set, namely competition between past
forms of verbs from the Corpus of Historical American English (COHA).
We further apply the BwS method to two data sets extracted from the Google
Books corpus (Michel et al., 2011). We first quantify the effect of competing motivations on language structure. We analyse the competition between inflectional
simplicity (i.e. regularity) and phonological simplicity in the past forms of English verbs whose irregular forms coalesce to avoid repeating of /d/ or /t/ sounds(e.g. he knit instead of he knitted). The BwS algorithm quantifies the net force
arising from these competing motivations through a selection strength parameter,
revealing that selection towards phonologically simpler irregular forms is more
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

257

Figure 1. (A) Performance of BwS algorithm with those utilising normal approximations (Federet al., 2014) and neural networks (Karsdorp et al., 2020), with reference to the likelihood of selection
in competition between regular and irregular past forms of COHA verbs. BwS aligns with the robustneural network results, while providing a nuanced continuous classification instead of a binary one.
Normal approximation is often unreliable due to non-normally distributed empirical frequency increments. (B) Application of BwS to competition between past tense verb forms that undergo coalescencein their irregular form. Positive selection strength implies favouring the irregular, phonologically simpler form, while the opposite is true for negative selection strength. Selection is concluded whenlikelihood is above 75%. Phonological simplicity beats inflectional simplicity in most cases, with halfof all verbs in the data set showing high likelihood of selection towards irregularity, while only one
shows regularisation. (C) Application of BwS to Spanish spelling reforms in the Google Books corpus. All reforms are detected with an error of 25 years or less, with three of them having an error of
10 years or less, even when trajectories are noisy (Real Academia Española, 1763, 1815, 1870).

common. If replicated in other domains, this result may shed light on how selection at different levels of linguistic structure might play out in language evolution.
Language is not only shaped by cognitive and internal factors, but also bysocial pressures that may change over time. This is illustrated in our second application, the dynamics of Spanish word spelling before and after reforms introducedby the Real Academia Española. Here, a new socially motivated bias appears thatheavily favours a new spelling. This is reflected in the model by a change in theselection strength, thereby quantifying the level of acceptance of the reform by theliterate population. Via likelihood maximisation, the BwS algorithm detects theyear of introduction of the spelling reform with high precision (see figure 1.C).
Thus this algorithm can detect and quantify major changes in social dynamics inthe data, even when the exact date or origin of those changes is unclear.
In summary, the BwS algorithm applies to both socially and internally motivated competition, quantifies the net selective force arising from competing motivations and detects variations in this force over time. It provides a tool for thenumerical analysis of diachronic linguistic data that is more insightful and reliable than previous methods. The continued development of such numerical tools
opens the door to the empirical analysis of the social, cognitive and internal pressures that shape the structure of language.

258

References
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cognition. Proceedings of the National Academy of Sciences, 113, 4530–
4535.

259

UNDERSTANDING DESIGN FEATURES OF MUSIC ANDLANGUAGE: THE CHORIC/DIALOGIC DISTINCTION
FELIX HAIDUK1, and W. TECUMSEH FITCH1,2*Corresponding Author: felix.haiduk@univie.ac.at
1Department of Behavioral and Cognitive Biology, University of Vienna, Vienna, Austria
2Vienna Cognitive Science Hub, University of Vienna, Vienna, Austria

Music and spoken language share certain characteristics: both consist of
sequences of acoustic elements that are combinatorically combined, and theseelements partition the same continuous acoustic dimensions (frequency, formant
space and duration). However, the resulting categories differ sharply: scale tones
and note durations of small integer ratios appear in music, while speech uses
phonemes, lexical tone, and non-isochronous durations. Why did music andlanguage evolve to become the two systems we have today, differing in these
specific features? Based on a reverse-engineering perspective on design featuresof language and music proposed by Hockett (1960) and Fitch (2006), we
developed a framework suggesting that design features of prototypical music andlanguage are a response to their differential deployment along three different
continuous dimensions, which we call the goal dimension, the novelty dimension
and the interactivity dimension. Also, we suggest that the hybrid nature ofintermediate systems like poetry, chant, or solo lament follows from theirdeployment in the less typical context. At the core of all three dimensions are
predictive processes about linguistic or musical sequences unfolding in time (seeKoelsch et al., 2019; Kuperberg & Jaeger, 2016). Predictions involve uncertainty
and surprise/ information which can be quantified using information theory(Shannon, 1948). Trajectories of information and uncertainty at multiple levels of
the unfolding sequence thus form the basis of our theorizing.
(1) The goal dimension concerns the broader purpose of linguistic or musicalsequence productions, whether to convey semantic messages, or to elicit andmodulate aesthetic responses in a broad sense (see Huron, 2016). Both language
and music can be deployed in propositional and aesthetic contexts, and similar
responses follow: with more propositional goals, the multiple levels of the speechor musical sequence are more interdependent, and vary their information density
to support successful inference of propositional content. For aesthetic goals,
independent variation across levels enables more unconstrained variation in
uncertainty and surprise, effectively exploiting the human reward system.

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

260(2) The novelty-repetition dimension involves the repeatability or novelty of(groups of) elements and their relations at different scales and at multiple levelsof musical or linguistic sequences. While language usually allocates repeatability
to the phonological level and novelty to the morphosyntactic and semantic levels(related to propositionality), music typically allows both novelty and repeatability
across all levels of the musical sequence, and meter seems to be especially crucial
as a predictive layer throughout, enabling both prediction and surprise.
(3) We argue that the familiar goal and novelty dimensions alone are not sufficient
to explain differences in design features between music and language: theinteractivity between individuals is crucial. The interactivity dimension, the polesof which we term ‘choric’ and ‘dialogic’, concerns the temporal coordination of
linguistic or musical productions of multiple participants. For dialogic contexts
the only coordinative constraint concerns the timing of the turn-taking between
individuals. Thus, we predict a lower information density and thus higher
predictability towards the end of phrases, across all levels of the sonic stream, forboth music and language. This aligns well with neural markers like the N400 (e.g.
Grisoni et al., 2017), changes in speech rate at turn completion points inconversations (Walker, 2010; Wightman et al., 1992) or the notion of musicalclosure in harmony and melody (Huron, 2006). In contrast, choric performancerequires tight temporal coordination of all contributing individuals, enabled byhigh predictability in timing and frequency of sonic events (Keller et al., 2014).
Isochronous meter and discrete pitches in scales are design solutions that enablea group of participants to join in making a coherent sound sequence, allowing
both novelty and repeatability. Simultaneous speaking of multiple talkers requires
uttering of precisely the same words at the same time, as happens for example inreligious chanting. Attention should then be much more focussed on coordination
than in dialogic speech acts, an isochronic and/or metrical scaffolding should
develop (cf. Bowling et al., 2013), and body motion, facial expressions or
prosodic intonation should be more pronounced in a spoken choric context.
Our framework avoids an overly simplistic dichotomy between language andmusic by also encompassing non-canonical forms of music and language likechant, poetry, or exchange of musical solos. It supports comparisons of different
forms of communication across distinct modalities and can help to generate new
hypotheses about optimal design of signals satisfying multiple different
requirements. We hope that it will also be fruitfully employed in animalcommunication research, especially for species engaging in chorusing or duetting,such as various bird species, dolphins, bats, or gibbons, broadening the scope ofcomparisons with music and/or language.

261

Bowling, D. L., Herbst, C. T., & Fitch, W. T. (2013). Social origins of rhythm?
Synchrony and temporal regularity in human vocalization. PLoS ONE,
8(11), e80402. https://doi.org/10.1371/journal.pone.0080402
Fitch, W. T. (2006). The biology and evolution of music : A comparative
perspective. Cognition, 100, 173–215.
https://doi.org/10.1016/j.cognition.2005.11.009
Grisoni, L., Miller, T. M., & Pulvermüller, F. (2017). Neural Correlates of
Semantic Prediction and Resolution in Sentence Processing. The Journalof Neuroscience, 37(18), 4848–4858.
https://doi.org/10.1523/JNEUROSCI.2800-16.2017
Hockett, C. (1960). The origin of speech. Scientific American, 203(3), 88–97.
Huron, D. (2006). Sweet Anticipation. Cambridge, Massachusetts: MIT Press.
Huron, D. (2016). Aesthetics. In S. Hallam, I. Cross, & M. Thaut (Eds.), The
Oxford Handbook of Music Psychology (2 ed., pp. 233–245). Oxford, UK:
Oxford University Press.
https://doi.org/10.1093/oxfordhb/9780198722946.013.19
Keller, P. E., Novembre, G., & Hove, M. J. (2014). Rhythm in joint action:
Psychological and neurophysiological mechanisms for real-time
interpersonal coordination. Philosophical Transactions of the Royal
Society B: Biological Sciences, 369(1658), 20130394.
https://doi.org/10.1098/rstb.2013.0394
Koelsch, S., Vuust, P., & Friston, K. (2019). Predictive Processes and thePeculiar Case of Music. Trends in Cognitive Sciences, 23(1), 63–77.
https://doi.org/10.1016/j.tics.2018.10.006
Kuperberg, G. R., & Jaeger, T. F. (2016). What do we mean by prediction inlanguage comprehension? Language, Cognition and Neuroscience, 31(1),32–59. https://doi.org/10.1080/23273798.2015.1102299
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System Technical Journal, 27(3), 379–423.
Walker, G. (2010). The phonetic constitution of a turn-holding practice: rushthroughs in English talk-in-interaction. In D. Barth-Weingarten, E. Reber,& M. Selting (Eds.), Prosody in Interaction. Studies in discourse andgrammar (23). (pp. 51–72). Amsterdam, Philadelphia: John Benjamins
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Journal of the Acoustical Society of America, 91(3), 1707–1717.

262

PRESSURE FOR AND AGAINST NON-ARBITRARINESS:
EVIDENCE FROM PHONESTHEMESDAVID A. HASLETT*1, ZHENGUANG G. CAI1,2*Corresponding Author: david.a.haslett@gmail.com
1Dept. of Linguistics and Modern Languages, The Chinese University of Hong Kong
2Brain and Mind Institute, The Chinese University of Hong Kong

Words with related meanings tend to sound dissimilar, and this arbitrariness allows forlarge vocabularies that make nuanced distinctions (like glint versus shine). Languages
violate arbitrariness in constrained ways, such as patterns in form that provide cues
indicating word class (e.g., English nouns and verbs tend to be stressed on different
syllables; Kelly & Bock, 1988), but phonesthemes (i.e., sound clusters associated withsemantic features, like /gl/ with light, as in glint and gleam) more directly defy arbitrarinessby pertaining to word meaning proper. We provide evidence from two experiments thatphonesthemes influence how people process word meanings (e.g., they’re slower to decidethat glove is unrelated to light, compared to a control like mitten) and that phonesthemes
aid in the recognition of visually degraded words. These findings support that, in English,
non-arbitrariness interferes with word meaning access and that the lexicon evolves tobalance competing needs: Arbitrariness maintains subtle distinctions, but non-arbitrarinesscan facilitate recognition in noisy environments.

1. Introduction
Word forms tend to be arbitrarily assigned to meanings (de Saussure, 1983;
Hockett, 1960). For example, bench and sofa refer to similar concepts but soundnothing alike. Evidence from simulations and novel word learning suggests that
this arbitrariness occurs not by chance but in response to communicative pressure.
When people need to distinguish referents, cultural evolution increases
arbitrariness (Kirby, Tamariz, Cornish, & Smith, 2015), which is consistent withthe finding that arbitrariness makes it easier to acquire a large vocabulary (Gasser,
2004) and to learn specific word meanings (Monaghan, Christiansen, & Fitneva,2011). There is selective pressure for an arbitrary lexicon. But non-arbitrarinesscan be advantageous, too, by facilitating categorization. For example, people readnouns in sentences faster if those nouns sound more like other nouns than likeverbs (Farmer, Christiansen, & Monaghan, 2006). Importantly, such patterning inThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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form (i.e., systematicity) pertains to low-level semantics, like word class(Dingemanse, Blasi, Lupyan, Christiansen, & Monaghan, 2015), and so isfunctionally compatible with arbitrariness at the level of individual word
meanings, like bench and sofa (Monaghan et al., 2011).
However, words that overlap substantially in meaning (not just that are
members of the same broad category) sometimes sound similar, as inphonesthemic words. Phonesthemes are sound clusters associated with semantic
features, like /gl/ with light (e.g., glimmer and glaze; Firth, 1930), and there is
speculation that words which contain phonesthemes but lack the corresponding
features, such as gland and glove (unrelated to light), are apt to acquire that feature(Bolinger, 1965; Blust, 2003). For example, English words beginning with thephonestheme /fl/ (as in flap or flow) have grown more likely to relate to “moving
through the air” (Smith, 2016). Such semantic changes defy the tendency for thelexicon to evolve to be more arbitrary or to limit non-arbitrariness to low-level
semantics. Novel word learning experiments are consistent with this corpus
analysis. People tend to pair novel words containing phonesthemes (e.g., glep,
like glow and glimmer) with definitions containing semantic features associatedwith that phonestheme (e.g., something related to light), and when asked to invent
labels for such definitions, people are more likely to produce novel words
containing the corresponding phonesthemes (Hutchins, 1998; Abelin, 1999;
Magnus, 2001).
Phonesthemes might motivate semantic change in an efficient lexicon even
if the resulting non-arbitrariness hinders our ability to distinguish similar concepts(like glint versus gleam). Consider how ambiguity is sometimes advantageous:
Languages reuse sound sequences that are easier to produce, and while this resultsin homophones, it eases the burden of articulation on speakers (Piantadosi, Tily,& Gibson, 2012). Similar-sounding words with related meanings might facilitate
comprehension, too. For example, Bergen (2004) found that phonesthemic primes
sped up recognition of targets containing the same phonestheme (glow → glitter)more than formal (glove) or semantic (shine) primes did.
There is suggestive evidence that phonesthemes motivate semantic change
and facilitate word recognition, but those studies rely on correlations and single
phonesthemes (Smith, 2016; Blust, 2003), on novel words rather than known
words learned in rich, realistic contexts (Hutchins, 1998; Magnus, 2001; Abelin,1999), and on priming rather than the ambient influence of the structure of thelexicon (Bergen, 2004). And while previous studies have found that nonarbitrariness facilitates processing of part of speech (e.g., Farmer et al., 2006),
those experiments do not apply to words that overlap in high-level conceptual

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semantics. We therefore conducted two experiments to provide evidence thatphonesthemes affect how people process the meanings of English words and thatthe resulting non-arbitrariness facilitates recognition in some conditions. All dataand scripts are available at osf.io/sna92.
2. Experiment 1
We first investigated whether people associate a word containing a phonestheme(e.g., /gl/) with the corresponding semantic feature (light) even when the word isunrelated to that feature (glove). In an online experiment (implemented on
Gorilla.sc; Anwyl-Irvine, Massonnié, Flitton, Kirkham, & Evershed, 2018), 57
adult native English speakers (recruited from Prolific.co) decided whether a cue
word associated with a phonestheme’s feature (e.g., shine, with light) was
semantically related to a target that either contains that phonestheme (glove) or isa control (mitten). We manipulated the target (pseudo-phonesthemic vs. control),for a one-factor, two-level design, manipulated within items and within subjects.
In each trial, the cue (e.g., shine) was unrelated to the target (glove/mitten), andthe pseudo-phonesthemic and the control targets were synonyms matched on
semantic distance from the cue, as measured by a distributional semantic model(computed using the LSAfun package in R; Günther, Dudschig, & Kaup, 2015).
The targets were also matched on formal similarity to cues (Levenshtein
distance), word frequency, age of acquisition, concreteness rating, bigram
frequency, orthographic neighbourhood density (OLD20), and length (all fromthe English Lexicon Project; Balota et al., 2007). We divided 126 items (unrelated
cue–target pairs) and 126 fillers (related pairs) into six lists, each comprising 21
pseudo-phonesthemic targets, 21 control targets, and 42 fillers. We assigned eachparticipant to one list. To incentivize fast and accurate responses, participants had
only two seconds to make each decision, with three seconds of feedback following
timeouts and incorrect responses. We excluded participants with accuracy below
75% (seven of 64 participants) and trials faster than 200ms (one trial). Linear
mixed effects modelling conducted using the lmerTest package in R (Kuznetsova,
Brockhoff, & Christensen, 2017) indicates that pseudo-phonesthemic targets were
judged to be unrelated to cues significantly more slowly than controls were (seeTables 1 and 2 and Figure 1). Importantly, semantic relatedness decisions involve
word meaning access, not just recognition or low-level categorization, so theseeffects stem from a phenomenon that is distinct from statistical regularities within
a broad category, as Dingemanse et al. (2015) define systematicity. Phonesthemes
pertain to word meaning proper and so are incompatible with arbitrariness.

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Figure 1: Semantic relatedness decision RT (correct unrelated decisions only) for targets containing
phonesthemes (glove) versus arbitrary controls (mitten) following unrelated cues (shine) in
Experiment. 1. Error bars indicate 95% confidence intervals.
Table 1: Comparison of RT by target type (sum contrast coded: pseudo-phonesthemic = 1, control =-1) for correct unrelated semantic relatedness decisions in Experiment 1, fitting the random interceptsof subject and item, with target type added as a random slope to the random intercept of subject, as
justified by forward model comparison (cf. Matuschek, Kliegl, Vasishth, Baayen, & Bates, 2017).
Intercept
Pseudo-phonesthemic

β775.18
13.59

SE
16.21
3.79

t
47.8
3.6

p
< .001< .001

Table 2: Comparison of accuracy (proportion of unrelated decisions) by target type (sum contrast
coded: pseudo-phonesthemic = 1, control = -1) for semantic relatedness decisions in Experiment 1,
fitting the random intercepts of subject and item, with target type added as a random slope to bothrandom intercepts, as justified by forward model comparison.
Intercept
Pseudo-phonesthemic

β
2.95
-0.16

SE
0.17
0.15

z
17.3
1.1

p
< .001.284

3. Experiment 2
We next investigated whether phonesthemes facilitate word recognition in noisy
conditions. In another online experiment, 59 participants made lexical decisions
about phonesthemic words (e.g., glitter, related to light and containing the /gl/phonestheme) or controls (sparkle) that were presented clearly or were visuallydegraded (i.e., the first and second half of the word flickering separately on thescreen, each half seen twice for 100ms). It was a 2 (target: phonesthemic vs.

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control) x 2 (presentation: clear vs. degraded) design, with both factors
manipulated within subjects and presentation manipulated within items. Target
type was manipulated between items, but phonesthemic targets were paired withsynonymous control targets and, as in Experiment 1, were matched on word
frequency, AoA, concreteness, OLD20, bigram frequency, and length. We evenly
divided 216 items (108 phonesthemic and 108 control targets) and 216 non-wordfillers into eight lists and assigned each participant to one list. We again
incentivized fast, accurate responses with a time limit and penalties, with the same
exclusion criteria as Experiment 1, excluding five of 64 participants. Logistic andlinear mixed effects modelling indicates that decisions were significantly fasterand more accurate for phonesthemic than control words and were faster for
degraded words that contained phonesthemes (i.e., a significant interaction oftarget type and presentation; see Tables 3 and 4 and Figure 2). Note that while themain effect of target type is significant in both analyses, the simple effect of target
type (i.e., when using treatment coding, with control targets and clear presentation
as the reference levels, thereby analyzing the effect of target type in the clear
condition; cf. Brehm & Alday, 2022) is not significant in the RT analysis, whichsuggests that the advantage for phonesthemes in the degraded condition is the realsource of the main effect on RT (see supplementary scripts on osf.io/sna92).

Figure 2: Lexical decision RT (correct decisions only) for phonesthemic targets (dark grey; e.g.,
glitter) versus controls (light grey; e.g., sparkle) presented clearly or visually degraded inExperiment 2. Error bars indicate 95% confidence intervals.

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Table 3: Comparison of RT by target type (sum contrast coded: phonesthemic = 1, control = -1) andpresentation type (visually degraded = 1, clear = -1) for correct lexical decisions in Experiment 2, withpresentation type added as a random slope to the random intercepts of subject and item, as justified byforward model comparison.
Intercept
Degraded presentation
Phonesthemic target
Degraded : Phonesthemic

β710.1469.36-11.52
-6.71

SE
14.24
4.03
4.18
2.90

t
49.9
17.2
2.8
2.3

p
< .001< .001.006.022

Table 4: Comparison of accuracy by target type (sum contrast coded: phonesthemic = 1, control = -1)
and presentation type (visually degraded = 1, clear = -1) in Experiment 2, fitting the random interceptsof subject and item, with presentation type added as a random slope to the random intercept of item,
as justified by forward model comparison.
Intercept
Degraded presentation
Phonesthemic target
Degraded : Phonesthemic

β
2.95
-0.47
0.44
-0.04

SE
0.17
0.12
0.13
0.07

z
16.9
4.0
3.3
0.5

p
< .001< .001.001.583

4. Discussion and Conclusion
We found that phonesthemes affect how people process word meanings. In
Experiment 1, participants were slower to decide that words containing
phonesthemes (e.g., glove, compared to mitten) were unrelated to meanings
typically associated with that phonestheme (e.g., shine). We also found thatphonesthemes confer an advantage in noisy conditions. In Experiment 2, people
made faster lexical decisions about visually degraded words when those words
were phonesthemic than when they were controls (e.g., glitter versus sparkle).
Experiment 1 supports that non-arbitrariness can interfere with word
meaning access (e.g., Gasser, 2004; Monaghan et al., 2011) and is consistent withstudies suggesting that phonesthemes, and similar-sounding words in general,motivate semantic change (Smith, 2016; Blust, 2003; Haslett & Cai, 2021). As
form influences interpretation, subtle shifts in word meanings can accumulate. At
first glance, these two implications seem to be at odds. If non-arbitrariness
impedes processing, then it should decrease rather than increase over generations,assuming the lexicon evolves for the sake of efficiency (e.g., Gibson et al., 2019).
However, Experiment 2 can help resolve this apparent inconsistency. While nonarbitrariness slows access to the precise meaning of a word, it facilitatesrecognition in challenging conditions. Compare this to the polysemy advantage
in lexical decisions. People are faster to recognize words with multiple relatedsenses (e.g., plant means both “situate” and “organism with roots”) than

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homonyms with multiple unrelated meanings (e.g., bark in the context of a dog
versus a tree), perhaps because related senses share a broad attractor basin insemantic space (Rodd, Gaskell, & Marslen-Wilson, 2002, 2004). We found thatwords which sound similar (rather than identical) to words with related meanings
confer the same sort of advantage when words are harder to read. It is possible,
though entirely speculative, that words’ overlapping in both form and meaning isreflected in the organization of semantic space, providing a conglomerate target
when encountering those words in noisy conditions.
These are small effects (a 28ms disadvantage for pseudo-phonesthemic
words in Experiment 1, a 35ms advantage for phonesthemic words in the
degraded condition in Experiment 2), but they are on par with comparable studies.
For example, Rodd et al. (2002) reported a 29ms disadvantage for homonyms,
compared to unambiguous words, and a 33ms advantage for polysemous words
with many senses, compared to those with few senses. Are differences on theorder of tens of milliseconds sufficient to drive language evolution, leading
people to favour some words over others or to alter words’ sounds or meanings?
Such minute advantages, even if imperceptible to the mind’s naked eye, areconsistent with what Christiansen and Chater (2016) call the now-or-neverbottleneck: We need to process words fast in order to shuttle information along.
Still, how fast is fast enough is an open empirical question, one that must weighthe value of word recognition against distinguishing word meanings.
Previous research has explored the competing pressures of word meaning
individuation and word learning (e.g., Monaghan et al., 2011; Kirby et al., 2015).
Here, we have provided evidence that patterns in sound and meaning influenceinterpretation and can thereby impede word meaning access but that those patterns
also facilitate recognition in noisy conditions. There are communicative pressuresboth for and against phonesthemes, depending on depth of processing.
References
Abelin, Å. (1999). Phonesthemes in Swedish. In Proceedings of XIV
International Conference of Phonetic Sciences, 99, 1333-1336.
Anwyl-Irvine, A. L., Massonnié, J., Flitton, A., Kirkham, N., & Evershed, J. K.
(2020). Gorilla in our midst: An online behavioral experiment builder.
Behavior research methods, 52(1), 388-407.
Balota, D., et al. (2007). The English Lexicon Project. Behavior Research
Methods, 39, 445-459.
Bergen, B. (2004) The Psychological Reality of Phonesthemes. Language,80(2), 291-311.

269

Blust, R. (2003). The Phonestheme ŋ- in Austronesian Languages. Oceanic
Linguistics, 42(1), 187-212.
Bolinger, D. (1965). Forms of English: Accent, Morpheme, Order. Harvard UP.
Brehm, L., & Alday, P. M. (2022). Contrast coding choices in a decade of mixed
models. Journal of Memory and Language, 125.
Christiansen, M. H., & Chater, N. (2016). The now-or-never bottleneck: A
fundamental constraint on language. Behavioral and brain sciences, 39.
Farmer, T., Christiansen, M., & Monaghan, P. (2006). Phonological typicality
influences on-line sentence comprehension. Proceedings of the National
Academy of Sciences, 103(32), 12203-122208.
Firth, J. R. (1930). Speech. Oxford UP.
Gasser, M. (2004). The origins of arbitrariness in language. Proceedings of theAnnual Conference of the Cognitive Science Society, 26.
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Methods, 47, 930-944.
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semantic shifts in word learning. Psychonomic Bulletin & Review.
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psychology: human perception and performance, 14(3), 389.
Kirby, S., Tamariz, M., Cornish, H., & Smith, K. (2015). Compression andcommunication in the cultural evolution of linguistic structure. Cognition,
141, 87-102.
Kuznetsova, A., Brockhoff, P. B., Christensen, R. H. B. (2017). lmerTest
Package: Tests in Linear Mixed Effects Models. Journal of Statistical
Software, 82(13), 1-26.
Magnus, M. (2001.) What’s in a word? Evidence for phonosemantics.
Dissertation, University of Trondheim.
Monaghan, P., Christiansen, M. H., & Fitneva, S. (2011). The arbitrariness of thesign: Learning advantages from the structure of the vocabulary. Journal of
Experimental Psychology: General, 140(3), 325-347.
Piantadosi, S., Tily, H., & Gibson, E. (2012). The communicative function of
ambiguity in language. Cognition, 122(3), 280-291.
Rodd, J., Gaskell, G., & Marslen-Wilson, W. (2002). Making sense of semantic
ambiguity: Semantic competition in lexical access. Journal of memory andlanguage, 46(2), 245-266.
Rodd, J. M., Gaskell, M. G., & Marslen‐Wilson, W. D. (2004). Modelling theeffects of semantic ambiguity in word recognition. Cognitive science, 28(1).
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THE STUDY OF ONOMATOPOEIA FROM THE
COMPARATIVE DEVELOPMENTAL PERSPECTIVE
HAYASHI MISATO*1,2, YAMADA TAKEMI1, UMEDA YUSUKE1, TANABEKEIKO3, TAKAMURA MAKI3, and NAKAJIMA KENSUKE3*Corresponding Author: hayashi-misato@chubu-gu.ac.jp
1Faculty of Education, Chubu Gakuin University (CGU), Kakamigaraha, Gifu, Japan
2Japan Monkey Centre, Inuyama, Aichi, Japan
3Hokuriku Gakuin University, Kanazawa, Ishikawa, Japan

1. Introduction
Onomatopoeia is the use of words that imitate the sounds associated with theobjects or actions they refer to. Onomatopoeias have been attracting attentions invarious fields in human linguistics (e.g., Laing, 2019), but have not previously
been explored from the cognitive developmental aspects for species comparisons.
This study examines the origin and context of onomatopoeia and determines its
significance in linguistic development from the evolutionary viewpoint.
1.1. A review on existing literatures on onomatopoeia
The Japanese language has a wide range of onomatopoeic expressions. Various
onomatopoeias have been used to convey subtle information that cannot be
expressed in normal words. Among the five categories used in a review ononomatopoeias in Japanese articles (Yamada et al., 2022), we specificallyfocused on the following two areas. (ii) Psychology and human relations:onomatopoeias have been referred to as sound symbolism (Imai & Kita, 2014),
facilitating linkage between a linguistic sound to a perceptual image in earlychildhood. (iii) Physical expression: onomatopoeias can be easily combinedwith physical/bodily actions and gestures, thus, facilitate sharing vivid images.
2. Methods and results of observational study on human children

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We conducted observations and video recordings in two settings by focusing on
onomatopoeias in the social interactions involving children in Japanese culture.
2.1. Onomatopoeias in mother-child interactions
Observations on mother-child interactions took place in a facility providing aplayroom and toys to mothers and children, located in CGU. We focused on an
episode which included onomatopoeic and gestural expressions from a three-yearold child during playing house. Some gestural patterns were linked toonomatopoeias, indicating the emergence of rudimentary forms of linguistic
categorization on internal action images in early childhood.
2.2. Onomatopoeias in interactions among peers
Observations on social interactions among peers took place in a kindergarten. We
focused on an episode which included onomatopoeic expressions from four-yearold children recorded during play activities. Children’s vocalizations may express
their feelings in communicative contexts as action words of onomatopoeias.
3. Implications for the comparative studies on cognitive development
Japanese children used a rich repertoire of onomatopoeias to communicate about
their internal images accompanied by gestures or bodily actions. Onomatopoeias
may bridge the babbling or simple actions in infancy with symbolic wordutterances, iconic gestures, or symbolic plays in early childhood, thus, have ascaffolding effect on the linguistic development. Since onomatopoeias connect
simple vocalizations/gestures to the subsequent linguistic abilities in humans,
more observations in early childhood will highlight the evolutional continuity
with gestural communications or manipulative plays in nonhuman primates.
References
Imai, M., & Kita, S. (2014). The sound symbolism bootstrapping hypothesis forlanguage acquisition and language evolution. Philosophical Transactions of
the Royal Society B, 369, 20130298.
Laing, C. (2019). A role for onomatopoeia in early language: evidence fromphonological development. Language and Cognition, 11, 173-187.
Yamada, T., Hayashi, M., Yageta, K., Umeda, Y., Mizuno, Y., Dalrymple, N.,
Nakajima, K., Tanabe, K., & Takamura, M. (2022) The study of
onomatopoeia from the developmental perspective: The process of language
production. The Journal of Chubu Gakuin University and Chubu Gakuin
College, 23, 77-89.

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EXPLORING OUR INNER-APE: SHARED UNDERSTANDING
OF GESTURE BETWEEN CHIMPANZEES AND HUMANS
MATTHEW G. HENDERSON*1, CATHERINE HOBAITER1, KIRSTY E. GRAHAM1,
PATRICK GEORG GROSZ2, and PRITTY PATEL-GROSZ2*Corresponding Author: mh295@st-andrews.ac.uk
School of Psychology and Neuroscience, University of St Andrews,
St Andrews, Scotland

1

2

Department of Linguistics and Scandinavian Studies, University of Oslo,
Oslo, Norway

1. Introduction
Decades of great ape gesture research revealed various parallels to humanlanguage, such as diverse repertoires of intentional signals used flexibly acrossvaried, everyday interactions (Pika et al, 2005; Liebal, Pika, & Tomasello, 2006;
Hobaiter & Byrne, 2011). Evidence for a shared origin to ‘ape’ gesturalcommunication is supported by extensive repertoire overlap between extant
apes, which increases between more phylogenetically related species (Byrne &Cochet, 2011; Graham et al, 2018). While it remains unclear how human fits
within this shared system of gesture, borrowing methods from ape gestureresearch seems promising. For example, Kersken and colleagues (2019) used‘ape-typical’ observational methods to find that children aged 1-2 years share
89% of their natural gesture use with chimpanzees (46/52 gestures).
To understand how human development and ability of language affects ourretention of innate ‘ape’ gestures, we use a novel online experiment that tests
human understanding of chimpanzee gestures when they are performed by otherhumans in a controlled environment. Using human models allows us to control
additional cues and contexts present in the ape environment that may affectparticipant performance, while also ensuring that participants do notaccommodate their interpretation of the signaler’s intended gestural meaning
based on personal perceptions and knowledge of nonhuman great apes.

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2. Methods
We pilot a novel online experiment (hosted on Gorilla.sc) that tests how adulthumans interpret the intended meaning of 10 established chimpanzee gestures(Hobaiter et al, 2014; Byrne et al, 2017) when performed by expert researchers.
Each video had participants choose one of four potentials ‘meanings’, with thecorrect response(s) varying depending on the gesture’s natural flexibility. There
were four Tight gestures with one correct response, and six Flexible gestureswith two correct responses (see Table 1). Trial order was randomized but eachgesture’s available responses were consistent. Binomial tests were used todetermine if participants were above chance at detecting correct chimpanzee
response(s) – note that we used the following corrected values of hypothesizedchance based on the available correct responses: Tight gestures=0.25, Flexible
gestures=0.5; and overall (all gestures combined)=0.4.
3. Results
We analyzed n=3000 responses from n=300 participants. Overall, participants
performed above chance across all 10 gestures (chance=0.4; binomial n=0.568,
p<0.001, LL=0.553, UL=1.000) but performed comparatively better on the six
Flexible gestures (chance=0.5, binomial: n=0.740, p<0.001, LL=0.722,
UL=1.000) than the four Tight gestures (chance=0.25, binomial: n=0.309,
p<0.001, LL=0.287, UL=1.000). Binomial tests for each gesture revealed
significance for 6/10 gestures (see Figure 1); 5/6 Flexible gestures (arm swing,
beckon, grab hold, hit object, reach palm) and 1/4 Tight gestures (hand fling);with four gestures at or below chance (arm raise, hand shake, push, reach wrist).
4. Discussion
Our experiment shows successful use of a new ‘proof of concept’ approach inunderstanding the root of ‘ape’ gesture, and supports the hypothesis that humans
retain some access to this shared system. Higher adherence of chimpanzee
responses in Flexible gestures than Tight gestures suggests our evolution may
have favored retaining more ambiguous gesture-types, and the mixed results
across individual gestures may represent our specified range of overlap tochimpanzees. However, additional factors may influence our results, such as our
specific selection of gestures (we avoided unusual gestural forms and meanings
for this type of typical human interaction, such as ‘pirouette’ and ‘sexualsolicitation’). Additionally, Tight gestures naturally have greater specificity that
may not have fit well within the context we presented (two adult humans resting
on a couch); while the less-specified Flexible gestures could include natural use
within similar contexts. Overall, we demonstrate practical adaptation of studying
chimpanzee gestural retention in humans and our results may hint at a shared,
underlying ‘ape’ gestural origin to human language.

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5. References
Byrne, R. W., & Cochet, H. (2017). Where have all the (ape) gestures gone?
Psychonomic Bulletin & Review, 24, 68–71.
Byrne, R. W., E. Cartmill, Genty, E., Graham, K. E., Hobaiter, C., Tanner, J.E.
(2017). Great ape gestures. Simple intentional communication with a rich setof innate signals. Animal Cognition, 20, 755-769.
Graham, K. E., Hobaiter, C., Ounsley, J., Furuichi, T., & Byrne, R. W. (2018).
Bonobo and chimpanzee gestures overlap extensively in meaning. PLOS
Biology, 16(2), e2004825.
Hobaiter, C., & Byrne, R. W. (2011). The gestural repertoire of the wild
chimpanzee. Animal Cognition, 14(5), 745–767.
Hobaiter, C., & Byrne, R. W. (2014). The meanings of chimpanzee gestures.
Current Biology, 24, 1596–1600.
Kersken, V., Gómez, J. C., Liszkowski, U., Soldati, A., & Hobaiter, C. (2019).
A gestural repertoire of 1-to 2-year-old human children: in search of the ape
gestures. Animal Cognition, 22(4), 577-595.
Liebal, K., Pika, S., & Tomasello, M. (2006). Gestural communication oforangutans (Pongo pygmaeus). Gesture, 6(1), 1-38.
Pika, S., Liebal, K., Call, J., & Tomasello, M. (2005). Gestural communicationof apes. Gesture, 5(1-2), 41-56.
6. Acknowledgements
We would like to thank everyone who helped contribute to make this researchpossible: the members of the Wild Minds Lab and the Super Linguistics Group;
everyone who selflessly shared and participated in this experiment; and thenonhuman apes whose study allowed for our insight into their gestural systems.
This research received funding from the European Union’s 8th Framework
Programme, Horizon 2020, under grant agreement no 802719.

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HORIZONTAL TRANSFER OF LINGUISTIC FEATURES AS AN
INTEGRAL PART OF THE EVOLUTIONARY HISTORY OF
LANGUAGE FAMILIES – PHYLOGENETIC RECONSTRUCTION OF
LINGUISTIC TREE–BASED NETWORKS
Lina Herbst*1,2,3 , Volker Gast1 , Denise Kühnert2,3 , and Mary Walworth2,3* Corresponding Author: lina.herbst@uni-jena.de

1 Department of English and American Studies, Friedrich Schiller University Jena, Jena, Germany
2 Max Planck Institute for the Science of Human History, Jena, Germany

3 Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany

More than 7000 different languages from more than 200 language families
are spoken worldwide today. The evolutionary relationships within a language
family are often illustrated and studied using a linguistic phylogeny, also calleda language family tree. Based on linguistic features this allows inference of theinternal structure and the time-depth of a language family. However, linguisticfeatures are not always “inherited”, they can also be acquired horizontally throughlanguage contact.
Non-treelike evolution is also common in evolutionary biology. Scholars haverepresented these horizontal relationships within phylogenetic networks, a treewith horizontal edges. By allowing linguistic features to move horizontally, amore accurate representation of linguistic evolution is possible. However, in historical linguistics network reconstructions are still in their infancy and no standard
has been established so far.
Pioneering work by Nelson-Sathi et al. uncovered hidden lexical borrowing in
Indo-European (IE) languages, showing that borrowing is more widespread thanpreviously thought (Nelson-Sathi et al., 2011). A software library based on thisapproach was provided by List and Moran (List & Moran, 2013). In addition, minimal lateral networks (MLNs) were applied to 40 IE languages by investigatingpresence-absence patterns of cognate sets (List, Nelson-Sathi, Geisler, & Martin,
2014). The MLN approach uses weighted parsimony and thus allows for a certain
proportion of parallel evolution (List et al., 2014). List et al. have also investigatedthe history of 40 Chinese dialects using the MLN approach (List, Nelson-Sathi,
Martin, & Geisler, 2014). In addition, Willems et al. used distance data from IE
languages to infer hybridization networks (Willems et al., 2016). Cathcart et al.
introduced a novel method for investigating areal dispersal of linguistic features,and they applied this approach to 117 IE languages (Cathcart, Carling, Larsson,
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Johansson, & Round, 2018). In 2019, a mixture-model approach was applied to
the Austronesian, Sinitic, IE and Japonic language families to detect non-treebased signals (Verkerk, 2019), and there are even more examples for the detectionof linguistic transfer events and the reconstruction of linguistic networks.
In this work, we develop a novel process that generates rooted and timed linguistic networks in which horizontal edges account for transfer events between
languages. These networks will be based on a birth-death process, which will
be adapted to account for horizontal transfers to detect the timing and the location of linguistic transfer. The novel process will be integrated in an open-source
Bayesian phylogenetic framework, such that knowledge about the past can be included e.g. on the depth of a sub-family. We will evaluate and quantify the effects
horizontal transfer has on different aspects of language from the grammar to thelexicon. The process will be applied to the Polynesian language family, which has
been suggested to have a highly reticulate history with many linguistic transfers.
This will shed light on the timing and phylogenetic placement of linguistic transfers among the Polynesian languages and reveal which linguistic features weretransferred.
Acknowledgements
Lina Herbst was funded by the Deutsche Forschungsgemeinschaft (DFG, German
Research Foundation) – 461279234.
References
Cathcart, C., Carling, G., Larsson, F., Johansson, N., & Round, E. (2018). Areal
pressure in grammatical evolution. Diachronica. International Journal for
Historical Linguistics, 35(1), 1–34.
List, J.-M., & Moran, S. (2013). An open source toolkit for quantitative historicallinguistics. Proceedings of the 51st Annual Meeting of the Association for
Computational Linguistics: System Demonstrations, 13–18.
List, J.-M., Nelson-Sathi, S., Geisler, H., & Martin, W. (2014). Networks of lexical borrowing and lateral gene transfer in language and genome evolution.
BioEssays: News and Reviews in Molecular, Cellular and Developmental
Biology, 36(2), 141–150.
List, J.-M., Nelson-Sathi, S., Martin, W., & Geisler, H. (2014). Using phylogenetic networks to model chinese dialect history. Language Dynamics andChange, 4(2), 222–252.
Nelson-Sathi, S., List, J.-M., Geisler, H., Fangerau, H., Gray, R. D., Martin, W.,& Dagan, T. (2011). Networks uncover hidden lexical borrowing in indoeuropean language evolution. Proceedings. Biological Sciences, 278(1712),
1794–1803.
Verkerk, A. (2019). Detecting non–tree–like signal using multiple tree topologies.
Journal of Historical Linguistics, 9(1), 9–69.

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Willems, M., Lord, E., Laforest, L., Labelle, G., Lapointe, F.-J., Sciullo, A. M. D.,& Makarenkov, V. (2016). Using hybridization networks to retrace the evolution of indo-european languages. BMC Evolutionary Biology, 16(180).

278

SEMANTIC SIMILARITY SHAPES HOW AGREEMENT MARKERSSPLIT OVER POSITIONS IN VERB MORPHOLOGY
Borja Herce1,2 † * , Carmen Saldana1,2 † , and Balthasar Bickel1,2* Corresponding Author: borja.hercecalleja@uzh.ch† These authors have contributed equally to this work

1 Department of Comparative Language Science, University of Zurich, Switzerland

2 Center for the Interdisciplinary Study of Language Evolution, University of Zurich, Switzerland

Agreement markers that refer to the same feature or argument tend to be foundin the same position within inflectional forms (e.g., all subject agreement in suffixes, all object agreement in prefixes; Mansfield, Stoll, & Bickel, 2020); however, little is known about the exceptions to this trend. In this study, we explore
the positional properties of subject and object person-number agreement markers
in a phylogenetically diverse sample of 227 languages from 97 different stocks—
based on AUTOTYP (Bickel et al., 2017) plus additional data collected from the
WALS 100-language sample (Dryer & Haspelmath, 2013). The result is 325
person-number paradigms, whose agreement morpheme order was surveyed to
explore trends and split patterns. We find that a majority of agreement paradigms
only require reference to a single position, thus obeying the principle of CATE GORY CLUSTERING (Mansfield et al., 2020). A sizeable minority (128 paradigms,
about 39%), however, show what we call POSITIONAL SPLITS, whereby different
person-number bundles are marked in different positions (e.g., prefix or suffix)
within the paradigm (as illustrated in Table 1). We ask whether positional splits
deviate from category clustering in systematic ways and whether their recurrence
is proportional to the amount of shared feature values.
Table 1.

1
2
3

Different patterns of syncretism in person-number verbal paradigms.

NATURAL PATTERNG UMER, open IPFVSG
PL

L - TYPE PATTERNKOASATI, hear ACT
SG

PL

SG

PL

9-k9ftt1-k9ft
y1-k9fthá:lo-lis-há:lhá:lil-há:lhas-há:lhá:lna-bilza-bil-tza
da-bilga-bil-tza
za-bil-tza-te
da-bil-tza

n1-k9ft-1n9
t1-k9ft-o
t1-k9ft-o

X - TYPE PATTERNBASQUE, walk PRS

Our survey suggests three patterns: natural, L-type and X-type patterns, which
are illustrated with orange, blue and green cells in the examples from Gumer(Ethiopia), Koasati (US) and Basque (Spain) in Table 1. In natural patterns, allforms with agreement affixes in the same position(s) share at least one feature
value throughout, e.g., prefixal forms share SG and circumfixal forms share PL in
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

279

proportion of correct responses

A

B

Accuracy by block and condition

Accuracy by condition

1.00

1.00

0.75

0.75

0.50

0.50

0.25

0.25

0.00

0.00
0

1

2

3

4

5

6

7

8

9

no−split

natural

block
10

20

30

L−typeX−typecondition
40

50

no−split

naturalL−typeX−typeFigure 1. Raw accuracy scores (colour faded) and Bayesian mixed model mean estimates(solid). Error bars and shaded areas show the 90% credible intervals of the mean estimates.

Gumer. In L-type patterns, forms with the same positional properties share values only partially, e.g., two of the prefixal forms in Koasati share PL but differ inperson; two share 2, but differ in number. X-type patterns share even less feature
values, e.g., circumfixal forms in Basque contain one form that shares no feature
value with the other forms. We fitted Bayesian mixed-effects models comparing the occurrences of each type (natural, L-type and X-type) to the occurrences
we would expect by chance from all logically possible configurations in personnumber 3×2 paradigms (with language and family as random slopes). We findthat the natural patterns are over-represented in our data, the most unnatural Xpatterns are underrepresented, and intermediate-naturalness L-patterns occur witha similar frequency as expected by chance.
These observations suggest a bias towards natural over L over X patterns when
languages evolve over time and space. We hypothesize that this bias is grounded ina preference for semantic similarity during the transmission and learning of wordforms. To test this hypothesis we conducted an online artificial language learning
experiment (N=247) where we trained and tested participants on a person-number
verbal agreement paradigm with positional splits according to natural vs. L-type
vs. X-type patterns (Saldana, Herce, & Bickel, 2022, January 21). We ran a further
control condition with no splits (i.e., following CATEGORY CLUSTERING). Results are consistent with the hypothesized learnability gradient no-split > natural> L-type > X-type (see Figure 1), thus matching the observed cross-linguistic tendencies. Our findings support the notion that semantic similarity shapes the evolution and transmission of morphological structure (Dautriche, Mahowald, Gibson,& Piantadosi, 2017; Mansfield et al., 2020) and that it does so in a gradient way.

280

References
Bickel, B., Nichols, J., Zakharko, T., Witzlack-Makarevich, A., Hildebrandt, K.,
Rießler, M., Bierkandt, L., Zúñiga, F., & Lowe, J. B. (2017). The AUTOTYP typological databases. Version 0.1. 0. Retrieved from https://github.
com/autotyp/autotyp-data/tree/0.1.0.
Dautriche, I., Mahowald, K., Gibson, E., & Piantadosi, S. T. (2017). Wordform
similarity increases with semantic similarity: An analysis of 100 languages.
Cognitive Science, 41(8), 2149–2169. https://doi.org/10.1111/cogs.12453.
Dryer, M. S., & Haspelmath, M. (Eds.). (2013). Wals online. Leipzig: Max Planck
Institute for Evolutionary Anthropology. Retrieved from https://wals.info/.
Mansfield, J., Stoll, S., & Bickel, B. (2020). Category clustering: A probabilistic
bias in the morphology of verbal agreement marking. Language, 96(2),
255–293. https://doi.org/10.1111/cogs.13107.
Saldana, C., Herce, B., & Bickel, B. (2022, January 21). Learnability of morphological patterns of syntagmatic splits. [Preregistration]
https://doi.org/10.17605/OSF.IO/YZCXP.

281

BEYOND BROCA: NEURAL ARCHITECTURE AND
EVOLUTION OF A DUAL MOTOR SPEECH COORDINATIONSYSTEMGREGORY HICKOK *1,2, JONATHAN VENEZIA 22,3, and ALEX TEGHIPCO 33*

Corresponding Author: greg.hickok@uci.edu
Departments of Cognitive Sciences & Language Science, University of California,
Irvine, CA USA
2
VA Loma Linda Health Care System and Dept. of Otolaryngology, Loma
Linda University School of Medicine, Loma Linda, CA, USA
3
Department of Communication Sciences and Disorders, University of South
Carolina, Columbia, SC USA

1

1. Introduction and preview
Classical neural architecture models of speech production propose a single
system coordinating all the vocal articulators from lips to larynx. Here we
propose a dual coordination system in which laryngeal control of pitch-related
aspects of prosody and song are controlled by a dorsal precentral gyrus system
while supralaryngeal articulation at the phonetic/syllabic level is controlled by aventral precentral system. The separability of these neural systems supports
models of the evolution of speech and language in which song-based
communication was an early foundational stage.
.
2.0 Evidence for distinct two systems
Evidence for the existence of two speech coordination systems comes fromfunctional imaging (Hickok, Buchsbaum, Humphries, & Muftuler, 2003; Priceet al., 1996; Wilson, Saygin, Sereno, & Iacoboni, 2004), direct corticalstimulation mapping (Lu et al., 2021), and post stroke apraxia of speech (GraffRadford et al., 2014; Hickok et al., 2014), all of which have identified speechrelated regions in both the ventral and more dorsal central gyrus. We refer tothese as the dorsal and ventral precentral speech areas (dPCSA and vPCSA).
3.0 Evidence for a voice pitch-related coordination in the dPCSA
Two recent published findings argue for a functional distinction between the two
speech areas, specifically that the dPCSA, but not vPCSA is involved in voice
pitch coordination for prosody and song. One is an intracranial recording study
showing that neural activity in the dPCSA (but not vPCSA) is correlated withThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

282

control of voice pitch and when stimulated elicits vowel-like vocalization(Dichter, Breshears, Leonard, & Chang, 2018). The other is an fMRI study
showing that the dPCSA (but not vPCSA) has auditory spectrotemporal
receptive fields that code pitch-related information (Venezia, Richards, &Hickok, 2021). We also provide new functional connectivity evidence: dPCSA
has greater connectivity to auditory cortex and vPCSA has greater connectivity
to secondary somatosensory cortex.
4.0 The Dual Speech Coordination Model
The observations described above led us to the following dual coordination
model:The dPCSA is part of a sensorimotor control circuit for pitch-related
vocalization, which would include prosodic aspects of speech as well as song,and predominantly acts on the laryngeal effector via the dorsal laryngeal motor
cortex.
The vPCSA is part of a sensorimotor control circuit for syllabic and phoneticrelated speech features and acts on supralaryngeal effectors as well as voicingrelated control of the larynx via the ventral laryngeal motor cortex.
We further propose and provide evidence for the following secondary
hypotheses.
-Both the dPCSA and vPCSA are part of hierarchy of speech control networks
in frontal cortex with both situated between lower-level primary motor systems
posteriorly and higher-level systems anteriorly, extending into the posterior
middle frontal gyrus and inferior frontal gyrus (Broca’s area), respectively.
-The location of the dPCSA can be explained in evolutionary terms, having
evolved out of a voluntarily controllable goal-directed orienting hub in premotor
cortex, which is present in both human and non-human primates, and whichmakes use of pitch and other acoustic cues.
-The vPCSA evolved out of voluntarily controlled supralaryngeal effectors, used
extensively for mastication, oral-grasping behaviors, and non-vocal oral
communication (e.g., lip smacking) with the major advance for speech being thecoordination of open-close jaw cycles (the syllabic frame) with articulatory
gestures (the phonetic content), as proposed in MacNeilage’s frame/content
model speech production evolution (MacNeilage, 1998).
-The dPCSA and its associated circuit for controlling vocal pitch, prosody, andsong evolved prior to the vPCSA system’s control over phonetic articulatory
gestures and represented an early stage of the evolution of language, as
originally proposed by Darwin (Darwin, 1871).

283

References
PREPRINT: https://psyarxiv.com/tewna/
Hauser, M. D., & Fitch, W. T. (2003). What are the uniquely human componentsof the language faculty? In M. H. Christiansen and S. Kirby (Eds.), Language
evolution (pp. 158-181). Oxford: Oxford University Press.
Pinker, S., & Bloom, P. (1990). Natural language and natural selection.
Behavioral and Brain Sciences, 13, 707-784.
Tomasello, M. (2003). Constructing a language: A usage-based theory oflanguage acquisition. Cambridge, MA: Harvard University Press.
Darwin, C. (1871). The descent of man and selection in relation to sex. London:
John Murray.
Dichter, B. K., Breshears, J. D., Leonard, M. K., & Chang, E. F. (2018). The
control of vocal pitch in human laryngeal motor cortex. Cell, 174(1), 2131 e29. doi:10.1016/j.cell.2018.05.016
Graff-Radford, J., Jones, D. T., Strand, E. A., Rabinstein, A. A., Duffy, J. R., &Josephs, K. A. (2014). The neuroanatomy of pure apraxia of speech instroke. Brain Lang, 129, 43-46. doi:10.1016/j.bandl.2014.01.004
Hickok, G., Buchsbaum, B., Humphries, C., & Muftuler, T. (2003). Auditorymotor interaction revealed by fMRI: Speech, music, and working
memory in area Spt. Journal of Cognitive Neuroscience, 15, 673-682.
Hickok, G., Rogalsky, C., Chen, R., Herskovits, E. H., Townsley, S., & Hillis, A.
E. (2014). Partially overlapping sensorimotor networks underlie speech
praxis and verbal short-term memory: evidence from apraxia of speechfollowing acute stroke. Front Hum Neurosci, 8, 649.
doi:10.3389/fnhum.2014.00649
Lu, J., Zhao, Z., Zhang, J., Wu, B., Zhu, Y., Chang, E. F., . . . Berger, M. S. (2021).
Functional maps of direct electrical stimulation-induced speech arrest
and anomia: a multicentre retrospective study. Brain.
doi:10.1093/brain/awab125
MacNeilage, P. F. (1998). The frame/content theory of evolution of speech
production. Behav Brain Sci, 21(4), 499-511; discussion 511-446.
doi:10.1017/s0140525x98001265
Price, C. J., Wise, R. J. S., Warburton, E. A., Moore, C. J., Howard, D., Patterson,
K., . . . Friston, K. J. (1996). Hearing and saying: The functional neuroanatomy of auditory word processing. Brain, 119, 919-931.
Venezia, J. H., Richards, V. M., & Hickok, G. (2021). Speech-Driven
Spectrotemporal Receptive Fields Beyond the Auditory Cortex. Hear
Res, 408, 108307. doi:10.1016/j.heares.2021.108307
Wilson, S. M., Saygin, A. P., Sereno, M. I., & Iacoboni, M. (2004). Listening tospeech activates motor areas involved in speech production. Nat
Neurosci, 7, 701-702.

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285

INCREMENTAL EVOLUTION OF SYNTAX:FROM ‘BIGRAMS’ TO ‘BRANCHING’Dieter Hillert*1*Corresponding Author: dhillert@gmail.com
1School of Speech, Language, and Hearing Sciences,

San Diego State University, USA

Here, we explore how syntax might have evolved after the hominin lineage branched offfrom our closest living relatives, monkeys and nonhuman great apes. Our approach focuseson ‘bigrams’ and ‘branching’ in terms of unordered, external and internal Merge inassociation with workspace and memory storage resources. The syntactic abilities ofmonkeys and nonhumans great apes but also various typologies of human syntax in children
and adults indicate that the language capacity incrementally involved in the hominin
lineage. Indirect paleoanthropological records let us moreover predict that already Homoerectus was equipped with a language capacity which was premodern or even modern.

1.

Introduction

All organisms are adapted to communicate with one another (or sometimes
across species). Cultural transmissions, however, can be found only in a subset oflife-forms, including fish, insects, birds, monkeys, and apes. The development andacquisition of these behavioral cultures are based on species-species genetic
endowments. However, we should not ignore that modern humans share with otherprimates or mammals most neurobiology (e.g., neuronal morphology,
neurotransmitter, subcortical structures) and the vocal tract of monkeys are
considered as ‘speech-ready’. Thus, the neurobiological differences between
nonhuman primates and modern humans can be considered as a ‘needle in ahaystack’ (Fitch, 2011; Fitch et al., 2016).
Modern humans (or possibly their closest extinct ancestors), however, added
another dimension by inventing a language system to share subjective experiencesor to express thoughts. Already, Lenneberg (1967) recognized that this dimension
is based on an innate human-specific capacity, and Chomsky (1968) described thehuman language capacity as an innate ‘universal grammar’ underlying all humanlanguages. Speakers make ‘infinite use of finite means’, that is, a finite set ofsyntactic rules are used to create an infinite number of sentences or text structures,aka “super-sentences” (Watumull et al., 2013). Our closest living relatives, thenonhuman great apes, clearly display superior cognitive abilities compared tomonkeys or other animals. We explore for this reason how this innate capacity

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

286

evolved in context of the expansion and refinement of (sub-) cortical circuits
after the hominin lineage branched off from monkeys and nonhuman great apes.
2.

Bigrams

The strong ‘minimalist program’ of generative linguistics explicitly claims
that syntactic recursion is an innate property of UG (Chomsky, 1995; Hauser etal., 2002; Berwick & Chomsky, 2016). This recursion concept is based on theoperation ‘unbounded Merge’, that is, it recursively merges words to createbottom-up larger phrases in a binary fashion. The simplest form of Merge is totake two distinct elements of the lexicon such as (X, Y), and combine them to anunordered binary set unit {X, Y}. It is a binary unit without a syntactic head which
determines the syntactic category of a phrase. Headless binary sets are still marked
by an unspecified (neutral) label. However, the pre-Merge conditions involve theability to form and recall discrete elements and create bigrams.
Bigrams can be found not only in our closest living relatives, monkeys andnonhuman great apes, but they also (re-) surface in varieties of modern spoken andsign languages. Bigrams are rooted in non-linguistic cognition across primate
species and represent the basic linear layer of human syntax across different
domains (e.g, Jackendoff, 2003; Miyagawa, & Clarke, 2019). In addition to longterm ‘memory storage’ (MS) for lexical elements, our account considers theincrease of temporary ‘workspace’ (WS), which is required for syntactic
operations (e.g., Rizzi, 2016). We do not exactly know which behavioral and/or
environmental changes triggered the expansion of (sub-)cortical regions andcircuits in the hominin lineage, but the increase of brain structures goes hand-inhand with an increase of WS capacity required for more complex intrinsic
structure-building processes.
2.1. Monkey Syntax
Monkeys primarily communicate by means of vocalizations. Their callrepertoire consists of a small set of discrete alarms calls. One or more calls can be
combined with another call, either discrete or suffixed. Female Diana monkeys(Cercopithecus diana), for example, emit four calls whereas call A, which refers
to caller identity, is systematically combined with the calls L, H, R to function asa social context modifier (Candiotti et al., 2012). Male putty-nosed monkeys(Cercopithecus nictitans) produce the general alarm call P (pyow) and the specificeagle-related alarm call H (hack). Both calls combined refer to a non-predator
situation, whereas H follows always P. The number of Hs and Ps in a sequence
informs about the distance of group movement (Schlenker et al., 2016). Cottontop tamarins’ (Saguinus oedipus) long-call combinations are emitted by socially287

isolated individuals to get in touch with their conspecifics. These contact calls
consist of one or more chirp-like syllables followed by two or more whistle-likesyllables. Again, two call types are combined in a fixed order and the variation is
the number of call type repetitions (Cleveland, & Snowdon, 1982). Black-fronted
titi monkeys (Callicebus nigrifrons) produce A-calls for predators in the canopy
and B-calls for predators on the ground. The combination of both call types
informs conspecifics not only about predator type but also about the location of
the threat and the number of continuous B-calls might indicate probabilistic rather
than categorial meaning (Casar et al., 2013; Berthet et al., 2019). These examples
show that the syntax of monkeys is restricted to the combination of maximal twoelements in a fixed order and their WS = 1 (e.g., Pasternak, & Greenlee, 2005;
Wakita, 2020).
2.2. Nonhuman Ape Syntax
Old World monkeys and apes branched of about 30-25 million yrs. ago (mya).
Thus, we expect to find qualitative differences between both clades about theirabilities to structure their ‘language’. Indeed, great apes coordinate often facial
expressions, postures, vocalizations, or gestures, and they outperform monkeys on
many cognitive tasks in an enculturated environment. The photographic memory
capacities of chimpanzees (Pan troglodytes) are impressive. Ai and her son Ayumu
outperformed humans in tasks which required to recall number sequences(Matsuzawa, 1985). Since the cognition of great apes’ centers on visuospatial
information and their communication is rather multimodal than relying on
vocalization, they have been primarily trained on sign language or graphic
symbols (lexigrams) to examine their language capacities.
Kanzi, the famous bonobo (Pan paniscus), acquired the meaning of lexigramsas an infant by observing his foster mother’s lessons. It is said that he learned
about 348 of those symbols. Kanzi accompanied often graphic communication
with an articulatory but distorted equivalent. He had the ability to communicate
without difficulties EVENT, OBJECT combinations such as HIDE ICE or WATER HIDE.
He also produced new combinations such as CAR TRAILER meaning he wanted tobe driven to the trailer (Greenfield, & Savage-Rumbaugh, 1991). Kanzi acquiredalso some American Sign Language by watching a video of the signing gorilla
Koko who learned thousand signs and understood about 2,000 spoken English
words (Patterson, 1978).
Chantek was an orang-utan (Pongo pygmaeus) who mastered to learn
approximately 150 signs and initiated communication to meet his needs. His
vocabulary matches that of a very young child (Miles, 1993). He learned single
concepts such as people names, places, food items, actions, animals, pronouns,
locations, attributes, etc. and immediately began to combine his acquired signs

288(e.g., He signed COKE DRINK after drinking a coke, or PULL BACK while pulling a
caregiver’s hair through the fence.) He began to over- or underextend themeanings of his signs (e.g., He used the sign DOG for dogs, barking noise, birds,
horses, noisy helicopter, etc). Chantek also invented new sign combinations (e.g.,
EYE DRINK for contact lens solution). He clearly imaged objects or actions (e.g.,
He pointed to objects when he was asked WHERE HAT or WHAT WANT?). His
signing was spontaneous, non-repetitious, did merely imitate his caregivers’
signing, actively initiated signing to meet his needs.
Nonhuman great apes acquire conceptual representations of events andobjects. In an encultured environment, they can flexibly combine two elements toexpress needs or ideas. Trigrams, however, are less often used. The order of theelements is in principle free but seems to follow mostly pragmatic strategies. For
example, in DAVE MISSING FINGER the topic is ‘Dave’; however, in HIDE ICE or
WATER HIDE, the order EVENT or OBJECT seems to be subjective or random. The
concepts are ordered linear, and it is unclear whether “grouping” takes place. WSof great apes is 2 ± 1 (Read et al., 2022).
3.

BranchingIn briefly reviewing monkey and nonhuman ape language, it is apparent thatmultiple components contributed to the evolution of syntax in modern humans.
The continuous increase of cognitive resources in terms of WS and MS in thelineage of great apes (including humans) may be the main factors for the ability togenerate complex structures between signals (cf. Zuberbühler, 2020). Moreover,complex structure-building processes can be found at all linguistic levels but also
across non-linguistic domains (e.g., Fujita, 2017; Asano et al., 2021). The
uniqueness of human syntax may be therefore based on an underlying universal
computational capacity which includes enhanced WS/MS resources.
3.1. Human Syntax
Typically developing (human) children produce at around the age of 18
months two-word utterances such as WANT-JUICE or CAR-GO which lackinflections and function words (Bloom, 1993). Some accounts assume that thisstage reflects WS limitations (Blake et al., 1994). At this point, human andencultured ape syntax are quite similar in production: (a) No significant
differences were found for the type of bigrams such as AGENT-EVENT, EVENTOBJECT, EVENT-LOCATIVE; (b) Auditory WS of 2 ± 1 matches between panins and2-year-old toddlers (Patkowski, 2014).
Adult human language differs from nonhuman ape language in two
fundamental aspects. The human child has the innate ability to acquire abstract

289

semantic, syntactic and phonological entries independent of immediate perceptual
experiences and goals, and over time it can build or tap into additional cognitive
resources to process complex sentence and discourse structures. Cowan (2001)
argues therefore that only 4 ± 1 items can be held within focus of attention.
Complex sentence processing ability is not enabled by WS per se but by its
strategic subcomponents. However, human languages tap into these resources todifferent degrees.
For instance: Independent of particular language pairs, adult L2 learners
acquire first a linear strategy with bigrams before they learn more complex
structures. Broca’s aphasic patients who suffer from agrammatism produce only
telegraphic-like speech without inflections and function words. Bigrams are also
the basic layer of particular sign languages. For example, the first generation ofdeaf children of the Idioma de Señas de Nicaragua combined signs to bigrams
such as AGENT-EVENT, and the next generation elaborated on these structures(Senghas et al., 2004).
About ten million people speak the Malayan dialect Riau Indonesian whichfunctions as a lingua franca. This language virtually has no syntactic categories or
inflections, and the word order is based on semantic principles (Gil, 2005). For
example, ayam means CHICKEN and makan EAT. The bigrams makan ayam orayam makan are open to the same readings and they are unmarked for numerus,
tense, aspect, semantic role, etc. Situational context is however sufficient that the
speakers getting the meaning across. Speakers of the indigenous, agglutinative
Amazonas language Pirahã get by without rearranging canonical structures andclear evidence of hierarchical syntactic structures is missing (Everett, 2009). In
general, the spectrum of typologies from analytical resp. isolating languages topolysynthetic languages varies strongly with respect to the use of syntactic
hierarchies and recursion.
3.2. Homo erectus
Paleoanthropological data indicate an incremental neurobiological process.
External evidence from phylogenetic and ontogenetic stages, let us hypothesize
milestones of syntactic evolution. There are many more intermediary steps which
need to be spelled out. Monkey’s initial alarm response will be presumably
replaced by a modifying response since they cannot keep more than one unit in
WS. In contrast, nonhuman great apes acquire concepts rooted in perception andcombine them into bigrams. Their lexicon may have an upper limit since they do
not make use of compositional structures.
We assume that the next milestone broadly corresponds to Homo erectus
since significant neurobiological and behavioral changes can be associated withthis species. Based on indirect evidence, we predict an intermediate stage between

290of structure-building processes between nonhuman great apes and Homo sapiens.
The appearance of the C variant of the protein SRGAP2 which involves neuronalmigration and differentiation is also associated with the rise of Homo erectus about
2 mya (Hillert, 2015; Martins et al., 2018). The endocasts of Homo erectus sharea pronounced Broca's cap, the bulge that appears at the level of the temporal polein Homo sapiens. The specimens' brain sizes, moreover, range between 6001,000 cc and had human-like prefrontal and temporoparietal regions. A recent
endocranial morphology study of early Homo supports the view that frontoparietal
areas did not evolve separately but in concert (Ponce de León et al., 2021). We
must, therefore, assume that the genetic endowment of a (pre-) modern language
capacity preceded cultural-linguistic accumulations. Acheulean tool industry
coincides with the appearance of Homo erectus about 1.8 mya and Acheulean
hand axes produced in Africa might fall in the category of art as their making
reflects planning and abstract thinking. In addition, late Homo erectus produced
non-functional, aesthetic engravings and decorations (Stout et al., 2015;
Mcbrearty & Brooks, 2000). Accumulated cultural processes began about 1 myain the merging species late Homo erectus and pre-archaic resp. superarchaic
Homo sapiens (e.g., Bergström et al. 2021).
In sum, indirect neurobiological and cognitive-behavioral changes indicatethat Homo erectus was equipped with a language capacity, whether it was
premodern or even modern, we cannot tell (Dediu, & Levinson, 2013; Hillert,2021). Possible WS differences can be considered as marginal. They might haveused basic structures such as a linear grammar and headless binary sets (unordered
Merge). Since direct evidence is lacking, we reserve asymmetric grouping(external Merge) and recursion (internal Merge) to the language of Homo sapiens.
4.

Conclusions

In discussing the biology capacity of syntax and language in general, it isimportant to separate the genetic endowment of species and the extent to which a
species makes use of its cognitive capacities. The evolution of syntax, frombigrams to branching, indicates that multiple factors played a crucial role,
including WS and MS resources. In particular, inner speech (rehearsal) may havecontributed to WS and representational resources required for complex sentence
and discourse processing. Syntax incrementally emerged in the hominin lineage,
and we must therefore assume that Homo erectus was already equipped with apremodern or even modern language capacity.
Acknowledgements
Supported by FY2020 JSPS invitational long-term fellowship at Kyoto University.

291

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294

PIECING TOGETHER THE BUILDING BLOCKS OF THE
VOCAL LEARNING BAT BRAINNIENKE HOEKSEMA*1,2, PETER HAGOORT,1, and SONJA VERNES2**Corresponding Author: nienke.hoeksema@mpi.nl
1 Neurobiology of Language Department, Max Planck Institute for Psycholinguistics,

Nijmegen, the Netherlands
2Neurogenetics of Vocal Communication Group, Max Planck Institute for

Psycholinguistics, Nijmegen, the Netherlands & St. Andrews University, St.
Andrews, Scotland

1. Background
Vocal learning is the ability to learn to produce novel vocalizations and is an
important component of the human capacity to acquire speech and spoken
language (Janik & Slater, 2000). Our close relatives, non-human primates, do notshow evidence of learning to produce novel vocalizations. However, a small
group of more evolutionarily distant animals, such as bats, display evidence for
vocal learning. This raises the question of how and when this skill evolved andhow it is encoded in our brains and genomes. Phyllostomus discolor is a highlysocial, vocal learning bat that can be studied in a laboratory setting (Vermes,2017). Little is currently known about the brain areas and networks involved in
vocal learning in P. discolor. Acquiring a better understanding of theneurobiology of this skill in bats will bring us closer to understanding how thisskill evolved, and subsequently shed light on the evolution of human speech andspoken language.
2. Approach
In the current study, we used two complementary approaches, neuroimaging andneurogenetic mapping, to start to map the connectivity of the P. discolor brain
and to highlight potential brain areas of interest for the study of vocal learning in
P. discolor.

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

295

We performed high quality structural and multi-shell diffusion tensor imaging of
post-mortem formalin-fixed P. discolor brains on an 11.7 T Bruker BioSpin
magnetic resonance imaging (MRI) scanner. Previous research has shown
increased and altered connectivity of brain areas involved in vocal
communication in vocal learners compared to vocal non-learners (e.g., Kumar etal., 2016). As no research thus far has been done on investigating the macro-scale
brain networks of P. discolor, we set out to exploratively map the brain-wide
connectivity of P. discolor.
In parallel, we performed immunohistochemistry, immunofluorescence, and insitu hybridization on juvenile and adult P. discolor brains. We investigated a setof genes of which previous research has shown that they have a distinct pattern ofexpression in voluntary vocal motor (planning) regions in the brains of humans
and vocal learning songbirds (Wang et al., 2015; Pfenning et al., 2014). We
hypothesized that these genes might show the same pattern of expression in
potential homologous bat vocal motor (planning) cortical regions, and could
hence point us to potential brain regions of interest.
3. Preliminary Results and Future Perspectives
We successfully acquired high-quality DTI data of post-mortem P. discolor brains
and are currently in the process of reconstructing the major white matter tracts in
the P. discolor brain and performing probabilistic tractography from two corticalregions involved in social communication in P. discolor: the auditory and frontal
cortex. Via neurogenetic mapping, we show that expression of our set of marker
genes is not dispersed uniformly across the P. discolor cortex, but is enriched or
reduced in specific layers and cortical areas. This suggests that these expression
patterns can be used to mark out distinct brain areas in P. discolor.
Exploring our dataset further in the future, we hope to define key areas andcircuits involved in vocal learning in the P. discolor bat brain and that this teaches
us about how this skill evolved in bats and humans.

296

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297

THE EFFECT OF IMPROVISATION AND LEARNING ON WORD
ORDER PREFERENCES
ANNIE HOLTZ*1 , SIMON KIRBY1 , and JENNIFER CULBERTSON1* Corresponding author: annie.holtz@ed.ac.uk

1 Centre for Language Evolution, University of Edinburgh, Edinburgh, United Kingdom

Background In this study we hypothesise that certain typological patterns may
have evolved as a result of competition between cognitive biases that tend to be
active during different stages of language evolution. Research has shown thatsome cognitive biases influence linguistic behaviour under conditions where language structure is improvised (Culbertson, Schouwstra, & Kirby, 2020; GoldinMeadow, So, Özyürek, & Mylander, 2008; Schouwstra & Swart, 2014), whereas
other biases influence learning behaviour, meaning some linguistic structures arefavoured or disfavoured during learning (Culbertson, Smolensky, & Legendre,
2012; Fedzechkina, Jaeger, & Newport, 2011; Hudson Kam & Newport, 2009).
This research can be thought to represent two stages of language evolution, suchthat the biases that affect improvisation are active during language creation, andbiases that influence learning affect language evolution only once there is a linguistic system for learners to acquire. However, recent research suggests that the
influence of some biases may persist from improvisation into learning tasks (Motamedi, Wolters, Naegeli, Schouwstra, & Kirby, 2021). The possibility of thesebiases being active simultaneously means that competition between different biases could shape language typology.
We focus our investigation on a possible example of such competition, namely
an exception to the typological tendency towards harmony, whereby languages
tend to order dependents on the same side of the head (Greenberg, 1963; Hawkins,
1990). The exception concerns how adjectives (e.g. ‘big house’) show a typological tendency for postnominal order (N-Adj = 879 vs Adj-N = 373, Dryer, 2013a)
whereas genitives (e.g. ‘The child’s toy’) tend to be prenominal (N-Gen = 468 vs
Gen-N = 685, Dryer, 2013b), despite a harmonic tendency among other nominal
modifiers. We report results from an experiment where participants had to chosean order to expressing descriptive (adjective) and possessive (genitive) meanings
in the absence of a language system. The second experiment explores if the ordering preferences identified in the first experiment continue to shape participants’linguistic behaviour in a learning task, where previous research has found that participants tend to have a preference for harmonic orders (Culbertson et al., 2012).
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

298

Experiment 1 Participants in experiment 1 were randomly assigned to either
an adjective condition (N=160) or a genitive condition (N=160). They saw images signifying meanings such as striped book (adjective condition) or cyclops’
hat (genitive condition) accompanied by two gesture videos. In one of thevideos the meaning of the image was conveyed with a prenominal gesture order(adjective/genitive-noun) and in the other with a postnominal gesture order (nounadjective/genitive). Participants each saw a single such trial, and were instructedto choose the video they thought best conveyed the meaning of the image. Resultsfrom this experiment show that participants preferences generally align with thoseseen in typology, namely that they prefer postnominal orders for adjectives (β =0.51, SE. = 0.16, z = 3.02, p < 0.01) and prenominal orders for genitives (β =0.56, SE = 0.16, z = 3.43, p < 0.001).
Experiment 2 In experiment 2 participants were randomly assigned to one of four
conditions which differed in the word order used to convey adjective and genitive
meanings. Each condition had a majority order shown 75% of the time, and aminority word order shown 25% of the time during training for each dependent
type. In the natural condition the majority order for genitive trials was prenominal and for the adjective trials it was postnominal (i.e., following the preferences
identified the typology, and replicated in experiment 1). The unnatural condition
was the opposite of this. In the remaining two conditions the majority order was
shared across both modifier types–either majority pre-nominal or post-nominal–
and thus was harmonic. In the test phase, participants were shown an image to beconveyed along with two videos featuring pre- or post-nominal order and had tochoose which they preferred, as in experiment 1. The results showed that participants generally learned the majority orders they were trained on (β = 1.34, SE =0.12, z = 11.24, p < 0.001). Further, there was an overall tendency to regularise(i.e., use one order for a given dependent type more consistently than in the input)
which was captured as reduction in conditional entropy (β = -0.10, SE = 0.02, t =-8.74, p < 0.001) and a tendency to harmonise (i.e., choose the same order acrossthe two modifier types) which was measured as reduction in Shannon entropy (β= -0.22, SE = 0.02, t = -5.15, p < 0.001). Crucially, there was no evidence thatthe preferences identified in experiment 1 for specific modifier types continued toinfluence people’s linguistic behaviour in this learning task (β = -0.14, SE = 0.10,
z = -1.40, p = 0.16). For example, participants did not learn more successfully, or
regularise the majority order more, in the natural condition.
Discussion While participants showed a strong preference for postnominal adjectives and prenominal genitives when the task involved choosing an expressionof descriptive and possessive meanings, this preference did not carry over to thelearning task in experiment 2. The results suggest that this exception to harmony
is not be caused by continuous competition between biases during learning. Future studies will examine if competition between these biases exist in languagetasks which involve both improvisation and learning, such as extrapolation tasks.

299

References
Culbertson, J., Schouwstra, M., & Kirby, S. (2020). From the world to word
order: deriving biases in noun phrase order from statistical properties of theworld. Language, 96(3), 696–717.
Culbertson, J., Smolensky, P., & Legendre, G. (2012). Learning biases predict aword order universal. Cognition, 122(3), 306–329.
Dryer, M. S. (2013a). Order of adjective and noun. In M. S. Dryer & M. Haspelmath (Eds.), The world atlas of language structures online.
Dryer, M. S. (2013b). Order of genitive and noun. In M. S. Dryer & M. Haspelmath (Eds.), The world atlas of language structures online.
Fedzechkina, M., Jaeger, T. F., & Newport, E. L. (2011). Functional biases inlanguage learning: Evidence from word order and case-marking interaction.
In Proceedings of the 33rd Annual Meeting of the Cognitive Science Society.
Boston, MA: Cognitive Science Society.
Goldin-Meadow, S., So, W. C., Özyürek, A., & Mylander, C. (2008). The natural order of events: How speakers of different languages represent events
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Greenberg, J. (1963). Some universals of grammar with particular reference
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When learners change languages. Cognitive psychology, 59(1), 30–66.
Motamedi, Y., Wolters, L., Naegeli, D., Schouwstra, M., & Kirby, S. (2021).
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Schouwstra, M., & Swart, H. de. (2014). The semantic origins of word order.
Cognition, 131(3), 431–436.

300

WORD ORDER CONVERGENCE IN THE HISTORY OF
ENGLISHMICHIO HOSAKA*1*

Corresponding Author: hosaka.michio@nihon-u.ac.jp
The Department of English Language and Literature, Nihon University, Tokyo, Japan1

1. Introduction
It is well known that early English had various types of word order, such as SXV,SVX, and XVS. However, in the course of the historical changes that have takenplace in English, its word order has gradually come to be fixed as SVX. Such aword order change has been the target of historical studies for ages (vanKemenade 1987, Roberts 2007, inter alia). This paper focuses on the word orderof the three-verb clusters (Modal+have/be+Participle) in OE and ME, and argues
how it has converged in the history of English.
2. Methods and Data Analysis
This paper aims to clarify the word order change based on the statistical analysisof the data from the YCOE and PPCME2, and explore the mechanisms of its
convergence from the perspective of the cultural evolution of language. As Table
1 shows, the word orders of 1-3-2 and 3-2-1 are the typical orders of Germanic
languages and have decreased in number. On the contrary, the 1-2-3 order, whichis the norm in PDE, has increased in the later period of OE. In addition, as Table
2 shows, it is obvious that the 1-2-3 order has been firmly established in ME.
1-2-3
1-3-2
3-2-1
3-1-2
2-1-3
2-3-1
Total

Early OE
113 (55.4%)
44 (21.6%)
42 (20.6%)
4 (2.0%)1 (0.5%)0 (0.0%)204

Late OE
478 (86.8%)
30 (5.4%)
38 (6.9%)
3 (0.5%)2 (0.4%)0 (0.0%)551

Table 1 Three-verb clusters in OE

1-2-3
1-3-2
3-2-1
3-1-2
2-1-3
2-3-1
Total

Early ME365 (90.1%)
34 (8.4%)
0 (0.0%)6 (1.5%)0 (0.0%)0 (0.0%)405

Late ME
1628 (98.4%)
1 (0.1%)0 (0.0%)24 (1.5%)
0 (0.0%)1 (0.1%)
1654

Table 2 Three-verb clusters in ME

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

Uniformity
Principle(Chomsky2001),
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2001),approachitthe
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Examples, Table, and Figure
Examples, Table, and Figured Figureþonne meahterimeþonne meahte
rime (1) ageðeodedbion he ðarahe ðararime(1) ageðeodedbion he ðarathen might
he 301of those number
then mightbe
he of those number joined
be he of those number joined‘thenhe might have been added to the n‘thenmight have been added to the number
of(cobede,Bede_5:14.442.1.4437)
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t have been added to the number
ofhethose’
b. & sægde
he to biscope geha
b. & sægde
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that
he to bishop cons& saidbe thatmight
he tobishopconsecrated
might
t he to bishop
consecrated
Notes: 1-2-3(Modal-be/have-Participle),
1-3-2(Modal-Participle-be/have),(Participle‘and said
that he might
be consecrated b
he might be consecrated
bishop’(cobede,Bede_4:1.254.20.2589)‘andthat he might be consecrated
be/have-Modal),
3-1-2said(Participle-Modal-be/have),
2-1-3 bishop’(be/have-Modal-Participle),
2-3-1(be/have-Participle-Modal)(2) a.
AcþæthireGodesmædenne lyffetun
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leofanDrihtne,(2) a.beon
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ne
mihte
beon
bepæhtþurhænigeNotes: 1-2-3 (Modal-be/have-Participle),
1-3-2 (Modal-Participleple), 1-3-2 Notes:(Modal-Participle-be/have),
3-2-1 (Participle1-2-3 (Modal-be/have-Participle),
1-3-2 (Modal-Participle-be/have),
3-2-1 (Participlebut
that
God’s
maiden
not
might
d’s
maidennotmight
beseducedthroughanyflatteryfromherbeloved
Lord
but
that
God’s
maidennotmight
beseducedthroughanyflatterybe/have-Modal),
3-1-2 (Participle-Modalbe/have), 2-1-3 (be/ha
Modal- be/have),
2-1-3 (be/have-Modal-Participle),
2-3-1
be/have-Modal),
3-1-2 (Participle-Modalbe/have),
2-1-3 (be/have-Modal-Participle),
2-3-1
‘But
the
virgin
of
God
could
not
be
allur
n of God couldnotbealluredbyany
flatteryfromherbelovedLord’(coaelive,æLS[Agnes]:85.1770)‘But
thevirgin
of
God
could
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flatteryfromherbeloved
Lord’
It
has
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philologicallyandtheoretically
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itmagon order
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rise
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functional
projections
underanevolutionary
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underevolutionary
assumptionof1-3-2functional
projections
under
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dynamic
1-2-33-1-2 Total1-3-2FP
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thatneeds.
areflexibly
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Main
268(88.7%)
23 (7.6%)
8tive(88.7%)
23 (7.6%)
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302
Maincl.(3.3%)
268(88.7%)
23 (7.6%)
10cl.
(3.3%)
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1-2-3
3-2-1
1-3-2

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1-3-2

3. Discussion
323 (71.3%)
51 (11.3%)
3 (71.3%)
51(11.3%) Sub.70
2 (0.4%)0 (0.0%)453
cl.(15.5%)323 (71.3%)
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2 (0.4
VP1
FP17 (1.5%) 51 (11.3%)
FP17 (1.5%)
FP1Sub.
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3. Discussion
Total
591 (78.3%)
74 (9.8%)
1 (78.3%) 3. Discussion
74 (9.8%) Total
80 (10.6%)7(0.9%)
3(0.4%)
0(0.0%)
755
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Given
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Notes:
1-2-3 (Modal-be/have-Participle),
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3-1-2 (ParticipleNotes:
1-2-3 (Modal-be/have-Participle),
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Modalbe/have),
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Modalbe/have),(be/have-Modal-Participle),(be/have-Participle-Modal)
3.4
ective(FP=Head-initial,
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3
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FP = Emergent

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Figure1 The
derivation
ofthree-verb
major
three-verb
clusters
in andOE andFigureThe
derivationof theclustersin
OE
Figureof major three-verb
clustersin
OEand ME
FigureThe
derivation
major
three-verb
cluste1 The
derivation
ofofMEthethree-verb
clusters
in1
Figure11The
derivation
major
three-verb
clusters
in OE
ME
2-1-3
3-1-2 2-3-1
2-1-3
3-1-2 2-3-1
2-1-3References
References
VP1
oberts
(2005)“Changing
EPP
parametersinthehistory
English,”EnglishLanguage
andLintuistics
9.1,
5-46EhE/
Biberauer,T
and I.VP1
Roberts(2005)
VP1
This
shows
that
the
word
order
of(2005)
theIt ofthree-verb
clusters
converged
into
1-2-3
VP1
Biberauer,
TVP1
and
I.VP1
Roberts“Changing
EPP
parametersinthehistoryof “Changing
English,”VP1
the three-verb
clusters
has
converged
into
1-2-3
shows
that
the
word
order
of
three-verb
clusters
It shows
that
the
word
order
of
the
three-verb
clusters
has
converged
into
1-2-3
“Derivationby
Phase,”
In
Ken
Hale,
by
Kenstowicz,
M.,
1-52,
MIT
Press
/
Haeverli,E.
&
S.
Pintzuk(2012)
Chomsky,N.
(2001)“Derivationby
Phase,”
I
Chomsky,N.
(2001)“Derivationby
Phase,”
In
Ken
Hale,
by
Kenstowicz,
M.,
1-52,
MIT
the
emergenceofnewfunctional
projections.
Thisprojections
canberegardedas theVP2It can beave emerged.
Itwithcanberesultofthe as
new
have
VP2
asVP2
new
functional
projections
have
canfunctionalbeas theofthe
VP2
VP2result
1emerged.
VP2as the1+2
1 emerged.
1+2
VP2It
1+3regarded
1+3reg
1+3regarded
ojection) Raisingin OldEnglish,”In
Grammatic
al often
Change:Nature,Outcomes,by al
Jonas,
D. etOrigins,
219“Revisiting
Verb(Projection)
Raising
inal.,Old
En
Verb(Projection)
Raising
inOrigins,
Old
English,”In
Grammatic
Change:resultof“Revisiting
the
rise
of
auxiliaryverbs
explainedingrammaticalization,which
plained VP3
in riseGrammaticalization,
whichisassumedriseofauxiliary
verbs
often
explained
inGrammaticalizof2 auxiliary
verbs
often
explained
inGrammaticalization,
whichisassumed
VP3
VP3
2+3with Special
VP3in Old
2 Phrase,
2+3with2English,
VP3
2 Univ.
2n, W. F. (1990) Word Order
Reference
toVP3
the
Verb
Doctoralthesis,of238,
OUP/ Koopman,
W. Special
F. VP3(1990)
Word
Orde
238,
OUP
/
Koopman,W.
F.
(1990)
Word
Order
in
Old
English,Referencetois assumed
to be
one oftheprocessesevolution(HeineKutevaevolution (Smculturalevolution(Smith2011).
Specifically,
toof
becultural
one (Smith
ofevolution
theOldprocesses
ofand
culturalbeoneof theprocessesof S.
cultural2011).
Specifically,, S.
(1999)toPhraseStructuresinCompetition:
Variation
andChangeinEnglishWord
Order,
Garland.
Amsterdam/ Pintzuk,S.
(1999)
PhraseStructu
Amsterdam/
Pintzuk,(1999)
PhraseStructuresinCompetition:
Variation
andChange
in
3
3
3
×
3
3
3
×andforition
3
Extraposition
Extraposition
2007,Extraposition
Smith
2011,
inter
alia).
precise,ModalP,
PassiveP
PerfectP
emerged ModalP,
adaptively
so as tocompensate
for Tothe beadaptively
ModalP,
PassiveP
PerfectP
emergedso aPassiveP
andPerfectP
emergedso as to andcompensate
the adaptively
Extrapos
Extraposition
25 the loss of verbal inflections in the25
order
to Thiscompensateforcourse ofloss
theemerged
historicalchange ofinin
English.
ofverbal change
inflectionsin the courseofverbaladaptively
inflections
the courseof loss
the historicalof English.
This of the historicalcourseofthehistoricalchanges
that
have
taken
place
in
English.
This
considered
one
of
good
examplesof
culturalconvergence
of
word
order
is
considered
oneof gooconvergence of word order is considered one of good examples of culturalconvergence
of
word
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regardedasagood
exampleofculturalevolution.
evolution.
evolution.

References
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Heine, B.
T. Kuteva(2007)
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Oxford:Oxfordan grammaticalization
tellandus about
the origins
of TheNewmeyer,
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(2006)
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aboutWhattheoriginsofUniversity
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and
K.
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(eds.)
The
Evolution
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In
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Cangelosi,
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Sm
language? In A. Cangelosi, A. D. M. Smith, and K. Smith (eds.) The Evolution
Kemenade,A.
van (1987)
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Language,
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ofthe
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of
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of English.
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Syntax. Smith,Oxford:Oxfordevolution.
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ticalization
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(2011)
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A.D.M.
(2011)
Grammaticalizationand language
InPress.
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Smith,
A.D.M.
(2011)
andlanguageevolution.
In H. Narrog
ford Handbook
ofB.
Grammaticalization.
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Heine (eds.)
The Oxford
Handbook of Gramm
and
Heine
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TheGrammaticalization
OxfordHandbookandof B.
Grammaticalization.
142-152.
and Oxford
B. HeineUniversity(eds.) ThePress.
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Verb
Raising
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Old
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Haeberli,
and S. Pintzuk(2012)
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Haeberli,E.(Projection)
andOxford
S. Pintzuk(2012)
Revisiting
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Raisingin Oldtman and A. Garret(eds.)
Change:English,In D.
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302

TO WHAT EXTENT CAN THE HUMAN AGENT BIAS BE CAPTUREDBY LINGUISTIC EXPERIENCE? EVIDENCE FROMMACHINE-LEARNING OF CORPORA
Eva Huber*1,2 , Paola Merlo3 , and Balthasar Bickel1,2* Corresponding Author: eva.huber@uzh.ch

1 Department of Comparative Language Science, University of Zurich.

2 Center for the Interdisciplinary Study of Language Evolution (ISLE), University of Zurich.
3 Département de Linguistique, University of Geneva.

Converging evidence from event apprehension (Wilson, Papafraou, Bunger, &Trueswell, 2011), language processing (Ferreira, 2003; Bornkessel-Schlesewsky& Schlesewsky, 2009; Kemmerer, 2012) and default grammar rules (Dryer, 2013;
Napoli & Sutton-Spence, 2014) shows that humans have a bias towards the Agentparticipant in events (Agent bias). We contrast two hypotheses on its evolution:(i) The biologically-driven hypothesis ascribes the bias to an innate property thatis already present in prelinguistic infants (Durrant et al., 2021) and that is potentially shared with other animals (Mascalzoni, Regolin, & Vallortigara, 2010); in
this case, the effects on language stem directly from an innate principle. (ii) The
experience-driven hypothesis ascribes the bias to our experience with the distributional properties in language use; in this case, the bias in processing is acquired,
and the parallels between cognition and language reflect parallel evolution. To
probe these hypotheses we focus on the specific effect that Agent Bias has on
the processing of role-ambiguous noun phrases. Here the effect reveals itself ina transient prediction of an agent role which triggers an electrophysiological deflection, typically an N400 effect, when the prediction fails later in the sentence(Bornkessel-Schlesewsky & Schlesewsky, 2014). The experience-driven hypothesis suggests that this effect can be simulated with computational models whoseknowledge of language is derived from distributional patterns in the linguistic
input and general architectural assumptions but without any explicit agent bias
built into them. Previous work on English (Levy, 2008; Frank, Otten, Galli, &Vigliocco, 2015; Szewczyk & Federmeier, 2022) has shown that N400 effects
correlate with surprisal, the negative log probability of a word given the previous
input extracted from such models. We use previously conducted EEG experiments
which contain an initial ambiguous NP to compare the extent to which suprisal
values in these models can capture N400 effects. Under the experience-driven hypothesis, we expect higher surprisal values in the conditions that also trigger anThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

303

N400 effect. Under the biologically-driven hypothesis, we expect higher surprisal
values not to correlate with the conditions that raise an N400 effect.
We selected experimental data from three languages that are maximally distinct in how they code Agents: Hindi (Bickel, Witzlack-Makarevich, Choudhary,
Schlesewsky, & Bornkessel-Schlesewsky, 2015), which flags Agents by an “ergative” case maker only under specific conditions (perfective transitive clauses);Basque (Isasi-Isasmendi Andaluze et al., 2022), which flags all Agents as ergative;
and German (Haupt, Schlesewsky, Roehm, Friederici, & Bornkessel-Schlesewsky,2008), which does not flag Agents by case but treats them as general subjects. All
experiments showed that a sentence-initial noun phrase disambiguated to a Patient
reading elicits an N400 response, although experimental conditions varied (Hindi:role-ambigous vs unambigous Patients × aspect; Basque: ambiguous Agents vsPatients × unambigous Agents vs Patients; German: ambigous Agents vs Patients× dative vs accusative-assigning verbs).
We extracted the surprisal values of two types of language model architectures:a Long Short-Term (LSTM) model (Hochreiter & Schmidhuber, 1997), a variantof a recurrent neural network, and a Transformer model (Vaswani et al., 2017).
LSTMs process units incrementally by recurrence, and hence contain a memory
bottleneck. Transformers, on the contrary, have ‘attention layers’ which allow for
direct access to parts of the previous input (Vaswani et al., 2017). Hierarchical
Bayesian Models are fitted to estimate surprisal values given the same conditions
as in the experiments. We then qualitatively evaluate whether the surprisal values
show the same trends as the N400, in line with the experience-driven hypothesis.
The results (see figure in supplementary material) for the Hindi LSTM show aslight trend in line with the N400 findings, but estimates overlap between conditions (89% CIs imperfective role-ambiguous: [13.29,14.55] vs role-marked:[12.9,14.12], perfective amb.: [11.57,12.74] vs marked: [11.24,12.31]); the Transformer model replicates the N400 findings slightly better in the imperfective
aspect (role-marked [16.53,17.88] vs role-unmarked [15.74,17.02]), unlike inthe experiments, where it held across aspects. Both Basque models replicatesthe N400 findings (LSTM A: [12.55,13.61 vs P: [13.63,14.47]; Transformer A:[16.18,16.26] vs P: [16.24,16.32]) but, unlike in the experiments, they generalize the pattern to unambigous cases (LSTM A: [12.71,13.78] vs P: [13.44,14.29];
Transformer: [16.18,16.26] vs P: [16.23,16.31]), where no N400 was observed.
The German models replicate the N400 findings but the effect is strong and reliable only in the Transformer model (for acc-verbs: A: [3.31, 4.1] vs P: [5.73,6.48];
LSTM: A: [8.96,9.76] vs P: [9.67,10.29]).
We conclude that there is at best marginal evidence for the experience-driven
hypothesis. The surprisal values replicate N400 effects only in German Transformer models. In all other cases, surprisal values are either more general(Basque) or less general (Hindi) than N400 findings; in addition most effects haveoverlapping credibility intervals.

304

Acknowledgments
This work was funded by the NCCR Evolving Language supported by Swiss National Science Foundation Agreement (#51NF40 180888).
References
Bickel, B., Witzlack-Makarevich, A., Choudhary, K. K., Schlesewsky, M., &Bornkessel-Schlesewsky, I. (2015). The neurophysiology of language processing shapes the evolution of grammar: Evidence from case marking.
PLOS ONE, 10, e0132819.
Bornkessel-Schlesewsky, I., & Schlesewsky, M. (2009). Minimality as vacuous
distinctness: Evidence from cross-linguistic sentence comprehension. Lingua, 119(10), 1541–1559.
Bornkessel-Schlesewsky, I., & Schlesewsky, M. (2014). Competition in argument
interpretation: Evidence from the neurobiology of language. Competing
motivations in grammar and usage, 107, 126.
Dryer, M. S. (2013). Order of subject, object and verb. In M. S. Dryer& M. Haspelmath (Eds.), The world atlas of language structures online.
Leipzig: Max Planck Institute for Evolutionary Anthropology.
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at 10 months predict vocabulary development? Language and Cognition,
13(1), 66–98.
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psychology, 47(2), 164–203.
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305

Mascalzoni, E., Regolin, L., & Vallortigara, G. (2010). Innate sensitivity for
self-propelled causal agency in newly hatched chicks. Proceedings of the
National Academy of Sciences, 107(9), 4483–4485.
Napoli, D. J., & Sutton-Spence, R. (2014). Order of the major constituents in sign
languages: implications for all language. Frontiers in psychology, 5.
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probability. Journal of Memory and Language, 123, 104311.
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Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances inneural information processing systems, 30.
Wilson, F., Papafraou, A., Bunger, A., & Trueswell, J. (2011). Rapid extractionof event participants in caused motion events. In Proceedings of the annual
meeting of the cognitive science society (Vol. 33).

306

PHYLOGENETIC RECONSTRUCTION FOR JAPONIC: NEW
DATA TYPES AND METHODOLOGICAL INSIGHTS
JOHN L. A. HUISMAN*1, BONNIE MCLEAN1, and CHIEH-HSI WU2*Corresponding Author: john.huisman@lingfil.uu.se
1Department of Linguistics and Philology, Uppsala University, Uppsala, Sweden
2Mathematical Sciences, University of Southampton, Southampton, UK

1. Background
While synchronic variation in the Japonic languages is well understood from themeticulous work of Japanese dialectology, the relationships between the various
linguistic groups from a historical perspective have received considerably lessattention. Existing studies tend to overrepresent either of the two main branchesof the family—i.e. Japanese (Lee & Hasegawa, 2011) and Ryukyuan (Pellard,
2009, 2015)—leaving some questions about the full family history unanswered.
Bayesian phylogenetic analyses have the potential to recover these deeper-level
branching events, but the complexity of such analyses requires large amounts ofdata, which have been argued to only be “really available from cognacy in thelexicon” (Greenhill et al., 2020). This can pose a challenge in contexts of recent
diversification, areal features, and regular contact where lexical diversity islimited—e.g., the situation found on the Japanese mainland (Hattori, 1973).
2. The current study
To address these issues, we analysed a sample of languages that more effectively
captures linguistic diversity of both the Japanese mainland and the Ryukyuan
islands, and experiment with analyses that—unlike the prevalent approach incomputational historical linguistics—include features other than cognate sets as
phylogenetic characters. Specifically, we use phonotactic traits as phylogenetic
characters using the method described in Macklin-Cordes et al. (2021), and assess
their informativeness for language families with a relatively limited time-depth.
2.1. Data and methods
We started by collating basic vocabulary data for 65 Japanese and 28 Ryukyuan
varieties using a 250-item list compiled from other commonly used concept lists(Greenhill et al., 2008; Swadesh, 1952; Tadmor, 2009) and coded the lexemes forcognacy. Subsequently, we used the vocabulary data to extract phonotactic traits
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

307

using the approach set out by Macklin-Cordes and colleagues (2021), i.e., thepresence/absence of sequences of sound segments, the relative transition
frequencies between sound segments, and the relative transition frequencies
between sound classes. We inferred phylogenetic trees using Bayesian
approaches (Bouckaert et al., 2019) with different tree priors and models of
evolution in three analyses (lexical traits only, phonotactic traits only, and lexicaland phonotactic traits combined), and compare our findings to previously
suggested phylogenies using different methods.
2.2. Results
As expected, we found that reduced levels of lexical diversity resulted in low
clade support, particularly among the Japanese mainland varieties, in the analysis
based on lexical traits alone. In the analysis on phonotactic traits alone, we foundseveral (albeit minor) conflicts with generally accepted phylogenies for the
Ryukyuan branch—which, we discuss, likely result from parallel changes in thephoneme inventory that reverberate in phonotactics. However, when combining
the lexical and phonotactic traits into a single holistic analysis, the model was able
to both capture language relationships accurately, while also considerably
decreasing the overall uncertainty in the model. We provide an overview of thestrengths of the method used here (e.g., “free” algorithmically inferred data), as
well as the challenges that remain with potential solutions, e.g., testing against
simulated data (Wichmann & Rama 2021). We also discuss how our results shedlight on ongoing questions in Japanese historical linguistics (e.g., the overall tree
topology; the timing of initial diversification), and previously raised concerns
about how the spatial structure of the Japonic language family complicates
phylogenetic inference and its interpretation (e.g., Murawaki, 2015).
3. Summary
We present a case study of the use of phonotactic traits in computational historicallinguistics (following Macklin-Cordes et al. 2021). Our findings suggest that nonlexical data can be a valuable addition to analyses that aim to untangle finegrained phylogenetic structures in contexts of recent diversification and language
contact. The reconstructed dated phylogeny of the Japonic language family
provides new insights into the linguistic history of Japan, which can e.g., further
our understanding of geographic factors in language diversification (cf. Huisman
et al., 2019), or facilitate assessing the validity of predictive mathematical modelsof language change (cf. Takahashi & Ihara, 2020).

308

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NON-LINEARITY IN HUMAN LANGUAGE EVOLUTION: A
GENERAL ECOLOGY PERSPECTIVE GROUNDED IN
PLEISTOCENE ARCHAEOLOGYSHUMON T. HUSSAIN1 and ALEXANDRA R. KRATSCHMER*2*Corresponding Author: romak@cc.au.dk
1Department of Archaeology and Heritage Studies, Aarhus University, Aarhus,

Denmark

2Department of Linguistics, Cognitive Science and Semiotics, Aarhus University,

Aarhus, Denmark

This paper reconsiders the emergence of language in the hominin lineage from theperspective of general ecology, multispecies theory, phenomenology, and semiotics. We
propose that changing communicative environments and their shifting foci of homininanimal and hominin-hominin interaction are fundamental to understanding the evolutionof language in the hominin lineage. After reviewing the available contextual evidence on
the nature and transformation of communicative ecologies in the deep-past, we pledge forinterpretive conservatism and argue that an “extended short chronology” for the evolutionof human language is currently best supported by the archaeological data (in sync with aprolonged period of intensified “indexical” communication). We further propose that thisevolutionary trajectory is best understood as a form of non-linear “co-evolution” and henceinterlaced with key developments in multiple hominin behavioral arenas and material
registers, such as animal relationships, technical behavior, artistic expression and themodalities and scale of social life. Systematically mapping and exploring this broader“general ecology” of communicative needs, concerns, functions, and horizons minimallyrequires the integration of archaeology, ethology, linguistics, and phenomenology.

1. Introduction: Shifting the Theoretical Focus
Asking for the origins and evolution of human language is a long-standing
conundrum. The available evidence is necessarily indirect – language does not“fossilize” – and assumptions about what precisely constitutes evidence for
language in the first place – and more difficult even, language evolution – differ
vastly across disciplinary and theoretical spectra. Accounts of the evolution ofhuman language range from cognitive approaches, localizing language as anextension or foundation of generalized capacities for symbolic thought (e.g.,
Noble & Davidson, 1991; Donald 1991; Henshilwood & d’Errico, 2011;
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

310

Tattersall, 2019), over cultural semiotic, mostly Piercean, ones (Kissel & Fuentes,
2017; Barham & Everett, 2021) to ethologically-founded communication studies
arguing for deep-historical roots of complex communication systems in diverse
animal taxa, especially birds, and the co-evolution of “speech”, tool-use, social
behavior, and potentially aesthetic display, in a number of unrelated species(Menninghaus, 2011; Stout, 2018). Yet, the main sources of evidence for human
evolutionary studies of language are material culture assemblages and specifictypes of artefacts uncovered by archaeologists and millennial-scale changes inhominin bio-cognitive makeup documented by paleoanthropologists, cognitive
scientists, and primatologists (Dediu & Levinson, 2018). The Darwinian optic that
goes together with these disciplinary configurations has vindicated the idea thatthe emergence of language – because it “makes us human” – is a watershed eventor turning point in hominin evolution, dividing the past into a “before” and“after”. The result has been a tendency to promote unilineal models of language
evolution rooted in persistent notions of progress, leading from the first
representatives of the Homo lineage to the only surviving hominin taxon, H.
sapiens. We suggest that the problematic vision of a set language threshold thatisolates a so-called “archaic” from a “modern” stage in human prehistory, where
full-blown humanity is achieved, is linked to an overemphasis, and at times
fetishization, of intrinsic “capacities” at the expense of context andcommunicative ecologies in hominin evolution.
Definitions and conceptual baselines: we here retain the conceptual distinction
between “language” as a fully-fledged symbolic sign system in the Saussurian
sense, where each sign obtains its meaning by virtue of its relation to other signs,
and “communication” as “[a] process involving signalling between a sender andreceiver, resulting in a perceptual response in the receiver, which extracts
information from the signal, potentially influencing the receiver’s behavior”(Stevens, 2013: 73). Communication in this view is a more basal and far-flung
phenomenon than language and as such deeply involved in most life-processes
and inter-organismic relations (Barbieri, 2008). Language can then be said torespond to specific communicative needs and serves particular functions, which
may in turn also change the total architecture of broader communicative ecologies– i.e., the contexts and relations that frame communicative processes at large. This
perspective shifts the attention to the social, cognitive, and environmental
embeddedness of language as a specific form of communication rather than
reducing it to a mere question of “language ability” (Lieberman, 2006).

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2. The Big-Picture Perspective: Changing Niches and “Ecologies ofCommunication” over the last 2 Million Years
This paper reconsiders language evolution among hominins in the framework ofgeneral ecology, multispecies theory, phenomenology and biosemiotics(meaning-making in living organisms). We contend that previous approaches
have paradoxically endorsed a human-centered point of departure with often
quasi-orthogenetic implications. Our advocated approach flips this perspective on
its head. We draw attention to the fact that early hominins were an extraordinarilyrare sights in Plio-Pleistocene landscapes, and in turn primarily had to grapple
with nonhuman-dominated life-worlds (Rodríguez et al., 2015). The
communicative needs and concerns in these contexts are fundamentally
multispecies: early hominin foraging niches relied on the coordination with otheranimals, especially predators and key scavengers, and biosemiotic relationships
at the hominin-animal interface were thus of key existential concern.
Communication systems therefore likely evolved in tandem with such broader
interspecies niche conditions as well as the changing demographic and social
contexts hominins found themselves in. Drawing on ethological data
demonstrating structured relationships between different niche-types andcommunicative environments, we explore the consequences of broad “humanniche” trajectories – with spatiotemporally divergent patterns of evolving
carnivory and generalist foraging (Ben-Dor et al., 2020) – and highlight theimportance of the Middle Pleistocene (ca. 600-130,000 years ago) associated withflourishing, wide-spread archaeological evidence for novel forms of socialorganization rooted in “soft” communicative technologies such as hearth-centeredlife, meat sharing and big-game hunting (Kuhn & Stiner, 2019).
The Middle Pleistocene not only documents important changes in the hominin
body plan including marked expansions in cranial capacity, but the archaeologicalrecord also points to significant reconfigurations of hominin social and technicalecologies. At Qesem cave in modern-day Israel, hominins were roasting anddining on tortoise shells about 400,000 years ago and differential ungulate bone
representation in the same layers indicates selective movement of meat-rich bodyparts to “homebase”-like places equipped with hearths (Stiner et al., 2011). Suchplaces not only promote and intensify social interaction among peers, but theyalso generate novel contexts for the sharing of experience and what has beenreferred to as “gossiping” (Dunbar, 2017), and thus ultimately precipitate theevolutionary dynamics between hominin place-making and storytelling resulting

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in the diagnostic “storied” landscapes documented among contemporary forager
groups. Evidence for the emergence of hafted spear tips used for hunting larger
animals, although sporadic, also reaches back ca. 500,000 years (Wilkins et al.,2012). Generally speaking, the Middle Pleistocene sees the proliferation,
diversification and formalization of so-called “prepared core technologies” basedon the principle of anticipated morphotechnical control over stone knapping
products as well as different forms of technical “predetermination”, often with anotable “division of technical labor” among varying coexisting technologies.
This goes hand in hand with the development of (non-linear) production
trajectories comprising “intermediate” products which in principle allow for the
separation of blank (unmodified artefacts) and stone tool production chains. Thisphenomenon is for example signaled by erupting evidence for tool hafting at theend of the Middle Pleistocene, and paves the ground for task differentiation inboth tools and tool users and thus the promulgation of “communities of practice”
with new requirements for social coordination beyond the spatiotemporal horizonof the “here and now“. We propose that such developments have re-directed thecommunicative foci of hominins from the wider more-than-human world to theirhominin peers. The development of taxing stone technologies such as “Levallois”,
which depend on faithful horizontal and vertical transmission of technicalknowledge and its cross-generational stabilization in the timeframe between 400
and 300,000 years ago has, consistent with this view, recently been interpreted as
evidence for a new quality of hominin intergroup interaction and sociotechnicalinformation exchange (MacDonald et al., 2021).
The Middle-to-Late Pleistocene transition (ca. 300-100,000 years ago)corresponds to the crystallization of new hominin behaviors linked to thecollaborative stalking and intercepting of isolated large animals such as rhinos
and bears or entire groups or even herds of animals such as horse or reindeer. The
implicated hunting tactics have been argued to require planning and cooperation,and hence reliable intragroup communication, even though collective hunting is
generally consistent with “language-free” explanations, as for example shown bylions, orcas and many other animals exerting cooperative foraging. Yet, it isnotable that the Middle-to-Late Pleistocene interface is also linked to a mosaic ofvarying hominin taxa populating different landscapes, and possibly coexisting in
some, providing a new context of “multispecies” interaction and communication(Wood & Boyle, 2016). That these different hominin forms at least occasionallyencountered and interacted with each other is demonstrated by aDNA research

313(Liu et al., 2021). This “pluralism” of hominin contexts and their overlap orintersection as well as the coercing climatic envelope of the developing Middle
Pleistocene bringing about high-amplitude 100,000-year warm-cold cycles withoften rapid transitions speaks to the dynamic and unsteady character of theassociated communication ecologies, so that hominin extinctions and dislocations
were probably not the exception (Hussain & Riede, 2020). The diverse physicalanthropological record of Eastern Eurasia and Africa not only suggests amplified
gene-culture coevolution but simultaneously points to novel and increasingly
existential requirements of navigating landscapes frequented by other hominins,and to communicatively attend to them.
As hominins become more frequent on the landscape – starting perhaps as earlyas 100,000 years ago in some areas – and as they begin to increasingly rely oneach other across vast geographic distances, communicative ecologies
presumably change once more and communication transcends its sole
information-conveyor service, developing into a medium of cultural signpostingand identity-formation within interconnected human-concentrated worlds(Roberts, 2013). Anthropological and philological observations indicate that such
communicative ecologies are prone to promote multi-linguality, and we may
surmise that such conditions were in place at least since the Late Pleistocene. The
Late Pleistocene (ca. 125-11,000 years ago), non-coincidentally, is associatedwith the emergence of forms of visual culture indicating a consequential role ofmaterial objects in grounding social memory and relaying storied worlds (Porr,
2010). The development of broadly delimited cultural geographies rooted in
distinct artefact designs (lithic tools, personal ornaments, ochre and ostrich shellengravings) has traditionally been linked to this timeframe as well (Vanhaeren &d’Errico, 2006; Brooks et al., 2006; Ruebens, 2013; Tylén et al., 2020; Hussain& Will, 2021). At the end of the Pleistocene, stone artefact forms begin to enactgroup awareness and possibly embody what Wiessner (1983) has called“emblematic style”. Bow-and-arrow technology which, based on current
knowledge, makes its first appearance in the archaeological record around 70,000
years ago has been argued to depend on complex technical knowledge and“delayed” problem-solving (Lombard & Haidle, 2012) and thus similarly
supports the idea that both the contents and the contexts of hominin
communication were subjected to transformative change in the Late Pleistocene.
We suggest it is no coincidence that such social learning-dependent technologies
flourish in the context of “generalist” hominin niches which depart from earlier

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carnivory-reliant hominin foraging strategies (Roberts & Steward, 2018; Ben-Dor
et al., 2021). Such “niche broadening” at the end of the Pleistocene has importantconsequences for ecologies of communication since foragers rely on a deeper andbroader vocabulary of places and resources, which promotes the development ofdifferentiated zoo- and pythotaxonomies and has implications for thecommunicative needs of task allocation.
3. Conclusion: Non-Linearity, Interpretative Conservativism and an“Extended Short Chronology” of Language in the Hominin Lineage
Searching for the origins of human language in the archaeological andpaleoanthropological records may be a futile exercise since it simply remains
unclear what constitutes unambiguous evidence for language vis-à-vis merely
indexical communication. Perhaps more importantly, the lines of evidence
traditionally linked to language evolution are greatly staggered in time, rather thanbeing correlated or chronologically “packed”, and thus suggest a gradual language
continuum rooted in changing ecological conditions, rather than a set of
mysterious hominin predispositions. The indirect archaeological evidence
available is additionally patchy and thus generally supports a non-linear evolutionof hominin communicative environments instead of a single context of language“origin”. Given these conditions, we pledge for interpretive conservatism andconclude that an “extended short chronology” for the evolution of language in thehominin lineage is currently best supported by the archaeological evidence (insync with a prolonged period of “indexical” communication). We propose that
this evolution is best understood as a form of “co-evolution”, and thus tied to key
developments in multiple behavioral arenas and material registers such as
hominin-animal interaction, technical behavior, artistic expression, and social
life. Systematically mapping and exploring this broader “general ecology” ofcommunicative needs, concerns and functions requires at least the integration ofarchaeology, ethology, linguistics, semiotics, and phenomenology. This coupledperspective makes room for variously emerging and collapsing ecologies ofcommunication without losing sight for the general lines of development inhominin evolution. The result is a more realistic, yet intricate picture of humanlanguage evolution giving rise to an original compromise between radical “short”
and “long” chronology renderings of language evolution.

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GENERATION OF STRUCTURED SPATIAL PATTERNSTHROUGH ADVERSARIAL IMITATION LEARNING
Hiroyuki Iizuka*1,2, Wataru Noguchi1, Taiki Sasaki3and Masahito Yamamoto1,2*Corresponding Author: iizuka@ist.hokudai.ac.jp
1Faculty of Information Science and Technology, Hokkaido University, Sapporo, Japan
2Center for Human Nature, Artificial Intelligence, and Neuroscience,

Hokkaido University, Sapporo, Japan
3Graduate School of Information Science and Technology,

Hokkaido University, Sapporo Japan

This study shows that systematic structures that appear in language and birdsong can begenerated by adversarial imitation learning, in which one wants to imitate the opponent's
patterns without the opponent imitating their patterns. In prior adversarial imitation studies,
the generated patterns became chaotic but not structured. Therefore, we extended aprevious model using the concept of generative adversarial networks (GANs) for deeplearning. The agents were modeled using generator and discriminator networks. Our results
show that mutual adversarial imitation learning can lead to higher fractal dimensions of thegenerated patterns and cause the structurization of patterns.

1. Introduction
Aside from being a complex time-series pattern, a language also features asystematic structure or grammar (Chomsky, 1965). Such a structure can beobserved in the time-series of the bird songs (Honda & Okanoya, 1999), the close
calls of banded mongoose (Jansen et al., 2012) and the humpback whale songs(Cholewiak et al., 2013) as well as in the behavior patterns of macaque monkeys(Hihara et al., 2003) and degus (Tokimoto & Okanoya, 2004) and even in spatial
patterns (Kondo & Miura, 2010).
To determine how systematic structures emerged, Kirby et al. conducted
experiments from a linguistic perspective (Kirby et al., 2008, Cornish et al., 2013,
Winters et al, 2015). These experiments showed that linguistic structures emerge
in the trade-off between learnability and expressivity through culturaltransmission, in which symbols are remembered and then transmitted to others
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

318

repeatedly (remembering here is equivalent to what we call imitation). However,these experiments required the assignment of distinct random sequences for each
expression at the initial stage in order to produce structured sequences. If theinitial sequences are equivalent among all expressions, the sequence is simply
transmitted without any structuring, and the system has no expressivity. Thismeans that other principles are needed to explain how the complexity required to
produce structural patterns emerges.
Accordingly, we hypothesize that patterns generated by organisms becomecomplex and structured through adversarial situations, in which one wants toimitate or understand an opponent’s signal patterns without the opponent
imitating or understanding their own signal patterns. For example, the ability toimitate the profitable behavioral patterns of others leads to one's own benefits. In
contrast, this may remove these benefits from those who are being imitated. In
other words, the strategy of imitating others while avoiding being imitated byothers may be evolutionarily dominant. Furthermore, individuals may want only
those in their in-group to understand profitable signals, which requires secrecy.
We believe that the complexity generated by interspecific adversarial
communication is adopted for intraspecific cooperative communication, which inturn leads structured communication to evolve.
Based on this concept, we propose a simulation model that complexifies
time-series patterns through adversarial imitation learning. Although the originalmodel only produced chaotic behavior in time-series (Yamazaki et al., 2020), we
extended it by introducing the recognition of patterns by individuals, thus
showing that recognition leads to simplification and pattern structuring, rather
than mere complexity.
2. Simulation model
In the field of machine learning, generative adversarial networks (GANs) can
imitate real data (e.g., images) to generate realistic artificial data (Goodfellow etal., 2014). GANs produce high-quality imitation data using a generator and adiscriminator that features adversarial learning. We used GANs to model asituation in which two individuals, Agents 1 and 2, are engaged in adversarial
imitation learning. Unlike the study upon which our work is founded, we
introduced the recognition of a discriminator to evaluate the imitation’s success(Suzuki & Kaneko, 1994, Yamazaki et al., 2020).
An overview of the proposed model is presented in Fig. 1. Each individual
consists of a generator (G1/G2) and a discriminator (D1/D2). The discriminator
determines whether a pattern (G1(Z) or G2(Z)) is produced by its own generator

319

or the opponent’s generator. The generator learns to generate a pattern such thatits own discriminator can easily recognize the pattern as being generated by itself.
This learning process works toward simplifying the pattern to facilitate
recognition. Furthermore, the generator learns to trick the opponent discriminatorby recognizing patterns produced by the opponent generator. Consequently, thediscriminator and generator learn to avoid being tricked by the opponent’s
generator, thus increasing pattern complexity. This learning process occurs under
mutual conditions.

Figure 1. Overview of our model. G1/G2 and D1/D2 represent the generators and discriminators
for Agent 1/2, respectively. Red and blue arrows indicate mutual and unidirectional adversarial
imitation learning, respectively. The generators and discriminators are realized by deep neuralnetworks with convolutions and de-convolutions. The generators receive random values as
seeds to generate patterns, as in the original GAN. G1/2(Z) are the patterns created byGenerators G1/2.

Both the generator and the discriminator are implemented with feed-forward
neural networks, with initial parameters given randomly. Those parameters aretrained by gradient descent. The loss function of Agent 1 can be expressed using
hinge loss as follows:
𝐿𝐷1 = −𝐸𝑥~𝐺1(𝑧) [min(0, −1 + 𝐷1(𝑥)] − 𝐸𝑥~𝐺2(𝑧) [min(0, −1 − 𝐷1(𝑥)],(1)𝐿𝐺1 = −𝐸𝑧~𝑁 [min(0, −1 + 𝐷1(𝐺1(𝑧))] − 𝐸𝑧~𝑁 [min(0, −1 + 𝐷2(𝐺1(𝑧))], (2)The loss function of Agent 2 is identical apart from exchanging D1/D2 andG1/G2. When this function is minimized, the discriminator learns to output
positive and negative values for the patterns created by its own generator andopponent generator, respectively, and the generator is trained such that both

320

discriminators output positive values. The batch size was set to 32, and one epoch
represents a single parameter update by the losses computed using the 32 patterns
produced by each generator.
To show that mutual adversarial imitation learning produces structural
patterns, we compared its results to the unidirectional condition, where only one
individual performs adversarial imitation learning. That model is denoted by blue
arrows in Fig. 1. Under unidirectional adversarial imitation learning, there is noD2 discriminator, and the term D2 in Eq. (2) for 𝐿𝐺1 and 𝐿𝐺2 is omitted from theloss function.
3. Results
In our simulations, the model was trained for up to 5,000 epochs under bothconditions. Figs. 2 and 3 show the patterns produces by each generator with eight
different z values under the unidirectional and mutual conditions, respectively.
Because all the network parameters were initialized with random values, thegenerated patterns became equally cluttered at the beginning for both conditions.

Figure 2. Examples of spatial patterns generated by G1 and G2 under unidirectional adversarial
imitation learning. Each block has 8 patterns generated from 8 different z values.

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However, as the learning process progressed, the generated patterns became
increasingly locally detailed and cluttered in unidirectional adversarial imitation
learning. Under mutual adversarial imitation learning, however, these patterns
appear to have formed a sort of global pattern.

Figure 3. Examples of spatial patterns generated by G1 and G2 under mutual adversarial
imitation learning.

To demonstrate the structuring progress under mutual adversarial imitation
learning, we calculated the fractal dimensions of the generated patterns using thebox-counting method with the patterns’ binary representations and the grayscale
median. We also calculated the entropy of the grayscale images to measurecomplexity. Figure 4 shows the fractal dimensions and entropies for mutual andunidirectional adversarial imitation learning. Here, entropy decreased in the earlystages of learning but remained high thereafter. The fractal dimension also did notincrease, instead converging to a low value. As can be seen in Fig. 2, as thelearning progresses, the pattern becomes more cluttered, but lacks an underlying
structure.
Under mutual adversarial imitation learning, however, the entropy andfractal dimension exhibited oscillation. The entropy decreased, and the pattern322

became less cluttered, but the fractal dimension temporarily increased. Fig. 3
shows that the pattern was not merely cluttered but developed a structure. In thecurrent simulation, this structure was not maintained, and the fractal dimension
remained high. Nevertheless, the fractal dimension increased again, andstructuring occasionally occurred. These results were confirmed through multiple
simulations. Thus, our results show that mutual learning promotes the structuringof generated patterns.

Figure 4. Changes in fractal dimensions and entropy of spatial patterns generated by G1 andG2. The left and right columns indicate learning results under the mutual and unidirectional
conditions, respectively.

4. Discussion
In the case of unidirectional adversarial imitation learning, thediscriminator/generator pair of Agent 1 should learn to escape from patterns
created by the opponent generator. It is sufficient to gradually make the patterns
chaotic to prevent the opponent from successfully imitating them. Oncecomplexity arises, structuring can occur as a result of simplifying pressures, sothat the patterns can be easily recognized by one's own discriminator. This
corresponds to Kirby's group experiment of structuring sequences to be easily
remembered in the process of cultural transmission through the learning force. In
our previous model, the generated patterns became chaotic; however,
simplification did not occur, and no structured patterns were found (Yamazaki etal., 2020). In the present model, the introduction of a discriminator makes it323

necessary to form patterns that are easy for the discriminator to recognize, whichleads to pattern structuring. Because it is not sufficient for the generator to simply
keep avoiding the opponent generator in mutual adversarial imitation learning, astronger adversarial learning pressure was applied to the generators, resulting instructured patterns.
Our simulation results generated a high fractal dimension, indicating thatthe generated patterns had a recursive structure, similar to that of language. We
believe that not only cooperative communication, but also adversarial
communication was necessary to create such structures, which may have been
incorporated into language.
Acknowledgements
This work was supported by JSPS KAKENHI (grant number JP20H04989).
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325

DEAF AND HARD-OF-HEARING PEOPLE CAN DETECTSOUND SYMBOLISM: IMPLICATIONS FOR THE
ARTICULATORY ORIGIN OF WORD MEANING
Mutsumi Imai*1, Kimi Akita2, Sotaro Kita3, Noburo Saji4, Masato Ohba5, and Miki
Namatame6*Corresponding Author: imai@sfc.keio.ac.jp
1 Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan
2 Department of English Linguistics, Nagoya University, Nagoya, Japan
3Department of Psychology, University of Warwick, Warwick, United

Kingdom
4 Faculty of Human Sciences, Waseda University, Tokorozawa, Japan
5 Graduate School of Media and Governance, Keio University, Fujisawa, Japan
6 Department of Industrial Information, Tsukuba University of Technology,

Tsukuba, Japan
Uncovering the relation between form and meaning of words is important forunderstanding the nature and evolution of language. Where does the sound-meaning
relationship of a word come from? In a long tradition of linguistics, it has been assumed
that language-specific conventions create arbitrary association between linguistic forms
and meanings (Hockett, 1960; de Saussure, 1916). However, words that sound like what
they mean have also been recognized, and this intrinsic link is called sound symbolism(Köhler, 1929). Does one need to be able to hear sound to detect sound symbolism? Here,
we show that one does not need to. Deaf-and-Hard-of-Hearing (DHH) participants, eventhose with profound hearing loss, could judge the sound symbolic match between shapes
and words just as well as hearing participants do, as long as DHH participants could movetheir tongue freely. This indicates that people can detect sound symbolism via inherent
resemblance between articulatory movements and the meanings. This further suggeststhat linguistic symbols can emerge through iconic mapping between oral gesture andsensory experience of the world.

1. Introduction
Uncovering the relation between form and meaning of words is important forunderstanding the nature and evolution of language. Form and meaning of words
are associated not only by language-specific arbitrary conventions (de Saussure,1916), but also by an intrinsic link, called sound symbolism (Köhler, 1929), whichreflects perceived resemblance between sound and properties of referents. One

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

326

may assume that the hearing ability is indispensable to sense sound symbolism.
However, if sound symbolism is originated from articulatory gesture—mimicryof things or events by mouth—people may be able to detect sound-meaning match
without the function of hearing. We propose the articulatory origin of soundsymbolism hypothesis. We examined this hypothesis by testing whether Deaf orHard of Hearing (DHH) people can detect sound symbolism.
2. Experiment 1.
2.1. Participants and Stimuli
Thirty-four DHH individuals, all with congenital and profound hearing loss
(minimum dB = 103), and 36 individuals with typical hearing participated in the
study. Participants received a 2-page booklet. On each page, either a spiky-shaped
figure or a round-shaped figure was depicted at the top, below which 38 novel
words were listed orthographically. A total of 38 novel words were created.
Previous findings (Köhler, 1929) indicate that, among these segments, sonorantconsonants (/m, n, j, ɾ/), bilabials consonants (/b, p/), and non-front vowels (/a, o,ɯ/) are likely to be associated with round shapes, whereas non-labial obstruents(/d, t, ɡ, k, z, s, ʑ, ɕ, tɕ, dʑ/) and front vowels (/i, e/) are good candidates for spiky
shape sound symbolism (D’Onofrio, 2014; Nielsen & Rendall, 2011). These
consonants and vowels were combined to form words with the /CVCV/ structure.
The participants were instructed to judge whether each word matched the figurein three degrees: good match, neutral, and mismatch.
2.2. Results
For each word-shape combination, we calculated the proportion of participants ineach group who gave “good match” judgment (Figure 1). In Figure 1, the words
were shown from the highest to the lowest acceptance (i.e., judgements of good
match) by the hearing group for the round and spiky shapes. The distribution ofmatch scores across items (word-shape combinations) was highly similar between
the two groups: by-item correlations between the two groups (N = 38) were very
high: r = .893 for the round figure (Figure 1a) and r = .832 for the spiky figure(Figure 1b). When both figures combined, r = .862, all ps < .001 (Figure 2). We
split the DHH participants into two groups according to the severity of hearing
loss to see whether DHH people’s judgements would differ depending on their
hearing level. Even the severest group showed high correlations, which did notstatistically differ from those shown by the milder group.

327

Figure 1. The percentage of participants in the hearing group and those in the DHH (Deafand Hard of Hearing) group who judged the novel words to be good sound symbolic matchwith the shapes, in Experiment 1. The words are arranged in the descending order of thepercentages for the hearing group.

A closer examination of Figures 1(a)(b) indicates that words that were judged
good matching for the round shape tend to start with sonorant sounds, and thosejudged good matching for the spiky shape tend to start with obstruent sounds.
In Figure 2, the 38 words are plotted along the proportion of good-match
judgements by the hearing (Y Axis) and the DHH group (X Axis). Here, the words
starting with sonorant sounds are represented in green, and those starting with
obstruent words are shown in red; values for the round shape and the spiky shapeare represented in a circle and a triangle, respectively. Here, it is clear that thesonorant-round shape and obstruent-spiky shape combinations received highvalues, while the sonorant-spiky shape and obstruent-round shape combinations
received low values in both groups.

Proportion of Acceptance response(DHH no Spoon)

328

0.8

0.6

0.4

0.2

0
0

0.2

0.4

0.6

0.8

Proportion of Acceptance response(Hearing no spoon)
Figure 2. Plots of the 38 items along the proportion of good match judgements by thehearing group (X Axis) and by the DHH group (Y Axis).

2.3. Discussion
The results supported the articulatory origin of sound symbolism hypothesis inthat DHH people show judgements of sound-shape correspondences that arehighly similar to those by hearing people. In both groups, participants judged
sonorant-round shape and obstruent-spiky shape to be good matches, while thereverse combinations to be poor matches.
3. Experiment 2.
Experiment 2 examined whether disturbance of articulatory movements changes
the judgements of sound-meaning correspondence both for hearing and DHH
participants. In Experiment 2, participants placed a spoon on the tongue andclosed the lips to hold the spoon, hence movement within the oral cavity and lips
was generally restricted.
3.1. Participants and Stimuli
Sixteen DHH students, all with congenital hearing loss, and 61 university students
with typical hearing, participated. None of the participants took part inExperiment 1. The stimuli and the procedure were identical to Experiment 1,
except that the participants were asked to hold a spoon with their mouth: they put

329

the spoon in the oral cavity with the concave part of the spoon facing down, andplaced the concave part on the tongue, and closed the lips to stabilize the handleof the spoon.
3.2. Results
3.2.1. By-item correlation analyses
As in Experiment 1, a by-item correlation analysis was conducted. For the DHH
group, the correlation coefficient between the baseline case (no-spoon in the
hearing group, Experiment 1) and the spoon on-tongue case (Experiment 2) was
r = .639, p < .001.This value was significantly lower than that between the twogroups in the no-spoon case in Experiment 1. For the hearing group, thecorrelation coefficient between the baseline and the on-tongue case was still very
high (r = .913, p < .001). The correlation coefficient in the on-tongue condition
was significantly lower in the DHH group than in the hearing group (Meng’s z =5.71, p = .000). The correlation analyses thus endorsed the view that oral
movement plays a critical role in sensitivity of sound symbolism.
3.2.2. Model analysis
The results of Experiment 1 suggested that the sonorant sounds and the obstruent
sounds play a prominent role for the shape sound symbolism. Figure 3 shows the
proportion of acceptance of words as good-matching in each of thesound/condition/group combinations for each shape. A mixed-effect logistic
regression analysis was conducted to predict the participants’ judgement (1:goodmatch/0: other responses), specifying Group (DHH:-0.5/Hearing: 0.5), Condition(articulation free:-0.5/articulation disturbed:0.5), shape (spiky:-0.5/round:0.5),
sound (sonorant:-0.5/obstruent:0.5), and their interactions as fixed-effects. The
participants are specified as random effects.
From the pattern shown in Figure 3, the interactions involving Sound x Shapestrongly predict the proportion of good-match judgements.
The Shape x Sound interaction was highly significant, z = -21.32, p = .000.
Also significant was two 3-way interactions: Condition x Shape x Sound
interaction, z = 3.08, p = .002, and Group x Shape x Sound interaction, z = -3.74,
p = .000. The effect of Condition was modified by Shape x Sound interaction insuch a way that disturbance of articulation decreased the sense of good match forthe round-sonorant combination but not so much for the obstruent-spiky
combination. Likewise, the effect of Group was modified by the Shape x Sound
interaction: the decrease of acceptance in round-sonorant combination was larger
for the DHH group than for the Hearing group. However, the 4-way interaction
effect was not significant.

330

Figure 3. The proportion of acceptance of words as good-matching in eachsound/condition/group combination for the Round shape (right panel) and for the Spiky
shape (left panel).

3.3. Discussion
The results strengthened the articulation origin of sound symbolism theory in that
disturbance of articulatory movements affected sensitivity to sound symbolism.
DHH individuals were affected by the disturbance of articulation more strongly
than individuals with typical hearing and that the decrease of sensitivity to soundshape correspondence mostly came from the sonorant sound-round shapecorrespondence, but not so much from the obstruent sound-spiky shapecorrespondence. The last result suggests that the mechanism of sound symbolism
sensitivity is complex and that different mechanism may underlie for roundshapes and spiky shapes (cf., Fort et al., 2018; Yang et al., 2019).
4. General Discussion
The results of the two experiments supported the articulatory origin of soundsymbolism hypothesis. Our finding offers great insights into a key question in theliterature of sound symbolism, i.e., whether sound symbolism arises fromarticulation (Sapir, 1929; Ramachandran & Hubbard, 2001; Margiotoudi &Pulvermüller, 2020). It is remarkable that Deaf and Hard-of-Hearing individuals
can sense sound symbolism by mapping oral movements to the meanings. Thisstrongly suggests that oral movements are crucial for sound symbolism detection.
Furthermore, this fact highlights the view that sound symbolism is truly a multimodal phenomenon, and not just the correspondence between audition and othersensory modalities (Perniss & Vigliocco, 2014; Pearlman et al., 2021). However,331

the current data do not rule out the possibility that hearing people recruit the
acoustic information in addition to articulatory movements.
The current results have profound implications for theories of language
evolution. Hand gesture is a prime candidate for how an open-ended sharedlexicon emerged in early stages of language evolution (Stokoe, 2002; Arbib,2005) because people can move their hands in a way that can iconically map toentities in the world such as events and objects(Goldin-Meadow et al., 1996;
Ortega & Özyürek, 2020). The hand-gesture origin theories of language evolution
maintain that hand movements have unique advantage over the movement ofarticulatory organs when it comes to iconically representing entities in theworld(Stokoe, 2002; Arbib, 2005). However, the current result questions suchassumption, and suggests that both hand movements and articulatory movements
can be the basis of iconic meaning, offering a possible account for why language
evolved in speech modality as well as in manual modality.
The current study has some limitations. First, because we presented the novel
words orthographically, the possibility that DHH people’s sensitivity may comefrom resemblance between the visual shape and shapes of letters. Although it isdifficult to completely rule out the influence of letter symbolism, the results ofExperiment 2 convincingly demonstrated that articulatory movements are criticalfor detecting sound-meaning correspondences (also see Cwiek et al, 2022 for theview that letter symbolism is minimum). Another limitation is that the spoon
manipulation in Experiment 2 affected only the round shape-sonorant sound.
Although this result suggests that different mechanism may underlie soundsymbolism for round shapes and spiky shapes (cf. Fort et al., 2018, Yang et al.,2019), we cannot draw a conclusion concerning this issue, as our stimuli were notdesigned to address this issue.
Acknowledgements
This study was supported by MEXT/JSPS KAKENHI Grant Numbers 20H00014,
16H01928, 18H05084 to Mutsumi Imai. We would like to thank Junko Kimura,
Daiki Kondo, and Fumika Murayama for their help with data collection. We
would also like to thank all participants in this study.
References
Arbib, M. A. (2005). From monkey-like action recognition to human language:
An evolutionary framework for neurolinguistics. Behavioral and Brain
Sciences, 28, 105–124.

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systems. Philosophical Transactions of the Royal Society B, 377, 20200390.
D’Onofrio, A. (2014). Phonetic Detail and Dimensionality in Sound-shape
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57, 367–393.
Fort, M., Lammertink, I., Peperkamp, S., Guevara‐Rukoz, A., Fikkert, P., &Tsuji, S. (2018). Symbouki: a meta‐analysis on the emergence of soundsymbolism in early language acquisition. Developmental science, 21, e12659.
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Margiotoudi, K., & Pulvermüller, F. (2020). Action sound–shape congruencies
explain sound symbolism. Scientific Reports, 10, 12706.
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categories and types of iconic representations in the manual modality: A
normed database of silent gesture. Behavior Research Methods, 52, 51–67.
Perniss, P., & Vigliocco, G. (2014). The bridge of iconicity: From a world ofexperience to the experience of language. Philosophical Transactions of theRoyal Society B: Biological Sciences, 369, 20130300.
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prelinguistic infant brain. Scientific Report, 9, 13435.

333

MALE NORTHERN GRAY GIBBON SONGS HAVE A PHRASEINSERTING STRUCTURE
YOICHI INOUE*1, WAIDI SINUN2, SHIGETO YOSIDA3 and KAZUO OKANOYA4*Corresponding Author: gibbonyoichi@hotmail.co.jp
1Cognition and Behavior Joint Research Laboratory, Riken Center for Brain Science

Institute, Wako, Japan
2Research and Development Division Yayasan Sabah Group, Kota Kinabalu,

Malaysia
3IQVIA Solusions Japan K.K., Tokyo, Japan
4Advanced Comprehensive Research Organization, Teikyo University, Tokyo, Japan

1. Introduction
To date, there have been no reports of a phrase-embedding structure in animal
vocal communication, although there are several reports of combinational animal
sounds (Hauser et al., 2014). Gibbons (Hylobatidae) living in South-East Asia aresmall apes, among the closest living relatives of humans. Gibbons are known for
their remarkable vocal behavior. The songs of male Northern Gray gibbons(Hylobates funereus) in the Danum Valley Conservation Area (DVCA), Sabah,
Malaysia consist of two notes, wa” and “oo”, and combinations of these are
flexible (Inoue et al., 2017). The “wa” notes were generally short, with a rapid
rise in frequency, and the “oo” notes were relatively monotonal. The phrases of asong have various note orders, i.e., the notes could be combined in different
phrase types. The Northern Gray gibbon is the species formerly named as
Mueller’s gibbon (Hylobates muelleri).
When phrase “N” is inserted within another phrase “AB”, the generated phrase isshown as “ANB”. We named this structure a phrase-inserting structure. In maleNorthern Gray gibbon songs in our study area, a succession of “wa” notes (trill)
are sometimes placed at both the start and end of phrases. When trills were placedat both the start and end of AB phrases, we named these AB phrases fixed phrases.
Trills were located in front of “A” and at end of “B.” In this case, each A and B
has a pairwise relationship and always cooccurs in a string. In the middle of a
fixed phrase “AB,” notes consisting of three or more notes were sometimes
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

334

included. If the included notes were also sung as a phrase independently, wedefined it as “N.”If the phrase “N” itself also has an inserting structure, then ANB is represented as
AABB, which can be called an embedding structure, as defined by Abe and
Watanabe (2011). When the animals sing songs by combining acoustic elements
flexibly, a complicated syntax may emerge. We hypothesized that there arephrase-inserting structures in male Northern Gray gibbon songs, and recorded
songs by a male in the DVCA for analysis. This report is based on a paper by
Inoue et al. (2020).
2. Methods
All observations were conducted in the DVCA located in Sabah, Malaysia, in thenortheast region of Borneo Island (Fig. 1). We studied a gibbon group named“SAPA”, whose territory was located around the Borneo Rainforest Lodge(BRL). With the aid of a field assistant, we conducted a survey over 4–7
successive days, biannually in August and December, from 2001 to 2009. During
the study period from 2001 to 2009, we recorded 70 songs in 107 days ofobservations. We arrived the sleeping trees where the SAPA group slept on theprevious day. We started following at 0500 h and ended about 30 min after the
gibbons had arrived at their sleeping trees. We started recording gibbon songs, assoon as they started singing. We used a digital audio recorder (R-09; Roland,
Hamamatsu, Japan) with a microphone (ATM57; Audio-Technica, Tokyo,
Japan), and recorded the gibbon voices under the trees in which the study malewas singing. The recorder was set at a 44.1-kHz sampling rate and had 16-bit
resolution. We analyzed 8,046 phrases in 70 songs from the SAPA adult male.
We converted the recorded sounds to sonograms using Avisoft-SAS Lab Pro
software (Avisoft, Berlin, Germany). Focusing on the fundamental frequency, we
performed the spectral and temporal measurements described below. To remove
ambient noise, we processed the sound through a high-pass filter to cut off soundbelow 500 Hz. Finally, sonograms were created for on-screen measurements(settings: 256-point fast Fourier transformation and Hamming windows). A songis a series of notes, generally of more than one type, uttered in succession and so
related as to form a recognizable sequence or pattern in time (Thorpe, 1961). A
phrase is a larger, loose collection of several notes preferentially voiced in
combination. Intra-phrase intervals are shorter than inter-phrase intervals(Geissmann et al., 2005). Most of the note intervals in our subject male’s songs
were less than 2.0 seconds (90.7%). Therefore, we identified different phrases
within a song as being separated by pauses of > 2 seconds.

335

In order to confirm two note types, we selected a 5 % random sample of all notes
and measured the onset time, offset time, start frequency, end frequency, middle
frequency, maximum frequency, and minimum frequency of each note and internote intervals for each song phrase. Based on the measured data, we calculated
the duration, Δ frequency, and Δ frequency/duration of each note. Two-tailed ttests were used to compare the acoustic characteristics of the two notes.
Furthermore, we analyzed whether or not the corresponding phrases wereindistinguishable in terms of the acoustic similarity between those with an
inserted phrase (A-N-B) and with no inserted phrase (A-B). We selected 20
patterns of phrases and calculated the acoustical similarity using AvisoftCORRELATOR for every pair of same type phrases. We calculated the acousticalsimilarity between all pairs of “A” and “B” notes within the A-N-B phrases(within-class condition), within the A-B phrases (within-class condition), andbetween the A-N-B and the A-B phrases (between-class condition). The AvisoftCORRELATOR allowed us to compute cross-correlations between spectrogramsby sliding them along with the time axis. The approach of the AvisoftCORRELATOR can be compared to computing correlations between twograyscale raster images, while sliding them on the X-axis. The highest correlation
coefficients for each pair of sounds were regarded as similarity scores. We
examined whether the acoustical similarity was the same regardless of whether
the pair of phrases were both from the same class (within-class condition) or fromdifferent classes (between-class condition). We performed a linear mixed-effectmodel entering the cross-correlation coefficients as the response variable and thewithin/between-class condition as the explanatory variable (fixed-effect). We
entered the phrase types (the above-mentioned 20 patterns of phrases) as random
intercepts because the average similarity scores were expected to differ among
them. We performed model diagnosis by visually assessing normality andhomogeneity of residual variance across the random groups and normality of therandom intercepts using group-wise boxplots and normal quantile-quantile plots.
We reported the mean and standard deviations of the similarity score for thewithin/between-class condition, and examined the statistical significance using alikelihood-ratio test (Pinheiro and Bates, 2000). R 3.6.1 (R Core Team 2019) andnlme package (Pinheiro et al., 2019) were used for the analysis.
3. Results
The mean values of the all examined acoustic parameters of notes were
statistically different between the two notes (Table 1). In this study, three or more
successive “wa” notes (trill) were placed at both the start and end of phrases in336

2,726 (33.9%) of the 8,046 phrases. Among these, we identified 1,891 phrases(23.5%) as fixed phrases. Phrases consisting of three or more notes weresometimes included within fixed phrases (Fig. 2). We found 448 phrases (5.6%)
that included phrases consisting of three or more notes.
The overall mean similarities for the within-class and between-class conditions
were 0.52 (SD = 0.12, n =1290) and 0.50 (SD = 0.11, n = 1346), respectively. The
diagnostic plots of the linear mixed effects model showed no indication ofviolating the model assumption (result not shown). The result of the linear mixed
effects model showed that the similarity was estimated to be 0.02 (standard error= 0.003) higher in the within-class condition, after controlling the phrase type (X1= 33.15, p < 0.01).
4. Discussion
We analyzed songs by a single male. There were no notes in the studied gibbon’ssongs with acoustic characteristics largely different from “wa” and “oo” notes.
The male had various types of note orders in his phrases. In his songs, we
identified fixed phrases, which were characterized by three or more successive“wa” notes (trills) placed at both the start and end of phrases. Phrases consistingof three or more notes were sometimes included within fixed phrases. We found
448 phrases (5.6%) that included phrases consisting of three or more notes. We
have concluded that these note orders suggest a phrase-inserting structure.
Corresponding phrases with an inserting structure and no inserting structure werevery similar in terms of sonograms, and hardly discriminable by human ear.
However, the statistical analysis showed a small but systematic difference inacoustic similarity between them; phrases were more similar among pairs thatboth had an inserting structure or did not have an inserting structure (within-classcondition), than among pairs where one had an inserting structure and the other
did not (between-class condition). Further study under experimental conditions
will be needed to examine whether gibbons are able to discriminate this small
acoustic difference.
One of the main differences between language and non-human animalcommunication is the grammar used to produce sequences. Human language uses
“context-free grammars” that are capable of generating recursive sequences(Chomsky, 2002). Among various discussions about the definition of recursion(e.g., Fitch, 2010), there is an interpretation that recursion consists of embedding
a constituent into a constituent of the same type (e.g., Pinker and Jackendoff,
2005; Martins and Fitch, 2014). In contrast, animal vocal sequences are usuallydescribed as “regular grammars”, a simple kind of concatenation system (Berwick

337

et al., 2012). Many researchers have considered that non-human animal
vocalizations would belong to regular ones. This paper tested the grammaticalstructures of primate “songs” with the comparative perspectives of the humanlanguage syntax and that of other animals. Our results showed that male gibbon’ssongs have a phrase-inserting structure which is considered to be a precursory
level to recursion.
A phrase-inserting structure occurred when combinations of notes in the male’ssongs were flexible. Our results lead us to hypothesize that complicated syntax
emerges when the animals sing songs by combining acoustic elements flexibly.
This may be the first evidence of a phrase-inserting structure in animal songs. Ourdata and linguistic perspectives may certainly be of use in future studies to
elucidate vocal communication in gibbons and other non-human primates.
However, as we collected data from only one male, further studies on many
gibbon groups will be necessary to confirm our results.
Table 1. Acoustic characteristics of “wa” and “oo” notes.

338

Figure 1. Location of the Borneo Rainforest Lodge (BRL) in the Danum Valley
Conservation Area, Sabah, Malaysia.

Figure 2. Examples of phrase A-N-B included within fixed phrases. Arrows
indicate where phrase N was included within phrase A-B. Solid line brackets
indicate phrase N included within phrase A-B.
5. References
Abe, K., & Watanabe, D. (2011). Songbirds possess the spontaneous ability todiscriminate syntactic rules. Nature Neuroscience, 14, 1067–1074.

339

Berwick, R. C., Beckers, G. J., Okanoya, K., & Bolhuis, J. J. (2012). A bird’s eyeview of human language evolution. Frontiers in Evolutionary Neuroscience,
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340

ELEMENTS FOR A CULTURAL EVOLUTION OF SUPERNATURAL
RITUAL LANGUAGESARITZ IRURTZUN
aritz.irurtzun@iker.cnrs.fr
CNRS–IKER (UMR 5478), Bayonne, France

I propose a characterization of ritual languages as ‘natural conlangs’, comparable to the ‘laboratory conlangs’ of iterated learning experiments. I present the Ritual Language DataBase(RLDB), which provides a typology of such languages around the world. A comparative analysis of the RLDB uncovers patterns, and the significant role of users (community size), functions,and alternate languages when shaping the grammatical structure of ritual languages.

1. Introduction: Speakers and communities as actors in language
evolution
Formal studies of language conceive of language acquisition and language change
as a dynamic process (cf. Andersen, 1973; Lightfoot, 1979; Yang, 2002):
Parents’ I-Language

Parents’ E-LanguageChildren’s I-LanguageChildren’s E-Language

Figure 1.

Dynamics of language acquisition and change (adapted from Andersen, 1973: 767).

The idea is that children (i.e., the language acquirers) do not have direct accessto the grammar/I(nternal)-language of their parents, but an indirect one, throughthe E(xternal)-language that they parse. This E-language will include some signatures or triggers that will guide the children in the choice of the ‘correct’ (compatible) target I-language. But rather than this being a deterministic system, language
acquirers have an active role in shaping their language, as they have to (re)interpretthe E-language of their environment (their primary linguistic data).
What is more, over the last decades intensive research on language evolution
and change has uncovered some of the biases that humans act upon when acquirThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

341

ing their languages (cf. Kirby, 2017; Culbertson, Smolensky, & Legendre, 2012;
Biberauer & Roberts, 2017; Raviv, Meyer, & Lev-Ari, 2019).
In this talk I want to extend this research paradigm to a new area: I give thefirst steps towards a characterization of the typology and analysis of the cultural
evolution of the linguistic practices employed for supernatural ritual functions.
2. Supernatural rituals and ‘natural’ conlangs
Very often, in all sorts of cultural niches across the world, people change theirlanguage when performing supernatural ritual functions. I analyze these special
languages as some sort of ‘natural’ conlangs, comparable to the ‘laboratory’ conlangs usually employed in iterated learning research on language evolution.
2.1. The Ritual Language Database
In order to analyze the nature and variability of ritual languages, I designed the
Ritual Language Database (RLDB), which at the moment comprises data from242 linguistic practices related to supernatural rituals across a wide range of different populations across the world.
The RLDB systematically documents 46 features of each ritual linguistic
use such as the population (Amharas, Antakarinjas, Apaches. . . ); its function(whether it is employed for cursing, divination, fortune-seeking, healing, initiation, mourning, or order-keeping); its user (whether it is a designated practitioner individual (a shaman, a priest, etc.) or a larger community that employs it);whether it implies possession by a spirit or ghost; whether it implies an alternate
language (which can be oral or gestual); whether the alternate language is an archaic variant of the local language or an independent language altogether; whether
it implies glossolalia; use of ventriloquism, animal and nature sounds, etc.; and if
there is no alternate language, whether the language employed has a special lexicon (which can be constituted of archaisms, borrowings, or ad hoc lexical items);
whether it comprises meaning shifts (metaphors, circumlocutions); whether its
segmental inventory is enriched with respect to the normal language (a larger setof consonants/vowels, lessened phonotactic restrictions) or impoverished (a more
restrictive set of consonants/vowels or stronger phonotactic constraints); whether
it involves aliterations and syntagmatic repetitions; morpho-phonological insertions; a special prosodic pattern; morphological and syntactic differences, etc.
The RLDB is a dynamic database, developed continuously with new data from
ethnographies, linguistic descriptions and religion studies reports.
2.2. Association between linguistic features
The linguistic phenomena of supernatural rituals at the RLDB show different degrees of association. These are summarized in Figs. 2 and 3 below. Fig. 2 displays
the association between features in ritual practices not involving an alternate language, whereas Fig. 3 is devoted to those practices that do involve an alternate

342

language. Numbers report the uncertainty coefficient (Theil’s U), a measure of
conditional entropy between variables (ranging from 0 to 1, where there is no
possible negative association). As such, this is an asymmetric measure, and thus
we can obtain an estimate about the nature of variable B, given our knowledgeof variable A, which is more informative than a simple (symmetric) correlation.
For instance, Fig. 2 shows that knowing the function of the ritual can be more
informative regarding the user (U = .31) than the other way around (U = .11).1

Figure 2. Heatmap of the association between features in the RLDB for ritual practices that do not
involve an alternate language.
1 For comparison, the symmetrical measure Cramér’s V associates the two variables at V

= .50.

343

Figure 3. Heatmap of the association between features in the RLDB for ritual practices that do involve an alternate language.

An overview of the data shows that the linguistic features of ritual languages
are not distributed at random but tend to cluster in patterned ways, which suggests
the effect of universal biases. This is a remarkable fact, given the fragmentary anduneven evidence reported in the ethnographic and linguistic records from populations around the world.2
Due to space restrictions here I can only comment a couple of them. Concentrating on function, we can observe that there are sharp differences vis-à-vis theemployment of an alternate language (X 2 (6, N = 217) = 38.584; p < .001):
2 The RLDB comprises references as varied as the recent and scholarly Jorgensen (2020) on thesign language of the Balgo or Hall (2019) on the incantations of Hup shamans, as weel as ancientethnographic reports such as Gabb (1875) on the Talamancans, or Martin (1817) on the Tongans.

344

Table 1.

Function ∼ alt. language

Alternate language
not available available

Function
cursingdivination
fortunehealing
initiation
keep ordermourning
T OTAL

1
12
21
25
25
10
8
102

3
8
6
18
30
5
45
115

Total
4
20
27
43
55
15
53
217

For instance, it is more likely for people to employ an alternate language inmourning than in fortune-seeking rituals (X 2 (2, N = 80) = 8.2573; p < .004).
Not only that, within the set of practices that do employ an alternate language,use of an alternate modality (a sign language like Marumpu Wangka employed by
the Balgo Aboriginals (Jorgensen, 2020)) is also non-randomly distributed acrossfunctions (X 2 (6, N = 107) = 45, 752; p < .001):
Table 2.

Function ∼ alt. modality in [+alt. langs]

Function

Alternate modality
not available available

Total

cursingdivination
fortunehealing
initiation
keep ordermourning
T OTAL

3
7
5
16
9
3
10
53

3
7
6
16
27
4
44
107

0
0
1
0
18
1
34
54

Last, in the populations that do not employ an alternate language but resort toa variant of their language (a natural conlang), we can observe sharp differencesin the grammatical devices employed (X 2 (4, N = 650) = 36.463; p < .001):
Table 3.

Grammatical strategies in [-alt. langs]

Strategymeaning shifts
morphosyntactic effects
special prosody
segmental effects
special lexicon
T OTAL

Availability
not available
available
92
122
106
120
99
539

38
8
24
10
31
111

Total
130
130
130
130
130
650

345

3. Elements for a cultural evolution of ritual languages
From this, a hierarchy of ritual languages can be established as a tension between
the search for Strangeness (the more the language employed departs from thenormal human ‘norm’, the more plausible the magical powers of the user look to
the community) and Learnability (the more systematic and predictable a language
is, the easier its replication by a next generation of speakers):+Strangeness
Animal/Spirit languages (users e.g. the Siberian shamans)
Liturgical languages (users e.g. the Catholic priests)
Initiation languages (users e.g. the Venda people)+Learnability
Figure 4.

Hierarchy of ritual languages.

On one extreme of the hierarchy we have the shamanic practices, archetypically represented by the Siberian shamans and their idiosyncratic speech whichconstitutes a ‘theatre of strangeness’ (Eliade, 1951; Kürti, 1994; Singh, 2018).
They do not show clear patterns of grammaticalization, but are full of extreme
renderings with high pitch, falsettos, ventriloquism, animal sounds, cries, mumblings, etc., and the particularities of each linguistic practice typically die with theshaman. Such practices are not transmitted to the next generation, and therefore
are not subject to cultural evolution pressures and dynamics (see, e.g. Tamariz& Kirby, 2016). At the other extreme we have community-wide practices whichalter the local language with special lexicons which rather than idiosyncratic, arederived via transformation rules (e.g. the semantic and phonological antonymic
patterns in Tenda ritual languages (Ferry, 1981)). As a matter of fact, ritual languages show again that ‘larger communities create more systematic languages’(Raviv et al., 2019). Last, in a middle point would be the liturgical languages
employed by designated individuals (and collectives) of established religious societies (priests, etc.). These are typically languages like Latin, Old Church Slavonic,
Sanskrit, Pali, or Ge‘ez (Bennett, 2017) which require explicit teaching, and where
adherence to the (archaic) norm is taken as an index of efficacy in the ritual.
Then, the grammatical devices employed for the generation of ritual variantsof languages show a restricted variation: regarding meaning, they recur to the
exploitation of the arbitrariness of the sign (often, with metaphors or antonymic

346

patterns), but no new semantic composition rules are invented. Special lexicons
are also employed, but they are generated by extending the meaning strategy;
an exploitation of the arbitrariness of the sign. As as consequence, obscuring
the message with new morpho-phonological labels for usual concepts is a very
common strategy that blatantly gives the user the image of an ability that others
do not possess, for he/she understands the meaning encoded by the new lexical
item. The prosodic means involve generally an exploitation of what Gussenhoven(2004) terms the ‘three biological codes’ (the frequency code, the effort code,and the production code), but no typological shift is attested (i.e, no turning a
stress-accent language into a tonal language for ritual purposes). Last, segmental
phonological effects also seem to be mostly restricted to the inventory (the introduction of new segments, or segment substitutions), but no new vowel harmony
operations. Last, the few morpho-syntactic strategies observed are circumscribed
to externalization simplifications (such as the unavailability of functional elementsin the ritual language, or a more productive pro-drop). Again, no new agreement
operations or new pied-piping domains.
All in all, when humans seek a non-human pattern in their language, the‘strangeness’ or ‘out-of-this-world-ness’ seems to be circumscribed to the lexicon and to morpho-phonology, and the patterns attested are within the limits predicted by theoretical work on natural language design (e.g. there are no syntactic rules that make reference to the nth word (Smith & Tsimpli, 1995; Mussoet al., 2003), no invention of nonconservative quantifiers (Pietroski, 2005), and no
syntactic rules that take as input phonological or semantic value-assignment processes (Irurtzun, 2009)). This bears testimony to the robustness of the syntactic
component of human language, which in turn suggests that a large part of the variability observed across languages may be due to externalization factors (Berwick& Chomsky, 2011; Boeckx, 2014).
Even if ritual languages are intended as ‘supernatural’, they are canalized bynatural language constraints and the biases of speakers and their communities.
Acknowledgements
This work has received financial support from the CNRS-MITI 80|PRIME grants 2021 PALEOSIGNES, and from the ANR ANR-21-CE27-0005 - MIND2WALL.
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Bennett, B. (2017). Sacred Languages of the World. Hoboken: Wiley.
Berwick, R. C., & Chomsky, N. (2011). The biolinguistic program: The current state of its development. In C. Boeckx & A. di Sciullo (Eds.), The
Biolinguistic Enterprise (pp. 19–41). Oxford: Oxford University Press.
Biberauer, T., & Roberts, I. (2017). Parameter Setting. In A. Ledgeway &347

I. Roberts (Eds.), The Cambridge Handbook of Historical Syntax (pp. 134–
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Boeckx, C. (2014). Elementary Syntactic Structures: Prospects of a Feature-freeSyntax. Cambridge: Cambridge University Press.
Culbertson, J., Smolensky, P., & Legendre, G. (2012). Learning biases predict aword order universal. Cognition, 122(3), 306–329.
Eliade, M. (1951). Le chamanisme et les techniques archaı̈ques de l’extase. Paris:
Payot.
Ferry, M.-P. (1981). Les ganles tecreses des des ndéta (les langues secrètes destenda). Objets et mondes, 21, 173–176.
Gabb, W. M. (1875). On the indian tribes and languages of Costa Rica. Proceedings of the American Philosophical Society, 14(95), 483–602.
Gussenhoven, C. (2004). The Phonology of Tone and Intonation. Cambridge &New York: Cambridge University Press.
Hall, K. (2019). Shamanic Language and Verbal Art: A Comparative Examinationof Hup Incantations. Honors thesis, University of Texas, Austin.
Irurtzun, A. (2009). Why Y: On the centrality of syntax in the architecture ofgrammar. Catalan Journal of Linguistics, 8, 141–160.
Jorgensen, E. (2020). A Phonological Analysis of Sign Used in a Western Desert
Community. Honours thesis, University of Melbourne, Melbourne.
Kirby, S. (2017). Culture and biology in the origins of linguistic structure. Psychonomic Bulletin & Review, 24(1), 118–137.
Kürti, L. (1994). Language, symbol and dance: An analysis of historicity inmovement and meaning. Shaman, 1/2, 23–82.
Lightfoot, D. (1979). Principles of Diachronic Syntax. Cambridge: Cambridge
University Press.
Martin, J. (1817). An Account of the Natives of the Tonga Islands in the South
Pacific Ocean (Vol. II). London: John Murray.
Musso, M., Moro, A., Glauche, V., Rijntjes, M., Reichenbach, J., Büchel, C., &Weiller, C. (2003). Broca's area and the language instinct. Nature Neuroscience, 6(7), 774–781.
Pietroski, P. M. (2005). Events and Semantic Architecture. Oxford: Oxford
University Press.
Raviv, L., Meyer, A., & Lev-Ari, S. (2019). Larger communities create more
systematic languages. Proceedings of the Royal Society B, 286, 20191262.
Singh, M. (2018). The cultural evolution of shamanism. Behavioral and Brain
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Smith, N. V., & Tsimpli, I. M. (1995). The Mind of a Savant. Oxford: Blackwell.
Tamariz, M., & Kirby, S. (2016). The cultural evolution of language. Current
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Yang, C. D. (2002). Knowledge and Learning in Natural Language. New York:
Oxford University Press.

348

ANALYZING PROPERTY PREDICATION FROM THE
PERSPECTIVE OF EVOLUTIONARY LINGUISTICS
MASAYUKI ISHIZUKA*1 and RYOKO UNO2
*Corresponding Author: ishizuka@l.u-tokyo.ac.jp
1Department of Linguistics, University of Tokyo, Tokyo, Japan
2Division of Language and Culture Studies, Tokyo University of Agriculture andTechnology, Tokyo, Japan

In the context of evolutionary linguistics, both constructive approaches (e.g., artificial
life, robotics, and experimental semiotics) and cognitive linguistics inquire about theembodied nature of language. However, there is still a huge gap between these two areas
when it comes to what constitutes language (Di Paolo et al., 2018). To bridge this gap,
this paper focuses on how property predication is expressed differently among
languages in a cognitive linguistic framework and argues in favor of noticing the aspectof pointing that does not directly contribute to establishing joint attention.
Joint attention is a proto-linguistic behavior that uses pointing with a gaze or finger
to attract other people’s attention to an object. Only humans can establish joint attention
to share intentionality. Thus, this act is often studied as key to revealing the relationshipbetween action and language (Gómez et al., 1993; Tomasello, 2008; 2019). Several
attempts to simulate the co-evolution of joint attention and language have been made(e.g., Kwisthout et al., 2008; Uno et al., 2011; Gong & Shuai, 2012). In cognitive
linguistics, demonstratives are well studied as a linguistic means to establish joint
attention (Diessel, 2006). To extend the argument, this paper sees predication sentences
as the linguistic equivalent of pointing. For example, if you are surprised by the rednessof a flower, you might point at the flower or say, “The flower is red!”—which is aproperty predication sentence.
In a typological study, Stassen (1997) showed that property predication sentences
have no prototypical encoding strategy of their own and they always align with one ofthree predicate categories: class, event, and location. Stassen characterized theprototypical strategy for each predicate category as follows: (1) The nominal strategy is
prototypical for class predicates and uses a support item that is (at least etymologically)
non-verbal. This strategy can be found in Waskia, for example. (2) The verbal strategy
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

349

is prototypical for event predicates, uses no support item (auxiliary), shows person
agreement, and has a specific negation strategy. This is exemplified by Mandarin
Chinese. (3) The locational strategy is prototypical for locational predicates and uses a
support item that is a verb. This strategy can be found in Finnish.
In a cognitive linguistic framework, we can interpret that property predicates haveto be construed in verbal, locational, or nominal ways. Construals are how weconceptualize particular semantic content. One of the central claims in cognitive
semantics is that we can portray the same situation in alternate ways (Langacker, 2008).
The three strategies can be interpreted as three types of construals for the same situation.
(1) a. Nominal construal: “The flower is classified as a red thing.”
b. Verbal construal: “The flower is going through an event of redness.”c. Locational construal: “The flower is in a location (or a state) of redness.”
From (1), we make the following argument: While verbal and locational construals
describe the situation in the world, nominal construal does not. Instead, nominal
construal describes the inner state of the speaker. Because of this difference, what isconceptualized by sentences with the nominal construal in their immediate scope cannot
be shared with others, unlike something observed in the speech context at hand. This
makes nominal construals more detached from the ground than the other two types. This
point is supported by another observation: many languages permit more than one
encoding strategy for property predicates, and Stassen (1997, p. 615) pointed out that if
a strategy switch involving the nominal strategy is interpretable in terms of time stability,
the nominal strategy will encode the more time-stable option. Time stability tends to belinked to a structural description rather than to a phenomenal description (Goldsmith &Woisetschlaeger, 1982; Langacker, 1997). To elaborate, the knowledge related to thenominal strategy can be more context independent or, in fact, less grounded.
To contribute to the theory of embodied language, we interpret the analysis ofproperty predication as an analysis of pointing in general. As in the case of property
predication, pointing can be semantically ambiguous: the agent can point at somethingoutside that can be shared to establish joint attention, but the agent can concurrently point
at it to express something that is inside the agent that cannot be seen and shared withothers through joint attention. The former is fully communicational, but the latter isrelatively monologic. Clark (1997) points out that the communicational aspect oflanguage attracts too much attention, often causing other aspects of language to beoverlooked. An analysis of pointing in evolutionary linguistics can be regarded as an
example. Our study implies that the monologic aspects of pointing must be focused on
more, as they are clues to linking action to linguistic constructions with abstract
meanings reflecting our structural knowledge of the world.

350

References
Clark, A. (1997). Being there: Putting brain, body and mind together. Cambridge:
MIT Press.
Di Paolo, E. A, Cuffari, E. C., & De Jaegher, H. (2018). Linguistic bodies: The
Continuity between life and language. Cambridge: MIT Press.
Diessel, H. (2006). Demonstratives, joint attention, and the emergence ofgrammar. Cognitive Linguistics, 17, 463–489.
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Gong, T. & Shuai, L. (2012). Modelling the coevolution of joint attention andlanguage. Proceedings of the Royal Society B: Biological Sciences, 279(1747), 4642–4651.
Kwisthout, J., Vogt, P., Haselager, P., & Dijkstra, T. (2008). Joint attention andlanguage evolution. Connection Science, 20, 155–171.
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Stassen, L. (1997). Intransitive predication. Oxford: Oxford University Press.
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351

NOMINAL PLURALITY IN SINO-TIBETAN: A DIACHRONICACCOUNT
Jessica K Ivani*1 , Taras Zakharko1 , and Chundra Cathcart1* Corresponding Author: jessica.ivani@uzh.ch

1 Department of Comparative Language Science, University of Zürich, Zürich, Switzerland

Grammaticalization, the process by which lexical elements lose their content status to serve a grammatical function, is extensively covered in the literature on language change (Heine & Kuteva, 2002; Hopper & Traugott, 2003, among others).
Several cross-linguistic qualitative studies describe this process as a unidirectional(Haspelmath, 2004; Börjars & Vincent, 2011, inter alia) and irreversible (Bybee,2011) cline, characterized in diachronic terms by gradual and incremental steps.
While existing research often conceives of grammaticalization as a whole, single
process, other studies argue that it might consist of the interplay of composite
and distinct processes. A body of research has focused on defining the individual
variables that play a role in grammaticalization (Hopper et al., 1991; Lehmann,
2015; Petré & Velde, 2018), and recent studies have explored them quantitatively
through the exploitation of corpus data (Saavedra, 2021).
In this study, we explore some aspects of the development of nominal plurality in a sample of 56 Sino-Tibetan languages, focusing on phonological and morphosyntactic integration behavior. Nominal plurality is a widespread category thathas been studied in the context of grammaticalization (Heine & Kuteva, 2002).
The Sino-Tibetan language family is characterized by a rich history, high variation in morphological types, and language-specific constraints that have proven to
shape the course of grammaticalization processes (Bisang, 2011). Our key contribution is a better understanding of the diachronic trajectories of different variables
frequently invoked in the grammaticalization literature.
The parameters used in this study describe phonological and morphosyntactic
degree of cohesion of the plural markers to the respective stem(s). These variables, extracted from descriptive grammars and typological literature, include: thepresence of phonological processes (PHONOLOGICAL RULES); inflectional properties such as exponence (MULTIPLE EXPONENCE), morpheme autonomy (CON CATENATIVE ), and position ( FIRST SLOT , FIXED POSITION ). Morphosyntactic
variables include adjacency (ADJACENCY) and interruptability (NOT INTERRUPTABLE ). It is important to note that our analyses solely involve abstract features
pertaining to integration of plural marking constructions, which can be gainedThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

352

Mult. exp. (0.0013)

0

2

4

Phon. rules (0.00015)

60.0First slot (0.99445)

0

10

20

0.5

1.0

1.5

Fixed pos. (0.90515)

2.00Concat (0.99385)

0

10

20

30

5

10

Not inter. (0.98515)

0

10

20

30

Adj (0.9886)

40

0

10

20

30

Figure 1. Posterior gain/loss ratios of different integration types, along with the proportion of samples where the ratio is greater than 1.

or lost, while grammaticalization deals with irreversible processes which embed
formal elements more deeply in a language’s grammar.
We use phylogenetic methods to explore whether there is a preference overtime for each integration parameter (code can be found at https://github.
com/jkivani/JCoLE2022-numevo). We model the diachronic change ofeach feature over the Sino-Tibetan phylogeny (Sagart et al., 2019) independently
according to a continuous-time Markov process for binary data, under which afeature evolves according to a gain and loss rate. We infer posterior gain andloss rates for each feature, employing a point mass prior, under which gain andloss rates can be equal or different. We place Gamma(1, 1) priors on all rates
and a Beta(1, 1) prior on π, the probability that gain and loss rates differ. For
each posterior sample in each feature, we compute the ratio of the gain rate to theloss rate, which indicates the degree of preference for a given integration feature.
These distributions are found in Figure 1, along with the proportion of samples
where the ratio is greater than 1. There is decisive preference (> .95) for thefeatures NOT INTERRUPTABLE, FIRST SLOT, CONCATENATIVE and ADJACENT
and strong preference (> .9) for FIXED POSITION, and decisive dispreference(< .05) for the features MULTIPLE EXPONENCE and PHONOLOGICAL RULES.
In general, we see that features involving the linear order of elements arehighly preferred, while phonological rules and multiple exponence are not. A
possible theory is that as language families expand and absorb speakers fromother families, certain features more difficult than others for second-language (L2)speakers to acquire. However, case studies suggest that phonological rules do notpose a problem for second-language speakers (Widmer, Jenny, Behr, & Bickel,
2021). At the same time, this finding does not preclude the possibility that language contact could militate against phonological and morphological complexity
on a large scale. Further research from other families taking into account demographic factors is needed to obtain a richer understanding of this phenomenon.

353

References
Bisang, W. (2011). Grammaticalization and linguistic typology. In The Oxford
Handbook of Grammaticalization. Cambridge University Press.
Börjars, K., & Vincent, N. (2011). Grammaticalization and directionality. In The
Oxford Handbook of Grammaticalization. Cambridge University Press.
Bybee, J. L. (2011). Usage-based theory and grammaticalization. In The Oxford
Handbook of Grammaticalization. Cambridge University Press.
Haspelmath, M. (2004). On directionality in language change with particularreference to grammaticalization. Typological Studies in Language, 59, 17–44.
Heine, B., & Kuteva, T. (2002). World lexicon of grammaticalization. Cambridge
University Press.
Hopper, P. J., & Traugott, E. C. (2003). Grammaticalization. Cambridge University Press.
Hopper, P. J., et al.. (1991). On some principles of grammaticization. Approaches
to grammaticalization, 1, 17–35.
Lehmann, C. (2015). Thoughts on grammaticalization. Language Science Press.
Petré, P., & Velde, F. Van de. (2018). The real-time dynamics of the individual
and the community in grammaticalization. Language, 94(4), 867–901.
Saavedra, D. C. (2021). Measurements of grammaticalization: Developing aquantitative index for the study of grammatical change. De Gruyter Mouton.
Sagart, L., Jacques, G., Lai, Y., Ryder, R. J., Thouzeau, V., Greenhill, S. J., & List,
J.-M. (2019). Dated language phylogenies shed light on the ancestry of
Sino-Tibetan. Proceedings of the National Academy of Sciences, 116(21),10317–10322.
Widmer, M., Jenny, M., Behr, W., & Bickel, B. (2021). Morphological structure can escape reduction effects from mass admixture of second language
speakers: evidence from Sino-Tibetan. Studies in Language. International
Journal sponsored by the Foundation “Foundations of Language”, 45(4),707–752.

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LETTERS AND THEIR SOUNDS ARE NOT PERFECTLY
ARBITRARY: EXPLORING GRAPHO-PHONEMICSYSTEMATICITY IN MULTIPLE ORTHOGRAPHY SYSTEMSHANA JEE 1, FIONA DU 1, MONICA TAMARIZ 2, and RICHARD SHILLCOCK 1*Corresponding Author: h.jee@yorksj.ac.uk
1Psychology, The University of Edinburgh, Edinburgh, UK
2Psychology, Heriot-Watt University, Edinburgh, UK

Language, as a complex system, suggests coordination between subsystems.
Recent studies demonstrated that semantically similar words tend to have similar
pronunciation (Blasi et al., 2016; Dautrich et al., 2017; Jee, Tamariz, & Shillcock,2022; Monaghan et al., 2014; Tamariz, 2008). The current research, for the firsttime, quantified mapping between letters and their canonical pronunciations, orgrapho-phonemic systematicity.
We examined naturally developed phonograms (Arabic, English, Greek, andHebrew), consciously designed phonograms (Korean, Shavian alphabet, andPitman’s shorthand), a logographic orthography (Chinese) and fictitious
orthography systems (Aurebesh and Klingon).
We measured all the pairwise phonological distances between phonemes in
the respective alphabet system, and the corresponding pairwise orthographicaldistances between letters. We then tested Pearson’s r between these two lists ofpairwise distances. The positive correlation coefficient means that similar lettershapes have similar canonical pronunciation. In contrast, the negative correlationmeans that similar letter-shapes have more distinct sounds, or vice versa. We
verified the significance of the correlations by conducting Monte-Carlo
permutation tests.
For the phonological distance, phonemes were encoded into vectors according
to the articulatory features and the distance between the vectors were calculated
in various ways. We applied three methods to measure the pairwise distances
between letter-shapes. Pixel count simply defines the distances as the differencein the number of pixels between two characters. Perimetric complexity is definedThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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as ink area divided by perimeter of the character, thus the distance means thedifference in complexity. Hausdorff distance (Huttenlocher et al., 1993)
quantifies the difference between two images. Since each letter was saved as animage file (PNG), we were able to compare the contribution of the font to thegrapho-phonemic systematicity.
We found the significant grapho-phonemic systematicity for all conventional
writing systems and two English shorthand systems. Those fictitious alphabets
did not show any systematicity. Considering each orthographic distance measurefocuses on distinct aspect of the letter-shapes, the fact that a certain method
maximised the systematicity of the writing system implies how it evolved.
Semitic orthography systems (Arabic, English, Greek and Hebrew) showed
highest grapho-phonemic systematicity when measured by pixel count (e.g.
English upper-cases r = .22, p < .001; English lower-cases r = .14, p = .02), whichindicates that more articulatorily complicated phonemes take up more space in
written forms. Effort in writing is easily understood as a letter’s elaborateness—
how long it takes to reproduce a character. Elaborateness is typically proportional
to the number of pixels. Korean, Shavian alphabet and Pitman’s shorthand were
all intentionally designed to exploit the systematicity between letters and sounds.
For instance, voiced-voiceless phoneme pairs share the identical visual featureswith slight variations. This topological difference was well-captured by Hausdorff
distance (e.g. Korean KCC Eun-young r = .39, p <. 001).
Although limited in number (N = 58), we found the significant graphophonemic systematicity in the Chinese characters that are acquired in the first andsecond year of the primary school. We found the negative correlation coefficient(r = - .12, p < .001), indicating that Chinese was influenced by an evolutional
force that distinguishes linguistic symbols. The finding implies that graphophonemic systematicity may exist to facilitate language learning and orthographyacquisition.
Our analyses are first a proof of concept: it is possible to quantify graphophonemic systematicity across a whole alphabet, for particular fonts and for
different languages. We also have confirmed and quantified the systematicity
intended by the authors of Korean writing system and English shorthand systems.
Our future research can shed more lights on sub-structure of grapho-phonemic
systematicity: the contribution of each phoneme/letter to the whole systematicity;
whether the more frequent phoneme/letter contributes more to the wholesystematicity; and most importantly, how this grapho-phonemic systematicity
bootstraps infants’ learning orthography.

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References
Blasi, D. E., Wichmann, S., Hammarström, H., Stadler, P. F., & Christiansen, M.
H. (2016). Sound–meaning association biases evidenced across thousands oflanguages. Proceedings of the National Academy of Sciences, 113(39),10818-10823.
Dautriche, I., Mahowald, K., Gibson, E., & Piantadosi, S. T. (2017). Wordform
similarity increases with semantic similarity: An analysis of 100
languages. Cognitive science, 41(8), 2149-2169.
Huttenlocher, D. P., Klanderman, G. A., & Rucklidge, W. J. (1993). Comparing
images using the Hausdorff distance. IEEE Transactions on pattern analysis
and machine intelligence, 15(9), 850-863.
Jee, H., Tamariz, M., & Shillcock, R. (2022). Exploring meaning-soundsystematicity in Korean. Journal of East Asian Linguistics, 31(1),Monaghan, P., Shillcock, R. C., Christiansen, M. H., & Kirby, S. (2014). How
arbitrary is language?. Philosophical Transactions of the Royal Society B:
Biological Sciences, 369(1651), 20130299.
Tamariz, M. (2008). Exploring systematicity between phonological and contextcooccurrence representations of the mental lexicon. The Mental
Lexicon, 3(2), 259-278.

357

PHYLOGENETIC MULTILEVEL MODELS REVEAL A SIMPLICITYBIAS IN URALIC
Yingqi Jing*1 , Miina Norvik23 , Outi Vesakoski4 , and Michael Dunn1* Corresponding Author: yingqi.jing@lingfil.uu.se

1 Department for Linguistics and Philology, Uppsala University, Uppsala, Sweden
2 Department of Modern Languages, Uppsala University, Uppsala, Sweden

3 Institute of Estonian and General Linguistics, University of Tartu, Tartu, Estonia

4 Department of Finnish language and Finno-Ugric linguistics, University of Turku, Turku, Finland

One central topic in comparative linguistics is to investigate the evolutionary
rates of typological traits between different levels of language systems. Varying rate of change could take place due to functional constraints such as generalprinciples of efficiency in communication and learning (Zipf, 1949; Culbertson& Kirby, 2016; Hahn, Jurafsky, & Futrell, 2020), or language contact and second
language acquisition that may affect the linguistic complexity (McWhorter, 2011;
Lupyan & Dale, 2010; Bentz & Berdicevskis, 2016; Housen, De Clercq, Kuiken,& Vedder, 2019). Previous research on rate variation across domains is however
unclear. Greenhill et al. (2017) and Carling and Cathcart (2021) show that grammatical features tend to evolve faster than lexical items (i.e., basic vocabulary)and morphological features at least in Austronesian and Indo-European, whereas
Dediu and Cysouw (2013) show that syntactic features including word orders are
more stable than morphological features, or there is no much rate difference acrossdomains (Greenhill, Atkinson, Meade, & Gray, 2010). Newly available Uralic typological data allows us to estimate the rates of change across typological domains(Norvik et al., 2022) and understand the evolutionary dynamics of the Uralic language system.
With the publications of the Uralic Typological Database (Norvik et al., 2022),
we use Bayesian phylogenetic inference to estimate the rates of change for different typological features in the history of Uralic. We go beyond the earlier approaches to fit phylogenetic models for each individual feature or estimate theaverage rates across all features. Instead, we introduce a novel multilevel phylogenetic Continuous-time Markov Chain model to investigate the evolutionary
trajectories of 110 features across different domains (phonology, morphology andsyntax) in 33 Uralic languages. The hierarchical model allows us to jointly infer
the evolutionary rates at both population and group levels, guarding against overfitting and underfitting (Nalborczyk, Batailler, Lœvenbruck, Vilain, & Bürkner,
2019; Stan Development Team, 2021). We first validate the model with simulatedThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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data and then apply our approach to Uralic typological data.
Our results reveal a slight directional change towards simple character states atthe population level (mean rate of q01 = 6.8 and 95% CI = [0.92, 23]; mean rate of
q10 = 8.26 and 95% CI = [0.76, 24.1]), suggesting that losing a complex feature
on average takes around 250 years less time than gaining a complex one. In eachdomain, we also observe consistent simplicity biases, though the estimated rates
of change are quite similar across different domains (mean rate in phonology: 8.1
and 95% CI = [0.79, 30.3]; mean rate in morphology: 7.29 and 95% CI = [0.52,
27.9]; mean rate in syntax: 8.6 and 95% CI = [0.47, 35]).
The evolutionary biases towards simplified states can be driven by the general
economic principle, which reduces the linguistic complexity to facilitate communication (Zipf, 1949). The observed trends are quite consistent across domains,suggesting that forces of simplification are persistent in the whole Uralic language
system. The simplification of language systems would also be expected in certain
contact situations (e.g., imperfect learning or language shift), which may lead tothe loss of complexity or redundancy (Heine & Kuteva, 2005; Grünthal et al.,2022). It is also worth noting that even though the differences in rate biases areslight, these evolutionary preferences can be amplified in a long period of language evolution or learning. Our results are not consistent with previous workthat suggests unequal rates of change across domains in other languages families.
Instead, we show that language evolves at very similar rates across typologicaldomains in Uralic. Further research is needed to expand our approach to otherfamilies to see whether there is a constant rate constraint in the multilayer systemof languages (Kroch, 1989; Kauhanen & Walkden, 2018).
References
Bentz, C., & Berdicevskis, A. (2016). Learning pressures reduce morphologicalcomplexity:: Linking corpus, computational and experimental evidence. In
26th international conference on computational linguistics (pp. 222–232).
Osaka, Japan.
Carling, G., & Cathcart, C. (2021). Reconstructing the Evolution of IndoEuropean Grammar. Language, 97(3), 561–598.
Culbertson, J., & Kirby, S. (2016). Simplicity and specificity in language:
Domain-general biases have domain-specific effects. Frontiers in Psychology, 6(1964).
Dediu, D., & Cysouw, M. (2013). Some Structural Aspects of Language Are
More Stable than Others: A Comparison of Seven Methods. PLoS ONE,
8(1), e55009.
Greenhill, S. J., Atkinson, Q. D., Meade, A., & Gray, R. D. (2010). The shapeand tempo of language evolution. Proceedings of the Royal Society B: Biological Sciences, 277(1693), 2443–2450.
Greenhill, S. J., Wu, C.-H., Hua, X., Dunn, M., Levinson, S. C., & Gray, R. D.

359(2017). Evolutionary dynamics of language systems. Proceedings of the
National Academy of Sciences, 201700388.
Grünthal, R., Heyd, V., Holopainen, S., Janhunen, J., Khanina, O., Miestamo, M.,
Nichols, J., Saarikivi, J., & Sinnemäki, K. (2022). Drastic demographic
events triggered the uralic spread. Diachronica (to appear).
Hahn, M., Jurafsky, D., & Futrell, R. (2020). Universals of word order reflect
optimization of grammars for efficient communication. Proceedings of the
National Academy of Sciences, 201910923.
Heine, B., & Kuteva, T. (2005). Language contact and grammatical change.
Cambridge University Press.
Housen, A., De Clercq, B., Kuiken, F., & Vedder, I. (2019). Multiple approaches
to complexity in second language research. Second language research,
35(1), 3–21.
Kauhanen, H., & Walkden, G. (2018). Deriving the Constant Rate Effect. Natural
Language & Linguistic Theory, 36(2), 483–521.
Kroch, A. S. (1989). Reflexes of grammar in patterns of language change. Language Variation and Change, 1(3), 199–244.
Lupyan, G., & Dale, R. (2010). Language structure is partly determined by social
structure. PloS one, 5(1), e8559.
McWhorter, J. H. (2011). Linguistic simplicity and complexity: Why do languages
undress? Walter de Gruyter.
Nalborczyk, L., Batailler, C., Lœvenbruck, H., Vilain, A., & Bürkner, P.-C.
(2019). An introduction to bayesian multilevel models using brms: A casestudy of gender effects on vowel variability in standard indonesian. Journalof Speech, Language, and Hearing Research, 62(5), 1225–1242.
Norvik, M., Jing, Y., Dunn, M., Forkel, R., Honkola, T., Klumpp, G., Kowalik, R.,
Metslang, H., Pajusalu, K., Piha, M., Saar, E., Saarinen, S., & Vesakoski,
O. (2022). Uralic typology in the light of new comprehensive data sets.
Journal of Uralic Linguistics, 1(1), 4-42.
Stan Development Team. (2021). RStan: the R interface to Stan. (R package
version 2.21.2)
Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Cambridge,
MA: Addison-Wesley Press.

360

POINTER EVOLUTION POINTS TO GRADUAL EVOLUTIONOF HIERARCHICAL COMPLEXITYSVERKER JOHANSSON *1*Corresponding Author: sja@du.se
1Dalarna University, Falun, Sweden

Chomsky (e.g. 2010) and others regard unlimited Merge as the defining feature oflanguage, that cannot evolve gradually. The neural implementation of Merge is not wellunderstood (Rizzi 2012, Zaccarella et al 2017), but must involve something functionallyequivalent to pointers in working memory. Every Merge requires two pointers, and fullsyntactic trees may require dozens. Other syntactic paradigms also need pointers.
Humans do hierarchies in general better than chimpanzees. Any hierarchical thinking
requires nested pointers in working memory, but they are neurologically expensive anddegrade with depth (Crawford et al. 2016). Humans have larger working-memory capacity
than chimpanzees, which has been proposed as key to human cognitive evolution (Read2008, Coolidge & Wynn, 2005). Gradual evolutionary growth of pointer capacity willallow gradually increasing syntactic complexity, without saltations in the underlying
computational machinery. Both depth degradation and pointer capacity naturally limit
Merge even in modern humans, consistent with corpus data (e.g. Karlsson 2010).

1. Can infinity evolve?
Language is commonly said to be infinite, and this is true at least in the limitedsense that there are no limits to what can be said. But some popular linguistic
paradigms, notably minimalism (Chomsky 1995), postulate that language isinifinite in a stronger, more literal sense, in that the language faculty can, in
principle, generate an infinite number of infinitely long sentences. The generating
hierarchy-building operation (Merge, in Chomsky’s case) can build trees ofarbitrary depth, without limits. This kind of inifinity is sometimes invoked as anargument against the gradual evolution of the human language capacity, roughly
along the following lines (e.g. Chomsky 2010, Berwick & Chomsky 2015):
1. Language is based on Merge, which is the defining feature of language.
2. Merge is unlimited.
3. Language, generated by unlimited Merge, is infinite.
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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4.

There is no gradual path from the finite to the infinite, no possible
intermediate “semi-infinite” proto-language.
a. Having a limited Merge, and then gradually evolving an
increased limit, will not get us to infinity.
5. Merge, and thus language, must have arisen in a single step. There isno such thing as “half-Merge”, no intermediate stage between no
Merge and full Merge (Berwick & Chomsky 2019).
6. Conclusion: Language did not evolve gradually.
But humans are indubitably descended from language-less ape-like ancestors.
And the notion that something as complex as language could have arisen in one
fell swoop, by a single super-mutation, is not tenable (e.g. Tallerman 2014,de Boer et al. 2019).
If the human language capacity did evolve in several steps, one of the points
above must be incorrect. The question is which one.
2 Merge is limited
I will focus first here on assumption #2, that Merge is unlimited, and show that
this is not a correct description of the actual human language capacity.
The term “Merge” sensu stricto is specific to Chomskyan minimalism, but
regardless of linguistic paradigm there will be a need for some kind of hierarchyhandling neural machinery in the language capacity, as human languages
indubitably do have some hierarchical structure. The form that the hierarchies
take differ widely between paradigms, but my argument here is intended to apply
over a broad range of hierarchy-handlers. I will use “Merge” here as a label, bothbecause it is the best-known hierarchy handler, and because most proponents of
the infinity argument above are Chomskyans, but I use it sensu lato, as a label fora generic hierarchy-handler.
From an empirical perspective, language is clearly finite. The human brain
has a finite size, and the human lifespan is finite, so infinite production is
obviously impossible in practice. Actual language usage shows that Merge inpractice is not just limited, but limited to fairly shallow depths – in written corpus
data, it is exceedingly rare to find examples of phrasal embedding more than threelayers deep, and the limits appear even tighter in spoken corpora (Karlsson 2010).
Similarly, the accuracy of grammaticality judgements approaches chance level as
embedding depth increases beyond what is commonly used (Christiansen &MacDonald 1999).
These performance limits keep real human languages from being infinite.
Languages are still very large; a few levels of embedding combined with a normal

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vocabulary still allows for an astronomical number of different sentences –unlimited for all practical purposes but not infinite.
However, it has been argued since Chomsky (1965) that performance is notinteresting, that performance limitations just distract attention from the real
underlying linguistic competence. The latter is what linguists should study, andthe latter is postulated to be infinite.
But is competence, a theoretical entity that is never directly observed,
actually the proper target for language evolution studies? Isn’t it enough toaccount for the evolution of actual language usage?
3 How can Merge be implemented in the brain?
The neural implementation of Merge is not well understood (Rizzi 2012,
Zaccarella et al. 2017). Discussions in the literature are mainly about thecomputational machinery, but I will focus instead on memory needs, that are nontrivial. In order to build a syntactic structure in the brain, two types of objects
need to be stored in memory: (1) lexicon storage in long-term memory (LTM),
and (2) syntactic nodes in working memory (WM). LTM and WM are distinct
types of memory, with distinct characteristics (Norris 2017). The leaf nodes in thesyntactic tree in WM must somehow refer to lexical items in LTM. Unless entire
lexical items are copied into WM nodes, this must involve something functionallyequivalent to pointers (e.g. Reilly 2003) in working memory, where a WM nodecontains a reference to a LTM item (Takac & Knott 2016); this would be 0-merge
sensu Rizzi (2016).
A Merge operation will create a new object in WM, which consists of two
pointers, one to each object that is merged (plus features and whatever else isstored at each node). If both pointers refer to lexical items in LTM, we have
1-merge sensu Rizzi (2016). The brain must be able to handle and refer to thisnew composite object as a single entity, a chunk of memory (cf. Gobet et al. 2016,
Isbilen & Christiansen 2018), for purposes of further merging; chunking is aprerequisite for Merge.
For the next level of merging, it is not enough with pointers from WM to
LTM; pointers from one WM location to another WM location are also needed.
WM-to-WM pointers are likely neurally distinct from WM-to-LTM pointers, as
the address space is different in kind. This means three different types of Merge
nodes are needed in WM:• Merging two lexical items (1-merge of Rizzi 2016).
• Merging a merged item with a lexical item (2-merge of Rizzi 2016).
• Merging two merged items (3-merge of Rizzi 2016).

363

All this is done in WM, and it can be noted that, while many animals do haveworking memory (Carruthers 2013), there is fair evidence that humans have moreof it than even our closest living relatives (Read 2008, Coolidge & Wynn, 2005).
In recent works, Chomsky does note the need for working memory (or“workspace”, as he calls it), for syntactic processing, but does not discuss either
implementation or limitations (e.g Chomsky et al. 2017).
4 Pointers
Pointers – having one memory location contain a reference to another memory
location – are used extensively in computer programming. Biological brains are
doing many operations for which a computer would use pointers. Something
functionally equivalent to pointers must be neurally implemented.
4.1 Who needs pointers?
Pointers in the brain are needed as soon as the brain in one location manipulates
information that is stored elsewhere in the brain. Notably, any WM computations
involving LTM items will require pointers. It is rather pointless to have both typesof memory, unless you have pointers as well. But both WM and LTM arewidespread among non-human animals. Fish have both WM (Hughes & Blight1999) and LTM (Lucon-Xiccato & Bisazza 2017), so presumably all vertebrates
do, though most likely not identical to human memory in capacity or capabilities(Carruthers 2013). Pointers, at least WM-to-LTM, thus have an ancient origin.
This means that 0-merge (Rizzi 2016) is available to all vertebrates. For 1-merge(or higher), chunking is required, for which there is evidence in e.g. rats (Fountain& Benson 2006) as well as some other mammals and birds, but negative results
for fish and amphibians (Wickelgren 1979), suggesting a more limited
distribution that nevertheless includes many (all?) non-primate mammals.
For 2+merge, WM-to-WM pointers are needed. Any WM operations
involving hierarchical structures would be evidence of WM-to-WM pointers.
There is some evidence of hierarchical cognition in non-human primates (e.g.
Seyfarth et al. 2005), but it is not strongly compelling. The jury is still out on
2+merge in non-humans.
Humans, however, are hierarchical thinkers par excellence, to the extent that
Fitch (2014) labels us “dendrophiles” for our propensity to use hierarchicalthinking and impose hierarchical structure on anything and everything. Martin &Doumas (2017) propose that this general mechanism for thinking hierarchically
can be repurposed for linguistic structures.

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4.2 Neural implementation of pointers
Pointers are inherently difficult to handle in the brain, as memory addressing is
not a matter of just storing the number of the addressed memory cell, like it is in
a computer. The content addressable memory in the brain requires a fundamentally different type of pointers, that are neurally quite expensive. This isparticularly true when attempts are made to scale up proposed pointer models toa human-sized address space; most models do not scale well and cannot addressa human-sized memory with the number of neurons available in a human brain(Blouw et al. 2016). Exact pointers are particularly vulnerable to scaling issues,
whereas different types of approximate pointers fare better (Crawford et al. 2016,
Legenstein et al. 2016).
The model of Crawford et al. (2016) is attractive in this context, as it has
explicitly been shown in simulation to manage the full human lexicon with areasonable number of neurons that will actually fit within the relevant brain areas.
This model is based on lossy compression of information; accuracy remains
adequate for single pointers, but degrades rapidly with depth when pointers arenested in recursive structures; the degradation mimics actual human performance(as opposed to theoretical competence) on multi-level embeddings.
But pointers remain expensive and consume WM fast, especially if you havehierarchical structures with multiple pointers-to-pointers.
5 The gradual evolution of limited Merge
As reviewed in the previous section, many non-human animals have LTM and
WM, of limited size, as well as WM-to-LTM pointers, which implies 0-merge.
Chunking, and thus 1-merge, likewise can be found in a fair range of animals(Wickelgren 1979), though the evidence for 2+merge outside the human lineage
is more limited.
Contrasting this with the capacities of modern humans, we can conclude thatthe evolutionary changes in the human lineage, after we parted ways with theother apes, most likely include:• Expanded WM.
• Expanded LTM (including possibly dedicated lexical storage).
• Dendrophilia.
• WM-to-WM pointers, and likely generally enhanced pointer handling.
• Node structures for 2-merge and 3-merge.
• Nested pointer handling.
• Enhanced chunking?

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None of these changes need to be linguistically motivated; they are plausible
components in the general human cognitive enhancement that took placeconcurrently (cf. Sherwood et al. 2008). There has obviously been considerable
selective pressure along the human lineage for enhanced brain size andpresumable enhanced cognition (Bailey & Geary 2009). Such a selective pressure
explains the evolution of expanded working memory, as there is a strong
correlation between WM size and general intelligence (e.g. Colom et al. 2008).
Given the WM cost of hierarchical structures, the WM expansion will makea big difference in hierarchy handling capacities. With a small WM, you cannot
build any significant tree structures even if full-blown Merge is computationallyavailable. A larger WM invites the evolution of enhanced pointer handling,including WM-to-WM pointers and recursively nested pointers, which werepointless before. There is also more scope for chunking in WM, when there isroom for more than a few chunks. Likewise, with a larger WM, dendrophilia(Fitch 2014) starts making sense.
This leaves us with a proto-human who has a fair-sized WM, a basic set ofpointer operations, including WM-to-WM pointers, and a general Merge-like
operation that can do all the merge levels of Rizzi (2016). Such a proto-humancan combine lexical items into two-word phrases, and can combine two phrases
into a composite utterance. Dedicated syntactic machinery such as featurechecking is still missing, leading to a rather anarchic proto-language, but the basichierarchical structure is there. Pointers remain limited in both address space andnesting depth, imposing limits on both lexicon size and tree size. But both limits
can be relaxed simply by gradually adding more neurons to the pointer machinery.
If there is selective pressure towards more expressive language, with more
complex syntax and enhanced narrative capacities, this can be dealt with in atleast three ways:• Further WM expansion.
• Pointer expansion as above, to handle both a human-sized address space
and the corpus-attested (Karlsson 2010) nesting depth.
• The general Merge operation already available may be augmented withlanguage-specific add-ons, gradually adding all the operations thatmodern Merge does beyond the actual merging (feature-checking etc.),
in order to make linguistic processing more precise and efficient.
The end result is modern humans, with a modern human language capacity.
Merge, or whatever hierarchical operation is actually running the human language
capacity, has gradually been enhanced to its modern full-fledged form, with noinfinity paradoxes blocking the way.

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References
Berwick, Robert C. & Chomsky, Noam. (2015) Why only us? Cambridge, MA:
MIT Press.
Berwick, Robert C. & Chomsky, Noam. (2019) All or nothing: No half-Merge
and the evolution of syntax. PLOS Biology 17:e3000539.
Bailey, D.H., Geary, D.C. (2009) Hominid Brain Evolution. Hum Nat 20, 67–79.
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Carruthers, Peter (2013) Evolution of working memory. Proc Nat Acad Sci
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THE COLOUR LEXICON IS SHAPED BY ENVIRONMENT AND
BIOLOGY: COMPARING HIMBA AND FRENCH COLOURPERCEPTION
Mathilde JOSSERAND*1, Serge CAPAROS2,3, François PELLEGRINO1, and Dan
DEDIU4*
Corresponding Author: mathilde.josserand@univ-lyon2.fr
Laboratoire Dynamique du Langage, CNRS & University Lumière Lyon 2, Lyon, France
2
Department of Psychology, University of Paris 8, Saint-Denis, France
3
Institut Universitaire de France, Paris, France
4
University of Barcelona & Catalan Institute for Research and Advanced
Studies (ICREA), Barcelona, Spain

1

The evolution and diversification of language is driven by evolutionary pressures,and by its repeated use, learning, and transmission across generations. One aspectof this process is the amplification of weak biases, potentially up to a whole
language and beyond, in large-scale cross-linguistic patterns. For example,
differences in subsistence mode (Blasi et al, 2019), in the anatomy of the vocal
tract (Dediu et al, 2019), or in climate (Everett et al., 2015) may influence thephonemic repertoires of languages. We show here that the lexicons of languages
can be influenced by weak biases due to environmental factors. UV radiations
affect the eye physiology by causing the lens to opacify, and this may reduce theability to perceive the blue part of the colour spectrum (Davies et al, 1998). As aconsequence, people living in areas with high levels of UV incidence may be lesslikely to possess a specific term for blue in their vocabulary (Lindsey & Brown,2002). However, the causes underlying the evolution of colour lexicons arecomplex and likely involve multiple factors, including variation in other culturaland physical factors. To investigate whether Lindsey and Brown's physiologicalhypothesis still holds when these other factors are considered, Josserand et al.
(2021) conducted a large-scale statistical study on 142 populations, and found that
populations living in areas with high levels of UV incidence are more likely tomerge ‘green’ and ‘blue’ colours under a single (‘grue’) term.
Here, we wished to complement these results by investigating the relationshipbetween colour perception and UV incidence in two representative populations
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

369

living in markedly different environmental conditions. Specifically, we compared
French participants (who are exposed to a medium amount of UV radiation) to
Himba participants (a population of Northern Namibia, living mostly outdoor
under high UV incidence). While French speakers have a specific term for ‘blue’,the Himba historically do not (Roberson et al., 2004; but see Mylonas et al.,2022). Here, we present new preliminary results from 76 Himba and 42 French
participants. We used the Farnsworth Munsell test, which evaluates and ranks
colour acuity for a broad range of colours and can reveal both inherited andacquired colour vision deficiencies. In addition, we used the JND (just noticeable
differences) task for blue and red colours. Here, participants performed a binary
forced choice stimulus task using a computerized adaptive testing design. Twocoloured rectangles were presented on the screen with a colour difference
magnitude ranging from 0 to 10 CIELAB units and applied differently per colour
and per axis (L, a and b). For both experiments, luminosity settings and screen
calibration were carefully controlled.
The results showed that older French participants were less able to discriminate
close colours in the blue range (only). This finding is consistent with the
cumulative nature of the exposure to UV radiation throughout lifespan. Second,older Himba participants were less able to discriminate close colours both in blue
and red ranges. Surprisingly, while we expected that the difference between
Himba and French participants would be the strongest in the blue range, on thecontrary, Himba participants had greater difficulty discriminating close colours
through the whole range of colours. However, a careful analysis is in progress toaccount for potential confounding factors, for instance related to the inadequacyof the tasks themselves.
These preliminary results may suggest that the effect of UV radiations on colour
perception occurs for all hues. If so, it may be one factor explaining the relatively
low number of colour terms used by the Himba people, by limiting the hues
individualized in the speakers’ perceptual space, in addition to other cultural andenvironmental factors. Indeed, recent computational work suggests that both
perceptual structure and communicative needs shape colour naming (Zavlasky et
al, 2019), and injecting variation in the initial perceptual space of these models is
an avenue we plan to explore in the future. Our contrastive study between two
populations contributes to unravelling the role of colour perception in mediating
physical/environmental factors on lexical characteristics of languages. Overall,this work suggests that language is deeply intertwined with its surroundings, thus
highlighting the relevance of considering weak biases related to the socio-cultural
system, the features of the environment, or the biology of its speaker.

370

Acknowledgements
We thank our two guides and translators Fanny Kavary and Chinho Lopez for
their great professionalism and help with the experiments, as well as for sharing
their knowledge on Himba culture. We also want to acknowledge Esther Boissin
for her resourceful advice and emotional support in this fieldwork. Most
importantly, we are immensely grateful to the Namibians who welcomed us andour project with openness, benevolence, and generosity.
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configuration. Science, 363(6432).
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tract anatomy shape the repeated transmission of vowels. Nature Human
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Scientific Reports, 11(1), 19095.
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Humanities and Social Sciences Communications, 9(1), 1–12.
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Development of Color Categories in Two Languages: A Longitudinal Study.
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Both Perceptual Structure and Communicative Need. Topics in Cognitive
Science, 11(1), 207-219.

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LANGUAGE WITHOUT SHARED MEANING: PRELIMINARYCONSIDERATIONS ON THE EVOLUTIONARY BENEFITS OFSUBJECTIVITY
Alexandre Kabbach*1,2 and Aurélie Herbelot2,3* Corresponding Author: alexandre@kabbach.net

1 Department of Linguistics, University of Geneva, Geneva, Switzerland
2 Center for Mind/Brain Sciences, University of Trento, Trento, Italy

3 Department of Information Engineering and Computer Science, University of Trento, Trento, Italy

We propose a characterization of language that does not rest on the hypothesis that meaning isnecessarily shared across interlocutors, since it is fundamentally grounded in the privacy andsubjectivity of mental content. We first argue that the function of language is thus best characterized as the coordination rather than the communication between minds, since it is the constraints on use—rather than on meaning itself —that are negotiated between interlocutors during
linguistic interactions. We then explore the evolutionary benefits of subjectivity and argue thatit positively contributes to adaptability through: 1) innovation, as conceptual variability at theindividual level increases the likelihood of the group of finding relevant conceptual innovations
when exposed to environmental challenges; and 2) transfer, as subjective coordination allows
for individual innovations to spread across a whole population, without individuals having toalign their conceptual spaces and thereby lose the benefits of conceptual variability.

1. Introduction
Communication through language rests on mutual understanding: to understandyou, I must figure out exactly what you mean by what you say. Successful communication therefore requires shared meaning and appeal to mental content. And
yet, mental content remains inherently private and idiosyncratic in nature, which
poses a major challenge to communication, for how can we indeed guarantee mutual understanding if the meaning speakers intend to communicate through language remains highly subjective and inaccessible to others (Pelletier, 2017, p.63)?
In this work, we propose to turn the problem on its head: rather than trying
to reconcile subjectivity with communication, we explore what it would mean forthe characterization of language to be structured around subjectivity, and what
benefits subjectivity could have from an evolutionary perspective.
Our first contribution is to argue that the function of language that unfolds is
actually best characterized as the coordination rather than the communication between minds—where communication and coordination are formally distinguishedwith respect to their requirement of shared meaning: strict for communication,
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

372

loose for coordination. Concretely, we argue that coordinating minds is best conceived as the private satisfaction of shared constraints, given that 1) speakers
never have direct access to what their interlocutors actually mean, so that; 2) it is
the constraints on use—rather than on meaning itself —that are actually negotiatedbetween interlocutors during linguistic interactions.
Our second contribution is to formalize a clear hypothesis about the evolutionary benefits of subjectivity in improving adaptability through cumulative culture—that is, the ability of humans to innovate by creating new knowledge aswell as to preserve existing knowledge by passing it on to other members of thegroup. We argue that subjectivity is advantageous in two ways: 1) for innovation, conceptual variability at the individual level increases the likelihood of thegroup of finding relevant conceptual innovations when exposed to environmental
challenges; and 2) for transfer, subjective coordination allows for individual innovations to spread across a whole population, without individuals having to aligntheir conceptual spaces and thereby lose the benefits of conceptual variability.
Through this work, we hope to contribute to the discussions on both the evolution and the function of language by decoupling the characterization of communication success from the characterization of interpretation success. Doing soallows us to treat the communicative function of language no longer as a theoretical prerequisite, “hardcoded” into our model of language, but as a mere hypothesisthat can now be analyzed comparatively for explanatory adequacy.
2. Language, communication and the problem of subjectivity
2.1. The code model of communication
According to Sperber and Wilson (1986/1995, p.2) “From Aristotle through tomodern semiotics, all theories of communication were based on [. . .] the code
model” later formalized by Shannon and Weaver (1949), which characterizes
communication as information transfer between a sender and a recipient who exchange messages encoded and decoded to and from (linguistic) signals through apotentially noisy channel.
The problem with such a characterization—as pragmatics has extensively
shown—is that there is more to language than what is said, so that linguistic
signals alone often do not suffice to unambiguously decode messages: they mustbe put into context (Sperber & Wilson, 1986/1995, ch.1 §2). But context here is tobe understood not only as the linguistic (e.g. sentential or discourse) context under which communication takes place, but also as the non-linguistic context suchas the set of background experiences, knowledge, beliefs, desires or assumptions
grounding the interpretation of messages and the mapping of linguistic form toconceptual meaning. Recipients must understand not only what is said, but also
what senders (or speakers) actually mean by what they say, and to do so they must
identify their intentions (Grice, 1989).

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Inferential processes are therefore required to properly decode messages, anddecoding is probably best formalized as interpretation, given how messages arethemselves best characterized in terms of mental content and as combinations ofpropositions and propositional attitudes (Sperber & Wilson, 1986/1995, p.57).
2.2. Subjectivity, similarity and communication success
Major problems then arise when trying to characterize communication success.
Because mental content is inherently private and background experiences grounding conceptual knowledge necessarily idiosyncratic and thus highly subjective andspeaker-specific (Pelletier, 2017, §6). Yet, the code model assumes that communication is successful when the message decoded by the recipient is identical to theone encoded by the sender. How can we guarantee, then, that senders and recipients do in fact understand one-another if the messages they map to and from identical linguistic signals are processed through different conceptual backgrounds?
The problem is well acknowledged1 and usually resolved along two lines.
First, by relaxing the identicity constraint on messages to sheer similarity. Second,by emphasizing that agents in linguistic interactions are not passive but actively
negotiate meaning and gradually align their conceptual representations throughconversation (Clark, 1996; Pickering & Garrod, 2006). But what does it mean
for two messages to be similar? And how much similarity is enough to guarantee communication success? In effect, the concept of similarity merely displaces
the problem while still posing major theoretical and empirical challenges (Medin,
Goldstone, & Gentner, 1993). Alignment-based approaches to communication are
not without problems either, as they still remain committed to the code model’s
characterization of communication success, inasmuch as conceptual representations are expected to align across interlocutors during conversation and misalignment between speakers is only tolerated when it pertains to aspects irrelevant tothe conversation at hand. Shared meaning is thus still very much of a requirement,
and actually expected, given how speakers of the same (linguistic) community are
assumed to share common ground (Kabbach & Herbelot, 2021, §2.3).
3. What exactly is the function of language?
3.1. The need for an alternative hypothesis
In any case, a characterization of communication success that rests on the hypothesis of shared meaning can only be an approximation at best, for speakers cannever actually verify that they do share meaning in practice. As Sperber and Wilson (1986/1995, p.18) emphasize, “when human beings try to communicate witheach other, they are aiming at something they can never, in fact, achieve”. The
1 And has been extensively discussed in the philosophy of language, notably through the questionof meaning holism and instability. See (Jackman, 2020), especially §3.2 and references therein.

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formalization of communication success in the code model is psychologically unrealistic, for it would actually require speakers to perform an infinite number ofrecursive mutual checks on their respective mental content to ensure their making use of the same context and guarantee their encoding and decoding identicalmessages (Sperber & Wilson, 1986/1995, ch1 §3).
Moreover, if all speakers can do during conversation is strive to avoid conflictrather than enforce agreement—given that overt disagreement is the only kind of
information they actually have access to in practice—the question boils down towhether the absence of (behavioral) conflict really equates (conceptual) agreement, which is far from obvious. The structure of language and cognition indeed
appears to be such that you and I can agree on the fact that kittens are cute without
having to agree on what kittens are, or up to what age one can call a cat a kitten, on why we find kittens cute, or even on what constitutes a good prototype ofcuteness. Many argue that, in such cases, conceptual variability between us does
not really matter: language can remain vague and vagueness can even serve communication (van Deemter, 2010). But saying that conceptual variability does notmatter does not mean that it is necessarily marginal. Regardless, it remains that
language appears to be structurally underdetermined regarding concepts, in thatmy uttering the word cat does not even begin to convey the richness of my concept CAT. Therefore, our agreeing on the fact that kittens are cute remains what it
is: an appearance of agreement between what are probably highly subjective andthus clearly distinct conceptual representations, compatible in this context only
inasmuch as they do not lead to overt disagreement between us.
Considering subjectivity to be negligible overall in language processing is thus
probably somewhat of an arbitrary choice—true by definition rather than by empirical observation—and the assumption of shared meaning may very well prove
to be biased by our tendency to actually overestimate how much we share meaning with others (Martı́, Piantadosi, & Kidd, 2019). Be that as it may, would
it really make sense to talk about “communication” if it did not involve shared
meaning in the first place? Or if the notion of mutual understanding was not
grounded in a form of “sameness of concept” among interlocutors? The languagefor-communication hypothesis still resists the relaxing of the shared meaning assumption, even if just for questions of clarity and consistency. If we are to call into
question its characterization of success, we thus need more than a new model ofcommunication: we need a new hypothesis about the function of language itself.
3.2. The language-for-coordination hypothesis
The primary contribution of our work is to propose a radical change of perspectiveon the role subjectivity is assumed to play in language processing (in general) andin interpretation (in particular). In considering that subjectivity is structural rather
than marginal, we argue that the function of language is actually best characterized

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as the coordination rather than the communication between minds.2 Concretely,
we distinguish coordination from communication with respect to their requirement of shared meaning: strict for communication, loose for coordination. In
effect, coordination dispenses itself from having to characterize success in termsof necessary conceptual alignment between speakers. Indeed, interpretation canbe thought of as a problem of constraint satisfaction, but one that admits multiple
solutions and that is resolved independently by speakers taken individually, so thatthey need not converge to identical solutions.
Let us take a concrete example to illustrate our point: it is common in thescientific literature to find counter-arguments to the subjectivity of meaning revolving around the problem of concept-to-word mapping. The argument goesmore or less as follows: if you call a dog what I call a cat, how can the two of us
communicate with one-another? Yet, things need not be so binary: there is ample
leeway between my saying cat when you say dog and you and I having identicalconcepts of CAT and DOG. The example is of course limited—what are identicalor similar concepts in the first place?—but it gives us an intuitive sense of theclaim: the mapping between words and concepts need not be one-to-one, so thatdifferent speakers can actually associate different concepts to identical words.
In fact, the process of making sense of linguistic signals could perfectly accommodate the pervasiveness of subjectivity specifically because it remains private and because meaning is never negotiated directly across speakers. You andI certainly do have to agree that what we have in front of us is referred to as a
cat and not a dog, but we will never negotiate our conceptual representations of
CAT and DOG directly: only the constraints they have to satisfy externally. And
since the words cat and dog are structurally underdetermined with respect to ourconcepts of CAT and DOG, the problem of constraint satisfaction so defined mayvery well admit multiple solutions that will be produced privately by each of us,
within the subjectivity of our respective conceptual spaces.3
2 Our use of the term “coordination” is somewhat arbitrary and primarily motivated by the desire
to clearly depart from the term “communication” without resorting to a neologism. Nonetheless, italso rests on the observation that, to coordinate, people need not do the same thing. The examples ofcoordination provided by Clark (1996, p.3) for instance—such as waltzing or playing music—give asense of the intuition. From not doing the same thing to not thinking the same thing, there is a bridge
between behavior and mind that we will take the liberty to cross here.
3 We neither provide nor commit to any particular characterization of the notion of “constraint”,
though we do commit to the view that concepts are mental entities. In the above example, constraints
could for instance be considered “referential”, insofar as you and I have to agree that the word catshould be used to refer to the animal we have in front of us. But in a different conversation, I may try
to convince you that cats are adorable creatures, and constraints on usage of cats or adorable will beformalized differently. Anyhow, the point made by the language-for-coordination hypothesis is thataligning usage does not require aligning concepts. The intuition is plain: we need not agree for thesame reasons. “Making sense” only requires that we find at least one way to agree, that is, one way toaccommodate our subjective concepts to the situation at hand.

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4. Coordination and subjectivity from an evolutionary perspective
4.1. Language-for-coordination and explanatory adequacy
Our proposition is not without empirical support. Kabbach and Herbelot (2021),
for instance, question the standard view that communication succeeds because theimpact of subjectivity is negligible (i.e. the view that speakers of the same linguistic community share significant common ground despite having private mental content (Clark, 1996)). They specifically investigate whether common groundemerges from linguistic interactions in the first place, and show using computational models of lexical meaning that aligning different models on parts of theirsemantic spaces does not necessarily lead to increased overall similarity between
them. Worse, they show that models often manage to improve superficial alignment by actually resorting to idiosyncratic rather than commonly shared aspectsof their conceptual spaces, providing thereby a computational characterization of
the distinction between agreeing and compatible semantic representations.
Such work, of course, is no definite proof of our argument. But it stands inthe long tradition of computational work that, without guaranteeing what is, canat least give us some intuition about what could be: in this case, superficial behavioral alignment does not have to equate deep conceptual agreement. To us, the roleof subjectivity in language should no longer be downplayed: in practice, conceptual similarity across people is extremely hard to validate experimentally (Kabbach & Herbelot, 2021, §2.2) and the fact of the matter is that “different subjects
give individually different results on the many tasks about meaning that have been
administered over the decades in cognitive psychology” (Pelletier, 2017, p.74).
Those empirical data have to be accounted for one way or another.
4.2. Adaptability: the evolutionary benefits of subjectivity
Interestingly, support for the language-for-communication hypothesis may comefrom considerations about the emergence of language and asking what benefits
subjectivity could have from an evolutionary perspective. One way to concretely
tackle the problem is to consider the impact of cumulative culture on adaptability.
According to Mithen (1996), human societies distinguish themselves from apes bya striking technological gap, giving our species a major survival advantage as it enables us to adapt much more easily to environmental changes. The superiority of
those technological abilities have been said to arise from cumulative culture, i.e.,from our ability to innovate by creating new knowledge as well as to preserve existing knowledge by passing it on to other members of the group (Mesoudi, 2011).
The question that arises, then, is how to provide a functional characterization of
the cognitive processes involved in cumulative culture, and more specifically of
the role language plays in both (conceptual) innovation and transfer.
One interesting take on the matter is the study of Toya and Hashimoto (2018)
which investigates the evolutionary benefits of recursion. Drawing a parallel be-377

tween mental operations and action sequences involved in toolmaking, they show
how specific types of recursive operations can lead to improved tool manufacturing strategies and to the production of more and more diverse tools—ultimately
leading to improved population fitness, better adaptability and increased survival
capabilities. Yet, if their study paves the way for an empirical investigation of
the question that interests us here—demonstrating how increased conceptual diversity can positively contribute to innovation and adaptability—it tells us little
about transfer and how a specific innovation may spread across a whole population. More specifically, will members of a group necessarily transmit whole production strategies or rather focus on enabling all members to produce the desired
outcome irrespective of the production method used? Which approach will prove
better able to entrench innovations and ultimately improve adaptability? This isprobably where we can best foresee the possible contribution of the languagefor-coordination hypothesis: if what matters is what you make and not how you
make it, relaxing the constraint on shared meaning could prove decisive. As Toyaand Hashimoto (2018) indeed detail, a single linearized sequence may correspondto different hierarchical structures (see Figure 1) so that if the point is to matchthe sequence rather than a particular structure (the what versus the how) diversity
in the how—what they call “diversity of production”—can actually prove beneficial. This directly echoes considerations of §3.2 regarding the underdeterminacyof language vis-a-vis concepts and the benefits of subjectivity for coordination.

a

b

c

d

a

b

c

d

a

b

c

d

Figure 1.: Different hierarchical structures corresponding to different combinations of recursive operations generating the linearized sequence abcd.
We can thus now formulate a clear hypothesis about the possible evolutionary benefits of subjectivity: by enabling agents to coordinate without having toalign their respective conceptual spaces, language would allow for the spread ofa particular conceptual innovation within a group, without losing the benefits forinnovation of maintaining distinct conceptual spaces across individuals.
5. Conclusion
Is the function of language best characterized in terms of communication or coordination? Inasmuch as communication entails that meaning must be shared acrossinterlocutors when coordination does not, we argue for the latter. Indeed, considering the major evolutionary benefits that subjectivity brings to cumulative culture and adaptability, we argue that we should reconsider the place of subjectivity

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as being central to any account of human language and cognition, rather than anegligible byproduct of marginally different background experiences grounding
conceptual knowledge.
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Press.
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Jackman, H. (2020). Meaning Holism. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy (Winter 2020 ed.). Metaphysics Research Lab,
Stanford University.
Kabbach, A., & Herbelot, A. (2021). Avoiding Conflict: When Speaker Coordination Does Not Require Conceptual Agreement. Frontiers in Artificial
Intelligence, 3, 95.
Martı́, L., Piantadosi, S. T., & Kidd, C. (2019). Same Words, Same Context,
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Shannon, C. E., & Weaver, W. (1949). The Mathematical Theory of Communication. Urbana, IL: University of Illinois Press.
Sperber, D., & Wilson, D. (1986/1995). Relevance: Communication and Cognition (Second ed.). Oxford: Basil Blackwell.
Toya, G., & Hashimoto, T. (2018). Recursive Combination Has Adaptability inDiversifiability of Production and Material Culture. Frontiers in Psychology, 9, 1512.
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University Press.

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LANGUAGE CHANGE IN HINDI FROM A PANINIAN PERSPECTIVE
Aniket Kali*1 and Gerald Penn1
1 Corresponding Author: aniket.kali@mail.utoronto.ca

1 Department of Computer Science, University of Toronto, Toronto, Canada

We examine the development of Hindi from Sanskrit using a spectral analysis algorithm. Thesemethods are exceptionally good at drawing out inconsistencies and discrepancies in a dataset,and are therefore well suited to questions around language change. Using a Hindi corpus developed from the Hindi/Urdu Treebank Project (Bhat et al., 2017) annotated in a Paninian (Sanskrit
based) scheme, we contrast Panini’s karaka role scheme against Hindi grammatical markers,
such as postpositions. More broadly, we contribute a novel approach to uncovering and analyzing specific changes in language through time.

1. Introduction
The most reliable means of documenting language change through time is by directly considering examples drawn from documents distributed over the historyof that language. There may, however, be another way. Many languages comewith linguistic traditions of their own. If these traditions come to be interpretedprescriptively, the community that participates in the tradition may attempt to accommodate an inherited (semi-)formal theory to their contemporary language, either to demonstrate that the language is still licensed by the tradition, or simply
because that is the only alternative that comes to mind for analyzing contemporary
language. These accommodations may not be acknowledged or documented, but
it may be possible to infer them from example explanations or derivations.
In this paper, we seek to investigate the possibility of such inference to document changes to Hindi syntax relative to the Paninian Sanskrit tradition. Using
spectral analysis techniques, we analyze a Hindi corpus that has been annotatedaccording to Paninian conventions by The Hindi/Urdu Treebank Project (Bhatet al., 2017). By treating Hindi as a kind of “noisy” Sanskrit, and imposing agrammar devised for Sanskrit onto it, we show that a simple technique for noise
reduction can in fact reveal certain specific changes. In particular, as we describe
in detail below, it has been able to reveal changes between the locative and genitiveroles and their grammatical markers, as well as a failure to classify the reflexive
pronoun apna, given the diversity of reflexives in Sanskrit. It also highlights thedevelopment of ergativity and Differential Object Marking, both of which developed well after classical Sanskrit.
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

380

While numerous mathematical approaches to language change have focusedon modelling the system of syntactic change itself (Niyogi & Berwick, 1997; Kodner & Cerezo Falco, 2018), or have sought to understand its cognitive or semantic
implications (Hamilton, Leskovec, & Jurafsky, 2016; Habibi, Kemp, & Xu, 2020),
to our knowledge, the vast majority have never been used as a means of discovery.
Those that have, some of them with very similar methods to the one used here
(Belkin & Goldsmith, 2002; Belkin & Niyogi, 2003; Thaine & Penn, 2019) do
not rely on a parallel linguistic tradition as a means of identifying changes. We
hope this work will contribute to advancing just such an approach.
2. Grammar and Corpus
The Ashtadhyayi, written c. 350 BCE by Panini, is perhaps the first serious grammar of a language - in its case, Sanskrit. It is admired by linguists of all persuasions for its wide range of descriptive devices, and its degree of formal rigour(Kiparsky, 2009).
One of the work’s defining traits is its use of karaka roles, abstract case markings with some connection to deep/semantic arguments, at least as they appear in
the corpus we used, although the correspondence between theta-roles and karaka
roles in Panini has been disputed elsewhere (Houben, 1999). Kiparsky (2009)
articulates three principles of karaka theory: (1) every karaka role must be “expressed” by a morphological element, (2) no karaka role can be expressed bymore than one morphological element, and (3) every morphological element must
express something. Hindi is a morphologically poorer language than Sanskrit.
As Bharati, Bhatia, Chaitanya, and Sangal (1998) have outlined, this requires ananalysis using inflections, postpositions, and auxiliary words rather than one withmorphology alone. Bhat et al. (2017) did such an analysis by hand in their corpus.
The Hindi-language fragment of the Hindi/Urdu Treebank Project (Bhat et al.,2017) contains 11,600 sentences and 242,600 tokens. This corpus is annotatedusing an extension of the Universal Dependencies scheme: roles like ‘vmod’ havebeen subdivided into ‘k1’ (agent), ‘k2’ (patient), and so on. These ‘k*’ roles
were inspired by traditional, Paninian karaka roles. We evaluate the relationshipbetween the actual distribution of Hindi morphemes and clitics and karaka roles
using spectral analysis.
3. Singular Value Decomposition
The corpus annotation scheme of Bhat et al. (2017) uses postpositions to indicate
role assignments to syntactic phrases. We define a matrix A that aligns postpositions with roles, in order to analyze discrepancies in their correspondence. Eachrow xi represents a role, with n total roles. Then row xi = [x1 , ..., xj , ..., xm ]where xj is the number of times the j-th postposition is the final element in aphrase marked with the i-th role, with m total postpositions.

381

We extracted a list of roles and postpositions from the corpus to construct thismatrix. We only counted postpositions at the end of phrases, as these determine
the role, and counted phrases without postpositions as inflected with a NULL
postposition. In the case of inflection by gender and number, e.g., the possessive
ka, which appears as ki (singular or plural feminine) or ke (plural masculine), we
count it as the stem ka. This provides consistency, as gender and number are
determined by the possessee and have no bearing on the role itself. Nonetheless,
we analyze occurrences of the form ke+PSP as is (where PSP is the POS tag forpostposition), since this is a postpositional construct in Hindi that is unrelated tothe possessive. In the case of pronouns, Hindi also permits postpositions to attach
as morphology, which we extract and analyze in the manner described above.
In our presentation, we retain all roles found in the corpus, but include only the
top 26 postpositions, to reduce clutter. These make up about 99.9% by occurrence.
In practice, including the rest has a negligible effect on our analysis. This leaves us
with a total of n=65 roles and m=26 postpositions. A complete list of roles andpostpositions, and their frequencies, are found in the supplementary materials.1
Singular-Value Decomposition (SVD) takes a co-occurrence matrix as input
and returns component vectors that capture the most salient dimensions of thedataset, as measured by variance, together with weights, called singular values,that describe the importance of each dimension. Here, we present only the dimensions corresponding to the six largest singular values and project the vectorized
roles onto them pairwise in a series of two-dimensional visualizations. We usedscikit-learn’s implementation of SVD.
The j-th component in a vector represents the relevance of the j-th postposition. Once normalized, entries with an absolute value close to 1 are the most relevant, and those close to 0, irrelevant. Negative entries indicate an anti-correlation,
which can still be very relevant.
Examining the projection of roles onto components outlines the relationshipbetween particular roles and postpositions. Components with a single dominant
postposition should attract roles highly related to the functional use of that postposition. Likewise, components containing multiple postpositions may either attract
multiple roles, or a single multi-purposed role, indicating an analytical gap.
4. Results
As stated above, the most relevant parts of a component are those entries withvalues furthest from 0. We provide the first three postpositions ranked this way,in Table 1. These entries already tend rapidly toward 0, which is where the vast
majority of entries lie anyhow. As we will explain, there are particular reasonsthat these postpositions appear on the components that they do, and that certain
roles associate with them.
1 https://hdl.handle.net/1807/117650

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Table 1. The first six components from our spectral analysis.
The three most extremal postpositions of each component are
given, as determined by the absolute value of their singular value.
Component
c0
c1
c2
c3
c4
c5

Postposition 1
ka (0.97)
NULL (0.9)
mein (0.92)
ne (0.86)
ko (-0.88)
ke liye (0.99)

Figure 1.

Postposition 2
NULL (0.21)
ne (0.32)
par (0.37)
ko (-0.41)
ne (-0.36)
se (-0.11)

Postposition 3
ne (0.06)
ka (-0.22)
tak (0.01)
NULL (-0.24)
NULL (0.25)
ke karan (0.0)

Projection onto components 0 and 2.

We consider roles mapped onto the components c0 and c2 first, shown in Figure 1. Each dot is a role, where the most relevant roles are those furthest awayfrom the main cluster. We do not annotate the main cluster for this reason in any
figure, although we do provide a list of cluster roles in the supplementary materials. It is worth noting that frequency plays a part in where roles land: a veryfrequent role can end up seeming more relevant than it is, with a weak correlation
exaggerated by its prevalence. On the other hand, SVD remains exceptionally
good at highlighting lower frequency roles that are actually relevant.
Considering c2 first, the roles k7t (location in time), k7p (location in space)and k7 (location elsewhere) as the most prominent. Given that c2 is mainly composed of mein (in) and par (on), this makes total sense: the locative postpositions
relate to the locative roles. Considering the other five components, it is striking
that neither mein nor par are relevant in any. Nor, as we will see, are the locativeroles as a group so clearly related to any other component. While this is a morestraightforward case, it also reinforces the empirical validity of this analysis.
We consider c0 next, where the role r6 (genitive) and its variant r6-k2 (complex
predicate with object) dominate, accompanied by k1 (agent), likely due to sheer
frequency. Notice that c0 is composed primarily of ka (possessive marker), along383with NULL, albeit more weakly. The relationship between ka and r6 is clear, withthe effect of relating the possessive marker to the genitive role.
r6 appears with the NULL marker in the corpus almost exclusively with the reflexive pronoun apna, which is marked in the corpus as r6, as are other possessive
pronouns. The inchoate category of “reflexive pronoun” only entered linguistics inthe late 19th century, partly due to comparative evidence from Sanskrit (Orqueda,2019). Other possessive determiners do exist in Sanskrit, although historically it
has always been more common to use the genitive case of the corresponding pronoun, which thus exhibits no agreement with its head noun. The reflexives standout in Sanskrit by being so heterogeneous in any account, including the Paninian
one, as they include an indeclinable (swayam), a noun (atman), a declinable determiner (svah) that, unlike every other possessive declinable determiner in Sanskrit, is not depronominal, and in certain cases, the verbal middle voice (known
to the traditional grammarians as atmanepada, arguably translatable as the reflexive voice). Against this backdrop, just about any choice of label for Hindi’s apna
would be a stretch, and indeed the r6 label belies the fact that apna is declinable
and, if it is de(pro)nominal at all, not transparently so.

Figure 2.

Projection onto components 1 and 3

Figure 3.

Projection onto components 4 and 5

Before we discuss the next group of components, we find it necessary to provide some background on ergativity in modern Hindi. In Hindi, the subject of
an intransitive clause and the object of a transitive clause are treated the sameway, whereas the subject of a transitive clause is marked with ne. But Hindi issplit-ergative: we only see the ergative pattern in the preterite and perfect tenses(Verbeke & De Cuypere, 2009). The origin of this construction is an open question with differing hypotheses, but crucially, it lacks any precedent in Sanskrit’s
case system itself (Anderson, 1977; Butt & King, 2004; Verbeke & De Cuypere,2009), presenting a problem for annotating Hindi using a Paninian scheme.
As for components c1 , c3 , and c4 (Figures 2 and 3), what is immediately clear384

is that there exists some relationship between NULL, ne (ergative marker), andko (to). These postpositions are almost entirely isolated to within this component
group, with the exception of NULL in c0 , which we have already addressed. Looking at Figure 2, k1 and k2 (patient) are consistently prominent throughout. Thisis rather conspicuous, as ne almost exclusively occurs with k1 to mark ergative
agents, and ko, primarily with k2 but rarely k1. NULL however occurs frequently
with both roles, and must therefore logically serve to unify them, although thisalone does not explain the linguistic relationship between ne and ko.
We believe the solution comes through a phenomenon known as Differential
Object Marking (DOM). DOM is a process of marking animacy and definitenessin a language. It is present in Hindi, and developed alongside the modern language- well after Sanskrit. In Hindi, its presence is typically expressed with ko, and its
absence, with NULL (Montaut, 2018). This latter point is crucial, as we can relate
it to ergativity: NULL marks both non-ergative agents, and non-animate or nondefinite objects. In other words, the development of ergativity and DOM in Hindi- phenomena both not present in Sanskrit - have come to intersect through NULL.
The component c4 also provides brief additional insight into role mergers connected with ko. As Butt and King (2004) note, ko is a postposition of many forms,
covering the accusative (including DOM as above), dative, and experiencer or dative passive. This is precisely what we observe in Figure 2, with k2, k4 (recipient),
and k4a (experiencer), respectively present on the negative side of c4 where ko has
a strongly negative singular value. Additionally, k7t appears in this group, because
temporal locatives in Hindi can also be marked with ko.
Finally, we examine c5 in Figure 3. The postposition ke liye (for) almost
always occurs under rt (purpose), whereas se (from) occurs primarily with k5(source). The relationship between these two is rather one-sided: se is a rather
versatile postposition, and occasionally occurs under rt, but we never observe keliye as expressing k5. se, moreover, is actually the third most frequent postpositionon the role rt, after ko. A post hoc analysis of the corpus reveals that under therole rt both postpositions are used to mark adverbials in Hindi, demonstrating a
convergence of one aspect of se with ke liye in this domain.
The reader familiar with Universal Dependencies will notice that we have notcommented on nmod (noun participle), pof (part of relation), or ccof (coordinationor conjunct), despite their prevalence. This is because in this corpus they either
serve as catch-all roles, or annotate more functional aspects of grammar. They
have less to do with the karaka scheme (Begum et al., 2008; Tandon, Chaudhry,
Bhat, & Sharma, 2016), and in turn the aim of this work.
5. Discussion
All three of our findings can be corroborated by existing knowledge among area
specialists in Hindi syntactic innovations. This suggests that spectral analysis relative to a prescriptive grammatical structure in which a descendant language is385

viewed as a noisy version of its ancestor has some promise as a means of discovering as-yet unknown changes.
The success of this work has relied on the alignment of several stars. First,
we picked a language pair with a very mature and well-documented linguistic
tradition; Sanskrit is the most analyzed language in the world after only modern
English. Second, the Hindi/Urdu Treebank Project methodically and, to our great
relief, electronically annotated enough Hindi data that SVD could reveal certain
anomalies. But most importantly, they did so in accordance with certain aspectsof the ancient Paninian tradition that made the application of our proposed method
straightforward. There are other approaches to Hindi grammatical analysis. Current analyses of modern Hindi are rooted in Prakritic grammars dating back to1698 at the earliest (Bhatia, 1987), but these grammars come from a colonial tradition based in the Western classical paradigms of Greek and Latin.
It is an open question as to whether the method proposed here can be extended
to pairs of cognate languages other than direct ancestor-descendant pairs. There
are a variety of more sophisticated methods for spectral analysis than SVD, thealgorithms for which were worked out in the 1960s. Some of them would perhaps reveal anomalies between more diverse pairs, or more subtle anomalies withancestors and descendants. Having located an anomaly, its interpretation furthermore remains non-trivial. We relied on the assistance of Professor Miriam Butt, an
area specialist, who was immediately able to interpret what the anomalies were.
The present authors would not have been able to do this themselves, even withconsiderable effort.
It should also be noted that we did not uncover every change from Sanskrit
to Hindi, nor even every noteworthy change by conservative estimates, such as
the development of the dative passive in Hindi. All of these shortcomings remain
areas for further investigation.
Acknowledgements
Our many thanks to Professors Miriam Butt and Dipti Sharma for their input andfeedback on the development of this work, and the reviewers for their comments.
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387

EMERGENCE OF NEGATION IN A TURKISH HOMESIGNSYSTEM: INSIGHTS FROM THE FAMILY CONTEXTUĞUR KAN*1,3, , KADİR GÖKGÖZ2, BEYZA SÜMER3,5, ELVAN TAMYÜREK2,and ASLI ÖZYÜREK3,4*Corresponding Author: ugur.kan@boun.edu.tr
1Cognitive Science, Boğaziçi University, İstanbul, Turkey
2Linguistics, Boğaziçi University, İstanbul, Turkey
3Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
4Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen,the Netherlands
5Amsterdam Center for Language and Communication, University of Amsterdam, theNetherlands

Deaf children without access to conventional sign language develop gesturesystems called homesigns to communicate with their immediate family. Despite
the lack of a language model, these systems exhibit many properties of natural
languages, including basic syntax [1], stable lexicon [2], complex sentences, andnoun phrases [3-4]. One of the challenges in such language creation situations isunderstanding whether and how the gestures of the family members play a role inthe creation of homesign systems. Despite acknowledging the complexity of thegestures in the input, previous studies did not conduct a systematic or detailed
analysis of gestures used by different caregivers in the family and their potential
impact on homesigners’ gestures [5-6]. Thus to what extent gestures used byfamily members surrounding a homesigner look analogous to homesigners’
gestures is understudied. Here, our study focused on a Turkish homesigner childand his family to examine the role of the language model in the emergence ofnegation, a universal component of human language.
Previously, Franklin et al. [7] analyzed the negation patterns of an American
homesigning child, David, on eight play sessions beginning from age 2;10 to 3;11
(years; months). They found that David predominantly used side-to-side
headshakes as the main negation marker (84% of 327 negative sentences).
Researchers assumed that he co-opted this negative marker from the surrounding
hearing culture because his mother and siblings have never been exposed to sign
language. Interestingly, the researchers concluded that his negated expressions
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

388

reflected the ability of the child to re-invent negation marker without an available
language model to guide him. However, in their study, gestures used by different
family members around the child were not analyzed to see whether they set up aconsistent negation model for him. Thus, the effect of the complexity of the input
from language models in the homesign system remains partially unanswered.
To address this issue, we investigate whether the hearing family members of
a Turkish homesigner child display a rich and consistent negation model with
their gestures, and the child benefits from this model. We observed natural playsessions (182 minutes) of a Turkish deaf child who had severe hearing loss
playing with his hearing mother and 13-years-old sister during six different time
points beginning from the child’s age 5;11 until 6;3. Since there is nocomprehensive study on the co-speech gestures of Turkish speakers, the coding
template consisted of the negation markers of the Turkish Sign Language [8-9].
The child’s and family members’ gestures were coded for the presence and formsof negative markers only and compared to each other in terms of form andfrequency. Table 1 (see supplementary materials) presents preliminary results ofnegation forms used by each family member. The child produced side-to-side
headshakes as the most frequent marker (39% of all his negative sentences
produced). Nevertheless, headshakes were not as common among his mother andsister’s negation forms (4,5% and 10%, respectively). Contrary to the child, hismother produced a backward head tilt as the most frequent marker (66% of all her
negative sentences), while his sister used a 1-handshape variant (60% of all her
negative sentences). They both seemed to present consistent negative strategies
as a model and differ from each other and the child. Although their presence inthe play sessions varied, the mother and the sister provided significant input (44and 30 negation gestures in 85 and 97 min. they interacted with the child insessions, respectively). In comparison, the child produced 102 negation gesturesin 182 minutes.
These preliminary results suggest that homesigners can receive richenvironmental input [10-12]. However, this input might not be consistent between
members of the family. The fact that we found a variation among family membersin the types of negation markers is in line with recent findings showing that
languages of small communities exhibit more variation than in larger ones [13].
These results also reinforce the previous observation that homesigners are notgetting their system from the input [14]: 39% of the child's negative sentences use
side-to-side headshake, which does not appear to be the most frequent strategy ofeither his mother or sister. Finally, the child’s most frequent marker supports
previous claims that some properties of language are resilient and are less inclined
to be affected by input during the creation [1], even when the input can be rich.
Rather, the resemblance of reliance on side-to-side headshake between the child389

and David also suggests a universally emerging negation strategy. Overall, thestudy aims to contribute to the scarce literature on how one of the main
characteristics of human language, negation, can emerge in a homesign
environment where hearing and deaf interlocutors actively participate. Further
functional analyses of the negation markers in the gesture utterances of theinteractants will be made.
References[1] Goldin-Meadow, S. (2003). The resilience of language: What gesture creation indeaf children can tell us about how all children learn language. Psychology Press.
Berlin, Boston: De Gruyter Mouton.
[2] Richie, R., Yang, C., & Coppola, M. (2014). Modeling the emergence of lexicons
in homesign systems. Topics in cognitive science, 6(1), 183-195.
[3] Goldin-Meadow, S., & Mylander, C. (1998). Spontaneous sign systems created bydeaf children in two cultures. Nature, 391(6664), 279-281.
[4] Hunsicker, D., & Goldin-Meadow, S. (2012). Hierarchical structure in a selfcreated communication system: Building nominal constituents in homesign.
Language, 88(4), 732.
[5] Goldin-Meadow, S., Mylander, C., de Villiers, J., Bates, E., & Volterra, V. (1984).
Gestural communication in deaf children: The effects and noneffects of parental
input on early language development. Monographs of the society for research inchild development, 1-151.
[6] Flaherty, M., Hunsicker, D., & Goldin-Meadow, S. (2021). Structural biases thatchildren bring to language learning: A cross-cultural look at gestural input tohomesign. Cognition, b211, 104608.
[7] Franklin, A., Giannakidou, A., & Goldin-Meadow, S. (2011). Negation, questions,and structure building in a homesign system. Cognition, 118(3), 398-416.
[8] Zeshan, U. (2006). Negative and interrogative structures in Turkish Sign
Language (TID). Interrogative and negative constructions in sign languages, 128164.
[9] Gökgöz, K. (2011). Negation in Turkish Sign Language: The syntax of nonmanual
markers. Sign Language & Linguistics, 14(1), 49-75.
[10] Haviland, J. (2020). Zinacantec family homesign (or “Z”). In O. Le Guen, J. Safar& M. Coppola (Ed.), Emerging Sign Languages of the Americas (pp. 393-400).
[11] Mesh, K., & Hou, L. (2018). Negation in San Juan Quiahije Chatino Sign
Language: The integration and adaptation of conventional gestures. Gesture,17(3), 330-374.
[12] Reed, L. (2021). Sign networks: Nucleated network sign languages and rural
homesign in Papua New Guinea. Language in Society, 1-35.
[13] Raviv, L., Meyer, A., & Lev‐Ari, S. (2020). The role of social network structurein the emergence of linguistic structure. Cognitive Science, 44(8), e12876.
[14] Carrigan, E., & Coppola, M. (2012). Mothers do not drive structure in adulthomesign systems: Evidence from comprehension. In Proceedings of the Annual
Meeting of the Cognitive Science Society (Vol. 34, No. 34).

390

EVOLVING LINGUISTIC DIVERGENCE
IN SOCIO-POLITICAL POLARITIESAndres Karjus*1 and Christine Cuskley2* Corresponding Author: akarjus@tlu.ee

1 ERA Chair for Cultural Data Analytics, Tallinn University, Tallinn, Estonia

2 School of English Literature, Language and Linguistics, Newcastle University, Newcastle upon

Tyne, United Kingdom

1. Introduction
All human languages change over time, as linguistic variants are discarded, innovated, and their meanings shifted. Most change stems from variation, be it geographical, cultural or social. Here we examine a division and source of variationintersecting these categories: political polarization. Particularly in the last decade,social and political scientists have been concerned with the causes and alarming
social effects of increasing media polarization and partisan segregation particularly in the US (Brown & Enos, 2021). However, political polarization may also
have an effect on the dynamics of language evolution and change, forming the basis for signals of in-group and out-group status (?) and potentially leading to more
dramatic language speciation at deeper timescales (?; Altmann, Pierrehumbert, &Motter, 2011). Using existing political categorizations, we collect and use an extensive corpus of social media posts to quantify divergence in American English
along the left-right axis, both in topics of conversation and lexical semantics.
Twitter data has been shown to be useful for mapping lexical innovation andvariation (Grieve, Nini, & Guo, 2018; Bhat & Klein, 2020) and analyzing polarization effects (Chen, Salloum, Gronow, Ylä-Anttila, & Kivelä, 2021). Studies of
linguistic divergence between political divides have often focused on politicians
and activists (Adamic & Glance, 2005; Li, Schloss, & Follmer, 2017; Gentzkow,
Shapiro, & Taddy, 2019). Those on the general population have grouped subjects
based on self-reporting (Halpern & Rodrıguez, 2018) and social media activity(Sylwester & Purver, 2015; Demszky et al., 2019; KhudaBukhsh, Sarkar, Kamlet,& Mitchell, 2020). We focus on everyday interactions, not just political communication, and scale up the latter sampling approach, mining and grouping a totalof half a billion follower listings across 72 large US news media accounts. We
use the Allsides Media Bias Rankings (Allsides, 2021, v4) to delineate likely left
and likely right biased news outlets (Allsides is not perfectly unbiased itself, but
serves as a useful starting point). Having access to the entire follower bases of theThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

391

news accounts allows us to carry out full set operations, and find users who only
follow one side but not the other. Further limiting these to active and identifiably
US-located users (a major bottleneck) leaves 6202 likely “left” and 4783 likely“right” aligned accounts. We mined their tweets between February-September
2021, yielding a corpus of 1.5 million tweets (750,814 and 732,521, respectively).

Figure 1. Divided they (micro)blog. UMAP dimension reduction of an LDA topic model of the tweet
corpus, with illustrating keywords. Each dot is an account, accounts with similar content are arranged
together (blue = “left”, red = “right”).

2. Results and Conclusions
We fit a topic model to the tweet corpus, visualized in Figure 1, showing how thetwo sides differ in their daily conversation topics. Word frequencies quantify thisfurther, revealing magnitudes of difference in the usage of some terms. Phrases
like President Trump and communist are used about 16x more by the “right” in
2021, who also prefer hand-shaped emoji in contrast to some face-shaped emoji
used predominantly by the “left”. We also apply word embeddings to detect semantic change and identify potentially competing variants. While users on eitherside still of course speak the same language, a number of interesting divergences
emerge, with differences in the meaning of some emoji and certain politicallycharged terms (e.g. woke referring to either “wokeness” or just waking up). Ongoing research aims to validate these findings with a crowd-sourced annotation
task (Schlechtweg, Walde, & Eckmann, 2018), and compare with recent largescale dialectal work (Grieve et al., 2018) to tease apart sources of variation.
By building on and scaling up previous methodologies of sampling utterancesby speaker political alignment, our results show that existing political divisions
are already being reflected in language use. Mapping this ongoing lexical andsemantic evolution in American English provides a model applicable to studying
similar phenomena in any language where sufficient data can be acquired. Weemphasize the potential for studies of this kind to shed light on the interplay ofinteracting evolutionary dynamics at socio-cultural and linguistic levels.

392

References
Adamic, L. A., & Glance, N. (2005). The political blogosphere and the 2004 U.S.
election: Divided they blog. In Proceedings of the 3rd international workshop on Link discovery (pp. 36–43). Association for Computing Machinery.
Allsides. (2021). Allsides Media Bias Ratings, version 4. Available from:https://www.allsides.com/media-bias/media-bias-ratings.
Altmann, E. G., Pierrehumbert, J. B., & Motter, A. E. (2011). Niche as a determinant of word fate in online groups. PLOS ONE, 6(5), 1–12.
Bhat, P., & Klein, O. (2020). Covert Hate Speech: White Nationalists and Dog
Whistle Communication on Twitter. In G. Bouvier & J. E. Rosenbaum(Eds.), Twitter, the Public Sphere, and the Chaos of Online Deliberation(pp. 151–172). Springer International Publishing.
Brown, J. R., & Enos, R. D. (2021). The measurement of partisan sorting for 180
million voters. Nature Human Behaviour, 5(8), 998–1008.
Chen, T. H. Y., Salloum, A., Gronow, A., Ylä-Anttila, T., & Kivelä, M. (2021).
Polarization of climate politics results from partisan sorting: Evidence from
Finnish Twittersphere. Global Environmental Change, 71, 102348.
Demszky, D., Garg, N., Voigt, R., Zou, J., Gentzkow, M., Shapiro, J., & Jurafsky,
D. (2019). Analyzing polarization in social media: Method and application
to tweets on 21 mass shootings. arXiv: 1904.01596.
Gentzkow, M., Shapiro, J. M., & Taddy, M. (2019). Measuring Group Differencesin High-Dimensional Choices: Method and Application to Congressional
Speech. Econometrica, 87(4), 1307–1340.
Grieve, J., Nini, A., & Guo, D. (2018). Mapping lexical innovation on Americansocial media. Journal of English Linguistics, 46(4), 293–319.
Halpern, D. J., & Rodrıguez, P. L. (2018). Partisan Representations: Partisan Differences in Semantic Representations and their Role in Attitude Judgments.
Proceedings of CogSci 2018, 6.
KhudaBukhsh, A. R., Sarkar, R., Kamlet, M. S., & Mitchell, T. M. (2020). We
don’t speak the same language: Interpreting polarization through machinetranslation. arXiv: 2010.02339.
Li, P., Schloss, B., & Follmer, D. J. (2017). Speaking two “Languages” in America: A semantic space analysis of how presidential candidates and their supporters represent abstract political concepts differently. Behavior Research
Methods, 49(5), 1668–1685.
Schlechtweg, D., Walde, S. Schulte im, & Eckmann, S. (2018). Diachronic usage
relatedness (DURel): A framework for the annotation of lexical semanticchange. In Proceedings of NAACL 2018 (pp. 169–174).
Sylwester, K., & Purver, M. (2015). Twitter Language Use Reflects Psychological Differences between Democrats and Republicans. PLOS ONE, 10(9),e0137422.

393

BEHAVIORAL EVIDENCE FOR REPRESENTATIONS OF THE
OTHER’S ACTION DURING A JOINT TASK IN RATSNORIKO KATSU*1, 2, and KAZUO OKANOYA1,3*Corresponding Author: katsuno0414@gmail.com
1

Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
2 Graduate School of Human Sciences, Osaka University, Osaka, Japan
3 Teikyo University Advanced Comprehensive Research Organization, Tokyo, Japan

1. Introduction
Shared intentionality is one of the essential basis for linguistic communication inhumans (Tomasello, Carpenter, Call, Behne, & Moll, 2005). It refers to themotivation to share a psychological state and collaborate with others for sharedgoals and actions. Various cooperative behaviors have been reported in nonhumanspecies including rats (Rattus norvegicus). Nevertheless, it is unclear whether
such behaviors are established based on each individual’s coordination or
independent contributions. Therefore, we used the Joint Simon task to examine
whether the rats have representation for other’s action to the shared goal.
The joint Simon task is based on the Simon task. In an auditory
discrimination task in which correct response is a left lever to stimulus A, and aright lever to stimulus B for example, the response is faster and more accurate
when the A is presented from the left than right side. This is because the stimulus
and the response are incompatible in the latter condition (Simon effect).
Generally, this effect disappears when the task is divided into left and right (halftask) but reappear when this task is performed by two people (joint Simon task).
This joint Simon task is used to examine the shared representation of the actionof a partner during joint activity in humans (Sebanz, Knoblich, & Prinz, 2003).
A previous study demonstrated the joint Simon effect in common marmosets
between familiar pairs (Miss & Burkart, 2018). However, the degree ofrepresentation sharing may differ according to familiarity between partners, as inhumans. Therefore, we also compared the effects based on familiarity with theThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

394

partner; cagemate and non-cagemate pairs. Subjects were also assigned to mixed
and single strain cagemate conditions to manipulate familiarity between strains.
2. Methods
The subjects were eight male rats (four Long-Evans and four Wistar strain),housed in pairs. We used an operant box that could be divided into two chambers
using a wire-mesh wall. Rats were trained individually on a two-choice auditory
discrimination task. All subjects learned to respond by pressing the left lever at 2
kHz, and the right lever at 4 kHz. Unlike in the training, the stimuli were presented
from either the left or right in the tests. The subjects experienced the four task
conditions; a single condition in which one individual performed a full Simontask, a control condition in which one individual performed a half task, a joint
condition in which two individuals shared the left and right half tasks, and a paired
control condition in which two individuals were in the operant box but only one
performed the half task. The subjects were also tested for joint and paired control
tasks both with a cagemate and a non-cagemate. The compatibility effect was used
for the index of the Simon effect, calculated by subtracting the correct rate forincompatible from compatible trials. The effect of task condition and familiarity
on the compatibility effect was examined by linear mixed models (LMMs).
3. Results and Discussion
The subjects showed larger compatibility effects in the single and joint conditions
than in control or paired control conditions. Overall, the rats showed both Simonand joint Simon effects. The difference between the paired control and joint
conditions indicates that this effect was caused by sharing the task, not merely
due to existence of the partner. The effects were significantly larger in cagemate
than in non-cagemate pairs. However, no significant differences were found basedon whether the partner was of the same or different strain. Therefore, familiarity
between partners only partially explain the differences in compatibility effects. In
summary, action co-representation for partner in joint tasks was shown in rats,
which is highly social species. Although cooperative tendency differed based onfamiliarity of the strain in the previous study, it is unclear whether social factors
modulated the joint Simon effect in this study. Therefore, further studies focusing
on individual differences in the effect, such as the frequency of paying attention
to the partner, would be informative.
Acknowledgements
This study was supported by JSPS KAKENHI (No. 19K20643 and No.
21J40080) to NK, and MEXT/JSPS KAKENHI (#4903, No. JP17H06380) to KO.

395

References
Miss, F. M., & Burkart, J. M. (2018). Corepresentation During Joint Action in
Marmoset Monkeys (Callithrix jacchus). Psychological Science, 29,984-995. doi:10.1177/0956797618772046
Sebanz, N., Knoblich, G., & Prinz, W. (2003). Representing others' actions: justlike one's own? Cognition, 88, B11-B21. doi:10.1016/s00100277(03)00043-x
Tomasello, M., Carpenter, M., Call, J., Behne, T., & Moll, H. (2005).
Understanding and sharing intentions: the origins of cultural cognition.
Behavioral and Brain Sciences, 28, 675-691; discussion 691-735.
doi:10.1017/S0140525X05000129

396

A CRITICAL POPULATION THRESHOLD FOR CONTACT-INDUCEDSIMPLIFICATIONHENRI KAUHANEN*1* Corresponding Author: henri.kauhanen@uni-konstanz.de

1 Department of Linguistics, University of Konstanz, Konstanz, Germany

Learning biases have long been theorized to play a causal role in the cultural evolution of linguistic systems. In particular, existing literature suggests thatthe difficulty encountered by second-language (L2) learners in acquiring complex
linguistic features may contribute to the loss of those features from the target language in situations of language contact (Bentz & Winter, 2013; Berdicevskis &Semenuks, 2022; Lupyan & Dale, 2010; Sinnemäki & Di Garbo, 2018; Trudgill,
2011; Walkden & Breitbarth, 2019; Weerman, 1993; also see Jansson, Parkvall,& Strimling, 2015 on modelling creolization). Against the backdrop of this bodyof research, it is reasonable to expect that the population fraction of L2 learners
may act as a bifurcation parameter: if sufficiently many L2 learners are present
in a speech community, the loss of L2-difficult features may be permanent. Without an explicit model combining population and learning dynamics, however, it is
impossible to say where the critical value of such a putative bifurcation parameter
might lie.
We propose such a model by extending the variational learner (Yang, 2002)
to cover L2 as well as L1 acquisition. For L2 (but not L1) learners, the extended
model includes a learning bias that works against the successful (native-like) acquisition of the L2-difficult variant. The asymptotic dynamics of this extended
learning model can be studied just like those of the ordinary linear reward–penalty
learning scheme (Bush & Mosteller, 1955) that underlies the variational learner.
In particular, we show that an L2 learner’s expected probability of employing an
L2-difficult grammar G1 over its easier-to-acquire competitor G2 tends to a definite value as learning iteration tends to infinity.
Taking the usual infinite learner limit (cf. Yang, 2000) then yields a deterministic dynamical system that describes the evolution of a mixed population of L1and L2 speakers. This system has three parameters: σ, the fraction of L2 speakers
in the population; D, the learning-theoretic strength of the L2-difficulty of G1 ;and α, the fitness ratio (Kauhanen & Walkden, 2018) of the two grammars.
We show analytically that this system always has exactly one stable equilibrium. The system’s dynamics are, however, separated into two phases: in one
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phase, the stable equilibrium satisfies p > 0 and q > 0, where p and q stand forthe probability of the L2-difficult grammar G1 in the L1 and L2 populations, respectively. In other words, the L2-difficult grammar is retained in each population
at some non-zero (and possibly high) frequency. In the second phase, however,
the attractor is the origin (p, q) = (0, 0), meaning that the L2-difficult grammar
is wiped out from both populations, including the L1 speaker population whichitself is not subject to the learning bias (but feels its effects through interactions
with the L2 population). This bifurcation occurs as σ crosses the critical value
σcrit =(α − 1)(D + 1)
,
αD

(1)

that is, fractions of L2 speakers σ > σcrit exhibit simplification dynamics (Fig. 1).
To provide some empirical support for the model, we estimate the parameters
σ and α from demographic and corpus data, and provide reasonable orders ofmagnitude for the learning bias D, for two historical developments: the loss ofverbal inflection in Afrikaans (Trudgill, 2011) and the partial loss of null subjects
in Afro-Peruvian Spanish (Sessarego & Gutiérrez-Rexach, 2018). Empirically, thesimplification process in Afrikaans went to completion, whereas in Afro-Peruvian
Spanish null subjects retain a partial status. These facts are predicted by the model,
in the sense that σ > σcrit in the former case but not in the latter.
α = 1.5

α=2

0.9

p (L1 population)

0.5
0.3
0.1
0.9
0.7
0.5
0.3
0.1

1.00
0.75
0.50

q (L2 population)

σ (fraction of L2 speakers)

0.7

1.0

3.0

5.0

7.0

9.0

1.0

3.0

5.0

7.0

0.25
0.00

9.0

D (L2−difficulty of G 1)

Figure 1. Stable equilibrium (p, q) of the mixed speech community (top row: probability of G1 in
L1 speakers; bottom row: L2 speakers). Full simplification occurs above the bifurcation threshold σcrit(equation 1), depicted as the dashed white curve.

Acknowledgements
Funded by the European Research Council as part of project STARFISH (851423).

398

References
Bentz, C., & Winter, B. (2013). Languages with more second language learners
tend to lose nominal case. Language Dynamics and Change, 3, 1–27.
Berdicevskis, A., & Semenuks, A. (2022). Imperfect language learning reduces morphological overspecification: experimental evidence. PLoS ONE,17(1), e0262876.
Bush, R. R., & Mosteller, F. (1955). Stochastic models for learning. New York,
NY: Wiley.
Jansson, F., Parkvall, M., & Strimling, P. (2015). Modeling the evolution of
creoles. Language Dynamics and Change, 5(1), 1–51.
Kauhanen, H., & Walkden, G. (2018). Deriving the constant rate effect. Natural
Language & Linguistic Theory, 36(2), 483–521.
Lupyan, G., & Dale, R. (2010). Language structure is partly determined by social
structure. PLoS ONE, 5(1), e8559.
Sessarego, S., & Gutiérrez-Rexach, J. (2018). Afro-Hispanic contact varieties atthe syntax/pragmatics interface: pro-drop phenomena in Chinchano Spanish. In J. King & S. Sessarego (Eds.), Language variation and contactinduced change: Spanish across space and time. Amsterdam: Benjamins.
Sinnemäki, K., & Di Garbo, F. (2018). Language structures may adapt to thesociolinguistic environment, but it matters what and how you count: a typological study of verbal and nominal complexity. Frontiers in Psychology,
9, 1141.
Trudgill, P. (2011). Sociolinguistic typology: social determinants of linguistic
complexity. Oxford: Oxford University Press.
Walkden, G., & Breitbarth, A. (2019). Complexity as L2-difficulty: implications
for syntactic change. Theoretical Linguistics, 45(3–4), 183–209.
Weerman, F. (1993). The diachronic consequences of first and second language
acquisition: the change from OV to VO. Linguistics, 31, 903–931.
Yang, C. D. (2000). Internal and external forces in language change. Language
Variation and Change, 12, 231–250.
Yang, C. D. (2002). Knowledge and learning in natural language. Oxford:
Oxford University Press.

399

POTS VERSUS CLOCKS: INTEGRATING ARCHAEOLOGICALEVIDENCE WITH BAYESIAN ESTIMATES OF DIVERGENCETIMES IN OCEANIC LANGUAGESBENEDICT KING*1, MARY WALWORTH1, AYMERIC HERMANN1,2, SIMON J.
GREENHILL1,3 and RUSSELL D. GRAY1*
Corresponding Author: benedict_king@eva.mpg.de
Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary
Anthropology, Leipzig, Germany
2
CNRS, Paris, France
3
Faculty of Science, University of Auckland, New Zealand

1

Bayesian phylogenetics provides a quantitative method with which to estimate
the timing of language divergences (Hoffmann et al. 2021). In such analyses, a“relaxed clock” of lexical change is used to date nodes in the language tree. A
relaxed clock assumes an approximately predictable rate of lexical innovation,
while allowing the rate to vary across the branches of the tree. In order to date aphylogenetic tree, the clock needs to be calibrated by integrating priorinformation to constrain the age of particular nodes in the tree (Maurits et al.
2020). The expansion of Austronesian speakers across Oceania is a topic ofresearch in which Bayesian methods should provide valuable insight. Since thepublication of the Austronesian phylogenetic tree of Gray et al. (2009), theAustronesian Basic Vocabulary Database (Greenhill et al. 2008) has undergone
significant expansion. New data, combined with improvements in analysis
methods and new archaeological dates should provide further insights into thetiming and expansion of Austronesians into Oceania.
To obtain a time-scaled tree of Oceanic languages, we analysed lexical datain the form of cognate sets in the Bayesian phylogenetic software BEAST2(Bouckaert et al. 2018). The tree was time-scaled using a relaxed clock andcalibration distributions on the divergence of the Oceanic, Polynesian andEastern Polynesian languages. Despite the relatively informative priorprobability distributions on divergence time, our analysis produced an estimate
for the divergence of Oceanic languages that strongly conflicted with thearchaeological record (Fig. 1), a result that holds for analyses using a variety ofdifferent models of tree shape. This ancient date estimated for Oceanic
languages evokes the “Rocks versus Clocks” debate in evolutionary biology, inwhich molecular clocks frequently produce ancient divergence dates that can
predate the earliest fossil record of a group by 10s or 100s of millions of years(Benton 1999, Puttick et al. 2016). Surprisingly, these older dates for the
Oceanic tree are found despite no apparent signal in the lexical data supporting
such a divergence.

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We investigated the ability of relaxed clock analyses to recover dates within
an age range consistent with the archaeological data using a series ofsimulations. Data was simulated on a tree taken from an analysis in which thedivergence time for Oceanic was enforced to be no more than 3300 years.
Although the relaxed clock performed reasonably when simulations were
performed according to randomised branch rates, we found that when data were
simulated using the observed branch rates, the age of Oceanic was consistently
overestimated, as were other nodes in the phylogeny. The clock model showedsome degree of statistical inconsistency, in that larger simulated datasets
returned more inaccurate age estimates. We also found that the relaxed clock
was unable to correctly estimate the rates of lexical innovation on branches,
consistently underestimating rates on branches simulated with a rapid rate. The
relatively old date estimated for Oceanic may therefore be a statistical artefact
resulting from a high rate of lexical innovation at the origin of the Central
Pacific subgroup of Oceanic.

Figure 1: Comparison of prior age calibrations (based on archaeological data) with ages
estimated from the data (posterior). For Oceanic as a whole, the estimated age strongly conflicts withthe prior.

The inability of these analyses to recover the known age of Oceanic in asimulation scenario calls into question the accuracy of ancient dates estimatedfrom relaxed clocks both in linguistics and evolutionary biology. In both fields,
groups chosen for study might often be expected to show rapid rates of evolution(lexical or molecular) at the base of the tree (Beaulieu, 2016), for example due tomigration to new territories or recovery from mass extinctions. Our study suggeststhat relaxed clocks overestimate the age of linguistic groups in at least somesituations.

401

References
Beaulieu, J. M., O'Meara, B. C., Crane, P., & Donoghue, M. J. (2015).
Heterogeneous rates of molecular evolution and diversification could explain
the Triassic age estimate for angiosperms. Systematic biology, 64(5), 869-878.
Benton, M. J. (1999). Early origins of modern birds and mammals: molecules vs.
morphology. BioEssays, 21(12), 1043–1051.
Bouckaert, R., Vaughan, T. G., Barido-Sottani, J., Duchêne, S., Fourment, M.,
Gavryushkina, A., ... & Drummond, A. J. (2019). BEAST 2.5: An advanced
software platform for Bayesian evolutionary analysis. PLoS computational
biology, 15(4), e1006650.
Gray, R. D., Drummond, A. J., & Greenhill, S. J. (2009). Language phylogenies
reveal expansion pulses and pauses in Pacific settlement. Science, 323(5913),
479–483.
Greenhill, S. J., Blust, R., & Gray, R. D. (2008). The Austronesian basic
vocabulary database: from bioinformatics to lexomics. Evolutionary
Bioinformatics, 4, EBO-S893.
Hoffmann, K., Bouckaert, R., Greenhill, S. J., & Kühnert, D. (2021). Bayesian
phylogenetic analysis of linguistic data using BEAST. Journal of Language
Evolution, 6(2), 119–135.
Maurits, L., de Heer, M., Honkola, T., Dunn, M., & Vesakoski, O. (2020). Bestpractices in justifying calibrations for dating language families. Journal of
Language Evolution, 5(1), 17–38.
Puttick, M. N., Thomas, G. H., & Benton, M. J. (2016). Dating placentalia:
Morphological clocks fail to close the molecular fossil gap. Evolution, 70(4),873–886.

402

COMPOSITIONALITY ARISES FROM ITERATED LEARNINGDESPITE A PREFERENCE FOR HOLISTIC SIGNALS: AN
EXPERIMENTAL MODEL OF SIGN LANGUAGE EMERGENCE
SIMON KIRBY*1 , YASAMIN MOTAMEDI1 , LUCIE WOLTERS1 , and KENNY SMITH1* Corresponding Author: simon.kirby@ed.ac.uk

1 Centre for Language Evolution, University of Edinburgh, UK

Background Cultural evolution has been proposed to account for the origins ofcompositionality (Smith, 2018), by which we mean the use of a segmented sequence of signal elements, each of which encodes part of the meaning of thewhole signal. Often in evolutionary models and experiments, the starting state ofa language is assumed to be holistic, lacking sequential segmentation of signals
into meaningful sub-parts. Ultimately, a cognitive bias is assumed, which leads
to the replacement of a holistic starting state with a compositional one. But if abias against holistic representations exists, why would they ever arise in the firstplace?
We use the example of the gradual emergence of segmented manner/path representations in Nicaraguan Sign Language (Senghas, Kita, & Ozyürek, 2004) toinvestigate whether this kind of segmented compositionality can arise through iterated learning even if there is an initial preference in favour of non-segmented representations. We use a series of large-scale experiments (N=1700) in which participants choose between two gestures describing events, specifically videos of balls
moving along different paths and in different manners, (Schouwstra, Abramova,
Motamedi, Smith, & Kirby, 2014). The gestures either describe the video by encoding manner and path simultaneously, or by segmenting them (e.g. gesturing
the path, then the manner).
Experiments In experiment 1, participants (N=100) were presented with 16
videos of balls moving with four different paths (e.g., down a slope, around a circle) and four different manners (e.g., bouncing, sliding). Each video was accompanied by two gesture videos representing the movement iconically, with mannerand path either segmented or simultaneous. For each of the 16 videos, participantschose which gesture video best conveyed the event. The results show a strong preference for simultaneous gesture (β = 2.30, SE = 0.34, z = 6.83, p < 0.001),
replicating findings from previous silent gesture improvisation experiments (Clay,
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

403

Pople, Hood, & Kita, 2014), and co-speech gesture (Senghas et al., 2004).
Next we tested whether this clear preference for simultaneous gesture is replicated in learning. In experiment 2, a new set of participants (N=100) went
through the same procedure as before except that they were first exposed to 12
training trials in which a sample of the ball videos were presented alongsidea gesture video. Half of the training trials appeared with segmented gestures,
half with simultaneous ones. In contrast to the preferences without training, we
now see a preference for segmented gestures (β = −0.82, SE = 0.28, z =−2.97, p = 0.003), and a significant difference between experiment 1 and 2,(β = −2.83, SE = 0.29, z = −9.60, p < 0.001).
Although this difference between preferences with and without exposure is
striking, the preference for segmented gestures after learning is not as strong as
the preference for simultaneous gestures without learning. In NSL, the use of
segmentation increases over cohorts, suggesting accumulation through cultural
evolution. In experiment 3, we ran 100 iterated learning chains using the learning paradigm from experiment 2 in which the participants’ choices at generation gis the training data for participants at generation g + 1, using the data from experiment 2 as the first generation and continuing for a further 7 generations (N=700).
We find that the preference for segmented gestures is amplified over generations(β = −0.10, SE = 0.05, z = −2.36, p = 0.02).
These results are compelling evidence that iterated learning can lead to languages that reflect biases that are the opposite of preferences of participants prior
to learning. However, to fully model the sign language emergence context, wheresegmented forms emerge following an initial preference for holistic structures, inexperiment 4 (N=800) we replicated experiment 3 using the output of the baseline preference from experiment 1 as seed rather than the 50:50 exposure of experiment 2. Here too we see a significant accumulation of segmented gestures overgenerations (β = −0.17, SE = 0.05, z = −3.68, p < 0.001).
Discussion We can treat language evolution as a Markov process and use all thedata from experiments 2, 3 and 4 to estimate the full transition matrix from language to language. From this we derive the stationary distribution for languages.
This shows a preponderance of segmented languages, but with some probability
mass on simultaneous ones, suggesting that although cultural evolution will tend
to lead languages towards segmented compositionality, some variability can nevertheless be expected across sign languages.
These results demonstrate that cultural evolution through iterated learning canlead to outcomes that appear to run counter to preferences prior to iterated learning. We use this to introduce a crucial distinction between naturalness biases
which affect signals that are not part of a set of conventions, and systematicity
biases that arise when signals are learned as part of a larger set.

404

References
Clay, Z., Pople, S., Hood, B., & Kita, S. (2014). Young children make their gestural communication systems more language-like: Segmentation and linearization of semantic elements in motion events. Psychological science,25(8), 1518–1525.
Schouwstra, M., Abramova, K., Motamedi, Y., Smith, K., & Kirby, S. (2014).
From silent gesture to artificial sign languages. In Workshop on evolutionof signals, speech and signs, Evolang X (p. 22).
Senghas, A., Kita, S., & Ozyürek, A. (2004). Children creating core properties oflanguage: evidence from an emerging sign language in Nicaragua. Science(New York, N.Y.), 305(5691), 1779–82.
Smith, K. (2018). The cognitive prerequisites for language: insights from iterated
learning. Current opinion in behavioral sciences, 21, 154–160.

405

THE RECURSIONTHAT THOSE WHO STUDY LINGUISTICS SEEKIS EVERYWHERE IF YOU KNOW WHERE TO LOOK FOR IT
Robert Kluender*1, Emily Davis*1, and Marc Rolnik*

1

Corresponding Author: e4davis@ucsd.edu
Department of Linguistics, UC San Diego, La Jolla, CA, USA

In this paper we build on previous claims that storytelling, music, and dance/performative
gesture comprise intrinsic parts of human ritual, occurring ubiquitously across cultures andacross time (Staal, 1979, 1980, 1984; Merker, 1999, 2009; Lewis, 2018). Ritual is uniquein its emphasis on structural form and proper execution over semantic/functional content.
Staal claimed that Vedic ritual in particular was recursive, based on its use of centerembedded symmetries. Here we trace the ubiquity of such structural principles throughout
history at every level of analysis in literary (frame narrative, ring composition, chiasma)
and musical composition (retrograde, sonata/minuet/rondo, arch form, twelve-tone) toargue that these persist as pervasive remnants of ritual culture. We then present the resultsof a corpus study showing that center embedding is more frequent in head-final languages,
and raise the possibility that even in head-initial languages, center embedding may be morecommon than assumed. Taken together, the evidence we present suggests that center
embedding is not only alive and well in human language and culture today, but that it has
also likely been prevalent throughout the history of our species.

1. Introduction
Twenty years ago, Hauser, Chomsky & Fitch (2002) threw down the gauntlet forthe study of language evolution with the strong claim that recursion (Fitch &Martins 2014:98, “the same hierarchical structure is repeated at multiple levels of
the hierarchy”) is the core property and sine qua non of the language faculty, andthat it cannot have been an adaptive property that was selected for (2002:1572-3).
Yet the concession is also made that recursion in language could have been
exapted from other, non-communicative domains of animal cognition, such as
navigation, number quantification, or social relationships (2002:1571,1578).
Here we take a slightly different tack in the quest for recursion by expanding
on proposals by Staal (1979, 1980, 1984) highlighting the similarity of recursive
properties in Vedic ritual and in language. Of particular note is Staal’s claim thatthe center embedding evident in ritual may have served as a template for recursion
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406

in human language—and not the other way around. In partial support of this idea,
animals as cladistically diverse as Bengalese finches (Abe & Watanabe, 2011)and macaque monkeys (Jiang et al., 2018) have been trained to recognize
recursive, center-embedded patterns; language therefore cannot be a necessary
condition for this ability. Yet if the center embedding found in ritual played sucha central role in the evolution of linguistic recursion, (1) why would center
embedding appear to be so infrequent today (Karlsson, 2007; Levinson, 2013),
and (2) shouldn’t there likewise be more traceable remnants of center
embedding in human culture? These are the questions we address in this paper.
and we present what we find to be striking evidence along these lines.
With regard to (1), Staal (1980) proposed that the much more luxurious timescale on which ritual typically unfolds accommodates the complexity of center
embedding without difficulty, whereas the speeded pace of spoken or signed
language communication in real time gives rise to obvious problems of verbal
working memory, as first pointed out by Chomsky & Miller (1963; see also
Warren & Gibson, 2002, regarding reference form). However, given the apparent
ease with which native speakers process head-final languages (e.g. Inoue &Fodor, 1995), we predicted that head-final languages should exhibit more centerembedding than head-initial or mixed-headedness languages, and conducted a
corpus search to this end. We also assembled a collection of center-embedded
examples from popular media in English. These results are found in section 4.
With regard to question (2) above, Lewis (2018) has described huntergatherer ritual as a communicative, transactional relationship between a society
and the forces of nature, manifesting in institutionalized communal reenactments
consisting of storytelling, music, and dance (with specified melodies, songs, andrhythms). This is remarkably consistent with traditional descriptions by Sanskrit
scholars of the more formalized, heavily constrained, and institutionally codified
ritual practice of Vedic culture. This in turn suggests that these may well beuniversal features of human ritual. Merker (1999, 2009) draws similar
conclusions: “Presumably, the vocal, the gestural and the social aspects of groupdisplay were never separate, nor are they kept separate in the conceptual
categories of a number of non-Western peoples even today. Their languages
subsume rhythm, song, dance and ritual (celebration) under a unitary concept….”(Merker, 2009:53; see also Cassirer, 1946:40-41). More crucially for our
purposes, Lewis (2018) emphasizes that the fundamental backbone and crucial
focus of hunter-gatherer ritual is not its semantic content, but its specified acoustic
properties, structural form, and manner of execution. This is precisely Staal’s(1979, 1980, 1984) claim with regard to the structure of Vedic ritual as well.
We build on such cross-cultural similarities to show that center embedding
persists and is well attested in the obvious descendants of early human ritual

407

culture today: structural features of (especially Western1) literary (section 2) andmusic composition (section 3). Taken together, the evidence we present suggeststhat not only is center embedding alive and well in human culture and cognition
today, but that it has likely been prevalent throughout human history—andarguably in some prior hominin species as well. As such, the ubiquity of center
embedding in human culture offers a tractable avenue for recursion to have gained
a foothold in language via its common association with ritual and music.
2. Center embedding in epic poetry
It turns out that not only is Vedic ritual center-embedded: the oral literature withwhich it is intertwined is as well. The so-called “frame story” structure of Indian
epics like the Mahābhārata, in which stories are repeatedly embedded one within
one the other, has been shown to reflect the structure of the rituals that serve as
the narrative framework for the recounting of those tales (Minkowski, 1989).
Alternatively, the epic embedding of one myth within another may be designed
to elucidate and justify the particulars of ritual practice (Witzel, 1987). However,
ABC…X…CBA narrative structure is not limited to Vedic culture, but ubiquitous
across Indo-European epic poetry (Watkins, 1995). Classicists refer to it as ringcomposition (van Otterlo, 1944). This symmetrical structure has been identified
in the Iliad and the Odyssey (van Otterlo, 1944; Whitman, 1958; Haig Glaser,1969), the Aeneid (Duckworth, 1962), La Chanson de Roland (Niles, 1973),
Beowulf (Niles, 1979), and in books of the Hebrew Bible (Fishbane, 1975; Alter,
1987; Rosenberg 1987; Douglas 2007). The same structuring device has been
claimed for literary works as diverse as 1001/The Arabian Nights (whose core
tales are recognized to be of Indian and Persian origins; Irwin, 1994), Boccaccio’s
Decameron, Chaucer’s The Canterbury Tales, Shakespeare’s plays (Rose, 1972),
Mary Shelley’s Frankenstein, and Sterne’s Tristram Shandy (Douglas, 2007).2
Importantly, however, the literary technique of center embedding applies not
only at the level of entire works, but also to the more fine-grained structure ofindividual hymns, poems, or verses. It is instantiated and signaled by a number ofboth narrative and linguistic bracketing devices, including case, tense/aspect,person, mood, connectives, and specific lexical forms (Minkowski, 1989). For
just one such example, so-called riddle hymns of the Ṛg Veda have a recognizable
structure in which the enigma itself is embedded in a verse in the middle of thehymn, with parallel and symmetric stylistic and lexical bracketing devices on
1
2

For literature at least, Douglas (2007:5,8) cites further examples from Madagascar, China, Burma(Myanmar), Thailand, Vietnam, Malaysia, Indonesia, Trans-New Guinea, and Hawai’i.
See Dane (1993) for a general critique of analyses of classical and medieval literature that rely onring composition as an organizing principle.

408

either side (Brereton, 1999; Jamison, 2004); Douglas 2007:36-37 includes bothof these conventions in her criteria for ring composition). Likewise, at the levelof individual verses or lines, chiasmus, the symmetrical (ABBA) structuring ofphrases or clauses via lexical and/or syntactic means, is a common poetic device.
3. Center embedding in musical composition
The same structural principle is prevalent in the composition of Western music.
Palindromic or mirror patterns in music are called retrograde, meaning that themusic is identical on some parameter when performed both forward or backward.3
Such early forms do exist, for example a 14th century rondeau by Machaut and a
16th century canon by Byrd. However, these are exceedingly rare, especially fromthe Baroque through the Romantic eras, due to the homophony that dominatedfrom the 17th through 19th centuries: chordal structure supporting an independent
melody, based on a series of harmonic progressions going from dissonance toconsonance. That said, as might be expected, eminent formalists like Bach and
Haydn both toyed with strictly palindromic composition in individual works.
However, musicologists have since expanded the definition of hierarchicalstructure in music to include composition at every level of analysis, from multimovement works, individual movements, and passages within movements tomusical gestures such as themes, motifs, rhythm, texture, and color. Recursive
structure provides the architectural basis for the sonata (ABA), so-called ternaryforms (ABA) like the minuet, and the rondo (ABACABA). Recursiveness also
manifests in the arch form, in which passages or movements are mirrored on thebasis of key, tonal center, or contour. A variety of well-known composers from
Beethoven and Schumann through Mahler and Bartok composed in arch form insome of their works. Rohrmeier (2011) has analyzed tonal harmonic progressions
as compatible with a context-free grammar. Palindromic permutations were also
fundamental to the 2nd (twelve-tone) Viennese school, the later return of tonality
in minimalism (Porter 1971), and 21st century eclecticism. Symmetry has thus
played a central role in the history of Western music as well as in its literature.
4. How frequent is center embedding?
The received wisdom in linguistics and psycholinguistics is that center embedding
is uncommon because of the burden it imposes on working memory resources
3

Interestingly, this is one (and the most difficult) of the techniques—called ghana-pāṭha
recitation—employed by Vedic pandits to learn by oral means the vast repertoire of hymns they
are required to commit to memory. Individual words of the hymn in various combinations arerepeated forward and backward, with total disregard for syntactic or semantic well-formedness(Egenes 1989:48).

409(Chomsky & Miller, 1963), as also noted by Staal (1980).4 Yet given the apparent
ubiquity of center embedding in other cultural domains derived from ritual, we
hypothesized that center embedding may actually be more common in language
than is generally assumed. For instance, center embedding is both more tolerable
and more common in writing than in speech, thanks to the use of externalized
cognitive representations that are stable over time (Karlsson, 2007). This
impression is buttressed by the number of naturally occurring examples weencounter in our casual reading and in corpora. Here is just one such example:…the idea [that only the people[who heard the tale straight from Homer's lips]
had the authentic experience of the epics] is facially absurd.
https://forums.somethingawful.com/showthread.php?threadid=3884594&userid=0&perpage=40&pagenumber=5#post493476903

Figure 1. The occurrence per 10,000 sentences of center embedded structures in corpus searches ofhead-final and head-initial languages, and of languages with mixed headedness.

However, the bulk of the linguistic literature on center embedding focuses on
English and other European languages (e.g. Karlsson, 2007). We predicted that
center embedding should be more common in head-final languages, given thatclausal complementation with SOV word order favors center-embedded structure,and subjects are therefore more frequently separated from their verbs anyway. We
4

Levinson (2013) has claimed that center embedding is more common in conversational discourse,
but see Legate et al. (2014) for a critique of Levinson’s claims regarding syntactic recursion.

410

searched the UD database (Nivre et al., 2018) for center-embedded structures ina variety of head-final and head-initial languages, as well as in languages with
mixed headedness. With the help of native speaker linguists, we eliminatedsentences with incorrect syntactic tagging and parenthetical clauses. Fisher's exact
tests confirmed a significant 3-way difference: head-final languages > mixed
headedness languages > head-initial languages (Figure 1; all p < .0001).
5. Conclusion
Starting from the rather uncontroversial notion that storytelling, music, song anddance are inextricably bound up in human ritual as a unified whole (Lewis, 2018;
Merker, 2009), we have attempted to show that the purported center-embedded
structure of ritual itself (Staal, 1979, 1980) is likewise an intrinsic part of (at least
Western) epic poetry and classical music. Our suggestion is that these remnantsof ancient ritual reflect a human preoccupation with symmetric structure(Douglas, 2007). Merker (2009:56) goes so far as to characterize language as ameta-ritual: “Language shares with the ritual mode out of which it grew aninsistence on proper form, yet differs from it by its emancipation from the finite
particularity of rituals.” It has been claimed that center embedding is more
prominent both in conversational discourse (Levinson 2013) and in writing(Karlsson 2007). We have shown that it is in fact more common in (the writingof) head-final than in head-initial languages, and we suspect that it is also morecommon than corpus studies would suggest, even in head-initial languages.
Taken as a whole, our proposal meshes well with others that seek the sourceof recursion in human language across levels of linguistic analysis, cognitive
domains, and spheres of human (and animal) cultural activity. Our findings
simultaneously broaden the body of available evidence for such proposals, help
to contextualize them in a larger cultural framework, and sharpen the empiricallinguistic facts relevant to the use of center embedding in human language.
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413

ECOLOGICAL AND SOCIO-CULTURAL CONSTRAINTS ON
LINGUISTIC DIVERSIFICATION: NEW INSIGHTS FROM THE
BANTU EXPANSIONEZEQUIEL KOILE *1, SIMON J GREENHILL 1,2, DAMIAN E BLASI 3, 1,4,
REMCO BOUCKAERT 5 and RUSSELL D GRAY 1,6*Corresponding Author: ezequiel_koile@eva.mpg.de
1Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary

Anthropology, Leipzig, Germany
2 School of Biological Sciences, University of Auckland, Auckland, New

Zealand
3 Department of Human Evolutionary Biology, Harvard University, Peabody

Museum, Cambridge, MA, USA
4 Human Relations Area Files, Yale University, New Haven, CT, USA
5 Centre for Computational Evolution, University of Auckland, New Zealand
6 School of Psychology, University of Auckland, Auckland, New Zealand

The Bantu expansion was a massive migration that reshaped the linguistic,
economic, and cultural landscape of Africa. It led to the proliferation of Bantuspeaking populations throughout sub-Saharan Africa and today more than 500
Bantu languages are spoken by 240 million people across an area of 9 million
square kilometers (de Filippo et al. 2012). This expansion has been associatedwith major economic and cultural changes across sub-Saharan Africa, including
a more sedentary way of life, iron working, and crop cultivation (Neumann et al.
2012, Grollemund et al. 2015, Currie et al. 2013).
Although there is a consensus about the time and location of the homeland of the
Bantu people, around 5,000 years before present, near the border between
current Nigeria and Cameroon, by the Guinea Gulf (Diamond et al. 2003,
Blench 2006, Vansina 1995), substantial uncertainty remains about the route andenvironmental conditions faced by early Bantu speakers during their migrations.
Until recently, it was believed that these populations, characterized by theiragriculturalist subsistence method, were unable to adapt to the West African
Rainforest, which is located in the way of their migration paths, according tocertain hypotheses.
We use the recently developed “break-away” geographical diffusion model(Bouckaert et al 2018), specially designed for modeling migrations, together
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

414with “augmented” geographic information, in order to reconstruct the Bantu
language family expansion. This Bayesian phylogeographic approach(Bouckaert et al. 2014) with augmented geographical data provides a powerful
way of linking linguistic, archeological and genetic data to test hypotheses about
large language family expansions.
We find that our analyses support the hypothesis of an expansion through central
African tropical forests at 4,420 BP (4,040-5,000 95% HPDI), well before thesavanna corridor known as the Sangha River Interval was open in its interior.
This is consistent with a slow adaptation of the Bantu speaking populations to
the rainforest, where interaction with their hunter-gatherer neighbors was
fundamental (Klieman 2003).
We take these results to show a wider trend in the study of language
diversification: While subsistence has shaped the expansion and the tempo ofmany language families, it does not impose an inescapable barrier to alternative
diversification patterns. This is critical for understanding the role of humanflexibility and ingenuity when thinking broadly about the processes underlying
language evolution.

Figure1. Bantu migrations reconstructed by using the break-away model in the augmented phylogeographic
tree in Figure 1. The homeland is marked with a star, and main nodes are numbered (1-3), as well as
main clades (0-23). Each circle represents the median value of the posterior distribution for theorigin of the respective clade, and their colors represent the geographical region spanned by thecorresponding languages nowadays. The span of the rainforest at 5,000 BP and at 2,500 BP isshown, according to (Maley 2001, Maley 2002).

415

References
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416

THE EVOLUTIONARY ROOTS OF HUMAN COMMUNICATIVEREPAIR: THE CASE OF CHIMPANZEE GROOMING
KAYLA KOLFF*1 and SIMONE PIKA1*Corresponding Author: kkolff@uni-osnabrueck.de
1Institute of Cognitive Science, Osnabrück University, Osnabrück, Germany

Human communication is characterized by several distinct aspects such as directionality,reference, and role reversal. If a communicative interaction fails, humans use distinct
mechanisms to repair it. Surprisingly, relatively little is known about the evolution of repair
and possible precursors in non-linguistic species. Thus, this paper introduces possible
precursors of communicative repair in grooming interactions of one of our closest living
relatives, the chimpanzees (Pan troglodytes schweinfurthii). In 1996, Dunbar had
suggested that early humans engaged in complex grooming interactions, before grooming
was replaced with a more efficient bonding system - language. Therefore, grooming offersa unique platform to investigate the evolutionary precursors of communicative repair.

1. Introduction
How humans perceive and interact with each other is predominantly throughlanguage. Why only humans developed this unique communicative systemremains a mystery (Hauser et al., 2014; Knight, Studdert-Kennedy, & Hurford,
2000) and has led to considerable scientific debate (Christiansen & Kirby, 2003).
Some researchers postulated that turn-taking is one of the most ancientmechanisms underpinning the layered language system (Levinson, 2016). This
cooperative interaction during conversations was first systematically described by
Sacks and colleagues (1974). It requires the combined effort of at least two
participants, who alternate short, reciprocal, flexible, and non-overlapping turns
to coordinate their interaction during conversations. Turn-taking involves distinct
temporal relationships (~200 ms time windows, Stivers et al., 2009), theestablishment of participation frameworks (who and when should they talk,move or act next), and the use of repair mechanisms (hereafter communicative
repair) to counteract problems during communication.
Recently, comparative researchers started to investigate whether turn-taking
is indeed an ancient mechanism that is already present in non-linguistic primate
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

417

species (e.g., Fröhlich et al., 2016; Pika, Wilkinson, Kendrick, & Vernes, 2018;
Ravignani, Verga, & Greenfield, 2019; Rossano, 2018). Individuals alternating
the production of signals can be found in several non-linguistic primate species,
with the majority of research focusing on the temporal relationships
characterizing turn-taking interactions and adjacency pair-like sequences (seePika et al., 2018 for a recent review). However, relatively little is known aboutcommunicative repair in non-linguistic species.
Communicative repair in human conversations is defined as 'fixing’ amisunderstanding or communicative problem (Schegloff, Jefferson, & Sacks,
1977), with researchers distinguishing between self-and-other-initiated repair.
Self-initiated repair refers to an actor producing communicative means tocounteract trouble in a conversation. It entails repetition, e.g., repeating the entire
or part of the previously spoken turn, or elaboration, e.g., rephrasing thepreviously spoken turn (Kitzinger, 2012). Dingemanse and colleagues (2015) ina cross-linguistic study, showed that across twelve languages, 48% ofcommunicative repair occurrences included individuals partially repeating theirinitial spoken turn during spontaneous conversations. In contrast, other-initiatedrepair refers to someone other than the speaker identifying the communicative
trouble and using means to counteract it (Dingemanse & Enfield, 2015). Hereindividuals may locate the source of trouble in the prior communicative turn (e.g.,
repeating a certain word), use questions words (e.g., “Who”, “What”, “Where?”,“Huh?”) or maintain silence and stare at the speaker, i.e., freeze looks(Dingemanse & Enfield, 2015; Manrique & Enfield, 2015).
In a study of native English speakers, communicative repair occurred at adelay (~700 ms) after the last spoken turn during face-to-face conversations(Kendrick, 2015). Compared to the average temporal gap of 0-500 ms between
turns found across several human cultures (Stivers et al., 2019). Furthermore,
Lerner and Raymond (2021) recently showed that communicative repair also
occurs in non-verbal bodily actions. They reported troubles during exchanges ofmanual actions, such as premature (mis)recognition of an emerging action, thatare repaired through adjustment of actions (e.g., retarding or reversing an action).
Communicative repair, therefore, seems to be crucial for different modes ofcommunication and was suggested as an essential element to facilitate thecoordination and cooperation between interacting individuals (Levinson, 2006).
However, relatively little is known about the evolution of repair and possible
precursors that may be found in non-linguistic species.
1.1 Brief overview of communicative repair in non-human primates418

To date, relatively little research attention has been focused on communicative
trouble and repair in non-human primates. For instance, Haimoff (1988) examined
duets songs (organized bouts comprised of three distinct sequences of vocal
behavior between pairs: the introductory, interlude, and great calls) of wild andcaptive gibbon species (Hylobates spp.). On about sixty occasions, gibbons were
found to counteract ‘errors’ (production of sounds not being produced normallyby an individual at a point in the bout) by abruptly terminating the duet bout andre-initiating a new sequence. Hence, two clear cases were found where Siamang(Symphalangus syndactylus) individuals repaired ‘troubles’ (not producing the
expected call at the appropriate time of a sequence) by ultimately initiating the
anticipated call and restarting the sequence. Additionally, Heesen and colleagues(2022) presented two cases on captive bonobos (Pan paniscus) and wild
chimpanzees (Pan troglodytes), where individuals may engage in communicative
repair during the context of grooming and respectively joint-travel. They
described that self-initiated repair could be found beyond modern humans throughsignal persistence, repetition, and elaboration, e.g., gesture repetition by achimpanzee mother to initiate joint-travel. Nonetheless, communicative repair
across non-human primates in their natural environment still remains unknown.
Hence, this paper aims to pinpoint possible precursors of communicative
repair and to propose a systematic framework to enable cross-comparison
between primates. We focused on the interactions of one of our closest living
relatives, the chimpanzees (Pan troglodytes schweinfurthii) in the wild.
Chimpanzees are an ideal species to investigate communicative repair due to theirrich vocal and gestural repertoires and high collaboration degrees (Goodall, 1986;
Mitani, 2009). Similar structures or traits found between chimpanzees and modern
humans may either be homologous relating to shared ancestry (Wrangham, 1987)
or convergent evolution of the Pan and Hominin ancestors in encountering similar
evolutionary pressures (Tooby, DeVore, & Kinzey, 1987). At the end of the lastcentury, Dunbar (1996) suggested that due to a considerable increase in groupsize, grooming was replaced in modern humans by the more efficient bonding
system of language. Communicative interactions involving repair may therefore
have characterized grooming interactions before the dawn of human language.
Thus, we centered on interactions in the context of grooming, a frequent behavior
in chimpanzee societies and has been suggested as a crucial platform for learningand exchanging communicative signals (Pika, 2009, 2014).
2. Communicative repair in wild chimpanzees: Employed signals andtemporal relationships

419

Here we introduce six grooming interactions that involve the production of
three distinct gestures, the DIRECTED SCRATCH1, PRESENT BODY PART, and TOUCH(see Fig. 1). These gestures are commonly produced during grooming interactions(either uni-directional - Fig. 1/A, or bi-directional grooming) by chimpanzees of
the Ngogo community, Kibale National Park in Uganda, to request, negotiate andmaintain grooming (Pika, 2009, 2014). The interactions involve hallmarks ofhuman communicative interactions, including first-order intentionality,
flexibility, directionality, reference, role negotiation, and role reversal (Pika,2014).

Figure 1. (A) Uni-directional grooming interaction of an adult male (left) grooming another adult male(right). (B) An adolescent male SCRATCHING the back of their head (front) after grooming an adultmale (back). (C) An adult male PRESENTING their side (front) and the adult male (back) that waspreviously grooming him. (D) An adult male (right) TOUCHING the rump of the adult male (left) that
he was grooming. All illustrated gestures are depicted in red circles.

The DIRECTED SCRATCH is an auditory and visual signal defined as anindividual making a relatively loud and exaggerated scratching movement on thepart of their body (Pika, 2014). When an actor (individual grooming the other)signals with a DIRECTED SCRATCH during a grooming interaction (seesupplementary materials Fig. S1), the recipient (individual being groomed) could
either (i) ignore, (ii) respond by repositioning themself (see supplementarymaterials Fig. S2 - role negotiation), (iii) groom the actor or (iii) groom the actor
at the allocated scratch area (see supplementary materials Fig. S1/C - role
reversal) (Pika, 2014). A DIRECTED SCRATCH by an actor stops the ongoinginteraction (breaks the temporal relationship). It presents a request to the recipient
to give a certain response to potentially ‘fix’ the interaction. For instance, role
reversal, where the recipient becomes the actor (see supplementary materials Fig.
S1). Or negotiation of the actor’s role where the recipient repositions themself toallow the actor to continue their interaction (see supplementary materials Fig. S2).
The potential illustrated repair is initiated by the gestural request of an actor whilein parallel disturbing the natural flow and temporal relationship of the groominginteraction (possible self-initiated repair). This forces the recipient to respond and1

Gestures are depicted from here in SMALL CAPITALS.

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act, to continue their interaction, and possibly return to the initial situation (seesupplementary materials Fig. S1, where the initial actor after the possible repair
ends up being the actor again).
PRESENT BODY PART is a gesture that involves an individual offering abody area such as their arm, back, or rump at a recipient (Pika, 2014). PRESENTINGBODY PART is a referential gesture (Hobaiter & Byrne, 2014) because it draws theattention of another individual to a certain location. A PRESENT BODY PART by anactor draws the recipient’s attention to request that it is their turn to groom theactor at the presented location. Howbeit, the recipient may still want the actor tocontinue grooming them and can negotiate this by PRESENTING BODY PART to theactor (see supplementary materials Fig. S3/G). The recipient could also ignore therequest by the actor, potentially ending the interaction (see supplementarymaterials Fig. S4). The recipient negotiating the actor’s role may represent a
potential other-initiated repair since the recipient communicates that it is still theactor’s turn to groom them. Ultimately, the use of PRESENT BODY PART by either
an actor or recipient interrupts the natural flow of the grooming interaction.
The TOUCH is a tactile gesture defined as an individual gently putting
their hand (<2 seconds) on any body part of the recipient (Pika, 2014) and may
accordingly pause (see supplementary materials Fig. S5/B). Simultaneously, thegrooming interaction stops, and the involved temporal relationship changes (theflow of grooming interaction is interrupted). This is a directed gesture from anactor to a recipient, where a recipient may respond by orientating themselves,giving access to the body area that the actor touched (i.e., intentional reference
request, see supplementary materials Fig. S6/C). The performed request achieves
a mutual understanding between the actor and recipient of where to groom next,
allowing for the continuation of the interaction. In the scenario where the recipient
does not entirely orient themself after a given TOUCH, the actor can orientate
themself towards another body area (see supplementary materials Fig. S5/D).
Here, the actor does not receive the adequate ‘desired’ response and counteracts
this by performing the wanted request themself (repositioning themself towards a
non-groomed body area of the recipient). Similar to the DIRECTED SCRATCH, theTOUCH depicts a possible repair during a grooming interaction. The actor initiates
a potential repair, and the recipient can respond by repositioning themself (seesupplementary materials Fig. S6). Alternatively, if the recipient’s response isinadequate, the actor may pause and reposition themself (see supplementarymaterials Fig. S5), both cases illustrating possible self-initiated repair.
The gestures DIRECTED SCRATCH, PRESENT BODY PART, and TOUCH producedby chimpanzees changed the temporal relationship of their grooming interaction.

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They, therefore, may represent means to ‘fix’ and eventually prolong therespective interactions, which can be understood as requests “Okay, what now?
Here? Your turn or still mine? Didn’t I groom you enough”. After a given request,
the interaction can change towards (1) role reversal (recipient becomes actor, e.g.,
producing a DIRECTED SCRATCH) or (2) role negotiation (the actor remains theactor, e.g., PRESENTING A BODY PART), or (3) during an actor’s turn they canreposition themself following a failed reference of a PRESENTED BODY PART after
a given TOUCH. The above scenarios demonstrate how both individuals interact
and communicate with each other to continue their grooming interaction andcommunicate their intentions. In doing so, the actor or recipient can be responsible
for employing certain signals to ‘fix’ and prolong their grooming interaction,
which may characterize potential self- and other-initiated repair.
3. Conclusion and future trajectory
Despite the growing evidence of distinct elements characterizing humanconversational turn-taking also being present in other primates, studies oncommunicative repair are still minimal. Here, we introduced possible precursors
of human communicative repair by focusing on three commonly produced
gestures by chimpanzees during grooming interactions to request, negotiate orreverse roles to maintain grooming. Future studies should therefore investigate
communicative signals employed by non-human primates during groominginteractions and the outcome of these signals. This will aid in understanding thepresence of communicative repair in non-linguistic species and how it relates tomodern human communicative repair and the hallmarks of humancommunication. We, therefore, propose a four-aspect framework (seesupplementary materials for further details) within the grooming context: (1)What does the signal entail? (2) Who initiates a signal? (3) What is the outcome
after the signal? (4) What is the time window between the signal and outcome?.
This framework may offer a systematic approach for future research to compare
the evolutionary roots of communicative repair across primates.

Acknowledgements
We thank the Uganda Wildlife Authority, Uganda National Council of Scienceand Technology, and Makerere University Biological Field Station for theirpermission to conduct research. A big thank you to K. Langergraber, J. Mitani, D.
Watts and to the Ngogo Field Station staff. We are grateful to the reviewers for422

their comments. KK was supported by the DFG Research Foundation - project
number GRK-2185/1 (DFG Research Training Group Situated Cognition).
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MECHANISMS UNDERLYING THE HERMENEUTIC CIRCLE
BETWEEN CONNOTATION AND DENOTATION IN THE
CONSTRUCTION OF SYMBOLIC COMMUNICATION SYSTEMSTAKESHI KONNO*1 , RYUSEI OTA1 , and RYOSUKE NAKANO1* Corresponding Author: konno-tks@neptune.kanazawa-it.ac.jp

1 Engineering Department, Kanazawa Institute of Technology, Ishikawa, JapanThe expression ”Can you pass the salt?” exchanged at the dining table can beinterpreted as either requesting a salt shaker or literally enquiring if the person
to whom the question is addressed to is able to pass the salt shaker. An expression has not only a literal meaning (denotation), but also an implied meaning(connotation), and they are interdependent. The process of determining the meaning of an expression involves a saying/implicating circle (Carston, 2002), whichis thought to develop eventually into a structure of individual parts and whole,called a hermeneutic circle (Tsuda, 1984; Levinson, 2000). However, the mechanism that lead to the formation of this structure from its primitive stage is unclear.
We therefore developed a coordination game with messages that simpliﬁes thedenotation and connotation based on the framework of experimental semiotics(Galantucci, 2009; Scott-Phillips & Kirby, 2010). Furthermore, while developing
a computational model that is positioned as model-based reinforcement learningthat performs as well as humans, we found several mechanisms underlying theprimitive circle.
The proposed coordination game is as a terminal-based computer game(Galantucci, 2005) in which players and their partners randomly place pieces inseparate 2×2 rooms and aim to move them to the same room. Before moving apiece, players can choose one of the four available shapes and exchange it with
their partners only once at any given time. Players can move their own piece only
once or keep it in the same position, but cannot move it diagonally between rooms.
The result is then disclosed, and the two pieces are again randomly placed. In theprocess of repeating this series of operations in each game round, most players
form symbol systems that map the shapes to their pieces to the room locations.
In this game, as the pieces cannot be moved diagonally, there is only a 50%
chance of matching the partner’s move if a piece is moved to a particular room
without exchanging messages with the partner. However, if the ﬁrst sender can tell
the location of the present room and the second sender can reply with a destination
where the two can meet, they can match their movements. Here, the relationshipThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

425

between game rooms and symbols corresponds to the denotation of a semantic element and the ﬁrst location and destination of a piece correspond to its connotationof a pragmatic element. In an experiment with 20 pairs of human participants, 14
pairs (65%) constructed a symbol system that could consistently match the rooms
to which the pieces were moved by the pair after 60 rounds of repetition. The
designed computational model of the proposed game probabilistically determines
the symbols, rooms, and when to send symbols. The probability changes throughtrial-and-error reinforcement learning, and the computational model has a parameter for interpreting symbols as indicating being/going to a room. This parameteris estimated from the partner’s action history. In computer simulations using thisparameter, the number of rounds required to achieve consistent matching of rooms
to which the pieces were moved by both players was reduced to approximately 1/5of that without using this parameter.
This reduction can be achieved by estimating the location of the partner’s piecebased on the received symbols and changing the probability of moving the pieceto that estimated location. For example, when the location of the partner’s pieceis known, the probability of moving the piece to a position with no contact should
become zero. This mechanism deviates from the framework of reinforcement
learning, which changes the behavioral probability based on the rewards obtained
from the results. However, this may be the mechanism by which symbols manipulate a partner’s behavior. It was also necessary to coordinate the symbols
when two players used the same symbol for different rooms or different symbols
for the same room. We considered the implicit arrangement of role divisions,such as leader and follower, or the introduction of ambiguity into the denotation
to solve this coordination. We adopted the latter mechanism owing to its lower
computational cost. The computational model with the above mechanisms could
consistently match the rooms to which the pieces were moved by both players for
9 pairs of participants (45%) out of the 20 pairs.
Computational models that deal with both literal and implied meanings canform symbol systems faster than computational models that only deal with literalmeanings. In addition, a mechanism that changes the action probability before
acting based on the literal and implied meanings inferred from the symbols isnecessary for the realization and mutual action coordination at a low cost. Thesefeatures improve the adaptive value of language acquisition in human evolutionary
processes (Scott-Phillips, 2015).
Acknowledgements
This work was supported by JSPS KAKENHI Grant Number JP17H06383.
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Carston, R. (2002). Thoughts and utterances: The pragmatics of explicit communication. John Wiley & Sons.

426

Galantucci, B. (2005). An experimental study of the emergence of human communication systems. Cognitive science, 29(5), 737–767.
Galantucci, B. (2009). Experimental semiotics: A new approach for studying
communication as a form of joint action. Topics in Cognitive Science, 1(2),393–410.
Levinson, S. (2000). Presumptive meanings: The theory of generalized conversational implicature. Cambridge.
Scott-Phillips, T. C. (2015). Speaking our minds: Why human communication
is different, and how language evolved to make it special. NY: Palgrave
Macmillan.
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Topics in Cognitive Science, 14(9), 411–417.
Tsuda, I. (1984). A hermeneutic process of the brain. Progress of Theoretical
Physics Supplement, 79, 241–259.

427

FREQUENCY AND CHARACTER COMPLEXITY IN 27 WRITINGSYSTEMS
Alexey Koshevoy*1 , Helena Miton2,3 , and Olivier Morin1,2* Corresponding Author: akoshevoy@ens.fr

1 Institut Jean Nicod, Département d’études cognitives, ENS, EHESS, CNRS, PSL University, UMR

8129

2 Minds and Traditions Group, Max Planck Institute for the Science of Human History, Jena, Germany
3 Santa Fe Institute, Santa Fe, USA

We are testing whether Zipf’s Law of abbreviation — the idea that more frequent symbols in acode are simpler than less frequent ones — is present at the level of individual characters. In its
original and most well-known manifestation, the law of abbreviation is a correlation between
the length and frequency of spoken or written words. Zipf’s Law of Abbreviation has beenshown to hold at the level of words in many languages, but it is unknown whether it holds at thelevel of characters. Characters’ complexity is similar to word length, requiring more cognitiveand motor effort from producing and processing the more complex symbols. We built a datasetof character complexity and frequency measures covering 27 different writing systems. Our
results suggest that Zipf’s Law of Abbreviation holds for every single system in our dataset– the more frequent characters have lower degrees of complexity and vice-versa. This result
provides further evidence of optimization mechanisms shaping communication systems.

1. Introduction
In his pioneering work, George Kingsley Zipf observed that more frequent words
tend to be shorter – a principle known as Zipf’s Law of Abbreviation (Zipf, 1949).
This law-like relation has since been confirmed on data coming from many different languages (see, for example, Piantadosi, Tily, & Gibson, 2011; Bentz &Ferrer-i-Cancho, 2016). Additionally, Zipf’s Law of Abbreviation has been shownto arise in artificial language learning experiments (Kanwal, Smith, Culbertson, &Kirby, 2017) and in communication systems of different species (see Ferrer-iCancho & Lusseau, 2009; Semple, Hsu, & Agoramoorthy, 2010, amongst many).
This effect is usually explained in terms of minimizing cumulative production
cost: speakers intend to reduce their average articulation effort. This results in a
reduction in the number of sounds that are pronounced overall.
Since scripts can be thought of as a communication system, which maps written characters to phonemes or syllables (Morin, Kelly, & Winters, 2020), we might
expect that the same effect will hold for individual characters. Characters do nothave length, unlike words. But visual complexity shares several relevant properties with word length. Complex characters take more effort to write and read,This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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just like long words are more effortful for speakers and hearers. Miton and Morin(2021) suggest that characters in writing systems are under similar pressures aswords in spoken languages. Rovenchak and Vydrin (2010) have found a negative
correlation between the complexity of characters and their frequency in the Nko
writing system (West Africa). Similar results were reported for the Vai writing
system in (Rovenchak, Mačutek, & Riley, 2008), and Mandarin Chinese characters (Shu, Chen, Anderson, Wu, & Xuan, 2003). The small number of studies thathave tested this hypothesis shows a negative association between the complexityand frequency of characters – consistent with Zipf’s Law of Abbreviation. However, no large-scale comparative testing was done in this domain. This study fills
this gap by using a dataset that consists of 27 writing systems and computational,
automated, and replicable measures to quantify character complexity. This is different from the idiosyncratic methods, primarily based on stroke counts used inprevious studies (see Changizi & Shimojo, 2005 for an example of such methodology). Large-scale cross-linguistic corpora and datasets containing data on character complexity such as GraphCom (Chang, Chen, & Perfetti, 2018) or the dataset
introduced in (Miton & Morin, 2021) offer a way to conduct cross-linguistic research on Zipf’s Law of Abbreviation in writing systems.
We hypothesize that writing systems will follow Zipf’s Law of Abbreviation.
As most writing systems are largely based on handwritten characters shaped by
centuries of reproduction, a minimization of the cumulative production cost is expected. There is evidence, based on high-quality but limited data, that writing
systems can become less complex over time (Kelly, Winters, Miton, & Morin,2021), indicating an overall trend towards simplification. Reduction of the complexity of symbols was also observed in interactive graphical communication experiments (Tamariz & Kirby, 2015; Garrod, Fay, Lee, Oberlander, & MacLeod,
2007) We expect that frequency should negatively correlate with complexity, i.e.,
more frequent characters should have become simpler visually due to pressures tominimize production cost over the course of their constant reproduction.
2. Data
2.1. Dataset description
The dataset used in this study combines complexity measures from (Miton &Morin, 2021) and frequencies for each character. The complexity measures for every character include perimetric complexity and algorithmic complexity. Perimetric complexity was introduced in (Attneave & Arnoult, 1956), and it is defined as
the inked surface divided by the perimeter of this inked surface. Miton and Morin(2021) computed this complexity measure using an implementation proposedin (Watson, 2012). Additionally, Pelli, Burns, Farell, and Moore-Page (2006)
demonstrate that perimetric complexity closely correlates with human visual processing effort. Algorithmic complexity is the number of bytes needed to store

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a compressed version of the character. The frequencies of individual characters
were obtained from biblical texts extracted from bible.com. If data on the desired writing system was not available on bible.com, we used data from (Bentz& Ferrer-i-Cancho, 2016), which is also based on biblical texts, instead. Additionally, for Shavian, we extracted the data from shavian.info/books/. The
texts were preprocessed to remove the punctuation, numbers, and characters that
do not belong to the writing system of interest (for instance, Latin characters were
removed). The character counts were computed from preprocessed texts and converted to relative frequencies by dividing each count by the sum of counts for the
given writing system. Additionally, as the distribution of relative frequencies ishighly skewed, these values were log-transformed. This transformation did notaffect the results we present here.
The resulting dataset has 27 writing systems. The median corpus size (in characters) is 711,785, with the smallest values for Shavian (97,566 characters) and thelargest for Thai (2,942,793 characters). The median number of characters is 42;
the writing system with the lowest number of characters is Syriac (22 characters),
and the largest writing system is Ethiopic (251 characters). Our dataset consistsof four abjads, fourteen abugidas, five alphabets, one featural system, and foursyllabaries (1560 characters in total). The geographic distribution of the writing
systems in the dataset is shown in Fig. 1:

Figure 1. Geographic distribution of the writing systems in the database, annotated with the ISO
15924 codes

2.2. Inclusion criteria
We included writing systems in our dataset based on several criteria:
1. A writing system was included if it had available Unicode-encoded text
files.
2. It is possible to identify one main language for which the writing system was

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designed. The Latin and Devanagari writing systems had to be excluded
because each of them is used to encode a multiplicity of languages, andeach was substantially transformed to encode these languages.
3. The writing system is not combined with other writing systems. For instance, Limbu writing consists of both Devanagari and Limbu characters.
Therefore, it was excluded from the sample. However, if the instances ofsuch use are not common, these cases would be kept. For instance, Korean
writing today is overwhelmingly based on the Hangul writing system, with
only occasional use of Hanja (Mandarin Chinese characters). We focusedon analyzing Hangul and disregarded Hanja.
4. Writing systems with less than a hundred thousand characters in the available texts were excluded.
3. Analysis
The proposed hypothesis was tested using a mixed-effect linear regression1 predicting a character’s complexity from its relative frequency (fixed effect FREQUENCY) and the writing system to which the character belongs (random effectWriting System). The model has both random slopes and random intercepts foreach writing system and was run separately on our algorithmic complexity measure and on our perimetric complexity measure.
First, we measured the null model’s Akaike information criterion (AIC). (Thenull model included only the random effect of Writing Systems.) We compared thenull model’s AIC with the full model’s AIC. The full model included a fixed effectfor FREQUENCY and the random effect of Writing Systems, with random slopes
for each writing system. If the full model has lower AIC values than the null model(with the conventional threshold being ∆AIC > 2), that means that the former ismore informative than the latter. For perimetric complexity, the ∆AIC value isequal to 172.8, and for algorithmic complexity, this value corresponds to 146.1,
meaning that they are both more informative than their respective null models.
The β coefficients for relative frequency in the perimetric complexity (-2.40, 95%
CI: [-1.76, -3.07]) and in the algorithmic complexity models (-26.6 95%CI: [34.15, -19.17])) are both negative. These values of the coefficients indicate thatthe higher the frequency is, the less complex is the character, as illustrated in Fig.
2:
1 We used the lme4 R-package to fit our models (Bates, Mächler, Bolker, & Walker, 2014)

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Figure 2. Predictions from perimetric complexity (A) and algorithmic complexity (B) mixed-effectlinear regression models for all the scripts combined. Each point corresponds to a unique character.
Shaded areas represent the 95% confidence interval for the predictions.

In Fig. 2, each dot represents an individual character. We added Thai characters to each plot for illustrative purposes. The arrows point to the most complex
and less complex characters. Additionally, our results suggest that the effects holdfor each writing system and are not an artifact from the aggregated data, see Fig.
3:

Figure 3. Predictions from perimetric complexity (A) and algorithmic complexity (B) models for individual writing systems. Each point represents an individual character, and each subplot corresponds
to an individual writing system (annotated by its ISO 15924 code).

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4. Conclusions
Using mixed effect linear regression models, we show that Zipf’s law of abbreviation holds on all of the individual writing systems in our dataset, not just on theaggregated data taken as a whole, validating our preregistered predictions. These
results hold for both our complexity measures and suggest that the law of abbreviation holds in a large variety of writing systems at the level of characters.
Zipf’s law of abbreviation was also attested in iterative learning experiments(Kanwal et al., 2017), where the authors suggested that the need for efficient andaccurate communication yields the inverse relationship between word frequency
and its length. Since our results support the presence of Zipf’s law of abbreviationin written communication, this suggests that the results from (Kanwal et al., 2017)
can be expanded to graphic communication. Overall, such evidence for Zipf’s
law of abbreviation in writing supports the idea that optimizing production andreception costs would have been an important factor in the evolution of spoken
and graphic communication.
Supplementary Materials
The preregistration for this study can be found here: https://osf.io/
ydr3n
A Git-Hub repository will all of the data and code can be found here: https://github.com/alexeykosh/2021-slojnost-project
Acknowledgments
We thank Christian Bentz and Ramon Ferrer-i-Cancho for their helpful advice on
this project, and for sharing their data.
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Changizi, M. A., & Shimojo, S. (2005). Character complexity and redundancy inwriting systems over human history. Proceedings of the Royal Society B:
Biological Sciences, 272(1560), 267–275.

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system.
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macaque vocal communication. Biology Letters, 6(4), 469–471.
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Zipf, G. K. (1949). Human behavior and the principle of least effort: An introduction to human ecology. Ravenio Books.

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MODELLING THE EMERGENCE OF VOCAL GROOMINGTom Kouwenhoven*1 , Ross Towns1 , and Tessa Verhoef1* Corresponding Author: t.kouwenhoven@liacs.leidenuniv.nl

1 Creative Intelligence Lab, Leiden University, Leiden, The Netherlands

The so-called Gossip and Grooming hypothesis (Dunbar, 1998, 2017), posits thatspoken language arose as an alternative to allogrooming as a means of maintaining social bonds in growing group sizes among early humans. Though aspectsof Dunbar’s theoretical framework have been criticised (e.g. Lindenfors, Wartel,and Lind (2021)), the role of language in social bonding is more widely supported(Tomasello, Carpenter, Call, Behne, & Moll, 2005), and is a key component of themusical protolanguage hypothesis (Fitch, 2017). Here we present a multi-agent
model based on work by Slingerland, Mulder, Vaart, and Verbrugge (2009), whoshowed that greater group sizes can stimulate the use of language within a grooming vs gossip paradigm. We update this model by simulating inter-group contact and subjecting agents to selection pressures acting on tolerance for out-groupmembers, drawing on evidence for increased social connectivity in archaeology(Belfer-Cohen & Hovers, 2020) and selection against reactive aggression in early
humans (Benı́tez-Burraco & Progovac, 2020).
The model
Slingerland et al. (2009) simulated a single group of agents who interact for a number of rounds. Whether this interaction takes the form of one-to-one grooming or
one-to-many gossiping is a function of the agents’ heritable gossip probability.
Agents maintain a memory (M ) of social interactions that are acquired by participating in, observing, or gossiping about an event. Selection is based on an equal
PEgroom 1
PEgossip 1
combination of social fitness (fsocial = 5 × x=0
x=0
px −1 + 4 ×
px −1, where px is the number of agents in a social event x out of all events E) andinformation fitness (finf o = M 2 ) determines which agents reproduce offspring
who inherit mutated preferences from their parents.
Our first addition involves investigating group dynamics by dividing the population into multiple groups. The likelihood that an agent interacts with an in- orout-group individual is a function of its heritable tolerance preference. Secondly,a recalculation of data (Nakamura, 2000, 2003) and new observations of bonobos
and chimpanzees in the wild (Girard-Buttoz et al., 2020) reveal that, contrary towhat was originally thought, social grooming is not strictly dyadic and one-way
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

435

but is often polyadic and mutual. In line with these findings, gossip and grooming are both one-to-many in the updated model. We investigate if, under thesecircumstances, gossip still becomes dominant and what the interaction between
group dynamics and tolerance is.
Figure 1 shows that a preference for gossip still evolves, in addition to clearly
distinct patterns for differently evolved group sizes: agents of large groups develop a preference for gossip with in-group members (low tolerance), while agentsof smaller groups rely on both high tolerance and gossip probability.

Figure 1. The evolution of tolerance and gossip probability over generations (left). The dynamic
between average group size, generation, tolerance, and gossip probability on population level (right).
Shaded regions are SD over 40 simulations of 125 generations initialised with 100 agents in 20 groups.

Discussion
We show that vocal grooming (gossip) can emerge as the dominant bonding strategy even when the assumption that clique sizes are different for gossip and grooming are relaxed. While Dunbar (2017) emphasises the role of time constraints in
restricting the number of social bonds that can be maintained through one-to-one
grooming, as a constraint of group size, our model suggests that the benefit of
having information about social events mainly drives the emergence of gossip.
Moreover, the results show that the evolution of tolerance is influenced by groupstructure in the community. Similarly, such ecological factors have been suggested to play a role in inter-group tolerance for primates (Lucchesi et al., 2020).
For humans, however, the evolution of inter-group tolerance was likely affectedby many other factors including mating patterns and food availability (Spikins,
French, John-Wood, & Dytham, 2021). Notably, the Human Self-Domestication
hypothesis (Hare, 2017) posits that direct selection against reactive aggression
increased tolerance in humans, which may have facilitated more inter-group encounters (Benı́tez-Burraco & Progovac, 2020). The precise interactions between
different factors affecting inter-group tolerance, population dynamics and how
these shape the evolution of language is what we propose to investigate further.

436

References
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Hohmann, G., & Wittig, R. M. (2020). Variable use of polyadic grooming
and its effect on access to social partners in wild chimpanzees and bonobos.
Animal Behaviour, 168, 211–224.
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Slingerland, I., Mulder, M., Vaart, E. van der, & Verbrugge, R. (2009). A multiagent systems approach to gossip and the evolution of language. In Proceedings of the 31st annual meeting of the cognitive science society (cogsci’09)(pp. 1609–1614).
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and brain sciences, 28(5), 675–691.

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SEMIOTIC ARCHAEOLOGY: CORRELATEDEVOLUTIONARY STAGES OF LANGUAGE, ART AND TOOLSALEXANDRA R. KRATSCHMER*1 and JACOB WAMBERG2*Corresponding Author: romak@cc.au.dk
1Department of Linguistics, Cognitive Science and Semiotics, Aarhus University,

Denmark

2Department of Art History, Aesthetics and Culture and Museology, Aarhus

University, Denmark

Bringing together the archaeological record from art history and tool production, this paperdelineates four different stages of semiotic and hence communicative abilities for hominin
evolution from the primate stage to modern homo sapiens: (1) reactive-mimetic; (2)proactive-mimetic; (3) hyperindexical; and (4) externalized iconic and symbolic. These
evolutionary stages presuppose a gradual emancipation of a virtual sphere detached fromnature and a corresponding giving over of hominin evolution to culturally rather than
genetically determined conditions. This transition comprises a spiraling turn in the Lower
Paleolithic, in which the phylogenetic lifeworld emerges as an enlargement of the intimate
space of postnatal ontogenesis, what we term a macro-cocoon. Although we hold that any
hominin line would possibly follow this route of development, either independently or ininteraction with other contemporary lines, only Homo Sapiens reached the fourth stage.

1. Introduction: an interdisciplinary approachThis paper presents a transdisciplinary model of human evolution, linking
linguistics with art history and prehistoric archeology. Its approach is systemsoriented, suggesting that humanoid mental capacities have evolved in increasing
interaction with diverse forms of cultural practices. Because of the closeinteraction between “outer” cultural practices and their imprints in “inner” brain
disposition, we must suppose certain stages of human evolution, in whicharchaeological traces of semiotic activities and utilitarian artifacts could be seen
as evidence of stages of certain brain dispositions, spanning cognition, emotions
and religious beliefs. We suggest a preliminary model with four basic steps,
whose ground tendencies are supposed to be applicable to any hominin line, living

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

438

or extinct. Although any hominin line would possibly follow this route ofdevelopment, either independently or in interaction with other contemporarylines, only Homo sapiens reached the fourth stage. In our concluding synoptic
table, we exemplify the different stages by samples from the currently available
archaeological record, but without claiming them to be exhaustive.
Our guiding presupposition is that the advancement of hominin culture creates avirtual niche, or sphere, that is increasingly autonomous from the living andsurviving conditions offered by the natural habitat (Harari [2011] 2014), and that
this involves a corresponding autonomization of semiotic signs in relation to theirreferents. It is indeed the crystallization of such a relatively autonomized virtual
sphere that pauses the genetically based anatomical evolution of the humanspecies. Human evolution is now predominantly transferred – sublimated – to thedomain of social learning, i.e. accumulative culture. Here, anatomical changes
only happen after birth in the form of micro-changes in cortical morphology anddiverse bodily techniques (Deacon, 2010; Krubitzer & Prescott, 2019).
However, the mature stage of this sphere, presenting the first externalized icons
and symbols, was only reached after a long evolutionary process that could becompared to a spiraling turn. In total, we distinguish the following four major
evolutionary stages: (1) reactive-mimetic: a stage, shared with other primates, aswell as dolphins and certain corvids, in which the early hominin can recognize its
mirror image as referring to itself; (2) proactive-mimetic (beginning 2,5 mio BP):the earliest specifically humanoid stage marked by stone tools, the ability to
project iconic shapes onto pre-existing natural forms, and mimetic body language;(3) hyperindexical (beginning ca. 700-500,000 BP): a stage in which thephylogenetic life world at large becomes modeled on the intimate space of early
postnatal ontogenesis, resulting in an animated ecology with the first thoroughly
externalized semiosis: a stylized reactualization of indexical communication,
including proto-symbolic speech (musical proto-language) and nonfigurative
pictorial patterns; and (4) externalized iconic and symbolic (beginning ca.
100,000-40,000 BP), marked by representational pictures and symbolic language.
2. The four evolutionary stages: semiosis of art, toolmaking and language
The initial stage (1) of hominin semiosis, reactive-mimetic, has to be the one
present in contemporary great apes (Anderson & Gallup, 2011), which we assume
was prevalent in both ancestral non-hominin primates and early hominins – the439

ability to recognize oneself in a mirror (e.g. of still water). This ability relies on
the immediate indexical association between the self and its reflection. Although
the hominin mind hereby identifies itself with something outside the body, thisidentification does not yet presuppose a consciousness of the distinction between
body and mirror reflection but merely their close association. As is also the casewith contemporary apes, this ability to self-reflect is allied with an ability to use
stone tools (Kortland, 2013) and to communicate by vocalizations and gestures,with a predominance of the latter in close-range communication, characterized byintentionality (deictic) reference, iconicity (pantomime), and turn-taking, i.e.
communication structured between dialogue partners (Fröhlich et al. 2019).
The second stage, the proactive-mimetic one (2), covers the two tool industry
periods of the Oldowan and the Acheulean. Its initiation is exemplified by theSouth African Makapansgat Pebble, a hominid face-like, but nature-created, stonethat 3-2,5 mio BP apparently was retrieved and transported from its finding site
to their habitation 50 miles away by supposedly Australopithecines. This effort
could be seen as a manifestation of the general idea of ‘human face’, in Peirce’s
categories of a so-called legisign, “a law that is a sign.” However, at this stage,
the representation is established only through reaction to something already given,
rather than production (Watson, 2015 as quoted in Hampton, s.a.). What is
specifically hominin and not found in apes is the ability to understand the double
nature of the image as a sign that is both similar and dissimilar to the thing it
represents. Probably the oldest documented hominid stone tool production,
consisting of stone tools coarsely chipped at the edges to yield a cutting edge (dela Torre, 2011), the East African Oldowan culture by Homo habilis, from ca. 2,61,7 mio BP, represents the first level of tool production linked to this mental levelof image recognition (Sernaw et al. 2003). The next level of stone tools within
this stage is the Acheulian type (hand axes and cleavers): bifacially knapped,
symmetrical artifacts, produced by the homo lineages Erectus andHeidelbergensis (Shipton & Nielsen, 2018). The earliest record so far dates backto 1.76 mio BP (Lepre et al., 2011), and the industry remained productive until
about 130,000 BP. Peterson et al. (2018) specify the existence of the Acheulean
hand axe as a type (category/model) to be realized in form of a token (the concrete
handaxe) in the terms of the Peircian sign types of respectively “legisign” (type/anidea) and “sinsign” (token/an actual singular thing) (Bergmann et al., 2003f.). We
hold that, while interior legisign and exterior sinsign are in fact highly detachedat this evolutionary stage, the sign user does not experience them as separate. At440stage (2), intra- and intergenerational technology was therefore still transferable
through mimetic-imitative embodied learning alone.
To approach the complex hyperindexical stage (3), we should understand it as aphase that moves the expanding world of inner representations closer to the outerworld of objects through dense indexical connections. It could be specified aswhat Tomlinson (2017: 13) calls “hyperindexicality,” that is, the “systematic andhierarchical arrangement of indexes in relation to one another, which brings themclose to one of the characteristic features of the symbol.” How this bridgebuilding between inner and outer worlds is realized could be illuminated byconsidering the further evolution of hominins as an exposure of a now more
vulnerable organism towards its accordingly more protective environment. For a
crucial price for the evolutionary rise of complex brains and sensoria seems to be
an intensification of the initial protection of individual organisms, which isunderway throughout the evolution of eukariotic organisms: the ontogenetic
protective worlds of eggs, wombs, nests, and warm spaces for suckling. Hominins
are located at a particularly intricate crossing point between rising autonomizationof the brain and this accompanying protection of embryos and infants, since now
this protective cocoon not only restricts itself to the ontogenetic evolution ofwomb and suckling environment. Rather, in a transferal from onto- tophylogenesis, it seems to be projected onto the physical environment at large in aprocess, which the German philosopher of culture, Peter Sloterdijk (1999), terms
Blasen (blowing). Corresponding with this giving-over of protection to the macrococoon of the surrounding world, the hominin body seems to become increasingly
fragile and open in its naked state. Prematurely born and continuing its neoteny,
the furless vulnerable state of young mammals into adulthood, the naked humanape (Morris, 1976) only becomes completed through wrapping itself in new
artificial furring: the macro-cocoon of semiotics and technology (Gehlen [1940]2016). It is this process of self-domestication (Deacon, 2010) that generates thehyperindexical stage, in which inner signs are connected to the outer world of
objects and events through intense indexical communication.
Although evolutionary linguistics seems to lack a systematized model of thisprocess of self-domestication, Darwin’s original idea about a “musical protolanguage” has lately gained renewed scholarly interest and might be a well-suited
explanatory candidate for accounting for the communicative underpinnings of thisprocess: an indexical form of proto-symbolic communication, in which sounds
are stylized according to intensity, rhythm and pitch (Ravignani & de Boer, 2021).
As suggested by Michael Tomasello (2008), this sort of proto-symbolic language

441

is very similar to the nonlinguistic communication between early infants andcaretakers. To further elaborate this connection between onto- and phylogenetic
development we should consult the paleo-art historian Ellen Dissanayake (2000)
who traces the origins of visual art to exactly a phylogenetic upscaling of theintimate indexical communication between parent and infant.
In terms of visual art, the pre-symbolic indexical patterns of parent-infant
communication are confirmed by the surviving remnants of Lower Paleolithic art,from Homo heidelbergensis to Homo neanderthalensis. Their exclusively nonfigurative patterns, such as parallel lines or criss-crossings, could thus beconceived of as ritualistic traces, imprints of performative actions, whose
significance is retained in indexical form. The main surviving evidence includes
the Pseudodon Shell DUB 1006-fL (540,000-430,000 BP) from Trinil, Java
(Joordens et al., 2015), the elephant bone from Bilzingsleben, Germany (400,000350,000 BP) (Mania & Mania, 1988), the ochre and ostrich eggshell fragmentsfrom the South African Blombos Cave and Diepkloof Rock Shelter (100,000 BP)(Tylén at al., 2020), and the criss-cross patterns engraved by Neanderthals in the
rocky walls of the Gorham Cave, Gibraltar (ca. 39,000 BCW) (Callaway, 2014).
The same semiotic mechanisms must have applied in the case of the Schöningenwooden hunting spears (nine spears, one lance, a double pointed stick, and a burnt
stick) from 300,000 BP, made by Homo heidelbergensis or early
Neanderthalensis (Schoch et al., 2015). The cultural niche, where abstract models(types) and concrete tokens must coexist in the minds of the artisans, allows forthe passage from indexical to symbolic thinking. On the practical level ofcommunication, the indexical event of namegiving of concrete tokens (living or
non-living entities) precedes the symbolic event of namegiving of
categories/types (legisigns), and is supposedly found at this cultural state (Deacon1997).
Stage (4) is finally characterized by externalized icons (representational pictures)and externalized symbols (spoken words), with the latter possibly preceding theformer, at least if we keep to what the currently available archaeological record
allows us to infer. The externalization of mammalian and avian fauna in the cave
paintings of Spain and France, and on the Indonesian island of Sulawesi, createdduring a period from ca. 45,500 to 12,000 BP (García-Diez et al., 2013; Ducasse& Langlais, 2019), were probably dependent on a beginning division of the Lower
Paleolithic macro-cocoon. Because humans now felt separated from an emergent
maternal underworld, a bridge was constructed in the form of pictorial animals
that seemed autochthonously born through the cave walls, the membrane between

442

the other world and ours (Clottes & Lewis-Williams, [1996] 1998). Suchexternalized pictorial signs thereby made remote realities of all kinds accessible,
including those of imagined abstractions. If we now look at the archaeologicalevidence for bow-and-arrow technology, the complexity of its complementary
design (hafting of different components with several-component glues, etc.)
demands communication of highly abstract information, which can only be
performed via a similarly externalized symbolic language system (Wadley, 2010;
Lombard & Haidle, 2012). The emergence of such a system is hence a prerequisite
for this cultural stage, whose archeological record can be traced back to 70,000
BCW.
In the following synoptic Table 1, we will, in conclusion, recapitulate the steps ofthis chronology, while emphasizing that the datings must be taken as
approximative and veiling the plausible assumption that there has been
considerable overlap in time as well as depending on space.
Table 1. Conservative evolutionary chronology of semiotic stages, art production, tool making andcommunication in hominins (approximative and simplified).
Datingprimate
3-2,5 mio
2,6 mio
1,76 mio– 130.000
540430.000
400350.000
300.000

Semiotic stages
reactive-mimetic

proactivemimetic;
legisigns andsinsigns

45.00020.000Tools
stones andsticksCommunication

OldowanAcheulean

vocal, gestural(mimetic)

gestural, vocal

Trinil Shell
hyperindexical

100.000
70.000Art
recognition ofown mirror
image (pre-stage)
Makapansgat
pebbleBilzingsleben
Bone
Blombos,
Diepkloof
engravings

externalized
symbolic andiconicSchöningenspearsnamegiving of
tokensbow & arrowsymbolic language

Sulawesi
paintings
Lascaux
paintings(namegiving of
categories/types)

443

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NEURAL CORRELATES OF SPEECH AND NON-SPEECHPRODUCTION IN CHILDHOODLANCHEROS, M.*1, ATANASOVA, T. 1, LAGANARO, M.1*Corresponding Author: monica.lancherospompeyo@unige.ch
1 Faculty of Psychology and Educational Science, University of Geneva, Geneva,

Switzerland

Speaking is a complex behavior at the intersection between cognitive and motor
processes that implies the coordination of multiple systems and of multiple vocal-tract
muscles. The uniquely-human ability to speak indicates that some mechanisms
underlying speech production have diverged from those of our prelinguistic ancestors.
However, the basic capacity to vocally produce sounds is shared with a surprisingly
diverse group of species. In fact, the oromotor structures in charge of articulating
speech are not only responsible of the production of speech targets but they also
generate other sounded orofacial movements, such as lip smacking or tongue clicks.
Although the brain dynamics underlying the production of speech and non-speech
gestures in adults are shown to differ (Lancheros, Jouen and Laganaro, 2020) the issue
is then when in ontogeny the production of speech differs from that of non-speech. In
the present study we explore this question, with the hypothesis of a specialized motor
control system for speech arising only after some years of full practice of speech.
Since the production of speech starts early after birth and its motor entrainment
extends over more than a decade (Ackermann, Hage & Ziegler, 2014), it is likely thata gradual specialization for speech emerges during childhood as a consequence of its
progressively extensive production. However, given that the phonetic inventory is notachieved before the age of seven, a distinct specialized speech neural architecture
might be established only some years ahead (i.e. >10 y.o.).
In the present study participants included fifteen French-native 7-to-9-year-old
children. They were asked to produce 20 sounded non-speech gestures, 20 highfrequency syllables and 20 low frequency syllables, paired on type and mode ofarticulation of the gesture onset. Stimuli were presented throughout video clips in atask targeting motor encoding processes: a delayed production task combined with an

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articulatory suppression task. Their brain activity was in parallel recorded withelectroencephalography (EEG) and event-related potential (ERP) analyses based on
microstates were performed on the time window preceding the vocal onset.
Behavioral results showed that children were less precise when producing low
frequency syllables as compared to non-speech. Concerning production latencies,
non-speech productions were initialized slower than both types of speech stimuli. The
ERP data aligned to the vocal onset showed activation of the same topographical maps
preceding the production of speech and of non-speech sequences, indicating therecruitment of the same neural networks for non-speech and speech. Additionally, thetemporal distribution of the same recruited brain circuits did not differ between thetwo oromotor behaviors, suggesting similar activation dynamics for speech and nonspeech.
Considering that adults do show different brain activation patterns for speech andnon-speech (Lancheros et al. 2020) with the exact same experimental setting, resultsof the present study seem to support the hypothesis that a specialized neural circuit forspeech is not established yet in children aged 7-9 years. Since a distinct neuralrepresentation for speech has been proposed to arise from its extensive motor learning,it might be the case that children at those ages have not practiced enough this oromotor
behavior and thus, speech is not yet a well-established overlearned motor activity with
its specialized brain circuits. Unquestionably, speech does not only depend on theamount of rehearsal it is subjected but it also relies on cognitive, linguistic and motor
developments occurring during childhood, which might also explain why a specializedneural circuity for speech is not yet mature, or at least it is not neurally evident, in 7to-9-year-olds.
References
Ackermann, H., Hage, S. R., & Ziegler, W. (2014). Brain mechanisms of acoustic
communication in humans and nonhuman primates: An evolutionary perspective.
Behavioral and Brain Sciences, 37(6), 529-546.
Lancheros, M., Jouen, A., & Laganaro, M. (2020). Neural dynamics of speech andnon-speech motor planning. Brain and language, 203, 104742.

447

THE IMPORTANCE OF THE FUNDAMENTAL FREQUENCY IN
CHIMPANZEES VOCAL COMMUNICATION
Marion Laporte*1,2, Louis-Jean Boë 2,3 Nicolas Audebert 4*Corresponding Author: marion.laporte@mnhn.com
1 UMR 7194, PaleoFED, Muséum National d'Histoire Naturelle, Paris, France
2 Institut des Sciences du Calcul et des Données, Sorbonne Université, Paris,

France3 GIPSA Lab, CNRS, Grenoble, France
4 CEDRIC (EA4629), Conservatoire National des Arts et Métiers, HESAM

Université, Paris, France

In the quest for understanding the evolution of human language, primates haveoften been taken as an approximation of the human ancestral condition despite
their equally long evolutionary path. Recent studies have shown that several
primates, including chimpanzees, have the capacity to produce vowel-like
vocalizations, despite a high larynx (Boë et al., 2017; Fitch et al., 2016;
Grawunder et al., 2022). However, many of these primates live in complex social
systems in environments where the visibility is low and where they need tocommunicate over long distances. In these cases, the vocal signals produced
involve high intensity signals associated to high fundamental frequencies (fo) thatare better suited for long-distance propagation as they maximize the sound powerradiation (Titze & Palaparthi, 2018). This is especially the case for chimpanzees,
who live in complex fission-fusion societies in forests or savannah habitats(Goodall, 1986).
Here, we argue that these long-distance vocalizations which constitute two-thirdsof all of male chimpanzees’ vocal communication (Arcadi, 2000) cannot always
provide complete formantic information or contrast, as is the case for humanvowels which are produced with low fundamental frequencies. One way aroundthis problem is to look at the fundamental frequency modulation, which is equallywell conserved over long distances and generally robust against surrounding
noise. While the chimpanzee vocal repertoire has been extensively studied overThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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the years, no study has yet looked at the fo modulation per se and its importancein the vocal repertoire of chimpanzees. We aim to evaluate this by determining a
vocal space of the entire vocal repertoire through different yet complementary
machine learning approaches using the fo modulation and Mel-frequency cepstralcoefficients (MFCCs) that are commonly used in human speech recognition andtake into account all the spectral information. We manually extracted the temporal
evolution of the fo for N=+6000 vocal units recorded from wild Ugandan
chimpanzees (11 adult males and 12 adult females). This allowed us to treat a
vocalization as a univariate time series, making them suitable for further analysis.
We tested both supervised classification methods (Support Vector Machines(SVM) (Schölkopf & Smola, 2001) using the GAK kernel (Cuturi, 2011)) andtrained an auto-encoder neural network on the frequential time series to performa non-linear dimension reduction for unsupervised classification. The interesting
aspect of working without supervision is that it does not rely on an a priori manualclassification of vocalizations into different groups and helps to identify patterns
in the data without human bias (t-SNE). We compared the results obtained on the
fo profiles with equivalent classification and dimension reduction techniquesapplied to the spectral information (MFCCs), similarly to how previous works
performed data analysis on primate vocalisations (Wadewitz et al., 2015) and
CNN 2D typically used for image classification but with good performances onspectrogram classification (Hershey et al., 2017).
Our results show that the modulation of the fundamental frequency is an importantfactor in differentiating the vocalisations of the chimpanzee repertoire, despite animportant gradation between calls. An analysis of the fo modulation alone obtains
good results. The classification does not improve with the MFCC approach, but
gains a finer-grained categorization with the CNN 2D approach. We present
clustering provided through t-SNE visualization which permits to better
understand the links between the different vocalisations and the importance of
their gradation.
We examine these results from an evolutionary perspective and discuss theimportance of the fo modulation in chimpanzee vocal communication comparedto human speech production.

449

Acknowledgements
This work is part of the Apesvoice project, funded by Sorbonne University(Emergence 2021-2022) and the ISCD as part of the Origins of Speechteam. We would like to thank Amélie Vialet and Pascal Perrier for theircomments.
ReferencesBoë, L.-J., Berthommier, F., Legou, T., Captier, G., Kemp, C., Sawallis, T. R.,
Becker, Y., Rey, A., & Fagot, J. (2017). Evidence of a Vocalic ProtoSystem in the Baboon (Papio papio) Suggests Pre-Hominin Speech
Precursors. PLOS ONE, 12(1), e0169321.
https://doi.org/10.1371/journal.pone.0169321
Cuturi, M. (2011). Fast Global Alignment Kernels. In Proceedings of the 28th
international conference on machine learning (ICML-11), pp. 929–936.
Fitch, W. T., de Boer, B., Mathur, N., & Ghazanfar, A. A. (2016). Monkey vocal
tracts are speech-ready. Science Advances, 2(12), e1600723.
https://doi.org/10.1126/sciadv.1600723
Goodall, J. (1986). The chimpanzees of Gombe: Patterns of behavior.
Cambridge: Harvard University Press.
Grawunder, S., Uomini, N., Samuni, L., Bortolato, T., Girard-Buttoz, C., Wittig,
R. M., & Crockford, C. (2022). Chimpanzee vowel-like sounds and voice
quality suggest formant space expansion through the hominoid lineage.
Philosophical Transactions of the Royal Society B: Biological Sciences,377(1841), 20200455. https://doi.org/10.1098/rstb.2020.0455
Hershey, S. et al. (2017) « CNN architectures for large-scale audio classification». In : 2017 IEEE International Conference on Acoustics, Speech andSignal Processing (ICASSP). p. 131-135.
Schölkopf, B., & Smola, A. J. (2001). Learning with Kernels: Support Vector
Machines, Regularization, Optimization, and Beyond. MIT Press.
Titze, I., & Palaparthi, A. (2018). Radiation efficiency for long-range vocal
communication in mammals and birds. The Journal of the AcousticalSociety of America, 143(5), 2813–2824. https://doi.org/10.1121/1.5034768
Wadewitz, P., Hammerschmidt, K., Battaglia, D., Witt, A., Wolf, F., & Fischer,J. (2015). Characterizing Vocal Repertoires—Hard vs. Soft Classification
Approaches. PLOS ONE, 10(4), e0125785.
https://doi.org/10.1371/journal.pone.0125785

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WORD SEGMENTATION IS FACILITATED IN ZIPFIAN
DISTRIBUTIONS
Lavi-Rotbain, O. *1, Arnon, I. 1
*Corresponding Author: orilavirotbain@gmail.com
1Psychology Department, Hebrew University, Jerusalem, ISRAEL

While languages differ from one another in many respects, they share certain
commonalties: these can provide insight on our shared cognition and the way it
may impact language structure. Here, we focus on one of the striking
commonalities between languages, the way word frequencies are distributed.
Across languages, words follow a Zipfian (or near-Zipfian) distribution, showing
a power law relation between a word's frequency and its rank (Piantadosi, 2014;
Zipf, 1949). The source of such distributions in language is debated (e.g., Chater& Brown, 1999), but they have been proposed to reflect foundational aspects ofhuman cognition and/or communication (e.g., Ferrer i Cancho & Sole, 2003;
Ferrer i Cancho et al. 2020; Manin, 2008). Regardless of their source, theirrecurrence in language may have advantages for learning: skewed distributions
may facilitate various aspects of language. Such individual biases could be
amplified over time, creating pressure to maintain skewed distributions (as has
been argued for other linguistic domains, e.g., Kirby et al. 2008).
In this paper, we explore the learnability consequences of Zipfian
distributions for word segmentation, a crucial aspect of early language
acquisition. Word segmentation has been studied extensively in the lab using
artificial language learning tasks (Saffran et al. 1996), but learners are usuallypresented with a uniform learning environment where each novel word appears
equally often. The few studies that examined learning from Zipfian distributions
suggest they are beneficial for word segmentation (Kurumada et al. 2013; Meylan
et al. 2012), but the extent and generality of this effect is unclear. More
importantly, we do not know what about Zipfian distributions impacts learning.
Here, we propose and test the prediction that Zipfian distributions are
facilitative because of their lower unigram entropy. We start by quantifying
unigram entropy (using efficiency, a normalized entropy measure, see Eq. 1) inchild-directed speech – children’s actual learning environment - across 15
languages (following Bentz et al. 2017 who did so for adult-to-adult speech). We
find that efficiency spans a surprisingly narrow range across languages(range=0.6-0.7, mean=0.63, SD=0.03). We then test the impact of those values on

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learning by manipulating unigram entropy and distribution shape using a classic
artificial word segmentation paradigm. We compare performance at threeefficiency levels: maximal (uniform distribution), reduced (skewed distribution,with lower unigram entropy than the uniform but higher than natural language),and language-like (within the range found for natural language). We find thatword segmentation in adults is uniquely facilitated in language-like efficiency
overall (see Fig. 1a), and for the low frequency words (see Fig. 1b), but does not
improve in reduced efficiency. We find similar results in a second study using a
differently skewed distribution with similar efficiency.
A
B

Figure 1. Adults' segmentation scores acrossconditions (a), and for low frequency words (b).

These findings show that child-directed speech has similar unigram entropy
across languages, and that these values are uniquely facilitative for word
segmentation. We discuss the possible role of learnability pressures in theemergence of such distributions.
References
Ferrer i Cancho, R., & Sole, R. V. (2003). Least effort and the origins of scalingin human language. Proceedings of the National Academy of Sciences,100(3), 788–791.
Kirby, S., Cornish, H., & Smith, K. (2008). Cumulative cultural evolution in thelaboratory: An experimental approach to the origins of structure in humanlanguage. Proceedings of the National Academy of Sciences, 105(31),10681-10686.
Piantadosi, S. T. (2014). Zipf’s word frequency law in natural language: A
critical review and future directions. Psychonomic Bulletin & Review,
21(5), 1112–1130. Retrieved fromSaffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8month-old infants. Science (New York, N.Y.), 274(5294), 1926–1928.
Zipf, G. K. (1949). Human behavior and the principle of least effort. In Human

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behavior and the principle of least effort. Oxford, England453

HOW THE STRUCTURES OF UMWELTS AND SIGNS ARE RELATED
Eric Leonardis**1 , Arturs Semenuks*1 , Greyson Sims2 , and Seana Coulson1* Corresponding Authors: ejleonar@ucsd.edu; asemenuk@ucsd.edu
1 Department of Cognitive Science, UCSD, La Jolla, USA
2 Department of Philosophy, UCSD, La Jolla, USA

Why do humans use symbols more than any other animal (Deacon, 1998)?
Here we argue that part of the explanation might lie in the effect certain propertiesof the human sensory world have on language.
Following Peirce (1955), semioticians usually distinguish three kinds of signs– indices, icons, and symbols – that are often treated as discrete, separate classes.
However, analyzing the structure of sign types using conceptual blending theory(CBT: Fauconnier and Turner (2008)) allows us to establish a continuity between
them. In CBT, mental spaces are integrated by a projection of those spaces as
inputs onto an emergent blend space, which develops structure beyond the input
spaces alone. Conceptual blends could be ordered on a cline based on how incongruous the structures of their input spaces are. Human thought frequently features
blends with input mental spaces whose structures are in ’fundamental conflict’(Turner, 2014). Signs can be analyzed as blends of the signifier (symbol or otherrepresentation) and the signified (referent) (Peirce, 1955). Indexical and iconic
signs have a relatively close match between the structures of the signifier and thesignified. In symbols, however, the signifier and the signified are inherently discordant due to the arbitrary nature of the former. Importantly, the structural similarity of mental spaces is not an all-or-nothing property, allowing for an accountof gradual emergence of symbols.
Furthermore, every sign includes an interpretant, i.e. the way it is interpretedby its receiver (Peirce, 1955). Because of this, communication should be analyzed relative to an animal’s Umwelt, i.e. the way the world presents itself to it(Uexküll, 1992). In this context the non-arbitrariness of indexical and iconic signs
as precursors to more complex blending in symbols becomes especially important. If we look closely at Umwelts of other animals, iconicity and indexicality
can be found much more readily than before. Perhaps most clearly it can be seenin the often overlooked case of olfaction, as behaviors like marking an object withalarm odor, sexual pheromone, or urine marks can be interpreted as indexical dueto their deictic function of demarcating presence, location, dominance, and territory. Additionally, the smells of those marks bear resemblance to the organism
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454

which produced it, suggesting an iconic relation as well (Parsons et al., 2018). A
clear example is the urine marking of fire hydrants by dogs. McGuire, Olsen, Bemis, and Orantes (2018) suggest that small dogs lift their legs significantly higher
than larger dogs for urine marking. The hydrant acts as a dominance display
record, where the highest urine mark indexes the presence of a higher ranking
individual. The suggestion is that dogs arriving at the fire hydrant interpret it asan iconic/indexical sign of their conspecifics in the area. The spatially arranged
layout of the urine samples is interpreted as systematically mapping onto suchproperties of those individuals as size or dominance. Additionally, it might afford
the perception of information about the temporal order of other dogs’ visits to thehydrant or about their relative size. Such an understanding is not accessible tohumans due the different Umwelt we inhabit (Quignon et al., 2003).
Turning to humans, language can be viewed as a high level hyper-blend(Turner, 2014), i.e. a blend using other blends as input mental spaces: in language
the linguistic conceptual space is blended with a production/perception modality,
most typically audition. Importantly, the semantic space of human language has ahigher dimensionality than that of audition, which, leaving aside timing and timbre, is often estimated to be 2D (Keller, 2017). Recent experimental evidence
suggests that iconicity is harder to maintain the more conflicting the topologiesof the meaning and the signal space are, e.g. if the meaning space has higher
dimensionality (Little, Eryılmaz, & De Boer, 2017). In such cases, more compositional or combinatorial structure is likely to emerge1 , leading to the developmentof more symbolic signs. This is indirectly supported by the observation that sign
languages typically exhibit higher levels of iconicity than spoken languages (Taub,2001). Interestingly, olfaction is often argued to have a more highly dimensional
psychophysical space compared to other senses (Mamlouk & Martinetz, 2004;
Magnasco, Keller, & Vosshall, 2015). While human olfactory abilities are often underestimated (Majid, 2021), humans exhibit abundant variation in olfactory
receptor protein genes (Hasin-Brumshtein, Lancet, & Olender, 2009), which affect odor perception (Trimmer et al., 2019), and have many more nonfunctional
olfactory receptor genes compared to other primates and other animals (Gilad,Man, Pääbo, & Lancet, 2003; Quignon et al., 2003). This could plausibly lead tomore variability in Umwelt structure for smell compared to other senses between
humans, and raises the possibility that olfaction – a high-dimensional perceptual
modality affording more means for iconic communication – is less readily available for communication to us than to other animals.
Taken together, the arguments above could suggest that the uniquely high reliance on symbols in humans might be partially explained by the specifics of ourperceptual and communicative Umwelt.
1 This can be seen as a way to increase the dimensionality of the signal space: each compositional/combinatorial element serves as a new temporally separated dimension.

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References
Deacon, T. W. (1998). The symbolic species: The co-evolution of language andthe brain. WW Norton & Company.
Fauconnier, G., & Turner, M. (2008). The way we think: Conceptual blending
and the mind’s hidden complexities. Basic books.
Gilad, Y., Man, O., Pääbo, S., & Lancet, D. (2003). Human specific loss ofolfactory receptor genes. Proceedings of the National Academy of Sciences,100(6), 3324–3327.
Hasin-Brumshtein, Y., Lancet, D., & Olender, T. (2009). Human olfaction: fromgenomic variation to phenotypic diversity. Trends in Genetics, 25(4), 178–
184.
Keller, A. (2017). Philosophy of olfactory perception. Springer.
Little, H., Eryılmaz, K., & De Boer, B. (2017). Signal dimensionality and theemergence of combinatorial structure. Cognition, 168, 1–15.
Magnasco, M. O., Keller, A., & Vosshall, L. B. (2015). On the dimensionality ofolfactory space. bioRxiv, 022103.
Majid, A. (2021). Human olfaction at the intersection of language, culture, andbiology. Trends in Cognitive Sciences, 25(2), 111–123.
Mamlouk, A. M., & Martinetz, T. (2004). On the dimensions of the olfactory
perception space. Neurocomputing, 58, 1019–1025.
McGuire, B., Olsen, B., Bemis, K., & Orantes, D. (2018). Urine marking in maledomestic dogs: honest or dishonest? Journal of Zoology, 306(3), 163–170.
Parsons, M. H., Apfelbach, R., Banks, P. B., Cameron, E. Z., Dickman, C. R.,
Frank, A. S., Jones, M. E., McGregor, I. S., McLean, S., et al.. (2018).
Biologically meaningful scents: a framework for understanding predator–
prey research across disciplines. Biological Reviews, 93(1), 98–114.
Peirce, C. S. (1955). Philosophical writings of peirce. Dover Publications.
Quignon, P., Kirkness, E., Cadieu, E., Touleimat, N., Guyon, R., Renier, C., Hitte,
C., André, C., Fraser, C., & Galibert, F. (2003). Comparison of the canineand human olfactory receptor gene repertoires. Genome biology, 4(12), 1–9.
Taub, S. F. (2001). Language from the body: Iconicity and metaphor in american
sign language. Cambridge University Press.
Trimmer, C., Keller, A., Murphy, N. R., Snyder, L. L., Willer, J. R., Nagai, M. H.,
Katsanis, N., Vosshall, L. B., Matsunami, H., & Mainland, J. D. (2019).
Genetic variation across the human olfactory receptor repertoire alters odor
perception. Proceedings of the National Academy of Sciences, 116(19),9475–9480.
Turner, M. (2014). The origin of ideas: Blending, creativity, and the human spark.
Oxford University Press.
Uexküll, J. von. (1992). A stroll through the worlds of animals and men: A picturebook of invisible worlds.

456

CALL COMBINATIONS IN CHIMPANZEESMAËL LEROUX*1,2,3, ANNE M. SCHEL4, CLAUDIA WILKE1,2,3, BOSCO
CHANDIA2, KLAUS ZUBERBÜHLER2,3,5,6, KATIE E. SLOCOMBE7 AND SIMON W.
TOWNSEND1,3,8*

Corresponding Author: mael.leroux@uzh.ch
Department of Comparative Linguistics, University of Zürich, Zürich, Switzerland
2
Budongo Conservation Field Station, Masindi, Uganda
3
Center for the Interdisciplinary Study of Language Evolution (ISLE), University ofZürich, Zürich, Switzerland
4
Animal Behaviour and Cognition, Utrecht University, Utrecht, Netherlands
5
Department of Comparative Cognition, University of Neuchâtel, Neuchâtel,Switzerland
6
School of Psychology and Neuroscience, University of St Andrews, St Andrews, UK
7
Department of Psychology, University of York, York, UK
8
Department of Psychology, University of Warwick, Warwick, UK
1

Through combining a finite set of words into larger compositional phrases, humanlanguage can express an open-ended, limitless number of messages. Thissyntactic capacity has been argued to be a key feature distinguishing language
from any other non-human animal communication system. However, recent
experimental evidence of syntactic-like structuring in monkeys has challenged
this assumption and suggests syntax might be evolutionary more ancient with its
origins deeply rooted in the primate lineage. Comparable data in great apes, ourclosest-living relatives, are central to reconstructing the more recent evolutionary
history of syntax, yet are currently lacking. In this study, we address this issue
and provide the first robust evidence for rudimentary syntactic structuring in ourclosest-living relatives, chimpanzees (though see Crockford and Boesch 2005;
Leroux et al. 2021, for evidence of combinatoriality). Chimpanzees produce“alarm-hoos” when surprised or frightened and “waa-barks” when potentially
recruiting other group members during aggression or hunting. Anecdotal datasuggested chimpanzees also combine these two calls together, specifically when
encountering a snake while being isolated from the main group. Throughpresenting wild chimpanzees with model snakes we i) confirmed combinations
were more likely to occur when an individual encountered the snake separated
from the group and ii) investigated receivers’ responses showing they were more
likely to join the caller after hearing the call combination compared with singlyoccurring “alarm-hoos”. Finally, to confirm these findings and verify theThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

457

meaning-bearing nature of the call combination, we conducted systematic
experiments, playing back an artificial call combination and both calls produced
independently. Chimpanzees reacted most strongly to the combination, showing
quicker and longer responses, compared with both individual calls. We conclude
the “alarm-hoo + waa-bark” combination represents a compositional syntacticlike structure, where the meaning of the sequence is derived from the meaning of
its parts. Our work supports previous work in monkeys and indicates language isunlikely to have evolved de novo in the human lineage but rather the cognitive
building blocks facilitating syntax were already present in our last common
ancestor with chimpanzees and are perhaps even older.
Acknowledgements
We are grateful to the staff of the Budongo Conservation Field Station forsupporting us with this study, and to the Royal Zoological Society of Scotland
for providing core funding. We also thank the Ugandan Wildlife Authority,
the Ugandan National Council for Science and Technology and thePresident’s Office for permission to conduct this research. We thank Denis
Lomoro for helping with data collection.
References
Crockford, C., & Boesch, C. (2005). Call combinations in wild chimpanzees.
Behaviour, 142, 397–421.
Leroux, M., Bosshard, A. B., Chandia, B., Manser, A., Zuberbühler, K., &Townsend, S. W. (2021). Chimpanzees combine pant-hoots with foodcalls into larger structures. Animal Behaviour, 179, 41–50.
https://doi.org/10.1016/j.anbehav.2021.06.026

458

COMMUNITY SIZE INFLUENCES WORD ORDERSHIRI LEV-ARI 1*1,
*Shiri.Lev-Ari@rhul.ac.uk
1Psychology Department, Royal Holloway, University of London, Egham, UK

There are six possible ways to order Subject, Verb, and Object in a sentence, but
the distribution of these orders across the world’s languages is highly skewed:
SVO and SOV are more than 100 times more common than OSV (Dryer, 2005).
It is often hypothesized that the preferred word orders reflect cognitive biases in
event perception and optimization for communication (e.g., Gibson et al., 2013;
Hahn et al., 2020; Schouwstra & de Swart, 2014). Then why do not all languages
use these orders? This study tests whether languages are more likely to manifest
universal tendencies if they face more communicative challenges. It specificallytests whether larger communities are more likely to use preferred word orders as
a way to overcome the greater communicative challenges they encounter.
Larger communities face greater communicative challenges: they have greater
input variability, less shared history, and information takes longer to travel.
Larger communities overcome these challenges by creating more robust
languages, including languages with more systematic grammar (Lupyan & Dale,
2010; Raviv et al., 2019) and greater sound symbolism (Lev-Ari et al., 2021).
These languages thus align with prior cognitive biases (e.g., sound-meaning
associations) to facilitate communication. Reliance on word orders that fit
cognitive biases can similarly facilitate communication.
To test this hypothesis, 15 groups of 8 participants (large groups) and 15 groupsof 4 participants (small groups) played a communication game in dyads for 14
rounds, changing partner every round. Participants described videos from Hall etal. (2013) to their partner by ordering pictures of the subject, object, and action.
The partner guessed which video they saw and both received feedback. Thenthey switched roles. Thirty additional participants played the game alone (solo).

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To test whether group size influences reliance on universally favored word
orders, word order on each trial was coded as 1 if it was SVO or SOV, or 0
otherwise. A logistic mixed effects model was conducted with Group Size (solo,
small, large; ref level=large), Round (scaled), their interaction, as well as
Participant’s Native Word Order as fixed effects, and Participant nested within
Session, Session, and Items as random variables. It revealed that reliance on acommon word order increased with time for large groups (β=0.57, p<0.001) but
that it did not increase as much among the small groups (β=-0.18, p<0.01) andsolo individuals (β=-0.24, p<0.001). The plot suggests that this is driven by
SVO order dominance. These results indicate that common word orders,
especially SVO, are driven by communicative pressures. The fact that the use ofcommon word orders increases with round, that it is significant even after
controlling for the word order in participants’ native language, and that it
depends on group size, indicates that performance was not driven by knowledgeof native language but by the properties of the interaction.

Figure 1. Word Order as dependent on Round and Group Size. Thin lines show each session, thicklines show condition averages.

The results of the study suggest that cross-linguistic variability in word ordermight reflect differences in communicative pressures, with less common word
orders being more common in smaller communities. A preliminary survey of1047 languages from WALS (Haspelmath et al., 2005) crossed with population
data from Ethnologue (Eberhard et al., 2020) supports this prediction, especiallyfor SVO order vs others (in line with experimental results; see figure 1). Future
research should further examine how cross-cultural differences in communicative
pressure influence exhibition of universally-favored patterns.

460

Acknowledgements
This research was supported by the BA/Leverhulme Small Research Grants
scheme (SRG1920\101183).
References
Dryer, M. S. (2005). Order of subject, object and verb. In M. Haspelmath, M. S.
Dryer, D. Gil, & B. Comrie (Eds.), The world atlas of language structures(pp. 330–333). Oxford, England: Oxford University Press.
Eberhard, D. M., Simons, G. F., & Fennig, C. D. (2020). Ethnologue:
Languages of the world (23rd ed.). Dallas, TX: SIL International
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A noisy-channel account of crosslinguistic word-order variation.
Psychological science, 24(7), 1079-1088.
Hahn, M., Jurafsky, D., & Futrell, R. (2020). Universals of word order reflect
optimization of grammars for efficient communication. Proceedings of the
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Lupyan, G., & Dale, R. (2010). Language structure is partly determined by social
structure. PloS one, 5(1), e8559.
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order. Cognition, 131(3), 431-436.

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LANGUAGE ACQUISITION AND LANGUAGE EVOLUTION:LESSONS FROM DUAL INHERITANCE THEORY
Josh Linden
Correspondence: jsl169@georgetown.edu
Department of Linguistics, Georgetown University, Washington, DC, United States

Dual Inheritance Theory (DIT; Richerson & Boyd 1978; Russell & Muthukrishna2021) is emerging in biological anthropology and related fields as a leading
candidate for a plausible and highly explanatory account of the recent
evolutionary history of our own species. A growing canon of literature spanningmany decades has explored its implications in detail and found robust empiricalsupport across the social sciences. The theory is built on three core assertions:
1) that early hominins gained the genetic capacity to acquire and transmit
information culturally as well as genetically, 2) that such cultural information
would itself be subject to natural selection, giving rise to a second system ofinheritance operating on a distinct set of selectional principles and 3) that culturegene coevolution would allow the human genome to continue to optimize for thechanging conditions created by the cultural environment (Henrich & McElreath2007). Together with the logic of evolution by natural selection, these axioms giverise to a detailed outline of the development of human cognition with consistentempirical support across such diverse domains as evolutionary biology(Herrmann et al. 2007), evolutionary psychology (Chudek & Henrich 2015),
social psychology (Gervais et al. 2021), psychoanalysis (Whitebook 2019),
psychological anthropology (Paul 2018), anthropology (Henrich 2016),
sociobiology (Boyd & Richerson 1980), economics (Laing 2008), animal
cognition (Horner & Whiten 2004) and many others.
Nonetheless, DIT remains all but absent from theoretical linguistics and its
vast implications for the study of language and particularly of language evolution
have not been explored. There has been relatively little agreement among
theoretical linguists about even how to study language evolution, although somesignificant progress has begun to appear in the last decade. Fitch (2017)
insightfully suggests a multicomponent approach, in which we refrain from
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assuming language was an innovation and instead ask which innovations mightmake it up, and he introduces the term Derived Component of Language (DCL)
to refer to any element of language that we hypothesize arose in humans or our
immediate ancestors after our evolutionary divergence from chimpanzees. He
further points out the necessity of creating multiple plausible hypotheses andcomparing them to each other experimentally rather than considering eachmodel’s plausibility in isolation.
Adopting and extending this framework, the current inquiry explores thetheoretical implications of assuming that language evolution happened primarily
as described by DIT: as a series of culturally transmitted innovations achieved onhuman timescales and continuously adapted to by our genetics on longer
timescales. I show how this assumption reinforces Fitch’s (2017) stagedprotolanguages model approach, and I create an explicitly nonspecific Fitchianmodel in which the uniquely human elements of language arise in some unknown
order DCL1, DCL2, …, DCLn. Situating these as a series of coevolved traits
arising within DIT, I extrapolate the model into the present and conclude that theempirical facts of language acquisition are especially likely to differ undercompeting Fitchian hypotheses.
Informed by existing DIT models of how information is transmitted between
communities and across generations (e.g. Boyer 1998; Linquist 2007), I proposethat two domains featuring partial acquisition are of most pressing interest to theevolutionary linguist: the overlapping but distinct stages of linguistic competence
we observe in first-language (L1) learners, and the empirical facts of homesign.
The former include the babbling stage, first words, the two-word stage, the multiword stage and an extended process of refinement and complexification (Salim &Mehawesh 2014). The latter refers to the structured communicative systems thatarise in Deaf children growing up without exposure to a signed language (Franklin
et al. 2011; Coppola & Newport 2005). Homesigners reliably engage in manual
babbling, invent signs with diverse meanings and begin to combine them to enter
the two-word and multi-word stages (Morford 1996). They do not, however,develop all further elements of Universal Grammar, and this is highly informativein light of the coevolutionary optimization loops the present model describes. I
borrow the term cognitive dependency (Seuren 2006) from psycholinguistics andcognitive semantics, which I formalize within the DIT framework by analogy toexaptation (Gould & Vrba 1982) in evolutionary biology. I then use it to argue
that if DCLi is reliably observed in homesign or at some stage of L1 acquisition
while DCLj is not, it is likely that i < j. Finally I consider briefly which potential
orderings of DCLs preliminarily appear best supported.

463

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SYNONYMS AND HOMONYMS IN SIGNALING GAME –WHICH ARE STABLE AND WHEN?
DOROTA LIPOWSKA*1* Corresponding Author: lipowska@amu.edu.pl

1 Chair of Linguistic Methodology, Adam Mickiewicz University in Poznań, Poland

Evolution and structure of language is often analysed using computationalmodelling (Cangelosi & Parisi, 2002; Nolfi & Mirolli, 2010; D’Ulizia, Ferri, &Grifoni, 2020). A particularly appealing research paradigm is inspired by the ideathat language might have spontaneously appeared in a population of communicating individuals, possibly with some adaptive features (Pinker & Bloom, 1990).
This standpoint prompted numerous analysis of multi-agent models, which mimic
such communication and try to infer the properties of the emerging language andits possible further evolution (Steels, 2012; Gong, Shuai, & Zhang, 2014; Kirby,
Griffiths, & Smith, 2014).
In certain models of this kind, language emergence and evolution is studied
using the signaling game (Lewis, 2002), where communicating agents must decide
which signal to send or how to interpret the signal they have received. To copewith this, agents very often use some form of the reinforcement learning (Skyrms,
2010; Lenaerts, Jansen, Tuyls, & De Vylder, 2005; Barrett, 2006; Franke, 2016;
Mühlenbernd & Franke, 2012; Lipowska & Lipowski, 2018).
By taking signals as words, we can regard the form-meaning mapping thatemerges during the signaling game as language. The mapping can be a one-toone correspondence (in a signaling game terminology, it is a signaling system),but there are also other possibilities, namely, homonyms or synonyms can emerge.
Neglecting some linguistic nuances (Ravin & Leacock, 2000), we can posit thatin the case of homonymy, one word carries different meanings while synonymy
means that a single concept is expressed by different words.
It seems quite plausible that synonyms or homonyms change in time. For example, the frequency of their usage may change and gradually one form will be
preferred over the other, and the latter eventually can even disappear. Although
linguistic data are difficult to interpret, there are some indications that in natural languages synonyms are quite rare in contrast to homonyms, which appear
to be more common (Hurford, 2003; Clark, 1990). Some linguists even insist
that true synonyms do not exist or at best are very rare compared to homonyms(Lyons, 1981; Goldberg, 1995). There are some arguments that the difference inThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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the frequency of synonyms and homonyms may be due to evolutionary pressuresfavouring speakers rather than hearers (Hurford, 2003), or to language acquisitionin childhood (Markman, 1989).
Let us notice that synonyms actually compete in a quite different way fromhomonyms, which can be demonstrated already within the framework of the signaling game. While synonymous words compete for being selected by a speaker,
for a homonymous word, it is the hearer’s role to assign an appropriate interpretation. It is thus possible that such a difference can affect an overall dynamics
of synonyms and homonyms and eventually result in different degrees of theirprevalence.
We approached the problem of evolution and stability of synonyms andhomonyms using computational modelling. Within the framework of the signaling game, we show that the reinforcement learning should operate in the so-calledsuper-linear regime with probabilities of selections increasing faster than linearlywith the accumulated weights. The linear regime would, instead, lead to languages with very stable synonyms and relatively fast decaying homonyms, which
is probably at odds with some linguistic observations (Hurford, 2003). Our work
indicates that the prevalence and evolution of synonoyms and homonyms in natural languages may give us some valuable clues as to the nature of the mechanismsthat drive linguistic processes. More details concerning our model and the results
obtained can be found in (Lipowska & Lipowski, 2022).
Perhaps an interesting question is why nonlinear rather than the simplest(maybe naively expected) linear feedback drives linguistic processes. Related
studies, under mathematically similar setup of the so-called urn models, in certain marketing or economic contexts showed that the value of the system grows
faster than linearly with the number of users. Such a behaviour seems to characterize competition between, e.g., video formats, operating systems, and eventypes of keyboards. In the literature, it is referred as Metcalfe’s Law (Shapiro &Varian, 1998; Arthur, 1994). In the signaling game and in linguistic context, it
would mean that a benefit of using a certain word (and thus a probability of its
future selection) increases faster than linearly with the number of successful communications. Considering the complexities of language evolution, with its various
social, biological, and cognitive aspects, it seems quite likely.
References
Arthur, W. B. (1994). Increasing returns and path dependence in the economy.
University of Michigan Press.
Barrett, J. A. (2006). Numerical simulations of the Lewis signaling game: Learning strategies, pooling equilibria, and the evolution of grammar. UC Irvine:
Institute for Mathematical Behavioral Sciences.
Cangelosi, A., & Parisi, D. (Eds.). (2002). Simulating the evolution of language.
London: Springer-Verlag.

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Clark, E. V. (1990). On the pragmatics of contrast. Journal of Child Language,17(02), 417–431.
D’Ulizia, A., Ferri, F., & Grifoni, P. (2020). A survey on modeling language
evolution in the new millennium. New Generation Computing, 38(1), 97–
124.
Franke, M. (2016). The evolution of compositionality in signaling games. Journalof Logic, Language and Information, 25(3), 355–377.
Goldberg, A. E. (1995). Constructions: A construction grammar approach toargument structure. Chicago: University of Chicago Press.
Gong, T., Shuai, L., & Zhang, M. (2014). Modelling language evolution: Examples and predictions. Physics of Life Reviews, 11(2), 280–302.
Hurford, J. R. (2003). Why synonymy is rare: Fitness is in the speaker. In
W. Banzhaf, T. Christaller, P. Dittrich, J. T. Kim, & J. Ziegler (Eds.), Advances in Artificial Life. Proceedings of the Seventh European Conferenceon Artificial Life ECAL 2003 (Vol. 2801, pp. 442–451). Berlin, Heidelberg:
Springer-Verlag.
Kirby, S., Griffiths, T., & Smith, K. (2014). Iterated learning and the evolution of
language. Current Opinion in Neurobiology, 28, 108–114.
Lenaerts, T., Jansen, B., Tuyls, K., & De Vylder, B. (2005). The evolutionary
language game: An orthogonal approach. Journal of Theoretical Biology,
235(4), 566–582.
Lewis, D. K. (2002). Convention: A philosophical study. Oxford, UK: Blackwell.
Lipowska, D., & Lipowski, A. (2018). Emergence of linguistic conventions in
multi-agent reinforcement learning. PLoS ONE, 13(11), e0208095.
Lipowska, D., & Lipowski, A. (2022). Stability and evolution of synonyms andhomonyms in signaling game. Entropy, 24(2), 194.
Lyons, J. (1981). Language, meaning and context. London: Fontana.
Markman, E. M. (1989). Categorization and naming in children: Problems of
induction. MIT Press.
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in small-world networks. In Proceedings of the 21st International Conference on Behavior Representation in Modeling & Simulation (BRIMS 2012)(pp. 37–42). Red Hook, NY: Curran Associates, Inc.
Nolfi, S., & Mirolli, M. (Eds.). (2010). Evolution of Communication and Language in Embodied Agents. Berlin, Heidelberg: Springer-Verlag.
Pinker, S., & Bloom, P. (1990). Natural language and natural selection. Behavioral and Brain Sciences, 13(4), 707–727.
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Oxford University Press.
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Amsterdam: John Benjamins.

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HOW MUCH DO WE AGREE ON WHAT WORDS MEAN?
GARY LUPYAN*1,, STEVEN PIANTADOSI2, LOUIS MARTÍ2, CELESTE KIDD2,
ELLA LIU1, JEROEN VAN PARIDON1 and YANCHAO BI3*Corresponding Author: lupyan@wisc.edu
1

Dept of Psychology, University of Wisconsin-Madison, USA 2Dept of Psychology, UC Berkeley,
USA 3IDG/McGovern Institute for Brain Research, Beijing Normal University, China

At the core of every language is a vocabulary—a set of building blocks fromwhich language users can construct arbitrarily complex meanings. Vocabularies
are, of course, learned. Members of a speech community must learn the form ofeach word—its phonology—but also, critically, its meaning. Where do word
meanings come from? And how similar are the meanings of the same word indifferent people within a speech community? These questions are of central
importance to the study of language evolution for at least three reasons. First,
understanding the emergence of vocabularies requires that we understand theextent to which words demarcate cognitively privileged categories (i.e., words
label our concepts, e.g., Snedeker & Gleitman, 2004), as opposed to demarcating
categories that are shaped by communicative needs and history—categories thatmight not be learned in the absence of labels (words help create concepts, e.g.,
Lupyan et al., 2007; Thompson et al., 2020). Second, for languages to function as
effective communicative systems, it is generally thought that people must closely
agree on what words mean (e.g., Hutchins & Hazlehurst, 2006). If word meaning
variability within a speech community is pervasive, it would raise the question of
how much agreement is really necessary for effective communication. Third, ifword meaning variability is indeed pervasive, how can we use language tocoordinate as effectively as we seem to? When we talk, misunderstandings seemto be more the exception than the norm. Here, we bring together several sourcesof recent evidence collected in our respective labs and observed in published word
norms that suggest that within a speech community, differences in meanings foreveryday words may be more extensive than previously realized (cf. Clark, 1998).
There is no gold standard for quantifying and comparing word meanings, andso we sought to estimate variability in a few different ways: (1) Sorting, e.g., sortthese words according to how similar they are to one another. (2) Similarity
judgments, e.g., is a penguin more similar to a whale or a seal? (3) Elicitation andendorsement of word meanings, e.g., what does “energy” mean? Which of these
meanings of “energy” best approximates your meaning? (4) Comparison ofpeople’s judgments of semantic dimensions such as concreteness. (5) Beyond

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explicit behaviors, we can also compare (using fMRI) neural representations
elicited by the same word in different people.
Each measure revealed clear similarities, e.g., people clustered body parts
together and separately from animals, clustered positive emotions together andseparately from negative emotions. In general, agreement for concrete words was
greater than for abstract words. But substantial variability existed even for
concrete words. Fig. 1A shows an example of how two people sorted a group ofcommon animals. Sorting-based correlations of such concrete words rarely
exceed r=.5 (Wang & Bi, 2021). We also saw substantial variability when using
similarity probes, e.g., when asked whether a seal is more similar to a penguin ora whale, 44% of people chose “whale”. Interestingly, people were largely unawarethat judgments like these produce divergent responses, believing a large majority
will respond as they themselves did. Those who responded with “whale” thoughtthat 75% would do the same (Martí et al., 2021). Fig. 1B shows endorsement
patterns of various senses of “energy” (generated by a separate set of participants),
revealing three distinct profiles. Fig. 1C shows concreteness ratings (Brysbaert etal., 2014). While many words show expected unimodal distributions, many others
show clear bimodality hinting at systematic differences in how different people
construe these words.

Figure 1. (A) Two solutions on an animal-sorting task. (B) Results from a meaning elicitationand endorsement task for the word “energy”. (C) Distributions of concreteness ratings for two
example words (rating of 1 corresponds to maximally abstract).

Do results such as these indicate true variability? If so, where do these differences
come from? We consider several possibilities including different learning biases,
different sensorimotor experiences, and different linguistic experiences. How dopeople communicate in the presence of these differences? We will discuss threepossibilities: (1) These differences have no consequences for everyday
communication, only showing up in specific contexts. (2) Misalignments arequickly repaired (Healey et al., 2018) or compensated through pragmatic
inference. (3) Consequential errors in communication are more common than
generally acknowledged.

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References
Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand
generally known English word lemmas. Behavior Research Methods, 46(3), 904–911.
https://doi.org/10.3758/s13428-013-0403-5
Clark, H. H. (1998). Communal Lexicons. In K. Malmkjær & J. Williams (Eds.), Context in
Language Learning and Language Understanding (p. 63). Cambridge University Press.
Healey, P. G. T., Mills, G. J., Eshghi, A., & Howes, C. (2018). Running Repairs: Coordinating
Meaning in Dialogue. Topics in Cognitive Science, 10(2), 367–388.
https://doi.org/10.1111/tops.12336
Hutchins, E., & Hazlehurst, B. (2006). How to invent a lexicon: The development of shared symbols
in interaction. https://doi.org/10.4324/9780203993699-17
Lupyan, G., Rakison, D. H., & McClelland, J. L. (2007). Language is not just for talking: Labels
facilitate learning of novel categories. Psychological Science, 18(12), 1077–1082.
Martí, L., Wu, S., Piantadosi, S. T., & Kidd, C. (2021). Latent diversity in human concepts.
PsyArXiv. https://doi.org/10.31234/osf.io/fb8s2
Snedeker, J., & Gleitman, L. (2004). Why is it hard to label our concepts? In D. G. Hall & S. R.
Waxman (Eds.), Weaving a Lexicon (illustrated edition, pp. 257–294). The MIT Press.
Thompson, B., Roberts, S. G., & Lupyan, G. (2020). Cultural influences on word meanings revealed
through large-scale semantic alignment. Nature Human Behaviour, 4, 1–10.
https://doi.org/10.1038/s41562-020-0924-8
Wang, X., & Bi, Y. (2021). Idiosyncratic Tower of Babel: Individual Differences in Word-Meaning
Representation Increase as Word Abstractness Increases. Psychological Science,
09567976211003877. https://doi.org/10.1177/09567976211003877

472

PHONOLOGICAL REPRESENTATION IN BONOBOS (PAN
PANISCUS) AS REVEALED THROUGH VOCABULARY ERRORSHEIDI LYN*1 and MARK YATES2*Corresponding Author: hlyn@southalabama.edu
1Joan M. Sinnott Chair of Psychology and Department of Marine Sciences, University ofSouth Alabama, Mobile AL USA
2Department of Psychology, University of South Alabama, Mobile AL USA

1. Objective
Human minds contain a complex set of associations with each word, morpheme, andphoneme in our language. When we mis-speak, those associations make some errors
more likely than others (Fromkin, 1980). This study seeks to clarify the possible
phonological associations within the mind of language-using bonobos.
2. Methods
Participants were two bonobos (Pan paniscus) that had been reared in a languageenriched environment and shown to understand English sentences similar to theabilities of a 2.5 year old child (Savage-Rumbaugh et al., 1993). Over the course of
10 years, these apes were given vocabulary tests regularly and all responses made to
the lexigram keyboard were recorded and coded for a previous study (Lyn, 2007).
These errors were shown to be non-random and associated with visual, conceptual,and auditory representations of the item. For example, lemons-lemonade, orangeapple, and cereal-milk.
To further explore the auditory components of these representations, phonologicaltranscriptions of the errors were coded as a match/no match from the sample to theThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

473

error. All examples that included shared morphemes (e.g. Orange-orange juice) were
removed, resulting in 1400 example of sample and error pairs.
3. Results
The bonobos made significantly more errors that matched the sample in phoneme thanwould be expected by chance (see Fig 1). Their responses varied by vowel andconsonant, with more consonant matches in the first and third positions, and more
vowel matches in the second and fourth positions. For example, BIG and BUG matchin the first and third phoneme (bIg and b^g) and JUICE and FOOD match in thesecond phoneme (jus and fud).
9%
8%
7%
6%
5%
4%
3%
2%
1%
0%
1st`

2nd

3rd

4th

Figure 1. Percentage of phonological matches across the first four positions of sample and error. Linerepresents conservative estimate of chance, 1/26 (the number of phonemes in each position of the samples
and errors ranged from 26-32). All are p<.001, binomial test.

4. Discussion
Our results suggest that the web of associations in the mind of language using bonobos
is more elaborate than simple auditory similarity and may include phonemic
information. These findings further suggest that this building block of humanlanguage was present before the evolutionary split of humans and bonobos.

474

Acknowledgements
Heidi Lyn holds the Joan M. Sinnott Chair which is funded by the University ofSouth Alabama Foundation.
References
Fromkin, V. A. (1980) Errors in linguistic performance. Slips of the tongue, ear, pen
and hand. Academic Press, New York.
Lyn, H. (2007). Mental representation of symbols as revealed by vocabulary errors in
two bonobos (Pan paniscus). Animal Cognition, 10(4), 461-475.
Savage-Rumbaugh, E. S., Murphy, J., Sevcik, R. A., Brakke, K. E., Williams, S. L.,
Rumbaugh, D. M., & Bates, E. (1993). Language comprehension in ape andchild. Monographs of the society for research in child development, i-252.
Vitevitch, M. S., & Luce, P. A. (2004). A web-based interface to calculate phonotactic
probability for words and nonwords in English. Behavior Research Methods,
Instruments, & Computers, 36(3), 481–487. https://doi.org/10.3758/BF03195594

475

TESTING THE ‘ROUNDNESS’ AND ‘SHARPNESS’ OFNONWORDS AND SHAPES IN GREAT APES.
Konstantina Margiotoudi*1 and Friedemann Pulvermüller 2,3,4*Corresponding Author: konstantina.margiotoudi@univ.amu.fr
Laboratoire de Psychologie Cognitive, Aix-Marseille University, CNRS, Marseille,
France2Brain Language Laboratory, Department of Philosophy and Humanities, Freie
Universität, Berlin, Germany
3Berlin School of Mind and Brain, Humboldt Universität, Berlin, Germany
4Einstein Center of Neurosciences, Berlin, Germany

1

Although sound symbolic effects of ‘bouba-kiki’ type have been reported acrosscultures (Ćwiek et al., 2022), it remains unclear whether this effect is an ability
unique to humans. Sound symbolic effects between speech sounds and shapes(Köhler, 1919) might have played an important role in shaping protolanguages.
Humans could have understood that certain speech sounds can evoke certain
sensory properties. This ability could have plausibly assisted referential insight inour human ancestors (Perniss and Vigliocco, 2014). The discussion on the role ofsound symbolism in shaping human languages gives rise to the following
question: “Can nonhuman great apes detect congruencies between ‘round’ and‘sharp’ nonwords and curved and sharp shapes?”. Previous research has shownthat a group of touchscreen trained chimpanzees (N=6) and gorillas (N=2), whentested with a two-alternative forced choice task on sound-shape mappings showed
no sound symbolic effects (Margiotoudi et al., 2019). Specifically, both gorillas
and chimpanzees failed to detect that a ‘round’ nonword, such as “bouba”, is agood fit to a curved shape and a ‘sharp’ nonword, such as “kiki”, is a good fit toa spiky shape. In parallel, humans were tested with the same task and showed asignificant sound symbolic congruency. In both tasks, neither the nonhuman nor
the human great apes were explicitly instructed to detect congruencies between
nonwords and shapes. Notably, when a second group of human participants were
explicitly instructed to detect sound symbolic congruencies in the same task, theyThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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showed above chance performance and detected congruencies 10% more often
compared to the previous implicit task. Conclusively, when humans are explicitly
instructed to detect sound symbolic congruencies, they achieve a better
performance. However, humans are exposed to speech stimuli compared tononhuman great apes and have referential insight. Could a nonhuman great ape
exposed to speech stimuli detect speech sound-shape consistencies? In a different
study, a language competent bonobo was tested with a match-to-sample task on
the ‘bouba-kiki’ effect. The bonobo has been able to match English words topictures with the same task. Hence, he tries to pick the best match from different
stimuli. Using his strategy, a second study investigated whether a nonhuman great
ape being exposed to speech stimuli would be able to detect sound symbolic
congruencies under an explicit match-to-sample task. Specifically, the bonobo
performed his familiar match English words to pictures task, while soundsymbolic trials of “bouba-kiki” type were interspersed. This kind of paradigm
allowed testing a nonhuman primate under explicit instructions. The bonobo tried
to guess the best matching shape for the ‘round’ and ‘sharp’ nonwords as he didfor the English word-picture matching. The results of the study revealed no soundsymbolic congruency detection but significantly above chance performance inmatching English words to pictures (Margiotoudi et al., 2022).
These findings suggest that the ‘bouba-kiki’ mapping is plausibly an ability
unique to humans. These results might be explained by neurobiologicaldifferences found between human and nonhuman great apes that are relevant to
the mechanism supporting the speech sounds-shape mappings. We suggest thatthe mechanism of this effect is found in the knowledge of the multimodal productsof our hand actions. This binding has special preconditions found at a neuronallevel, with auditory-visual and motor systems linked in the brain via long whitematter tracts.
ReferencesĆwiek, A., Fuchs, S., Draxler, C., Asu, E. L., Dediu, D., Hiovain, K., ... &Winter, B. (2022). The bouba/kiki effect is robust across cultures and writing
systems. Philosophical Transactions of the Royal Society B, 377(1841),
20200390.
Köhler W. 1929 Gestalt psychology. New York, NY: Liveright.
Margiotoudi, K., Allritz, M., Bohn, M., & Pulvermüller, F. (2019). Sound
symbolic congruency detection in humans but not in great apes. Scientificreports, 9(1), 1-12.
Margiotoudi, K., Bohn, M., Schwob, N., Taglialatela, J., Pulvermüller, F.,
Epping, A., ... & Allritz, M. (2022). Bo-NO-bouba-kiki: picture-word477

mapping but no spontaneous sound symbolic speech-shape mapping in alanguage trained bonobo. Proceedings of the Royal Society B, 289(1968),
20211717.
Perniss, P., Thompson, R., & Vigliocco, G. (2010). Iconicity as a generalproperty of language: evidence from spoken and signed languages. Frontiersin psychology, 1, 227.

478

VOCAL LEARNING IS AT ODDS WITH NON-VOCAL DEXTERITY
Pedro Tiago Martins*1* Corresponding Author: pt.martins@ub.edu

1 Psycholinguistics and Neurolinguistics Lab, University of Ljubljana, Ljubljana, Slovenia

1. Introduction
In this work, I propose that vocal learning complexity is at odds with non-vocal
dexterity. In order words, given some conditions generically required for vocal
learning behavior, species lacking appendages fine-tuned for grasping, object manipulation, etc. (‘prehensile appendages’) are more likely to display complex vocal behavior than species which do have and expertly use such appendages. I
hypothesize that the use of the mouth or analogous structures (and those adjacent
to it) for all or most object manipulation and interaction promotes complex vocal
behavior. Conversely, the existence of appendages tuned for fine control relegates
the vocal apparatus to simpler vocal, feeding, and manipulation behaviors. In
conjunction (and indeed in constant interaction) with several other factors, thisrelationship helps shape the vocal learning phenotype.
This hypothesis has implications for how vocal learning and related abilities
evolved across species, and emphasizes the importance of ecological and behavioral factors for this capacity. This is in contrast with much of the vocal learning
literature which tends to be focused on neural mechanisms alone.
2. A closer look
The species to look at in order to study this relationship are accepted and possible
vocal learners. Here I consider vocal learning in a broad sense (Ghazanfar, Liao,& Takahashi, 2019; Martins & Boeckx, 2020; Fischer, Wegdell, Trede, Dal Pesco,& Hammerschmidt, 2020), not tied to a very specific neural circuit nor limited toimitative behavior, which is crucial for the current hypothesis.
Given a set of factors in place (sound production anatomy, some kind of neuralcircuitry subserving control of that anatomy, functional and ecological pressures
promoting and shaping vocal behavior), this hypothesis adds that well-developed
prehensility in appendages not related to vocal behavior (e.g., “hands”), pushes
species away from complex vocal behavior, while the lack of such appendages ordexterity thereof brings species closer to it. Indeed, for the most part, vocal learning is displayed most clearly by species with not a lot of dexterity (several bird
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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orders, pinnipeds—flyers and swimmers), while other, more dexterous species
with apparently the right mix of factors in place display it in a more arduous, peculiar, or simple manner—to the point where they are often not accepted as vocal
learners (e.g., non-human primates, rodents).
The following independent reasons for this relationship are put forward as partof the hypothesis:
1. lack of manual dexterity puts emphasis on mouth and adjacent structures forobject manipulation, fostering the development of those structures, which
are recruited for volitional control when vocalizing
2. for prehensile species, vocal control requires greater effort than forelimb/manual control (Koda, Kunieda, & Nishimura, 2018)
3. flyers and swimmers tend to cover much larger areas, and need to communicate across larger distances (Janik and Slater (1997) allude to this)
3. Flyers, swimmers, and beyond
This work brings together ecological, anatomical, developmental, and neural dataon a range of species (birds, cetaceans, pinnipeds, elephants, bats, rodents, andprimates) in an attempt to corroborate the hypothesis proposed. Humans, which
are clearly a dexterous and vocal learning species, are seemingly an outlier inthe relationship I propose here and deserve special attention. A multidimensional
approach is followed, according to which there are no magic-bullet explanations
for the evolution of particular cognitive abilities. Indeed, this work defends andadds to the notion that the vocal learning capacity and the factors that contribute to
it are not all-or-nothing affairs, but rather nuanced (Wirthlin et al., 2019; Martins& Boeckx, 2020; Ravignani & Garcia, 2022).
Acknowledgements
I thank Cedric Boeckx, Andrea Ravignani, and members of the Cognitive Biology
of Language Group (University of Barcelona) for discussion and suggestions on
this idea and on other ideas and topics that led to it. This proposal relies in part onwork funded by the Portuguese Foundation for Science and Technology (FCT) in
the form of a PhD fellowship (grant SFRH/BD/131640/2017).
References
Fischer, J., Wegdell, F., Trede, F., Dal Pesco, F., & Hammerschmidt, K. (2020).
Vocal convergence in a multi-level primate society: Insights into the evolution of vocal learning. Proceedings of the Royal Society B: Biological
Sciences, 287(1941), 20202531.
Ghazanfar, A. A., Liao, D. A., & Takahashi, D. Y. (2019). Volition and learningin primate vocal behaviour. Animal Behaviour, 151, 239–247.

480

Janik, V. M., & Slater, P. J. (1997). Vocal Learning in Mammals. In Advances in
the Study of Behavior (Vol. 26, pp. 59–99). Elsevier.
Koda, H., Kunieda, T., & Nishimura, T. (2018). From hand to mouth: Monkeys
require greater effort in motor preparation for voluntary control of vocalization than for manual actions. Royal Society Open Science, 5(11), 180879.
Martins, P. T., & Boeckx, C. (2020). Vocal learning: Beyond the continuum.
PLOS Biology, 18(3), e3000672.
Ravignani, A., & Garcia, M. (2022). A cross-species framework to identify vocal
learning abilities in mammals. Philosophical Transactions of the Royal
Society B: Biological Sciences, 377(1841), 20200394.
Wirthlin, M., Chang, E. F., Knörnschild, M., Krubitzer, L. A., Mello, C. V., Miller,
C. T., Pfenning, A. R., Vernes, S. C., Tchernichovski, O., & Yartsev, M. M.
(2019). A Modular Approach to Vocal Learning: Disentangling the Diversity of a Complex Behavioral Trait. Neuron, 104(1), 87–99.

481

EVOLUTION OF TOPICALIZATION IN HUMAN LANGUAGE
DAIKI MATSUMOTO*1,2*

matsumoto.daiki.24x@st.kyoto-u.ac.jp
Graduate School of Human and Environmental Studies, Kyoto University
2
Japan Society for the Promotion of Science

1This paper shows that the comparative approach to each subcomponent of our mosaic
language capacity can also explain the evolution of seemingly theory-internal specificlinguistic phenomena, by taking what is traditionally called “Topicalization” as an
example. The first section sets the framework, overviews what Topicalization is with someexamples, and introduces the questions to be addressed and answered. The results andhypotheses from the previous analyses of primate calls are sketched in Section 2, whichplay a significant role in the proposal. Section 3 hypothesizes that the origin of
Topicalization can be found in how primate calls are formed. The same section further
argues that Self-domestication played a pivotal role in the genesis of Topicalization inhuman language. Section 4 concludes the paper.

1. Introducing the Question to be Asked and Answered
Studies in the evolution of language center around various topics from thehierarchical syntax to the communicative function of our language. As Boeckx(2021) a.o. rightly points out, language is not a monolithic cognitive capacity but
a mosaic property of our cognition, consisting of various sub-components. Thereis mounting evidence in the literature that once this mosaic property isdecomposed into pieces, remarkable similarities and relations with other species
can be discovered with respect to those subcomponents of our language
competence including intentionality (Tomasello 2008), recursive syntax (Hauser& Fitch 2004), semantics (Hurford 2007), and phonology (Samuels 2011).
However, language as a mosaic whole is not entirely limited to thoseconspicuous cognitive sub-modules. It is widely known that there are many
seemingly language-specific semantic, syntactic, phonological and pragmatic
phenomena that are considered to be universal across individual languages. One

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

482

example can be obtained from what is called “Topicalization1”. Topicalization is
widely known in the relevant literature (see e.g., Culicover, 1999) as a syntactic
operation that fronts a “topic” of a sentence to the sentence-initial position:(1) (Talking about the book over there and your conversation partner asks
you about who bought the book when. Then you reply:)
The book over there, John bought (it) when Mary visited him.
In (1), “the book over there” serves as a topic of the conversation as the question
by the addressee is about it, and it is fronted to the sentence-initial position in youranswer sentence. The rest of the sentence serves as a comment on this topic, which
essentially elaborates what this topic is about.
Interestingly, in the same context, (2) sounds infelicitous. This is because “whenMary visited him” is not a topic of the sentence but is a part of the comment about
the topic “the book over there” (* indicates that the sentence is ungrammatical).
(2) *When Mary visited him, John bought the book over there.
Furthermore, it is known that (3) is dispreferred, in which the topic is placed at
the end of a sentence rather than at the initial position.
(3) *John bought when Mary visited him the book over there.
This talk seeks to show that the nature of Topicalization, which is seemingly
language-specific, can also be captured through the lens of the decompositional
approach to narrow down the gap between human languages and other species’
cognition. The questions to be asked are: why does Topicalization always “front”
a topic rather than postposing etc. the topical element in human language? Is thisa purely language-specific phenomenon, the analog of which cannot be observed
elsewhere? In what follows, it is argued based upon previous studies (Arnold &Zuberbühler, 2006a, b, 2008, 2012, 2013; Schlenker et al., 2016) that it is not
human language specific and the analog can be found in primate calls. We will
also see that the fact that Topicalization always fronts a topic finds its
evolutionary rationale in how primates form a sequence of alarm calls.
2. How Primate Alarm Calls are Pragmatically Formed
1

By “Topicalization,” I mean the grammatical operation that yields the result such as the one in (2)in the text.

483

As is widely known, male Putty-nosed monkeys have two main alarm calls: pyows
and hacks (see Seyfarth et al., 1980a, b for an initial work in the field). Pyows
were initially analyzed as calls that signal that a leopard (Panthera pardus) is in
the vicinity (Arnold & Zuberbühler, 2006a, b). However, in later studies, Arnold
and Zuberbühler (2013) modify their previous analysis and propose that pyows
should be analyzed as calls that simply draw attention to the presence and locationof the caller, signaling the presence of a threat.
Hacks were assumed to be indicative of the existence of crowned eagles(Stephanoaetus coronatus) nearby (Arnold and Zuberbühler, 2006a, b). However,
Arnold & Zuberbühler (2013) has further refined this analysis and proposed theidea that they are true alarm calls that indicate high arousal, which can betriggered not just by an eagle’s presence but also by many other phenomena
related to aerial threats such as tree fall (Schlenker et al., 2016). These calls
facilitate movements appropriate as a reaction to escape the threats.
In what follows, we assume following these authors’ later work that pyows are
calls that signal the presence of a threat and draw attention of the listeners to thecaller while hacks signal that there is an alert causing high arousal triggered by
the presence of an aerial threat. Below, P stands for pyows and H does for hacks.
In their series of work, Arnold & Zuberbühler (2006a, b, 2008, 2012, 2013)
have shown that Putty-nosed monkeys sometimes produce distinct P+-H+
sequences. These P+-H+ sequences consist of a small number of Ps followed by asmall number of Hs. According to Arnold & Zuberbühler, P+-H+ sequences areanalyzed as signals that initiate “group movement”.
In sum, the following “semantics” of Putty-nosed monkeys’ alarm calls is
generalized (from Schlenker et al., 2016 with a slight modification):(4) The “semantics” of the alarm calls by Putty-nosed monkeys
a. P+ signals that there is an alert (and it draws attention to the presence
and location of the caller).
b. H+ signals that there is an aerial predator (an eagle and tree fall, tomention just two).
c. P+H+ initiates a group movement.
One fact that is particularly noteworthy regarding (4) is that P+ sequences are
not observed when an eagle is present in the vicinity. Under the definition in (4a),this absence of P+ is unexpected: since (4a) basically says that P+ can be used if
there is an alert and the presence of an eagle is clearly an alert for Putty-nosed
monkeys, (4a) expects P+ to be observed in this case, contrary to fact.

484

To explain this fact while maintaining the crux of the generalization in (4),Schlenker et al. (2016: 15) propose the following principle:2(5) Urgency Principle
If a sentence S is triggered by a threat and contains calls that convey
information about its nature and location, no call that conveys suchinformation should be preceded by any call that does not.
Urgency Principle in (5) basically states that calls that convey “more urgent”
information should precede ones that convey less urgent one. According to (4),
P+ is a call that signals that there is an alert while H+ signals that there is an aerial
alert. Therefore, (5) predicts that at the presence of an eagle, P+ is preempted by
H+. This is what is observed according to the experiments by Arnold &Zuberbühler, and hence Urgency Principle in (5) neatly explains the fact that (4)fails to capture (I refer the reader to Schlenker et al., 2016 and Arnold &Zuberbühler, 2006a, b, 2008, 2012, 2013, for the details of the experiments).
Furthermore, (5) predicts that not only P+ but also P+H+ is unavailable when
there is an aerial alert such as the presence of an eagle or tree fall. According toSchlenker et al. (2016: 17), this is what we observe (again, due to space limitations,
I refer the reader to Schlenker et al., 2016 and Arnold & Zuberbühler, 2006a, b,
2008, 2012, 2013, for details).
Summing up this section, the distribution of P+, H+, and P+H+ is restricted by
Urgency Principle defined in (5). Before adopting this idea to the biologicalunderpinning of Topicalization, note in passing that Urgency Principle has anevolutionary rationale: it is plausible to assume that the principle in (5) has
emerged evolutionarily since those that obey this principle obviously increase thechance of survival by quickly reacting the potential threats to their lives. Thus, we
can claim that it is a residue of group selection.
In the next section, we hypothesize that Urgency Principle is the origin for
Topicalization observed in (virtually) every human language.
3. Urgency Principle as the Origin of Topicalization
Urgency Principle dictates that those calls that convey information about thenature and location of a threat precede ones that do not convey such information.
2

Of note here is that by this principle Schlenker et al. seek to propose a compositional semantic
account of those alarm calls. Since their compositional semantics does not play a role in what
follows and this principle still plays its role even if their compositional semantics is abstracted away
from, this paper does not delve into the details of Schlenker et al.’s semantic account.

485

In the wild world of the animal kingdom, this abstractly means that calls thatconvey information “about their lives” precede ones that do not convey suchinformation. Given the fact that those calls further evoke escaping movement of
those addressees, this can be further interpreted as follows: those calls uttered inaccord with (5) “are about” the comment (i.e., types of movement that they evoke).
Under this interpretation of the pragmatic function of Urgency Principle, thesimilarity between the principle and human language Topicalization becomes
obvious. Both “front” the information that conveys what the comment is about.
Based upon this, this article hypothesizes the following:(6) Topicalization is evolutionarily derived from Urgency Principle.
(6) claims (i) that Topicalization, which is seemingly not just human-specific but
also language-specific, can find its analog in how primate calls are shaped in aparticular manner, and (ii) that it has Urgency Principle as its evolutionary basis.
Below, the rationales for this hypothesis are provided.
Of note first is that Topicalization has a communicative function and it plays
little role (if any) in constructing a propositional meaning of a sentence: even if
Topicalization does not happen, the basic semantics of (2) remains the same, andits primary role is to front a topic of the current discourse and create a structure inwhich the topic and the comment about it are divided in a communicatively
efficient fashion. This suggests that Topicalization is not required for semantic
composition and hence should not be observed in the first place if nocommunicative function exists in our language use. Succinctly put, Topicalization
primarily has a communicative function.
Notice at this point that Urgency Principle is also a principle that forms asequence that conveys important/urgent information in an efficient way in accord
with the external condition under which those calls are produced.
From these, it is not unreasonable to assume that the functional basis of
Topicalization can be found in Urgency Principle. Then, how did Topicalization
evolve from Urgency Principle? Notice already that Topicalization in humanlanguage is not triggered by urgency in any sense.
To address this issue, I hypothesize that human self-domestication played a keyrole in the evolution of Topicalization from Urgency Principle. As is widely
discussed in the relevant literature (see Theofanopoulou et al., 2017 for an
excellent overview and insights), modern humans are claimed to have undergone
a process of self-domestication which caused the globularization of the brain caseand a reduction in tooth size among many other features peculiar to domesticated486

species (see also Wilkins, Wrangham, & Fitch, 2014 for some pioneering work in
the field). It has also been widely argued that self-domestication reduced reactive
aggression in humans. Self-domestication in humans is argued to be triggered bymany types of socio-environmental chaos such as the climate catastrophe during
the Last Glaciation, which facilitated prosocial behavior of our ancestors (Spikins
et al., 2021). And this is claimed to have resulted in a selection for lessemotionally reactive group partners and for receptiveness towards extra-groupindividuals (Hare, Wobber, & Wrangham, 2012; Pisor & Surbeck, 2019).
Recently, Benítez-Burraco, Ferretti, & Progovac (2021: 1) argue that “thereduction in reactive aggression, one of the key factors in self-domestication
processes, enabled us to fully exploit our cognitive and interactional potential asapplied to linguistic exchanges,” claiming that it played a decisive role in theevolution of our pragmatic capacities.
Based upon this illuminating previous work, I hypothesize that Topicalization,
which has a clear communicative (and hence, pragmatic) function, became
available to humans through self-domestication via socialization, emancipating
us from the prison of urgent threats and reactive aggression. 3 In other words,
because of self-domestication, Urgency Principle could be safely applied to ourlanguage use with no urgency. Once it became nonurgent, it has a clear
communicative function that forms a signal sequence that conveys information inan efficient way in accordance with the discourse, which eventually culturally
evolved and stabilized in the species along with other pragmatic capacities.
As one of the anonymous reviewers suggests, to which I am indebted, Urgency
Principle in (5) can be further restated as a principle that requires “calls that
contain more information that is currently relevant come first.” Under thisabstracted interpretation of (5), the similarity between (5) and Topicalization
should be fairly obvious. Based upon this suggestion by the reviewer, I submitthat what self-domestication facilitated was to liberate our ancestors from urgency,
and it allowed us to generally front more relevant information in the discourse.
In sum, the hypothesis is that though Topicalization resulted from Urgency
Principle, it became nonurgent due to self-domestication. An immediate virtue of
this hypothesis is that it can be further tested experimentally, by designing a

3

One of the reviewers wonders whether the current proposal assumes that “Topicalization emerged
fairly late in human language evolution and so that self-domestication episode is the most relevantone”. I indeed hypothesize so. This can be tested experimentally via simulation by letting a language
evolve and seeing in what stage(s) Topicalization (or a phenomenon analogous to it) emerges. I
leave this important work for future research.

487

computer simulation which examines whether liberation from urgency indeed
facilitates the generalization of (5) as a condition of relevance.
4. Conclusion
To the best of my knowledge, specific grammatical operations have thus far
escaped serious investigation in the circle of language evolution. This paper hasbrought up the operation called “Topicalization” to the body of research questionsin the field of evolution of language and has sought to show that the comparative
approach that has been proven to be fruitful in this field sheds light on how thisspecific, seemingly language-specific operation arose in our lineage.
If language evolution is about how our language biologically evolved, thenthose specific linguistic operations/phenomena should also be taken as serious
objects of evolutionary inquiry. This paper is an attempt to show with a specificexample that those theory-internal linguistic operations that have hitherto escaped
serious evolutionary attention can be studied in a basic “divide and conquer,”
comparative fashion. Needless to say, there are scores of other specificgrammatical operations/phenomena discussed neither in this paper nor in theprevious evolinguistic literature. It is hoped that those will also be studied in thecomparative way, along with search for neurological underpinning among others,in future research.
Acknowledgements
I thank Koji Fujita for a series of discussions on this issue. This work is supported
by JSPS KAKENHI Grant Number JP20J20039.
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643-653.
Arnold, K., & Zuberbühler, K. (2006b). Semantic combinations in primate calls.
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Arnold, K., & Zuberbühler, K. (2008). Meaningful call combinations in a nonhuman primate. Current Biology 18(5), R202-R203.
Arnold, K., & Zuberbühler, K. (2012). Call combinations in monkeys:
Compositional or idiomatic expressions? Brain Language. 120(3), 303-309.
Arnold, K., & Zuberbühler, K. (2013). Female putty-nosed monkeys use
experimentally altered contextual information to disambiguate the cause of
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alarm
calls.
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http://dx.doi.org/10.1371/journal.pone.0065660.

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Boeckx, C. (2021). Reflections on language evolution: From minimalism topluralism. Berlin: Language Science Press.
Culicover, P. W. (1999) Syntactic nuts. Oxford: Oxford University Press.
Elordieta G., Lee, C., Gordon M., & Büring D. (eds.) (2007). Topic and focus:
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Hare, B., Wobber, V., & Wrangham, R. (2012). The self-domestication
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aggression. Animal Behavior, 83(3), 573-585.
Hauser, M. D., & Fitch, W. T. (2004). Computational constraints on syntactic
processing in a nonhuman primate. Science, 303(5656), 377-380.
Hurford, J. (2007). The origins of meaning. Oxford: Oxford University Press.
Pisor, A. C., & Surbeck, M. (2019). The evolution of intergroup tolerance innonhuman primates and humans. Evolutionary Anthropology, 28(4), 210-223.
Samuels, B. D. (2011). Phonological structure: A biolinguistics approach.
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Schlenker, P., Chelma, E., Arnold, K., & Zuberbühler, K. (2016). Pyow-hackrevisited: Two analyses of Putty-nosed monkey alarm calls. Lingua, 171, 123.
Seyfarth, R.M., Cheney, D. L., & Marler, P. (1980a). Monkey responses to threedifferent alarm calls: evidence of predator classification and semantic
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Spikins, P., French, J. C., John-Wood, S., & Dytham, C. (2021). Theoretical andmethodological approaches to ecological changes, social behaviour andhuman intergroup tolerance 300,000 to 30,000 BP. Journal of Archaeological
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CO-EVOLUTIONARY LINKS BETWEEN LINGUISTICALIGNMENT AND COOPERATION –AN EXPERIMENTAL STUDY
THERESA MATZINGER*1,2, MAREK PLACIŃSKI1, ADAM GUTOWSKI1,MARIUSZ LEWANDOWSKI1, PRZEMYSLAW ZYWICZYNSKI1 and SŁAWOMIRWACEWICZ1*

Corresponding Author: theresa.matzinger@univie.ac.at
Centre of Language Evolution Studies, Nicolaus Copernicus University, Toruń, Poland
2
Department of English, University of Vienna, Vienna, Austria

1

Linguistic communication requires a high degree of cooperation between
interaction partners (Tomasello, 2008; Fitch, 2010; Grice, 1975; Hurford, 2007;
Knight, 2016). At the same time, language unlocks mechanisms for maintaining
and strengthening cooperation: it facilitates cooperation through verbal
coordination (Gärdenfors, 2004), or raises the costs of non-cooperation by
spreading the track record of individuals via gossip (Dunbar, 1996). These close
links between language and cooperation make it highly plausible that these twotraits co-evolved during their evolutionary emergence. Our study explores theseco-evolutionary links between cooperation and language and helps to shed lighton possible causalities and directionalities of these links.
In particular, we hypothesize that people are more likely to cooperate withindividuals that align with them linguistically, i.e. share lexical or syntactic
choices (Pickering & Garrod, 2004). Such correlations have already been
observed with respect to task success (Reitter & Moore, 2014), and we expect thatthe same positive relationship exists between language and cooperation.
To test this, we conducted an online experiment with 40 native English-speaking
participants. Each participant communicated with two interaction partners in a
picture-naming task (Bock, 1986), in which the participants and their partners
took turns describing pictures that represented ditransitive events. After an initialcommunication stage, where both partners (which were in fact standardized chatbots) did not align their syntactic constructions with the participants, one of thepartners switched to an aligned syntactic construction, whereas the other partner

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continued to use a non-aligned syntactic construction (Fig. 1). After thiscommunicative interaction, participants had to decide in a two-alternative forced
choice task with which of the two partners they would like to play a cooperation
game that determined the financial bonus that they would receive forparticipation. The participants’ choices in this task were our main variable ofinterest. We predicted that linguistically aligning communication partners would
be preferred as cooperation partners over non-aligning ones.
Our results did not confirm this prediction. Instead, aligning (47.5%) and nonaligning (52.5%) partners were chosen equally often as cooperation partners(confidence intervals include 50%). Further exploratory analyses showed that
there was no correlation between the attention that participants paid to thelanguage used by their partners and their partner choices (χ2 = 0.17, p = 0.68, φ =0.07): also when participants reported that they actively paid attention to andbased their decisions on the “correctness” of their partners’ responses, they did
not choose the aligned partners significantly more often than the unaligned ones
(note that “correctness” is the participants’ subjective impression, since bothaligned and non-aligned partners answered grammatically correctly).
Our study did not find evidence that syntactic alignment is a decisive factor for
people when choosing their cooperation partners. Other yet unexplored factors
may override the influence of linguistic alignment in such decisions. For example,
people may avoid partners who mimic them exactly, and rather regard those using
different syntactic structures as more competent, honest or trustworthy partners
in cooperative interactions. However, since our design relied on only a single
instance and a single type of alignment (syntactic alignment), it might not havebeen sensitive enough to capture subtle effects. Follow-up research is necessary,in particular with alignment unfolding over a longer course of interaction, to reacha firmer conclusion regarding the interdependence, and possible co-evolution, of
linguistic alignment and cooperation.
The farmer lends thehammer to the singer.

The farmer lends thehammer to the singer.

Alignment

Cooperation

The farmer lends the
singer the hammer.

No alignment

No cooperation

Figure 1. Experimental design and hypotheses.

491

References
Bock, J. K. (1986). Syntactic persistence in language production. Cognitive
Psychology, 18, 355–387.
Dunbar, R. I. M. (1996). Grooming, gossip and the evolution of language.
London: Faber and Faber.
Fitch, W. T. (2010). The Evolution of Language. Cambridge: Cambridge
University Press.
Gärdenfors, P. (2004). Cooperation and the evolution of symbolic
communication. In K. Oller & U. Griebel (Eds.), The Evolution ofCommunication Systems (pp. 237–256). Cambridge, MA: MIT press.
Grice, H. P. (1975). Logic and conversation. In P. Cole & J. Morgan (Eds.),
Syntax and semantics (pp. 41–58). New York: Academic Press.
Hurford, J. R. (2007). The origins of meaning: Language in the light of evolution
I (Vol. 8). Oxford and New York: Oxford University Press.
Knight, C. (2016). Puzzles and mysteries in the origins of language. Language &Communication, 50, 12–21.
Pickering, M. J., & Garrod, S. (2004). Toward a mechanistic psychology of
dialogue. The Behavioral and Brain Sciences, 27(2), 169–226.
Reitter, D., & Moore, J. D. (2014). Alignment and task success in spoken
dialogue. Journal of Memory and Language, 76, 29–46.
Tomasello, M. (2008). Origins of human communication. Cambridge, MA: MIT
Press.

492

AESTHETIC PERCEPTION OF PROSODIC PATTERNS AS A
POTENTIAL FACTOR IN THE CULTURAL EVOLUTION OFLANGUAGE
THERESA MATZINGER*1,2,3, EVA SPECKER4, NIKOLAUS RITT1 and TECUMSEH
FITCH2*

Corresponding Author: theresa.matzinger@univie.ac.at
Department of English, University of Vienna, Vienna, Austria
2
Department of Behavioral and Cognitive Biology, University of Vienna, Vienna, Austria
3
Centre of Language Evolution Studies, Nicolaus Copernicus University, Toruń, Poland
4
Department of Basic Psychological Research, University of Vienna, Vienna, Austria
1

All human cultures appreciate art and can perceive visual, verbal, or musicalstimuli in terms of their aesthetic appeal. For example, aesthetic preferences
regarding prosodic patterns play a prominent role for the appeal of poetry (Nadal& Vartanian, 2019; Rastall, 2008). If such preferences also apply in the perceptionof spontaneous everyday speech, they may pose a constraint on language change(Rastall, 2008): we hypothesize that aesthetically appealing linguistic features arelearned easily and used frequently, and will thus be culturally transmitted to future
generations of speakers more successfully than less appealing features (cf. Smith& Kirby, 2008).
In our exploratory study, we investigated a crucial baseline for this hypothesis,
namely if there were indeed differences in listener’s aesthetic judgements oflinguistic features. Specifically, we focused on the aesthetic perception of
temporal rhythmic patterns in polysyllabic words. On the one hand, words might
be regarded as most aesthetic if their syllables are isochronous because isochrony
has a facilitatory effect on auditory processing, and people have a generalpropensity for regular patterns (Ravignani & Madison, 2017). On the other hand,
listeners may also perceive irregular patterns as aesthetically appealing(Westphal-Fitch & Fitch, 2013). In that case, words with deviations fromisochrony might be judged as more pleasing than isochronous stimuli.
To explore the potential link between words’ rhythmic patterns and aesthetic
perception, we tested 180 native-German-speaking participants on their aesthetic
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evaluation of artificially generated trisyllabic pseudo-words. Each participant
made valence ratings of 20 words that were each presented in 3 different
conditions in a random order: a) with isochronous syllables, b) with the initial,
medial or final syllable lengthened and c) with the initial, medial or final syllable
shortened by 50% of its original duration (400 ms). Each participant ranked eachword three times, namely on its ‘likability’, on its ‘beauty’ and on its‘naturalness’, which together served as indicators of ‘aesthetic appeal’.
Cumulative Link Mixed Models revealed that, overall, isochronous syllables werepreferred over deviations of isochrony. Especially, shortened syllables had aprominent negative effect on aesthetic appeal. The only modification thatparticipants judged as slightly more aesthetically appealing than isochrony was
word-final lengthening. These results were similar for ‘likability’, ‘beauty’ and‘naturalness’.
The positive ratings of rhythmic patterns are unlikely to have been influenced bytheir occurrence frequencies in the participants’ native language (Bybee, 2007)
because word-medial syllables, which are typically stressed and thus lengthened
in German (Domahs, Plag, & Carroll, 2014) have not been evaluated as
aesthetically appealing when lengthened in our experiment.
Interestingly, the aesthetic appeal of prosodic patterns in our study corresponded
to their effectiveness for speech segmentation in other experiments, where words
with finally lengthened syllables could be extracted from continuous speech more
successfully than words with finally shortened syllables (Matzinger, Ritt, & Fitch,2021). Together, these findings indicate a potential connection between aesthetics
and language learning. Thus, overall, this study serves as an important starting
point for testing the role of aesthetic perception of linguistic input for the cultural
evolution of linguistic patterns.
Further research should test the role of aesthetic appeal in language change more
directly, for example in iterated learning experiments (Kirby, Cornish, & Smith,2008). Also, future research should consider that aesthetic preferences are likely
to arise from a combination of factors, including the occurrence frequencies of
the target patterns, their position withing a sentence, or an interplay of familiarity
and novelty biases (Sluckin, Hargreaves, & Colman, 1983).

References
Bybee, J. (2007). Frequency of Use and the Organization of Language. Oxford:
Oxford University Press.
https://doi.org/10.1093/acprof:oso/9780195301571.001.0001

494

Domahs, U., Plag, I., & Carroll, R. (2014). Word stress assignment in German,
English and Dutch: Quantity-sensitivity and extrametricality revisited. The
Journal of Comparative Germanic Linguistics, 17(1), 59–96.
https://doi.org/10.1007/s10828-014-9063-9
Kirby, S., Cornish, H., Smith, K. (2008). Cumulative cultural evolution in thelaboratory: an experimental approach to the origins of structure in humanlanguage. Proceedings of the National Academy of Sciences, 105(31),10681–10686.
Matzinger, T., Ritt, N., & Fitch, W. T. (2021). The Influence of Different
Prosodic Cues on Word Segmentation. Frontiers in Psychology,
12(March). https://doi.org/10.3389/fpsyg.2021.622042
Nadal, M., & Vartanian, O. (Eds.). (2019). The Oxford Handbook of Empirical
Aesthetics. Oxford: Oxford University Press.
Rastall, P. (2008). Aesthetic responses and the “cloudiness” of language: Is
there an aesthetic function of language? La Linguistique, 44, 103–132.
https://doi.org/10.3917/ling.441.0103
Ravignani, A., & Madison, G. (2017). The paradox of isochrony in the evolutionof human rhythm. Frontiers in Psychology, 8, 1–13.
https://doi.org/10.3389/fpsyg.2017.01820
Sluckin, W., Hargreaves, D. J., & Colman, A. M. (1983). Novelty and humanaesthetic preferences. In J. Archer & L. I. A. Birke (Eds.), Exploration in
animals and humans. Wokingham: Van Nostrand Reinhold, 245–269.
Smith, K., & Kirby, S. (2008). Cultural evolution: implications forunderstanding the human language faculty and its evolution.
Philosophical Transactions of the Royal Society of London. Series B,
Biological Sciences, 363(1509), 3591–3603.
https://doi.org/10.1098/rstb.2008.0145
Westphal-Fitch, G., & Fitch, W. T. (2013). Spatial Analysis of “Crazy Quilts”, aClass of Potentially Random Aesthetic Artefacts. PLoS ONE, 8(9).
https://doi.org/10.1371/journal.pone.0074055

495

DISENTANGLING THE STATISTICAL PROPERTIES THAT DRIVELANGUAGE ACQUISITION: EVIDENCE FROM MAXIMALLY
DIVERSE LANGUAGES
Jekaterina Mažara*1,2 , Giuachin Kreiliger1,2 , Sabine Stoll1,2 , and Balthasar Bickel1,2* Corresponding Author: jekaterina.mazara@uzh.ch

1 Department of Comparative Language Science University of Zurich, Switzerland

2 Center for the Interdisciplinary Study of Language Evolution, University of Zurich, Switzerland

A key property of the human language faculty is that any of its instantiations
can be acquired by any child in roughly the same time frame. A powerful suggestion for what makes this possible is the idea that children can rely on statisticalproperties for segmenting and learning elements such as morphemes or words(Saffran, 2003; Saffran, Aslin, & Newport, 1996; Gerken, 2005; Pelucchi, Hay,
& Saffran, 2009; Hay, Pelucchi, Estes, & Saffran, 2011; Thiessen, 2011; Höhle,2015). However, an unresolved question is which specific aspects of statistical distributions drive or differentiate the acquisition process in real-world data: is it the
sheer number of items that need to be learned? Or distributional differences acrossstructural or semantic types, or across entire languages? Or frequency classes?
Here we address these questions by assessing potential factors that might influence the acquisition of verb forms in morphologically highly diverse languages.
Our data consists of longitudinal recordings of 20 children and their surrounding speakers from five naturalistic corpora from the ACQDIV data base (Moran,
Schikowski, Pajović, Hysi, & Stoll, 2016): Chintang (Sino-Tibetan), English(Indo-European), Japanese (Japonic), Turkish (Turkic), and Yucatec (Mayan). As
potential factors we tested the following: (i) the difference between heads anddependents. Heads include the main stem of a verb form that carries structurally
independent information, while dependents comprise elements that provide modifying lexical, derivational, and grammatical information, such as affixes and secondary stems (Stoll, Mazara, & Bickel, 2017). We hypothesize that heads areeasier to learn than dependents because they are cognitively more salient. (ii) the
difference between languages, hypothesizing that some languages are generally
acquired faster than others. (iii) the deviation of the empirical input distributions from the theoretical Zipf distribution (quantified by the contribution of aparabolic function to fitting the log-log rank-frequencies), hypothesizing that convexity in rank-frequency distributions facilitates learning because there are morehigh-frequency items than expected. (iv) the log number of distinct heads andThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

496

dependents, assuming that a higher number would indicate a more difficult system for learners. (v) the entropy of items (measured with the Chao-Shen estimator(Chao & Shen, 2003) to account for possible unseen elements), hypothesizing thatlower entropies facilitate learning (Lavi-Rotbain & Arnon, 2022).
To evaluate the children’s development of productivity in verb form use overtime, we computed the (log) ratio in usage entropies between the target child andsurrounding adult speakers within each recording session (Stoll et al., 2012).
We applied a hierarchical nonlinear scale/location (“distributional”) model(Bürkner, 2018) to fit the development of the log entropy ratios over time using the
exponential function to describe the learning curves, which allows us to evaluate
the speed of acquisition. To allow for variation at an individual level, we model
the children as random effects. We fitted models with one of the five factors each(plus an ancillary model of item type and language together) and compared themthrough model stacking, estimating the relative weight of each factor in predictive
performance during leave-one-out cross-validation (Vehtari, Gelman, & Gabry,2017).
Model stacking (Yao, Vehtari, Simpson, & Gelman, 2018) shows that the data
are best predicted by the convex deviation from the theoretical Zipfian distribution,i.e. distributions with more higher-frequency types faciliate learning and increase
the rate of acquisition. The (log) number of items has a slightly weaker but still
appreciable impact (stacked prediction model weight .45 vs .5 for the deviationfrom the Zipfian distribution). All other factors predict the data much less well(receiving no weight in stacking), i.e., differences between languages, item types,
and entropies have no impact on the acquisition process.
Our findings suggest that the children in our sample indeed do make use ofstatistical properties of the input during learning, and that it is specifically the
number of high-frequency items and the total number of items that matters.
ReferencesBürkner, P.-C. (2018). Advanced Bayesian multilevel modeling with the R package brms. The R Journal, 10, 395–411.
Chao, A., & Shen, T.-J. (2003). Nonparametric estimation of Shannon’s index
of diversity when there are unseen species in sample. Environmental andEcological Statistics, 10(4), 429–443.
Gerken, L. (2005). Decisions, decisions: infant language learning when multiple
generalizations are possible. Cognition, 98(3), B67–B74.
Hay, J. F., Pelucchi, B., Estes, K. G., & Saffran, J. R. (2011). Linking sounds
to meanings: infant statistical learning in a natural language. Cognitive
Psychology, 63(2), 93–106.
Höhle, B. (2015). Crosslinguistic perspectives on segmentation and categorizationin early language acquisition. In E. L. Bavin & L. R. Naigles (Eds.), The
cambridge handbook of child language (p. 159-182). Cambridge University

497

Press.
Lavi-Rotbain, O., & Arnon, I. (2022). The learnability consequences of zipfian
distributions in language. Cognition, 223, 105038.
Moran, S., Schikowski, R., Pajović, D., Hysi, C., & Stoll, S. (2016). The acqdiv
corpus.
Pelucchi, B., Hay, J. F., & Saffran, J. R. (2009). Statistical learning in a natural
language by 8-month-old infants. Child Development, 80(3), 674–685.
Saffran, J. R. (2003). Statistical language learning mechanisms and constraints.
Current Directions in Psychological Science, 12(4), 110–114.
Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by
8-month-old infants. Science, 274, 1926 - 1928.
Stoll, S., Bickel, B., Lieven, E., Banjade, G., Bhatta, T. N., Gaenszle, M., Paudyal,
N. P., Pettigrew, J., Rai, I. P., Rai, M., & Rai, N. K. (2012). Nouns andverbs in chintang: children’s usage and surrounding adult speech. Journalof Child Language, 39, 284-321.
Stoll, S., Mazara, J., & Bickel, B. (2017). The acquisition of polysythetic verb
forms in chintang. In M. Fortescue, M. Mithun, & N. Evans (Eds.), Handbook of polysynthesis (p. 495-517). Oxford University Press.
Thiessen, E. D. (2011). Domain-general constraints on statistical learning. Child
Development, 82(2), 462–470.
Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation
using leave-one-out cross-validation and WAIC. Statistics and computing,27(5), 1413–1432.
Yao, Y., Vehtari, A., Simpson, D., & Gelman, A. (2018). Using stacking to average bayesian predictive distributions (with discussion). Bayesian Analysis,13(3), 917–1007.

498

THE EMERGENCE OF GAZE CONVENTIONS IN
INTERPERSONAL COORDINATION
Gregory Mills*, Remko Boschker*Corresponding Author: g.j.mills@rug.nl

Centre for Language and Cognition Groningen,
University of Groningen, Netherlands

A central finding in psycholinguistic research is that when people interact witheach other they rapidly develop new idiosyncratic referring expressions andassociated meanings with their conversational partner (Clark, 1996; Pickering andGarrod, 2004)
In addition to natural language, conversational interaction is underpinned bymyriad non-verbal signals which are used, inter-alia, to regulate turn-taking(Argyle, 1988; Holler et al., 2016). For example, speakers tend to look away fromtheir addressee while speaking and then re-establish eye-contact to signal the endof their turn.
However, it is currently unclear whether such turn-taking signals are static andfixed, or whether, like natural language, they are negotiated by participants during
interaction. To address this question participants play a novel collaborative task,
in virtual reality. The task is played by 3 participants, and is inspired by games
such as guitar hero and dance-dance revolution. The three key differences are: (1)Instead of performing target sequences of musical notes or dance moves,participants need to perform sequences of gaze events. E.g. a typical target
sequence might be: “Person 2 must look at person 3. Then person 3 must look at
person 1. Then person 1 and person 2 look at each other” (2) On each trial, only
one participant (the director) sees the target sequence. This means that in orderfor the group to complete the target sequence, the director has to instruct the others(3) Crucially the participants are not allowed to use natural language – they may
only communicate by looking at each other. Solving the task, therefore, requiresthat participants bootstrap a communication system, solely using their gaze
patterns.

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We conducted a set of experiments where triads played this game for 25 minutes
followed by a manipulation which swapped participants` identities: In Participant1`s view, Participant 2`s avatar is controlled by Participant 3, while Participant3`s avatar is controlled by Participant 2. In Participant 2`s view, Participant 1`savatar is controlled by Participant 3 and vice versa. Similarly for Participant 3`sview. The intention behind this manipulation is that if participants establish pairspecific routines with each of their partners, this swap will disrupt co-ordination,
since after the swap, participants will be interacting with a different partner who
is using different routines.
The results show that during the first 25 minutes of the task, triads are able toestablish a communication system that allows them to solve sequences of up to 8
gaze events. Moreover, the results show that after swapping participants`
identities, triads perform worse at the task, suggesting that the routines established
to coordinate the timing and sequencing of eye-gaze can be flexibly negotiatedduring interaction.

References
Argyle, M. (1988). Bodily Communication. 2nd Edition. Methuen, London
Clark, H. H. (1996). Using language. Cambridge University Press.
Holler, J., Kendrick, K. H., Casillas, M., & Levinson, S. C. (2016). Turn-taking
in human communicative interaction. Frontiers Media SA.Pinker, S., &Bloom, P. (1990). Natural language and natural selection. Behavioral andBrain Sciences, 13, 707-784.
Pickering, M. J., & Garrod, S. (2004). Toward a mechanistic psychology of
dialogue. Behavioral and brain sciences, 27(2), 169-190.

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SELF-REPAIR INCREASES ABSTRACTION OF
REFERRING EXPRESSIONSGregory Mills*, Gisela Redeker*Corresponding Author: g.j.mills@rug.nl

Centre For Language and Cognition, Groningen, University of Groningen

When interlocutors repeatedly describe referents to each other, they rapidly
converge on referring expressions which become increasingly systematized andabstract as the interaction progresses. This occurs for a wide range of referents,
e.g. when referring to spatial locations (Garrod and Doherty, 1994; Roberts et al.,2016; Mills, 2014), music (Healey et al., 2007), concepts (Schwartz, 1995),
confidence (Fusaroli et al., 2012), and temporal sequences (Mills, 2011).
Cumulatively, these findings suggest that interaction in dialogue places important
constraints on the semantics of referring expressions.
However, there is currently no consensus about how best to account for how
convergence develops. The iterated learning model (Kirby, Griffiths and Smith,2014) explains convergence as arising out of individual cognitive biases; theinteractive alignment model of Pickering and Garrod (2004; 2021) favoursalignment processes, while the collaborative model of Clark (1996) emphasizes
the role of positive feedback. By contrast, Healey et al., (2007) argues thatnegative evidence of understanding plays the central role: When interlocutors
initiate repair, this allows them to interactively identify, diagnose and resolve any
differences in interpretation between them and their conversational partner.
Addressing these differences accelerates convergence.
To investigate in closer detail how negative evidence contributes toward
convergence, we report a variant of the “maze task” (Pickering and Garrod, 2004).
Participants communicate with each other via an experimental chat tool (Healey
and Mills, 2006), which automatically transforms participants' private turnrevisions into public self-repairs that are made visible to the other participant. For
example, if a participant, A types:
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A: Now go to the square on the left, next to thebig block on top
and then before sending, A revises the turn to:
A: Now go to the square on the left, next to thethird column
The chat server automatically detects the revised text and inserts a hesitation
marker (e.g. "umm" or "uhhh" immediately preceding the revision). This would
yield the following turn, sent to B:

A: Now go to the square on the left next, to thebig block on top umm.. I meant next to the thirdcolumnParticipants who received these transformed turns used more abstract andsystematized referring expressions, and also used a larger vocabulary (i.e. moreunique words). However, dyads who received the interventions solved fewer
mazes and produced more turns than participants in the control group.
We argue that this effect is due to the artificial self-repairs causing
participants to put more effort into diagnosing and resolving the referential
coordination problems in the task. At the start of the experiment this leads to fewer
solved mazes, but ultimately yields better grounded spatial semantics andconsequently leads to increased use of abstract referring expressions.
References
Clark, H. H. (1996). Using language. 1996. Cambridge University Press:
Cambridge
Fusaroli, R., Bahrami, B., Olsen, K., Roepstorff, A., Rees, G., Frith, C., &Tylén, K. (2012). Coming to terms quantifying the benefits of linguistic
coordination. Psychological Science, 0956797612436816.
Garrod, S., & Doherty, G. (1994). Conversation, co-ordination and convention:
An empirical investigation of how groups establish linguistic
conventions. Cognition, 53(3), 181-215.
Garrod, S., & Pickering, M. J. (2009). Joint action, interactive alignment, anddialog. Topics in Cognitive Science, 1(2), 292-304.
Healey, P. G., Swoboda, N., Umata, I., & King, J. (2007). Graphical language
games: Interactional constraints on representational form. Cognitive
Science, 31(2), 285-30

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Kirby, S., Griffiths, T., & Smith, K. (2014). Iterated learning and the evolutionof language. Current Opinion in Neurobiology, 28, 108-114
https://doi.org/10.1016/j.conb.2014.07.014.
Mills, G. J. (2011). The emergence of procedural conventions in dialogue. In
Proceedings of the Annual Meeting of the Cognitive Science Society(Vol. 33, No. 33).
Mills, G. J. (2014). Dialogue in joint activity: Complementarity, convergenceand conventionalization. New Ideas in Psychology, 32, 158-173.
Pickering, M. J., & Garrod, S. (2021). Understanding dialogue: Language use
and social interaction. Cambridge University Press.
Pickering, M. J., & Garrod, S. (2004). Toward a mechanistic psychology of
dialogue. Behavioral and Brain Sciences, 27(2), 169-190.
Roberts, G., Lewandowski, J., & Galantucci, B. (2015). How communication
changes when we cannot mime the world: Experimental evidence for theeffect of iconicity on combinatoriality. Cognition, 141, 52-66.
Schwartz, D. L. (1995). The emergence of abstract representations in dyad
problem solving. The Journal of the Learning Sciences, 4(3), 321-354.
.

503

EFFECT OF ENVIRONMENTAL RICHNESS ONRESPONSIVENESS OF RATS TO SPECIES-SPECIFIC
EMOTIONAL CALLSMarika Minamii*1,2, Yumi Saito3, and Kazuo Okanoya2,1*Corresponding Author: mnmmrk-1066@g.ecc.u-tokyo.ac.jp
Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
2
Advanced Comprehensive Research Organization, Teikyo University, Tokyo,
Japan
3
Animal Science and Biotechnology, Azabu University, Kanagawa, Japan

1

Language and speech communication are closely intertwined, and acomprehensive understanding of the evolution and origin of language cannot be
achieved without the study of sound communication in a variety of animal
species. Rats produce two types of ultrasonic vocalization (USV) in response toemotional and social contexts. Previous studies have suggested that 50 kHz“pleasant” USV increases heart rate with a significant tendency, while 22 kHz“unpleasant” USV decreases heart rate (Saito, 2019). However, the environment
in which laboratory rats are raised is quite different from the environment inwhich wild rats live. Brydges et al. (2011) suggested that rats raised in richenvironments tend to judge intermediate stimuli as positive stimuli. It has also
been shown that environmental enrichment stimulates progenitor cellproliferation in the amygdala (Okuda et al., 2009). Based on these studies, because
the laboratory rearing environment may alter the innate response to USVs, it isnecessary to conduct further studies with more wild-like enrichment
environments. Therefore, in this study, we made a hypothesis that rats reared in arich environment respond less to 22 kHz USV or more to 50 kHz USV andexamined whether the environmental richness affects the responsiveness to USV.
In this study, rats raised in an enriched environment were exposed to USV,
and their responses to USV were examined in comparison with those of rats raised
in a standard environment. Following Saito's study (2019), the responses to USV
were indexed by increases or decreases in heart rate. If the rats raised in theenriched environment are more optimistic and the decrease in heart rate when
listening to 22 kHz USV is not significant, or if the heart rate of rats listening to
50 kHz USV is significantly increased, it is assumed that the enrichment altered
the rats' responsiveness to USV.
Of the 12 weaned male rats, 6 were pair-reared in cages (approximately 25
×40×20 cm) lined with recycled paper bedding. The others were kept in large
cages (approximately 40 × 65 × 30 cm) with a thick layer of pepa-kurin(JapanSLC, Shizuoka, Japan) and several enrichment supplies, three animals per
cage. Two months later, 50 kHz and 22 kHz USV were presented, and heartbeats
were measured during listening.
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MATLAB was used for the following analysis. The baseline was definedas the time up to 7 seconds before the start of voice presentation, and the test was
defined as 7 seconds after. The RRI, the interval between R waves, wascalculated, and the mean of the RRI (mean RRI) was calculated as the heart rateinterval. Prolongation of the RRI indicates a falling heart rate and shortening of
the RRI indicates a rising heart rate (Jose, 1966). For these indices, baseline values
were subtracted from the tests in the block. For example, if the difference issignificantly greater than zero, we can say that listening to the USV decreased
heart rate.
The following analysis was then conducted using R. To examine whether
each index changed from baseline due to the presentation of USV, we tested the
median values by one-sample Wilcoxon signed rank test and found no significant
changes in any of the conditions. The test results were shown in Fig. 1.

Figure 1. Change in mRRI from baseline. ST: standard group (N = 4), EN: enriched group (N= 6), ×: the median.

In conclusion, both the environmental richness and the category of USV
did not affect the response of the autonomic nervous system in rats. However,some individuals did not return their heart rate to baseline after listening to the
USV, which may have prolonged the effect of the preceding stimulus. Thus, weneed to review whether the baseline and interstimulus interval settings wereappropriate. Moreover, we would like to investigate whether the enrichedenvironment operation was appropriate through further experiments.

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Environmental enrichment induces optimistic cognitive bias in rats. Animal
Behaviour, 81(1), 169-175.
Okuda, H., Tatsumi, K., Makinodan, M., Yamauchi, T., Kishimoto, T., &Wanaka, A. (2009). Environmental enrichment stimulates progenitor cellproliferation in the amygdala. Journal of neuroscience research, 87(16),3546-3553.
Saito, Y. (2019). Categorical perception and processing of emotional calls: The
study of rat ultrasonic vocalizations. [Doctoral dissertation, The Universityof Tokyo]. (In Japanese.）

506

LINGUISTIC LAWS IN PRIMATE VOCAL COMMUNICATION
Steven Moran*1,2 , Quentin Gallot1 , Auriane Le Floch1,3 , Marco Maiolini1 , Adrian Soldati1,4 , andKlaus Zuberbühler1,4* Corresponding Author: steven.moran@unine.ch

1 Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland

2 Department of Anthropology, University of Miami, Coral Gables, USA
3 Institut des Sciences Cognitives Marc Jeannerod, CNRS, Lyon, France

4 School of Psychology and Neuroscience, University of St Andrews, St Andrews, UK

Researchers have increasingly focused on the commonalities between animal communicationand language with the aim to identify what makes language unique and to shed light on how it
evolved. One area of recent study is whether linguistic laws also characterize vocal communication in other animals. Given their relevance for the evolution of language, here we provide
a comprehensive review of the presence or absence of linguistic laws reported in studies on
primate vocal communication. We find that primate vocal production follows Menzerath’s law,while adherence to the other laws is mixed. Moreover, we raise three important points to consider when studying linguistic laws: the role of sexual dimorphism in vocal production, thecriteria used to define a vocal sequence, and the choice of vocal units for analysis. Thus, thisreview provides a road map for future studies investigating linguistic laws in primates and is
aimed at making results more comparable across species and signals.

1. Introduction
In the last two centuries linguists and biologists have increasingly focused on thecommonalities between animal communication systems and language. The aim is
to identify what makes language unique and to shed light on its origin.
The best known account is Hockett’s (1960) universal design features of human language. Hockett used them to distinguish between animal communication
systems and language, noting that most features (originally 9 of 13) are attested
in other species. Ever since Hockett proposed the design features, they have beenused to juxtapose human language with communication systems of other animals
for descriptive purposes, even though the study of language evolution itself has
evolved in the last sixty years. Their use is limited as an evolutionary theoreticalframework (Wacewicz & Żywiczyński, 2015). Furthermore, the increased accessto large datasets and machine learning has given rise to the application of quantitative and information-theoretic approaches in biological systems more generally.
We provide a comprehensive review of the current state of the art of linguistic
laws reported in primate vocal communication. We note in which species, signals,
and contexts that there is support for the presence or absence of different linguistic
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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laws. Our work builds on that of Semple, Cancho, and Gustison (2021) and we
provide an online and openly accessible table of the current reported findings.
These results shed light on, and provide a road map for, future studies on primatesand other animal species to investigate communication systems, linguistic laws,
and language evolution.
2. Linguistic laws
Linguistic laws are mathematically formulated statistical regularities discoveredby linguists studying properties of (written) language. These patterns appear atvarious linguistic levels (e.g., sounds in words, words in text) and their existence
is often attributed to universal principles of efficiency in human language (cf. Levshina and Moran (2021)). There are four well-studied linguistic laws.
The most well-known is referred to as Zipf’s law or Zipf’s principle of least
effort (Zipf, 1936). It states that the frequency distribution of words in a language is inversely proportional to their frequency rank. Zipf theorized that this
distribution of words is due to the principle of least effort, i.e., speakers minimize
their effort when transmitting messages (hence why the most frequent words ina language tend to also be the shortest). Work by Yu, Xu, and Liu (2018) showsthat Zipf’s law holds across a sample of 50 languages. However, why the Zipfian
distribution – one of several power law distributions – occurs is still not well understood. Power law distributions are found in numerous biological, physical, andhuman-made phenomena, and they emerge naturally from latent variables (Aitchison, Corradi, & Latham, 2016) or from almost nothing (Piantadosi, 2014).
Zipf’s law of abbreviation (Zipf, 1949), also known as the law of brevity, statesthat more frequently used words will tend to be shorter than less frequently used
words within a language. This law in particular captures an aspect of the efficiencyof language use (Kanwal, Smith, Culbertson, & Kirby, 2017).
Menzerath-Altmann’s law states that the size of constituents within a linguistic construction decrease in size as constituents get longer, and vice versa (Menzerath, 1954; Altmann, 1980). For example, longer words tend to have shorter
syllables and shorter morphemes; longer sentences tend to have shorter clauses.
Menzerath-Altmann’s law appears in many multi-level systems, e.g., proteins,
genes, genomes, and genetics (Nikolaou, 2014; Ferrer-I-Cancho & Forns, 2010;
Shahzad, Mittenthal, & Caetano-Anollés, 2015; Sun & Caetano-Anollés, 2021).
Lastly, Herdan-Heaps’ law (Heaps, 1978; Herdan, 1960, 1964; Egghe, 2007)
states that as the size of a text increases, the number of discoverable unique words(i.e., word types) decreases as a function of the text’s length. For example, Kornai(2002) verified Herdan-Heaps’ law in a corpus of 50 million words, i.e., the distribution the type-token ratio of words did not flatten, indicating that adding more
text would lead to more unique words. Brants and Franz (2006) created the first
one trillion word corpus of English by aggregating internet webpages and they
showed that there were nearly 14 million word types in the corpus – again with no508

indication that all word types had been discovered.
Of the four linguistic laws, to the best of our knowledge only Herdan-Heaps’
law has not yet been studied in animal communication systems.
3. Materials and methods
We have identified 19 published studies on linguistic laws and primate vocalizations (see supplementary materials). These include species from different taxa.
We identified and extracted information for the following variables (together withthe bibliographic metadata): species, linguistic law investigated, evidence (i.e.,
was the linguistic law supported or not by the data), dependent variable (e.g., units,
sequences, bouts), signal investigated (e.g., pant hoots, wobbles), signal modality (e.g., wobble cycles, vocal sequences), definition and criteria of the signal (asper the authors’ description), the context of production, sample size, number ofsubjects, sex, age, social system, size of repertoire reported, and any pertinent orinteresting comments or findings.
4. Results
The oldest paper that we identified is by McCowan, Doyle, and Hanser (2002),
who use comparative measures from information theory to compare the development of vocal repertoires of bottle nose dolphins, squirrel monkeys, and humans. No linguistic laws were tested. Instead, they used the Zipf coefficient toapproximate diversity and repertoire complexities. Their findings suggest that in
species that are capable of vocal learning, repertoire structure diversity decreases
and becomes more organized into adulthood, i.e., less entropic. Thus, they reportthat chuck calls of squirrel monkeys exhibit similar developmental patterns as inhuman language acquisition. Other authors use a similar approach in that they
investigate power law coefficients in relation to Shannon entropy (Kershenbaum
et al., 2021), study developmental trends from infancy to adulthood (Gultekin,
Hildebrand, Hammerschmidt, & Hage, 2021), or compare one or more linguistic
laws across different species, e.g., macaques, marmosets, and uakaris (Bezerra,
Souto, Radford, & Jones, 2011; Ferrer-I-Cancho & Hernández-Fernández, 2013;
Ferrer-I-Cancho et al., 2013; Kershenbaum et al., 2021).
The majority of the current published research, however, investigates a linguistic law within a single species’ repertoire: chimpanzees (Fedurek, Zuberbühler, &Semple, 2017), geladas (Gustison, Semple, Cancho, & Bergman, 2016), gibbons(Clink, Ahmad, & Klinck, 2020), gorrilas (Watson, Heesen, Hedwig, Robbins,& Townsend, 2020), indris (Zanoli et al., 2020), and macaques (Semple, Hsu, &Agoramoorthy, 2010). Follow-up studies on the same species, but perhaps on different aspects of vocalizations, include (some of) the same authors, e.g., the workon Menzerath’s law in geladas (Gustison et al., 2016) or Zipf’s law of abbreviationin macaques, marmosets, and uakaris (Semple et al., 2010).

509

Out of the 19 studies, six investigate Menzerath’s law and six investigate Zipf’s
law of abbreviation. A further three investigate both laws. Regarding Menzerath’s
law in primate vocal repertoires, there is overwhelming support reported, but withsome caveats. Gustison et al. (2016) find that adult male gelada vocalizations
follow Menzarath’s law. Gustison and Bergman (2017) report that wobble cycle
duration was shorter when the number of wobbles or lip smacks was greater. The
authors also report that Menzerath’s law was identified separately in both inhaledand exhaled wobbles and suggest therefore that the compression of vocal signals
by geladas operates at multiple levels (as Menzerath’s law does at different linguistic levels in language).
Menzerath’s law is also found in tarsiers, titi monkeys, and male Bornean gibbons (Clink & Lau, 2020). These findings raise several important points for consideration when studying linguistic laws in animal communication systems (seealso Semple et al. (2021)). First, sexual dimorphism may play an important partin the evolutionary development of vocal repertoires (e.g., in gorillas, who differ
greatly in size by sex). Additionally, sexes may differ with regard to the context
of production. Thus, ideally the vocalizations of both sexes need to be tested toprevent bias, e.g., Odom, Hall, Riebel, Omland, and Langmore (2014). Second,
at what level or which part of the vocal production is analyzed (e.g., duration ofnotes, proportion of call types, single vs multi-unit sequences) and how a sequence
is defined, i.e., which criteria are used to separate the units from each other (e.g.,length of silence gaps). Third, a linguistic law may be found in certain call types
or certain levels of analysis, but not others. For example, Watson et al. (2020)
investigated close-calls of mountain gorillas in sequences. The authors initiallyfound positive evidence for Menzerath’s law, but then report that the relationshipwas due to the difference between single and multi-unit sequences (leaving single
units out of the analysis resulted in longer sequences being typically composedof longer units). Hence, Watson et al. (2020) report that close calls by mountaingorillas only partially adhere to Menzerath’s law.
Other than mountain gorillas, the only non-human great ape that has so far
been studied with regard to linguistic laws is the chimpanzee. Fedurek et al.
(2017) find support for Menzerath’s law in the number and duration of calls within
the pant hoot and for entire vocal sequences. They also report that these findings
hold between the duration of adjacent phases in the pant hoot. Pant hoots were
investigated in the context of feeding and traveling and only pant hoots producedby males and that contained the climax phase were included in their investigation.
The results from studies of various species and whether their vocalizations
adhere to Zipf’s law of abbreviation are more mixed. Bezerra et al. (2011) investigate 12 call types of marmosets (excluding predator-specific alarm calls) and7 from uakaris produced by adults across all contexts. They find no support for
Zipf’s law of abbreviation. Likewise, Gultekin et al. (2021) find no support for
Zipf’s law of abbreviation in marmosets throughout their development. However,510

Semple et al. (2010) examine the full repertoire of macaque vocalizations, in allcontexts, from all age ranges and sexes. They show that more frequent vocalizations are shorter in duration. In a follow up study, Semple, Hsu, Agoramoorthy,
and Cancho (2013) report that the support for Zipf’s law of abbreviation that theyfound is not an artefact of their previous analysis of mean call duration.
Support for Zipf’s law of abbreviation and Menzerath’s law was recently reported by Valente et al. (2021) for indris’ songs, in contexts of territory defense
and long distance communication. Although not an investigation of any particularlinguistic law in indris, we note the work of Zanoli et al. (2020), who undertake aLevenshtein distance analysis of adult male and female indri songs. They reportthat the songs of female indris are less stereotyped that those of males. This work
again highlights the importance of evaluating both sexes.
Lastly, we note two studies on whether gibbons’ vocalizations adhere to Zipf’s
law of abbreviation and Menzerath’s law. Clink, Tasirin, and Klinck (2020) findno support for Zipf’s law of abbreviation, but they report strong support for Menzerath’s law. Huang, Ma, Ma, Garber, and Fan (2020) report that both laws are
confirmed in male gibbon calls. These two studies highlight the importance of
which species and which calls are considered and how sequences are defined.
Clink et al. (2020) study solo singing bouts in multi-phase vocal sequencesby male Bornean gibbons. These lengthy singing bouts are comprised of a discrete number of note types with a large repertoire of phrases. Sequences of notes
are calculated by two-second breaks or more. The authors also report a strong
negative correlation between the number of notes in a phase and the notes’ mean
duration. They conclude that individual variation produces strong individual signatures. In contrast, the study by Huang et al. (2020) focuses on western blackcrested gibbons and Cao-vit gibbons. The authors study the loud morning calls of
sub-adult and adult males in various contexts. They define a song bout as all notes
in a song with silence periods of less than ten minutes, where a note is a single
continuous sound produced either through inhalation or exhalation. They reportthat the most common notes of the male gibbons follow Zipf’s law and that longer
sequences follow Menzerath’s law.
5. Discussion
Here we focus on reported findings of linguistic laws in the vocalization systems
of primates because of its potential link to the origin and evolution of speech.
However, we note that this review is restricted because it does not include studies
on gestures, facial expressions, or multi-modal systems (cf. Liebal, Slocombe, andWaller (2022)). Although such work is beyond the scope of this paper, we havenevertheless begun to collect this information for future research, as we plan tointegrate more animal species and communicative signals for meta-analysis.
We find that studies of primate vocalizations and Menzerath’s law are in general supportive. However, investigations of Zipf’s law of abbreviation in primates

511

are more mixed. Nevertheless, we highlight that within the research of linguistic
laws in primate vocalizations, most studies are descriptive in nature. Therefore,
we encourage future researchers to consider the four levels of analysis raised by
Semple et al. (2021) and what questions they aim to address.
Finally, while we acknowledge the disparate nature of the studies, there areissues and limitations regarding the comparability of species, vocalization types(e.g., duration, number of calls per phase), choice of dependent variables, sexual
differences, context of call production, age range, among others. To address theseissues, we provide a list in the supplementary materials in which we highlight differences in authors’ definitions of units and sequences, the number of subjects intheir sample, sample size of vocalizations, and discrepancies in reported repertoire
sizes. Moreover, we note that how to test for linguistic laws is still an area of debate even within linguistics, e.g., Zipf’s distribution (Piantadosi, 2014). Thus, wehope that studies on animal communication will strengthen this research avenue
and ultimately help to elucidate how language evolved in our species.
6. Supplementary Materials & Acknowledgements
See: https://github.com/bambooforest/jcole2022.
Fundingfrom the Swiss NSF: SM & MM (#186841) and KZ, ALF & QG (#185324).
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Clink, D. J., & Lau, A. R. (2020). Adherence to Menzerath’s law is the exception(not the rule) in three duetting primate species. Royal Society Open Science,7(11), 201557.
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Science and Technology, 58(5), 702–709.

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Fedurek, P., Zuberbühler, K., & Semple, S. (2017). Trade-offs in the productionof animal vocal sequences: insights from the structure of wild chimpanzee
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Hsu, M. J., & Semple, S. (2013). Compression as a universal principle ofanimal behavior. Cognitive Science, 37(8), 1565–1578.
Gultekin, Y. B., Hildebrand, D. G. C., Hammerschmidt, K., & Hage, S. R. (2021).
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Gustison, M. L., & Bergman, T. (2017). Divergent acoustic properties of gelada
and baboon vocalizations and their implications for the evolution of humanspeech. Journal of Language Evolution, 2(1), 20–36.
Gustison, M. L., Semple, S., Cancho, R. Ferrer-i, & Bergman, T. J. (2016). Gelada
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GREAT APE VOCAL REPERTOIRES ARE ALL SIMILAR IN SIZE:NOW WHAT?
Steven Moran*1,2 , Marco Maiolini1 , and Adriano Lameira*3* Corresponding Authors: steven.moran@unine.ch, Adriano.Lameira@warwick.ac.uk
1 Institute of Biology, University of Neuchâtel, Neuchâtel, Swizterland

2 Department of Anthropology, University of Miami, Coral Gables, USA
3 Department of Psychology, University of Warwick, Coventry, UK

There is an uncanny similarity between the size of nonhuman great ape vocal repertoires and that of the phonological inventories documented in humanlanguages. Great ape repertoires vary from a conservative estimate, and lowerbound, of 18 calls in both species of gorillas (Gorilla), to the low-to-mid 20s in
chimpanzees and bonobos (Pan), to the mid 30s in orangutans (Pongo). These
numbers are in line with the average number of contrastive speech sounds in modern and in ancient and reconstructed languages.
This observation suggests a common evolutionary pressure for a two digit
system repertoire, one that existed before the origin of language, and present at
the split with our evolutionary ancestors. The difference between systems is well
documented – language combines speech sounds (or signs) into an open-ended
communication system that infinitely produces new words and meanings. And
although we do not yet know what led to the cognitive abilities that gave rise tothis system in Homo, there are many similarities between the vocal repertoires ofgreat apes and those in languages left to explore.
Like language, great ape calls are produced in non-random combinations(Lameira et al., 2021). Like speech, orangutan vocal repertoires are composedof consonant- and vowel-like calls (Lameira, 2014). Orangutan vocalizations also
involve modest airstream and articulatory control (Wich et al., 2008) and experiments done in captivity show precise vocal fold motor control, including voicing(Lameira & Shumaker, 2019). And along with other cognitive skills, e.g., tool
making, some great ape vocalizations are arguably learned (Hopkins, Taglialatela,& Leavens, 2007) with some groups in captivity reportedly transmitting raspberries to their young, which they use to get the attention of caretakers (Hopkins
et al., 2007). Finally like languages, the call repertoires of great apes are shapedby socio-cultural factors (Lameira et al., 2022), they have dialects (Crockford,
Herbinger, Vigilant, & Boesch, 2004), change through contact (Mitani & GrosLouis, 1998), and their existence and diversity are under the threat of extinction(Meijaard et al., 2011).
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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We conducted an extensive review of the existing literature (66 published articles) and found that call repertoires are similar in size across extant great apes.
However, we also discovered that there is little in terms of a comparative articulatory and acoustic analysis of these vocalizations. This is because there is a serious
gap in the descriptions of the articulatory features of great apes in the literature,including for both vocal and gestural data.
A step towards a comprehensive comparative phonetic analysis of articulations of all great apes is needed to shed light on the similarities between vocal
communication systems and how each evolved in its own right over the last 6-7
million years of divergent evolution. It is crucial for language evolution researchthat we have articulatory and acoustic phonetic analyses, of the sort conductedby Perlman and Clark (2015) through audio-visual recordings, for all great ape
species (and ideally for each community). This would allow, for example, morestudies along the lines of Grawunder et al. (2022), who explored the evolution of
the vowel space in chimpanzees. The challenge, however, is not trivial because
it is difficult to collect the necessary field data – unlike in linguistics, in which
fieldworkers have access to speakers and can also ask them questions directly.
So far we have identified the gaps in the literature and created an ontology ofthe disparate terminology used in great ape studies. Our research suggests thatone pertinent area to explore for acoustic analysis is the spectral envelope, sothat we can ask whether prosodic features can be modified to give information to
the receiver (Zimmermann, Leliveld, & Schehka, 2013). Such findings will shedlight on whether paralinguistic features are shared across all great apes. Anotherarea ripe for investigation is what effects exist due to sexual dimorphism, e.g., inorangutans and gorillas, and to understand how to categorize these vocalizations.
We know that the size of great ape vocal repertoires are similar across all
extant great apes and that we share eight innate vocalizations, e.g., screaming,
crying, laughing (Anikin, Bååth, & Persson, 2018). However, there is still muchto learn about what we share vocally, and gesturally (Liebal, Slocombe, & Waller,2022), and how and when we diverged linguistically.
Supplementary Materials & Acknowledgements
Supplementary materials and all references are available online.1 SM & MM
were funded by the Swiss NSF (#186841). ARL was funded by the UKRI Future
Leaders Fellowship (MR/T04229X/1).
References
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1 https://github.com/bambooforest/great_ape_vocal_repertoires

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Crockford, C., Herbinger, I., Vigilant, L., & Boesch, C. (2004). Wild chimpanzees
produce group-specific calls: a case for vocal learning? Ethology, 110(3),221–243.
Grawunder, S., Uomini, N., Samuni, L., Bortolato, T., Girard-Buttoz, C., Wittig,
R. M., & Crockford, C. (2022). Chimpanzee vowel-like sounds and voice
quality suggest formant space expansion through the hominoid lineage.
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ON THE RELATION BETWEEN CONTEXT DEPENDENCY AND
VOCABULARY IN HUMAN LANGUAGE AND BIRDSONG
Takashi Morita*1,2 , Ryosuke O. Tachibana3 , Kazuo Okanoya3,4,5 , and Hiroki Koda2,3* Corresponding Author: tmorita@alum.mit.edu
1 SANKEN, Osaka University, Ibaraki, JAPAN2 Primate Research Institute, Kyoto University, Inuyama, JAPAN3 Graduate School of Arts and Sciences, the University of Tokyo, Tokyo, JAPAN

4 Advanced Comprehensive Research Organization, Teikyo University, Tokyo, JAPAN5 Behavior and Cognition Joint Research Laboratory, RIKEN Center for Brain Science, Wako, JAPANHuman language production is often characterized by long dependency on previous output
records, or context. Recent studies on deep learning-based models of language generation haveshown that the dependent context can range over hundreds of words. In this study, we investigated the relation between context dependency and vocabulary type/size, by case-studying
English. We found that the long context dependency vanished when words were replaced with
their grammatical categories and purely syntactic dependency was considered, which suggeststhat the long dependency in human language is largely attributed to semantic factors. We also
performed clustering in word-embedding spaces and showed that a larger number of clusters(i.e., larger vocabulary) led to longer context dependency. Moreover, a parallel analysis of birdsong (of Bengalese finch, Lonchura striata var. domestica) revealed the opposite relation between context dependency and vocabulary size (i.e., larger vocabularies shortened dependency
length) and moderate vocabulary sizes amounted to comparable dependency lengths to English
syntax. We also showed that English phoneme sequences have a much shorter dependency
length, which casts doubt on the previous generalization in comparative studies that birdsong ismore homologous to human language phonology than to syntax.

1. Introduction
Human language production is often characterized by long dependency on previous output records, or context (Larson, 2017). Recent studies on deep learningbased models of language generation have shown that the dependent context can
range over hundreds of words (Khandelwal, He, Qi, & Jurafsky, 2018; Dai et al.,2019). In this study, we investigated the relation between context dependency andanother characteristic property of human language: rich vocabulary. Specifically,
we clustered English words into various types/numbers of categories and analyzed
its effect on the length of context dependency.
The results of the English analysis will be discussed in comparison with context dependency in birdsong, reproducing our recent study on Bengalese finch(Lonchura striata var. domestica; Morita, Koda, Okanoya, & Tachibana, 2021).
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

518

As pointed out above, the rich vocabulary is one of the most distinguished properties of human language; no other animal seems to handle tens of thousands of
vocal categories like human language words. Thus, it is of interest to see whether
birdsong exhibits language-like long-distance dependency if it consists of a larger
vocabulary, and our previous study addressed this question using the same analytical paradigm as in this study. Here, we complement the cross-specific investigation of the relation between vocabulary size and context dependency through ananalysis of English data.
2. Materials & Methods
2.1. Measuring Context Dependency by Deep Language Modeling
We measured context dependency in human language and birdsong by using a
deep neural network (Khandelwal et al., 2018; Dai et al., 2019; Morita et al.,2021). We first trained a neural network on the language modeling task: the network computed the predictive probability P (xt | x1 , . . . , xt−1 ) of each token xt(e.g., word, phoneme, or birdsong syllable) conditioned on the preceding tokens
x1 , . . . , xt−1 (i.e., context). Then, the trained network was used to compute thepredictive probability of test tokens conditioned on the full and truncated contexts(Fig. 1A). Intuitively, truncation of a context decreases the predictive probability
when dependent tokens are excluded from the context. Thus, the effective context
length (ECL) was defined by the minimum length of the truncated context where
the difference in the predictive probabilities based on the two contexts faded away;
in practice, we adopted the canonical threshold of 1% difference in perplexity following the previous studies (Khandelwal et al., 2018; Dai et al., 2019; Morita
et al., 2021).
Following Morita et al. (2021), we adopted a Transformer with six layers,
eight heads, hidden dimensionality of 512 (for both the self-attention and feedforward modules), and relative position encoding (Dai et al., 2019) for language
modeling. See Morita et al. (2021, S2 Text) for more details including the training
procedure.
2.2. Human Language Data
We studied the English portion of Wiki40B dataset for word-level analysis of context dependency owing to syntax and/or semantics (Guo, Dai, Vrandečić, & AlRfou, 2020). The raw text data were tokenized into word(-like) sequences using
the Stanza package of Python (Qi, Zhang, Zhang, Bolton, & Manning, 2020).
This resulted in 1,542,787,693 training and 85,462,957 test tokens, which werechunked into 19,873,689 and 1,104,835 paragraphs respectively based on the tagsin Wiki40B. Due to limitations in computational resources and time, we performed the analysis of context dependency on only 10,000 test paragraphs (butstill amounting to 84,664,199 tokens) that were randomly selected among those

519

A

Prediction Target

Full Context

D

word2vec (English)

Truncated Context
The photographs that were taken in the cafe and sent to Mary were ...

Trained Language Model(Transformer)
log P( were | The ... Mary ) log P( were | taken ... Mary )Measure Di erence in Prediction Loss

EnglishE

GloVe (English)

C

Bengalese FinchF

BERT (English)

ff

B

Figure 1. (A) Metric of context dependency based on deep language modeling. Log predictive probabilities of test tokens were computed conditioned on the full context (framed with a black line) andtruncated context (framed with a light-blue line). The effective context length (ECL) is the minimum
length of the truncated context where the prediction difference went below a threshold (1% in perplexity, or ppl.). (B–F) The differences in the mean loss (negative log probability) between the truncatedand full-context predictions of Wiki40B English words (B), part-of-speech (POS) tags (B), and kmeans cluster labels in word2vec (D), GloVe (E), and BERT (F) word-embedding spaces; phoneme
transcription of LibreSpeech (B); and Bengalese finch songs, consisting of syllable sequences (C).
The x-axis corresponds to the length of the truncated context. Both of the axes are in log scale. The
horizontal dashed line indicates the canonical threshold at 1% ppl. The error bars in (C) represent the
90% confidence intervals estimated from 10,000 bootstrapped samples, so that the loss difference isstatistically significant (p < 0.05) if the lower side of the intervals is above the threshold.

520

containing at least 128 tokens.
Three major types of word representation were investigated:• 10,000 most frequent word tokens, replacing the rest with a special label<unk>.
• Part-of-speech (POS) tags in the universal dependency format (Nivre et al.,2016), induced by a pretrained model provided in Stanza (Qi et al., 2020).
• L2-normalized k-means clustering of three pretrained word embeddings (k :=10, 20, . . . , 80, 160, 320, 400, 480):– Word2vec embeddings (Mikolov, Chen, Corrado, & Dean, 2013).
– GloVe embeddings (Pennington, Socher, & Manning, 2014)– Word embeddings of BERT (Devlin, Chang, Lee, & Toutanova, 2019).
Only alphanumeric words included in the BERT’s vocabulary (the smallest
among the three) were clustered and the rest were labeled with <unk>.
The POS representation removed semantic distinctions among words (e.g., apple,
dog → NOUN; give, run → VERB; a, the → DET) and, thus, allowed us to benchmark a purely syntactic context dependency. Word embeddings are real-valued
vector representations that are derived ultimately from co-occurrence statistics ofwords. Word embeddings are known to reflect syntactic and semantic properties
of words in numerical ways. Therefore, clustering in the embedded space allowed
us to manipulate the vocabulary size and inquire into its relation with context dependency.
In addition to the word-level analyses above, we also assessed the context
dependency in phoneme sequences (i.e., phonotactics). We used the phoneme
transcription of the LibriSpeech corpus (Panayotov, Chen, Povey, & Khudanpur, 2015) that was induced by the Montreal Forced Aligner (McAuliffe, Socolof, Mihuc, Wagner, & Sonderegger, 2017).1 The Transformer language model
was trained on the train-clean-360 portion of the corpus (consisting of
13,611,485 tokens extracted from 104,008 wav files), and test-clean portion(203,760 tokens from 2,620 wav files) was used for estimating the dependency
length. We adopted the original wav files of the corpus as the unit of sequence.
2.3. Birdsong Data
We reproduced the estimation of context dependency in Bengalese finch song,
originally reported in (Morita et al., 2021). In that study, we performed Transformer language modeling on syllable sequences produced by eighteen birds.
Syllables were classified into discrete categories by an unsupervised clustering
method with speaker normalization (see also Morita & Koda, 2020) and the resulting symbolic representation was used for the language modeling and analysis
of context dependency. The number of syllable categories was automatically esti1 Precomputed transcription is provided by Lugosch, Ravanelli, Ignoto, Tomar, and Bengio (2019)in https://doi.org/10.5281/zenodo.2619474.

521

mated by the clustering method as 37. We also reported the context dependency
based on finer- and more coarse-grained classifications of the syllables (into 10,
20, . . . . 80, 160, 320 categories) by L2-normalized k-means clustering.2 Here, we
reproduced the analysis of context dependency for each of those syllable clusterings. The dataset consisted of 457,992 and 6,557 syllables (segmented into 7,779
and 100 sequences by recording sessions) for training and test respectively.
3. Results
While sequences of raw English words exhibited the context dependency beyond
128 tokens, the effective dependency length dropped to eleven when the words
were replaced with their POS tag (Fig. 1B). The dependency in phoneme sequences was even shorter—only on four tokens—despite their larger vocabulary
size than the POS categories.
Manipulation of the vocabulary size by word-embedding clustering showedthat larger vocabularies led to longer context dependency (Fig. 1D–F). By contrast, the context dependency of Bengalese finch song decreased as finer-grained
syllable classifications were adopted (Fig. 1C; due to the small size of test data,
the dependency length was estimated considering the statistical significance ofthe difference between full- and truncated-context predictions against the threshold with p < 0.05, following Morita et al., 2021). When coded by the automatically detected vocabulary consisting of 37 syllable categories, the birdsong
exhibited effective dependency on eight tokens. Smaller vocabularies with 10–30
categories increased the dependency length to ten to fifteen tokens. Conversely,
larger vocabularies decreased the dependency length, up to five when 160/320
syllable categories were assumed.
4. Discussion
We found that the word-level context dependency of English became drasticallyshorter when tokens were replaced with their POS tag. This result suggests thatthe long context dependency in human language is mostly attributed to semantic factors and purely syntactic dependencies can be handled relatively locally,by referring to eleven recent tokens in the production history. This view is also
consistent with the positive correlation between the number of word-embedding
clusters and the dependency length; finer-grained clusterings of embeddings recovered more semantic information in the original continuous space, which led to
the longer dependency length in turn.
By contrast, finer-grained classification of Bengalese finch syllables decreased
the dependency length (as originally reported in Morita et al., 2021). This find2 All the clustering results are publicly available in https://doi.org/10.17605/OSF.IO/
R6PAQ.

522

ing indicates that expanding vocabulary size does not lead to language-like longdistance dependency by itself. Specifically, minor acoustic variations in Bengalese
finch syllables—which are encoded in fine-grained classifications but ignoredin coarse-grained classifications—are unlikely to carry semantics-like information (Okanoya, 2007; Berwick, Okanoya, Beckers, & Bolhuis, 2011; Miyagawa,Berwick, & Okanoya, 2013). Instead, acoustic properties of the birdsong syllables are known to be affected by surrounding syllables (Wohlgemuth, Sober, &Brainard, 2010) and, thus, paying more attention to local context improved theprediction of fine-grained syllable categories (e.g., five previous tokens when 160
and 320 categories are assumed). Such local interactions are more akin to humanlanguage phonotactics; as demonstrated by our analysis of phoneme sequences inEnglish, phonotactic dependencies are much shorter (four tokens; although longer
and potentially unbounded phonotactic dependencies have been suggested for limited patterns in several languages; Sapir & Hoijer, 1967) than syntactic dependencies encoded by POS sequences (eleven tokens).
Meanwhile, it should be noted that Bengalese finch song—under the assumption of a moderate number of syllable categories (10–40)—exhibited longer dependency on eight to fifteen tokens than English phonotactics (four tokens). The
estimated dependency length of the birdsong instead amounted to that of English
syntax (eleven tokens), which casts doubt on the previous generalization in comparative studies that birdsong is more homologous to human language phonology
than to syntax (Berwick et al., 2011). The non-local context dependency comparable to human language syntax also implies that the birdsong cannot be modeled efficiently by traditional n-gram grammars (Hosino & Okanoya, 2000) because exponentially more transitional rules are needed as the dependency length increases.
The long dependencies would be captured more succinctly by latent structures asin hidden Markov models (Rabiner, 1989; Katahira, Suzuki, Okanoya, & Okada,2011) and hierarchical grammars (Berwick, 2015; Morita & Koda, 2019), or by
distributed representation as in biological/artificial neural networks (Nishikawa,Okada, & Okanoya, 2008; Dai et al., 2019).
Acknowledgements
This study was supported by JSPS Grant-in-aid for Scientific Research on Innovative Areas #4903 (Evolinguistic; JP17H06380) to HK and KO, JSPS Grant-in-Aid
for Scientific Research (JP19KT0023, JP21H03781) to ROT, and for Early-Career
Scientists (JP21K17805) to TM, the JST Core Research for Evolutional Scienceand Technology 17941861 (JPMJCR17A4) to HK and ACT-X 21454934 (JPMJAX21AN) to TM, and the Mitsubishi Foundation Research Grants in the Natural
Sciences (202111014) to TM. We also gratefully acknowledge the support of the
Academic Center for Computing and Media Studies, Kyoto University, regarding
the use of their supercomputer system.

523

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structure in human language. Frontiers in Psychology, 4, 71.
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D. (2016). Universal Dependencies v1: A multilingual treebank collection.
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THE EFFECT OF NETWORK STRUCTURE ON LEXICALVARIABILITY IN SIGN LANGUAGE COMMUNITIES
Katie Mudd1 , Marnix Van Soom1 , Connie de Vos2 , and Bart de Boer1* katie.mudd@ai.vub.ac.be

1 AI lab, Vrije Universiteit Brussel, Belgium

2 Department of Communication and Cognition, Tilburg University, the Netherlands

The study of sign languages allows us to observe what features characterizea language in its early stages. The initial phase of emergence is characterized
by a high degree of lexical variability, where synonyms for a concept appear tocoexist in a community. Overtime, lexical variation may persist; for example, it
was found that Kata Kolok, a sign language used in a small community in a Balinese village with a tightly knit social network, exhibits a high degree of lexical
variability (Mudd et al., 2020). Meanwhile, in communities with different social
configurations, we suggest that lexical variation may decrease within groups of
frequent interlocutors (i.e. on the local level) but remain high across the entire
population (i.e. on the global level). For example, British Sign Language (BSL),
used by the large Deaf community across the UK, was found to be lexically uniform at the local level (i.e. within regions), but at the global level BSL actually
has a higher degree of lexical variability than Kata Kolok (Mudd, Lutzenberger,
Schembri, Ohanin, & Stamp, 2022).
Several factors have been proposed to explain the retention of lexical variability in sign language emergence: de Vos (2011) suggests that populations that retain
a high degree of lexical variability typically have frequent face-to-face interaction,
are small and lack formal deaf education. Previous models have investigated population size (Thompson, Raviv, & Kirby, 2020) and the role of shared context(Mudd, de Vos, & de Boer, 2022), but the role of network structure on lexical
variation has yet to be explored. In addition, variation at the local and global levels has yet to be considered, with previous models implicitly considering variation
only at one, unspecified level.
Building on the shared context model by Mudd et al. (2022), which formalizes
the hypothesis that social and psychological information shared within groups ofsigners affords the use of iconic signs and thus the retention of lexical variability,
we investigate if network structure can explain differences in the degree of lexical
variation at the local and global levels. In more detail, in the initial phase of themodel, agents improvise signs to refer to different concepts based on their social
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

526

and psychological information, such that agents in the same groups are likely to
produce similar signs. However, we made the arguably unrealistic assumptionthat group membership does not influence interaction patterns. Here, we generalize our previous model by allowing the interaction probability between pairs ofagents to depend on their group membership through Newman (2003)’s assortativity coefficient r. A continuous spectrum of assortative network structures canthus be probed by varying r, from agents interacting completely at random (r = 0)
to interacting only with others in the same group (r = 1).
To study the effect of assortative network structure on lexical variability at the
local (i.e. within group) and global (i.e. across the entire population) levels, we
keep a fixed set of parameters1 and vary the value of r, as shown in Fig 1. We
observe that r correlates with the lexical variability at both levels. Interestingly,
the local and global levels decouple only at high values of r, i.e. for strongly
assortative networks.

Figure 1. Network visualizations (left) depict interactions (edges) between groups (nodes colored according to group membership) for different values of r. The plot (right) shows the effect of assortative
network structure on local and global lexical variability. For high values of r, the degree of variation
at the global level is much higher than at the local level.

Social networks tend to high values of r (Newman, 2003), and if we suppose that the interaction between BSL signers living in different regions can bemodeled by a strongly assortative network structure, our result may help explain
the observed difference between local and global levels. Network structure is
likely one suitable candidate in determining the degree of local and global lexical
variability in languages, most pronounced for communities consisting of several
locally tightly knit groups (where r is high). Finally, with this model, a range ofassortative network configurations can be considered, without needing to compare
different categorizations of sign languages (Hou & Vos, 2021).
1 The fixed model parameters are: number of agents = 100, number of groups agents are randomly
assigned to = 5, number of concepts = 10, number of bits for the form and culturally salient features =10, overlap between the culturally salient features and the form = 0.9. The model is run for 2000 time
steps. 100 iterations of the run are averaged, shown as mean ± standard deviation in Fig 1.

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Referencesde Vos, C. (2011). Kata Kolok Color Terms and the Emergence of Lexical Signs
in Rural Signing Communities. The Senses and Society, 6(1), 68–76.
Hou, L., & Vos, C. de. (2021). Classifications and typologies: Labeling sign
languages and signing communities. Journal of Sociolinguistics, 1–8.
Mudd, K., de Vos, C., & de Boer, B. (2022). Shared context facilitates lexical
variation in sign language emergence. Languages, 7(1), 31.
Mudd, K., Lutzenberger, H., Schembri, A., Ohanin, O., & Stamp, R. (2022).
The social structure of signing communities and lexical variation: A crosslinguistic comparison of three unrelated sign languages. In Theoretical Issues in Sign Language Research (TISLR). Osaka, Japan.
Mudd, K., Lutzenberger, H., Vos, C. de, Fikkert, P., Crasborn, O., & Boer, B. de.
(2020). The effect of sociolinguistic factors on variation in the Kata Kolok
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Thompson, B., Raviv, L., & Kirby, S. (2020). Complexity can be maintainedin small populations: a model of lexical variability in emerging sign languages. In Proceedings of the 13th International Conference on the Evolution of Language (p. 3). Brussels, Belgium.

528

MODELING THE ORIGINS OF COMMUNICATION: ALIGNMENT OF
INTERESTS AND SOCIAL CUES THEREOF
ROLAND MÜHLENBERND*1 and ANDREAS BAUMANN2* Corresponding Author: muehlenbernd@leibniz-zas.de1 Leibniz-Centre General Linguistics, Berlin, Germany
2 University of Vienna, Vienna, Austria

Alignment of interests is a fundamental factor for the evolutionary stability of communication. We will apply tools from evolutionary game theory to analyze the relationship between
the degree of alignment of interests among interlocutors and the evolutionary stability of theirpotential signaling strategies. We will study different scenarios and show that there is an evolutionary benefit for adopting a communication strategy which is conditioned on social cues:
hints about the degree of alignment of interests in a given situation. We will show that thisstrategy i) is the only evolutionarily stable strategy in a scenario with social cues, ii) has very
low underlying requirements (with respect to alignment of interests) to be evolutionarily stable,
and iii) is less reliable for the receiver, since it determines the sender to be partially dishonest.

1. Introduction
A fundamental underlying factor for the stability of communication is alignmentof interests (AOI). Searcy and Nowicki (2005) illustrate in a number of scenariosthat the evolutionary stability of animal signaling depends on the AOI between
sender and receiver. They distinguish a) scenarios where interests overlap, suchas food begging signals or alarm calls, where stable signaling is suitable and common, from b) scenarios where interests diverge or oppose, such as mating signals
or displays of aggression, where stable signaling is rather unexpected and can only
be explained through additional conditions. Naturally, AOI does also play an important role in human language. For example, the Gricean cooperation principle
tells us that interlocutors’ interests are generally aligned in at least one aspect,
namely to communicate appropriately and cooperatively (Grice, 1975).
Often, the purpose of a communicative signal depends on the extent to whichinterests are aligned. For example, birds such as the great titmouse use alarm
calls in cases when there is actual danger (high AOI), but they also use them as‘false alarm’ in situations where they want to move other birds away from somefood source (low AOI) (cf. Matsuoka, 1980). That is, titmice are able to adapt
their signaling strategies not only towards the actual information they want totransfer (the source of danger) but also towards properties of the receiver, i.e.,social information. This form of deception is frequently found in human language
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

529

as well. For example, the usage of polite speech can serve for sweetening theconversation (high AOI) or for persuading the hearer for doing something that israther in the interest of the speaker (low AOI).
In this article, we will apply evolutionary game theory to show how the stability of communication strategies under population dynamics depends on the AOI
in the underlying scenario. Moreover, we will demonstrate that the establishmentof stable communication is more likely when senders adapt their signaling strategies towards social cues, i.e., hints about the AOI between interlocutors in a given
situation. Finally, we will show that the benefit of the integration of social cues
into communication comes with a hitch: it promotes dishonest signaling.
2. The mating game: communication with (partially) aligned interests
We study a fundamental question concerning the origin of communication: how
can a signal become an entity of (meaningful) communication at all? We takea look at a simple form of communication, namely signaling, where a sender
produces a signal that evokes a response from a receiver. One important aspectof signaling being sustainably successful is the alignment of interests between asender and a receiver. When the sender has an advantage in evoking a particularresponse from a receiver by sending a signal, and the receiver, in turn, has anadvantage in performing this response upon the signal, then both parties have analigned interest in successful communication.
To emphasize the role of alignment of interests in communication, we wantto draw the attention to a signaling scenario where the interests between senderand receiver are only partially aligned. Diverse game-theoretic versions of this
scenario have been studied with tools from evolutionary game theory (cf. Crawford & Sobel, 1982; Grafen, 1990).We will present a simple model version of this
scenario, which we call the mating game.
The scenario is as follows: we have a situation where a male and a femaleof a particular species get together to get involved in a potential mating act. The
male can be a high quality (H) or low quality (L) type. The female can agree tomate (M ) or not to mate (M̄ ). For any type of male, high or low type, it is always
evolutionarily beneficial to mate with the female. For the female, however, it isevolutionarily beneficial to mate with high type males and reject low type males.
The simplest form of the underlying payoff structure of this scenario is given in
Figure 1(a), where each strategy profile is attributed to two utility values, the firstfor the male (row player), the second for the female (column player). Utility
values are either 1 (evolutionarily beneficial) or 0 (evolutionarily detrimental).
In this scenario, interests are only aligned for the case where the male is a hightype. It can be shown that sustainable signaling cannot evolve. Let’s assume thathigh type males start sending a signal to the females, upon which females mate,otherwise they don’t mate. That works fine for the females as long as only hightype males send that signal. But now low type males have an evolutionary benefit

530

H
L

M
1, 1
1, 0

M̄
0, 0
0, 1

(a) underlying payoff structure
Figure 1.

⟨s, s⟩⟨s, s̄⟩⟨s̄, s⟩⟨s̄, s̄⟩⟨M, M̄ ⟩1, 1/2
1/2, 1
1/2, 00, 1/2⟨M̄ , M̄ ⟩0, 1/20, 1/20, 1/20, 1/2(b) mating gameThe mating game’s (a) underlying payoff and (b) normal-form representation.

in sending the signal, and will therefore adopt it. But then the signal is meaningless for females, because it lost its function, namely to distinguish high type from
low type males. Therefore, they can ignore it and signaling stops working.
This line of thought can be studied mathematically. Whether a signaling strategy is sustainable can be mathematically deduced by proofing if it is an evolutionarily stable strategy (Maynard Smith & Price, 1973). As a first step, we definea space of signaling strategies. We define a sender strategy as a tuple ⟨xH , xL ⟩,whereby xH represents the signaling behavior of high types, and xL represents thesignaling behavior of low types. In its simplest version, our scenario entails four
different sender strategies: ⟨s, s⟩, ⟨s, s̄⟩, ⟨s̄, s⟩ and ⟨s̄, s̄⟩, whereby s stands forsending a signal, s̄ stands for not sending a signal. Similarly, a receiver strategy
is defined as a tuple ⟨ys , ys̄ ⟩, whereby ys represents the response behavior uponreceiving a signal, and ys̄ the response behavior upon not receiving a signals. For
simplicity, we assume that receivers never mate upon not receiving a signal. Thenthe receiver strategies are: ⟨M, M̄ ⟩ and ⟨M̄ , M̄ ⟩.
From the underlying payoff structure we can deduce a utility table over pairsof signaling strategies by computing expected utilities (see the Online Appendix
Sec. B) which represent how beneficial (on average) a combination of a sender
strategy and a receiver strategy (a strategy profile) is for the sender (first value)
and the receiver (second value). The resulting utility table is given in Figure 1(b).
From these utilities we can identify strategy profiles that are evolutionarily
stable (Maynard Smith & Price, 1973). Selten (1980) has shown for asymmetricgames, such as the mating game, that a strategy profile is evolutionarily stable
if and only if the row player’s utility is unique maximum in its column, and thecolumn player’s utility is a unique maximum in its row. This means that any
unilateral switch out of an evolutionarily stable strategy profile leads to a strictly
lower payoff for the switching player. The readers can check for themselves thatnone of the strategy profiles of Figure 1(b) is evolutionarily stable. Particularly,
the strategy profile that represents separating signaling (⟨s, s̄⟩, ⟨M, M̄ ⟩) is not
evolutionarily stable, since the sender has a benefit from switching to ⟨s, s⟩. And
then, (⟨s, s⟩, ⟨M, M̄ ⟩) is not evolutionarily stable, since the receiver is not strictly
worse off when switching to ⟨M̄ , M̄ ⟩. A similar argument can be found for any
other strategy profile of the mating game in Figure 1(b).

531

H
L

M
1, 1
0, 0

M̄
0, 0
1, 1

(a) underlying payoff structure⟨s, s⟩⟨s, s̄⟩⟨s̄, s⟩⟨s̄, s̄⟩⟨M, M̄ ⟩1/2, 1/2
1, 1
0, 0
1/2, 1/2⟨M̄ , M̄ ⟩1/2, 1/2
1/2, 1/2
1/2, 1/2
1/2, 1/2(b) lethal mating game

Figure 2.

Lethal mating game’s (a) underlying payoff and (b) normal-form representation.

Let us now consider a scenario where interests are completely aligned, as represented by the payoff structure in Figure 2(a). This could represent a mating
scenario where the female is extremely selective and aggressive. For example,
after agreeing upon mating, the female tests the strength of the male before theactual mating act happens. High-quality males pass the test and mate with thefemale, whereas low-quality males fail the test and get killed by the female before
the mating can happen. Here, low types have an evolutionary benefit of not initiating a mating act with this type of female. Let us call this game the lethal matinggame. Its normal-form game representation is given in Figure 2(b).
This game has exactly one evolutionarily stable strategy profile, namely(⟨s, s̄⟩, ⟨M, M̄ ⟩), since any unilateral switch of strategy leads to a strictly worse
payoff for the switching player. In this strategy profile, high-quality males senda signal upon which females mate, and low-quality males don’t send a signal andwon’t get killed by the female. The bottom line of this section is this: in contrary
to the standard mating game, signaling in the lethal mating game is evolutionarily
stable, particularly because the interests of sender and receiver are totally aligned.
3. Transfer to a scenario of cultural evolution
Note that these mating game scenarios study stability aspects of signaling strategies in the light of biological evolution, where utility represents fitness which
drives the rate of biological reproduction. But when it comes to the evolutionof human communications and language, we are frequently concerned with cultural evolution, where utility equals fitness which drives the rate of behavioral
reproduction, for example in form of imitation or learning. Since the concept ofevolutionary stability is assumed to be applicable for biological evolution as wellas cultural evolution (O’Connor, 2020), we can be agnostic about the concrete
underlying dynamics. Moreover, very often we can transfer games that describe
scenarios that a relevant in the light of biological evolution to scenarios that are
relevant in the light of cultural evolution. We will exemplify this hereafter.
Here is an idea how we can reinterpret the mating game as a communication
scenario primarily exposed to dynamics of cultural evolution: Assume that wehave a group of individuals which happen to use alarm calls for dangerous events,
such as raptor attacks, etc. Let’s say that the individuals can distinguish between

532

events of high danger (H) and events of low danger (L). In our game, the row
player is a watchpost, who is in charge of giving alarm calls. The column player
is a gatherer, who is doing a utility-relevant activity, such as picking berries.
In a first scenario the gatherer is selfish: he is picking the berries for himself.
He has an interest to be warned whenever there is a high danger event, so he canstop picking and look for a save place to move to (M ). However, in a low danger
event, it is more beneficial not to move (M̄ ) and continue picking berries. The
watchpost, however, has an interest in always interrupting the gatherer, since there
will be more berries left for her when her watchpost shift is over. The underlying
payoff structure of this scenario is exactly the one in Figure 1(a), and the resulting
communication game is exactly the one in Figure 1(b). And the analysis leads tothe same conclusion: the strategy profile of separating signaling (⟨s, s̄⟩, ⟨M, M̄ ⟩)is not evolutionarily stable. And also the reason is the same: interests are notaligned. Picking a lot of berries is good for the gatherer, but bad for the watchpost.
Let us think about a similar scenario where the gatherer is not picking berries
for himself, but for the whole group instead, including the watchpost. Then thewatchpost would prefer only to interrupt the gatherer when the gatherer’s life is
at stake (in high danger situations), but otherwise prefers him to continue picking
berries, since more work done by the gatherer means more food for the wholegroup, including the watchpost herself. This scenario is exactly the one presented
in Figure 2(a). Accordingly, Figure 2(b) represents the resulting communication
game. Here again, the conclusion is the same: the separating signaling strategy(⟨s, s̄⟩, ⟨M, M̄ ⟩) is evolutionarily stable since interests are aligned. Picking a lotof berries is good for both the gatherer and for the watchpost.
4. Combined communication games and social cues
Note that both scenarios of the last section differ with respect to the social behaviorof the gatherer. In the first case, he is selfish; in the second case, he is social.
In this section we want to merge both scenarios to a combined one, where thegatherer is sometimes selfish and sometimes social. In more general terms, insuch a combined scenario the situation of totally aligned interests (Figure 2(a))
occurs with a frequency p in (0, 1), and the situation of partially aligned interests(Figure 1(a)) occurs otherwise, with frequency 1 − p. When we combine theutility tables of both situations weighted by probability p, we obtain the underlying
payoff structure as given in Figure 3(a). Its normal form game representation withsignaling options is given in Figure 3(b), which we label communication game 1.
Here, it can be shown that the separating signaling strategy (⟨s, s̄⟩, ⟨M, M̄ ⟩) isevolutionarily stable if and only if p > 1/2; for the proof see the Online Appendix
Sec. A.1. In other words, when situations of totally aligned interests occur more
frequently than situations of partially aligned interests, then the separating signaling strategy is evolutionarily stable. With respect to our concrete example, this is
the case when gatherers behave more often social than selfish.

533

H
L

M
1, 1
1 − p, 0

M̄
0, 0
p, 1

(a) underlying payoff structure⟨s, s⟩⟨s, s̄⟩⟨s̄, s⟩⟨s̄, s̄⟩⟨M, M̄ ⟩1 − p/2, 1/2
1/2 + p/2, 1
1/2 − p/2, 0p/2, 1/2⟨M̄ , M̄ ⟩p/2, 1/2
p/2, 1/2
p/2, 1/2
p/2, 1/2(b) communication game 1

Figure 3. The (a) underlying payoff and (b) normal-form representation of communication game 1.
The alignment of interests between row and column player depends on p.

⟨s, s, s, s⟩⟨s, s, s̄, s⟩⟨s, s, s̄, s̄⟩⟨s̄, s̄, s̄, s̄⟩⟨M, M̄ ⟩1 − p2 , 21
1 , 1+p
2

1+p
2 ,1
p 1
2 , 2

⟨M̄ , M̄ ⟩
p 1
2, 2
p 1
2, 2
p 1
2, 2
p 1
2, 2

Figure 4. A subset of four selected strategies in communication game 2. The full game entails thesame finding: there is only one evolutionarily stable strategy pair (and only for p > 0), namely(⟨s, s, s̄, s⟩, ⟨M, M̄ ⟩) (the boxed entry).

The definition of communication game 1 implies that individuals cannot distinguish situations with totally aligned interests from those with partially aligned
interests. Let’s assume that the sender is able to distinguish both scenarios, forexample via social cues that give hints about the receiver’s attitude. For example,
when the sender can identify if the gatherer is behaving selfish or social, she cancondition her strategy accordingly. We can model this by extending the sender’sstrategy space to a quadruple ⟨xHt , xHp , xLt , xLp , ⟩, where xHt is the signaling
behavior of a sender in a scenario with totally aligned interests in a high-danger
event, xHp is the signaling behavior of a sender in a scenario with partially aligned
interests in a high-danger event, and so on. In each case, the sender can either senda signal s or not s̄, leading to 24 = 16 sender strategies. We call the resulting gamecommunication game 2. Figure 4 shows a subset of strategies in communication
game 2. The full utility table is given in the Online Appendix Sec. A.2.
It can be shown that there is exactly one evolutionarily stable strategy pair,namely (⟨s, s, s̄, s⟩, ⟨M, M̄ ⟩), where the sender always sends the signal in highdanger events, but also in low-danger events in case that the receiver is selfish. Importantly, this strategy pair is evolutionarily stable for any p in (0, 1); for the proof
see the Online Appendix Sec. A.2. The bottom line of this section is this: there
is a much higher AOI requirement for evolutionary stability without a sender’saccess to social cues (communication game 1; requirement: p > 21 ) than with asender’s access to social cues (communication game 2; any p in (0, 1)). This is
our main finding and we will come back to this point in the conclusion.

534

Note that the scenarios in this section assume that signaling comes without
costs. In the Online Appendix Sec. C, we studied communication games 1 and 2with signaling costs. The result shows that signaling costs strengthen the conditions for separating signaling strategies to be evolutionarily stable in both games.
Since the separating signaling strategy in communication game 2 is not conditioned on social cues, we can conclude that signaling costs weaken the prerequisites for the evolutionary stability of signaling strategies that involve social cues.
5. Reliability and honesty
We want to study the communication strategies with respect to the concepts reliability and honesty. Following Searcy and Nowicki (2005), a signal is reliable ifi) some characteristic of the signal (including its presence/absence) is consistently
correlated with some attribute of the signaler or its environment, and ii) receivers
benefit from having information about this attribute. Moreover, a signal does nothave to be perfectly reliable, but it is enough when the signal is honest on average,such that the receiver on average is better off assessing the signal than ignoring it.
Following these lines of thought, we define a signal s to be honest, if andonly if it is sent in situations such that the expected response of the receiver is
evolutionarily beneficial to the receiver. Hereof, we define the reliability of asignal s as the ratio of the honest usage of s to the total usage of s. More precisely,
when a sender uses a signal s with frequency fT in total and uses it honestly with. Thus,frequency fH , then the reliability of the signal is given by the ratio ffHTreliability of a signal is a value between 0 and 1 (never/always honestly used).
Let’s take a look at the reliability of signal s in sender strategy ⟨s, s, s̄, s⟩ aspart of the only stable signaling strategy pair (⟨s, s, s̄, s⟩, ⟨M, M̄ ⟩) in communication game 2. Here, totally aligned situations occur with frequency p andpartially aligned situations with frequency 1 − p. High danger and low danger events are implicitly assumed to occur both with frequency 21 . This gives
us the following frequencies for the four situations that the sender can distin1−p
guish: f (xHt ) = p2 , f (xLt ) = p2 , f (xHp ) = 1−p2 and f (xLp ) =2 . Inthe stable signaling strategy ⟨s, s, s̄, s⟩ the sender sends the signal with total frequency fT = f (xHt ) + f (xHp ) + f (xLp ) = 2−p
2 . Furthermore, since the signal s is only honestly sent when there is a high danger event, we get frequency
fH = f (xHt ) + f (xHp ) = 12 . Now we can compute the reliability of signal s as
1

1
2
follows: ffH
= 2−p
= 2−p
.
T
2
From this analysis we can follow that the reliability of the signal s in strategy⟨s, s, s̄, s⟩ is greater than 12 for any p in (0, 1). However, this result also shows thatstrategy ⟨s, s, s̄, s⟩ is not totally reliable (save for the borderline case with p = 1).
On the other hand, it is easy to see that in the evolutionarily stable sender strategy⟨s, s̄⟩ of communication game 1, signal s has a reliability value of 1 (since s isonly and always sent in H), therefore it is totally reliable.

535

6. Conclusion
We have argued that classical game-theoretical models of animal signaling can betransferred to scenarios that might have played an important role in the evolutionof human language. The evolutionary analyses of the models in this paper havedemonstrated a well-known general aspect of any form of communication: theevolutionary stability of meaningful signaling/communication depends to a great
degree on the alignment of interests (AOI) between sender and receiver. The moreparticular results of our analyses are connected to the juxtaposition of the communication games 1 and 2. This analysis showed that access to social cues lowers theunderlying AOI requirements for signaling strategies being evolutionarily stable.
We conclude that the ability to integrate social cues into communication is aconducive feature for stable communication and therefore a potentially important
propulsive factor in the evolution of language. The latter analysis showed thatthe integration of social cues comes with a hitch, though: it promotes dishonest
signaling. But then, dishonest signaling isn’t necessarily something adverse in thepresented scenario. It can also be seen as a form of punishment that the senderexerts on the receiver for selfish behavior. Here, dishonesty might have evolved
for a good cause, namely to suppress the behavior of nonprosocial individuals inorder to enhance sociality. This aspect ought to be pursued in subsequent research.
Online Appendix: The Online Appendix is part of the proceeding’s supplementary material and accessible under https://doi.org/10.6084/m9.figshare.20004395
Funding: Roland Mühlenbernd was funded by the German Research Foundation(DFG) within the SFB 1412 under DFG agreement number 41659133.
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Grafen, A. (1990). Biological signals as handicaps. Journal of Theoretical Biology, 144, 517–546.
Grice, H. P. (1975). Logic and conversation. In Speech acts (pp. 41–58). Editedby P. Cole/J. Morgan, New York: Academic Press.
Matsuoka, S. (1980). Pseudo warning call in titmice. Tori, 29, 87-90.
Maynard Smith, J., & Price, G. (1973). The logic of animal conflict. Nature(146),
15–18.
O’Connor, C. (2020). Games in the philosophy of biology. Cambridge University
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Searcy, W. A., & Nowicki, S. (2005). The evolution of animal communication.
Princeton University Press.
Selten, R. (1980). A note on evolutionarily stable strategies in asymmetric animal
conflicts. Journal of Theoretical Biology, 84, 93–101.

536

MACHINE IMPOSTORS IMPEDE HUMAN DETECTION AND
THE SUCCESS OF STABLE CONVENTIONS BY IMITATING
PAST INTERACTIONSTHOMAS F. MÜLLER*1, LEVIN BRINKMANN1, JAMES WINTERS2, and NICCOLÒPESCETELLI3*Corresponding Author: mueller@mpib-berlin.mpg.de
1Center for Humans and Machines, Max Planck Institute for Human Development,

Berlin, Germany
2School of Collective Intelligence, Mohammed VI Polytechnic University, Ben

Guerir, Morocco
3Department of Humanities and Social Sciences, New Jersey Institute ofTechnology, Newark, United States

How can successful communication arise and stabilize between humans?
Artificial language games have shown that we create novel conventions even fromminimal communicative means (Galantucci, 2005; Garrod et al., 2007; ScottPhillips et al., 2009). A convention is defined as the arbitrary solution to arepeated coordination problem (Lewis, 1969), established through, e.g.,
precedence (Clark, 1996). Meanwhile, results from the perceptual crossing
paradigm (Auvray et al., 2009; Barone et al., 2020) have shown that humaninteraction in minimal environments is distinct from interaction with automatedagents in its reciprocal patterns, i.e. interdependence between participants.
However, these studies deliberately avoid conventional paths to success. In thecurrent study, we build on these experimental approaches to investigate
conventional and reciprocal routes to communication in a minimal environment.
In a Turing test setup (Turing, 1950) stripped of the use of natural language, we
manipulate bot behavior to assess how it affects the detection of bots, andparticipants’ behaviors. Our main hypothesis is that access to past interactions
makes bot impostors more deceptive by interrupting convention formation.
We recruited 200 participants online for the experiment and assigned them to
one condition in pairs. Their goal was to find out whether their random partner ineach trial was the human partner or the bot impostor. Participants interacted
within a 2D space containing an orange square and a blue circle. Their only way
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

537

to communicate was to move the square, while the human partner or the bot
moved the circle. In both conditions, bot behaviors were identical replays ofprevious behavior from human pairs, but their source differed: While bots in thepartner impostor condition repeated behaviors shown earlier by the participant’s
own human partner, bots in the foreign impostor condition imitated an unrelatedparticipant from a previous partner impostor condition. Participants received
feedback on their own and their partner’s performance after each trial.
We tested our preregistered predictions via model comparison of mixedeffects models. Predicting performance by condition revealed that participants
were more successful in the foreign impostor condition than in the partner
impostor condition (β = −1.92, SE = 0.16, ΔAIC = 69; Fig. 1a). We measured theconventionality of participants’ behaviors by the Earth Mover’s distance to theirown spatial positions over trial blocks. Human performance in the partner
impostor condition suffered from conventional behavior, while the foreign
impostor condition profited from it (β = 0.30, SE = 0.08, ΔAIC = 10; Fig. 1b).
Last, we measured reciprocity by computing the transfer entropy between
participants’ and their partners’ movements, and found that it was only useful todetect bots in the partner impostor condition (β = 0.17, SE = 0.07, ΔAIC = 3.4).

Figure 1. a) Performance results by participant. Violin plots show the density in the two conditions.
The top line represents the performance ceiling, the middle line performance at chance. b) Relation
between conventionality and accuracy in the two conditions. Note that higher Earth Mover’s distancetranslates to lower conventionality. The shaded area shows the 95% confidence interval.

Our results suggest bots can avoid human detection and prevent conventional
behaviors from succeeding by imitating past interactions, emphasizing the role ofthe interaction history for human communication. Although both conventionaland reciprocal behaviors were adaptive under the right conditions, participants
struggled when conventionality was maladaptive. We show how manipulating bot
behavior can provide new insights into emergent communication, combining
ideas from language evolution, pragmatics, and social cognition.

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References
Auvray, M., Lenay, C., & Stewart, J. (2009). Perceptual interactions in aminimalist virtual environment. New Ideas in Psychology, 27(1), 32–47.
https://doi.org/10.1016/j.newideapsych.2007.12.002
Barone, P., Bedia, M. G., & Gomila, A. (2020). A Minimal Turing Test:
Reciprocal Sensorimotor Contingencies for Interaction Detection. Frontiers in
Human Neuroscience, 14, 102. https://doi.org/10.3389/fnhum.2020.00102
Clark, H. H. (1996). Using language. Cambridge University Press.
Galantucci, B. (2005). An experimental study of the emergence of humancommunication systems. Cognitive Science, 29(5), 737–767.
https://doi.org/10.1207/s15516709cog0000_34
Garrod, S., Fay, N., Lee, J., Oberlander, J., & MacLeod, T. (2007). Foundationsof representation: Where might graphical symbol systems come from? Cognitive
Science, 31(6), 961–987. https://doi.org/10.1080/03640210701703659
Lewis, D. (1969). Convention. Harvard University Press.
Scott-Phillips, T. C., Kirby, S., & Ritchie, G. R. S. (2009). Signalling
signalhood and the emergence of communication. Cognition, 113(2), 226–233.
https://doi.org/10.1016/j.cognition.2009.08.009
Turing, A. (1950). Computing Machinery and Intelligence. Mind, 59, 433–460.

539

THE ROLE OF CROSS-CULTURAL DIFFERENCES IN
REFERENTIAL STRATEGY EMERGENCE: DATA FROMARTIFICIAL SIGN LANGUAGE LEARNING
Danielle Naegeli*1, David Peeters1, Emiel Krahmer1, Marieke Schouwstra2, YasaminMotamedi3, and Connie de Vos1
*Corresponding Author: d.s.u.naegeli@tilburguniversity.edu
1Department of Communication and Cognition, Tilburg University, Tilburg, Netherlands
2Institute for Logic, Language and Computation, University of Amsterdam,

Amsterdam, Netherlands
3Centre for Language Evolution, University of Edinburgh, Edinburgh, United

Kingdom

The grammatical use of space for referential strategies such as spatial
modification and role shift can be found across many sign languages (e.g.,
Engberg-Pedersen, 1993; Liddell, 2003). Signers, for example, use pointing signs
towards arbitrary locations in space to refer to different agents. However, not all
sign languages use spatial strategies for non-spatial relations. The rural sign
languages Kata Kolok (used in the North of Bali) and Al-Sayyid Bedouin Sign
Language (used in southern Israel) have not developed strategies relying on
anaphoric use of space (de Vos, 2012; Padden et al., 2010). This raises thequestion of why there are such typological differences. A proposed explanation
attributes the absence of such strategies to the relatively young age of these sign
languages, suggesting that these strategies have not developed yet (Sandler et al.,2005). However, while Kata Kolok and Al-Sayyid Bedouin Sign Language areyounger than, for example, American or British sign language they differ
considerably in age and furthermore, another “young” sign language, Nicaraguan
Sign Language, developed spatial strategies early on in its emergence (Senghas
et al., 1997). Moreover, hearing English-speaking participants in a silent gestureexperiment that simulated intergenerational transmission with an iterated learning
design produced referential strategies using space anaphorically within just fivegenerations (Motamedi et al., 2021). Therefore, the age of respective sign
languages alone cannot serve as an explanation, and hence, the question remains:
What can account for such differences across sign languages?
This preregistered study (https://osf.io/w4sgx) aims to investigate anotherpossible factor contributing to spatial strategies emerging in a sign language: To

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

540

what degree can these typological differences be explained by differences in theirrespective surrounding co-speech gesture system? Indeed, the Balinese co-speech
gesture system is different from most Western ones, for example, using a
geocentric instead of an egocentric pointing system (Wassman & Dasen, 2006) –a feature it shares with Kata Kolok (de Vos, 2012). Thus, if the ambient co-speech
gesture system is relevant to the development of spatial referential strategies, weshould see speakers of Balinese produce strategies that differ from the ones
employed by speakers of Indo-European languages like Dutch in a silent gesturetask. This in turn could shed light on why Kata Kolok has not developed anaphoricuse of space.
We are currently conducting replication studies of Motamedi et al.’s (2021)
silent gesture experiment in the Netherlands and in Bali allowing for crosscultural comparison of the results. Fifty hearing participants of either language
group (speakers of Dutch or speakers of Balinese) are paired up and asked toperform a communicative director-matcher task using only gestures. The designof the experimental stimuli is such that participants have to distinguish agents inorder for their partner to be able to choose the right option from an array. The
experimental set-up was slightly adapted to use the online video conferencing app
Zoom to function in a remote setting during the pandemic. Participants’ gestured
responses are recorded and adhering to an iterated learning design (like Motamedi
et al., 2021), the videos of one participant were used in training trials for thefollowing dyad. Using a director-matcher task in combination with iterated
learning allows us to analyse the strategies emerging through language use and torecord the possible entrenchment of these strategies through the simulation of
repeated generational transmission.
Our set-up not only provides the opportunity to explore and compare thestrategies produced by participants from different cultural backgrounds but alsocollects data from individuals that are users of the exact co-speech gesture systemthat surrounds Kata Kolok signers.
Pilot data from our Balinese experiment has already given us someindications that the strategies developed by participants there do not make use of
space the same way as our Dutch participants do and the English-speaking
participants in Motamedi et al. (2021) did. Thus, at the conference, we will present
our comparative data, discuss what strategies the different participant groups haveproduced, and evaluate what this suggests about the role of the co-speech gesturesystem in the emergence of sign languages.

541

Referencesde Vos, C. (2012). Sign-spatiality in Kata Kolok: How a village sign language inBali inscribes its signing space. PhD Thesis, Max Planck Institute for
Psycholinguistics, Nijmegen.
Engberg-Pedersen, E. (1993). Space in Danish Sign Language. The Semantics
and Morphosyntax of the Use of Space in Visual Language. Hamburg:
Signum.
Liddell, S. K. (2003). Grammar, Gesture and Meaning in American Sign
Language. Cambridge: Cambridge University Press.
Motamedi, Y., Kenny, S., Schouwstra, M., Culbertson, J., & Kirby, S. (2021).
The emergence of systematic argument distinctions in artificial sign
languages. Journal of Language Evolution, 1-22.
Padden C., Meir, I., Aronoff, M., Sandler, W. (2010). The Grammar of Space in
Two New Sign Languages. Sign Languages: A Cambridge Language
Survey.
Sandler, W., Meir, I., Padden, C., & Aronoff, M. (2005). The emergence ofgrammar: Systematic structure in a new language. Proceedings of the
National Academy of Sciences, 102(7), 2661-2665.
Senghas, A., Coppola, M., Newport, E. L., & Supalla, T. (1997). Argument
Structure in Nicaraguan Sign Language: The Emergence ofGrammatical Devices. In E. Hughes, M. Hughes, & A. Greenhill(Eds.), Boston University Conference on Language Development 21
Proceedings, 550-561. Somerville, MA: Cascadilla Press.
Wassmann, J., & Dasen, P. R. (2006). How to Orient Yourself in BalineseSpace: Combining Ethnographic and Psychological Methods for the
Study of Cognitive Processes. In J. Straub, D. Weidemann, C. Köbl,
and B. Zielke (Eds.), Pursuit of Meaning. Advances in Cultural andCross-Cultural Psychology (pp. 351-376). Bielefeld: Transcript
Publishers.

542

EVOLUTION OF THE WORD ORDER RULES THROUGH
COMMUNICATION AND CULTURAL TRANSMISSION: AN
ITERATED LEARNING EXPERIMENT
Seiya Nakata*1,2,4, Akari Ohira1, and Masanori Takezawa*1,3,4*Corresponding Author: snakata@eis.hokudai.ac.jp, m.takezawa@let.hokudai.ac.jp
1Department Behavioral Science, Hokkaido University, Sapporo, Japan
2Japan Society for the Promotion of Science, Japan
3Center for Experimental Research in Social Sciences, Hokkaido University,

Japan
4Center for Human Nature, Artificial Intelligence and Neuroscience, Hokkaido

University, JapanThe grammatical structure found in human language may have emerged throughcommunication and cultural transmission. To focus on the role of communication in theevolution of word order rules, we designed an iterated learning experiment in which
participants use a given set of words. The results of the experiment showed that
communication facilitates the cultural evolution of word order rules. In culturaltransmission chains with communication, the structural language with word order rules
emerged rapidly and remained stable throughout the generations. On the other hand, in thecultural transmission chain without communication, the evolution of word order rules wasvery slow, and the word order distribution didn't show stability. These findings suggestthat word order rules culturally evolve by sharing the intention to communicate.

1. Introduction
Human languages have a systematic grammatical structure. For example, theorder of subject-object-verb (SOV) is determined according to the grammaticalrules of each language (Schouwstra, & de Swart, 2014). The systematic orderingof elements by grammatical rules allows for smooth communication among
speakers of the same language. The systematic word order rules, rather thansimply listing words, are rare in the communication systems of non-humananimals (Hauser, Chomsky, & Fitch, 2002; but see also Suzuki, 2014; Suzuki,
Wheatcroft, & Griesser, 2017). The question of where the systematic structure
found in human language emerges from is one of the central questions inevolutionary linguistics. Studies using iterative learning experiments have shown
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

543

that the linguistic structure emerges from a trade-off between learnability (culturaltransmission) and expressivity (communication) (Kirby et al., 2008; 2015;
Winters, 2015). In these experimental studies, reusable parts of strings
corresponding to individual meanings evolved from the random strings of theinitial language. They used small and simple meaning spaces and showed theemergence of basic compositional structure. Saldana et al. (2019) expanded thesemantic space and investigated the emergence of the compositional hierarchicalstructure of language in the iterated learning experiments. They made the task
more complex and implemented communication among participants. Through aseries of iterated learning experiments, they found that compositional linguistic
structures having hierarchical word order rules inherent to natural languages
evolved under cultural transmission and communication.
In the current study, we focused on the cultural evolution of grammaticalstructures exhibited as word order rules. Initial language in Saldana et al. (2019)was holistic strings each corresponding to a unique stimulus. They found theemergence of two levels of the hierarchy of constituents: word-like forms andsentence-like structures. In our experiment, the initial language was a sequence ofrandomly ordered words. Each word referred to either an actor or an action. As
there was no systematic rule determining word order, a sequence is regarded as
holistic language each of which corresponds to a unique meaning. By employing
a similar experimental setting as experiment 2 in Saldana et al. (2019), we
investigated whether grammatical rules for determining word orders emerge
through the processes of cultural transmission and communication.
We were particularly interested in whether communication plays an essential
role in the evolution of the word order rules. We compared a solo condition, in
which languages were transmitted in a single transmission chain, with a paircondition, in which pairs of individuals communicate with languages. If
communication plays an essential role, we will observe the rapid emergence of aword order rule in the pair condition. If the cultural transmission is sufficient for
the emergence of word order rules, we won’t observe much difference between
the conditions.
2. Methods
2.1. Participants
120 (51 females and 69 males) undergraduates of Hokkaido University, whose
native language was Japanese, were recruited. Eighty and forty participants wereassigned to the pair condition and the solo condition respectively. Mean age was
19.93 years (SD = 1.55; ranging from18 to 23). Participants received 500 JPY544(about 4.33 USD) and additional payment based on the number of successes inthe task (described later).
2.2. Stimuli
Participants were asked to learn a combination of a two-second animation and asentence. All the animation consisted of two actors and the action of each actor(Fig. 1). A sentence was made of four words; two words represent actors (a red
rabbit, a blue rabbit, a red bird, or a blue bird) and the other two words represent
an action of actors (a rabbit punching, a rabbit getting angry, a bird punching, ora bird getting angry). Each word was randomly made of two to three Japaneseletters and participants could not modify the words. Participants were allowed tosee the correspondence table of the words and their meaning during theexperiment. The initial sentences presented to the participants in the first
generation were created by randomly ordering the four words representing theelements in an animation. The order of words representing actions was fixed for
representing the chronological order of the actions – the action word appearing
first represents the action of the left actor, who first initiates an action, in the
animation.

Figure 1. An example of animation and a sentence. Each animation started with an action of aleft actor followed by an action of a right actor (the text in brackets was not shown).

2.3. Experimental procedures
The experiment consists of two phases. In the learning phase, participants wereasked to memorize pairs of animations and sentences presented on a computer
screen. There were 16 pairs of stimuli and eight were randomly selected for thelearning phase. In each round of the learning phase, a pair of a sentence and545

animation was presented on the computer screen for 6 seconds. At the end of eachround of presentation, one of two confirmation tests was randomly administered.
In the sentence production test, participants were presented with only the
animation again and were asked to reproduce the presented sentence byrearranging the four given words. In the animation selection test, participants were
presented with only the sentence again and asked to select the corresponding
animation from a given set of four animations. Each stimulus was presented threetimes and the learning phase consisted of 24 rounds (three blocks of eight stimuli).
Next, participants in the pair condition engaged in a communication phase
where two participants worked on the communication task for a total of 64 rounds(four blocks of sixteen stimuli). In the communication phase, a total of 16 stimuli
were used, including 8 stimuli that did not appear in the learning phase. The 16
stimuli were randomly ordered and presented in each block. The roles of speakerand hearer were alternated every round so that a participant played the roles of a
speaker and a hearer twice for each stimulus. Participant assigned a speaker’s role
was shown an animation and asked to produce a corresponding sentence byrearranging the four words presented. Participants assigned a hearer’s role were
presented with a sentence produced by the speaker and were asked to choose acorresponding animation from four candidate animations. If the animation
selected by the hearer matched the one presented to the speaker, both participantsreceived 20 JPY as an additional payment. After each round, both participantsreceived feedback on their communication; the animation presented to the
speaker, the sentence produced by the speaker, the animation selected by thehearer were displayed with the cumulative number of successes. After completingthe experiment, participants answered a post-questionnaire and received
payments based on the number of successes.
Instead of the communication phase, participants in the solo condition
engaged in the test phase. Participants were given one of the two tests similar tothe ones used in the communication phase of the pair condition – either creating
a sentence corresponding to the displayed animation by rearranging the four
presented words or choosing the animation corresponding to the displayed
sentence out of the four candidates. The number of rounds of the test phase was
identical to the communication phase in the pair condition. At the end of every
round in the test phase, participants received feedback and 40 JPY was added tothe payment for each correct answer.
Participants were organized into an independent transmission chain each withfive generations. In the solo condition, 16 pairs of animations and sentences
produced by a participant in the third and last blocks of the test phase were546

transmitted. In the pair condition, one of the participants was randomly selected
and the 16 pairs of sentences and animations produced as a speaker in the thirdand last block of the communication phase were transmitted. In both conditions,eight pairs were randomly selected and displayed in the learning phase of the next
generation. The animation was created by Adobe Animate 2021, and theexperiment was programmed in oTree v. 3.3.11 (Chen, Schonger, & Wickens,
2016).
3. Results
3.1. Emergence of word order rules
In the following, “N1” refers to a word meaning the actor who initiates the action,and “N2” refers to a word meaning the actor reacted to the action of N1. “V1”and “V2” refer to the action of N1 and N2, respectively. All the sentences are
made by ordering these four words (N1, N2, V1, and V2). Initial sentences weremade by randomly ordering words although V1 always preceded V2 forrepresenting the order of actions. The number of possible orderings is 24. For
analyzing the emergence of word order rules, we calculated the Shannon entropy(Shannon, 1948) for a frequency distribution of each order on 16 sentencestransmitted to the next generation. If all the 16 sentences were made by a single
ordering, the value of entropy becomes zero.
Fig. 2 (left) shows the mean Shannon entropy per generation for eachcondition. Values in generation zero were calculated based on the initial 8
sentences participants in the first generation learned in the learning phase. In thepair condition, entropy approached zero at the first generation and remained
unchanged. In the solo condition, entropy did not change until the third generationand gradually lowered from the fourth generation. It is suggested that a single
word order rule quickly emerged in the pair condition while a word order rulevery slowly evolved in the solo condition.
We fitted a linear mixed model where conditions, generations, and interaction
effect were entered as predicting factor variables (chain was treated as a random
effect variable). We then calculated estimated marginal means for post-hoc
contrasts. We found that, except for the generation zero, the entropy in the paircondition was significantly smaller than the solo condition in all the generations(M (pair – solo) = –2.41 ~ –1.06, ps = .000). In the pair condition, the entropy in thegeneration zero was significantly larger than the other generations (M (0 – 1, 2, 3, 4, or
5) = 2.18~2.51, ps = .000). In the solo condition, entropy in the generation 4 and
5 were significantly smaller than the other generations (M (0,1,2 or 3 – 4 or 5) 0.59~1.38,
ps = .05 ~ .001).

547

Figure 2. (left) Entropy of word order distribution plotted by generation. Lines and shadowsshow mean and bootstrapped 95% CI respectively. (right) Similarity of the word order
distribution between neighboring generations. Distances between t–1 and t are shown as t on
the horizontal axis. Lines and shadows show mean JSD between previous generation andbootstrapped 95% CI respectively.

3.2. Word order stability
For analyzing the similarity of word orders within a chain, we calculated theJensen–Shannon divergence metric (JSD) as the distance of word order
distributions between neighboring generations (i.e., t–1 vs. t). The lower JSD
indicates word order distributions are more similar (Fig. 2 (right)). As we’vealready discussed, in the pair condition, a single word order rule quickly emergedin generation 1 and remained unchanged for the rest of the generations. Thispattern is also exhibited in the trend of JSD distance in the pair condition. On theother hand, in the solo condition, the language changed a little each generation,and no stable word order distribution emerged.
We fitted the similar LMM on the JSD and found that, in the solo condition,
JSD were not significantly different across generations (M = –0.05~0.09, ps= .082~.999). In the pair condition, JSD in the first generation significantly larger
than the other generations (M (1 – 2~5) = 0.42~0.46, ps = .000).
3.3. Evolved word order rules reflected causal order in the referent
Table 1 shows the most frequently used word orders in the final generation ofeach chain. In both conditions, “N1-V1-N2-V2” and “V1-N1-V2-N2” (italics inthe table) were the most frequently used although word orders were more diversein the solo condition. Although any word order could achieve successfulcommunication in the pair condition, we found that the word orders reflecting the548

order of action in the animation were more likely to evolve in the experiment,
which was similar to that of the participants' native language, Japanese.
Table 1. The most frequently used word orders utilized in the final
generation (numbers in parentheses are frequencies).
Pair condition

Solo condition

Chain 1

V1-N1-V2-N2 (75%)

N1-V1-N2-V2 (38%), V1-N1-N2-V2 (38%)

Chain 2

N1-V1-N2-V2 (100%)

N1-V1-N2-V2 (100%)

Chain 3

V1-N1-V2-N2 (100%)

N1-N2-V1-V2 (25%), N1-V1-V2-N2 (25%),
V1-N1-N2-V2 (25%), V1-N1-V2-N2 (25%)

Chain 4N1-V1-N2-V2 (100%)

N1-V1-N2-V2 (50%)

Chain 5

N1-V1-N2-V2 (100%)

N1-V1-N2-V2 (100%)

Chain 6

N1-V1-N2-V2 (97%)

N1-V1-V2-N2 (94%)

Chain 7

N1-V1-N2-V2 (100%)

N1-V1-N2-V2 (31%), N1-V1-V2-N2 (31%)

Chain 8N1-V1-N2-V2 (97%)

V1-N1-V2-N2 (50%)

4. Discussion
We investigated the hypothesis that communication facilitates the cultural
evolution of the word order rules. Our iterated learning experiments showed that
communication is a powerful pressure on the evolution of word order rules. In thepair condition where communication is necessary, structural languages with word
order rules emerged rapidly. The word order distributions in the pair condition
showed stability throughout the generation. In contrast, in the solo condition, theevolution of word order rules was much slower, and the word order distributionchanged from generation to generation.
Specific word orders became dominant in the final generation of all chains in
the pair condition. On the other hand, in the solo condition, various word orders
remained even in the final generation. Throughout the experiment, participants
frequently produced word orders to reflect the order of the actions in the
animation. In our experimental design, participants could communicate even with
the initial random word order sentences because they could see thecorrespondence table of the words and their meanings. Nonetheless, it isinteresting to note that specific word order rules were preferred. In other words,
the emergence of word order rules could be explained by the intent of the549

communication, not by the need to understand the meanings of sentences. It issuggested that the rapid emergence of word order rules in the pair condition is dueto the shared intention to communicate appropriate meaning to each other.
However, the impact of an incentive to reproduce the initial language in the solo
condition (which would not be present in the pair condition) should also be
considered in the future. Finally, the participants' acquired languages could
attribute to the high frequency of causal order emergence. Therefore, it would benecessary to conduct future experiments with participants from various language
families at various stages of language development.
Acknowledgements
This work was supported by JSPS KAKENHI Grant Number 17H06383. We
would like to thank Koji Fujita on the helpful advice for the experimental design.
References
Chen, D. L., Schonger, M., & Wickens, C. (2016). oTree—An open-source
platform for laboratory, online, and field experiments. Journal of Behavioral
and Experimental Finance, 9, 88-97.
Hauser, M. D., Chomsky, N., & Fitch, W. T. (2002). The faculty of language:
what is it, who has it, and how did it evolve? Science, 298(5598), 1569-1579.
Kirby, S., Cornish, H., & Smith, K. (2008). Cumulative cultural evolution in thelaboratory: An experimental approach to the origins of structure in humanlanguage. Proceedings of the National Academy of Sciences, 105(31), 1068110686.
Kirby, S., Tamariz, M., Cornish, H., & Smith, K. (2015). Compression andcommunication in the cultural evolution of linguistic structure. Cognition,
141, 87-102.
Saldana, C., Kirby, S., Truswell, R., & Smith, K. (2019). Compositional
hierarchical structure evolves through cultural transmission: an experimental
study. Journal of Language Evolution, 4(2), 83-107.
Schouwstra, M., & de Swart, H. (2014). The semantic origins of word
order. Cognition, 131(3), 431-6.
Shannon, C. E. (1948). A mathematical theory of communication. The Bell systemtechnical journal, 27(3), 379-423.
Suzuki, T. N. (2014). Communication about predator type by a bird using discrete,
graded and combinatorial variation in alarm calls. Animal Behaviour, 87, 5965.
Suzuki, T. N., Wheatcroft, D., & Griesser, M. (2016). Experimental evidence forcompositional syntax in bird calls. Nature communications, 7(1), 1-7.
Winters, J., Kirby, S., & Smith, K. (2015). Languages adapt to their contextual
niche. Language and Cognition, 7(3), 415-449.

550

CHALLENGING A WIDELY-ACCEPTED ACCOUNT OF
VOWEL METATHESIS IN NAGOYA JAPANESE WITH NO
REFERENCE TO PRECEDENCEKUNIYA NASUKAWA*1, and SHIN-ICHI TANAKA2*Corresponding Author: nasukawa@mail.tohoku-gakuin.ac.jp
1Department of English, Tohoku Gakuin University, Sendai, Japan
2Department of Language and Information Sciences, University of Tokyo, JapanTo understand the nature of the language faculty as part of the program of research
into the origins of human language, it is essential to establish which properties ofhuman language make it species-specific. The Minimalist Program of Generative
Grammar sees internal language as a computational system (CS) which generates
an infinite number of hierarchically-organised (syntactic) objects, each of which
underlies an array of instructions to two interfaces: the Sensory-Motor (SM)systems and the Conceptual-Intentional (CI) systems. In this model of thelanguage faculty, a linguistic expression is constructed by taking lexical items
from the lexicon and merging them to form a new object (e.g., γ + {α, β} → {γ{α,β}}). The repeated application of Merge to its own derivatives generates aninfinite number of recursively structured expressions, where precedence relations
are not encoded between constituents (Hauser, Chomsky & Fitch, 2002;
Chomsky, 2010). In this model, precedence relations are viewed as
epiphenomenal, a by-product of linearisation taken at the SM interface.
To make all types of structure-building systematically coherent, Precedencefree Phonology (PfP: Backley & Nasukawa, 2020, and others) claims that CS
takes not only morpho-syntactic objects but also phonological features called
elements as its arguments. Then, through the merging of elements CS builds ahierarchical phonological representation for each morpheme before being stored
in the lexicon. In this model, as in syntax, precedence is the natural result ofcomputing and interpreting the head-dependent relations which hold between
units in a structure. This paper challenges the common assumption that theanalysis of phonological processes relies on precedence. It illustrates this byThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

551

focusing on the following type of vowel metathesis in Nagoya Japanese, which istypically analysed as a swapping process operating between two adjacent vowels
ai, ae, oi and ui: the palatality of the second vowel becomes an on-glide to thefirst vowel, while the quality of the first vowel is preserved in the newly formed
CV (see also Tanaka, 2022, for an analysis of vowel fusion in Tokyo Japanese).
Table 1. On-gliding of the second member of a sequence of two vowels(‘→’ = ‘corresponds to’).
Tokyo Standard J.

Nagoya J.

a.

umai ‘delicious’omae ‘you, dear’

→
→

umjaː
omjaː

b.

suɡoi ‘amazing, great’zurui ‘go home’→→suɡjoː
zurjuː

Since PfP makes no reference to precedence relations, it rejects any analysis
based on metathesis. Instead, it regards the process in question as coalescence, inwhich two sounds merge into one by combining their properties (‘resonance’
elements (|I| (dip), |U| (rump), |A|(mass), see Backley, 2011), as illustrated below.
Table 2. Element fusion and |I| salience (salient elements underlined).
V1
a.
b.

V2

V1,2

|A|

+

|I|

→

|AI|

|A|

+

|AI|

→

|AI|

|AU|
|U|

+
+

|I|
|I|

→
→

|AUI|
|UI|

Traditionallydescribed as
jaː
jaː
joː
juː

phoneticallyrealised as
[æː]
[æː]
[øː]
[yː]

Fusion takes place between V1 and V2, which in PfP are structured hierarchically(V1 is dominated by V2) rather than ordered sequentially (Backley, 2021). In
addition, a language-specific rule makes |I| (palatality) structurally dependent,
meaning that it makes a bigger contribution to the acoustic signal of the fusedstructure and is therefore perceived as having greater prominence than otherelements (THE PRINCIPLE OF PHONETIC REALISATION OF HEAD-DEPENDENCYSTRUCTURE: Backley & Nasukawa, 2020). As a result, the structures |AI|, |AUI|,|UI| are realised as palatalized [æː] [øː] [yː] (rather than [aː] [oː] [uː]) (Harris,
1994; Backley, 2011). As a salient property, palatality may yield glide-vowel
sequences such as [jaː] as phonetic variants in Nagoya Japanese. This analysis
serves as a starting point for exploring other processes that have been accounted
for in terms of precedence. The aim is to strengthen the claim that precedence is
not a formal structural property anywhere in the language faculty.

552

References
Chomsky, N. (2010). Some simple evo devo theses: How true might they be for
language? In R. Larson, V. Deprez and H. Yamakido (Eds.), The evolution ofhuman language: Biolinguistic perspectives (pp. 45-62). Cambridge:
Cambridge University Press.
Backley, P. (2021). Elements and structural head-dependency. In S. Bendjaballah,
A. Tifrit, & L. Voeltzel (Eds.), Perspectives on Element Theory (pp. 9-31).
Boston and Berlin: Mouton de Gruyter.
Backley, P., & K. Nasukawa (2020). Recursion in melodic-prosodic structure. In
K. Nasukawa (Ed.), Morpheme-internal recursion in phonology (pp. 11-35).
Boston and Berlin: Mouton de Gruyter.
Harris, J. (1994). English sound structure. Oxford: Blackwell.
Hauser, M. D., Chomsky, N., & Fitch, W. T. (2002). The faculty of language:
What is it, who has it, and how did it evolve? Science, 298, 1569-1579.
Tanaka, S. (2022). Vowel coalescence as head-dependent merge. PhonologicalStudies, 25, 77-88.

553

THE COMPLEXITY OF A LANGUAGE IS SHAPED BY THE
COMMUNICATIVE NEEDS OF ITS USERS: AN ITERATEDHIERARCHICAL BAYESIAN MODELVLAD C. NEDELCU*1 and KENNY SMITH1* Corresponding Author: v.c.nedelcu@sms.ed.ac.uk

1 Centre for Language Evolution, University of Edinburgh, Edinburgh, UK

Recent experimental and modelling work has found that languages are shapedby the referential context in which they operate (Müller, Winters, and Morin,
2019; Nölle, Staib, Fusaroli, and Tylén, 2018; Lupyan and Dale, 2010). Wrayand Grace (2007) argues that even compositionality (the fact that the meaningof an utterance is a function of the meaning of its parts and the way in whichthese parts are combined) may have only evolved in response to changing social
contexts. But how can the referential contexts of individual interactions come toshape the level of compositionality in the language of an entire community?
To explore these questions, we propose an iterated hierarchical Bayesianmodel that shows how partner-specific linguistic innovations can be generalized ascommunity-wide features via a context-sensitive pathway. We combine insightsfrom the approaches of Winters, Kirby, and Smith (2018) and Hawkins et al.
(2021), manipulating reliability of context in pair-based interaction, while also
considering abstractions of socio-linguistic knowledge at a higher, populationwide level. More specifically, an agent maintains a separate partner-specific representation of context for each communication partner, but a single overhypothesis about the distribution of types of contexts within the community, and a single
community-wide distribution of possible languages. This setup allows an agent todynamically adapt its language use to the communicative needs of individual partners, but also offers a mechanism for generalization of knowledge across different
partners. We model communication between agents as an asymmetric reference
game, where each agent is assigned the role of speaker or listener. Each game issplit into multiple rounds in which the speaker is provided with a target meaning
to convey and the listener is confronted with a context consisting of that target
plus a number of distractors. An agent’s type of context determines the semantic
features in which the referents in any given context can differ for that agent. In
a one-feature-different context-type, a single feature is sufficient for discriminating among all the referents in a context, and that feature is furthermore consistentacross all rounds. In a mixed context-type, the meanings in any given context alsoThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

554

differ in one feature, but that feature differs on a round-by-round basis. Following the iterated learning framework (Kirby, 2001), agents in a generation observe
data produced by speakers in the previous generation, then communicate with one
another, with these processes being repeated for a large number of generations.
We first simulate a single-partner variant to evaluate our model’s predictions
against the experimental results of Winters, Kirby, and Smith (2015). In the sharedcontext version, we assume that the speaker has direct access to the listener’s context. We compare results to the unshared context version, where the speaker has
uncertainty both about the context-type of the listener and which specific context
the listener faces on any given round. In case of a shared context and one-feature
context-type, we find the emergence of one-feature languages (i.e., which map all
meanings sharing one of the features to the same signal), as these are the simplest
communicatively functional in one-feature contexts. In the shared mixed contexttype condition, the optimal strategy that guarantees communicative success leads
to the emergence of compositional languages (i.e., which have consistent mappings for all features that make up the meaning). When context is not shared,
speakers must infer the context-type of the listener over the course of interaction.
This introduces an additional level of uncertainty, and it becomes a possibility forthe speaker to design their utterance for the wrong context-type. Speakers are thus
encouraged to produce more autonomous and less context-dependent utterances,
generally resulting in the emergence of a higher proportion of compositional systems than in the case of a shared access to context.
We then examine the results for the multi-partner model. Where the speaker’s
communicative partners are homogeneous in facing one-feature or mixed contexts, the hierarchical model produces a similar pattern of results to the singlepartner simulations: we find the emergence of languages that best compromise between ease of learning, adaptation to the homogeneous context-type, and robustness to uncertain inferences. However, we also find that the successive swappingof partners causes more overall uncertainty over context-types compared to thesingle-partner simulations, resulting in the emergence of more compositional languages. Furthermore, when a speaker interacts with partners with heterogeneouscontext-types, we find additional effects of this heterogeneity: when encounteredpartners have one-feature contexts, but differ in the specific feature that needs tobe encoded for disambiguation, compositional languages become dominant, as
speakers must compromise on using a language that encodes both features.
In sum, we show that the degree of compositionality that evolves in the language of a community is dependent on the communicative needs of its members,
but also on the degree of uncertainty over the nature of those needs. Our model
also tests the theory proposed by Wray & Grace for the emergence of compositionality, and finds that compositionality can emerge in communities where simpler
languages would satisfy the individual needs of its users, as long as the community’s needs as a whole are heterogeneous.

555

Acknowledgements
This research received funding from the European Research Council under theEuropean Union’s Horizon 2020 research and innovation program (Grant 681942,
held by K. Smith), and from a University of Edinburgh College of Arts, Humanities and Social Sciences Research Award (held by V. Nedelcu).
References
Hawkins, R. D., Franke, M., Frank, M. C., Goldberg, A. E., Smith, K., Griffiths,
T. L., & Goodman, N. D. (2021). From partners to populations: A hierarchical bayesian account of coordination and convention. arXiv preprint
arXiv:2104.05857.
Kirby, S. (2001). Spontaneous evolution of linguistic structure-an iterated learningmodel of the emergence of regularity and irregularity. IEEE Transactions
on Evolutionary Computation, 5(2), 102–110.
Lupyan, G., & Dale, R. (2010). Language structure is partly determined by social
structure. PloS one, 5(1).
Müller, T. F., Winters, J., & Morin, O. (2019). The influence of shared visualcontext on the successful emergence of conventions in a referential communication task. Cognitive science, 43(9), e12783.
Nölle, J., Staib, M., Fusaroli, R., & Tylén, K. (2018). The emergence of systematicity: How environmental and communicative factors shape a novelcommunication system. Cognition, 181, 93–104.
Winters, J., Kirby, S., & Smith, K. (2015). Languages adapt to their contextual
niche. Language and Cognition, 7(3), 415–449.
Winters, J., Kirby, S., & Smith, K. (2018). Contextual predictability shapes signal
autonomy. Cognition, 176, 15–30.
Wray, A., & Grace, G. W. (2007). The consequences of talking to strangers: Evolutionary corollaries of socio-cultural influences on linguistic form. Lingua,117(3), 543–578.

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INTEGRATION AND SEGREGATION OF AUDITORYSTREAMS DURING RHYTHM PERCEPTIONJUN NITTA*1, SOTARO KONDOH1, KAZUO OKANOYA1,2, and RYOSUKE O.
TACHIBANA1,*

1

Corresponding Author: nitta-jun477@g.ecc.u-tokyo.ac.jp
Department of Life Sciences, Graduate School of Arts and Sciences, The University ofTokyo, Tokyo, Japan
2
Advanced Comprehensive Research Organization, Teikyo University, Tokyo,
Japan

Among auditory information, rhythm is one of the important factors in humanlanguage (Tachibana et al., 2013), and in vocalization of other species (Fitch,2013). In the present study, we examined the rhythm perception of human,especially in relation with the effect of timbre.
Research on rhythm perception has mainly focused on temporal structure,including periodicity (Bendixen et al., 2010). However, research on the auditory
stream segregation has reported that sounds with different timbres are difficult toperceive as a single unit (Bregman, 1990). This suggests that rhythmic sequencescomposed of multiple timbres might be more difficult to discriminate theirrhythmic patterns than those composed of single timbre.
In the present study, we aimed to reveal the perception of rhythmic pattern
including different timbres, and investigated the accuracy of rhythm
discrimination by 2 Alternative Forced Choice Task. The center frequency of theband noise stimuli was manipulated to provide conditions with and without
frequency difference. In the experiment, participants listened to two rhythmic
stimuli, standard stimuli and comparative stimuli, and were asked to choose thebouncing one. Point of subjective equality (PSE) and discrimination accuracy (σ)
were estimated by sigmoid approximation.
The result (Fig. 1) showed that the accuracy of rhythm discrimination
decreased in the condition with frequency difference. A paired t-test for σ showedsignificant differences between conditions (p < 0.001, t = 5.36, df = 39). Thereeffects were found even when the standard rhythm pattern was varied from 2:1,
1:2, to 1:1. This suggests that rhythm perception is affected not only by temporal
structure but also by sequential grouping. Similar experiments on other species
would give an insight into the origin of language.

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

557

Figure 1. Proportion of response that the comparative stimulus was "bouncing" for eachinterval ratio. The blue line indicates the condition with frequency difference, and the redline indicates the condition without frequency difference. Error bars are standard errors.
Curves are sigmoidal approximations.

Acknowledgements
This work was supported by JSPS KAKENHI Grant Number JP20K21804.
References
Bendixen, A., Denham, S. L., Gyimesi, K., & Winkler, I. (2010). Regularpatterns stabilize auditory streams. The Journal of the AcousticalSociety of America, 128(6), 3658-3666.
Bregman, A. S. (1990). Auditory Scene Analysis: The Perceptual Organizationof Sound. MIT Press.
Fitch, W. T. (2013). Rhythmic cognition in humans and animals: distinguishingmeter and pulse perception. Frontiers in Systems Neuroscience, 7,
68.
Tachibana, R. O., Sasaki, Y., & Riquimaroux, H. (2013). Relative contributionsof spectral and temporal resolutions to the perception of syllables,
words, and sentences in noise-vocoded speech. Acoustical Scienceand Technology, 34(4), 263–270.

558

MORE THAN AFFECT: HUMAN FACIAL EXPRESSIONSPROVIDE ICONIC AND PRAGMATIC FUNCTIONSJONAS NÖLLE*1, YICHEN WU1, PABLO ARIAS2,3, OLIVER G. B. GARROD1,
PHILIPPE G. SCHYNS1 and RACHAEL E. JACK1*

Corresponding Author: jonas.noelle@glasgow.ac.uk
School of Psychology & Neuroscience, University of Glasgow, UK
2
Cognitive Science, Lund University, Sweden
3Sound Perception and Design, IRCAM, Paris, France

1

Human facial expressions are a powerful tool for social communication. In
addition to the visually salient appearance of the human face (Tomasello et al.,2007; Vick et al., 2007), it has a high capacity to generate many different facial
expressions that can each represent different nuanced social messages. Thoughused daily for a variety of social interactions, facial expressions have mainly beenstudied as displays of affect in humans (Ekman, 1994) and nonhuman primates(Van Hooff, 1972; Waller & Micheletta, 2013) and less so as a pragmatic tool forcommunication (cf. Crivelli & Fridlund, 2018). Thus, while multimodal accountsof communication continue to gain attention (e.g., Perniss, 2018; Holler &Levinson, 2019), including in discussions of language origins (Zlatev et al., 2017;
Fröhlich et al., 2019), the contribution of facial expressions to multimodal
communication remains understudied. One main challenge to this endeavor is the
sheer number and complexity of facial expressions (Jack et al., 2018).
To unravel this complexity, we used a powerful data-driven method (Yu etal., 2012) that agnostically generates facial movements and objectively measures
their effect on social perception (see Jack & Schyns, 2017 for a review). Here, we
examined the semiotic potential of facial expressions combined with speech aspart of multimodal signaling, addressing two questions: whether facial
expressions can 1) express meanings iconically (e.g., representing size) and/or 2)
serve as pragmatic markers, akin to speech prosody, described below.
Experiment 1. We tested whether facial expressions can communicate iconic
meanings, similar to manual gestures, e.g., pinching to express ‘tiny’ numbers
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

559

referred to in speech (Woodin et al., 2020). We presented participants (20 native
English speakers, 10 females) with a scenario (e.g., “There are 108 animals in
the field”) followed by a series of multimodal stimuli—i.e., speakers eachdisplaying a facial expression and commenting on the scenario one of three vague
quantifiers (“Of these, several/many/few were cows”). Participants viewed eachstimulus and estimated the number communicated by the speaker. Facial
expressions displayed on each trial comprise combinations of eye/eyebrow
movements (called Action Units—AUs; Ekman & Friesen, 1978) that do notaffect speech, agnostically generated by a generative model of real human facial
movements (Yu et al., 2012). We generated different voices using recordings of
two native English speakers, normalized and transformed via pitch and spectralenvelope shifts to create naturalistic but tightly controlled speaker identities(Arias et al., 2021). We generated synchronized lip movements using a neural net(Cudeiro et al., 2019) and aligned the onset of each facial expression with the
spoken quantifier. Finally, we displayed each facial expression on a randomly
generated face identity using a generative model of human face shape/complexion(Zhan et al., 2019). Analysis of the statistical relationship between the facial
movements presented on each trial and each participant’s responses (Ince et al.,2017) revealed that specific facial movements modulate the quantities estimatedby the participants.
Experiment 2. Using the same data-driven approach, we examined thepragmatic function of facial expressions to mark confidence or doubt, also known
as ‘Feeling of Knowing,’ akin to prosodic cues when answering questions (Jiang& Pell, 2017). Here, the scenario comprised a question (e.g., “Is she a good
leader?”) followed by a multimodal stimulus (i.e., speakers displaying a facial
expression) responding ‘yes’ or ‘no.’ Participants rated the perceived confidenceof the speaker’s answer using a 5-point scale (‘very doubtful’ to ‘very confident’).
Analysis using ordinal logistic regression revealed that specific facial movements
influence the perception of confidence (AU43 eyes closed, AU1-2 brow raiser)and doubt (AU4 brow lowerer, AU7 lid tightener).
In sum, we used a data-driven psychophysical approach combined a withgenerative model of the human face to precisely characterize the facial
movements that serve pragmatic functions in multimodal communication. Our
results suggest that facial movements originally evolved to control sensory input(Susskind et al., 2008) can be exapted to ground more abstract social signals or
iconic representations, with direct implications for central theories of multimodal
communication and language origins (e.g., Holler & Levinson, 2019).

560

Acknowledgements
This project has received funding from the European Research Council (ERC)
under the European Union's Horizon 2020 research and innovation programme(Grant agreement No. [759796]) to REJ; Wellcome Trust [Senior InvestigatorAward; 107802], Multidisciplinary University Research Initiative/Engineeringand Physical Sciences Research Council [172046-01] awarded to PGS; theSwedish Research Council [2014-1371] awarded to PA. We would also like tothank Bodo Winter for discussions about the design, Philip McAleer and Gaby
Mahrholz for help with sound recordings, as well as Niamh Mary McLaughlin
and Jude Kiltie, who kindly lent us their voices.
References
Arias, P., Rachman, L., Liuni, M., & Aucouturier, J.-J. (2021). Beyond
Correlation: Acoustic Transformation Methods for the Experimental Studyof Emotional Voice and Speech. Emotion Review, 13(1), 12–24.
https://doi.org/10.1177/1754073920934544
Crivelli, C., & Fridlund, A. J. (2018). Facial Displays Are Tools for Social
Influence. Trends in Cognitive Sciences, 22(5), 388–399.
https://doi.org/10.1016/j.tics.2018.02.006
Cudeiro, D., Bolkart, T., Laidlaw, C., Ranjan, A., & Black, M. J. (2019).
Capture, Learning, and Synthesis of 3D Speaking Styles. 10101–10111.
https://openaccess.thecvf.com/content_CVPR_2019/html/Cudeiro_Capture_
Learning_and_Synthesis_of_3D_Speaking_Styles_CVPR_2019_paper.html
Ekman, P. (1994). All emotions are basic. In P. Ekman & R. J. Davidson (Eds.),
The nature of emotion: Fundamental questions (pp. 65–58). Oxford
University Press.
Ekman, P., & Friesen, W. V. (1978). Facial action coding system: Investigator’s
guide. Consulting Psychologists Press.
Fröhlich, M., Sievers, C., Townsend, S. W., Gruber, T., & Schaik, C. P. van.
(2019). Multimodal communication and language origins: Integrating
gestures and vocalizations. Biological Reviews, 94(5), 1809–1829.
https://doi.org/10.1111/brv.12535
Holler, J., & Levinson, S. C. (2019). Multimodal Language Processing in
Human Communication. Trends in Cognitive Sciences, 23(8), 639–652.
https://doi.org/10.1016/j.tics.2019.05.006
Ince, R. A. A., Giordano, B. L., Kayser, C., Rousselet, G. A., Gross, J., &Schyns, P. G. (2017). A statistical framework for neuroimaging data
analysis based on mutual information estimated via a gaussian copula.
Human Brain Mapping, 38(3), 1541–1573.
https://doi.org/10.1002/hbm.23471

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Jack, R. E., Crivelli, C., & Wheatley, T. (2018). Data-Driven Methods toDiversify Knowledge of Human Psychology. Trends in Cognitive Sciences,22(1), 1–5. https://doi.org/10.1016/j.tics.2017.10.002
Jack, R. E., & Schyns, P. G. (2017). Toward a Social Psychophysics of Face
Communication. Annual Review of Psychology, 68(1), 269–297.
https://doi.org/10.1146/annurev-psych-010416-044242
Jiang, X., & Pell, M. D. (2017). The sound of confidence and doubt. SpeechCommunication, 88, 106–126.
https://doi.org/10.1016/j.specom.2017.01.011
Perniss, P. (2018). Why We Should Study Multimodal Language. Frontiers in
Psychology, 9. https://doi.org/10.3389/fpsyg.2018.01109
Susskind, J. M., Lee, D. H., Cusi, A., Feiman, R., Grabski, W., & Anderson, A.
K. (2008). Expressing fear enhances sensory acquisition. Nature
Neuroscience, 11(7), 843–850. https://doi.org/10.1038/nn.2138
Tomasello, M., Hare, B., Lehmann, H., & Call, J. (2007). Reliance on headversus eyes in the gaze following of great apes and human infants: The
cooperative eye hypothesis. Journal of Human Evolution, 52(3), 314–320.
https://doi.org/10.1016/j.jhevol.2006.10.001
Van Hooff, J. (1972). A comparative approach to the phylogeny of laughter andsmiling. Non-Verbal Communication, 209–241.
Vick, S.-J., Waller, B. M., Parr, L. A., Smith Pasqualini, M. C., & Bard, K. A.
(2007). A Cross-species Comparison of Facial Morphology and Movementin Humans and Chimpanzees Using the Facial Action Coding System(FACS). Journal of Nonverbal Behavior, 31(1), 1–20.
https://doi.org/10.1007/s10919-006-0017-z
Waller, B. M., & Micheletta, J. (2013). Facial Expression in Nonhuman
Animals. Emotion Review, 5(1), 54–59.
https://doi.org/10.1177/1754073912451503
Woodin, G., Winter, B., Perlman, M., Littlemore, J., & Matlock, T. (2020).
‘Tiny numbers’ are actually tiny: Evidence from gestures in the TV News
Archive. PLOS ONE, 15(11), e0242142.
https://doi.org/10.1371/journal.pone.0242142
Yu, H., Garrod, O. G. B., & Schyns, P. G. (2012). Perception-driven facial
expression synthesis. Computers & Graphics, 36(3), 152–162.
https://doi.org/10.1016/j.cag.2011.12.002
Zhan, J., Garrod, O. G. B., van Rijsbergen, N., & Schyns, P. G. (2019).
Modelling face memory reveals task-generalizable representations. Nature
Human Behaviour, 3(8), 817–826. https://doi.org/10.1038/s41562-0190625-3
Zlatev, J., Wacewicz, S., Żywiczyński, P., & van de Weijer, J. (2017).
Multimodal-first or pantomime-first?: Communicating events throughpantomime with and without vocalization. Interaction Studies, 18(3), 465–
488. https://doi.org/10.1075/is.18.3.08zla

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NEURAL SIGNATURES OF PROSODIC PROCESSING
CHANTAL ODERBOLZ*1, ELISABETH STARK2,3, and MARTIN MEYER1*Corresponding Author: chantal.oderbolz@uzh.ch
1Department of Comparative Language Science, University of Zurich, Zurich,

Switzerland
2Zurich Center for Linguistics, University of Zurich, Zurich, Switzerland
3Institute of Romance Studies, University of Zurich, Zurich, Switzerland

1. Introduction
Generating complex meaning from continuous speech requires the neuralmapping of this incoming signal onto syntactic structure. In the evolution of
language, prosody might have been integral in facilitating this mapping in thehuman brain – essentially providing a physical nexus to the abstract (Kreiner &Eviatar, 2014). Support for the possible phylogenetic significance of prosody
comes from research on human ontogeny: Infants use a range of prosodic cues toinfer different aspects of language in a process called prosodic bootstrapping(Hawthorne & Gerken, 2014). In trying to elucidate this mapping in adults, werefer to cortical oscillations as the biophysical infrastructure that might be suited
to enabling the transformation from the physical to the abstract (Ding & Simon,
2014; Giraud & Poeppel, 2012; Meyer, 2018; Murphy, 2020). More precisely,
cortical oscillations are hypothesized to synchronize with the speech signal – a
process referred to as speech tracking. However, the extent to which this isfunctionally relevant for continuous speech processing and which prosodic
features serve as driving forces is still not fully understood.
2. Methods
We therefore sought to better understand the dynamics of speech tracking and theextent to which it is modulated by prosodic features. To this end, we conducted
an EEG experiment with a sample of native Swiss German speakers (N = 26).
Participants listened to spoken sentences manipulated in terms of their rhythm
and intonation. Roughly the first half of a sentence could either follow aThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

563

predictable regular or an unpredictable irregular rhythm. The second half theneither confirmed these predictions or violated them. Additionally, sentences either
had a natural or pitch-flattened intonation contour. To test whether thesemanipulations were perceptually relevant, sentences were embedded in noise. We
expected the quality of speech tracking to be influenced by the prosodic featuresof speech and this relationship between speech signal and brain signal to predictspeech perception. To test these hypotheses, we calculated generalized linear
mixed models. Specifically, we assessed the phase-locking value (Lachaux et al.,1999) as an indication of speech tracking and percentage of correctly identified
syllables in noise as a perceptual marker.
3. Results
Results showed that the brain is sensitive even to slight variations in rhythmic(ir)regularity. Cortical oscillations in the theta range (3.5–4.7 Hz) were able to
track the speech signal more faithfully when the rhythm was regular compared toirregular. However, the predictability of this rhythm did not seem to influence
speech tracking: Neither the violation of a predictable rhythm nor the continuationof an unpredictable rhythm caused a reduction in the PLV. In contrast to rhythm,
theta oscillations were less sensitive to intonation as evidenced by the equallyhigh PLV values in either intonation condition.
4. Discussion
Continuous speech tracking was differentially influenced by the prosodic featuresof speech. A regular rhythm ⎯ as compared to an irregular rhythm ⎯ facilitatedthe brain’s ability to phase-align with speech in the theta range. These findings
are in accordance with studies showing advantages in evoked potentials andperception afforded by rhythmic regularity in auditory stimuli (Abecasis et al.,2005) as well as speech (Bohn et al., 2013; Roncaglia-Denissen et al., 2013).
Underlying this facilitation is presumably an evolutionarily rooted connection
between the speech motor system, auditory perceptual system, and neural
infrastructure (Giraud & Poeppel, 2012; Poeppel & Assaneo, 2020; Strauss &Schwartz, 2017). As such, rhythm may have evolved to take on the role of an
endogenous tool, structuring speech input and optimizing sensory processing.
Intonation, on the other hand, was not related to differences in speech tracking.
Given this non-existent effect, it appears that these two prosodic features and theirrespective functions are dissociable in and dissociated by the brain and thus might
have also had different evolutionary trajectories. Along these lines, intonation
may come into play for more strictly high-level linguistic operations such assyntactic parsing or disambiguation (Marslen-Wilson et al., 1992; Selkirk, 2011).

564

Acknowledgements
This work was supported as a part of NCCR Evolving Language, a National
Centre of Competence in Research, funded by the Swiss National
Science Foundation (grant number #51NF40_180888).
References
Abecasis, D., Brochard, R., Granot, R., & Drake, C. (2005). Differential brain
response to metrical accents in isochronous auditory sequences. Music
Perception,
22(3),
549-562.
doi:https://doi.org/10.1525/mp.2005.22.3.549
Bohn, K., Knaus, J., Wiese, R., & Domahs, U. (2013). The influence of rhythmic(ir)regularities on speech processing: Evidence from an ERP study on
Germanphrases.
Neuropsychologia,
51(4),
760-771.
doi:https://doi.org/10.1016/j.neuropsychologia.2013.01.006
Ding, N., & Simon, J. Z. (2014). Cortical entrainment to continuous speech:
Functional roles and interpretations. Frontiers in Human Neuroscience,
8(311). doi:https://doi.org/10.3389/fnhum.2014.00311
Giraud, A.-L., & Poeppel, D. (2012). Cortical oscillations and speech processing:Emerging computational principles and operations. Nature
Neuroscience, 15, 511. doi:https://doi.org/10.1038/nn.3063
Hawthorne, K., & Gerken, L. (2014). From pauses to clauses: Prosody facilitateslearning of syntactic constituency. Cognition, 133(2), 420–428.
https://doi.org/10.1016/j.cognition.2014.07.013
Kreiner, H., & Eviatar, Z. (2014). The missing link in the embodiment ofsyntax: Prosody. Brain and Language, 137, 91–102.
https://doi.org/10.1016/j.bandl.2014.08.004
Lachaux, J.-P., Rodriguez, E., Martinerie, J., & Varela, F. J. (1999). Measuringphase synchrony in brain signals. Human Brain Mapping, 8(4), 194-208.
doi:10.1002/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C
Marslen-Wilson, W. D., Tyler, L. K., Warren, P., Grenier, P., & Lee, C. S. (1992).
Prosodic effects in minimal attachment. The Quarterly Journal of
Experimental
Psychology
SectionA,
45(1),
73-87.
doi:https://doi.org/10.1080/14640749208401316
Meyer, L. (2018). The neural oscillations of speech processing and language
comprehension: State of the art and emerging mechanisms. European
Journalof
Neuroscience,48(7),
2609-2621.
doi:https://doi.org/10.1111/ejn.13748
Murphy, E. (2021). The oscillatory nature of language. Cambridge University
Press.
Poeppel, D., & Assaneo, M. F. (2020). Speech rhythms and their neuralfoundations. Nature Reviews Neuroscience, 21(6), 322–334.
https://doi.org/10.1038/s41583-020-0304-4

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Roncaglia-Denissen, M. P., Schmidt-Kassow, M., & Kotz, S. A. (2013). Speechrhythm facilitates syntactic ambiguity resolution: ERP evidence. PLOS
ONE, 8(2), e56000. doi:https://doi.org/10.1371/journal.pone.0056000
Selkirk, E. (2011). The syntax-phonology interface. In J. Goldsmith, J. Riggle, &A. C. L. Yu (Eds.), The Handbook of Phonological Theory (2nd ed., pp.
435-484). West Sussex, UK: Blackwell Publishing Ltd.
Strauß, A., & Schwartz, J.-L. (2017). The syllable in the light of motor skills andneural oscillations. Language, Cognition and Neuroscience, 32(5), 562569. doi:https://doi.org/10.1080/23273798.2016.1253852

566

NEUROFEEDBACK TRAINING AS A METHOD FOR
EVALUATION OF BRAIN SYSTEMS FOR LANGUAGEJIRO OKUDA*1, HIROKI KATAOKA1, MASAYUKI FUJIWARA2, and TAKASHIHASHIMOTO2*

Corresponding Author: jokuda@cc.kyoto-su.ac.jp
Division of Frontier Informatics, Kyoto Sangyo University, Kyoto, Japan
2
School of Knowledge Science, Japan Advanced Institute of Science andTechnology, Nomi, Ishikawa, Japan1

1. Introduction
Since language is a complex faculty concerning multiple domains, it is thoughtthat the brain system for language has evolved in an integrative manner suchthat numbers of componential systems make their own contributions to thewhole system. Recently, on the other hand, neuroscience studies have begun toshow that a neurofeedback training of repeated self-induction of a specific brain
activity pattern could alter behavioral ability corresponding to the trained brain
activity (Shibata et al., 2011). Given this causal effect, we wondered that theneurofeedback training might serve as a new method to test causal relevance of
hypothetical neuro-linguistic models. That is, if behavioral performance on acertain linguistic ability changed according to the self-induction training ofactivity of a specific brain system, we could argue that such brain system might
have effective contribution to that ability in an adaptive manner.
Based on this idea, we specifically tried to examine organization andpossible adaptive capacity of brain systems contributing to an ability to express
intentions by hierarchical structures of written symbol strings. Although boththe hierarchical structure (Everaert et al., 2015) and the intention-sharing(Tomasello, 2003) are believed to be essential for human language, relation
between the two remains unknown. Therefore, we first proposed a neural model
concerning intention-sharing by the hierarchical structures of symbol strings
through an electroencephalography (EEG) experiment, and then tested the effectof the EEG neurofeedback training with respect to the model.
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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2. EEG experiment and neurofeedback training
We adopted experimental semiotics approach (Galantucci, 2009) that could
simulate emergence and evolution of a novel language system through trial anderror across participants. In each trial of our experiment, we assigned a pair ofparticipants either a sender or a receiver of a string of three symbols involving
the hierarchical structures. The sender composed a symbol string to indicate atarget object in the experimental screen, and was additionally required to imply
an instruction whether the receiver should choose the target or not. As a result,most participants expressed the meaning of the choose/not-choose instruction
within specific hierarchical structures of the string. EEG analyses showedsignificant suppression of 11 Hz EEG power at the left frontal, bilateral parietal,
and occipital electrodes during a composition period of the symbol string. Thus
we proposed a neuro-linguistic model that desynchronization of alpha-band
neural activity in multiple brain systems (Murphy, 2020) would contribute torealization of the intention expression through the hierarchical structures.
We conducted 4-days neurofeedback training (1.5h per day) to independent
participants. We visually presented feedback signals that represented magnitudeof 11 Hz power suppression at the frontal, parietal, and occipital electrodes, andasked the participants to try to increase the feedback signals as high as possible.
Although effects of the neurofeedback training on performance change between
pre- and post-training behavioral tests varied across participants, we observed
the representative case in which richness of intention-reading in a picture
description task (Cummings, 2019) dramatically improved after successful
training of the EEG suppression at the parietal and occipital electrodes. Thiscase also showed less improvement of utilization of the hierarchical structures toexpress intentions in the string composition task, in parallel with lessachievement of the left frontal power suppression during the EEG training.
3. Discussion
Differential training effects across pragmatic understanding of intentions andutilization of the hierarchical structures may suggest partially independent
evolutionary organization of brain systems for these aspects of language. We
speculate that incorporating connectivity information into the neurofeedback
method might help to further elucidate integrative organization of these systems.
Acknowledgements
Supported by KAKENHI JP (20H05019, 20H04256, 18H05087, 17H06383).

568

References
Cummings, L. (2019). Describing the Cookie Theft picture: Sources ofbreakdown in Alzheimer’s dementia. Pragmatics and Society, 10(2), 153176.
Everaert, M. B. H., Huybregts, M. A. C., Chomsky, N., Berwick, R. C., &Bolhuis, J. J. (2015). Structures, not strings: Linguistics as part of thecognitive sciences. Trends in Cognitive Sciences, 19(12), 729-743.
Galantucci, B (2009). Experimental semiotics: A new approach for studying
communication as a form of joint action. Topics in Cognitive Science, 1(2),393-410.
Murphy, E. (2020). The oscillatory nature of language. Cambridge: Cambridge
University Press.
Shibata, K., Watanabe, T., Sasaki, Y., & Kawato, M. (2011). Perceptual learning
incepted by decoded fMRI neurofeedback without stimulus presentation.
Science, 334(6061), 1413–1415.
Tomasello, M. (2003). Constructing a language: A usage-based theory oflanguage acquisition. Cambridge, MA: Harvard University Press.

569

SIMILARITIES AND DIFFERENCES IN A CROSS-LINGUISTICSAMPLE OF SONG AND SPEECH RECORDINGSYUTO OZAKI1, JIEI KUROYANAGI2, JOHN MCBRIDE3, POLINA PROUTSKOVA4,ADAM TIERNEY5, PETER PFORDRESHER6, EMMANOUIL BENETOS4, FANG
LIU7, PATRICK E. SAVAGE2**

Corresponding Author: psavage@sfc.keio.ac.jp
Graduate School of Media and Governance, Keio Univ., JP
2
Faculty of Environment and Information Studies, Keio Univ., JP
3
Center for Soft and Living Matter, Institute for Basic Science, KR
4School of EECS, Queen Mary Univ. of London, UK
5
Department of Psychological Sciences, Birkbeck, Univ. of London, UK
6
Department of Psychology, Univ. at Buffalo, NY, US
7
School of Psychology and Clinical Language Sciences, Univ. of Reading, UK
1

1. Introduction: Music and language are prominent forms of acoustic
communication across human societies. The relationship between these twomodes of human sound communication has been investigated from theperspective of music and language evolution (e.g. Fitch, 2006; Patel, 2007). Songand speech are usually considered as distinct categories, but the boundary is notalways clear (Brown, 2017; Cummins, 2020; Deutsch et al., 2011; Engelhardt &Bretèque, 2017; Feld & Fox, 1994). Many studies have documented acoustic
differences between song and speech in specific languages, but few studies haveidentified consistent cross-cultural similarities or differences across a diverse setof languages (see the references cited in the Result section). Therefore, we aim to
conduct a series of comparative analyses with diverse cross-cultural samples to
explore more general acoustic similarities and differences. In particular, weanalyzed the following acoustic features: fundamental frequency (F0) of voice,inter-onset interval (IOI) of vowel onsets, ratio of F0 between adjacent IOIsegments (interval). Onset annotations were performed manually and intervals
were calculated by taking the outer product of an F0 vector at an IOI segment andThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

570

the reciprocal of the F0 vector at the previous IOI segment which resulted in the
distribution of pitch ratio of adjacent IOI segments1.
2. Dataset: We analyzed 23 pairs of song and speech recordings (i.e. 46 audio
files). Each pair was recorded by the same person. 36 recordings were sampledfrom Hilton et al. (in press) that include English, Mandarin and Spanish. The
remaining 10 recordings were newly collected ones in Japanese, English,
Mandarin and Korean. The latter data was created by singing a song first and thenreciting the text of the sung lyrics.
3. Results: We observed that song has higher F0 and longer IOI than speech,
as reported previously (Hansen et al., 2020; Merrill & Larrouy-Maestri, 2018;
Sharma et al., 2021, but see also Ding et al., 2017 who used amplitude
modulation). We also observed that song has a sharper concentration at an IOIratio (Roeske et al., 2020) of 0.5 than speech though both data has a medianaround 0.5. Our nPVI analysis both failed to support Patel & Daniele (2003)'s
song-speech relationship hypothesis and failed to sort languages into traditional
syllable-/stress-/mora-timed categories. Speech and song showed similar rangesof melodic intervals (within ±700 cents). However, the interval distribution ofsong has distinct peaks at around ±200 cents in addition to 0 cent, while speech
had only one peak at 0 cents. In addition, we measured the variability of F0 by
entropy (like Ozaki et al., 2022), and English and Spanish showed that singing
has lower entropy than speech indicating greater pitch stability of singing(Merrill & Larrouy-Maestri, 2018; Raposo de Medeiros et al., 2021; Sharma etal., 2021; Thompson, 2014), while Mandarin showed the opposite pattern1.
4. Conclusion: In summary, we observed the above potential systematicdifferences and similarities between song and speech. Why and how thesedifferences have emerged and how evolutionary theories of language andmusicality can account for these are the key questions to be addressed in future
research (Darwin, 1871; Brown, 2000; Tierney et al., 2011; Savage et al., 2021;
Mehr et al., 2021). For example, slower and more regular vocal communication
may facilitate synchronization (social bonding hypothesis, Savage et al., 2021),
while similar melodic interval range and 1:1 IOI duration focus may be due toshared constraints on the vocalization mechanism (motor constraint hypothesis,
Tierney et al., 2011). Future steps to explore more comprehensive and robust
relationships between two universal human acoustic communication forms
include increasing sample size and language diversity, timbre/formant analysis,
and measuring inter-rater reliability for the syllable/note annotations.
1
Further details and figures can be found in the supplementary materials.
https://drive.google.com/file/d/1MDnk9miUzRMupb8pMtqlpdcuQ-I7w-CY/view?usp=sharing

571

Acknowledgements
We thank Sangbuem Leonard Choo for providing Korean language data. This
work was supported by Grant-in-Aid no. 19KK0064 from the Japan Society for
the Promotion of Science and by startup grants from Keio University (Keio Global
Research Institute, Keio Research Institute at SFC, and Keio Gijuku Academic
Development Fund) to P.E.S, and by Grant Number JPMJSP2123 of Support for
Pioneering Research Initiated by the Next Generation from the Japan Science andTechnology Agency to Y.O.
References
Brown, S. (2000). The “Musilanguage” model of music evolution. In N. L.
Wallin, B. Merker, & S. Brown (Eds.), The origins of music (pp. 271–300).
MIT Press.
Brown, S. (2017). A Joint Prosodic Origin of Language and Music. Frontiers in
Psychology, 8. https://www.frontiersin.org/article/10.3389/fpsyg.2017.01894
Cummins, F. (2020). The Territory Between Speech and SongA Joint Speech
Perspective.
Music
Perception,
37(4),
347–358.
https://doi.org/10.1525/mp.2020.37.4.347
Darwin, C. (1871). The descent of man. Watts & Co.
Deutsch, D., Henthorn, T., & Lapidis, R. (2011). Illusory transformation fromspeech to song. The Journal of the Acoustical Society of America, 129(4),2245–2252. https://doi.org/10.1121/1.3562174
Ding, N., Patel, A. D., Chen, L., Butler, H., Luo, C., & Poeppel, D. (2017).
Temporal modulations in speech and music. Neuroscience & Biobehavioral
Reviews, 81, 181–187. https://doi.org/10.1016/j.neubiorev.2017.02.011
Engelhardt, J., & Bretèque, E. A. de la. (2017). Guest Editors’ Preface: Speech,
Song, and In-Between. Yearbook for Traditional Music, 49, xv–xix.
https://doi.org/10.5921/yeartradmusi.49.2017.00xv
Feld, S., & Fox, A. A. (1994). Music and Language. Annual Review of
Anthropology, 23, 25–53.
Fitch, W. T. (2006). The biology and evolution of music: A comparative
perspective.
Cognition,
100(1),
173–215.
https://doi.org/10.1016/j.cognition.2005.11.009
Hansen, J. H. L., Bokshi, M., & Khorram, S. (2020). Speech variability: A crosslanguage study on acoustic variations of speaking versus untrained singing.
The Journal of the Acoustical Society of America, 148(2), 829.
https://doi.org/10.1121/10.0001526
Hilton, C. B., Moser, C. J., Bertolo, M., Lee-Rubin, H., Amir, D., Bainbridge, C.
M., Simson, J., Knox, D., Glowacki, L., Galbarczyk, A., Jasienska, G., Ross,
C. T., Neff, M. B., Martin, A., Cirelli, L. K., Trehub, S. E., Song, J., Kim, M.,

572

Schachner, A., … Mehr, S. A. (in press). Acoustic regularities in infantdirected speech and song across cultures. Nature Human Behaviour.
https://doi.org/10.1101/2020.04.09.032995
Mehr, S. A., Krasnow, M. M., Bryant, G. A., & Hagen, E. H. (2021). Origins ofmusic in credible signaling. Behavioral and Brain Sciences, 44.
https://doi.org/10.1017/S0140525X20000345
Merrill, J., & Larrouy-Maestri, P. (2017). Vocal Features of Song and Speech:
Insights from Schoenberg’s Pierrot Lunaire. Frontiers in Psychology, 8, 1108.
https://doi.org/10.3389/fpsyg.2017.01108
Ozaki, Y., Sato, S., Mcbride, J., Pfordresher, P. Q., Tierney, A. T., Six, J., Fujii,
S., & Savage, P. E. (2022). Automatic acoustic analyses quantify pitch
discreteness within and between human music, speech, and birdsong.
Proceedings of the 10th International Workshop on Folk Music Analysis. The
10th International Workshop on Folk Music Analysis, Sheffield, United
Kingdom.
Patel, A. D. (2007). Music, Language, and the Brain. Oxford University Press.
https://doi.org/10.1093/acprof:oso/9780195123753.001.0001
Patel, A. D., & Daniele, J. R. (2003). An empirical comparison of rhythm inlanguageandmusic.
Cognition,87(1),
B35–B45.
https://doi.org/10.1016/S0010-0277(02)00187-7
Raposo de Medeiros, B., Cabral, J. P., Meireles, A. R., & Baceti, A. A. (2021). A
comparative study of fundamental frequency stability between speech andsinging.
SpeechCommunication,128,
15–23.
https://doi.org/10.1016/j.specom.2021.02.003
Roeske, T. C., Tchernichovski, O., Poeppel, D., & Jacoby, N. (2020). Categorical
Rhythms Are Shared between Songbirds and Humans. Current Biology,30(18), 3544-3555.e6. https://doi.org/10.1016/j.cub.2020.06.072
Savage, P. E., Loui, P., Tarr, B., Schachner, A., Glowacki, L., Mithen, S., & Fitch,
W. T. (2021). Music as a coevolved system for social bonding. Behavioral
and Brain Sciences, 44. https://doi.org/10.1017/S0140525X20000333
Sharma, B., Gao, X., Vijayan, K., Tian, X., & Li, H. (2021). NHSS: A speech andsinging parallel database. Speech Communication, 133, 9–22.
https://doi.org/10.1016/j.specom.2021.07.002
Thompson, B. (2014). Discrimination between singing and speech in real-world
audio. 2014 IEEE Spoken Language Technology Workshop (SLT), 407–412.
https://doi.org/10.1109/SLT.2014.7078609
Tierney, A. T., Russo, F. A., & Patel, A. D. (2011). The motor origins of humanand avian song structure. Proceedings of the National Academy of Sciences,108(37), 15510–15515. https://doi.org/10.1073/pnas.1103882108

573

THE ADAPTIVE SIGNIFICANCE OF HUMAN SCLERALBRIGHTNESS. AN EXPERIMENTAL STUDY.
JUAN PEREA GARCÍA1, DARIUSZ DANEL2,, ZDZISŁAW LEWANDOWSKI3,SLAWOMIR WACEWICZ*4*Corresponding Author: wacewicz@umk.pl
1 National University of Singapore, Singapore
2 Hirszfeld Institute of Immunology and Experimental Therapy, Wrocław, Poland
3 University School of Physical Education in Wrocław, Poland
4 Center for Language Evolution Studies, Nicolaus Copernicus University,Toruń, Poland

1. Objective
Several of the most influential language evolution scholars (e.g. Tomasello et al.
2007, Knight and Lewis 2017, cf. also e.g. Hare 2017) have opined that the
emergence of the homogeneously pale sclera characteristic of modern humans
was a prerequisite for, and turning point in, the evolution of language. However,the understanding of the exact role and origin of this trait has remained highly
speculative, with initial investigations suggesting that it evolved to facilitate eyegaze following by conspecifics. It is unlikely that our species went from thetypically pigmented sclera of most other primate species to its current state in theabsence of proximate functions that made partial depigmentation adaptive. The
literature (see esp. Danel et al. 2020) enumerates several non-mutually exclusiveproximate functions that could have mediated the transition from pigmented todepigmented sclerae. These functions include the signaling of: attractiveness and health (e.g. through symmetrical depigmentation thataffords the perception of changes in vasculature or pigmentation), reliability and trustworthiness (e.g. by facilitating the perception of one’s
gaze and emotions and, thus, intentions),
 reduced emotional reactivity (possibly sharing proximal mechanisms
with self-domestication).The aim of our study was to investigate whether
partial scleral depigmentation could have been adaptive by signaling
reduced aggressiveness, attractiveness, trustworthiness, health, and age.

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

574

2. Stimulus and procedure
STIMULUS. We morphed and photo-edited images of the faces of a range ofreconstructed hominin species, to create realistic facial images of 20 individually
distinct hominins with relatively diverse facial morphologies. We adjusted thebrightness of the scleral areas of each individual to achieve a perceptually (i)“humanlike” bright sclera and (ii) “generalised apelike” dark sclera version ofeach individual. PROCEDURE. 250 demographically diverse participants,
recruited via Prolific, rated 10 bright-sclera and 10 dark-sclera hominins
randomly presented with online software, by moving sliders between the left (0)and right (100) extremes for each trait. ANALYSIS. We fit separate generalized
mixed models (GLMM) for each of our dependent variables (except Age, see
below) with our manipulation (dark/bright sclerae) as an independent variable.
For age, we fit a linear regression model (LM). We included stimuli pairs andparticipants as random effects in all our models. We predicted that bright-sclera
hominin faces would be rated as less aggressive, more attractive, moretrustworthy, healthier, and younger than the same faces with a dark sclera. Our
study was pre-registered at https://aspredicted.org/TM4_PSK
3. Results
All models found statistically significant differences between pairs of stimuli inthe predicted direction (Table 1).
Table 1. Summary of the LM (Age) and GLMMs (the remaining dependent
variables) for “dark-sclera” vs “bright-sclera” pairs of hominin faces.
Intercept

Dark-Brightdifferencet/z valuepAge (LM)

63.13

4.03

5.30

<0.01

Aggression

-0.44

0.28

38.369<0.01

Attractiveness

-0.19

-0.37

-50.76<0.01

Health

0.51

-0.39

53.17

<0.01

Trustworthiness

0.24

-0.31

-43.25<0.01

4. Conclusions
Our results show that partial scleral depigmentation in hominins may have been
adaptive for signaling functions that possibly predate the deictic function of eyegaze. Further studies are required to evaluate the role of the familiarity (i.e.
human-likeness) of the stimulus in the effects detected in our study.

575

References
Danel, D. P., Wacewicz, S., Kleisner, K., Lewandowski, Z., Kret, M. E.,Żywiczyński, P., & Perea-Garcia, J. O. (2020). Sex differences in ocularmorphology in Caucasian people: a dubious role of sexual selection in theevolution of sexual dimorphism of the human eye. Behavioral Ecology andSociobiology, 74(10), 1-10.
Hare, B. (2017). Survival of the Friendliest: Homo sapiens Evolved via Selection
for Prosociality. Annual Review of Psychology 68:1, 155-186.
Knight, C., & Lewis, J. (2017). Wild voices: Mimicry, reversal, metaphor, andthe emergence of language. Current Anthropology, 58(4), 435-453.
Tomasello, M., Hare, B., Lehmann, H., & Call, J. (2007). Reliance on head versus
eyes in the gaze following of great apes and human infants: the cooperative
eye hypothesis. Journal of human evolution, 52(3), 314-320.

576

BODILY ROTATION IN THE GESTURING, PLAY, ANDDISPLAY OF GREAT APESMARCUS PERLMAN
m.perlman@bham.ac.uk
Department of English Language and Linguistics, University of Birmingham,
Birmingham, UK

Study of the gesturing and displays of great apes is, arguably, fundamental tounderstanding the evolution of human language, as well as other forms of humanexpression like dance. This paper examines the case of bodily rotation in ape
gesturing and display, such as in various forms of spinning, rolling, flipping, etc.
Such rotational movements are a recurring element in the intentional
communication of great apes, for example, noted by Byrne et al. (2017) in six
distinct gesture types and variants: pirouette, pirouette with object, ice skating,side roulade, somersault, and rope spinning. Byrne et al. hypothesized that thesegestures, and dozens of others, are part of the innate repertoires of ape species,
evolved through phylogenetic ritualization. By this idea, although such gestures
are used intentionally and flexibly in context, they lack the open-endedness inform that is characteristic of human language. This hypothesis also implies thatthe human propensity for rotating their bodies—common in creative activities likedance and play—is not related to similar looking behaviors in apes, as it isdoubtful our own diverse behaviors stem from a shared inventory of innately
specified gestures. Alternatively, humans and great apes might spin, roll, and flipfor homologous reasons, perhaps for the stimulating, dizzying effects of thesebehaviors, which drive their widespread occurrence.
Here, the rotational behavior of great apes was analyzed in a large sample ofonline videos collected from YouTube. Videos were identified by search phrases
that combined terms for primate clades (e.g., chimpanzee, gorilla, ape) withdifferent rotational movements (e.g., spinning, rolling, flipping). Data include 276
videos of great apes performing 793 rotational behaviors comprising 361 single
rotations and 442 sequences ranging from 2 to 40 rotations. Video captions
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

577

Proportion of Sites

available for about half of the videos confirm footage from many different sites.
Table 1 shows the number of observations by clade, including the number ofbehaviors, videos, and caption-confirmed sites. Sites included zoos andsanctuaries, animals handled as pets and trained acts, and animals observed in thewild.
Analysisshowsthat
Behaviors Videos
Zoo/Handled/Wildindividuals across species, Cladeages, and sexes performed a Bonobo
60
18
7/1/0
range of rotational behaviors,
Chimp
195
49
17 / 12 / 2
including those previously
332
117
30 / 0 / 3
documented (Byrne et al., Gorilla
2017), and others like Orang
206
92
19 / 3 / 0
backflips and leaping 360s.
Behaviors were most often Table 1. Number of observations by clade.
performed in contexts of play and display. Figure 1 shows the frequency of major
behavior types by clade, measured as the proportion of confirmed sites at which
the behavior was observed. For example, gorillas are notable in their frequent
performance of standing spins—spinning around the vertical bodily axis while
standing upright on varying numbers of hands and feet—in about three-quartersof sites. Not visible in this plot is that, although the behaviors are classifiable as
types based on certain formal
1.00
Clade
characteristics, they often show
Bonobogreat variation and creativity,
Chimpanzee
0.75
Gorillaproducedwithdistinctive
Orangutan
movements tuned to particular
0.50
features of the environment (e.g.,
0.25
ropes, swings, hills, water, floor
types, various objects).
0.00
These preliminary results areSomersault Roll
Standing Rope
Other
1. Proportion of identified
sites at which
spin
spin
interpreted as evidence that apes Figure
different rotational behaviors were observed.
perform rotational behaviors for
similar reasons to humans, a primary one appearing to be that they are fun as aresult of being perceptually stimulating and dizzying. Similar-looking inventories
of rotational behaviors across ape species might result from this shared inclination
channeled through the affordances common to our ape bodies. Importantly, thedata show that the forms of rotational behaviors are flexible and subject tocreativity, suggesting that they are open-ended, perhaps comparable to humanlanguage and gesture.

578

References
Byrne, R. W., Cartmill, E., Genty, E., Graham, K. E., Hobaiter, C., & Tanner, J.
(2017). Great ape gestures: intentional communication with a rich set of innate
signals. Animal Cognition, 20, 755-769.

579

THETA RHYTHM IS WIDESPREAD IN VOCAL PRODUCTIONACROSS THE ANIMAL REALM.
THÉOPHANE PIETTE*1, CHUNDRA CATHCART2,, CHIARA BABRIERI3, DIDIER
GRANDJEAN4, ÉLOÏSE DÉAUX1, ANNE-LISE GIRAUD1*

Corresponding Author: theophane.piette@unige.ch

1

Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland

2

Center for the Interdisciplinary Study of Language Evolution, University of Zurich,
Zurich, Switzerland
3

Department of Evolutionary Biology and Environmental Studies, Universityof Zurich, Zurich, Switzerland
4

Swiss Center for Affective Sciences, University of Geneva, Geneva,
Switzerland

1. Introducti on
Speech production rate, conserved between 2 to 8 syllables per second, plays a
key role in comprehension. Recent studies that highlighted the presence of thesame rhythm in vocal productions and mouth movements of non-human primates,suggest that it may be inherited from mastication (Risueno-Segovia et al, 2020).
However, other factors could constrain vocal rhythm. Notably, syllabic rate also
matches the frequency of theta brain oscillations (2 -7 Hz). While oscillations arerhythmic electrical activity spontaneously generated by neural tissues, they canalso be stimulus-driven and research has demonstrated that theta oscillations
entrain to speech during listening (Giraud et al, 2014). As brain oscillations
frequencies are conserved in animals, syllabic rate may instead be constrained by
the recipient’s neuronal activity (Buzaski et al, 2013). To determine what factor(s)may have contributed to the emergence of the theta vocal rhythm, we collected
vocalizations for 90 species of animals. We used phylogenetic regression to assess
whether weight, mastication or environmental constraints explain the variation in
animal vocal rhythms while controlling for phylogenetic relationships.
2. Methods
2.1 Vocal, physical and environmental data
We gathered, when they were available, recordings for one species per infra-orderof tetrapods as well as some fishes and insects (N=92, mammal = 24, bird = 58,
amphibian = 4, reptile =1, insect = 4 and fish= 1). To obtain vocal production

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

580

rhythm, we then extracted sequences, consisting of one subject vocalizing for atleast 2s, with less than 1s of silence between two sounds. For each sequence, theenvelope, was computed, and analyzed using a wavelet transform to extract thepeak rhythmic changes of amplitude (Tilsen et al 2014). Bodyweight data werecollected (N=92), habitat type was defined as either closed, semi-closed, open,
wetland or water, and mastication status as yes or no. We obtained data on thecontext of production, but these were only available for 35% of the recordings.
Therefore, we assumed that such effect would relate to flexible rhythm adjustment
within an already physically constrained rhythmic range (Martin et al, 2022).
2.2 Statistical analyses
Given the extent of the current dataset, we are still in the process of building thecorresponding phylogenetic tree. Thus using log-transformed weight, mastication,
environments, and their interactions as fixed effects, and random slopes of log
weight by groups and random intervals for orders, we built a linear mixed model
investigating the variation of the median log-transformed rhythm frequency.
3. Results
We can observe in our preliminary results that most of our species (74.4%) havea median frequency located between 2Hz to 7Hz. When testing our linear mixed
model, we noticed that our random effects did not explain the variance of rhythmin our species. We also found that none of our predictors significantly influenced
vocal rhythm (Most impacting factor: Environment, F=1.0570, p.value=0.3748).
4. Conclusion
Theta production rhythm seems widespread across animal vocal productions,
independently of their physical characteristics, mastication status or environment.
As it is present in most species, even the ones that do not masticate, it appears
unlikely that this rhythm is constrained by a limit of the natural movement of thearticulators. Furthermore, the lack of effect of weight and environmental
constraints tends to point towards a more common and endogenous constraint for
vocalization rhythm. As brain oscillations and neurons characteristics, in general,
are well conserved across species, it could be that the rhythm of the vocalizations
has adapted to them as a form of ‘neuronal exploitation’, to ensure the efficient
reception by the listener. As the range of oscillations in animals’ brains is not
documented for most species, further work on phylogeny and the direct lin k
between vocalizations and brain oscillations in animals will be needed tostrengthen this hypothesis or lead the way for alternative explanations.

581

Acknowledgements
The NCCR Evolving Language, Swiss National Science Foundation Agreement
Nr. #51NF40_180888, funded this work. The authors are grateful to the CSIRO
Australian National Wildlife Collection, (https://ror.org/059mabc80), Fonoteca
Zoologica (https://www.fonozoo.com/), the Museum für Naturkunde of Berlin(https://www.museumfuernaturkunde.berlin) and the Xeno-Canto Foundation for
Nature Sounds (https://xeno-canto.org) for their assistance in undertaking thisresearch. The authors thank all the researchers from the NCCR Evolving
Language as well as Adrien Meguerditchian, Pascal Belin and Steffen R.Hage for
their insights on the project. The authors would like to thank Aaron Bauer,
Andrew Spencer, Alex Kwet, Alex Rohtla, Camila Ferrara, Daniel Blumstein ,Élodie Briefer, Émilie Genty, Frank Lambert, Isabelle Charrier, Gerald Carter,
Hans Schneider, Marc D. Hauser, Nikola Falk, Peter Boesman and Robert
Seyfarth for providing us with recordings for this study. The authors also thanktwo anonymous reviewers for their helpful comments on an earlier version of themanuscript.

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timing : evolutionary preservation of brain rhythms. Neuron, 80(3),751‑764. https://doi.org/10.1016/j.neuron.2013.10.002
Fujii, S., & Wan, C. Y. (2014). The role of rhythm in speech and language
rehabilitation: the sep hypothesis. Frontiers in Human Neuroscience, 8.
https://doi.org/10.3389/fnhum.2014.00777
Giraud, A.-L., & Poeppel, D. (2012). Cortical oscillations and speech processing :emerging computational principles and operations. Nature Neuroscience,15(4), 511‑517. https://doi.org/10.1038/nn.3063
Hoyo, J. del, Elliott, A., Sargatal, J., & Cabot, J. (Éds.). (1992). Handbook of thebirds of the world. Lynx Edicions.
Martin, M., Gridley, T., Elwen, S. H., & Charrier, I. (2022). Feel the beat: Cape
fur seal males encode their arousal state in their bark rate. The Science of
Nature, 109(1), 5. https://doi.org/10.1007/s00114-021-01778-2
Noujaim, S. F., Lucca, E., Muñoz, V., Persaud, D., Berenfeld, O., Meijler, F. L.,& Jalife, J. (2004). From mouse to whale : a universal scaling relation forthe pr interval of the electrocardiogram of mammals. Circulation, 110(18),2802‑2808. https://doi.org/10.1161/01.CIR.0000146785.15995.67
Ravosa, M. J., Ross, C. F., Williams, S. H., & Costley, D. B. (2010). Allometryof masticatory loading parameters in mammals. The Anatomical Record:
Advances in Integrative Anatomy and Evolutionary Biology, 293(4),557‑571. https://doi.org/10.1002/ar.21133

582

Risueno-Segovia, C., & Hage, S. R. (2020). Theta synchronization of phonatory
and articulatory systems in marmoset monkey vocal production. Current
Biology, 30(21), 4276-4283.e3. https://doi.org/10.1016/j.cub.2020.08.019
Tilsen, S., & Johnson, K. (2008). Low-frequency Fourier analysis of speechrhythm. The Journal of the Acoustical Society of America, 124(2),EL34‑EL39. https://doi.org/10.1121/1.2947626
Wilson, D. E., Mittermeier, R. A., & Cavallini, P. (Éds.). (2009). Handbook of
the mammals of the world. Lynx Edicions: Conservation International:
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Worthington, J., Young, I. S., & Altringham, J. D. (s. d.). Short communicatio n
the relationship between body mass and ventilation rate in mammals. 4.

583

HOW SENSES ARE GROUPED WITHIN WORD FORMS OVER TIME:
AN ANALYSIS OF DIACHRONIC COLEXIFICATION PATTERNSLucı́a Pitarch*1 and Thomas Brochhagen2* lpitarch@unizar.es

1 Universidad de Zaragoza

2 Universitat Pompeu Fabra

Languages commonly express multiple meanings with single forms (Piantadosi, Tily, & Gibson, 2012). However, while ambiguity is pervasive, the reasons
why some meanings are more likely to be conflated than others –i.e., why theycolexify more often (François, 2008)– are still under investigation (Xu, Duong,
Malt, Jiang, & Srinivasan, 2020; Brochhagen & Boleda, 2022).
Originally the term colexification referred to several senses sharing the sameword form (e.g. the senses of right as ’direction’ and ’being correct’ colexify).
Later studies broadened this term to apply to concepts instead of senses (Xu et al.,2020, e.g.), relying on CLICS3 (Rzymski et al., 2020), the largest database on
cross-linguistic colexifications available to date. CLICS3 provides English glosses
for meanings lexicalized in source languages (e.g., sentir in Catalan expresses
both FEEL and LISTEN). When different glosses (e.g., LISTEN and FEEL) sharethe same word form in the original language, as sentir in Catalan, those concepts
colexify. This approach enabled many quantitative studies by being more easily
and automatically computable. However, relying on English as meta-language
leads to a decrease in the granularity of the original term (François, 2008, cf.).
For the present study, we linked the data from CLICS3 for romance languages toBabelNet (Navigli & Ponzetto, 2012), one of the largest multilingual sources forstructured semantic data. In BabelNet words are split into senses which have auniversal identifier shared by all languages. We searched pairs that colexified in
CLICS3 in a certain language in BabelNet, and retrieved the information related to
the BabelNet senses shared by both colexifying wordforms in CLICS3. By doing
so we describe colexifications not only at the concept level, nor through English asa mediator, but rather at the sense level with universal sense abstractions. Beyond
enriching the available data, this study offers a novel, quantitative and diachronic,
perspective on colexification. It asks what form- and concept-based information
is predictive of the maintenance or loss of colexifications over time. For instance,
while the concepts FEEL and LISTEN did not colexify in Latin (with the different
word forms sentire and audire respectively), in Catalan they do, and the same
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584

word form sentir is used to refer to both concepts. Yet, in other languages such as
Spanish, Latin’s semantic organization is maintained.
We focus on the following intralinguistic features: word length, form confusability,1 part of speech, number of lexifications per sense (in how many otherwords of a language it appears) and semantic relations (e.g., part/whole and subsumption). The data was processed by grouping senses belonging to the sameword forms into pairs, and then fit using logistic regression models with the above
features as predictors and, as response, whether a given colexification from Latin
was maintained diachronically in at least one of its daughters. That is, whether
sense pairs expressed in Latin with the same word are also still colexified in otherromance languages. We fit two different models. In the smallest, more imbalanced, model, encompassing all 782 colexifications annotated with semantic relations, only semantic relation information and form confusability were estimatedto impact prediction. In fact, the information that two senses are semanticallyrelated (no matter the type of relation) was so informative that all colexifications
were predicted as maintained, incurring a 10% error rate. The larger model covers
all 406777 pairs, but does not include relational information. The most informative feature for diachronic colexification maintenance in this model was form
confusability, followed by the number of lexifications of the synset. The rarer a
synset’s lexification is, the more colexifications it appears in will be maintained.
In both models the more different a word is, on average, from other words in its
lexicon –i.e., the less mistakable it is– the more the colexifications it hosts aremaintained. Both part of speech and word length were the least informative predictors for diachronic colexification maintanance. Word length’s lesser impact
compared to other features may be due to its overlap with form confusabillity.
As for part of speech, Latin’s structure may play a role, as its syntactic functions
are mainly expressed by case rather than by functional words (e.g., prepositions).
Further study of other language families with different peculiarities should shedfurther light on these matters. The accuracy obtained with both cross-validated
models lies between 80% and 90%.
The enrichment of BabelNet data is ongoing (e.g., (Declerck & Bajčetić, 2021)
for phonology or (Nayak, Majumder, Goyal, & Poria, 2021) for relation extraction). In the future we aim to improve our diachronic research with this new
information to gain a deeper insight on linguistic change.
Acknowledgements
This research has been partially supported by DGA/FEDER.
1 Mean Levenshtein distance between a form and the lexicon it draws from. The latter was estimatedby scraping unique words from 1250 random wikipedia pages for each language. Initially we focus on
orthography but we are currently working on enriching the data with phonological resources.

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different meanings? The Goldilocks principle in colexification. Cognition.
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to relation triplets extraction: A comprehensive survey. Cognitive Computation, 13(5), 1215–1232.
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ambiguity in language. Cognition, 122(3), 280–291.
Rzymski, C., Tresoldi, T., Greenhill, S. J., Wu, M.-S., Schweikhard, N. E.,
Koptjevskaja-Tamm, M., Gast, V., Bodt, T. A., Hantgan, A., Kaiping, G. A.,
et al.. (2020). The database of cross-linguistic colexifications, reproducible
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relations predict colexification across languages. Cognition, 201.

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RE-EVALUATING HOCKETT’S DESIGN FEATURES FROM A
COGNITIVE AND NEUROSCIENCE PERSPECTIVE: THE
CASE OF DISPLACEMENTMICHAEL PLEYER*1,2 and ELIZABETH QING ZHANG1,2*Corresponding Author: pleyer@umk.pl
1 Center for Language Evolution Studies, Nicolaus Copernicus University inToruń, Toruń, Poland
2 University Center of Excellence IMSErt - Interacting Minds, Societies,

Environments, Nicolaus Copernicus University in Toruń, Toruń, Poland

Hockett’s Design features of language (e.g. Hockett, 1960; 1963) have provenhighly influential within the field of linguistics in describing the differences
between human language and animal communication. However, they have notremained without criticism over the many years since their inception. In
particular, although frequently mentioned, they have “generally failed to motivate
theoretical discussion or empirical tests” (Hauser, 1996: 47) in animalcommunication research. Focusing on language evolution, Wacewicz &Żywiczyński (2015) have argued that the design feature approach does not pay
enough attention to the actual cognitive capacities underlying these features andthe ecological and social settings in which they are used.
Despite these criticisms, Hockett’s design features are still frequently appealed toby linguists. Here we argue that if linguistics wants to continue using them, thisneeds to be complemented by a decompositional approach that explicates a given
design feature with regard to the cognitive and neuroscientific component parts
that underlie the feature. Only by re-evaluating Hockett’s design features from a
cognitive and neuroscience perspective can linguistics be brought into a
productive dialogue with animal communication research and language evolution
research. Our proposal follows that of Wacewicz & Żywiczyński (2015) but
extends it by adding a neuroscientific perspective where all of Hockett’s design
features are decomposed in a systematic fashion.
We illustrate this approach by focusing on the property of displacement, whichsuggests that, instead of being specific to human language, displacement isevolutionarily continuous. Displacement in language depends on episodic
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memory, which has been detected in nonhuman animals, and is supported by anancient subcortical region—the hippocampus. This has important implications foran overall re-evaluation of Hockett’s design features.
Displacement describes linguistic messages referring to events remote in bothtime and space (Hockett, 1963). Episodic memory, which enables one to revisit
the past and imagine the future (Tulving, 2001), is necessary to achievedisplacement. Comparative evidence reveals that although it might be rare,neither displacement nor episodic memory are unique to humans. As Hockett(1963) observes, the waggle dance of honeybees is always displaced (RootBernstein, 2010). In addition, chimpanzees have been shown to communicate
about objects that are absent or displaced (Lyn et al. 2014; Bohn et al. 2015).
Regarding episodic memory, “what-where-when” has been taken as a reference
for identifying episodic-like memory in nonhuman animals. For example, foodcaching birds, rodents, and nonhuman primates have been shown to be able toaccomplish episodic-like memory tasks (Clayton & Dickinson, 2010).
Neurological studies reveal that the hippocampus could play a key role indisplacement. In humans, the hippocampus has been shown to be involved not
only in episodic memory, but also in encoding spatial and temporal information(Howard & Eichenbaum, 2015). It is also assumed to store when, where and what
information (Tsao et al., 2018). In birds, the behavior of food-caching engages
the hippocampus. For instance, Black-capped chickadees and dark-eyed juncos
whose hippocampus is lesioned exhibit memory impairment for location, which
is a crucial part for food-caching (Colombo & Broadbent, 2000). It has also beenreported that the seasonal change in hippocampal size is correlated with seasonalchange in caching food in food-storing birds (Sherry & Hoshooley, 2010). In
rodents, different subregions of the hippocampus are involved in subcomponentsof episodic memory like recognition, temporal order and spatial memories(Aggleton & Pearce, 2001). The hippocampus has been shown to take part inobject recognition and time delay between sample and test sessions (Cohen &Stackman, 2015). Specifically, the dorsal part is critical for temporal information
processing, and the ventral part is crucial for temporal order of the spatial
information (Hoge & Kesner, 2007; Howland et al., 2008). In nonhumanprimates, the what-where association has been investigated by recording single
neurons in macaques, and the results showed that the hippocampus is involved inseparation and combination of representations of objects and the places where
they are located (Roll et al., 2005). Zola et al. (2000) also found that hippocampal
lesions affected object recognition memory.
As the case of displacement shows, a cognitively and neuroscientifically informed
re-evaluation of Hockett’s design features therefore offers the potential of turningthem into a useful analytical device for comparisons of human language andanimal communication.

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References
Aggleton, J. P., & Pearce, J. M. (2001). Neural systems underlying episodic
memory: Insights from animal research. Philosophical Transactions of theRoyal Society B: Biological Sciences, 356(1413), 1467–1482.
Bohn, M., Call, J., & Tomasello, M. (2015). Communication about absent entities
in great apes and human infants. Cognition, 145, 63-72.
Clayton, N.S., & Dickinson, A. (2010). Mental Time Travel: Can Animals Recall
the Past and Plan for the Future? In Encyclopedia of Animal Behavior (pp.
438–442). Elsevier.
Cohen, S. J., & Stackman, R. W. (2015). Assessing rodent hippocampal
involvement in the novel object recognition task. A review. BehaviouralBrain Research, 285, 105–117.
Colombo, M., & Broadbent, N. (2000). Is the avian hippocampus a functional
homologue of the mammalian hippocampus? Neuroscience andBiobehavioral Reviews, 24(4), 465–484.
Hocket, C. D. (1960). The origin of speech. Scientific American, 203, 88-111.
Hockett, C.F. (1963). The problem of universals in language. In J. H. Greenberg(Ed.), Universals in language (pp. 1-22). MIT Press
Hoge, J., & Kesner, R. P. (2007). Role of CA3 and CA1 subregions of the dorsal
hippocampus on temporal processing of objects. Neurobiology of Learning
and Memory, 88(2), 225–231.
Howard, M. W., & Eichenbaum, H. (2015). Time and space in the hippocampus.
Brain research, 1621, 345-354.
Howland, J. G., Harrison, R. A., Hannesson, D. K., & Phillips, A. G. (2008).
Ventral hippocampal involvement in temporal order, but not recognition,
memory for spatial information. Hippocampus, 18(3), 251–257.
Lyn, H., Russell, J. L., Leavens, D. A., Bard, K. A., Boysen, S. T., Schaeffer, J.
A., & Hopkins, W. D. (2014). Apes communicate about absent and displaced
objects: methodology matters. Animal Cognition, 17(1), 85-94.
Rolls, E. T., Xiang, J., & Franco, L. (2005). Object, space, and object-space
representations in the primate hippocampus. Journal of Neurophysiology,
94(1), 833–844.
Root-Bernstein, M. (2010). Displacement activities during the honeybee
transition from waggle dance to foraging. Animal Behaviour, 79(4), 935–938.
Sherry, D. F., & Hoshooley, J. S. (2010). Seasonal hippocampal plasticity in foodstoring birds. Philosophical Transactions of the Royal Society B: Biological
Sciences, 365(1542), 933–943.
Tsao, A., Sugar, J., Lu, L., Wang, C., Knierim, J. J., Moser, M. B., & Moser, E.
I. (2018). Integrating time from experience in the lateral entorhinal cortex.
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Tulving, E. (2001). Episodic memory and common sense: How far apart?
Philosophical Transactions of the Royal Society B: Biological Sciences,356(1413), 1505–1515.

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Wacewicz, S., & Żywiczyński, P. (2015). Language evolution: why Hockett’s
design features are a non-starter. Biosemiotics, 8(1), 29-46.
Zola, S. M., Squire, L. R., Teng, E., Stefanacci, L., Buffalo, E. A., & Clark, R. E.
(2000). Impaired recognition memory in monkeys after damage limited to the
hippocampal region. Journal of Neuroscience, 20(1), 451–463.

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AN EVOLUTIONARY PERSPECTIVE ON IM/POLITENESSAND THE PRAGMATIC CONCEPT OF FACEMONIKA PLEYER*1 and MICHAEL PLEYER2,3*Corresponding Author: monika.pleyer@as.uni-heidelberg.de
1 Department of English Literature and Linguistics, Ruprecht-Karls-Universität

Heidelberg, Heidelberg, Germany
2 Center for Language Evolution Studies, Nicolaus Copernicus University inToruń, Toruń, Poland
3 University Center of Excellence IMSErt - Interacting Minds, Societies,

Environments, Nicolaus Copernicus University in Toruń, Toruń, Poland

The concepts of im/politeness and face are central to pragmatic accounts of humancommunication. Im/politeness can be defined as behaviour designed to disarm or enhance
aggression, whereas the notion of face broadly refers to a person’s reputation in interaction.
Given their centrality, both concepts should also play an important role in accounts of theevolutionary emergence of language and human communication. Here, we adopt anevolutionary perspective on im/politeness and face. We first describe the roles
im/politeness and face play in human interaction, with a special focus on thesubcomponents comprising the concept of face: self-image or self-worth, interpersonalaffect, identity, and reputation. We then briefly sketch the evolutionary benefits ofim/politeness and face, highlighting their role in co-operation and group management.
Lastly, we discuss aspects of face shared with other primates, focussing especially on theprocess of reputation formation. We conclude that although reputation formation can beseen as an evolutionary foundation of the evolution of face, face management is uniquely
expressed in language and human interaction.

1. Introduction
In this paper we adopt an evolutionary perspective on im/politeness and thepragmatic concept of face. Whereas language evolution research has increasingly
focused on the importance of pragmatics (e.g., Scott-Phillips 2017), the conceptof im/politeness and its relation to the pragmatic concept of face so far have hardly
received any attention. Given its centrality to human interaction, an evolutionary
perspective on im/politeness and face can prove highly valuable in shedding
further light on the pragmatic foundations of language use and evolution (cf.

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Pleyer & Pleyer 2016; Mühlenbernd et al. 2021). In this paper, we present suchan evolutionary perspective, first focussing on theoretical and definitional workin pragmatics and im/politeness studies on the concepts of im/politeness and face.
We will then take a look at the potential evolutionary benefits of im/politenessand face, and explore aspects of face that humans share with other primates.
Overall, we will argue that many aspects of im/politeness and face are shared withother primates, but that they are uniquely expressed in human language.
2. Im/Politeness
Before we can discuss the evolution and evolutionary benefits of being im/polite,
we must first define the object under study. In linguistics, there is a plethora ofdefinitions of im/politeness (Culpeper 2011); for us, the following two are themost central. For Brown and Levinson (1987: 1), “politeness, like formal
diplomatic protocol […] presupposes that potential for aggression as it seeks todisarm it, and makes possible communication between potentially aggressive
parties.” In other words, politeness relies on the interactants’ wish to cooperate
and to avoid conflict (see also Leech 1983). Impoliteness, on the other hand, seeks
to create antipathy and non-cooperation (see also Kienpointner 1997): “Situatedbehaviours are viewed negatively – considered ‘impolite’ – when they conflictwith how one expects them to be, how one wants them to be and/or how one
thinks they ought to be.” (Culpeper 2011: 23). This means that impolitenessincludes a violation of (social) norms and/or expectations. It may trigger negative
emotions, such as anger, shame, or sadness, and can have negative consequences,
ranging from one or more participants feeling offence to the potential of physicalaltercations.
In our view, both politeness and impoliteness rely on the same shared cognitive
mechanisms, and further, interpretations of behaviours as polite or impolite rely
heavily on contextual factors. Thus, politeness and impoliteness should be seen
as two end points of a scale rather than two distinct phenomena (see also Watts
2003). It is relevant for the evolution of im/politeness that both can be expressedusing a potentially open-ended set of verbal and non-verbal behaviours. Polite
verbal behaviours include e.g. complimenting, showing interest in H, or giving
deference, whereas non-verbal politeness may include smiling or doing an action
beneficial to H – in short, behaviours that make H feel good and thereby
demonstrate the other’s wish to cooperate and avoid conflict. Impoliteness, inturn, includes behaviours that make the hearer ‘feel bad’ (Culpeper 2011: 9), suchas verbally insulting them, offering dispraise, showing disinterest or upsetting
power relations; non-verbal behaviours may include shouting or frowning at H,

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or doing an action detrimental to their well-being. In short, many of these actions
include an unmitigated potential for aggression. Many of these non-verbal
expressions of im/politeness may well be shared with other non-human primates(Mason & Mendoza 1993).
3. Face – what it is, what it isn’t
The pragmatic concept of face can be described as “the positive social value aperson effectively claims for himself [sic] by the line others assume he has taken
during a particular contact” (Goffman 1967: 5). This means that face is associatedwith personal, relational and social value, and is concerned with people’s senseof worth, reputation, competence, etc. As such, it is understood as a dyadic,
interpersonal phenomenon that is co-constituted in interaction (Arundale 2006).
Face is thus a way of managing rapport, or self and other (Spencer-Oatey 2008).
One must distinguish between individual face and group face, a form of face thathighlights the concerns of the group over the individual (see e.g. many nonWestern societies, Nwoye 1992). Face is further relevant for the expression of
im/polite beliefs: polite behaviour seeks to maintain or enhance the hearer’s facein that all potential face-threats must be redressed with politeness (see Brown &Levinson 1987), while impoliteness sets out to threaten it (Culpeper 1996).
In pragmatics, research has highlighted several concepts that are closely related
to face, and that may be shared to a degree with other primates. These are selfimage or self-worth, interpersonal affect, identity, and reputation.
One’s self-image are ideas a person has about themselves and would like others
to entertain, as well. It is distinct from face as one can have a self-image separate
from interactions, but face can and is only relevant in interactions with others (seeGoffman’s 1967: 5 view of face as being “on loan from society”). Speakers may
be able to modify their self-image (e.g. by dressing in a certain way) which might
inform their face, but threats to face might, but do not automatically have to, incurloss of self-worth.
Interpersonal affect is similar to, but cannot be fully equated to face. For instance,
the speaker’s malign intent in a potentially face-threatening act can be overcome
by H in pretending S had an originally supportive intent. Thus, in a group setting,
H would not lose face, but the interpersonal relation between S and H might suffer(see O’Driscoll 2011: 20-21). Interpersonal affect requires quite a high level ofsocial awareness, as well as knowledge about the distinction of individual andgroup face.
Identity and face are closely interconnected aspects. Some researchers in essence
equate the two concepts; for instance, for Sifianou (2011: 42), face “can be seen

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as a positive social image akin to identity.” Zimman (2018: 178-79) describes
identity creation as an intersubjective construction together with otherinteractants, which bears strong similarities to Goffman’s (1967) understandingof face. Other stances see a clear distinction between these concepts in that faceis bestowed upon ego by society post-factum, whereas identity is also bestowed
upon ego by ego (Spencer-Oatey 2007). Arundale (2006: 202) echoes this view,and further understands identity as a phenomenon much broader than face. In ourview, identity then requires a knowledge of the self as an individual, and as theindividual having some control over how self is presented to others. Some aspectsof this phenomenon are likely shared with a small subset of animals, e.g. thosethat can recognise themselves in mirrors, such as cetaceans, great apes, magpies,
crows, and Asian elephants (De Waal 2019). Some animals, such as chimpanzees,
might even have a more developed concept of self extending across time andspace, as indicated in their ability to recognise themselves in delayed video
footage (Hirata et al. 2017).However, the concept closest to face that is mostcentral for our understanding of primate interactions seems to be that ofreputation. Reputation can be described as “a universal currency for human social
interactions” (Milinski 2016), and signals the likely behaviour of an individual(see also Mühlenbernd et al. 2021). Thus, there is quite a strong overlap with
Goffman’s (1967: 5) ‘line’ that others assume we take in a given interaction. As
the concept of reputation is used both in pragmatics and evolutionary studies, it
seems an ideal concept for interdisciplinary research
4. The evolutionary benefits of im/politeness and faceIn our view, the aspects of politeness most relevant to early humans are that ofcooperation and avoiding conflicts. Behaving in accordance with the group face,and avoiding conflicts secures the group’s cohesion, and as such, can be beneficial
to both the group’s survival and, more importantly, the survival of individual
members within the group. This can be strengthened with emerging conventionsof how to do things (with words), i.e. shared sets of pragmatic norms which, iffollowed, demonstrate the individual’s belonging to a group. In this way,
individuals who adhere to these emerging conventions within such groups accrue
benefits to their own evolutionary fitness. We can observe similar behaviours in
non-human apes. For example, research has shown that baboons, who live ingroups up to 100 individuals, show an awareness for social norms and power
structures (Bergman et al. 2003).
However, there are also evolutionary benefits to impoliteness, as it can also be
used as a method of in-group management (Kienpointner 1997). As such,

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impoliteness secures group norms by excluding those individuals who areunwilling or unable to follow them. This generates a group of like-minded
individuals who share the same conventions, which, as stated above, may aid insurvival. It further enhances group stability in generating an ‘us’ vs ‘them’mentality, which may aid in e.g. resource distribution. Just as for politeness, anindividual who successfully manages their belonging to and status within a stablein-group will enhance their own evolutionary fitness.
5. Aspects of face shared with other primates
Many non-human animals also exhibit complex social cognition. This holds
especially for non-human primates. Chimpanzees, for example, understandpsychological states like seeing, knowledge and ignorance, know that others makeinferences, and understand others’ perceptions and intentions (e.g., Bettle &Rosati 2020). These capacities have the potential to shed light on the evolutionary
origins of the human interactive concept of face and the question of which aspectsof face are shared with other primates.
As mentioned in Section 3, one aspect of face that is also crucial to primate andmany non-human animal interactions is that of reputation. From a comparative
psychological perspective, reputation can be defined as “knowledge of anindividual’s typical behaviour in a situation based on their past behaviour”(Russel et al. 2008). Acquiring knowledge about an individual’s reputation is
known as reputation formation (Herrmann et al. 2013). There are three principal
ways of reputation formation (Smith & Harper 2003): through direct experience,
through observation of other individuals interacting with each other, and throughgossip, that is, receiving third-party information about somebody’s behaviour. In
humans, all three sources contribute to reputation formation. Other animals,
however, seem not to make use of communicative signals that inform others of
the reputation of a third party. However, for some highly social non-human animal
species, reputation formation might not only be based on direct interactions, butalso on observations of others’ interactions. For example, Subiaul et al. (2008)
found that chimpanzees preferred to interact with individuals that they observed
to be ‘generous’ to others versus those that they observed to be ‘selfish’.
Similarly, Russel et al. (2008) found that chimpanzees preferred individuals theyobserved to exhibit ‘nice’ behaviour to those that exhibited ‘nasty’ behaviour.
However, they found no such preference for gorillas, bonobos, and orangutans.
Herrmann et al. (2013) found that both chimpanzees and orangutans preferred‘nice’ to ‘mean’ individuals, with bonobos showing no such preference. In all
three experimental designs, ‘generous’/’nice’ vs ‘selfish’/’nasty’/’mean’ wasoperationalised as whether an individual would share food with somebody else

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who begged for food. This research therefore indicates that in addition to direct
reputation formation through interactions with conspecifics, indirect reputation
judgement is present to at least some degree in some of the other great apes.
However, the potential mechanisms involved in this process are still being
debated. Earley (2010) describes this process as ‘social eavesdropping’, where
bystanders attend to, and use, information emitted by signallers; Russel et al.
(2008) refer to a more specific process of ‘image scoring’, where person A
monitors the giving behaviour of person B towards person C. Finally, Abdai &Miklòsi (2016) speak of a general process of social evaluation, which theydescribe as “a mental process that leverages the preference toward prosocial
partners (positivity bias) against the avoidance of antisocial individuals(negativity bias) in a cooperative context.” Given that social evaluation has also
been observed in dogs and cleaner-client reef fish interactions (Abdai & Miklòsi
2016), it is possible that a general prosocial vs antisocial bias provides an
evolutionary ancient foundation for the evolution of the human concept of face.
There is, however, one aspect related to face and reputation that seems not to bepresent in other primates to the same degree, namely that of reputation
management. Humans seem to be uniquely aware and concerned with how others
see them, and how their behaviour is evaluated collectively (Tomasello 2019).
For example, Engelmann et al. (2012) showed that 5-year-old children steal
significantly less and help significantly more in a scenario where they areobserved by others, as opposed to a scenario where they were unobserved. The
same was not found for chimpanzees, where there was no significant difference
in stealing behaviour depending on whether they were observed or not. Actively
trying to manage one’s reputation therefore seems to be a crucial aspect of humanface.
6. Conclusion
In this paper, we have addressed some important aspects of the evolution of
im/politeness and face that have so far not received enough attention in pragmatic
accounts of the evolution of language and human communication. Bothim/politeness and face have evolutionary benefits, e.g. for group and resource
management. Both humans and non-humans have complex sociocognitive
capacities, but humans seem to be especially good at cultural learning, including
im/politeness strategies, and group-mindedness.
Many components of face seem to be shared with other highly social species,
especially other primates. As such, mapping animal behaviours on aspects of facecan help develop an evolutionary perspective on its emergence in humans (cf. Lim& Bowers 1991). However, the explicit management and negotiation of aspects

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of face seems to be uniquely expressed in human interaction, and especially sothrough the use of language.
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and indirect reputation formation in nonhuman great apes (Pan paniscus, Pantroglodytes, Gorilla gorilla, Pongo pygmaeus) and human children (Homosapiens). Journal of Comparative Psychology, 127(1), 63.
Hirata, S., Fuwa, K., & Myowa, M. (2017). Chimpanzees recognize their own
delayed self-image. Royal Society Open Science, 4(8), 1-9.
Kienpointner, M. (1997). Varieties of rudeness: Types and functions of impoliteutterances. Functions of Language 4, (2), 251–87.
Leech, G.N. 1983. Principles of pragmatics. London: Longman.
Lim, T.-S., and Bowers, J.W. (1991). Facework: solidarity, approbation, and tact.
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Milinski, M. (2016). Reputation, a universal currency for human social
interactions. Philosophical Transactions of the Royal Society B: Biological
Sciences, 371(1687), 20150100. https://doi.org/10.1098/rstb.2015.0100
Mühlenbernd, R., Wacewicz, S., & Żywiczyński, P. (2021). Politeness andreputation in cultural evolution. Linguistics and Philosophy, 44(6), 11811213.
Nwoye, O.G. (1992). Linguistic politeness and socio-cultural variations of thenotion of face. Journal of Pragmatics, 18/4, 309–328.
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Pleyer, M., & Pleyer, M. (2016). The Evolution of Im/politeness. In S. G. Roberts,
C. Cuskley, L. McCrohon, L. Barceló-Coblijn, O. Feher, & T. Verhoef (Eds.),
The Evolution of Language. Proceedings of the 11th International Conference
on the Evolution of Language (EVOLANG 11).
Russell, Y. I., Call, J., & Dunbar, R. I. (2008). Image scoring in great apes.
Behavioural Processes, 78(1), 108-111.
Scott-Phillips, T. C. (2017). Pragmatics and the aims of language evolution.
Psychonomic Bulletin & Review, 24(1), 186-189.
Sifianou, M. (2011). On the concept of face and politeness. In F. BargielaChiappini & D. Z. Kádár (Eds.), Politeness across cultures (pp. 42-58).
Basingstoke: Palgrave Macmillan.
Smith, J. M., & Harper, D. (2003). Animal signals. Oxford University Press.
Spencer-Oatey, H. (2008). Face, impoliteness and rapport. In Spencer-Oatey,
Helen (Ed.), Culturally speaking: Managing rapport through talk acrosscultures (pp. 11-47). 2nd edn. London: Continuum.
Spencer-Oatey, H. (2007). Identity, face and (im)politeness. Journal of
Pragmatics, 39(4), 635-638.
Subiaul, F., Vonk, J., Okamoto-Barth, S., & Barth, J. (2008). Do chimpanzees
learn reputation by observation? Evidence from direct and indirect experience
with generous and selfish strangers. Animal cognition, 11(4), 611-623.
Tomasello, M. (2019). Becoming human. Harvard University Press.
Watts. R.J. (2003). Politeness. Cambridge: Cambridge University Press.
Zimman. L. (2018). Pronouns and possibilities. Transgender language activism
and reform. In N. Avineri, L.R. Graham, E.J. Johnson, R.C. Riner and J. Rosa
(Eds.), Language and social justice in practice (pp. 176-183). New York;
London: Routledge.

598

THE ‘MUSICAL’ ORIGIN OF LANGUAGE GRAMMAR AS A
MILESTONE FOR THE DEVELOPMENT OF A COMPLEXCONCEPTUAL MINDPIOTR PODLIPNIAK1*

Corresponding Author: podlip@amu.edu.pl
Department of Musicology, Adam Mickiewicz University in Poznań, Poland

1

1. Abstract
Speech is composed of two elements: 1) affective prosody (Brown, 2017) and 2)
articulate vocalizations which are claimed to be processed by two different
neurological pathways (Ackermann et al., 2014). At the psychological level,affective prosody is based on continuous changes of parameters such as pitch
and loudness, whereas articulate vocalizations consist of perceptually discrete
entities such as phonemes and words that enable combinatoriality (Hilliard &White, 2009). While articulate vocalizations transmit meaning by means of theexchange of mental concepts, affective prosody acts as a source of emotional
induction. Moreover, compared to articulate vocalizations, affective prosody isless susceptible to volitional control (Ackermann et al., 2014). As language
grammar seems to be a framework for the exchange of complex conceptual
meanings, its origin is usually claimed to be related to articulate vocalizations
and the conceptual mind (Ginsburg & Jablonka, 2019). However, the crucial
features of language grammar – hierarchy and recursion – are also present inmusic (Pinker & Jackendoff, 2005; Rohrmeier et al., 2015) which is devoid ofpropositional semantics (Lerdahl, 2013), and which shares many traits withaffective prosody. The main aim of this theory-based proposal is to present a
scenario in which language grammar might have originated from primary ‘felt’sound relations used to induce emotions, rather than from articulate symbols.
From this perspective, the mechanisms that enabled proto-musical forms ofrhythm and pitch hierarchies could have been exapted to serve a hierarchicalframework for sound (and gestural) symbols in order to enable the exchange ofThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

599

information about increasing social complexity. This exaptation could have led
to the emergence of language grammar and a complex conceptual mind.
In order to trace the sequence of events that led to the evolution of language
grammar, a comparison between speech and music is proposed. For instance, thepresence of the elements of affective prosody in many mammalian
vocalizations, as well as in all forms of human vocal expressions (Ross et al.,2009), can lead us to assume that speech prosody consists of features thatevolved long before the emergence of language. In addition, propositional
meaning in the form of referential concepts seems to be much older than Homosapiens. These concepts could have probably been externalized by means ofboth sounds and gestures long before the appearance of hominins. After all, thesimple forms of such externalizations are still observed in apes (Kalan et al.,2015).
It has been proposed that language could have evolved as a result of a merger
between two functionally different communicative protolanguages: one
designed to express external meaning and another designed to express internal
meaning (Podlipniak, 2022). If this scenario is true, the human conceptual mind
owes its complexity to the evolution of both of these communicative systems.
Although prosody is usually perceived in a continuous way it can also be
experienced, under some conditions, as being composed of hierarchicallyorganized discrete pitch and rhythm units (Deutsch et al., 2008), which suggeststhat hierarchization is achievable without semantics. Moreover, as simple
temporal hierarchies have been observed in infant-directed speech and song
(Falk & Kello, 2017), as well as in non-human vocalizations (Ravignani et al.,2019) which are devoid of propositional meaning, it is reasonable to assume that
hierarchical communication has its roots in the affective prosody. This view is
supported by the fact that in the least grammatically complex natural languages
the grammatical relations depend mainly on prosody (Gil, 2005; Jackendoff &Wittenberg, 2014; Sandler, 2017). From this perspective, the appearance of acomplex semantic compositionality was possible thanks to the exploitation of
the previously existing mechanisms that enabled hierarchical ordering ofaffective sounds in the domain of symbolic articulate vocalizations. In otherwords, the emergence of language grammar could have been the result of theexaptation of cognitive mechanisms used in preconceptual proto-musicalexpressions into the creation and externalization of hierarchical relations
between concepts and words.

600

References
Ackermann, H., Hage, S. R., Ziegler, W. (2014). Brain mechanisms of acoustic
communication in humans and nonhuman primates: An evolutionary
perspective. Behavioral and Brain Sciences, 37(06), 529–546.
Brown, S. (2017). A Joint Prosodic Origin of Language and Music. Frontiers in
Psychology, 8, 1894.
Deutsch, D., Lapidis, R., Henthorn, T. (2008). The speech-to-song illusion. The
Journal of the Acoustical Society of America, 124(4), 2471.
Falk, S., Kello, C. T. (2017). Hierarchical organization in the temporal structureof infant-direct speech and song. Cognition, 163, 80–86.
Gil, D. (2005). Word Order Without Syntactic Categories: How Riau Indonesian
Does It. In A. Carnie, S. A. Dooley, H. Harley (Eds.), Verb first: on thesyntax of verb-initial languages (pp. 243–263).
Ginsburg, S., Jablonka, E. (2019). The Evolution of the Sensitive Soul - Learningand the Origins of Consciousness. The MIT Press.
Hilliard, A., White, S. A. (2009). Possible Precursors of Syntactic Components
in Other Species. In D. Bickerton, E. Szathmáry (Eds.), BiologicalFoundations and Origin of Syntax (pp. 161–183). The MIT Press.
Jackendoff, R., Wittenberg, E. (2014). What you can say without syntax: ahierarchy of grammatical complexity. In F. J. Newmeyer L. B. Preston(Eds.), Measuring Grammatical Complexity (pp. 65–82).
Kalan, A. K., Mundry, R., Boesch, C. (2015). Wild chimpanzees modify foodcall structure with respect to tree size for a particular fruit species. Animal
Behaviour, 101, 1–9.
Lerdahl, F. (2013). Musical syntax and its relation to linguistic syntax. In M. A.
Arbib (Ed.), Language, Music, and the Brain (pp. 257–272). MIT Press.
Pinker, S., & Jackendoff, R. (2005). The faculty of language: What’s special
about it? Cognition, 95(2), 201–236.
Podlipniak, P. (2022). Pitch syntax as part of an ancient protolanguage. Lingua,271, 103238a. https://doi.org/10.1016/J.LINGUA.2021.103238
Ravignani, A., Dalla Bella, S., Falk, S., Kello, C. T., Noriega, F., Kotz, S. A.
(2019). Rhythm in speech and animal vocalizations: a cross‐species
perspective. Annals of the New York Academy of Sciences, nyas.14166.
Rohrmeier, M., Zuidema, W., Wiggins, G. A., & Scharff, C. (2015). Principles
of structure building in music, language and animal song. PhilosophicalTransactions of the Royal Society of London. Series B, Biological
Sciences, 370(1664), 20140097.
Ross, M. D., Owren, M., Zimmermann, E. (2009). Reconstructing the Evolutionof Laughter in Great Apes and Humans. Current Biology, 19, 1106–11.
Sandler, W. (2017). What comes first in language emergence? In N. J. Enfield(Ed.), Dependencies in language: on the causal ontology of linguistic
systems (pp. 63–83).

601

EVOLANG DATABASE: A COMPREHENSIVE SOURCE OF
INFORMATION ON THE SCIENCE OF LANGUAGEEVOLUTION COMMUNITY
ALEKSANDRA PONIEWIERSKA *1, ALEKSANDRA SZCZEPAŃSKA 1, YEN YING
NG1, MARTA SIBIERSKA 1, MAREK PLACIŃSKI1, PRZEMYSŁAW
ŻYWICZYŃSKI1 and SŁAWOMIR WACEWICZ1*

1

Corresponding Author: maestro.aleksandra@gmail.com
Center for Language Evolution Studies, Nicolaus Copernicus University in Toruń,Toruń, Poland

1. Introduction
In recent years, language evolution has further consolidated its identity as a
distinct field of research (see e.g. Dediu & de Boer 2016; Kirby 2017; cf. the“Science of Language Evolution”, Żywiczyński 2018) and has become theobject of interest of metascientific analyses (see e.g. Bergmann and Dale 2016;
Roberts et al. 2020). To contribute to this trend, we created a comprehensive
database of information on the Evolang conference series (available at
http://sle.cles.umk.pl/), as representative of the language evolution academic
community. In what follows, we discuss the applicability of this database forscientometric research and present a case study of the academic profiles of thoseresearchers involved in Evolang who self-identify with interests and disciplines
most commonly associated with language evolution.
2. Data collection
The database contains information on Evolang abstracts (n = 1085) and Evolang
contributors (n = 1401) over the period of 9 most recent iterations of theconference (2004-2020). The database was created in four steps: (1) A list ofcontributors was compiled based on automatic text mining of the Evolang booksof abstracts. (2) Basic bibliographic information was collected from theproceedings volumes through automatic data collection, which allowed us toThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

602

identify the author profiles in Google Scholar (GS). Despite certain drawbacks,
most importantly incomplete coverage of author profiles, GS has already been
acknowledged as a useful tool in scientometric analyses (e.g. Gusenbauer 2019),
and it proves particularly informative in studying self-reported data, such as
one’s discipline or research interests (see below). (3) Each author’s profile was
supplemented with data from GS (automatic data collection). The data collected
includes self-reported affiliation and research interests, the year of the author’s
first publication shown in GS, as well as scientometric information such as
h-index, i10-index, and citation count. (4) All the data collected was verified bythree independent experts (manual data verification).
3. Database design
The database is organized into four tables: (1) “authors”, including all Evolang
contributors, (2) “publications”, including bibliometric information on all
Evolang abstracts, (3) “references”, including full references used in the
abstracts, and (4) “texts”, including all the abstracts themselves. Tables (1), (2),and (3) are available in the user interface view of the database (see:http://sle.cles.umk.pl/). The data is fully interoperable and reusable, and in the
future extended implementations it will be supplemented with additional
information, such as on the geographical distribution of the authors.
authors
field

publications
sourcefield

sourceaffiliationGS

author(s): first andlast names,
affiliations

B

countryGS

excerpt

B

ORCID

M

references: allreferences used in the
abstractB

disciplineGS

citations: all citations
used in the abstractB

first Evolang
contribution

Btype of document:
short/long abstract

B

603

number of Evolang
contributions

B

type of presentation:
regular talk/plenaryB

first scientificpublicationGSbibliographic
informationB

h-index (5-year andoverall)GS

i10 index (5-year andoverall)GS

citations

GS

Table 1. A summary of the fields available in tables (1) and (2) of the database, and the relevant
sources of data, where M stands for manual, GS stands for Google Scholar, and B for books ofabstracts. Table (3) corresponds to the “publications/references” field; table (4) is partly available
through “excerpts”.

4. Analyses
To exemplify the type of questions that can be addressed with the current
implementation of the database, we present an analysis of the self-identificationof the Evolang contributors with a number of labels most often used to denotelanguage evolution as a field of research. 642 authors in our database had
identifiable GS profiles, with a total of 916 tokens of associated discipline/fieldlabels (self-reported in GS as “Areas of interest”). Here, we looked into fivelabels: “language evolution”, “evolutionary linguistics”, “evolution of
language”, “language origin(s)” / “origin of language”, and “biolinguistics” (seeTable 2 below). This analysis helps us explore the subtle connotational
differences between those five labels that are taken to be near-synonymous oroverlapping. Of those five, “language evolution” is by far the most frequently
used label by Evolang contributors, whereas “evolutionary linguistics” appears
to be preferentially used by more senior and more accomplished researchers,oriented towards more computational research, and more strongly involved in
the Evolang conference (of the total of four existing Google Scholar profiles
with this label, three belonged to Evolang contributors). Perhaps surprisingly,
only three Evolang contributors report “biolinguistics” as their area of researchin their GS profile.

604

languageevolution

evolutionary
linguistics

evolutionoflanguage

languageorigin(s),origin oflanguagebiolinguistics

1. Number ofauthors

69

15

21

4

3

2. Proportion
Evolang to GS

.570

.750

.636

.333

0.125

3. Other mostcommon labels

culturalevolution,
cognitive
science,linguistics

computational
linguistics,
cognitive
sciencecognitive
linguistics,
comparativecognition

languageevolution,
linguistics

NA*

4. Per cent longpapers

17.85

31.67

23.15

30

33.33

5. Seniority (years
since first GS
publication)

15.94

26.27

18.63

16

16.33

6. Mean citationcount

1494.664825.42436.74

464

899

Table 2. A summary of the analysis of selected Google Scholar labels (self-declared “areas ofinterest”) of Evolang contributors. Row 1 states the total number of Evolang authors for a given GSlabel. Row 2 states the share of Evolang authors among all GS profiles for that label. Row 3 states
the other GS labels most commonly declared by Evolang authors with a given label. Row 4 states the
proportion of papers (6-8 pages) to abstracts (2 pages) submitted by Evolang authors; we assumed
the submission type to reflect the distinction between review / theoretical papers (typically longer)
and reports of empirical results (typically shorter). Row 5 reports author seniority, operationalised as
the time of active publishing, counting from the first publication listed on GS. Row 6 reports GS
citation count.

5. Conclusions
As the field of language evolution research has rapidly grown and now
constitutes to develop as a broad and highly interdisciplinary field, researchers
have expressed the need to seek new methods to systematize and explore thescientific production in this field (Bergmann & Dale, 2016; Roberts et al. 2020).
We believe that the database reported in this contribution provides a valuable605

tool and resource to this end, with a range of theoretical and practicalapplications. In future work, we plan enriching the database with informationfrom complementary resources, which would further extend the range ofpossible analyses. One example is the inclusion of GS citation data on individual
publications, which will make it possible to identify the classic references (i.e.
the publications most often – and most interdisciplinarily widely – cited in the
Evolang proceedings), and address questions on the nature of the contribution of
the different disciplines, research centers and author networks and its dynamics
across the successive iterations of the Evolang conference. Another interesting
direction is integrating our database with thematically related databases – inparticular, the CHIELD database (Roberts et al. 2020), which contains entries
for a large number of Evolang articles, with manually coded information such as
on the methods used in the reported studies (e.g. “experiment”, “observation”)
and the stages of language emergence to which they refer (e.g. “biologicalevolution”, “language change”).
Acknowledgements
This research was supported by the Polish National Science Centre under grant
agreement UMO-2019/34/E/HS2/00248.
References
Bergmann, T., and Dale, R. (2016). A Scientometric Analysis Of Evolang:Intersections and Authorships. In S. G. Roberts et al. (eds) The Evolution of
Language: Proceedings of the 11th International Conference (EVOLANG11).
New Orleans: Evolang Scientific Committee. Availa.
Dediu, D., & Boer, B.G. (2016). Language evolution needs its own journal.
Journal of Language Evolution, 1, 1-6.
Gusenbauer, M. (2019). Google Scholar to overshadow them all? Comparing thesizes of 12 academic search engines and bibliographic databases.
Scientometrics, 118(1), 177-214.
Kirby, S. 2017. Culture and biology in the origins of linguistic structure.
Psychonomic Bulletin & Review 24(1), 118-137.
Roberts, S. et al. (2020). CHIELD: The Causal Hypotheses in Evolutionary
Linguistics Database, Journal of Language Evolution, 5(2), 101-120, DOI:
10.1093/jole/lzaa001.
Żywiczyński, P. (2018). Language Origins: From mythology to science. Berlin:Peter Lang.

606

ELEPHANTS AS A NEW ANIMAL MODEL FOR STUDYING
THE EVOLUTION OF LANGUAGE AS A RESULT OF SELFDOMESTICATIONLIMOR RAVIV*1,2,3, SARAH L. JACOBSON4,5, JOSHUA M. PLOTNIK4,5, JACOB
BOWMAN6, VINCENT LYNCH6 AND ANTONIO BENÍTEZ-BURRACO7*Corresponding Author: limor.raviv@mail.huji.ac.il
1 LEADS group, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
2 cSCAN, University of Glasgow, UK

3 Artificial Intelligence Lab, Vrije Universiteit Brussels, Belgium
4 The Graduate Center, City University of New York, USA5

Hunter College, City University of New York, USA

6 Department of Biological Sciences, University at Buffalo SUNY, USA

7 University of Seville, Spain

1. Self-domestication and the evolution of language
Humans are unique in their sophisticated culture and societal structures, theircomplex languages, and their extensive tool use. But why did humans, and noother animal, develop these sets of complex traits? According to a prominent
theory which is gaining more and more attraction, namely, the Human SelfDomestication hypothesis (HSD), this unique set of traits may be the result of anevolutionary process of self-induced domestication, in which humans have
evolved to be more cooperative and prosocial as a result of environmentalpressures in the middle and late Paleolithic (Hare, 2017; Benítez-Burraco, Clay
& Kempe, 2020). According to the HSD hypothesis, self-domestication has
resulted in less aggressive individuals who were more prone to interact with others(e.g., with kins and in-group members, but also with strangers), leading toincreased social contacts and community structure, as well as more sophisticatedforms of explicit teaching, learning, and exploration. Crucially, these properties
may have ultimately given rise to the specific niche that enabled many of humans’distinctive traits, including our sophisticated linguistic abilities. The potential
explanatory link between self-domestication and language evolution is gaining
momentum in the field, with HDS invoked to explain many of the biological andcognitive changes that underlie the process of the cultural evolution of language(Thomas & Kirby, 2018; Progovac & Benítez-Burraco, 2019), as well as many
critical features of modern languages such as pragmatics and turn-taking(Benítez-Burraco, Ferretti & Progovac, 2021), child-directed speech (BenítezBurraco & Kempe, 2018), grammar sophistication and innovation (Langley,
Benítez-Burraco & Kempe, 2019), , and cross-linguistic variability (BenítezBurraco, 2020; Benítez-Burraco & Progovac, 2020).
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

607

While HSD is a compelling theory, it is hard to test. Most notably, the only otherspecies that has been argued to be self-domesticated besides humans is Bonobos(Hare et al, 2012; Hare 2017; Kovalskas et al., 2021), resulting in a very narrow
scope for investigating this theory beyond the primate family.
2. Are Elephants also self-domesticated?
Our work poses the novel question of whether Elephants may have also undergone
a process of self-domestication, similar to humans and bonbons. If true, Elephants
may serve as a new animal model for studying the potential impact and triggers
of a self-domestication, and specifically the cultural evolution of language. Since
the most recent common ancestor of humans and Elephants is likely the mostrecent common ancestor of all placental mammals, comparing the process of selfdomestication and its outcomes in these species can lead to important insights
about convergent evolution beyond the primate taxa, and open the door for
exciting new research and better understanding of this process and its implications
to communicative complexity in the broad sense.
We first motivate our hypothesis of Elephants as a self-domesticated species withan extensive cross-species comparison of relevant cognitive, behavioral, andphysiological similarities between Elephants and the two other species that havebeen put forth as self-domesticated (i.e., humans and bonobos). This cross-species
comparison demonstrates that Elephants indeed exhibit many of the featuresassociated with self-domestication, including reduced aggression, increased
prosocial behavior, extended juvenile period, increased play behavior, sociallyregulated cortisol levels, and complex vocal behavior. With respect to linguistic
abilities, Elephants rely on a rich multimodal communication system that includes
an extensive repertoire of vocal, visual, tactile and chemical signals to mediate
the intricate teamwork displayed by members of an Elephant family (Jacobson &Plotnik, 2020). Their vocal signals have been shown to encompass specificmeanings and intents shared with other members of the herd (e.g., Kahl &Armstrong, 2000; Poole & Granli, 2003; Wierucka, Henley & Mumby, 2021),
and it shows a degree of intra-specific variation within and between individuals
and across different herds - which is seen as an important feature of humanlanguage (e.g., Evans & Levinson, 2009). Next, we present novel genetic
evidence to support our proposal and expose the positive selection of candidate
genes for domestication in African Elephants. These novel analyses provide
empirical evidence for our hypothesis: they show that genes positively selected in
African Elephants are enriched in pathways that are involved in domestication,and that several candidate genes associated with domestication have been
positively selected in African Elephants. We also discuss several explanations forwhat may have triggered a self-domestication process in the Elephant lineage, anddiscuss the potential implications of our theory to the field of language evolution.

608

References
Benítez-Burraco, A. (2020). The Self-Domestication Hypothesis of Language
Evolution. Paradigmi [Preprint].
Benítez-Burraco, A., Clay, Z. & Kempe, V. (2020). Self-Domestication and
Human Evolution. Frontiers in Psychology, 11.
Benítez-Burraco, A., Ferretti, F. & Progovac, L. (2021) ‘Human SelfDomestication and the Evolution of Pragmatics’, Cognitive Science, 45(6),e12987.
Benítez-Burraco, A. & Kempe, V. (2018). The emergence of modern languages:
Has human self-domestication optimized language transmission?. Frontiers in
Psychology, 9.
Benítez-Burraco, A. & Progovac, L. (2020). A four-stage model for language
evolution under the effects of human self-domestication. Language &Communication, 73, 1–17.
Evans, N. & Levinson, S.C. (2009). The myth of language universals: Language
diversity and its importance for cognitive science. Behavioral and Brain
Sciences, 32(5), 429–448.
Hare, B. (2017). Survival of the Friendliest: Homo sapiens Evolved via Selection
for Prosociality. Annual Review of Psychology, 68(1), 155–186.
Hare, B., Wobber, V. & Wrangham, R. (2012). The self-domestication
hypothesis: evolution of bonobo psychology is due to selection against
aggression. Animal Behaviour, 83(3), 573–585.
Jacobson, S.L. & Plotnik, J.M. (2020). Elephant Cognition. In Zoo Animal
Learning and Training. John Wiley & Sons, Ltd, 191–196.
Kahl, M.P. & Armstrong, B.D. (2000). Visual and tactile displays in Africanelephants, Loxodonta africana: a progress report. Elephant, 2(4), 19–21.
Kovalaskas, S., Rilling, J.K. & Lindo, J. (2021). Comparative analyses of the Pan
lineage reveal selection on gene pathways associated with diet and sociality inbonobos. Genes, Brain and Behavior, 20(3), e12715.
Langley, M.C., Benítez-Burraco, A. & Kempe, V. (2019). Playing with language,
creating complexity: Has play contributed to the evolution of complex
language?. Evolutionary Anthropology: Issues, News, and Reviews, 29(1).
Poole, J.H. & Granli, P. (2011). Signals, Gestures, and Behavior of African
Elephants. In The Amboseli Elephants: A Long-Term Perspective on a LongLived Mammal. University of Chicago Press, pp. 109–124.
Progovac, L. & Benítez-Burraco, A. (2019). From Physical Aggression to Verbal
Behavior: Language Evolution and Self-Domestication Feedback Loop.
Frontiers in Psychology, 10.
Thomas, J. & Kirby, S. (2018). Self domestication and the evolution of language.
Biology & Philosophy, 33(1).
Wierucka, K., Henley, M.D. & Mumby, H.S. (2021). Acoustic cues toindividuality in wild male adult African savannah elephants (Loxodonta
africana). PeerJ, 9, e10736.

609

THE POWER OF IDEAS FROM INFORMATION THEORY FORSTUDYING ANIMALS’ NATURAL COMMUNICATIONZHANNA REZNIKOVA *1,2*

zhanna@reznikova.net
Institute of Systematics and Ecology of Animals, Novosibirsk, Russia
2
V. Zelman Institute for Medicine and Psychology, Novosibirsk State
University, Russia

1

Evolutionary linguistics has emerged as an attempt to answer questions
concerning language evolution and human nature based on an interdisciplinary
collaboration (Bickerton, 1990; Tomasello, 2008; Gong et al., 2014). The
fundamental question about the uniqueness of human language is still far frombeing understood (Wacewicz & Żywiczyński, 2015). One of the possible paths
forward suggested by Hauser et al. (2014) includes observations andexperiments of naturally communicating animals and experiments assessing
animals' computational and perceptual capacities, focusing on abilities necessary
for human language processing. Here I compare three main experimental
paradigms in studying animal language behavior and highlight the new
dimension in this field provided by information theory.
The first approach is aimed at direct decoding the function and meaning ofanimal signals, which is a notoriously tricky problem. Two types of natural
messages decoded up to the present concern the symbolic honeybee “dance
language” (for a review, see Kohl et al., 2020) and fragments of acoustic
communications in several species such as monkeys (review in Fischer, 2020)and dolphins (King & Janik, 2013). Girard-Buttoz et al. (2022) recently revealed
a highly versatile vocal system in chimpanzees; however, larger datasets are stillneeded to prove animals’ capacity to produce flexible vocal sequences that
support numerous differentiated meanings. The second approach based on
artificial intermediary languages uncovered significant “linguistic” andcognitive potential in some species that contradicts limitations in understanding
their natural communications. The third approach applies ideas of Shannon
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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entropy and Kolmogorov complexity. The main point is to study naturalcommunications and evaluate their capabilities by measuring informationtransmission rates (details in: Ryabko & Reznikova, 2009; Reznikova & Ryabko,2011). Without studying the nature of the signals used for communication, thisapproach provides a new dimension for understanding the essentials of animalcommunication systems.
Ants appeared to be even better candidates for studying general communication
rules than the iconic honeybee. Among about 15 000 ant species, the majority ofwhich display relatively simple modes of communication, a few Formica rufa
species possess the leader-scouting system based on a consistent personal
difference between scouting and foraging individuals (Reznikova, 2021). The
idea of experiments is that the experimenters know the quantity of information(in bits; Shannon, 1948) to be transferred, which corresponds to the number ofturns in a “binary tree” maze towards a “leaf” containing syrup. There were nocues that could help the ants find the food (including olfactory ones) except the
information contacts with scouts. The duration of the contacts between thescouts and foragers appeared to be ai + b, where i is the number of turns, a is thetime duration required for transmitting one bit of information, and b is an
introduced constant. The rate of information transmission was about 1 minuteper bit in ants, which is at least 10 times smaller than in humans. Ants appeared
to be able to grasp regularities in the “text” (sequence of turns, see below) to betransferred and use them to “compress” and thus optimize their messages. This
series of experiments was inspired by the concept of Kolmogorov complexity(Kolmogorov, 1965) applied to words (or “texts”) composed of the letters of analphabet, for example, consisting of two letters: L (left) and R (right)
corresponding to a sequence of turns in a “binary tree”. Informally, theKolmogorov complexity of a word (and its uncertainty) equates to its mostconcise description. For example, the word “LRLRLRLR” can be represented as“4LR”, while the “random” word of shorter length “LRRLRL” probably cannot
be expressed more concisely, and this is more complex. The hypothesis being
tested was H0, that is, the time for transmission of information by the scout does
not depend on the complexity of the “text”. The alternative hypothesis was H1
that this time actually depends on the complexity of the “text”. The hypothesis
H0 was rejected (P = 0.01), thus showing that the more time ants spent on theinformation transmission, the more complex – in the sense of Kolmogorov
complexity – was the message. This surprisingly resembles “learning withchunking and generalization” during the foraging process in structured
environments suggested by Kolodny et al. (2015). In sum, ideas frominformation theory help to evaluate cognitive and flexible aspects of naturalcommunication systems and thus understand better the evolution of language.

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Acknowledgements
This work was funded by Russian Fund for Basic Research (No. 20-04-00072)
and the Federal Fundamental Scientific Research Program for 2021-2025(122011800268-1).
References
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Chicago Press.
Fischer, J. (2020). Nonhuman primate alarm calls then and now. Animal
Behavior and Cognition, 7(2), 108-116.
Girard-Buttoz, C., Zaccarella, E., Bortolato, T., Friederici, A. D., Wittig, R. M.,& Crockford, C. (2022). Chimpanzees produce diverse vocal sequenceswith ordered and recombinatorial properties. Communications biology, 5(1),1-15.
Gong, T., Shuai, L., & Comrie, B. (2014). Evolutionary linguistics: theory oflanguage in an interdisciplinary space. Language Sciences, 41, 243-253.
Hauser, M. D., Yang, C., Berwick, R. C., Tattersall, I., Ryan, M. J., Watumull,J., ... & Lewontin, R. C. (2014). The mystery of language evolution.
Frontiers in psychology, 5, 401.
King, S. L., & Janik, V. M. (2013). Bottlenose dolphins can use learned vocal
labels to address each other. Proceedings of the National Academy of
Sciences, 110(32), 13216-1322
Kohl, P. L., Thulasi, N., Rutschmann, B., George, E. A., Steffan-Dewenter, I., &Brockmann, A. (2020). Adaptive evolution of honeybee dance dialects.
Proceedings of the Royal Society B, 287(1922), 20200190.
Kolmogorov, A. N. (1965). Three approaches to the quantitative definition of
information. Problems of information transmission, 1(1), 1-7.
Kolodny, O., Edelman, S., & Lotem, A. (2015). Evolution of protolinguistic
abilities as a by-product of learning to forage in structured environments.
Proceedings of the Royal Society B: Biological Sciences, 282(1811),
20150353.
Reznikova, Z., & Ryabko, B. (2011). Numerical competence in animals, with an
insight from ants. Behaviour, 148(4), 405-434.
Reznikova, Z. (2021). Ants’ Personality and Its Dependence on Foraging Styles:
Research Perspectives. Frontiers in Ecology and Evolution, 9, 269.
Ryabko, B., Reznikova, Z. (2009). The use of ideas of information theory forstudying “language” and intelligence in ants. Entropy, 11(4), 836-853.
Shannon, C. E. (1948). A mathematical theory of communication. The Bell
System Technical Journal, 27(3), 379-423.
Tomasello, M. (2008). Origins of Human Communication. Cambridge, MA:
MIT Press.

612

Wacewicz, S., & Żywiczyński, P. (2015). Language evolution: Why Hockett’s
design features are a non-starter. Biosemiotics, 8(1), 29-46.

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INVESTIGATION OF OCTAVE SIMILARITY IN PITCHPERCEPTION IN RATS : IN SEARCH OF COMMON
AUDITORY PROPERTIES BETWEEN HUMANS AND
ANIMALS
RISERU KOSHIISHI*1 and KAZUO OKANOYA1,2*
Corresponding Author: koshiishi-riseru5414@g.ecc.u-tokyo.ac.jp
Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
2
Teikyo University Advanced Comprehensive Research Organization, Tokyo, Japan
1

Pitch is the psychological representation of tone height. Pitch is a very importantsound attribute that conveys information in speech, music, and animal
vocalizations (Doupe & Kuhl,1999; Rothenberg et al., 2014). Various models
have been proposed for how we perceive pitch, but they have yet to be fully
demonstrated.
It has been found that mental representations of pitch are more likely to
explain various phenomena if they are multidimensional. The spiral structure
model (Shepard, 1964) is based on the theory that there are two aspects of pitch
perception: pitch height, which has a linear relationship with fundamental
frequency, and pitch chroma, which is a repeated sequence of notes. These aspects
are illustrated in the model of a sound spiraling up the outside of a cylinder. In
this model, one revolution of the pitch chroma represents an octave.
When two sounds have a frequency ratio of 1:2, i.e., are in octaves, theperceptual impression of the two sounds is similar. This phenomenon calledoctave similarity. Octave similarity forms the basis of the pitch chroma concept
and can be said to be the foundation for examining pitch perception models.
Octave similarity is known to be a general characteristic that is not limited tomusical experience and has been observed in infants and in rhesus monkeys(Demany & Armand, 1984; Wright et al., 2000). This suggests that octave
similarity is common in a wide range of animal species, not only humans, and thatit may have a biological origin. However, the mechanism and evolutionary
function of octave similarity are still unclear.
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In this study, we used the rat (Rattus norvegicus), which is frequently used
as a model animal for speech communication, to aim to investigate the mechanism
and biological origin of octave similarity in more detail. Although the detectionof octave similarity has been explored in other species other than primates, it has
not yet been fully demonstrated (Deutsch, 1943; Burns, 1999; Cynx,1993). We
conducted behavioral experiments using operant conditioning to elucidate
whether octave similarity can be detected in the auditory system of rats.
The experiment was conducted in an operant apparatus. Five rats were first
rewarded with a sucrose solution to form left and right lever-pressing behavior.
Two groups of octave-related sound stimuli (A: 1250 Hz, 2500 Hz, 5000 Hz; B:
1000 Hz, 2000 Hz, 4000 Hz) were set up, and the rats were trained to discriminate
between left and right lever presses according to the pitch of these sound stimuli.
Finally, the rats were probed with a sound 1-2 octaves away from the soundpresented in the training.
As a result of the discrimination training, all rats achieved the training
criterion and were able to perform the probe test twice. As a result of probe tests,
all rats perceived the probe sounds and showed left or right lever-pressing
responses. However, the results did not show octave similarity, i.e., similarity ofperception of the probe sound to that of the training sound, and the rats showed
different responses depending on the individual and the probe sounds. The
analysis of response latency suggested that compared to the sound stimuli
presented in the training trials, probe sounds were perceived as different, and thistendency was particularly pronounced for probe sounds that were two octaves
apart.
When we examined the difference in response shown by rats, it wassuggested that comparing the relative pitch of the presented sound stimuli may
have caused a deflection of the left/right response. Furthermore, it was consideredthat some rats responded not only to the presented sound stimuli but also toleft/right preference and past response content in lever pressing during the session
as a cue during the probe test. From now on, we will adjust the experimental
method to eliminate response cues other than the presented sounds. In addition,
we plan to conduct a test in which rats are trained to discriminate a part of theprobe sounds and then presented with another new probe sounds, in order tofurther verify the phenomenon of pitch perception.
The progress of this study will allow us to examine the possibility that octave
similarity is a common mechanism in many animal species, and will provide new
insights into auditory research. Furthermore, the possibility that octave
equivalence is related to the specificity of human speech signals can be examined.

615

References
Cynx, J. (1993). Auditory frequency generalization and a failure to find octave
generalization in a songbird, the European starling (Sturnus
vulgaris). Journal of Comparative Psychology, 107(2), 140.
Demany, L., & Armand, F. (1984). The perceptual reality of tone chroma inearly infancy. The Journal of the Acoustical Society of America, 76(1), 5766.
Doupe, A. J., & Kuhl, P. K. (1999). Birdsong and human speech: common
themes and mechanisms. Annual Review of Neuroscience, 22(1), 567-631.
Dowling, W. J., & Harwood, D. L. (1986). Music Cognition. New York:
Academic Press.
Deutsch, D. (1972). Octave generalization and tune recognition. Perception &Psychophysics, 11(6), 411-412.
He, C., & Trainor, L. J. (2009). Finding the pitch of the missing fundamental ininfants. Journal of Neuroscience, 29(24), 7718-8822.
Hoeschele, M. (2017). Animal pitch perception: melodies andharmonies. Comparative Cognition & Behavior Reviews, 12, 5.
Richards, D. G., Wolz, J. P., & Herman, L. M. (1984). Vocal mimicry ofcomputer-generated sounds and vocal labeling of objects by a bottlenosed
dolphin, Tursiops truncatus. Journal of Comparative Psychology, 98(1), 10.
Rothenberg, D., Roeske, T. C., Voss, H. U., Naguib, M., & Tchernichovski, O.
(2014). Investigation of musicality in birdsong. Hearing Research, 308, 7183.
Shepard, R. N. (1964). Circularity in judgments of relative pitch. The Journal of
the Acoustical Society of America, 36(12), 2346-2353.
Titze, I. R., & Palaparthi, A. (2020). Vocal loudness variation with spectral
slope. Journal of Speech, Language, and Hearing Research, 63(1), 74-82.
Tomlinson, R. W., & Schwarz, D. W. (1988). Perception of the missing
fundamental in nonhuman primates. The Journal of the Acoustical Society ofAmerica, 84(2), 560-565.
Wright, A. A., Rivera, J. J., Hulse, S. H., Shyan, M., & Neiworth, J. J. (2000).
Music perception and octave generalization in rhesus monkeys. Journal of
Experimental Psychology: General, 129(3), 291.

616

THE COMMON TASK FRAMEWORK:USING CAUSAL THEORIES FROM LINGUISTICANTHROPOLOGY TO EXPLORE THE EMERGENCE OF
SYMBOLIC COMMUNICATION
SEÁN G. ROBERTS*1, ELIZABETH IRVINE1, FIONA M. JORDAN2*

Corresponding Author: RobertsS55@cardiff.ac.uk
English, Communication and Philosophy, Cardiff University, Cardiff, UK
2
Anthropology and Archaeology, University of Bristol, Bristol, UK

1

We present the Common Task Framework approach to testing causal theories about theevolution of language. There are now many theories about how symbolic communication
emerged, but less work trying to compare, synthesise and test these theories. We suggestthat the first step is to formalize the theories as causal graphs using tools from the field of
causal inference. This helps recognize the critical causal links that differentiate theories.
The second step is to use tools from experimental semiotics to specify a “common task”:
an experimental environment and a task for individuals to complete. The different theories
suggest different design solutions for this common task, and the success of the individuals
can be used as a measure of the relative success of each theory. In this paper, we provide
an example from anthropological theories of the emergence of symbolic communication,
including coding 11 theories as causal graphs using the CHIELD database. We hope thecommon task framework will be applicable to many aspects of language evolution.

1. Introduction
Human language is the most complex communication system on earth, with noother animal coming close to our linguistic abilities. How did this ability evolve,
and why only in humans? While genetic and cognitive differences are obviously
part of the story, perspectives from anthropology and archaeology are making it
increasingly clear that our ancestors also benefitted from critical social, economicand ecological situations. Some species including apes, birds and even insects
have advanced communicative abilities (Berwick et al., 2011; Arnold &Zuberbühler, 2006; von Frisch, 1967) or can acquire them through intense training(e.g. Premack, 1971). However, apparently their natural habitats do not provide
the right selective pressures for these latent abilities to evolve further. Only forhumans did the right factors come together to motivate the evolution of complex
language. Therefore, we argue that there is a critical unanswered question: What

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617

are the social, economic and ecological conditions for the evolution of complex
communication systems? (Roberts, 2018). We suggest that theories fromanthropology and archaeology can help us answer this question.
There are two main challenges. The first is identifying the most prominent
theories. More importantly, how do they agree or conflict with each other? Are
there specific predictions that can be tested in order to evaluate the relativeplausibility of each theory? Addressing this will require a systematic review ofthe literature and a way of formally representing the causal structure of theories.
We suggest that causal inference can help researchers to do this.
The second challenge is how to test these theories against each other empirically.
Since we cannot observe language evolution directly, and naturalistic methods
provide limited flexibility, support for theories must come from a robust
combination of approaches (Irvine, Roberts & Kirby, 2013). We suggest that acommon task framework is needed that combines control from experimental
semiotics and ecological validity from anthropology. This should be flexible
enough to simulate many scenarios while being consistent enough to makecomparisons across theories.
In this paper we outline how causal inference and a common task framework canmeet the two challenges above. We illustrate the approach with examples related
to the emergence of symbolic signals. However, because many questions about
the evolution of language involve complex causal connections from multiple
sources of evidence, and where direct experimentation is impossible, we hope thatthe common task framework will be a useful more generally for the field of
evolutionary linguistics.
2. Formalising theories from anthropology and archaeology
There is now a wealth of theories from anthropology and archaeology about thekinds of tasks that plausibly created a need for complex communication in human
evolutionary history (see e.g. Dor et al., 2014; Power et al., 2016). These include
predation (Dunbar, 2017), hunting (Knight & Lewis, 2017; Sterelny, 2012),
navigation (Bednarik, 1997), tool use (Davidson & Noble, 1989), fire making(Twomey, 2013; Wiesser, 2014), and reproductive strategies (Knight et al., 1995).
While theories abound, there are few attempts to synthesise them andsystematically test them against each other. We argue that causal inference
provides a powerful approach that can express, explore and evaluate theories. One
tool for helping researchers do this is the Causal Hypotheses in Evolutionary
Linguistics Database (CHIELD, Roberts et al., 2020, http://chield.excd.org).
CHIELD is a database of hypotheses which have been hand-coded as causal

618
Bednarik (1997)
Dunbar & Gowlett (2014)
Dunbar (2017)
Isbell & Young (1996)
Knight & Lewis (2017)
Knight, Power & Watts (1995)

Noble & Davidson (1991)
Davidson & Noble (1989)
Sterelny (2012)
Twomey (2013)
Wiessner (2014)

Figure 1: A causal graph of 11 theories of the emergence of symbolic signals.
graphs. This allows users to discover formal links between hypotheses, assess
conflicting evidence and spot weak links that currently have little support.
Using CHIELD, we formalized 11 theories as causal graphs (Fig. 1, seesupporting materials). There were two major findings from this exercise. The firstis that the theories are deeply interconnected. For example, the move from dense
jungle out into open savannah could have changed the availability of resources
and created pressures to change foraging strategies and group dynamics (Isbell &Young, 1996). This could have made larger social networks more feasible, andcreated greater pressures for advanced social communication (Dunbar, 2004).
However, a complex infrastructure that facilitates trust and material support
within a group may have been necessary before symbolic communication was
viable (Knight et al., 1995). These infrastructures themselves may have been
shaped by specific ecological and economic conditions, such as the ability toharness the environment more effectively by crafting tools (Gibson et al., 1994;
Kolodny et al., 2018), collaborative hunting (Sterelny, 2012), or the need tomaintain sources of fire (Twomey, 2013). We also noted some links that, as far
as we know, have not been remarked on in the literature: Knight et al.’s ritual
theory involves beliefs about hunting; hunting animals provides bones that can beused as fuel for fires; fire is used to reduce predation risk.
The second finding is that, despite the interconnectedness, the majority of theories
are not mutually exclusive. There are only two points of formal conflict in thecausal graph. Firstly, Knight et al. do not think that gossip on its own can create
effective alliances, in contrast to Dunbar. Secondly, Noble & Davidson suggestthat symbolic language facilitates hunting, rather than the other way around.
Finally, around half of the causal connections were hypothesized in the source
papers without empirical support. This suggests there is much outstanding work

619

to make these theories robust. In order to accomplish this work, we need apractical testing method, for which Experimental Semiotics is a candidate.
3. Experimental semiotics
Experimental semiotics uses methods from psychology to explore the role ofcognition, acquisition, and usage in shaping language (Galantucci, 2009; Roberts,2017). Studies use lab-based experiments with human participants who must
construct, use, and transmit artificial languages. These methods have been used
to explore the emergence of compositional structure (Kirby et al., 2015). Initial
results suggested that inter-generational transmission was a key causal property,
but recent studies test whether the same pressure can be created by needing tocommunicate with a large population (Raviv, Meyer & Lev-Ari, 2019).
Essentially, the experiments provide a way of comparing design solutions to the“common task” of creating conditions for the emergence of compositionality.
However, in order to obtain a high level of experimental control, theseexperiments tend to use very idealised tasks based on formal communication
games, and impose strict limits on the ways that individuals can interact: one
individual speaks at a time; pointing is only allowed during feedback; orindividuals can’t initiate repair (see Macuch Silva & Roberts, 2016). Irvine &Roberts (2016) noted that several experimental semiotics studies assume that thepressure to communicate derived from the need for individuals to collaborate inorder to manipulate physical objects or build complex constructions. However,
the experimental tasks are so constrained that they remove the possibility of any
other strategies. We argue that a different kind of common task is needed to
explore the origins of symbolic communication.
4. A common task framework for studying symbolic communication
A common task framework approach involves iterating on the solution of apractical task in order to understand how different pressures lead to different
outcomes. For example, in the field of robotics, the DARPA Grand Challenge is
a common task framework where an autonomous robotic vehicle must complete
a task in the real world such as driving across a desert. Different robotic designs
can be evaluated against each other according to how well they complete the task.
While the DARPA Challenge focuses on designing robots, for studying the
emergence of symbolic communication we suggest that the experimenter's task is
to design a situation (including a physical environment and survival task that
individuals must complete) that creates a pressure for individuals to develop a
symbolic communication system. The effectiveness of the situation is tested byplacing agents (with a capacity for symbolic communication, but no existing620

conventions within the challenge context) into the situation and observing if they
do indeed invent a symbolic communication system.
These situations may be somewhat abstract, but they should reflect relevant andplausible analogues of early hominid life. Using the causal graphs as a guide,
different theories are translated into minimally contrasting situations. Thesesituations are then tested (using human participants or simulated agents), and theresults can be contrasted to evaluate the theories against each other. The aim of
the common task framework is not to recreate the exact, true emergence ofsymbolic communication in human ancestors: we cannot know what this situationwas. Instead the procedures are used as grounded thought experiments in order toexplore and refine theories (e.g. Webb, 2000; Steels, 2003).
One candidate framework for studying the emergence of symbolic
communication comes from Irvine & Roberts (2016). This was an experimentwith two human participants in a 3D virtual world, using the video game
Minecraft. The environment was a field outdoors, and the survival task was tobuild a shelter together. Each participant had half of the plan for the shelter,
providing a reason to communicate. Participants were prevented from using
speech during the experiment, but they were given the resources and capacity toconstruct a simple symbolic communication system to help them in the task: they
could use their virtual avatar to point and they could also knock on the table. This
gave them the option of developing a simple symbolic system to refer to the four
different building materials (e.g. one knock for red blocks, two knocks for yellow
blocks etc.). That is, participants were placed in a similar situation to our prelinguistic ancestors: they had the capacity to construct a symbol system, but the
crucial question was whether the task would motivate them to do so. The results
showed that all participants used pointing and trial-and-error strategies tosuccessfully complete the task. Post-experiment interviews confirmed that there
was not enough motivation to invent a symbolic system, and so none evolved.
After all, setting up a symbolic system takes time, and the pointing strategy wasproductive enough to be successful. Irvine & Roberts concluded that the need tocollaborate for construction would not have been a strong enough selective
pressure to motivate the emergence of a symbolic system for our ancestors.
In summary, a pressure to develop a complex communication system relies on aparticular practical situation. It might seem obvious that various theories would
create a pressure for the emergence of symbolic communication. However, by
putting the theory into practice, Irvine & Roberts realized that the opportunity
cost of setting up a symbolic system is high, and participants may find unexpected621

solutions that avoid having to do so. The question now is: what situations would
motivate them to invest time in creating a symbolic system?
5. Candidate situations
Based on the causal review of the literature, we propose that situations which
promote the emergence of symbolic communication will have various key
properties. First, there will be an asymmetry of information between
individuals. That is, some individuals will know facts or skills that the others do
not, creating something to communicate about. A second property is that thereferents to be communicated about will be distant in time or space, making
pointing less effective. These first two properties are found in some advanced
non-human communication systems, such as honey bees needing to communicate
the location of pollen (von Frisch, 1967). Another key property is the need fordivision of labour. This requires coordination and scheduling between
individuals, requiring trust and cooperation. Principles from video game design(Brown, 2018) suggest more key properties, including: More tasks than people
to promote role-switching; dead time to promote multitasking; disruptions
which change task demands to prevent ritualization of strategies; public goods topromote coordination of who does tasks with no direct reward. We predict thatthese key properties will emerge as common elements in successful situation
designs. These will define a set of properties that solve the “meta-task” ofpromoting the emergence of symbolic communication.
There are two candidate situations motivated by anthropological theories that may
meet these key properties. The first involves fire maintenance. Humans began
using fire long before being able to create it (Twomey, 2013), and even today firemaking technology is not universal in human societies (McCauley, Collard &Sandgathe, 2020). Keeping a fire lit and fuelled create strong pressures to organiseand divide labour between individuals. This could provide the right pressure to
start referring to distant locations (sources of fuel) or points in time (agreements
about tending the fire). The situation also involves dead time, disruptions andpublic goods. Manipulations of this situation could include how far away and how
sparsely distributed the fuel sources were. The most successful experimental
conditions could then be compared to plausible scenarios of early hominids.
The second situation is collaborative hunting. Sterelny (2012) suggests that thepotential rewards of hunting big game would provide pressures to cooperate andcommunicate. An asymmetry of information exists between skilled and unskilledhunters. Some hunting strategies involve different roles (e.g. flushing andambushing), and these roles may require individuals to be out of sight of eachother at key moments. This would create a need for division of labour and622

referring to future times and distant places in order to plan the hunt. Conditions
could vary the number of participants or the speed of the animal. Fast animals
might encourage strategies involving stealth or splitting the group into different
roles, requiring communication about locations, time and coordinated action. The
core causal components of these candidate situations can be recreated in acommon task framework. We note that Minecraft has various features that help
with this: fire dynamics; hearths that require a constant supply of fuel; projectile
weapons; and animals that can be ‘hunted’. It also supports computational agents.
6. Discussion
We presented a common task framework approach to investigating the evolutionof symbolic communication. Causal inference tools were used to formally relatetheories and identify critical differences. These were used to design a common
task framework to evaluate those theories against each other. We suggested thattheories related to fire maintenance and collaborative hunting meet some key
properties for the emergence of symbolic communication. Of course, insightsfrom this approach need to be integrated with other findings, including the role ofrepeated actions leading to conventionalisation and differences in cognitive
abilities between our ancestors and modern human participants.
Acknowledgements
Supported by AHRC grant AH/T006927/1. We thank our reviewers.
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624

THE EVOLUTIONARY TRENDS OF NOUN CLASS SYSTEMS IN
ATLANTIC LANGUAGES
Neige Rochant1 , Marc Allassonnière-Tang2 , and Chundra Cathcart*3* Corresponding Author: chundra.cathcart@uzh.ch

1 Sorbonne Nouvelle University/Lacito, CNRS, Paris, France
2 EA, MNHN/ CNRS/ University Paris City, Paris, France

3 Department of Comparative Language Science, University of Zurich, Zurich, Switzerland

Nominal classification systems such as grammatical gender (e.g., the masculine/feminine distinction in French) and noun classes (e.g., Bantu noun classes based on fruits, plants, liquids,
among others) provide a window on how the human brain perceives and categorizes objects andexperiences it encounters. While the diachronic development of grammatical gender systems
is well studied, noun class systems have received less attention. We use phylogenetic comparative methods to analyze where noun classes are marked (on nouns, pronouns, demonstratives,
articles, adjectives, numbers, and verbs) in thirty-six Atlantic languages and how these markers change diachronically. Our results show that noun class marking is generally preferred andmore stable within the noun phrase, i.e., on nouns, demonstratives, and adjectives.

1. Introduction
Languages can rely on various strategies to categorize nouns of the lexicon (Seifart, 2010; Kemmerer, 2017). One the most common strategies is noun classsystems (Corbett, 2007), in which each noun of the lexicon is assigned to a specific category (i.e., noun class), which can relate to humans, plants, fruits, liquids,
among others (Corbett, 2013). For example, in Swahili, nouns are affiliated tomore than ten noun classes.
Two of the most common formal criteria to define noun classes are flexibility of category assignment and grammatical agreement (Corbett, 1991). First,
noun class systems typically have a rigid assignment system. Each noun of thelanguage belongs to one of the noun classes found in the language. This assignment is not flexible and using the agreement pattern of another noun class would
result in ungrammaticality. Second, noun class systems may have marking on
nouns, as shown in Swahili, with prefixes on the nouns, e.g., m-toto (CLASS .1child) ‘child’. However, noun classes also generate grammatical agreement withwords associated to the noun and its referent. The noun classes can be marked
on the adjectives, verbs, demonstratives, numerals, among others. Taking Swahili
again to illustrate this type of agreement: m-toto yu-le a-li-anguka (CLASS .1-child
CLASS .1-that CLASS .1. SUBJ -past-fall) ‘that child fell’. These requirements of asThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

625

signment and agreement distinguishes noun class systems from inflection classes
such as on number marking. Grammatical number systems have agreement; however, number is not a fixed inherent feature of a noun/referent. For example, countnouns can generally be singular or plural depending on the context while the nounclass of a noun is fixed. It is generally held that languages may develop a nominal
classification system that proceeds through different stages of grammaticalization(Grinevald, 2002; Aikhenvald, 2016). At the beginning, classification is basedon lexical nouns, which then develop into classificatory morphemes, which inturn further grammaticalize and become agreement markers. For example, theNiger-Congo noun class systems may have developed from nouns, along with thenominal classification systems found in modern Amazonian languages (Grinevald& Seifart, 2004). At the same time, questions remain regarding their developmentthat are relevant to the diachronic change of linguistic complexity (Walchli, Olsson, & Di Garbo, 2020). For example, were noun classes first marked on nouns?
If so, how did they spread to pronouns or verbs? While several studies have investigated the synchronic distribution of noun class systems in languages of theworld (Corbett, 2013; Allassonnière-Tang et al., 2021), most diachronic quantitative studies have focused on the evolution of agreement marking in Indo-European
languages (Allassonnière-Tang & Dunn, 2020; Carling & Cathcart, 2021). Few
studies have investigated the evolution of agreement marking in languages withnoun class systems, and even fewer studies have approached this question froma quantitative perspective with phylogenetic methods. The current study aims tofill this gap, quantifying the diachronic preference, speed of change, and stabilityof noun class markers across multiple morphosyntactic domains in the Atlantic
languages of West Africa, with an eye to inferring the relative chronology of theirdevelopment.
2. DataIn this section, we describe how the data on noun class marking was gathered ina sample of 36 Atlantic languages. We also provide information about how thephylogeny of the languages in the sample was generated.
2.1. Noun class systems
The Atlantic languages were selected due to their frequent presence of noun classsystems. We consider noun class marking on the noun, in the noun phrase, andthe verb phrase. To be more precise, we consider noun class marking on the noun
itself (via a PREFIX), ARTICLES, PRONOUNS, DEMONSTRATIVES, ADJECTIVES,CARDINAL NUMBERS, and VERBS. For instance, Segerer (2002, 85-92) showsthat Bijogo (Glottocode bidy1244) has fourteen noun classes that are marked on
the noun itself (e-we [class.E-goat] ‘goat’), adjectives and demonstratives (ño-ogo
n-nE n:-gbon [CLASS . M-rock CLASS . M - DEM CLASS . M-be.big] ‘These rocks arebig.’), pronouns (ya-g [CLASS . YA - PR] ‘them’), cardinal numbers (ya-to ya-nsom

626[CLASS . YA-two CLASS . YA-people] ‘two people’), and verbs (bisaw wO-gbe najOkO na-kotong [Bissau CLASS . WO - ACC.have CLASS . ÑA-house CLASS . ÑA-big]‘There are big houses in Bissau.’). A language is encoded as marking a domain
as long as one instance of marking is attested. Data were extracted manuallyfrom language grammars and sketches with sufficient available information, while
maintaining a balance across the sub-branches of the language family. This process resulted in a sample of 36 languages, displayed in Figure 1.
2.2. Tree Sample
The models used in this paper require a phylogenetic representation of the languages in our sample, in the form of a tree sample. We matched each speechvariety in our data set to its closest correspondent in a data set of automaticallygenerated lexical cognacy and sound class characters (Jäger, 2018). We inferred aphylogeny of the Atlantic languages using RevBayes (Höhna et al., 2016), using aBirth-Death tree prior (Yang & Rannala, 1997) and a General Time-Reversiblemodel of character evolution (Tavaré, 1986). We employed clade constraints,
enforcing a split between the North and Bak languages and including the sevenhigher-order subgroups found in Glottolog (Cangin, Central Atlantic, Fula-Sereer,Jaad, Naluic, Tenda, and Wolof-BKK), ensuring that trees that do not contain thesesubgroups are assigned zero posterior probability. We run 500,000 iterations ofMarkov chain Monte Carlo over 4 chains, discarding the first half of samples asburn-in and monitoring convergence by comparing the log posteriors of the chains.
3. Method
We explore differences in the diachronic behavior of noun class marking acrossthe seven domains of marking in our sample via phylogenetic comparative methods. We assume that marking in each domain evolves independently according
to a continuous-time Markov process parameterized by two rates, a gain rate αdand loss rate βd : d ∈ {1, ..., 7}. We infer the rates for all features jointly using RStan (Carpenter et al., 2017). We place Gamma(λ, λ) and Gamma(µ, µ)
priors over α and β, respectively, where λ, µ ∼ Exp(1), and incorporate phylogenetic uncertainty by inferring rates for 100 trees from our tree sample andaggregating posterior samples of rates across trees. We use posterior rate values in order to generate a number of quantities of interest to the properties mentioned above, as described below. Code employed in this paper is available athttps://github.com/chundrac/JCoLE2022-atlantic.
Stationary probabilities of noun class marking We make use of the stationary probability of noun class marking in each marking domain in order to operationalize the LONG - TERM PREFERENCE FOR NOUN CLASS MARKING acrossdifferent morphosyntactic elements. The stationary probability of a continuoustime Markov chain represents the probability that the system will be in a particular627

maas1239
pula1262
west2454
nige1253
adam1253
pula1263
sere1260
bedi1235
bass1258
wame1240
biaf1240
bady1239
nucl1347
noon1242
leha1242
saaf1238
palo1243
ndut1239

Marker absent

bain1261

Marker present

gube1234
bain1259
kobi1241baga1275
karo1294
kuwa1246
kera1248
jola1263
band1340
gusi1246
aram1260
bayo1255
mand1419
mank1251
pape1239
bala1302
bidy1244

EF
PR B
ER
V
O
PR
RD
CA
J
D

EM
RT

A

D

A

Figure 1.

Maximum clade credibility tree of languages in data set, along with datastate as time approaches infinity, as well as the long-term preference of the system
for a particular state. For a binary feature with a gain rate α and loss rate β, theαstationary probability is equal to α+β. We compute posterior stationary probabilities of noun class marking in each domain from the posterior distribution ofrates.
Speed of change Inferring gain and loss rates also allows us to compute the
OVERALL SPEED OF CHANGE , irrespective of the direction of change for all posterior samples. Given a gain rate α and a loss rate β, the overall speed of change
is α + β.
Phylogenetic stability We carry out ancestral state reconstruction for the internal
nodes of the tree on the basis of the inferred rates. For each pair of rates in theposterior sample, we compute the probability of noun class marking at each nodein the tree and draw a Bernoulli variate indicating the presence or absence of noun

628

class marking. We then average these values, yielding a posterior probability ofpresence between 0 and 1. On the basis of these reconstruction probabilities,
we infer the phylogenetic stability of marking in each domain according to theBayesian method of Borges et al. (2019), which provides an index of a feature’s
relative invariance over time, regardless of whether it is preferred or dispreferred.
We infer posterior values of the stability metric jointly across marking domains
for all trees in the sample.
Marker preference

Speedgreater in > 75%

VERB

greater in > 75%

PREF

greater in > 85%
PRO

0.903

greater in > 85%

greater in > 95%

ADJ

greater in > 95%

0.809

ART

0.924

CARD

0.984

0.943

0.903

VERB

ADJ

0.975

0.903

0.861

ART

0.979

0.829

0.841

0.873

PREF

0.96

0.868

0.836

CARD

0.98

0.838

0.848

0.869

DEM

0.984

0.96

0.952

0.862

0.85

0.774

PRO

0.994

0.945

0.949

0.933

0.781

0.784

VERB

PRO

ART

CARD

ADJ

PREF

PREF

ADJ

DEM

VERB

ART

CARD

DEM

Figure 2. Cross-sample pairwise differences in long-term preference fornoun class marking between marking domains

Figure 3. Cross-sample pairwise differences in speed of change between
marking domains

Stability
greater in > 75%

PRO

greater in > 85%

Figure 4.

greater in > 95%

ART

1

CARD

1

0.971

VERB

1

0.996

0.908

ADJ

1

0.999

0.903

PREF

1

1

0.985

DEM

1

1

1

0.929

0.989

0.864

PRO

ART

CARD

VERB

ADJ

PREF

0.848

Cross-sample pairwise differences in stability between marking domains, across samples

4. Results
We compare posterior distributions or each metric of interest across pairs of domains. Following Gelman, Hill, and Yajima (2012), feature pairs display deci-629

sively contrasting behavior if differences between values in 95% or more of samples are greater/less than zero.1 The 85% cutoff serves as strong but not decisive
evidence. Inter-feature comparisons for long-term noun class marker preference,
speed, and stability can be found in Figures 2, 3 and 4, respectively. Features areorganized according to their median posterior values, in increasing order.
The following cline can be found with respect to long-term noun class marking: VERB < PRO , ART < CARD , ADJ , PREF, DEM (where << indicates decisiveand < indicates strong evidence for a difference). There is additionally decisive evidence that cardinal numbers, adjectives, prefixes and demonstratives show
greater preference for noun class marking than verbs and that demonstratives show
greater preference for noun class marking than pronouns and articles.
Prefixes exhibit the lowest speed of change, followed by adjectives, demonstratives, verbs, articles, cardinal numbers and pronouns. Unlike preference fornoun class marking, no decisive breaks can be found along this cline. Thereis decisive evidence that articles, cardinal numbers, and pronouns exhibit fasterchange than prefixes; additionally, there is strong evidence that pronouns exhibit
faster change than adjectives, demonstratives, and verbs, and that articles, cardinal
numbers and pronouns exhibit faster change than verbs.
In some ways, results for stability mirror those of speed, with pronouns, articles and cardinal numbers exhibiting lowest stability and highest speed, verbs exhibiting intermediate values for both metrics, and adjectives, prefixes and demonstratives exhibiting highest stability and lowest speed. Here, however, we see
decisive and strong evidence for breaks along the cline of stability: PRO << ART<< CARD , VERB , ADJ < PREF, DEM.
5. Concluding discussion
The high preference and stability for demonstratives is in line with the literature.
From a diachronic point of view, demonstratives are frequently the source of grammaticalization processes in Atlantic languages. Demonstratives inflected for classfrequently grammaticalize as class pronouns, which then reinforce and extend
class agreement (Creissels & Pozdniakov, 2015). The high preference and stability for marking on nouns also dovetails with received wisdom. For example, innoun class languages that lost grammatical agreement, markers are generally stillfound on nouns (Kießling, 2018). The lower preference for noun class marking
on verbs is expected as noun class marking typically starts from within the nounphrase and then extends to the verb phrase (Tang & Her, 2019). Results show thatthe marking on pronouns is the least stable and most in flux, which reflects that
1 Debate exists regarding the danger of false discoveries under multiple comparisons in a Bayesianframework. Our hierarchical model has a partial pooling index of 0.67 (Ogle et al., 2019), whichcorresponds to a low rate of false positives but a high rate of false negatives. A full appraisal of thisissue is outside of this paper’s scope but will be addressed in future work, e.g., via a mixture model.

630

the presence of class pronouns is likely to interact with the grammaticalization ofdemonstratives and other diachronic factors.
The results also indicate that marking on demonstratives, prefixes, and adjectives is the most stable and preferred in comparison to articles, cardinal numbers,
pronouns, and verbs. This speaks to the development of noun class marking first innouns and demonstratives, subsequently spreading to other elements in the nounphrase and finally to the verbs. However, our models do not quantify the complex
diachronic interactions between these different markers. For example, are nounclass markers most likely to be found on nouns before demonstratives, or viceversa? In future work, models of correlated evolution can be employed to further
assess the interactive dynamics between different noun class markers.
Acknowledgments
The first and second authors acknowledge the support of Sorbonne Nouvelle
University and Lacito – UMR 7107, CNRS, and the French National Research
Agency grant EVOGRAM (ANR-20-CE27-0021), respectively.
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Her, O.-S., Hammarström, H., & Carling, G. (2021). Expansion by migration and diffusion by contact is a source to the global diversity of linguistic
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about multiple comparisons. Journal of research on educational effectiveness, 5(2), 189–211.
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languages: Towards a comparison. Linguistic Typology, 8(2), 243–285.
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B. R., Huelsenbeck, J. P., & Ronquist, F. (2016). Revbayes: Bayesian
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632

THE LANDSCAPE OF GESTURAL RESEARCH IN LANGUAGEEVOLUTION: A SYSTEMATIC REVIEW
Evelina D. Rodrigues*1, António J. Santos1, Flávia Veppo2, Joana Pereira3, and Catherine
Hobaiter 4*Corresponding Author: erodrigues@ispa.pt
1 William James Center for Research, ISPA–Instituto Universitário, Lisbon, Portugal
2 Department of Applied Psychology, School of Psychology, University of

Minho, Braga, Portugal
3 Centre for Ecology, Evolution and Environmental Changes, Faculdade deCiências, Universidade de Lisboa, Lisboa, Portugal
4 School of Psychology and Neuroscience, University of St Andrews, St

Andrews, Scotland, UK

1. Introduction
The presence of common features in human and nonhuman primate
communication can be used to suggest the evolutionary trajectories of potential
precursors to language (Byrne et al., 2017; Fitch, 2010; Tomasello & Call, 1997).
However, the overrepresentation of some species or populations1 and differencesin definitions, methodology and context distort our ability to make phylogenetic
comparison (Fröhlich & Hobaiter, 2018; Leavens et al., 2019; Scott & Pika,2012). This systematic review describes the current landscape of data available
from studies of spontaneous gestural communication (produced without explicit
training2) in human and nonhuman primates that make an explicit connection tolanguage evolution.
2. Methods
For example WEIRD - Western, Educated, Industrialized, Rich, and Democratic – in humans(Henrich et al., 2010); and BIZARRE - Barren Institutional Zoo And other Rare Rearing
Environment – in chimpanzees (Leavens et al., 2010).
2
Studies on home-sign development were included because of its often spontaneous emergence, but
we excluded formal signed language studies to avoid explicit teaching of signs and gestures
1

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

633

We investigated how these studies varied in terms of study domains (Table S1),
concept of gesture (Bourjade et al., 2020), study scope, inclusion of additionalsources, and of research settings and study design (Table S2). In March 2020, we
conducted a search of peer‐reviewed articles and book chapters in Web of Scienceand PsycINFO using the search terms “gestur*,” AND “evolutio*” OR “origin*,”AND “languag*” OR “communicat*. From the 963 studies identified we retained
163 according to a predefined set of criteria (Figure S1).
3. Results
We found a similar number of studies on human (N=80) and nonhuman primates(N=87), but very few studies included data on both human and nonhumanprimates (N=4). As a result, evolutionary inferences remain restricted tocomparison across studies. We identified areas of focus, bias, and apparent gaps
within the field. Of the nonhuman primate species studied, the majority focusedon great apes (N=75/87), but no studies were found in small apes, monkeys of theAmericas, or strepsirrhines (Figure S2). Most human participants were from
WEIRD societies (N=68/80). There were few nonhuman primate studies of
ontogeny and relatively few human studies of gesture form (Figure S3). We foundvariation in the conceptual and methodological approaches used between humanand nonhuman primate studies. Definitions of gesture and criteria for intentionaluse are absent in most human studies (TableS3). Human studies focused more on
specific gesture types or contexts (N=65/80) and were more likely to include
additional sources to gesture (N=53/80). Studies of nonhuman primates wereconducted more often in familiar settings (N=71/87) and using observational
designs (N=61/87), whereas studies with humans were conducted more often inlaboratories (N=53/80) using experimental designs (N=58/80; Table S4).
4. Discussion
Diversity in focus, methods, and socio‐ecological context fill important gaps andprovide nuanced understanding, but only where the source of any difference
between studies is transparent. We highlight important areas in a call to action
through which we can strengthen our ability to investigate gesturalcommunication's contribution within the evolutionary roots of human language,
including the need for: i) explicitly testing evolutionary hypotheses in ourempirical work; ii) more data from diverse species, social groups, andenvironments, iii) studies exploring the spontaneous use of gesture forms inhumans and gesture ontogeny in non-human primates.

634

Acknowledgements
EDR was supported by the Portuguese Foundation for Science and Technology(SFRH/BD/138406/2018). CH is funded by the European Union’s 8th Framework
Programme, Horizon 2020, under grant agreement no 802719.

References
Bourjade, M., Cochet, H., Molesti, S., & Guidetti, M. (2020). Is Conceptual
Diversity an Advantage for Scientific Inquiry? A Case Study on the Conceptof ‘Gesture’ in Comparative Psychology. Integrative Psychological and
Behavioral Science, 54(4), 805–832. https://doi.org/10.1007/s12124-02009516-5
Byrne, R. W., Cartmill, E. A., Genty, E., Graham, K. E., Hobaiter, C., & Tanner,
J. E. (2017). Great ape gestures: Intentional communication with a rich set of
innate signals. Animal Cognition, 20(4), 755–769.
https://doi.org/10.1007/s10071-017-1096-4
Fitch, W. T. (2010). The Evolution of Language. Cambridge University Press.
Fröhlich, M., & Hobaiter, C. (2018). The development of gesturalcommunication in great apes. Behavioral Ecology and Sociobiology, 72(12),194. https://doi.org/10.1007/s00265-018-2619-yHenrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in theworld? Behavioral and Brain Sciences, 33(2–3), 61–83.
https://doi.org/10.1017/S0140525X0999152X
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USA.

635

COGNITION AND THE STABILITY OF EVOLVING COMPLEX
MORPHOLOGY: AN AGENT-BASED MODEL
Erich Round*1,2 , Stephen Mann1,3 , Sacha Beniamine1 , Emily Lindsay-Smith1 , Louise Esher4 , andMatt Spike5* Corresponding Author: e.round@surrey.ac.uk

1 Surrey Morphology Group, University of Surrey, Guildford, UK

2 School of Languages and Cultures, University of Queensland, St Lucia, Australia
3 Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
4 CNRS LLACAN, Paris, France

5 School of Philosophy Psychology & Language Sciences, University of Edinburgh, Edinburgh, UK

Cultural attractors enable evolving cultural traits to gain the stability that underpins cumulative
cultural evolution, yet the conditions that support their existence are poorly understood. We examine conditions affecting the stability of a salient kind of complex cultural attractor in humanlanguage, known as inflectional classes. We present a model of the evolution of inflectionalclasses, as they are reconstructed across generations via a combination of direct transmission
and analogical inference. Parameters examined pertain to diversity of the lexicon and the cognitive policies governing inferential reasoning. We discover that persistence of stable inflection
classes interacts in complex ways with features which affect how inflection classes are inferred.
Thus we contribute to a greater understanding of factors affecting cultural attractors’ existence,and to insights into a widespread and complex trait of human language.

1. Introduction
Human languages present an explanatory challenge: to reconcile their seemingly
endless structural diversity with an equally striking tendency to exhibit recurrent
similarities. Cultural evolutionary perspectives offer a solution. The adaptive flexibility of cultural transmission introduces the noise which makes linguistic innovation and change inevitable, while the cognitive nature of language influences
the origin, direction and uptake of these changes, and their subsequent stability.
Cultural Attraction Theory (Claidière & Sperber, 2007) foregrounds the role ofcognition in shaping the adaptive landscape over which culture evolves. Cultural
attractors are regions of meta-stability in cultural trait space. Their existence enables traits to remain stable in certain states but not others, thus promoting the
emergence of shared, cumulative culture (see Griffiths & Kalish, 2007; Kirby,
Tamariz, Cornish, & Smith, 2015). Here we examine inflectional class systems
as cultural attractors. We investigate how parameters governing the inferential reconstruction of inflectional classes affect their evolutionary stability. Inflectionalclass systems are known to persist across millennia, thus the cognitive strategies
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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common among language speakers are likely to be those which promote stability.
Consequently, we view our approach as a method for identifying parameter settings of cognitive policies that may promote the persistence of a highly complex
cultural attractor known to be a salient, recurrent trait of human language.
Table 1.

Paradigms illustrating three inflectional classes in Swedish nouns.

IC1‘school’‘bottle’‘chair’‘box’‘idol’‘Basque’IC2
IC3

SG . INDEF
skola
flaska
stol
ask
idol
bask

SG . DEF
skolanflaskanstolenasken
idolenbaskenPL . INDEF
skolorflaskorstolaraskar
idolerbaskerPL . DEF
skolornaflaskorna
stolarnaaskarna
idolernabaskerna

Inflectional Classes as Cultural Attractors In many languages, lexemes
can possess a set of multiple, contextually-conditioned wordforms, termed an inflectional paradigm as illustrated in Table 1 for Swedish nouns. In paradigms,
each wordform occupies a cell. The wordforms in a paradigm typically containformal differences, termed the exponents of the cell, and Inflectional Classes (ICs)are distinct patterns of exponents found in paradigms. In Table 1, IC 1 has exponents {-a,-an,-or,-orna} whereas IC 2 has {Ø,-en,-ar,-arna}.
As languages change, ICs are known to serve as attractors in the sense thatanomalous paradigms (which may arise as the result of minor disruptions) often
undergo alteration, to become more like other ICs in the language (Maiden, 2018).
Recently, an explanation for this has been formulated in terms of the reconstruction and transmission of paradigms.
Reconstruction and Transmission Paradigms are reconstructed not en bloc
but in piecemeal fashion, one cell at a time: a single utterance might contain thewordform ‘sang’, but not the whole paradigm ‘sing–sings–singing–sang–sung’.
In many languages, paradigms are sufficiently large (often comprised of dozens
if not hundreds of wordforms) that it is implausible that speakers would always
have heard a specific wordform of a lexeme before they first need to produce it(Ackerman, Blevins, & Malouf, 2009; Blevins, Milin, & Ramscar, 2017). Yet
speakers can perform this task and they agree upon the solution. Psycholinguistic
results show that speakers have clear inferential intuitions about paradigm forms
they have not previously heard. English speakers for instance nominate splungbut not splong as a plausible past tense form of novel spling (Albright & Hayes,
2002). Thus, cognitively, ICs are supported by processes of inferential reasoningin addition to mere storage of wordforms.
Conditional Predictability In natural languages, the distribution of exponents in different cells is not random, rather the exponent of one cell is typicallyrelatively predictable from the exponent of another (Carstairs-McCarthy, 1987);
conditional entropy is low compared to random covariation (Ackerman & Mal-

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ouf, 2013). Linguists have been interested in understanding this interpredictability
as a potentially emergent property of IC evolution (Ackerman & Malouf, 2015;
Round, Beniamine, & Esher, 2021).
2. Modelling of Attractor Evolution
Inflectional Classes The task of inferring the content of a paradigm cell is known
as the paradigm cell filling problem, or PCFP (Ackerman et al., 2009). Ackerman
and Malouf (2015) modelled an analogical PCFP process, in which one cell ofa lexeme’s paradigm (which we call the focal cell) is inferred from analogicalrelationships that exist between it and one other cell (which we call the pivot cell,and which is sampled at random). To infer the focal cell’s exponent, the agent
attends to other lexemes that share a similar pivot cell, and samples from theirfocal cells. In an iterated learning simulation, with a single agent who learns all
forms of the language except for one, which is inferred analogically, a systemthat is seeded initially with random paradigms will gradually self-organize into alimited number of ICs. Ackerman and Malouf (2015) show that across the lexicon,
the mean conditional entropy of pairs of cells falls over time, and interpret this asreflecting a rising interpredictability between cells.
However, Round et al. (2021) note that conditional entropy can fall for two reasons: because interpredictability is increasing or because total entropy is fallingas a system becomes uniform. They show that in Ackerman and Malouf (2015)’s
model, conditional entropy falls for the latter reason: unlike the stable distinctiveness found in real IC systems, ICs in the model are inherently unstable andinevitably collapse together entirely. They advocate an improved measure of interpredictability based on mutual information. Mutual information will rise andfall as interpredictability does, including when total entropy is changing.
Inferential Policies In Ackerman and Malouf (2015) and Round et al.
(2021), the PCFP is based solely on the contents of lexemes’ inflectional
paradigms. However, other information sources, including similarities along semantic and phonological dimensions, are known to affect the probability withwhich one lexeme influences another, both psycholinguistically and in languagechange (Hayes, Zuraw, Siptár, & Londe, 2009; Maiden, 2020). Here we investigate the emergence and stability of inflectional classes when analogical inferencein the PCFP makes reference to extra-paradigmatic similarity: features other thanmorphological exponents that lexemes can share.
3. Model Description
We examine conditions for the (in)stability of cultural attractors, namely inflectional classes, in a multi-agent model in which paradigms evolve via a PCFP
mechanism.1 An initial population of agents is created who all share the same
1 Model code and a Wiki guide are available at bit.ly/ELXIV.

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lexicon. In each iteration, a child population is created, all of whom begin life
with an empty lexicon. During acquisition, children learn from adults, who utter wordforms including by using the PCFP (see below) to infer forms which theadults themselves have not heard. Finally, adults die, children become adults, andthe next iteration begins with the creation of a new child population.
Simulations commence with an initial population of A adult agents, who sharea randomised lexicon of L lexemes2 {LEX1 , LEX2 , . . ., LEXL }. Each lexeme has
a paradigm of C cells, whose exponents are represented as integers drawn fromthe range {1, 2, . . . V }. The initial lexicon contains Iinit distinct inflection classes
which may share some (but not all) exponents. The model runs for G iterations(‘generations’). At each iteration, children during acquisition receive an input totaling W wordforms, transmitted in equal measure by Q randomly selected adultacquisition sources. If a child hears multiple wordforms for the same cell of alexeme, it stores the first that it encounters. Adults sample wordforms to transmit
as follows. A lexeme is sampled from among the lexemes known to the agent, according to a Zipfian distribution derived from the frequencies with which lexemes
were heard by the agent as a child. A cell for that lexeme is chosen, again according to a Zipfian distribution. The exponent for the cell is retrieved from memory
if the adult learned it as a child. Otherwise it is inferred by the PCFP process.
The PCFP The PCFP proceeds as follows. To infer the focal cell of the focal
lexeme, (1) sample E evidence lexemes; (2) accord them a weight of +1 for each
non-focal cell that matches the corresponding cell of the focal lexeme; (3) for eachexponent value v in {1, 2, . . . ,V }, assign it a weight equal to the summed weights
of all evidence lexemes whose focal cell contains v. The selected exponent value
is the one with the greatest weight; ties are broken randomly.
At step (1), the sampling of evidence lexemes is biased by lexemes’ similarity
in their extra-paradigmatic features. Moreover, the relative importance attachedto each feature when calculating similarity is controlled by a feature weight which
the agent learns at the conclusion of acquisition, as a function of the correlations it finds between a feature’s values and lexemes’ exponents.3 As a language
evolves, agents’ feature weights—and the associated bias introduced into their
PCFP solutions—will change over time, reflecting the shifting relationships between lexemes’ unchanging extra-paradigmatic features and changing paradigms.
In this study, we investigate the effects of different kinds of correlations in theinitial population’s lexicon between extra-paradigmatic features and lexemes’ ICs.
Lexemes are given three features, allowing us to examine four conditions: High,in which one feature correlates exactly with ICs; Medium, in which one featurecorrelates perfectly with ICs for the first 50% of the lexicon; Double medium,
which is identical to Medium but with the additional of a second feature that cor2 In the remainder of this section, italicised single letters denote model parameters.

3 Implemented using Random Forest Feature Importance. See model code for details.

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relates perfectly with the ICs of the second 50% of the lexicon; and Zero, in which
no feature correlates in these ways with initial ICs. Thus, in the High, Medium
and Double conditions, whenever lexeme LEX i is the subject of the PCFP, the setof evidence lexemes against which it is compared is more likely to be drawn fromlexemes which shared LEX i’s IC in the initial population’s lexicon.
Table 2.

Parameter definitions and values used. See main text for explanation.

Parameter

Value(s)PARADIGMS

Lexemes
Cells
Possible cell values
Inflection classes (initial)

Parameter

Value(s)TRANSMISSION

L
C
V
Iinit

200
5
5, 50
5

AgentsAcquisition sources
Generations
Wordforms in acquisition

E
B

10
High, Medium, Double, Zero

A
Q
G
W

4
3
5,000
24,000PCFP

Evidence lexemes
Extra-paradigmatic bias

4. Simulation Experiments and Results
Parameter settings investigated are shown in Table 2. Varied parameters were B(extra-paradigmatic bias) and V (range of available exponents), taking 4 and 2
values respectively for a total of 8 conditions. Each condition ran 30 times.
The outcomes of primary interest are the change in number of ICs, the degree
to which different agents conform in the ICs in their grammars, and the interpredictability between cells in agents’ grammar. To examine these we measure
five population-wide statistics every 50 generations: (1) IC Diversity, the total
number of distinct ICs in the grammars of the population of agents (lexemes withmissing wordforms are ignored); (2) IC Disparity, defined as 1 − A/D where A
is the average number of distinct ICs for individual agents and D is IC Diversity;(3) Conditional entropy, the mean conditional entropy between pairs of cells inan agent’s paradigm system, averaged across all agents; (4) Mutual information,
the mean mutual information between pairs of cells in an agent’s paradigm system, averaged across all agents; (5) Maximum prediction weight, the weight of thefeature with respect to which the agent’s selection of evidence lexemes is moststrongly biased. Figure 1 visualises the four measures across 5,000 generations.
The results show initial, transient spikes in IC Diversity, IC Disparity and conditional entropy as the system departs from its initialisation state (first-generation
agents share the same lexicon). Then, under all conditions, we find broad falls
in IC Diversity and IC Disparity, falls in conditional entropy, and falls in mutual
information as the newly-formed ICs begin to collapse together. The analogising effect of the PCFP is bringing different lexemes into line with one another.
In almost all cases, each statistic takes a larger value in the 50-exponent case(blue lines). More exponents entail greater entropy and information because thereMutual Info

Cond. Entropy

IC Disparity

IC Diversity

640

High

Medium

Double medium

Zero

40
30
20
10
0.3
0.2
0.1
0.0
0.9
0.7
0.5
0.3
0.1
1.2
0.9
0.6
0.3

Max weight

0.0
0.7
0.6
0.5
0.4
0.3

Figure 1. Five measures of ICs over 5,000 generations, observed at intervals of 50 generations. Conditions with 50 available exponents are in blue, with 5 in green. Each condition was run 30 times.
Lines show medians, ribbons show 80% of variation. Each column of panels shows one bias level.

is a smaller chance of the same exponents being used across different ICs. And
more exponents entail greater IC Diversity and Disparity because there is a greater
source of variation with which to recombine existing ICs into new ones.
Differences in different bias conditions (i.e. across different columns in figure1) can be understood as follows. First note all measures’ uniqueness in the Highcondition. In High, evidence lexemes are very likely to be drawn from the same
initial IC as the focal lexeme. Consequently, initial groups of lexemes will stick
together through generations: they constitute reliable clusters exerting an internal gravitational pull, preventing individual lexemes from escaping to form newclasses. Mutual information remains high because the initial ICs are mostly conserved. The other conditions show accelerated declines in conditional entropy andmutual information. Inflection classes are collapsing together across the lexicon;
because the feature correlations are not strong enough to generate insular clusters, lexemes from different initial classes might be used to inform the PCFP for
any given lexeme. Second, note the difference between the Medium and Double
conditions. On the face of it, these patterns are paradoxical: the Double condition
contains greater correlation across the lexicon, so one would expect its statistics toresemble those of the High condition. But its IC Disparity and mutual information
are more similar to the Zero condition. How can more correlation across multiple
features accelerate collapse, when maximum correlation in a single feature retards
collapse? The answer is revealed by the ‘Max weight’ statistic, measuring how
strongly an agent favours one set of extra-paradigmatic features over the others. In
the High condition, agents favour the strongly-correlated feature. In the Medium
condition, there is no strongly correlated column, but agents favour the mediumcorrelation feature. This leads to classes beginning to ‘cluster’ according to thosefeatures (even though they do not perfectly correspond to initial ICs), thus thecollapse (decrease in IC Disparity and mutual information) is somewhat slowed.

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By contrast, in the Double condition, agents cannot strongly favour any single
feature; their assignment of lexemes to groups is pulled in two different directions
indicated by the two distinct medium-correlation features. Two medium-strength
correlations that pull in different directions cancel each other out; as a result, thestatistics in this condition more resemble those of the Zero condition, where there
is no information by which to favour certain lexeme clusters at all. These results
indicate that IC stability may be affected in strikingly different ways by correlations which agents detect between inflection classes and extra-paradigmatic features, such as lexical semantics or phonological stem shape.
5. Conclusions and Future Directions
In this paper we presented a model of evolution in systems of inflectional classes,
which are known to act as complex cultural attractors in languages around theworld. Framing the study in terms of Cultural Attractor Theory, we modeled thereconstruction of ICs in terms of the paradigm cell filling problem (PCFP), which
has attracted considerable recent attention in linguistics. We ran simulations chosen to highlight a key potential parameter of variation in the cognitive, inferential
processes that underlie the PCFP and which we therefore expect to play a role inshaping the cultural attractor landscapes which ICs traverse. We find that multiple sources of analogical inference can prevent distinct categorisation, because
those sources can conflict with each other. Therefore, explaining how agents categorise by analogy is not just a case of quantifying correlational relationships, but
understanding the different sources of those correlations and their interactions.
Future elaborations of our approach include adding the effects of noise andchannel biases during transmission, and transforming interaction into a communication game involving costs for communicative failure. More complex storageof heard exponents could allow agents to track the grammatical variation theyencounter. Further possibilities are implemented in our model, but have not been
explored in this study. Our model design enables agents to converse as adults,
leading to paradigm gaps being filled via horizontal inheritance among a peer
generation. Recent evidence (e.g. Raviv, Meyer, & Lev-Ari, 2020) underlines
the importance of population demographics, e.g. population size and network
structure (which our model implements), and overlapping, non-synchronous generations. Additional parameter values of the morphological system to be examined
include larger paradigms with more lexemes, more cells, and multiple exponents
per cell. Finally, it will be important to examine exogenous impacts on paradigm
systems such as sound change and borrowing of lexicon from other languages;
our model implementation anticipates the addition of these extensions.
Acknowledgements
The authors wish to thank three anonymous reviewers for helpful feedback. This
work was supported by British Academy Global Professorship GP300169 to ER

642

and British Academy Newton International Fellowship NIF23\100218 to SB.
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GROUP BINDING AS THE MAIN DRIVER OF LANGUAGEEVOLUTION
Daniil Ryabko*1,2 and Alvaro Mbeju Moreno2* Corresponding Author: daniil@ryabko.net
1 INRIA, France (on leave from)
2 Fishlife Research, Belize

A novel hypothesis concerning language evolution is advanced. It posits that languages have
evolved as a means of binding individuals to a group, as well as for defining these groups. The
key evolutionary adaptation is the loss, in adults, of the ability to learn languages with the easethat children possess. The proposed hypothesis helps to explain the diversity of human languages, their complexity, as well as the apparent uniqueness of the language faculty to humans.

1. The main hypothesis
The problem of language evolution has fascinated researchers for centuries (Wangand Minett, 2005), with the question of language diversity being one of the mostintriguing ones (Lupyan and Dale, 2016). Why do peolple speak different languages? Whereas for groups of people that comminucate little or not at all somelanguages differences are expected to appear due to the effects akin to genetic
drift, the langauge divergence between groups that live next to each other is muchharder to explain (Labov, 2010). Moreover, unlike in biological reproduction
where each new individual inherits genetic material from one or two parents, eachnew carrier of a language samples from a much larger set (dozens or even hundreds of carriers). Thus, it may be expected that mutations would average out andthe new learner would adopt the norm that is most common; according to Nettle(1999), this problem in modelling language evolution has been recognized sinceat least Sapir (1921).
The importance of language in the individual’s social identity is wellestablished, and is manifest in children from an early age (Kinzler, 2021; Cohen,2012). We suggest that the need for social identity has been the main driver ofthe evolution of language. More specifically, language evolved in such a way as
to make it difficult to learn as an adult. Consider the fact that children have theability to learn a language perfectly and the adults do not, in the sense that it isextremely difficult or often impossible for an adult to learn to speak a language aswell as someone who learned it as a child. The cornerstone question that we poseis the following:This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

644

Can the loss of the language-learning ability in adults be adaptive?
In other words, can the loss of this ability serve some purpose, and if so thenis it possible that languages have evolved to fulfill this purpose? Put this way,
the question may appear baffling, since it is difficult to imagine what advantage aloss of a cognitive ability may confer to an individual. However, the advantages
become clear if this question is considered on the level of groups. The loss oflanguage-learning ability defines, for each language, the group of its native speakers, and effectively binds them to this group.
2. Altruism and group-level selection
Binding individuals to a group allows for the evolution of altruistic behaviour
via group-level selection, which means that the evolution of language has to beconsidered in the context of the evolution of altruism. Let us consider this latter
point in more detail.
Group-level (or, more generally, multilevel) selection is used to explain theevolution of altruistic behaviour. Altruistic behaviour is a behaviour that is detrimental to the individual but is beneficial to others which may be only distantly
related; non-altruistic behaviour is called selfish. If selection acts on the superindividual level, that is, if groups compete between themselves, then groups whichhave more altruists get an advantage, even though an altruist is disadvantaged
within their group (so long as it has at least one selfish member). It is worth noting that the idea of group-level selection as an explanation for the emergence ofaltruistic behaviour can be traced back to Darwin (1871).
In sociolinguistics, it is generally acknowledged that language should be considered a property of a group first and individual only second. In the words ofLabov (2010), who calls is the central dogma of sociolinguistics, language is seen
as an abstract pattern located in the speech community and exterior to the individual. The function of language as a source of social markers is widely recognized and well-studied (Nettle and Dunbar, 1997; Roberts, 2013). Moreover, thediversity of language seems interfere with the its other functions, such as communication (Labov, 2010; Roberts, 2013). Cohen (2012) gives a list of properties oflanguage (more specifically, of accent) that makes it a particularly reliable sourceof social markers. Of particular importance for the hypothesis proposed is that it issalient and hard to fake. Indeed, one’s native accent is hard to impossible to hide
in a foreign language (Cohen, 2012; Sigmund and Nowak, 2001; Roberts, 2008),
and it is extremely difficult for an adult to learn to speak a foreign language aswell as a native speaker (Birdsong, 2009; Roberts, 2013; Kinzler, 2021).
Mathematical and computational models of various complexity have been proposed to explain the evolution of altruism through group-level selection, and, more
specifically, tag-based cooperation; see (Rand and Nowak, 2013) for a review.
Again, for the purpose of the proposed the hypothesis the most important aspectis the need for a mechanism that binds individuals to groups. Intuitively, it is645

clear that between-group migration works against cooperation; specifically, one
might think of individuals fleeing a group in the face of danger, e.g. under attackby another group. Mathematical modelling not only confirms this intuition, butshows that already random migration has this effect. One of the simplest models
that allows for migration is that by Traulsen and Nowak (2006). In this model,
the population is divided into groups and individuals reproduce within the groups.
Groups may split upon reaching a certain size, and individuals may migrate at
random with a certain probability. The authors show that group-level selection
favours altruism if b/c > 1 + z + n/m, where b/c is the ratio between the benefit and cost of the altruistic act, n and m denote the maximum group size andthe number of groups, and, finally, z is the average number of migrants arisingfrom one group during its lifetime. Thus, at least in this model, migration works
directly against altruism: the higher the migration (z) the bigger the benefit ofaltruism should be related to its cost (b/c) in order for altruism to survive.
Group-level selection in our own species had almost certainly taken place; inparticular, Tattersall (2016) notes that the spread of Homo sapiens over the planet
between 100kyr and 50kyr ago occurred far too rapidly to be accounted for by
the slow workings of natural selection at the individual level (and suggests an
explanation based on the invention of language, but does not consider its role as agroup-binding tool).
3. Other mechanisms of group-binding
If binding individuals to groups is an essential part of group evolution, and inparticular of the evolution of altruism, then one should expect there to be morethan one means of achieving it. Some examples of the manifestations of this phenomenon can be found in artificial body deformations. These have been foundacross a wide variety of human cultures, and include cranial deformations, footbinding, scarification, tattooing and so on, which can often be linked to class,status, religion or other forms of social identity (Mackie, 1996; Hoshower et al.,
1995; Ludvico and Kurland, 1995). While it is clear that these phenomena arecultural, their widespread presence in only distantly related populations is an evidence of an underlying common genetic adaptation. It is worth noting that manyof the artificial body deformations mentioned are applied to young children, i.e.,at the same age that language acquisition starts; others are applied at adolescence,
where group membership is being ascertained (e.g., as a right of passage).
The presence of these group-binding practices provides an indirect support tothe hypothesis advanced in this note.
One can also ask the question of whether there is anything that makes thelanguage a better group-binding tool than these practices. One possible answer
it the universality of language: everyone has it. Indeed, Cohen (2012) lists thisproperty as one of those making the accent an especially good source of social
markers. While some of the other social practices mentioned above may be more

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flexible, none is based on something that readily exists in all humans.
4. Discussion
We have proposed a hypothesis that language has a evolved as a tool to bind
individuals to groups; thus, the difficulty with which adults can learn it is a grouplevel evolutionary adaptation. The need for languages to be complex is a directconsequence of this hypothesis (they need to be complex so as to be difficult for
adults to learn). Moreover, for group-level selection to take place, groups needs toreproduce, which entails language diversity (languages diverge to accommodate
or to define group splits). Thus, the proposed hypothesis fits well the empiricallyobserved phenomena – the complexity and diversity of languages – that have so
far remained largely enigmatic from the theory point of view. Moreover, it is
supported by theoretical models of group evolution as well as by the evidence ofother group-binding practices that permeate human populations around the world.
There are many questions left to be answered. Most importantly, while theproposed hypothesis attempts to explain why the language evolved and what purposes it serves, it remains so far to speculate exactly how this evolutionary process took place. One can envisage two distinct possibilities: either the language
evolved on the basis on some pre-existing cognitive abilities that children possess and that they lose growing up; or, the evolutionary process has somehow
suppressed some existing cognitive ability; or, which is most likely, it was somecombination of the two. If some pre-existing abilities played the main role, wewould perhaps be able to find some other cognitive abilities that children have but
adults do not. These appear rather difficult to find in humans. However, looking
beyond our own species, a related mechanism can be found, namely that of imprinting. It is widespread in the so-called precocial species, i.e. those in which theyoung are born relatively mature. Specifically, filial imprinting establishes social
preference for an object that should typically be the parent, during the first hours
or days of life; sexual imprinting works similarly to establish sexual preference(see, e.g., Reznikova (2007) for review). Most importantly, these mechanisms are
only active during certain sensitive periods of life. Clearly, humans are not precocial species and do not need to imprint the parental image for the same reasons thatgees chicks do. However, similar cognitive mechanisms could be present. Sexual
imprinting is also interesting to consider from this point of view, as the choice ofsexual partner is largely affected by their group membership and social standing.
Parallels can be drawn with language acquisition and change. Thus, children
are sensitive to language variation from an early age, and they start to use it is astrong source of social markers (Johnson and White, 2020; Kinzler, 2021); for example, 5-6 year-old children were demonstrated to prefer native-accented speackers and this preferences was stronger than that for race (Kinzler et al., 2009).
However, it is during the adolescence that the propensity to make changes to thelanguage is the strongest Eckert (2004); Kinzler (2021). It thus may be interesting647

to study the emergence of new language forms from the point of view of reproduction of groups and its relation to the sexual sexual partner choice.
As with any hypothesis, an important question to consider is what evidence
would disprove it. Finding other compelling reasons for the loss of the languagelearning ability in adults, corroborated by studies on related species would be one
way of doing it. Finding other learning abilities that have nothing to do withlanguage (so that it could not be argued that language has developed on their basis) that disappear in adulthood would considerably undermine the hypothesis. It
could also be useful to consider under which circumstances a language can actually become simpler. One example, that may appear contradicting the hypothesis proposed, is that languages spoken by larger with many non-native speakers
have a tendency to lose some morphological complexity (Lupyan and Dale, 2010).
However, it can be argued that precisely the fact that there are many non-native
speakers (and that the is spoken by a large group) weakens its role as a source ofsocial identity. In such cases this role is perhaps relegated to dialects, accents andother local language variations.
Another question that acquires a new perspective in light of the hypothesis
advanced is the exclusivity of the capacity for language to our species. Language
is often considered a uniquely human ability (e.g. Jackendoff, 2011), or at least
partially so, in particular, Hauser et al. (2002) suggest that the FLN is uniquely
human. From the perspective of the evolution of groups and altruism, our species
does appear to be unique, though by no means the most advanced. Humans areperhaps the only species to live in large, highly-structured but at the same time
genetically heterogeneous communities. Eusocial species, such as bees, ants andmole rats, are more advanced in the sense that their communities are much moregenetically homogeneous. Some of these latter species, in particular bees and aselect few species of ants, possess communication systems that allow for transmission of quantitative information, and, at least in the case of ants, are flexible
enough to allow for information compression (review in Reznikova and Ryabko,2011). Since they are eusocial, the groups in these species are families, i.e., arebound genetically, and so do not need to bind their members further using language or other means (this said, in many ant species the colonies are actuallygroups of families, and thus are genetically heterogeneous). Therefore, if the hypothesis advanced in this note is correct, their languages should be much simpler
and less diverse. While their relative (with respect to humans’) simplicity is perhaps a foregone conclusion, the diversity aspect appears an interesting subject for
a comparative study.
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650

HUMAN LANGUAGE LAWS IN CHIMPANZEE SEXUAL
SOLICITATION GESTURES
ALEXANDRA SAFRYGHIN1, CATHARINE CROSS1, BRITTANY FALLON1,RAPHAELA HEESEN3, RAMON FERRER-I-CANCHO2&, and CATHERINEHOBAITER1,4&**Corresponding Author: clh42@st-andrews.ac.uk (CH)
School of Psychology and Neuroscience, University of St Andrews, St Andrews, UK
2
Departament de Ciències de la Computació, Universitat Politècnica de Catalunya,
Barcelona, Spain
3
Department of Psychology, Durham University, Durham, UK
4
Budongo Conservation Field Station, Masindi, Uganda&
These authors contributed equally to this work1

1. Introduction
Two language laws have been identified as manifestations of universal principles
of animal behaviour (Semple et al., 2021). Zipf’s law of brevity describes anegative relationship between the length of a behaviour and the frequency of its
use (Zipf, 1949). Menzerath’s law describes a negative correlation between thenumber of behaviours in a sequence and the average length of behaviours
composing it (Menzerath, 1954). Following Torre et al., 2019’s physicalhypothesis on the emergence of language laws and given the key role gesturalcommunication played in human language evolution (review: Rodrigues et al.,2021), chimpanzee gestural communication represents a powerful model in which
to explore compression and language laws (Ferrer-i-Cancho et al., 2022), withrepertoires of over 70 distinct gesture types (Byrne et al., 2017). Menzerath's law
appears to hold in play gesture sequences, but the play repertoire represents a rare
failure of Zipf's law, perhaps due to the nature of that specific context (Heesen etal., 2019). Here, we test Zipf’s law of brevity and Menzerath’s law in malechimpanzee sexual solicitation gestures, which, in contrast to play, are subject tostrong selection pressures for success (Hobaiter & Byrne, 2012).
2. Methods
We measured 560 male-to-female sexual solicitation gestures from 173 videos of
16 wild, habituated East African chimpanzees (Pan troglodytes schweinfuthii)
from the Sonso community, Budongo Forest Reserve, Uganda. We calculatedThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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gesture duration following Heesen et al. (2019). Gestures were grouped into types,
with each instance being an individual token (see supp mat). Tokens performed
with less than 1s between them formed a sequence of n tokens. Single gestures
formed sequences of length 1. Following Heesen et al. (2019), we computed
correlations and compression values related to the respective laws. We also
performed subset analysis, grouping gesture types into manual and whole-bodygestures.
3. Results
Zipf’s law of brevity did not hold. A tendency towards an opposite pattern was
detected (Spearman correlation - all data: rs=0.30, n=26, p=0.066; manual subset:
rs=0.42, n=21, p=0.031; supp mat Figure 1) and was corroborated by the
permutation analysis. The expected mean code length L of the data tended to besignificantly big, rather than small, compared to the distribution of L calculated
via 105 permutations (all data: L=0.239s, pright=0.05; manual subset: L=2.26s,
pright=0.058). For whole-body gestures no pattern was detected.
While Spearman’s test revealed only a trend towards correlation between average
gesture duration within sequence t and sequence size n (Spearman correlation:
rs=-0.08 n=359, p=0.076; supp mat Figure 2), the total sum of the duration of eachsequence M was significantly small (M=1300.67, n=359, p=0.003) suggesting alinear association between n and t following Menzerath’s law.
4. Discussion
Our results challenge the view that compression is a universal principle in animalcommunication (Ferrer-i-Cancho et al., 2013; Börstell et al., 2016) as it does not
act on a communicative system uniformly. Although 77% of gesture types
described here matched those in play sequences (Heesen et al., 2019), and data
belonged to the same individuals, collected over the same period, the two different
contexts produced conflicting results: in sexual solicitations we detected an
opposite pattern to Zipf’s law of brevity and no clear evidence for Menzerath’slaw. In chimpanzee solicitation the benefits of successful communication toindividual fitness appear to outweigh the energetic costs associated with the
production of prolonged signals in such a highly competitive environment, fittingwith theories on sexual selection of traits (Zahavi, 1975). Although an apparent
absence of a pattern may depend on the unit of analysis (Demartsev et al., 2019),
there is scope for investigating different parts of gesture structure other thanduration, which may shed light on the physical or environmental mechanisms that
led to the emergence of these patterns in modern human languages.

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Acknowledgements
We thank the staff and field assistants of the Budongo Conservation Field
Station, the Ugandan National Council for Science and Technology and the
Uganda Wildlife Authority for making this research possible. We thank theRoyal Zoological Society of Scotland for its funding of the field station. This
research received funding European Union’s 8th Framework Programme,
Horizon 2020, under grant agreement no 802719.
References
Börstell, C., Hörberg, T., & Östling, R. (2016). Distribution and duration of signs andparts of speech in Swedish Sign Language. Sign Language and Linguistics, 19(2),143-196. doi: 10.1075/sll.19.2.01bor
Byrne, R., Cartmill, E., Genty, E., Graham, K., Hobaiter, C., & Tanner, J. (2017). Great
ape gestures: intentional communication with a rich set of innate signals. Animal
Cognition, 20(4), 755-769. doi: 10.1007/s10071-017-1096-4
Cover, T. M., & Thomas, J. A. (2006). Elements of information theory. 2nd ed. Hoboken,
N.J: Wiley-Interscience.
Demartsev, V., Gordon, N., Barocas, A., Bar‐Ziv, E., Ilany, T., & Goll, Y. et al. (2019).
The “Law of Brevity” in animal communication: Sex‐specific signaling
optimization is determined by call amplitude rather than duration. Evolution
Letters, 3(6), 623-634. doi: 10.1002/evl3.147
Ferrer-i-Cancho, R., Hernández-Fernández, A., Lusseau, D., Agoramoorthy, G., Hsu, M.,& Semple, S. (2013). Compression as a Universal Principle of Animal
Behavior. Cognitive Science, 37(8), 1565-1578. doi: 10.1111/cogs.12061
Ferrer-i-Cancho, R. & Bentz, C & Seguin, C. (2022). Optimal coding and the origins ofZipfian laws. Journal of Quantitative Linguistics, 29(2), 165-194. doi:
10.1080/09296174.2020.1778387
Heesen, R., Hobaiter, C., Ferrer-i-Cancho, R., & Semple, S. (2019). Linguistic laws in
chimpanzee gestural communication. Proceedings of The Royal Society B:
Biological Sciences, 286(1896), 20182900. doi: 10.1098/rspb.2018.2900
Hobaiter, C., & Byrne, R. W. (2012). Gesture use in consortship: Wild chimpanzees’ useof gesture for an ‘evolutionarily urgent’ purpose. In S. Pika & K. Liebal (Eds.),
Gesture Studies (6), 129–146. John Benjamins Publishing Company.
https://doi.org/10.1075/gs.6.08hob
Menzerath, P. (1954). Die architektonik des deutschen wortschatzes. F. Dümmler (Ed).
Semple, S., Ferrer-i-Cancho, R., & Gustison, M. (2022). Linguistic laws inbiology. Trends In Ecology & Evolution, 37(1), 53-66. doi:
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Rodrigues, E., Santos, A., Veppo, F., Pereira, J., & Hobaiter, C. (2021). Connecting
primate gesture to the evolutionary roots of language: A systematic
review. American Journal Of Primatology, 83(9). doi: 10.1002/ajp.23313
Torre IG, Luque B, Lacasa L, Kello CT, Hernández-Fernández A. (2019). On thephysical origin of linguistic laws and lognormality in speech. Royal Society OpenScience, 6, 191023. doi:10.1098/rsos.191023
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Zipf, G. K. (1949). Human behavior and the principle of least effort: an introduction tohuman ecology. Addison-Wesley Press.

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THE FUNCTION AND EVOLUTION OF CHILD-DIRECTED SPEECHJOHANNA SCHICK*1,5 , CAROLINE FRYNS2,5 , FRANZISKA WEGDELL1,5 , MARIONLAPORTE7,8 , KLAUS ZUBERBÜHLER2,4,5 , CAREL P. VAN SCHAIK3,5 , SIMON W.
TOWNSEND1,5,6 , and SABINE STOLL1,5* Corresponding Author: johanna.schick@uzh.ch

1 Department of Comparative Language Science, University of Zurich, Zurich, Switzerland
2 Department of Comparative Cognition, University of Neuchatel, Neuchatel, Switzerland,
3 Department of Anthropology, University of Zurich, Zurich, Switzerland

4 School of Psychology and Neuroscience, University of St. Andrews, St. Andrews, United Kingdom
5 Center for the Interdisciplinary Study of Language Evolution (ISLE), University of Zurich, Zurich,

Switzerland

6 Department of Psychology, University of Warwick, Warwick, United Kingdom

7 Histoire naturelle de l’Homme préhistorique, UMR 7194, Paleo FED, Muséum National d’Histoire

Naturelle, Paris, France

8 Institut des Sciences du Calcul et des Données, Sorbonne Université, Paris, France,

Abstract
Humans exhibit unusual vocal behaviour when interacting with infants andsmall children. In this ‘child-directed speech’ (CDS) the speech signal differs from speech addressed to adults, both acoustically and structurally, exhibiting increased conspicuousness and comprehensibility compared to regular adultdirected speech. CDS has been observed in numerous cultures and it is widely
considered a human universal (Ferguson, 1978; Fernald et al., 1989; Soderstrom,2007).
Over the past few decades, a plethora of studies has shown that these structural and acoustic features support the acquisition of language by infants at bothcomprehension (e.g. Singh, Nestor, Parikh, & Yull, 2009) and production (e.g.
Porritt, Zinser, Bachorowski, & Kaplan, 2014; Rowe, 2012) levels, suggesting afunctional teaching role.
Here, we discuss how CDS evolved by examining which elements of human
CDS are present in our closest-living relatives and thus presumably also in the lastcommon ancestor. We are interested both in frequency of CDS, i.e., how often the
different species address their infants directly and whether they deploy features
which increase conspicuousness/ostention (e.g. repetitions, F0, pitch modulation).
Research on non-human great-apes suggests that apes rarely direct vocal behaviour at their infants (chimpanzees, Pan troglodytes: (Laporte & Zuberbühler,2011); bonobos, Pan paniscus: (Oller et al., 2019)), and if so, most likely inways indistinguishable from adult-directed communication (ongoing work). This
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would then suggest that infant great apes mostly rely on infant-surrounding communication in the acquisition of their vocal communicative competence, and that
infant-directed communication evolved de novo among our hominin ancestors. As
such, we argue that child-surrounding speech in humans requires much more research attention to understand whether this originally predominant source of input
remains significant in humans, or has been replaced by CDS (see Figure 1) as themain source of learning. The potential relevance of child-surrounding speech is
supported by the fact that the interplay of child-directed and child-surrounding
speech shows major cross-cultural variation in human child-rearing. Indeed, insome linguistic communities, surrounding speech seems to be the primary sourceof input in the early phases of child development since adults rarely directly address children (Kaluli and Samoan: (Ochs & Schieffelin, 1984); Yucatec Mayan:(Shneidman & Goldin-Meadow, 2012), Tsimane: (Cristia, Gurven, & Stieglitz,2019)), at least in their first year of life. Despite these differences in input type,children still become competent native speakers (Brown, 2011; De León, 2011;
Ochs & Schieffelin, 1984).
This raises critical questions regarding current theories and the importance of
CDS compared to child-surrounding speech in the evolution of language. We conclude that, even though CDS does indeed serve to facilitate language acquisitionin humans, it also remains plausible that child-surrounding speech can compensate for limited CDS, at least during some developmental stages, thus deserving
more consideration in ontogenetic studies of language evolution.

Figure 1. Transition of child-surrounding to child-directed communication. The transition of
the importance of use of child-surrounding communication to child-directed communication. Darker
colour shows importance/presence and brighter colour possible insignificance of CSS and CDS fromearly hominins to extant humans.

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References
Brown, P. (2011). The cultural organization of attention. In A. Duranti, E. Ochs,& B. B. Schieffelin (Eds.), The handbook of language socialization (pp.
29–55). Malden, MA: Wiley Online Library.
Cristia, A., Gurven, M., & Stieglitz, J. (2019). Child-Directed Speech Is Infrequent in a Forager-Farmer Population: A Time Allocation Study. Journalof Child Development, 90(3), 759–773.
De León, L. (2011). Language Socialization and Multiparty Participation Frameworks. In E. O. A. Duranti & B. B. Schieffelin (Eds.), The handbook of
language socialization.
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Fernald, A., Taeschner, T., Dunn, J., Papousek, M., Boysson-Bardies, B. de, &Fukui, I. (1989). A cross-language study of prosodic modifications in mothers’ and fathers’ speech to preverbal infants. Journal of Child Language,16(3), 477–501.
Laporte, M. N., & Zuberbühler, K. (2011). The development of a greeting signal
in wild chimpanzees. Developmental Science, 14(5), 1220–1234.
Ochs, E., & Schieffelin, B. (1984). Language acquisition and socializations:Three developmental stories and their implications. In R. A. Shweder &R. A. LeVine (Eds.), Culture theory: Essays on mind, self and emotion (p.
276–320). Cambridge: Cambridge University Press.
Oller, D. K., Griebel, U., Iyer, S. N., Jhang, Y., Warlaumont, A. S., Dale, R., &Call, J. (2019). Language Origins Viewed in Spontaneous and Interactive
Vocal Rates of Human and Bonobo Infants. Frontiers in Psychology, 10.
Porritt, L. L., Zinser, M. C., Bachorowski, J.-A., & Kaplan, P. S. (2014). Depression diagnoses and fundamental frequency-based acoustic cues in maternal
infant-directed speech. Language Learning and Development, 10(1), 51–
67.
Rowe, M. L. (2012). A Longitudinal Investigation of the Role of Quantity andQuality of Child-Directed Speech in Vocabulary Development. Child Development, 83(5), 1762–1774.
Shneidman, L. A., & Goldin-Meadow, S. (2012). Language input and acquisition
in a Mayan village: How important is directed speech? Developmental
Science, 15(5), 659–673.
Singh, L., Nestor, S., Parikh, C., & Yull, A. (2009). Influences of infant-directedspeech on early word recognition. Infancy, 14(6), 654–666.
Soderstrom, M. (2007). Beyond babytalk: Re-evaluating the nature and content ofspeech input to preverbal infants. Developmental Review, 27(4), 501–532.

656

THE PUZZLE OF GREAT APE GESTURE & A SOLUTION
THOM SCOTT-PHILLIPS1,* and CHRISTOPHE HEINTZ1
1 Department of Cognitive Science, Central European University*Corresponding Author: thom.scottphillips@gmail.com

Searching for evolutionary homologies between humans and non-human great
apes, one promising approach is to focus on social cognition and in particular incognitive capacities for attention manipulation. Indeed, arguably the most intriguing thing about great ape interaction is how they often inform one anotherin ways that can seem very ‘human’ (for an example see e.g. Genty & Zuberbühler, 2014). How to describe great ape gesture in a way that accounts for bothits behavioural similarity with some forms of human communication, but also its
limited range relative to the human case? One proposed solution to this puzzle
focuses on the role of ‘we intentionality’ in human communication (Tomasello,2008). In our view we-intentionality is not a cognitive process but a behavioural
phenomenon itself in need of explanation. Here we summarise a novel approach.
Figure 1 presents five embedded subsets, each a more or less specific meansof manipulating others’ attention (from Scott-Phillips & Heintz, in press). This
‘special case of’ approach is a novel alternative to existing frameworks for animal communication that are based on a supposed hierarchy of ‘levels of intentionality’ (see e.g. Townsend et al., 2017). It also helps to meet a growing demand for analyses that go beneath the surface of behaviour to consider underlying cognitive processes (e.g. Graham et al., 2020).

Figure 1. Cognitive means of attention manipulation. See main text for detailed description.
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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To describe these subsets we begin in the middle. With ‘Gricean’ we follow
established characterisations in the Relevance Theory literature (e.g. Wharton,
2008; Wilson & Sperber, 2012; inter alia). This is the intentional manipulationof attention towards one’s own informative intentions. Working inwards,’Lewisian’ is Gricean communication by means of convention (following Lewis,1969). A great many instances of Gricean communication entail communicative
conventions, such as nodding, winking and pointing. At the same time, humans
can improvise non-conventional communication when necessary, sometimes
called ‘pantomime’ (e.g. Zlatev et al., 2020). A further subset of Lewisian communication is when the conventions in question are organised in structured networks, commonly called ‘languages’. We call this layer ‘Saussurian’, following
the emphasis that Ferdinand de Saussure placed on structure in linguistic analysis. At the other extreme, the outermost subset includes all instances of the intentional manipulation of attention. We assume this subset extends well beyond
humans. We label it ‘Washburnian’ after Margaret Floy Washburn, who pioneered the study of cognition in a wide variety of non-human animals, at a time
when more behaviourist approaches were coming into vogue (Washburn, 1908).
The key novelty in our analysis is specification of the second innermost
subset (see also Warren & Call, 2022, who present a somewhat similar analysis).
Here, individuals intentionally manipulate others’ attention towards evidence of
the intended outcome, which could be, for instance, grooming, play or sex. Thisis possible if observers can identify such intentions on the basis of the observed
behaviour. We name this subset ‘Ladyginian’ after Nadezhda Ladygina-Kohts(born Nadezhda Ladygina), who was one of the first scientists to document in asystematic way the similarities and differences between the expressive behaviours of humans and chimpanzees. The difference with Washburnian is that
whereas Washburnian behaviour is intentional towards any ends, Ladyginian
behaviour intentionally reveals intentions (to be groomed, to play, etc). The difference with Gricean is that Gricean behaviour intentionally reveals not simply
intentions but specifically informative intentions.
We suggest that most great ape gesture may be best characterised as Ladyginian but not Gricean. This suggestion is potentially convergent with other,previous analyses of great ape gesture (in particular Moore, 2016; 2017). However, those previous analyses maintain the label ‘Gricean’. This conflates two
distinct modes of interaction that can and, in our view, should be separated. Explaining the evolutionary origins of human communication, and hence languages, requires identification of the ecological reasons why humans, and apparently only humans, transitioned from Ladyginian to Gricean modes of interaction (Heintz & Scott-Phillips, in press).

658

Funding statement
TSP & CH were financially supported by the European Research Council, under
the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC
grant agreement no. 609819 (Somics project).
References
Byrne, R. W., Cartmill, E., Genty, E., Graham, K. E., Hobaiter, C., & Tanner, J.
(2017). Great ape gestures: Intentional communication with a rich set of
innate signals. Animal Cognition, 20(4), 755-769.
Genty, E., & Zuberbühler, K. (2014). Spatial reference in a bonobo gesture. Current Biology, 24(14), 1601-1605.
Graham, K. E., Wilke, C., Lahiff, N. J., & Slocombe, K. E. (2020). Scratching
beneath the surface: intentionality in great ape signal production. Philosophical Transactions of the Royal Society B, 375(1789), 20180403.
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Evolution, 5(2), 156-174.

659“MUSIC” PRODUCTION BY COCKATIELSYOSHIMASA SEKI *1*Corresponding Author: yoshimasa.seki@gmail.com
1Department of Psychology, Aichi University, Toyohashi, Japan

Elucidating the biological substrates essential for music (i.e., musicality) could
increase our understanding of the evolution of human language. Researchers havedone work towards this aim using both humans and non-human animals; however,defining the concept of “music” is difficult and discussion of the evolution ofmusicality has not yet converged on a single idea (Savage et al., 2021). Somebirdsongs may be similar to human music (Baptista & Keister, 2005); however,this does not mean these songs are universally considered to be music. Further,studies on music production (or, imitation) by non-human animals are very
scarce, though some avian species (e.g., bullfinches, starlings, Amazon parrots
and African Greys) occasionally imitate human music.
Therefore, I documented four types of sound production behaviors (similar
to human music production) in 3 cockatiels. First, (1) cockatiels exposed to a
melody of a popular song (so it was universally considered music) spontaneously
imitated the melody without any food reinforcement. Then, (2) the cockatiels
spontaneously sang the song in synchrony with a playback of the melody; (2a)when the melody was played back shortly after a bird started singing the song, thebird paused singing and resumed after a short period to synchronize the timing of
his singing to that of the playback melody; (2b) when the melody was played backwhile a bird was not vocalizing, the bird started singing and skipped some initial
notes to synchronize the timing of his singing to that of the playback melody. Sofar, only a few studies have reported unison-like singing by several wild
songbirds; however, the present examples might be a striking demonstration ofthis because the cockatiels sang a melody of human music in unison. Further, a
few months later, (3) the birds spontaneously rearranged (or, customized) the
melody. Some of them inserted novel sound elements around the tail of the
melody. Finally, (4) they later produced novel rhythmic sound sequences which
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did not share similarities with sounds which occurred in their living environment,
which means they created original sound sequences. The motor commands to
produce these sounds were not likely inherent in this species because the acoustic
patterns varied greatly among the individuals. Further, one of the birds produceda sound sequence by utilizing both vocal sounds and sounds generated bydrumming a food cup with his beak (see, Le Covec et al., 2019). This is similar
to a behavior observed in Palm cockatoos (Heinsohn et al., 2017). However, thecockatiel combined learned vocalizations (an imitation of a human word) and thesound made by hitting his beak against a hard surface, and repeated them in
regular short intervals (similar to a human song that contains both vocals anddrumming).
The results suggest that the cockatiels and humans share some of thecapabilities necessary for music production. Because we only have a few reports
on sound production behavior in wild cockatiels, it is difficult to discuss how thefindings are involved in their ecology. As this report demonstrates, captive
cockatiels are prominent imitators of man-made music; however, interestingly,
they are not known to be proficient at imitating long stretches of human speech.
Therefore, some elements of music (e.g., tonality and meter) may assist them inproducing structured and hierarchical sound sequences. The results may suggest
a connection between musicality and the production of long-form speech, at leastin the earliest stage of the evolution of vocal language in humans (Brown, 2017).
Acknowledgements
This study was supported by MEXT/JSPS Grant-in-Aid for Scientific Researchon Innovative Areas #4903 (Evolinguisitics) JP17H06380.
References
Savage, P., Loui, P., Tarr, B., Schachner, A., Glowacki, L., Mithen, S., & Fitch,
W. (2021). Toward inclusive theories of the evolution of musicality.
Behavioral and Brain Sciences, 44, E121.
Baptista, L. F., & Keister, R. A. (2005). Why birdsong is sometimes like music.
Perspectives in Biology and Medicine, 48(3), 426-443.
Heinsohn, R., Zdenek, C. N., Cunningham, R. B., Endler, J. A., & Langmore, N.
E. (2017). Tool-assisted rhythmic drumming in palm cockatoos shares key
elements of human instrumental music. Science Advances, 3, e1602399.
Le Covec, M., Aimé, C., & Bovet, D. (2019). Combinatory sound object play incockatiels: a forerunner of music? Behaviour, 156(5-8), 595-617.
Brown, S. (2017). A joint prosodic origin of language and music. Frontiers in
Psychology, 8, 1894.

661

INVESTIGATING CROSS-LINGUISTIC VARIABILITY IN
PSYCHOLINGUISTIC DIFFICULTY THROUGH INFORMATIONDENSITY VARIABILITY MEASURES
Arturs Semenuks**1,2* Corresponding Author: asemenuks@ucsd.edu

1 Department of Cognitive Science, UCSD, La Jolla, USA

2 Center for Academic Research and Training in Anthropogeny, UCSD, La Jolla, USA

Are all languages equally complex? In recent years, this question has attracted attention of researchers from multiple scientific fields (Miestamo, 2017;
Ehret, Blumenthal-Dramé, Bentz, & Berdicevskis, 2021). If language is a complex adaptive system (Beckner et al., 2009), we expect languages to differ in theircomplexity, and a growing number of studies indeed provide support for that position (McWhorter, 2001; Lupyan & Dale, 2010; Trudgill, 2011; Bentz & Winter,
2014; Ehret et al., 2021). This leads to an important question from the perspectiveof evolutionary linguistics: what leads to the changes in complexity that languages
exhibit?
One of the most widely discussed hypotheses attempting to explain (someaspects of) cross-linguistic variability in complexity suggests that languages adapt
to the cognitive and sociocultural niches they inhabit, i.e. languages change incomplexity to accommodate the cognitive and communicative constraints of theirspeakers, resulting in, for example, language simplification in communities withmore speakers or a higher percentage of non-native learners (Lupyan & Dale,
2010; Bentz & Winter, 2014).
Given this, we can expect a relation between descriptive complexity and psycholinguistic difficulty – less complex languages should be easier to learn, produce and/or comprehend. However, this assumption still requires empirical support, as theory-based complexity and empirically observed cognitive difficulty do
not necessarily entail one another (Miestamo, 2017). Surprisingly, the existenceof this relation is underresearched. Furthermore, while some studies support thishypothesis (Berdicevskis & Semenuks, 2022), a number of recent studies do notfind support for the theorized links between facets of complexity and psycholinguistic difficulty (Atkinson, Smith, & Kirby, 2018; Semenuks & Berdicevskis,
2018; Wagner, Smith, & Culbertson, 2019; Johnson, Gao, Smith, Rabagliati, &Culbertson, 2021). Thus, the question of whether descriptively simpler languages
are also easier does not yet have a clear answer, which makes it less clear why
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the observed cross-linguistic variability in complexity and the relationships between different complexity dimensions and properties of language speakers andcommunities exist.
One way to tackle this issue is to use metrics that are more informed by psycholinguistic research and can be reasonably assumed to be transparently related
to (or even operationalize) facets of learning, production or comprehension difficulty. A promising candidate for such a metric is the average information density
variability of a language at a particular structural level. Information density is theamount of information transmitted per unit at a particular level of organization(e.g. word or syllable), and it can be operationalized as the information content of the unit in its context, i.e. the negative log probability of the unit given
its context. For example, in the sentence “I like coffee with milk and sugar”,
the word “like” has a relatively high information density, as it is relatively unexpected given the preceding context, is more surprising, and has a higher information content, whereas “sugar” has a relatively low information density due to its
high predictability (low information content) at the end of the sentence. Theoretical considerations and empirical research suggest that the difficulty of processing
a linguistic unit is predicted by its information density. Based on this, Jaeger
and Levy (2006) put forward the Uniform Information Density (UID) hypothesis, which argues that speakers aim to minimize the variability in the information
density of their utterances, as a UID strategy minimizes the total difficulty of processing an utterance. A variety of studies provide empitical support for the UID
hypothesis, e.g. see Genzel and Charniak (2002), Aylett and Turk (2004), Frankand Jaeger (2008).
Thus, we can expect that languages argued to be under higher pressure to be
more efficiently structured, such as languages with more speakers, should be moreeasily processed, and thus have more constrained information density variabilityof units at different levels of organization. I investigate this hypothesis by calculating the variability in the word information density at the syntactic level using
the part-of-speech annotated data for a sample of languages from the Universal
Dependencies corpora. Mixed-effects models show that languages vary on thismeasure and, surprisingly, exhibit higher values of information density variability
with more speakers. However, we also find that the average information density level decreases with the number of speakers and is negatively correlated with
the information density variability, thus lowering the average unexpectedness ofa syntactic unit and creating a tradeoff. Taken together, the results suggest thatwhile individual facets of language complexity correlate with extralinguistic sociocultural properties, they also sometimes trade off with each other, potentiallyproviding a solution to why some previously studied facets of complexity do notcorrelate with psycholinguistic difficulty.

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References
Atkinson, M., Smith, K., & Kirby, S. (2018). Adult learning and language simplification. Cognitive science, 42(8), 2818–2854.
Aylett, M., & Turk, A. (2004). The smooth signal redundancy hypothesis: A functional explanation for relationships between redundancy, prosodic prominence, and duration in spontaneous speech. Language and speech, 47(1),31–56.
Beckner, C., Blythe, R., Bybee, J., Christiansen, M. H., Croft, W., Ellis, N. C.,
Holland, J., Ke, J., Larsen-Freeman, D., et al.. (2009). Language is acomplex adaptive system: Position paper. Language learning, 59, 1–26.
Bentz, C., & Winter, B. (2014). Languages with more second language learners
tend to lose nominal case. In Quantifying language dynamics (pp. 96–124).
Brill.
Berdicevskis, A., & Semenuks, A. (2022). Imperfect language learning reducesmorphological overspecification: Experimental evidence. PloS one, 17(1),e0262876.
Ehret, K., Blumenthal-Dramé, A., Bentz, C., & Berdicevskis, A. (2021). Meaning
and measures: interpreting and evaluating complexity metrics. Frontiers in
Communication, 6, 61.
Frank, A. F., & Jaeger, T. F. (2008). Speaking rationally: Uniform information
density as an optimal strategy for language production. In Proceedings ofthe annual meeting of the cognitive science society (Vol. 30).
Genzel, D., & Charniak, E. (2002). Entropy rate constancy in text. In Proceedingsof the 40th annual meeting of the association for computational linguistics(pp. 199–206).
Jaeger, T., & Levy, R. (2006). Speakers optimize information density throughsyntactic reduction. Advances in neural information processing systems,
19.
Johnson, T., Gao, K., Smith, K., Rabagliati, H., & Culbertson, J. (2021). Investigating the effects of i-complexity and e-complexity on the learnability ofmorphological systems. Journal of Language Modelling, 9(1), 97–150.
Lupyan, G., & Dale, R. (2010). Language structure is partly determined by social
structure. PloS one, 5(1), e8559.
McWhorter, J. H. (2001). The worlds simplest grammars are creole grammars.
Miestamo, M. (2017). Linguistic diversity and complexity. Lingue e linguaggio,
16(2), 227–254.
Semenuks, A., & Berdicevskis, A. (2018). What makes a grammar difficult?
experimental evidence. In The evolution of language: Proceedings of the
12th international conference (evolangxii).
Trudgill, P. (2011). Sociolinguistic typology: Social determinants of linguistic
complexity. Oxford University Press.

664

Wagner, S., Smith, K., & Culbertson, J. (2019). Acquiring agglutinating andfusional languages can be similarly difficult: Evidence from an adaptive
tracking study. In Cogsci (pp. 3050–3056).

665

INVESTIGATING THE RELATIONSHIP BETWEEN NUMBER OF
SPEAKERS, I-COMPLEXITY, AND E-COMPLEXITY
Arturs Semenuks**1,2* Corresponding Author: asemenuks@ucsd.edu

1 Department of Cognitive Science, UCSD, La Jolla, USA

2 Center for Academic Research and Training in Anthropogeny, UCSD, La Jolla, USA

A number of theoretical proposals and computational models suggest that thesociocultural niche a language occupies affects the morphosyntactic complexityof that language, e.g. see McWhorter (2001), Wray and Grace (2007), Trudgill(2011), Dale and Lupyan (2012), Spike (2017). A common denominator of manysuch proposals is the focus on the difference between exoteric and esoteric societies, i.e. societies more (exoteric) and less (esoteric) open to outsiders. Wrayand Grace (2007) propose arguably the most detailed psycholinguistic explanation for how esotericity promotes morphosyntactic complexity to date, suggestingthat shared insider knowledge and implicit encoding leads to opaque, irregular,
and more complex forms of language1 .
Recently, correlational cross-linguistic and experimental studies have provided empirical support to these theories, e.g. Lupyan and Dale (2010), Bentz
and Winter (2014), Sinnemäki and Di Garbo (2018), Koplenig (2019), Kocab,
Ziegler, and Snedeker (2019), Raviv, Meyer, and Lev-Ari (2019), Berdicevskis
and Semenuks (2020, 2022). It is encouraging that researchers arrive at similar
conclusions using approaches differing in methodological details, which could betaken as showing the robustness of the hypothesized relationship. At the sametime, what morphological complexity is and what measures best capture it is
not yet settled (Berdicevskis et al., 2018; Ehret, Blumenthal-Dramé, Bentz, &Berdicevskis, 2021). Additionally, how different dimensions of complexity interact with each other is far from resolved and what specific dimension(s) of complexity are affected by sociocultural niche is not yet clear. For example, Sinnemäki
and Di Garbo (2018) find their verbal morphological complexity measure to becorrelated with the total amount of speakers a language has and the percentage of
L2 speakers in its population, but find no similar relationship for nominal complexity. Thus the aforementioned variability in methodological details leaves open
1 In turn, these properties themselves make it harder for outsiders to understand the language, cre-ating a feedback loop.

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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some important questions, including (i) are languages in exoteric niches simpler
on all dimensions of complexity? and (ii) how are different dimensions of complexity related?
Here I explore whether languages of more exoteric societies (operationalized
as the total number of speakers) tend to have morphological paradigms of lower
i-complexity. Following Ackerman and Malouf (2013), we define i-complexity
as the average conditional entropy between the word forms in a paradigm, which
captures the average amount of information needed for a speaker to predict all
inflectional forms of a new lemma. On the one hand, it should be expected thatlower values of i-complexity facilitate learning and would be under stronger selective pressure in exoteric societies with more speakers. On the other hand, some
information theoretic measures of language structure have been reported to behighly similar across languages due to psycholinguistic constraints (Coupé, Oh,
Dediu, & Pellegrino, 2019). As Ackerman and Malouf (2013) hypothesize thati-complexity is constrained to facilitate preservation of efficient linguistic structures, we can similarly expect it to not differ substantially across languages.
Additionally, recent studies have provided evidence for a negative correlation
between i-complexity and e-complexity, i.e. the number of morphosyntactic distinctions that a particular language makes (Cotterell, Kirov, Hulden, & Eisner,
2019; Johnson, Gao, Smith, Rabagliati, & Culbertson, 2021). I extend the previous analyses using methods more closely aligned with the proposal in Ackerman
and Malouf (2013) and following the advice for best practices and directions inMalouf, Ackerman, and Semenuks (2020).
I use the data (71 languages, 47 language families) from the Surrey Morphological Complexity Database (Baerman, Brown, Evans, Corbett, & Cahill, 2015),
as well as the data from the UniMorph 3.0 Project (60 languages, 11 language
families) (McCarthy et al., 2020). The former database provides high quality annotated data for inflectional paradigms from a typologically and geographicallydiverse sample of languages, whereas the latter provides a less diverse sample,
but allows for a more theory-neutral data-driven process of estimating paradigm
information for a language. In the UniMorph 3.0 data, the set of noun paradigms
for each language is estimated by removing the maximal shared subset of characters within word forms for all lemmas in a language. I use mixed-effect linear
models to control for phylogeny and geography in analyzing the data. No significant correlation between i-complexity and the number of speakers a language
is observed. However, similarly to Ackerman and Malouf (2013), languages arefound to frequently have lower values than expected as shown through Monte
Carlo simulations. Additionally, both data sets suggest a negative correlation
between i-compexity and e-complexity. Taken together, the results suggest thati-complexity is not significantly affected by the sociocultural niche a language
occupies, however we see evidence of optimization of this aspect of linguistic
structure, potentially supporting its hypothesized psycholinguistic importance.

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References
Ackerman, F., & Malouf, R. (2013). Morphological organization: The low conditional entropy conjecture. Language, 429–464.
Baerman, M., Brown, D., Evans, R., Corbett, G. G., & Cahill, L. (2015). Surreymorphological complexity database. (University of Surrey. http://dx.
doi.org/10.15126/SMG.23/1)
Bentz, C., & Winter, B. (2014). Languages with more second language learners
tend to lose nominal case. In Quantifying language dynamics (pp. 96–124).
Brill.
Berdicevskis, A., Çöltekin, Ç., Ehret, K., Prince, K. von, Ross, D., Thompson, B.,
Yan, C., Demberg, V., Lupyan, G., Rama, T., et al.. (2018). Using universal
dependencies in cross-linguistic complexity research. In Proceedings of thesecond workshop on universal dependencies (udw 2018).
Berdicevskis, A., & Semenuks, A. (2020). Different trajectories of morphologicaloverspecification and irregularity under imperfect language learning. The
Complexities of Morphology, 283–305.
Berdicevskis, A., & Semenuks, A. (2022). Imperfect language learning reducesmorphological overspecification: Experimental evidence. PloS one, 17(1),e0262876.
Cotterell, R., Kirov, C., Hulden, M., & Eisner, J. (2019). On the complexity
and typology of inflectional morphological systems. Transactions of the
Association for Computational Linguistics, 7, 327–342.
Coupé, C., Oh, Y. M., Dediu, D., & Pellegrino, F. (2019). Different languages,
similar encoding efficiency: Comparable information rates across the human communicative niche. Science advances, 5(9), eaaw2594.
Dale, R., & Lupyan, G. (2012). Understanding the origins of morphologicaldiversity: The linguistic niche hypothesis. Advances in complex systems,
15(03n04), 1150017.
Ehret, K., Blumenthal-Dramé, A., Bentz, C., & Berdicevskis, A. (2021). Meaning
and measures: interpreting and evaluating complexity metrics. Frontiers in
Communication, 6, 61.
Johnson, T., Gao, K., Smith, K., Rabagliati, H., & Culbertson, J. (2021). Investigating the effects of i-complexity and e-complexity on the learnability ofmorphological systems. Journal of Language Modelling, 9(1), 97–150.
Kocab, A., Ziegler, J., & Snedeker, J. (2019). It takes a village: The role ofcommunity size in linguistic regularization. Cognitive Psychology, 114,
101227.
Koplenig, A. (2019). Language structure is influenced by the number of speakers
but seemingly not by the proportion of non-native speakers. Royal Society
open science, 6(2), 181274.
Lupyan, G., & Dale, R. (2010). Language structure is partly determined by social

668

structure. PloS one, 5(1), e8559.
Malouf, R., Ackerman, F., & Semenuks, A. (2020). Lexical databases for computational analyses: A linguistic perspective. Proceedings of the Society for
Computation in Linguistics, 3(1), 297–307.
McCarthy, A. D., Kirov, C., Grella, M., Nidhi, A., Xia, P., Gorman, K., Vylomova,E., Mielke, S. J., Nicolai, G., Silfverberg, M., et al.. (2020). Unimorph 3.0:
Universal morphology.
McWhorter, J. H. (2001). The worlds simplest grammars are creole grammars.
Raviv, L., Meyer, A., & Lev-Ari, S. (2019). Larger communities create more
systematic languages. Proceedings of the Royal Society B, 286(1907),
20191262.
Sinnemäki, K., & Di Garbo, F. (2018). Language structures may adapt to thesociolinguistic environment, but it matters what and how you count: A typological study of verbal and nominal complexity. Frontiers in psychology,
9, 1141.
Spike, M. (2017). Population size, learning, and innovation determine linguistic
complexity. In Cogsci.
Trudgill, P. (2011). Sociolinguistic typology: Social determinants of linguistic
complexity. Oxford University Press.
Wray, A., & Grace, G. W. (2007). The consequences of talking to strangers: Evolutionary corollaries of socio-cultural influences on linguistic form. Lingua,117(3), 543–578.

669

GRAMMATICAL COMPLEXITY IS ONLY WEAKLYINFLUENCED BY THE SOCIOLINGUISTIC ENVIRONMENTOLENA SHCHERBAKOVA *1, SUSANNE MARIA MICHAELIS2, HANNAH J.
HAYNIE3, SIMON J. GREENHILL4, DAMIÁN E. BLASI 5, 2, 6, RUSSELL D.
GRAY2,7, VOLKER GAST8, and HEDVIG SKIRGÅRD2*Corresponding Author: shcherbakova@shh.mpg.de
1Max Planck Institute for the Science of Human History, Jena, Germany
2Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
3University of Colorado, Boulder, CO, USA
4School of Biological Sciences, University of Auckland, Auckland, New

Zealand
5Harvard University, Cambridge, MA, USA
6Yale University, New Haven, CT, USA
7School of Psychology, University of Auckland, Auckland, New Zealand
8Friedrich-Schiller University of Jena, Jena, Germany

Recent studies claim that the social environment influences the evolution of
language structures. In particular, grammatical complexity has been proposed to
be lower in communities with looser social networks, higher numbers of L1
speakers, and higher proportions of L2 speakers (among others, Kusters 2003,
Trudgill 2011, Lupyan & Dale 2010, Sinnemäki & Di Garbo 2018). The
explanation for these relationships relies on the assumption that larger
communities are exposed to more contact than smaller ones. Specifically, due tosubstantial proportions of L2 speakers in large communities, the more complex
features are not always transmitted to further generations, and hence languages
become simpler over time. Here we test these claims on a global scale using two
metrics of grammatical complexity while controlling for phylogenetic and spatial
non-independence.
In previous studies, metrics of grammatical complexity often capture twodifferent phenomena (cf. Lupyan & Dale 2010): the amount of phonologically
fused marking ("boundness") and the number of semantic distinctions("informativity"). In our study, we separate these into two different metrics toevaluate potential differences in their behavior.
We use the typological information from Grambank (The Grambank Consortium2022) to construct metrics for each of these concepts and test whether variationin the metric scores depends on the number of L1 speakers, the proportion of L2This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

670

speakers, the status of the language (official/not official) obtained from
Ethnologue (Eberhard et al. 2021), number of linguistic neighbors, and population
density available in Bromham et al. (2022). The status of the language has notbeen previously used in investigating these questions, but we include it in thisstudy to glean the information on the function of the language in a community. Inline with previous research, we expect the two dimensions of grammaticalcomplexity, boundness and informativity, to be negatively correlated with allsocial variables, except for population density which serves as a proxy for social
network density and can be expected to positively correlate with complexity
dimensions (c.f. Trudgill 2011).
To explore relationships between language and social structures, we adopt a
spatiophylogenetic modeling technique introduced in Dinnage et al. (2020), a
Bayesian approach that uses an Integrated Nested Laplace Approximation (INLA)(Rue et al. 2009, Martins et al. 2013). The models fit social variables as fixed
effects and two structured random effects to estimate the phylogenetic and spatial
influence on variation in metric scores. This method allows us to not only control
for phylogenetic and spatial non-independence of languages but also to estimate
the effects of phylogeny and geographical distance between languages onvariation in grammatical complexity and evaluate if adding the social variables
improves the explanatory power of the model above and beyond the phylogenetic
and geographic similarity. We compare the models combining sets of random andfixed effects based on obtained WAIC values (Watanabe 2010).
Contrary to prior studies, our Bayesian spatiophylogenetic modeling results do
not support a hypothesis that morphological complexity (boundness) is strongly
influenced by the number of L1 speakers, the proportion of L2 speakers, thenumber of linguistic neighbors, or population density. In contrast to previous
studies, we disentangle boundness from informativity in our analyses, and our
findings can be explained by the use of an extensive dataset and our rigorous
control for phylogenetic and spatial non-independence of languages. As a result,the claimed link between the grammatical complexity and social variables does
not appear to be strong and is found only weakly between informativity scoresand some of the social variables, such as a positive effect of L1 speaker populationand the official language status on informativity. This indicates that the evolutionof these complexity dimensions is better explained from the perspective ofinheritance and areal diffusion rather than constraints imposed by different social
environments.
Acknowledgements
The authors are thankful to Sam Passmore for his advice on the methods.

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References
Bromham, L., Dinnage, R., Skirgård, H., Ritchie, A., Cardillo, M., Meakins, F.,
Greenhill, S., & Hua, X. (2022). Global predictors of language
endangerment and the future of linguistic diversity. Nature Ecology &Evolution, 6(2), 163–173.
Eberhard, D. M., Simons, G. F., & Fennig, C. D. (Eds.). (2020). Ethnologue:
Languages of the World (24th ed.). SIL International.
www.ethnologue.com
Dinnage, R., Skeels, A., & Cardillo, M. (2020). Spatiophylogenetic modelling of
extinction risk reveals evolutionary distinctiveness and brief flowering
period as threats in a hotspot plant genus. Proceedings of the Royal Society
B, 287(1926), 20192817.
Kusters, W. (2003). Linguistic complexity. Netherlands Graduate School of
Linguistics.
Lupyan, G., & Dale, R. (2010). Language Structure Is Partly Determined bySocialStructure.
PLOS
ONE,
5(1),
e8559.
https://doi.org/10.1371/journal.pone.0008559
Martins, T. G., Simpson, D., Lindgren, F., & Rue, H. (2013). Bayesian computing
with INLA: new features. Computational Statistics & Data Analysis, 67,
68–83.
Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian inference forlatent Gaussian models by using integrated nested Laplace approximations.
Journal of the Royal Statistical Society: Series b (Statistical Methodology),71(2), 319–392.
Sinnemäki, K., & Di Garbo, F. (2018). Language structures may adapt to thesociolinguistic environment, but it matters what and how you count: A
typological study of verbal and nominal complexity. Frontiers in
Psychology, 9, 1141.
The Grambank Consortium (eds.). (2022). Grambank 1.0. Leipzig: Max Planck
Institute for Evolutionary Anthropology. http://grambank.clld.org
Trudgill, P. (2011). Sociolinguistic Typology: Social Determinants of Linguistic
Complexity. OUP Oxford.
Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation andwidely applicable information criterion in singular learning theory. Journalof Machine Learning Research, 11, 3571–3594.

672

CONSTRAINTS ON COMMUNICATING ORDER OF EVENTSIN PANTOMIMEMARTA SIBIERSKA*1,2, PRZEMYSŁAW ŻYWICZYŃSKI1, JORDAN ZLATEV2,MONIKA BORUTA-ŻYWICZYŃSKA1, JOOST VAN DE WEIJER3, ADAMGUTOWSKI1, and MARIUSZ LEWANDOWSKI1*Corresponding Author: sibier@umk.pl
1Centre for Language Evolution Studies, Nicolaus Copernicus University, Toruń, Poland
2Division for Cognitive Semiotics, Lund University, Sweden
3The Humanities Lab, Lund University, Sweden

Pantomime, understood as a self-sufficient, predominantly non-conventional
means of communication based on bodily mimesis (Żywiczyński et al. 2018), has
become a strong candidate for “the original human-specific communicative
system” (Zlatev et al. 2020). Although pantomime affords successfulcommunication in many contexts, it also has its limitations (see e.g. Żywiczyński
et al. 2021). In this study, we looked at its constraints on communicating order of
events. We assumed that as pantomime unfolds in real time, it is fit for conveying
simple narratives, where events are arranged in a chronological sequence. At thesame time, it is less suitable for expressing events in a non-chronological order,
characteristic of more complex narratives (Boyd 2017; Żywiczyński et al. 2018).
This assumption was put to test in a study based on basic “semiotic games” (seee.g. Krauss & Winheimer 1966; Fay et al. 2010; cf. Fay et al. 2013, 2014).
The study consisted of 4 games, during which 52 participants, working inpairs, took turns as directors and matchers. The director was presented with averbal representation of a story consisting of three events. In the chronologicalcondition, the story was arranged so that the resultative event was at the end (e.g.
“A man opened the door. Then he saw a bear. Then he ran away”). In the nonchronological condition, the resultative event was placed at the beginning (e.g.
“A man ran away, because when he opened the door, he saw a bear”). The task ofthe director was to communicate the story to the matcher by means of body
movements. The matcher was presented with 4 comic strips: one which
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673

corresponded to the director’s input and three distractors. Their task was to choose
the correct alternative. Communicative success was operationalised as theaccuracy of the matchers’ responses.
We found that non-chronological representations had lower communicative
success (34% correct matches) when compared to representations ofchronological order of events (94% correct matches). To explore this result, weused a logistic regression model which included an interaction between two
predictors (condition and game), and pairs of participants as a random effect. The
model showed significant differences in communicative success between theconditions, as well as between the games (see Fig. 1).

Figure 1. Predicted proportions of correct matches with 95% intervals, corresponding to the log odds
from the logistic regression model described above. Post-hoc tests indicated that differences between
all the games in the non-chronological condition were significant.

A possible explanation of this result is that some pairs of participants managed toindicate a disruption of chronology by means of specific movements, whichconventionalised over the games. These results suggest that while it is fairly easy
to communicate events in a chronological order in pantomime, the need to convey
more chronologically complex narratives calls for a successful negotiation of
specific communicative strategies over time, thus pushing pantomime towards“protolanguage”. We discuss these findings in the context of the bodily mimesis
theory and the evolutionary trajectory of pantomime and narrative (e.g. Arbib
2012; Collins 2013; Donald 2001; Zlatev 2014; Zlatev et al. 2020).

674

Acknowledgements
This research was funded by the Polish National Science Centre (NCN) under theagreement UMO-2017/27/B/HS2/00642. Marta Sibierska was funded by thePolish National Agency for Academic Exchange (NAWA) under the agreement
PPN/BEK/2020/1/00319/U/00001.
References
Arbib, M. A. (2012). How the brain got language. Oxford: Oxford University
Press.
Boyd, B. (2017). The evolution of stories: From mimesis to language, from fact
to fiction. WIREs Cognitive Science, 9(1), 1444, doi: 10.1002/wcs.1444.
Fay, N. et al. (2014). Creating a communication system from scratch: gesturebeats vocalization hands down. Frontiers in Psychology, 5, 354, doi:
10.3389/fpsyg.2014.00354.
Fay, N., Arbib, M. A., Garrod, S., (2013). How to bootstrap a humancommunication system. Cognitive Science, 37, 1356–1367, doi:
10.1111/cogs.12048.
Fay, N. et al. (2010). The interactive evolution of human communication systems.
Cognitive Science, 34(3), 351–386.
Krauss, R. M. & Weinheimer, S. (1966). Concurrent feedback, confirmation andthe encoding of referents in verbal communication. Journal of Personality andSocial Psychology, 4, 343–346.
Zlatev, J., Żywiczyński, P., Wacewicz, S. (2020). Pantomime as the originalhuman-specific communicative system. Journal of Language Evolution, 5(2),156–174, doi: 10.1093/jole/lzaa006.
Żywiczyński, P. et al. (2021). Evolution of conventional communication. A crosscultural study of pantomimic re-enactments of transitive events. Language &Communication, 80, 191–203.
Żywiczyński, P., Wacewicz, S., Sibierska, M. (2018). Defining pantomime for
language evolution research. Topoi, 37(2), 307–318.

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SHALL I POINT IT OUT?
REBECCA SLAGLE LUENSER* 11 and HEIDI LYN 2 1,2*Corresponding Author: rl2122@jagmail.southalabama.edu
1Department of Psychology, University of South Alabama, Mobile AL USA
2Joan M. Sinnott Chair of Psychology and Department of Marine Sciences, University ofSouth Alabama, Mobile AL USA

1. Ostensive Communication
A great debate exists when it comes to ostensive (intentional) communication andnon-human primates. Some argue that non-human primates likely lack ostensive
communication or are at least very limited in it (Scott-Phillips, 2015). ScottPhillips (2015) contends that this limitation is due to great ape communication
relying on a code model in contrast to human communication’s ostensiveinference model. Still, others argue that the behaviors that suggest ostensive
communication in human research exist in apes as well, including eye contact,and goal-directed, intentional communication (Moore, 2015).
Previous research has found bonobos in environments with rich language
exposure are quite successful at following pointing gestures (Lyn et al., 2010).
The bonobos studied here are unique in their high levels of exposure to natural
English and lexigrams (Savage-Rumbaugh, 1993). The bonobos have also
successfully passed previous pointing tests, likely due to daily pointing
exposure. These environmental conditions gave Kanzi and the other bonobos a
possible advantage in displaying their comprehension of ostensive
communications.
Here, we separated the act of pointing into two components: the pointmechanism (the gesture) and the ostensive communication (eye contact and gaze
direction) in the hopes of exploring whether the bonobos understand pointing asa simple association, or as we hypothesize, they understand the communicative
intent, including the ostensive cues that accompany the act itself.

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2. Methodology
The present study used an object-choice task in which six bonobos were eachtested on contralateral point gestures in five conditions in which the eye gaze andattention of the researcher were manipulated. Subjects were presented with two
paper bags in front of their home enclosure, one which contained a small food
reward. A researcher pointed to the baited bag contralaterally, focusing attention
as described by each condition: (1) Contralateral (2) Drop Gaze, (3) Object-Ape,(4) Gesture-Object and (5) Gesture-Ape. For the Contralateral condition, theresearcher’s attention shifted equally between the ape, the object, and the gesture.
For the Drop Gaze, attention focused on either the gesture or the floor. For theremaining conditions (Object-Ape, Gesture-Object, and Gesture-Ape), attention
shifted equally between the two targets. The trial was successful when the ape
pointed to the correct object. The trial was unsuccessful when the ape failed topoint within 15 seconds or pointed to the incorrect object.
3. Discussion
Our results support previous findings that language enculturated bonobos havesome cognitive capacity for understanding ostensive communication (Lyn et al.,2010). Aligning with the hypothesis, the bonobos with a more extensive historyof language exposure performed better than those with less exposure (KruskalWallis test, chi-square = 54.8, df = 5; p < .001). Mali, who has had limited
language enculturation, scored much lower than the other apes on all conditions(Dwass-Steel-Critchlow-Fligner pairwise comparisons for each ape, For each p< .001). Additionally, in contrast to pilot data, apes performed equally well on thedrop-gaze condition as the more ostensive conditions, though results were notsignificant (Kruskal-Wallis test, chi-square = 5.90, df = 4; p = 0.207). This
discrepancy may be due to altered methodologies, underscoring the need for carewhen designing studies of this nature. While some argue that ostension cannot be
separated from the communication act itself, that they are inherently the samething (Heintz & Scott-Phillips, in press), it is our plan to continue attempting topiece apart these components in further studies.
References
Heintz, C., & Scott-Phillips, T. (in press). Expression unleashed: The
evolutionary and cognitive foundations of human communication.
Behavioral and Brain Sciences.

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Lyn, H., Russell, J.L., & Hopkins, W.D. (2010). The impact of environment on
the comprehension of declarative communication in apes. Psychological
Science, 21(3), 360-365.
Moore, R. (2015). Meaning and ostension in great ape gestural communication.
Animal Cognition, 19, 223-231.
Savage-Rumbaugh, E.S., Murphy, J., Sevcik, R.A., Brakke, K.E., Williams, S.L.,& Rumbaugh, D.M. (1993) Language comprehension in ape and child.
Monographs of the Society for Research in Child Development, 58(3-4), 1222.
Scott-Phillips, T. C. (2015). Nonhuman primate communication, pragmatics, andthe origins of language. Current Anthropology, 56(1), 56-80.

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SIMULTANEITY AS AN EMERGENT PROPERTY OF SIGNLANGUAGESANITA SLONIMSKA* 1,2, ASLI OZYUREK 1,2,3, and OLGA CAPIRCI 4*Corresponding Author: Anita.Slonimska@mpi.nl
1 Centre for Language Studies, Radboud University, Nijmegen, The Netherlands
2 Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
3 Donders Centre for Cognition, Nijmegen, The Netherlands
3

ISTC, National Research Council, Rome, Italy

One of the defining properties of natural languages is segmenting holistic
representations into smaller meaning units that allow to combine them into larger
meaning units, leading to compositionality (Kirby et al., 2008). This has beenshown to constitute an emergent property of linguistic systems that evolved
initially from holistic representations to accommodate the pressures ofcommunicative efficiency during language use and language transmission to newlearners (e.g., Kirby et al., 2008; Motamedi et al., 2019; Senghas et al., 2004).
One of the evidence for this claim comes from sign language emergence researchwhich has shown the emergence of segmentation out of holistic representations
and linear sequencing of these meaning units (Senghas et al., 2004). However, insign languages, due to the affordances of the visual modality to use multiple
articulators and iconicity, meaning units can be organized not only linearly but
also simultaneously. In the present study, we investigate if simultaneity, in
addition to linearity, is an emergent property of sign languages by comparing theuse of simultaneous constructions in LIS (Italian Sign Language) to that of silent
gestures used by hearing Italian speakers.
Recent research has shown that LIS signers use simultaneous and iconic
constructions (i.e., diagrammatic iconicity) as modality-specific properties toachieve communicative efficiency through clustering related meaning units closer
together when they are asked to encode informatively rich events in an interactive
task (Slonimska et al., 2020). The study showed that as the events to becommunicated became more informationally dense (i.e., contained more semantic
information units) LIS signers increased the use of simultaneous constructions aswell as information density of these constructions. However, it remained to be
explored whether such simultaneity might constitute a general affordance ofcommunication in visual modality rather than a linguistic property that has
evolved for greater communicative efficiency. Therefore, here we asked whether
hearing participants with no knowledge of any sign language using only their
gestures to communicate could recruit multiple articulators and iconicity torepresent multiple elements of the event simultaneously to the same extent as
signers.

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In the present study, we conducted the same experiment as in Slonimska etal. (2020) with 23 Italian speakers with no knowledge of any sign language (12females, M age = 26.04) using silent gesture to describe the events of varying
information density in a director-matcher task. We coded whether movement
segments (MS), i.e., segments based on gesture strokes (Kendon, 2004) used bygesturers contained simultaneity, where more than one articulator is used torepresent distinct semantic information units. We also coded the information
density (i.e., the number of simultaneously represented semantic information
units) of these MS. We then compared frequencies and information density of MS
with simultaneity in descriptions of silent gesturers and LIS signers (N = 23).
The findings showed that in comparison to signers, silent gesturers used MSwith simultaneous constructions less frequently than signers (β = -0.98, CI[-1.23;-0.73], SE = 0.13, z = -7.65, p <.001), even though their use increased in bothgroups as information density of the pictures they needed to describe increased(Fig.1). Furthermore, when silent gesturers used simultaneous MS they weresignificantly less informationally dense (β = -0.21, CI[-0.26; -0.17], SE = 0.02,
z= -9.16, p <.001). Simultaneous constructions were also qualitatively different
in the two groups. Gesturers relied on imagistic iconicity to represent
simultaneous aspects of the event (e.g., to hold and caress) in separate movement
segments (Fig. 2). In turn, signers relied on diagrammatic iconicity and broughtvarious information units encoded on different articulators together (also relating
to the two different referents) in their movement segments (MS 3, 4 and 5).

Figure 1. Mean proportions of simultaneous
MS out of the total number of MS per trial forsilent gesturers and signers when asked todescribe events with increasing information
density.

Figure 2. Prototypical encoding sequence for an
event from silent gesturers and signers. Colored
circles represent semantic information units ofdifferent referents (purple circles for the bird, redcircles for the bunny).

The present study indicates that not only linear but also simultaneous expressionsof segmented meaning units constitute an emergent property in sign languages
that potentially evolved for achieving greater communicative efficiency. The
findings highlight the role of modality-specific adaptive capabilities in linguistic
expression and are relevant for the broader discussion about the multi-modal
origins of language.

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References
Goldin-Meadow, S., So, W. C., Özyürek, A., & Mylander, C. (2008). The naturalorder of events: How speakers of different languages represent events
nonverbally. Proceedings of the National Academy of Sciences, 105(27),9163-9168.
Kendon, A. (2004). Gesture: Visible action as utterance. Cambridge University
Press.
Kirby, S., Cornish, H., & Smith, K. (2008). Cumulative cultural evolution in thelaboratory: An experimental approach to the origins of structure in humanlanguage. Proceedings of the National Academy of Sciences, 105(31), 1068110686.
Motamedi, Y., Schouwstra, M., Smith, K., Culbertson, J., & Kirby, S. (2019).
Evolving artificial sign languages in the lab: From improvised gesture tosystematic sign. Cognition, 192, 103964.
Senghas, A., Kita, S., & Özyürek, A. (2004). Children Creating Core Propertiesof Language: Evidence from an Emerging Sign Language in Nicaragua.
Science, 305(5691), 1779-1782.
Slonimska, A., Özyürek, A., & Capirci, O. (2020). The role of iconicity andsimultaneity for efficient communication: The case of Italian Sign Language(LIS). Cognition, 200, 104246.

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THE ONTOGENY OF BEHAVIOURAL RESPONSES TO PANTHOOTS IN WILD CHIMPANZEESADRIAN SOLDATI*1,2,3, PAWEL FEDUREK3,4, GUILLAUME DEZECACHE3,5,
GERESOMU MUHUMUZA3, KLAUS ZUBERBÜHLER1,2,3, and JOSEP CALL1*Corresponding Author: as391@st-andrews.ac.uk
1School of Psychology and Neuroscience, University of St Andrews, St Andrews, UK
2School of Biology, University of Neuchâtel, Neuchâtel, Switzerland
3Budongo Conservation Field Station, Masindi, Uganda
4University of Stirling, Faculty of Natural Sciences, Stirling, UK
5Université Clermont Auvergne, CNRS, LAPSCO, Clermont-Ferrand, France

1. Introduction
Comparative researchers study the vocal systems of animal species to identify thepresence or absence of traits shared with human language to explain its origins(Fitch & Zuberbühler, 2013). Traditionally, the vocal development of non-humanprimates, like their adult vocal communication, has been regarded as rather
inflexible and hard-wired, which led to the idea that few parallels exist with thehighly flexible and socially mediated acquisition of human language (Egnor &Hauser, 2004; Owren et al., 2011). However, limitations to this view include
being largely based on studies conducted on monkey species, on alarm calls, andin artificial or captive settings (Seyfarth & Cheney, 1997; Snowdon, 2009).
The development of pant hoots, complex vocal sequences used by wild
chimpanzees as long-distance social calls (Fedurek et al., 2014; Marler &Hobbett, 1975; Mitani & Nishida, 1993), is largely unknown. Pant hoots are themost frequently used calls by adults and are flexibly used across most behaviouralcontexts (Marler & Tenaza, 1977). Chimpanzees either spontaneously produce
pant hoots or respond vocally to others’ pant hoots, sometimes by chorusing
together. Given that this call is very rarely produced by immature individuals, one
approach to determine if and how social factors mediate its ontogeny is toinvestigate how young receivers respond when exposed to others’ pant hoots.
2. Methods
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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We collected data in the Sonso community (Pan troglodytes schweinfurthii) of
the Budongo Forest, Uganda, from six infants (0–4 years; M:F, 4:2), seven
juveniles (5–9 years; M:F; 4:3), all dependent on their mother, as well as from 25
mature individuals (M:F, 13:12). We used focal animal sampling as the main
method of data collection (Altmann, 1974). We recorded the behavioural activityof the focal, information about pant hoots produced by the focal and by othergroup members, behavioural changes of the focal after being exposed to others’
pant hoots, and continuous party composition. We used long-term data collectedby field assistants to assess the gregariousness of individuals by measuring how
frequently they were observed in a party while not being focal followed.
3. Results
Immature individuals spontaneously called 10 times less often than matureindividuals but responded vocally only 2 times less often. The two most common
behavioural responses to pant hoots were a head movement towards the call andvocal responses. When compared to mature chimpanzees, immature individuals
produced both responses less often. More specifically, the older offspring, themale offspring, and the offspring of more gregarious mothers were more likely tomove their head towards calls. The older male offspring, the older offspring of
more gregarious mothers, and the offspring of more gregarious mothers in thepresence of adult males were more likely to respond vocally. In addition,
immature individuals were more likely to respond vocally when their mother also
produced a vocal response. Finally, we observed that the male offspring of moregregarious mothers were exposed to more pant hoots overall.
4. Discussion
Our study shows that the ontogeny of vocal responses undergoes developmental
changes, varies according to sex, and is socially mediated. The rather slow
ontogeny of pant hoots is consistent with the idea that vocal usage andcomprehension learning are less hard-wired in social calls. Our observations arealso in line with the idea that the development of social skills tends to occur earlier
in male great apes, likely as a result of different selection pressures. Higher levelsof social and communicative exposure likely increase opportunities for immatureindividuals to learn appropriate responses. Chorusing between mother andoffspring might function as a bonding signal or as auditory reinforcement. Whilea clear distinction between the development of human language and that ofprimate vocalisations exists, we demonstrated that chimpanzees’ vocal ontogeny
can be flexible and mediated by social factors, contrary to previous assumptions.

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References
Altmann, J. (1974). Observational Study of Behavior: Sampling Methods.
Behaviour, 49(3/4), 227–267.
Egnor, S. E. R., & Hauser, M. D. (2004). A paradox in the evolution of primate
vocal learning. Trends in Neurosciences, 27(11), 649–654.
Fedurek, P., Donnellan, E., & Slocombe, K. E. (2014). Social and ecologicalcorrelates of long-distance pant hoot calls in male chimpanzees.
Behavioral Ecology and Sociobiology, 68(8), 1345–1355.
Fitch, W. T., & Zuberbühler, K. (2013). Primate precursors to human language:
Beyond discontinuity. In E. Altenmüller, S. Schmidt, & E.
Zimmermann (Eds.), Evolution of Emotional Communication (pp. 26–
48). Oxford University Press.
Marler, P., & Hobbett, L. (1975). Individuality in a Long-Range Vocalization ofWild Chimpanzees. Zeitschrift Für Tierpsychologie, 38(1), 97–109.
Marler, P., & Tenaza, R. (1977). Signalling behaviour of apes with specialreference to vocalisations. In P. Marler, R. Tenaza, & T. A. Sebeok(Eds.), How animals communicate (pp. 965–1033). Indiana University
Press, Bloomington.
Mitani, J. C., & Nishida, T. (1993). Contexts and social correlates of longdistance calling by male chimpanzees. Animal Behaviour, 45(4), 735–
746.
Owren, M. J., Amoss, R. T., & Rendall, D. (2011). Two organizing principles of
vocal production: Implications for nonhuman and human primates.
American Journal of Primatology, 73(6), 530–544.
Seyfarth, R. M., & Cheney, D. L. (1997). Some general features of vocal
development in nonhuman primates. In C. T. Snowdon & M.
Hausberger (Eds.), Social Influences on Vocal Development (1st ed.,pp. 249–273). Cambridge University Press.
Snowdon, C. T. (2009). Plasticity of Communication in Nonhuman Primates. In
M. Naguib, K. Zuberbuhler, N. S. Clayton, & V. M. Janik (Eds.),
Advances in the Study of Behavior (Vol. 40, pp. 239–276). Elsevier.
Acknowledgments
We are grateful to the management, staff, field assistants, and researchers of theBudongo Conservation Field Station (BCFS) for their support and discussions.
We thank the Uganda Wildlife Authority and the Uganda National Council for
Science and Technology for permission to conduct the study. We are thankful toCharles Paxton for statistical advice. The study was funded by a St Leonard
College Inter-University scholarship awarded to J.C. and K.Z., a Swiss National
Science Foundation grant (310030_185324) awarded to K.Z., and individual
mobility grants from Swissuniversities and St Leonard College

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awarded to A.S.

685

HOW SELF-DOMESTICATION AND PROSOCIALITY MAYSHAPE CROSS-MODAL LANGUAGE
KEES SOMMER*1,2, CHRISTINE CUSKLEY3, SIMON KIRBY4 and TESSAVERHOEF2*

Corresponding Author: k.sommer@hhs.nl
IT & Design, The Hague University of Applied Sciences, The Hague, Netherlands
2
Leiden Institute for Advanced Computer Science, Leiden University, Leiden,
Netherlands
3
Center for Behaviour and Evolution, Newcastle University, Newcastle, UK
4Center for Language Evolution, University of Edinburgh, Edinburgh, UK

1

An emerging literature in language evolution has highlighted the key role of selfdomestication, with at least two crucial features for language evolving in otherspecies through domestication: vocal learning in birds and the recognition ofcommunicative intent in dogs (Thomas & Kirby, 2018). Selection for lessaggressive individuals is also associated with the appearance of higher
prosociality, and closely linked to increased levels of serotonin and other'bonding' neurochemicals (Hare, 2017). Interestingly, substances that interact
with such neurochemicals can heighten integration of the senses (synesthesia) in
humans (Brang & Ramachandran, 2008; Luke & Terhune, 2013). Bothsynesthesia and the related phenomenon of shared cross-modal mappings may
have played a key role in the early evolution of language (Bankieris & Simner,
2015; Cuskley & Kirby, 2013; Imai & Kita, 2014) as a way to bootstrap sharedlinguistic form-meaning mappings. The current paper links self-domesticationand cross-modality, using a task intended to enhance participants’ prosociality
and measuring their sensitivity to linguistic cross-modal associations.
A total of 62 participants were recruited at the University of Edinburgh andpaid £5. Half of the participants performed a social clapping task with theexperimenter using a composed rhythm, aimed at stimulating prosociality:
synchronising through a rhythm has been shown to increase prosociality inindividuals (Kirschner & Tomasello, 2010; von Zimmermann et al., 2018). The
remaining participants completed an asocial clapping task, using a random rhythmwith the computer. Participants in both groups then performed a task designed totest their cross-modal sensitivity to linguistic stimuli, where pre-recordedThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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pseudowords had to be matched with a shapethat could be manipulated in size and weight
through a circular slider (see figure 1). The
pseudowords were designed based on
previously found strong associative
Figure 1. Cross-modal interface. Leftreactions between consonant voicing andto right: Slider at initial position, at
visual weight, and vowel openness andlargest position and at smallest position.
visual size (Schmidtke, Conrad, & Jacobs,
2014; Cuskley, 2013), and were therefore distinctive for both these features, aswell as place of articulation (bilabial vs alveolar) to create more variation in thedata. This created a total of 8 pseudowords: /ipi/, /ibi/, /iti/, /idi/, /apa/, /aba/, /ata/,
and /ada/. Lastly, all participants were asked to answer an open question, where
word count was used as a prosociality measure (Baumsteiger & Siegel, 2019).
Since size and weight were simultaneously adjusted with one slider, we referto size for the shape produced. Pseudowords were grouped into 4 types (ordered):
1: closed-voiceless, 2: closed-voiced, 3: open-voiceless, 4: open-voiced, sinceparticipants were expected to map closed vowels and voiceless consonants withsmaller sizes, where openness was expected to have a stronger effect, and nostrong relationship between size and place of articulation was expected. Size in
relation to condition, word type and prosociality was analyzed using linear mixedeffects models with maximum likelihood estimation (P-values calculated using
the Satterthwaite's method). Sizes produced for word types followed the expectedcross-modal pattern (1<2<3<4). Shape sizes were significantly larger for openvoiceless than for closed-voiceless (b = 0.97 ± 0.28 SEM, P <0.001) and for openvoiced items relative to closed-voiceless items (b = 1.02 ± 0.28 SEM, P <0.001).
This also interacted with task final measures of prosociality: participants with ahigher prosociality score had even larger shapes for both open-voiceless (b = 0.39
± 0.18 SEM, P = 0.03) and open-voiced words (b = 0.40 ± 0.18 SEM, P = 0.03)
relative to closed-voiceless items, suggesting a link between prosociality andcross-modal associations. However, the effect of the clapping task is less clear:social clapping only affected open-voiceless items, actually dampening
associations relative to asocial clapping (b = -0.36 ± 0.15 SEM, P = 0.02).
Differences between the social and asocial clapping tasks may not have been
sufficient, since both involved rhythmic coordination. Since the clapping task did
not measurably influence cross-modal sensitivity but prosociality did, perhaps theeffect relates more strongly to more stable neurochemical interactions. For
example, while mu-opioids are more sensitive to prosocial manipulations(Manninen et al., 2017), serotonin is much more stable (Mitchell, 2006). Future
work in this direction could take this into account.
Overall, this experiment presents an initial exploration of a role forprosociality in mechanisms facilitating intuitive meaning sharing. This could
potentially open up new comparative and experimental research directions in thecontext of self-domestication.

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References
Bankieris, K., & Simner, J. (2015). What is the link between synaesthesia andsound symbolism? Cognition, 136, 186-195.
Baumsteiger, R., & Siegel, J. T. (2019). Measuring prosociality: The developmentof a prosocial behavioral intentions scale. Journal of personality assessment,
101(3), 305-314.
Brang, D., & Ramachandran, V. S. (2008). Psychopharmacology of synesthesia;
the role of serotonin S2a receptor activation. Medical hypotheses, 70(4), 903904.
Cuskley, C. (2013). Mappings between linguistic sound and motion. Public
Journal of Semiotics, 5(1), 39-62.
Cuskley, C. and Kirby, S. (2013). Synaesthesia, cross-modality and language
evolution. In: Simner, J. and Hubbard E.M. (Eds) Oxford Handbook ofSynaesthesia, pp. 869-907.
Hare, B. (2017). Survival of the friendliest: Homo sapiens evolved via selection
for prosociality. Annual review of psychology, 68, 155-186.
Imai, M., & Kita, S. (2014). The sound symbolism bootstrapping hypothesis forlanguage acquisition and language evolution. Philosophical transactions of
the Royal Society B: Biological sciences, 369(1651), 20130298.
Kirschner, S., & Tomasello, M. (2010). Joint music making promotes prosocial
behavior in 4-year-old children. Evolution and Human Behavior, 31(5), 354364.
Launay, J., Tarr, B., & Dunbar, R. I. (2016). Synchrony as an adaptive mechanism
for large‐scale human social bonding. Ethology, 122(10), 779-789.
Luke, D., & Terhune, D. B. (2013). The induction of synaesthesia with chemical
agents: a systematic review. Frontiers in psychology, 4, 753:1-12.
Manninen, S., Tuominen, L., Dunbar, R. I., Karjalainen, T., Hirvonen, J.,
Arponen, E. & Nummenmaa, L. (2017). Social laughter triggers endogenous
opioid release in humans. Journal of Neuroscience, 37( 25), 6125-6131.
Mitchell, A. J. (2006). Two-week delay in onset of action of antidepressants: newevidence. The British Journal of Psychiatry, 188( 2), 105-106.
Schmidtke, D., Conrad, M., & Jacobs, A. M. (2014). Phonological iconicity.
Frontiers in psychology,5,80.
Thomas, J., & Kirby, S. (2018). Self-domestication and the evolution of language.
Biology & philosophy, 33(1), 1-30.
von Zimmermann, J., Vicary, S., Sperling, M., Orgs, G., & Richardson, D. C.
(2018). The choreography of group affiliation. Topics in Cognitive Science,10(1), 80-94.

688

COMBINING POINTING AND LANGUAGE DURING THE EARLYSTAGES OF DEVELOPMENT: A CASE STUDY OF RUSSIAN AND
CHINTANG
Sabine Stoll1,2 , Jekaterina Mažara*1,2 , and Elena V. M. Lieven3* Corresponding Author: jekaterina.mazara@uzh.ch

1 Department of Comparative Language Science University of Zurich, Switzerland

2 Center for the Interdisciplinary Study of Language Evolution, University of Zurich, Switzerland

3 Emeritus Professor, Division of Human Communication, Development and Hearing, University ofManchester, United Kingdom

Both language and pointing are universal features and central components ofhuman societies (cf. Kita, 2003). A milestone of early ontogeny is learning how tocommunicate intentionally. One of the first entry points into this process is pointing (Bates, 1976; Iverson & Goldin-Meadow, 2005). A number of cross-cultural
studies have shown that pointing emerges around the same age and is used for similar communicative functions cross-culturally (Butterworth, 2003; Butterworth &Morissette, 1996; Callaghan et al., 2011; Carpenter, Nagell, & Tomasello, 1998;
Liszkowski, Brown, Callaghan, Takada, & Vos, 2012; Ohama, 1984).
Pointing alone, however, is often under-specified without additional linguistic
information and adult language users generally accompany points with language(Greenfield & Smith, 1976; Enfield, 2009). While the co-development between
gestures and language is well-studied (Iverson & Goldin-Meadow, 2005), so far,little research has been dedicated to the specific content of point-accompanying
language. Here, we examine the relationship between pointing and accompanying vocalizations and utterances in the production of 1 to 4-year-old children in
two very different cultural settings, Russia (St. Petersburg) and Chintang (arural subsistence community in Eastern Nepal). We analyze the speech acts children perform and the information they convey while pointing to assess whether
the way the two domains interact is independent of language and culture. Even
though the specific systems of language and pointing differ greatly between cultures (Wilkins, 2003), the development of this interplay is a candidate for a universal of human communication.
In this pilot study, we narrowed our focus on the development of utterancesand vocalizations accompanying finger points, since they are proposed to be themost commonly and probably most frequently used type of pointing by young
children across cultures (Butterworth, 2003). Even though index finger pointing
is ubiquitous in the world’s societies, pointing behavior is an integral part of theThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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style of communication between parents and children and might, thus, be subject
to specific cultural reinforcement that would result in a different path of development (Masataka, 2003). To understand how children and adults behave in naturalcommunicative situations, we rely on observational longitudinal data. We evaluate
whether pointing behavior differs between adults and children of the two culturesand how closely the children’s production is linked to child-surrounding adult input. The video and audio data was hand-annotated for type of point as well as typeof sentence, intent, and content of accompanying utterances/vocalizations.
First, we compared the proportion of finger points in both cultures in the target children’s and adults’ production. Second, we examined the development ofaccompanying utterances to understand how often pointing occurs without accompanying utterances, whether this changes with development, and whether it differsfrom the production in the ambient language. In a last step, we take a more detailed look at the content of the utterances that accompany finger pointing in bothlanguages.
We found that finger points represent a larger proportion of all points in Russian (65%) than in Chintang (35%). Russian children tend to point more frequently but both Chintang and Russian children followed the distribution of pointtypes found in their surrounding adults. Both Chintang and Russian adults use significantly fewer points unaccompanied by language or vocalizations than the children and neither children nor adults differ significantly between cultures. Overtime, fewer of the children’s points are silent or accompanied by vocalizations,which reflects their linguistic maturation. This is also reflected in the changes of
the utterances’ content in children’s production over time. Children in the older
age groups show a diversification of the information content they try to convey
while pointing. 1 to 2-year-olds predominantly use finger points and vocalizations to draw their interlocutor’s attention, older children increasingly point while
making an additional comment about the referent they are pointing out. They also
diversify the sentence types they use (declaratives, interrogatives, imperatives).
The composition of information content and sentence types in the inputchanges less across all recordings but, over time, we saw a reduction in simple
identifications by adults. Across both languages and age groups (excluding theyoungest children’s production), the most commonly used utterances occurringwith finger points are statements. Points and language are used in symbiosis, eachadding information to the other.
This case study contributes to understanding the role of language in communicative pointing during the earliest stages of language development. Children inboth languages differ from the input in term of content of their utterances but display cross-cultural similarities. Despite the very different cultural environments,
overall gestural behavior, and adults’ attitudes towards linguistic instruction, theinteraction of finger pointing with language shows considerable similarities.

690

References
Bates, E. (1976). Language and context: The acquisition of pragmatics. Academic Press.
Butterworth, G. (2003). Pointing is the royal road to language for babies. In
S. Kita (Ed.), Pointing: Where language, culture and cognition meet (pp.
9–33). New York: Erlbaum Associates.
Butterworth, G., & Morissette, P. (1996). Onset of pointing and the acquisition of
language in infancy. Journal of Reproductive and Infant Psychology, 14(3),219–231.
Callaghan, T. C., Moll, H., Rakoczy, H., Warneken, F., Liszkowski, U., Behne, T.,& Tomasello, M. (2011). Early social cognition in three cultural contexts.
Wiley-Blackwell.
Carpenter, M., Nagell, K., & Tomasello, M. (1998). Social cognition, joint attention, and communicative competence from 9 to 15 months of age. Monographs of the Society for Research in Child Development, 63(4), i–143.
Enfield, N. J. (2009). The anatomy of meaning: Speech, gesture, and composite
utterances (Vol. 8). Cambridge University Press.
Greenfield, P. M., & Smith, J. H. (1976). The structure of communication in earlylanguage development. New York, NY: Academic Press.
Iverson, J. M., & Goldin-Meadow, S. (2005). Gesture paves the way for language
development. Psychological Science, 16(5), 367–371.
Kita, S. (2003). Pointing: A foundational building block of human communication. In S. Kita (Ed.), Pointing: Where language, culture, and cognition
meet (pp. 1–8). Mahwah, NJ: Lawrence Erlbaum.
Liszkowski, U., Brown, P., Callaghan, T., Takada, A., & Vos, C. de. (2012). A
prelinguistic gestural universal of human communication. Cognitive science, 36(4), 698–713.
Masataka, N. (2003). From index-finger extension to index-finger pointing: Ontogenesis of pointing in preverbal infants. In S. Kita (Ed.), Pointing: Where
language, culture and cognition meet (p. 69-84). Mahwah, NJ: Lawrence
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Ohama, K. (1984). Development of pointing behavior in infants and mother’s
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The development of verbal behavior VI. Bulletin of the Faculty of Education,
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Wilkins, D. (2003). Why pointing with the index finger is not a universal (in sociocultural and semiotic terms). In S. Kita (Ed.), Pointing: Where language,
culture and cognition meet (pp. 171–215). Erlbaum Mahwah, NJ.

691

VERBAL LABELS INCREASE CONCEPTUAL ALIGNMENTELLISE SUFFILL1,, JEROEN VAN PARIDON1, and GARY LUPYAN*1*
Corresponding Author: lupyan@wisc.edu
Dept of Psychology. University of Wisconsin-Madison, USA1

Successful linguistic communication requires conversants to mean at least
approximately the same thing by the same words. But how is this alignment achieved?
One possibility is that participants have pre-existing concepts to which verbal labels
are mapped. Alignment is then a matter of ensuring that in members of the same speechcommunity, the same word points to the same concept. But how do the underlying
conceptual representations become aligned in the first place? One source of alignmentis shared sensory experiences mediated by similar perceptual systems. But is thisenough? We test the possibility that language itself serves to align conceptual
representations. Participants were asked to sort novel shapes and we measured thesimilarity between people’s sorts. By separately manipulating previous perceptual
experience with the shapes, and exposure to (entirely redundant) category labels, wetested (1) the role of shared perceptual experience and (2) the effect of labels on
representational alignment. The results showed that shared experience with labels
increased representational alignment more than shared perceptual experience alone.
We consider the implications of this finding for the cognitive functions of language
and for how language may be used to enable coordination in the face of non-sharedperceptual experiences.

Introduction
The idea that it is possible to communicate through direct transfer of mental states
between minds has been a popular trope in science fiction for over a century. It
has now has become the subject of empirical investigation, e.g., Rao et al. ask“Can information that is available in the brain be transferred directly in the formof the neural code, bypassing language altogether?” (2014). A common thread inscience fiction treatments of telepathy and its modern revival is that natural
language obscures communication because it is ambiguous, imprecise, and slow.
For example, the computer scientist Yan LeCun, the chief AI scientist at
Facebook, recently asserted that “Language is an imperfect, incomplete, and lowbandwidth serialization protocol for the internal data structures we call thoughts”(LeCun, 2021). Bypassing language is hence seen as a way of improving, or atleast speeding up, communication.

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

692

The work we describe here is motivated by challenging a core assumption
behind the telepathy trope: that our mental states are naturally aligned such thatone person’s thought is syntactically and semantically homologous to anotherperson’s. In the absence of this prior such alignment, transferring neural patterns
that constitute mental states between people—even if technologically possible—
would not lead to successful communication. What is at stake is important to thestudy of language evolution because understanding the evolution of a trait isgreatly helped by understanding the range of its functions (Griffiths, 1993) andwork on the functions of language are curiously under-represented in the study oflanguage evolution (cf. Dessalles, 2007).
Conceptual alignment and language: what is the connection?
Everyday communication seems to require conceptual alignment (e.g., Pickering& Garrod, 2021). When one person says “Pass the salt please” and another person
passes them the salt, the two have achieved some amount of alignment: one
person’s request was successfully represented by the other. But where does thisalignment comes from and what role, if any, natural language plays in establishing(rather than obscuring) it.
The idea that linguistic communication is possible only because our thoughts
are already sufficiently aligned is a basic premise of philosophical positions suchas Fodor’s language of thought (Fodor, 1975). It is also a common starting pointin theories of language learning that view children as mapping words onto preexisting (and largely shared) concepts (Bloom, 2002; Pinker, 1994; Snedeker &Gleitman, 2004).
But another possibility is that alignment is achieved—in part—throughlanguage itself (e.g., Casasanto & Lupyan, 2014; Dingemanse, 2017; Gomila,
2011; Lupyan & Bergen, 2016). On this view, learning and using the syntax andsemantics of a natural language helps people to structure and convey theirthoughts in ways that are (more or less) understandable to others. Rather than justa device for conveying our thoughts, language provides an interface between
minds (e.g., Clark, 1998; Gentner & Goldin-Meadow, 2003; Lupyan, 2012). If
true, then telepathy could in principle be possible by transferring neuralrepresentations of words from one person to another, but such a scheme would
not be the language-bypassing telepathy we’ve been promised, but rather an overengineered form of texting.
The idea that language may play a causal role in promoting conceptualalignment is supported by several lines of evidence, some circumstantial, others
more direct. First, there is the simple fact of substantial cross-linguistic diversity

693

in all aspects of language (Evans & Levinson, 2009). If our conceptual
representations were naturally aligned—either due to our shared biology, sharedenvironment, shared goals, or all three—one might expect lexicons to show moresimilarity than they do. And although it is clear that the lexical systems of natural
languages occupy a small space of all possible systems (e.g., Zaslavsky et al.,2018), it is striking that finding universal basic units of linguistic meaning has
been so difficult. Even in the domain of perception, where one might findvocabularies to be most constrained by shared biology, one finds tremendous
diversity of naming schemes (Majid, 2020; Majid et al., 2018). Diversity withina language is smaller (Forder & Lupyan, 2017) although it can depend on themeasure one uses (Kuehni, 2004). Second, experimental evidence suggests thatverbal labels can increase conceptual alignment across people, in bothcommunicative (e.g., Markman & Makin, 1998) and non-communicative contexts(Suffill et al., 2016, 2019).
Current study
Here we tested a strong version of the prediction that language promotes
conceptual alignment. We exposed people to novel shapes grouped into two
distinct categories (Figure 1). We then computed alignment among participants
assigned to each of group using a sorting task. Our design allowed us to compare
how conceptual alignment is affected by shared labels compared to alignment
achieved through shaped perceptual experiences. Using materials with a clear preexisting category structure allowed us to test whether labels help to aligncategories even when the existence of the categories is made plain by perceptual
discontinuities (Fig. 1). This makes the current experiment substantially different
from work examining the ways that labels can help mark distinctions inperceptually equidistant continua such as colors (Davidoff, 2001) and shapes(Plunkett et al., 2008) as well as from past work showing that labels facilitate thelearning of new categories (Lupyan et al., 2007).
Methods
Participants
We recruited 129 (85 female, ages 18-22) psychology students from
University of Wisconsin-Madison. Participants were randomly assigned to a
Baseline (N = 45), No Labels (N = 43) or With Labels (N = 41) condition.

694

Materials
We constructed two visual family-resemblance categories designed to beeasy to distinguish and difficult to name. We began by generating two prototype
shapes by creating a random collection of points and connecting them with aspline (Fig 1A, 1B). We then generated 18 exemplars per category by perturbing
the points and fitting a new spline, creating low, medium, or high distortions (e.g.,
Fig. 1 bottom).

Figure 1. Category A (left) and B (right) prototypes with examples of“low”, “medium” and “high” category exemplars.

Procedure.
Pre-exposure. Participants assigned to the With Labels or No Labels
conditions began with a match-to-sample task designed to familiarize participants
with the stimuli and have them repeatedly contrast within-category and betweencategory stimuli. On each trial, participants saw one of the shapes (the standard)for 1 sec followed by a 1 sec blank screen. Two shapes then appeared side by side.
One of these was identical to the standard (target) and the other was a foil—ashape from the contrasting category. Participants had to choose which of the two
shapes exactly matched the standard, i.e., on each trial they had to choose thetarget and not the foil. In the With Labels condition, the standard was presented
with its corresponding nonsense label; half the With Labels participants heard
category A shapes labeled as “a talp”; half heard them labeled as “a gek”. Noticethat the label is completely unnecessary for making a correct response and istherefore informationally redundant. Errors were signaled with a short buzzing
sound. Participants assigned to the Baseline condition did not complete this phaseand proceeded directly to the free sort.
Free sort. We quantified alignment by measuring how participants in thethree conditions arranged the shapes in a free sort—a common method for695

assessing people’s conceptual representations (Goldstone, 1994; Kriegeskorte etal., 2008; Malt et al., 1999). Participants were shown 20 shapes (10 A shapes and
10 B shapes) arranged around the perimeter of the screen. These included 3
previously seen exemplars, 6 novel exemplars, and the previously unseen
prototype. Participants were asked to cluster the shapes together in way that madesense to them, creating as many or as few clusters as they needed.
Analytic approach
We computed alignment between people’s item arrangements as follows: Foreach participant, we take the pairwise distances between all item pairs (20*19/2= 190). We then compute the rank correlations between that participant’s pairwise
item distances and the pairwise item distances of the other participants in the same
condition. The Fisher’s z-transformed mean of these correlations represents theparticipant’s average alignment to other participants. These are the values shownin Fig 2A. To statistically compare the groups in an unbiased way we counted
each participant pairing as a single observation, but attributed the varianceassociated with this observation to both participants in the pairing using
lmerMultiMember (van Paridon et al., 2022), an R package that allows for
specifying multiple membership random effects. In addition, we computed foreach participant a measure of categoricality, the median Euclidean distance
between exemplars from different categories (e.g., A1 and B2) minus the median
distance between exemplars from the same category (e.g., A1 and A2).
Results
Pre-exposure. Accuracy on the delayed match-to-sample task was nearly
identical for the No Labels (M = 0.98) and With Labels groups (M= 0.98). Given
the task’s simplicity, this was expected, and confirms that the categories weretrivially easy to distinguish, regardless of labels.
Categoricality. Participants in all groups grouped within-category items
closer together than between-category items: Categoricalitybaseline=151 pixels,
Categoricalityno-labels=171 pixels, Categoricalitywith-labels=264 pixels. All threevalues were significantly greater than 0, t’s> 5, p<.0001, confirming that even

696

Figure 2. A. Mean alignment for the three tested conditions. B. The relationship betweenalignment and categoricality for each condition.

Baseline participants—who had no prior experience with seeing or contrasting theshapes—were still sensitive to the designed category structure. Categoricality was
not significantly different between the Baseline and No Labels groups, t<1. In
contrast, participants exposed to the nonsense labels produced more categoricalsorts than those who had identical experience seeing and contrasting the shapes,
but without being exposed to their names (b=93, t=2.4, p=.02).
Alignment. Average alignment for each group is shown in Fig. 2A. Those in
the Baseline condition were as similar to one another in their shape arrangements
as those who encountered the shapes several hundred times, but without theaccompanying labels (b=.02, t=1.1, p=.24). In contrast, participants who were
exposed to category labels, sorted shapes more similarly to one another than thosein the Baseline condition (b=.09, t=4.9, p<.0001) and those in the No Labels
condition (b=.07, t=3.7, p=.0004). The increase in alignment caused by labels was
significantly greater than that caused by shared perceptual experiences (t=2.27,
p=.03).
Relationship between categoricality and alignment. Why did labels
increase alignment? One possibility is that alignment was mediated bycategoricality: labels increased categoricality—leading to an increase in between
category distance and a decrease in within-category, and these more tightlyclustered sorts were more aligned. As shown in Fig. 2B, there was indeed a strong
relationship between categoricality and alignment (overall r=.83, p<.0001). And
yet, categoricality (b=.08, t=21.9, p<.0001) and condition (b=.02, t=6.4, p<.0001)
accounted for unique variance in predicting alignment; including categoricality

697

made the effect of condition on alignment even stronger with the two predictors
accounting for 83% of the variance. Fig. 2B also makes clear that there was acondition-by-categoricality interaction (t=7.71, p<.0001). For the same level ofcategoricality, exposure to labels yielded greater alignment. In short, labels
increased both alignment and categoricality, but there was no evidence ofmediation of alignment by categoricality.
General Discussion
Language allows one to activate thoughts, old and new, in other people. The
promise of telepathy—a direct exchange of mental states that bypasses natural
language—is predicated on the assumption that thoughts are entirely independentof language; language is merely a medium by which the thoughts are transmitted.
This assumption, however, may be wrong. The study we describe here provides avery limited, but nevertheless strong test of the hypothesis that even very strippeddown forms of language—redundant and seemingly uninformative verbal
category labels—can increase conceptual alignment and do so to a greater extent
than shared perceptual experiences alone. Our finding that verbal labels increase
conceptual alignment is just one result using specific stimuli and task. Our hopeis that future investigations can map out the generality of this result and themechanisms by which labels achieve this effect.
The technology to transfer mental states may one day exist. Will it enable
telepathy? The present results offer an early hint that however “imperfect andincomplete” language may be, attempts to bypass it may lead to a semantic
disconnect and communicative failure. It may be possible to devise a system forre-aligning our thoughts into a mutually understandable form. Natural language
is just such a system and the role natural language plays in aligning our thoughts
may be another piece in the puzzle of its evolution.
Acknowledgements
This work was supported by NSF-PAC Awards 2020969 to G.L.
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Casasanto, D., & Lupyan, G. (2014). All Concepts are Ad Hoc Concepts. In E. Margolis & S. Laurence(Eds.), Concepts: New Directions (pp. 543–566). MIT Press.
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Davidoff, J. (2001). Language and perceptual categorization. Trends in Cognitive Sciences, 5(9), 382–387.

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Dessalles, J.-L. (2007). Why We Talk: The Evolutionary Origins of Language (J. Grieve, Trans.). Oxford
University Press, USA.
Dingemanse, M. (2017). On Brain-to-Brain Interfaces, Distributed Agency and Language. In N. J. Enfield& P. Kockelman (Eds.), Distributed Agency (pp. 59–66). Oxford University Press.
Evans, N., & Levinson, S. C. (2009). The myth of language universals: Language diversity and its
importance for cognitive science. Behavioral and Brain Sciences, 32(05), 429.
Fodor, J. A. (1975). The language of thought (Vol. 5). Harvard university press.
Forder, L., & Lupyan, G. (2017). Facilitation of color discrimination by verbal and visual cues. Meeting of
the Vision Sciences Society.
Gentner, D., & Goldin-Meadow, S. (2003). Language in mind: Advances in the study of language andthought. MIT Press.
Goldstone, R. (1994). An efficient method for obtaining similarity data. Behavior Research Methods,
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Branches of Systems Neuroscience. Frontiers in Systems Neuroscience, 2.
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699

A COMPUTATIONAL FRAMEWORK FOR STUDYING THE
EVOLUTION OF CULTURAL TRAITS AMONG CHATTINGAGENTS USING A GENERATIVE MODEL
Reiji Suzuki*, Soichiro Hirata and Takaya Arita*Corresponding Author: reiji@nagoya-u.jp

Nagoya University, Japan

Constructive approaches have played a significant role in the understanding of
cultural evolution and meme transmission, showing social relationships among
hosts can affect the diversification of memes (Kobayashi et al. 2013, Weng et al.
2021). However, it is still unclear how novel memes emerge from complex
communications in human language in the real world, such as chatting in a faceto-face situation and text-based communication in SNSs, and propagate in the
population while a bigdata analysis contributed to clarifying the open-ended
evolutionary dynamics of Web services such as social tags (Ikegami et al. 2019).
We propose a computational framework for studying the cultural evolution ofmemes among chatting agents using a generative model (Fig. 1 (i)). Individuals
are represented as agents and arranged in a two-dimensional social space. The
space abstracts their social relationships (i.e., the closer distance between agents
represents more intimate relationships among them), which is inspired by thesocial particle swarm model (Nishimoto et al. 2013). When another neighboring
agent is in its interaction radius, a communicative interaction occurs between
them ((i)-(a)). Each agent has genetic and cultural traits, each composed of a listof words. The former represents non-evolvable and intrinsic preferences to the
topic of a sentence to utter (e.g., gossip, (Mesoudi et al. 2006)). The latter
represents the cultural ones, which evolve in the model. An agent utters a sentence
produced by a pre-trained model of Japanese sentence generation (rinna/japanesegpt2-small) based on GPT-2 (Radford et al. 2019). This model predicts andgenerates Japanese words that follow a given phrase. We generate an utterance ofan agent by using a list of words in its genetic traits followed by its cultural traits
as a given phrase, which is regenerated with a certain probability at each time
step. We assume that agents who share more words among their utterances areThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

700(a) interaction phase

An uttering sentence of the agent A

repulsion

J<D

Rinteraction

人工生命と脳といえば
 自然生命のイメージがありましたが 人工生命ではなく ”自然に生まれる人工生命” というイメージがあります 人工生命は細胞を成長させ、脳を鍛え、知性と判断力を高めるもの。その一方 人工生命と人工生命の間に境界線を定めるものです。人工生命と人工生命の間にあるのは脳なのです。
((Translated) When we think of artificial life and the brain
, we have an image of natural life, notartificial life, but "naturally occurring artificial life." Artificial life grows cells, trains the brain, andincreases intelligence and judgment. On the other hand, it defines the boundary between artificial life andartificial life. It is the brain that lies between artificial life and artificial life.)

J = the Jaccard coefficient of the nouns in theutterances between A and B (similarity)
attraction J > D (a threshold for active interactions)
An uttering sentence of the agent B

A

recently
received meme

人工生命と自然といえば
 生命というものが存在するということだけを思い浮かべる人が多いと思う。 人工生命というのは,自然界の本能によってできている人工の生命。それに対して人工生
命には,自然界の本能にかなった人工の生命が自然界には存在しないので,自然界では自然界が生きている。人工生命には,自然界の本能はまったく存在しない。

B((Translated)) When we think of artificial life and nature, most people think only of the existence of life.
Artificial life is artificial life created by the instincts of the natural world. On the other hand, artificial life
has no natural instincts, because artificial life does not exist in the natural world. In artificial life, theinstincts of the natural world do not exist at all.

no neighbors:
random movement(ii) The spatial distribution of agents.

(i) Overview.
step 1step 499

step 49
I

Japan

novel

thing

movie

step

year

blog

drama

Tokyo

(iii) The change in the frequency of major uttered nouns and word clouds of unique nouns in several time steps.

Fig. 1: (i) Overview of the model and (ii, iii) results.

speaking about the topics more attractive to both. We measure the Jaccard index
between the sets of the agents’ uttering nouns. If the index is above the thresholdvalue, the interparticle attraction occurs, and the repulsion occurs otherwise, of
which strength is inversely proportional to the distance between the agents. We
also introduce cultural transmission of traits into the interaction process ((i)-(b)).
Each agent randomly extracts a noun from the sentence uttered by each closely
neighboring agent with a certain probability and adds it to its cultural traits (andremoves the oldest one if the number of traits exceeds the limit).
The preliminary experiments with agents, each of whose genetic traits had oneof the names of famous novelists mainly in Heian - Reiwa periods in Japan,
showed that agents were actively moving and exchanging novel traits withneighboring agents and the emergence and collapse of chatting clusters happened
repeatedly (ii). In addition, the agents initially tended to utter words related toJapanese literature, and then these were taken over by more various words, andfurther trends of Japanese literature emerged (iii). These indicate that the proposed
framework enables us to discuss the cultural evolution of novel traits incommunicating agents in language.

701

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and multi-agent system for meme discovery and prediction in social network.
Knowledge-Based Systems, 229, 107368.

702

AUDIO-VOCAL MIRROR NEURON IN THE SONGBIRD BASALGANGLIA
YUKA SUZUKI*1,2, SHIN YANAGIHARA2, and KAZUO OKANOYA2,1*

1

Corresponding Author: suzuki-yuka236@g.ecc.u-tokyo.ac.jp
Department of Life Sciences, Graduate School of Arts and Sciences, The University ofTokyo, Tokyo, Japan
2
Advanced Comprehensive Research Organization, Teikyo University, Tokyo,
Japan

Speech acquisition in humans is parallel to vocal learning in songbirds (Doupe& Kuhl, 1999; Prather et al., 2017). Both learnings are held by imitating adults’
vocalizations and have critical periods that need auditory and motor information(Fromkin et al., 1974; Marler, 1970). In studying vocal learning of humans andsongbirds, it is important to understand the neural mechanisms how to integrate
auditory and vocal-motor information. Since songbirds have anatomically welldefined brain circuits dedicated to vocal learning, we focused on songbirds tounderstand the neural mechanisms underlying the audio-vocal integration at thecellular levels that is hard to research on human subjects.
The cortico-basal ganglia circuitry, which includes premotor cortical nucleus(HVC) and a part of the basal ganglia (Area X), is crucial for vocal learning in a
songbird (Kao et al., 2005). HVCx neurons, which project to Area X, fire bothwhen the bird is singing and when it is listening to the bird’s own song (BOS)(Prather et al., 2008; Hessler & Okanoya, 2018). These neurons are called “audiovocal mirror neurons”. Audio-vocal mirror neurons should have important roles
to integrate auditory and vocal motor information. Because HVCx neurons
projects to Area X that is important for vocal leaning, there should be audio-vocal
mirror neurons not only in HVC but also in Area X. In this study, we used
Bengalese finch (Lonchura striata var. domestica) and examined whether
neurons in Area X shows singing-related and auditory-related activity byrecording multiple single-unit activity in freely behaving condition.
We found two types of audio-vocal mirror neurons in Area X that exhibited
both singing and auditory-related activity. One type of neurons fired at theThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

703

specific syllable timing when a bird was singing and when it was listening to BOS(Fig.1). Based on the firing property and spike waveform (Goldberg & Fee, 2010;
Goldberg et al., 2010), these neurons were classified as putative striatum medium
spiny neurons (MSNs). The other type of neurons was active when a bird was
singing. The same neuron also showed increase in firing rate when a bird waslistening to BOS during sleep, while the neuron did not show such auditory
responses when a bird was awake. These neurons were classified as internal
globus pallidus (GPi) neurons.
MSN is the input stage of Area X from HVC (Farries et al., 2005). Thus, MSN
may exhibit similar audio-vocal mirror neuron properties by receiving inputs from
HVCx neurons. On the other hand, GPi is the main output in Area X to thethalamic nucleus DLM (Farries et al., 2005). Thus, we assumed that GPi send to
DLM both auditory and vocal-motor information necessary for vocal learning.
However, GPi neurons did not show any auditory response while MSN showed
BOS selective responses when a bird was awake. These results suggest that theneural transmission from MSN to GPi is gated off when a bird is awake.
In conclusion, we found audio-vocal mirror neurons in Area X and these are
putative MSN and GPi neurons. We assume that the neurons integrate auditory
and vocal-motor information in the cortico-basal ganglia circuitry necessary for
vocal learning. We also suggested that the auditory information processing is
modulated by arousal levels. Our findings support the idea that audio-vocal mirror
neurons in cortico-basal ganglia circuitry are important in vocal learning thatneeds to integrate sensory and motor information. The mirror neurons that
integrate auditory and vocal information may be a common neural substrate not
only for vocal learning in birds but also for speech learning in humans, and thisstudy provides new clues for considering the evolution of language.

a
.
b

c
.

Figure 1. An example of firing
pattern of audio-vocal mirror neuron
in adult Bengalese finch basal
ganglia nucleus Area X. (a)Sonagram. (b) Mean firing rate (FR)when a bird was singing. (c) Mean
FR when a bird was listening during
wakefulness. Note that the phasic
firing occurs at the time of specificsyllable during singing and listening
to the song.

704

Acknowledgements
This work was supported by MEXT/JSPS KAKENHI Grant Number #4903,
JP17H06380, and JP20H00105.
References
Doupe, A. J., & Kuhl, P. K. (1999). Birdsong and human speech: common
themes and mechanisms. Annual Review of Neuroscience, 22, 567–631.
Farries, M. A., Ding, L., & Perkel, D. J. (2005). Evidence for “direct” and“indirect” pathways through the song system basal ganglia. The Journal ofComparative Neurology, 484(1), 93–104.
Fromkin, V., Krashen, S., Curtiss, S., Rigler, D., & Rigler, M. (1974). The
development of language in genie: a case of language acquisition beyond
the “critical period.” Brain and Language, 1(1), 81–107.
Goldberg, J. H., Adler, A., Bergman, H., & Fee, M. S. (2010). Singing-Related
Neural Activity Distinguishes Two Putative Pallidal Cell Types in the
Songbird Basal Ganglia: Comparison to the Primate Internal and External
Pallidal Segments. Journal of Neuroscience, 30(20), 7088–7098.
Goldberg, J. H., & Fee, M. S. (2010). Singing-Related Neural Activity
Distinguishes Four Classes of Putative Striatal Neurons in the Songbird
Basal Ganglia. Journal of Neurophysiology, 103(4), 2002–2014.
Hessler, N. A., & Okanoya, K. (2018). Physiological identification of corticostriatal projection neurons for song control in Bengalese finches.
Behavioural Brain Research, 349, 37–41.
Kao, M. H., Doupe, A. J., & Brainard, M. S. (2005). Contributions of an avian
basal ganglia–forebrain circuit to real-time modulation of song. Nature,433(7026), 638–643.
Marler, P. (1970). A comparative approach to vocal learning: Song developmentin white-crowned sparrows. Journal of Comparative and PhysiologicalPsychology, 71(2, Pt.2), 1–25.
Prather, J. F., Okanoya, K., & Bolhuis, J. J. (2017). Brains for birds and babies:
Neural parallels between birdsong and speech acquisition. Neuroscience& Biobehavioral Reviews, 81, 225–237.
Prather, J. F., Peters, S., Nowicki, S., & Mooney, R. (2008). Precise auditory–
vocal mirroring in neurons for learned vocal communication. Nature,451(7176), 305–310.

705

HOW MUCH LANGUAGE USE IS ACTUALLY ON SOCIALTOPICS?
ANNA SZALA*1,2, MAREK PLACIŃSKI1, ALEKSANDRA PONIEWIERSKA1,
ALEKSANDRA SZCZEPAŃSKA1 and SŁAWOMIR WACEWICZ1*Corresponding Author: szala@oakland.edu
1Centre of Language Evolution Studies, Nicolaus Copernicus University, Toruń, Poland
2Department of Psychology, Oakland University, Rochester, MI, USA

1. Introduction
How much language use is on social topics? Dunbar and colleagues (1997)
claimed that “socially relevant topics” accounted for about two-thirds of timespent on conversation, and later studies largely confirmed this pattern, including
cross-culturally (e.g., Kubik et al., 2018). In addition to its considerable popular
impact, Dunbar et al.’s study broke new theoretical grounds, helping motivate theevolutionary Social Brain theory and the influential “gossip” theory of language
origins (however, the term “gossip” is problematic and should be used withcaution – see esp., Dores Cruz et al., 2021).
Here, we revisit Dunbar’s question about the proportion of conversational time
devoted to social topics. In this effort, we are motivated by the substantial
limitations of both the dataset and approach of the original study. Dunbar et al.
(1997) relied on a relatively small number of conversations (N = 45), collected
exclusively in open public environments, between a sample of participants with avery limited demographic and geographic distribution. Secondly, their definitionand operationalisation of “social topics” lacked clarity and sufficient connection
to the proposed adaptive functions of language. For example, Dunbar et al.’sclassification of the categories of sport/leisure, culture/art/music, or politics as
non-social appears arbitrary in the light of our own data (see below) and grouping“personal experiences” within the larger category of social topics does not appear
to demonstrate a straightforward link to the proposed adaptive social functions oflanguage that motivate the evolutionary conclusions drawn from this study.

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

706

2. Materials and methods
We used Spokes (Pęzik, 2014), a corpus of Polish informal, casualconversations (N = 668; over 2.6 million word tokens) with speakers from alarge variety of Polish social backgrounds. Mindful of the limitations ofDunbar’s original approach, we re-defined "social topics'' as conversations
concerning 1) people that at least one participant knew in person (e.g.,neighbour), 2) people they did not know in person (e.g., musician), and 3)
social groups they either knew or did not know in person (e.g., classmates,
football teams). As a consequential decision, content related to the speakers’
personal experiences which did not involve others was considered non-social.
We randomly selected 56 conversations (ca. 10% of the dataset excluding thetraining set), which were independently assessed by two judges, who rated
each text line in each transcript as concerning a social or non-social topic.
3. Results
The expert judges converged on 85% of their judgments (moderate interrater
agreement; κ = 0.68). Portions of conversations classified differently by thejudges were removed from the analysis. The total number of word tokens was
52436 for social topics (50.9%) and 50528 for non-social topics (49.1%). We
fitted age and gender to a logistic regression model to check how they linkwith conversing on social topics. Age positively correlated with conversing
on social topics (β = 0.015, p < 0.001); gender did not show an effect but atrend was for males to speak less on social topics (β = -0.073, p = 0.09).
4. Conclusions
We found a roughly equal share of conversation devoted to social versus nonsocial topics, which differs considerably from the roughly 2/3 reported by
Dunbar et al. (1997). One key difference was our classification of “personal
experiences” (content limited to the individuals involved in the conversation)
as non-social. Unsurprisingly, the proportion of social topics in conversationis very sensitive to the definition applied, and since developing a standard
multipurpose definition does not appear feasible (cf. e.g. phatic
communication is technically not on “social topics”), further research should,
in particular, compare the different definitions of “social topics” that are
directly rooted in the different socially adaptive functions of language.

707

Acknowledgements
This research was supported by the Polish National Science Centre under
grant agreement UMO-2019/34/E/HS2/00248.
References
Dores Cruz, T. D., Nieper, A. S., Testori, M., Martinescu, E., & Beersma, B.
(2021). An Integrative Definition and Framework to Study Gossip. Group &Organization
Management,
46(2),
252–285.
DOI:
10.1177/1059601121992887
Dunbar, R. I. M., Marriott, A., & Duncan, N. D. C. (1997). Humanconversational behavior. Human Nature, 8, 231–246. DOI:
10.1007/BF02912493
Kubik, B. G., Pápay, B. T., Takács, K., & Galántai, J. (2018). The Gossip
Hypothesis.
[Prezislides].
European
Research
Council.
www.prezi.com/bs2dnr14_j2x/the-gossip-hypothesis.
Pęzik, P. (2014, October 24–25). Spokes – a search and exploration service
for conversational corpus data. [Selected Papers]. CLARIN 2014:
Soesterberg, The Netherlands. www.ep.liu.se/ecp/116/009/ecp15116009.pdf.

708

WHAT LINGUISTICS TEXTBOOKS TEACH ABOUTLANGUAGE EVOLUTION
ALEKSANDRA SZCZEPAŃSKA *1, ALEKSANDRA PONIEWIERSKA 1, MICHAELPLEYER1 , PRZEMYSŁAW ŻYWICZYŃSKI1 and SŁAWOMIR WACEWICZ1*Corresponding Author: szczepansk29@gmail.com
Center for Language Evolution Studies, Nicolaus Copernicus University in Toruń,Toruń, Poland

1

1. Introduction
In 2016, Wacewicz et al. (2016) conducted a preliminary study of topics related
to language evolution research as presented in 14 introductory-level linguistic
textbooks, finding that in contrast to areas such as language acquisition,
language change or language and the brain, language evolution received
insufficient attention, often based on out-dated and inadequate conceptual
frameworks. Here, we further investigate this issue with a first-of-its-kind, large
computer-assisted qualitative study, in which we inspected 18 textbooks for allcontent related to the evolutionary emergence of language and its uniqueness innature, in order to evaluate its thematic scope, selection of topics, theories
covered, researchers cited, structural soundness, currency, and factual accuracy.
2. Materials and methods
Our dataset consisted of the most recent editions of 18 introductory textbooks tolinguistics, selected based on the popularity measures OS Appearances and OS
Score on the Open Syllabus Project (opensyllabus.org). We used the qualitative
data analysis software nVivo 1.3 to mark and code any content related to theorigins of language and its status among animal communication systems,
appearing anywhere in the content of each book, including boxes, footnotes,
captions, or exercises (but not indexes or references). We followed a 5-stepcoding procedure consisting of training (two language evolution experts
instructed two expert coders), individual coding by the expert coders, consensus
discussion of all coded passages by the coders, review by two language
evolution experts, and consensus discussion by all four experts. This resulted inThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

709

a hierarchy of 462 codes in the main thematic groups: Animal Communication(AC), Language Evolution (LE), and Researchers (R).

Fig. 1. Numbers of references to the main thematic categories of interest in 15 introductions tolinguistics. The remaining three textbooks did not contain any references to either AnimalCommunication or Language Evolution.

3. Results and discussion
Overall, we found that the content of interest lacks a canonical representation
across the textbooks (e.g. of the 189 researchers whose work was cited, only 17
were cited in 5 or more textbooks). The coverage of animal communication was
relatively broad, with some recurring classic examples, such as vervet monkeys
or honeybees. As its main problem, we see overreliance on the outdated
framework of “design features” (11 textbooks, 183 references), to the exclusionof other important frameworks such as signaling theory (0 references) or
phenomena such as turn-taking (3 books, 2 references). In contrast, the coverageof topics related to language origins and evolution was much less extensive andsystematic, and suffered from an overrepresentation of content whose value isalmost exclusively historical (i.e. creation myths, “bow-wow” theories), at theexpense of current research in this fast-paced field; resulting in almost complete
exclusion of topics such as cultural evolution (1 book, 3 references) or tool use(3 books, 7 references). We found the textbooks to be mostly factually correct,
with a total of 60 problematic passages of different dimensions found in all
textbooks (38 for AC and 22 for LE). We conclude with a detailed discussion of
the results and several recommendations for future editions of the textbooks,
most importantly changing the proportion of historical content to recent results,
and including signalling theory and cultural evolution.

710

Acknowledgements
This research was supported by the Polish National Science Centre under grant
agreement UMO-2019/34/E/HS2/00248.
References
Wacewicz S., Żywiczyński P. and Jasiński A. (2016). "Language Evolution And
Language Origins In Teaching Linguistics At The University Level". In S.G.
Roberts, C. Cuskley, L. McCrohon, L. Barceló-Coblijn, O. Fehér & T.
Verhoef (eds.) The Evolution of Language: Proceedings of the 11th
International Conference (EVOLANG11).

711

INFERRING THE TEMPORAL AND SPATIAL EVOLUTION OF
ACCENT SYSTEMS IN JAPANESE DIALECTS: A
PHYLOGEOGRAPHIC APPROACH
Takuya Takahshi*1, Ayaka Onohara2, and Yasuo Ihara3*Corresponding Author: takuya.14966@gmail.com
1Meiji Institute for Advanced Study of Mathematical Sciences, Tokyo, Japan
2Center for Statistics and Information, Rikkyo University, Tokyo, Japan
3 Department of Biological Sciences, the University of Tokyo, Tokyo, Japan

1.

Abstract

Accent systems of Japanese dialects over Japan’s mainland arecharacterized by pitch changes, which are further subdivided into two
phonological features; pitch accent and word tone (Hayata, 1999; see also
Labrune, 2012; Uwano, 2012). As for the former, a majority of the dialects
accentually distinguish words, that is, words are distinguished by the mora wherethe voice-pitch changes (McCawley, 1968; Uwano, 2012). For example, consider
three words “hashi” (chopsticks), “hashi” (bridge), and “hashi” (edge) followedby the topic marker “ga.” In Tokyo dialect, the phrase “hashiga” (mora structure:
ha-shi-ga) is pronounced in a high-low-low manner for “chopsticks,” low-highlow for “bridge,” and low-high-high for “edge”. These words are thus
distinguished by the position of pitch accent, where the pitch drops, which isplaced on the first and second morae for “chopsticks” and “bridge,” respectively,
but absent for “edge.” On the other hand, “word tone” (Hayata, 1999) or “tonalregister” (Uwano, 2012) concerns the change of voice pitch in a whole word,
which characterizes the accent system of some regions such as the Kyoto andKagoshima dialects, but is absent in other dialects. Utsugi (2007) describes thatword tone (Hayata, 1999) is an almost equivalent concept to N-pattern accent(Uwano 2012).
Studying these two phonological features potentially gives insight into theancestry of the Japanese language for the following reasons. First, the word tone
has been pointed out to be a continuous feature of the Chinese tones (Hayata,1999). Second, since pitch accent and word tone affect the whole phonologicalThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

712

system, they change only infrequently, and their low speed of replacement enables
the trace of far past lineages and phylogenic reconstruction of regional dialects.
In the current research, we distinguish four accent patterns according to thepresence/absence of pitch accent and word tone, aiming at elucidating how thisvariation was formed after the regional dialects had split from the common
ancestor, in which both pitch accent and tone were present. In particular, thepurpose of this research is two-fold: (1) inference of transition rate between accentpatterns and (2) estimation of phylogenic tree of regional dialects based on theiraccent systems. To create empirical data, we randomly select 100 locations inJapan and specify the accent pattern at every location. To consider the spatial
contact patterns between human groups, we develop a network of populations,which reflects the real geography and population density of Japan’s mainland, andsimulate the spread of accent patterns on the network (see Takahashi & Ihara,2020). Simulating the genealogy of accent patterns, and applying a methodborrowed from Bayesian phylogenetics (Felsenstein, 1981; Pagel & Meade,2017), we obtain the posterior distribution of transition rates among accentpatterns, the range of spatial interaction between populations, and thephylogenetic topology and divergence time of the present dialects.
Thus far, we have quantified the loss rates of pitch accent and word tone,
but are unable to reconstruct a clear phylogenetic tree with a sufficiently highposterior clade probability. Future study needs to include more data.

713

Acknowledgements
This research was supported by JSPS KAKENHI, grant numbers JP20J13493,
JP18J00484, and JP17H06381. This research was also supported under theframework of the Japanese-Swiss Young Researchers’ Exchange Programme of
the Leading House Asia at ETH Zurich.
References
Felsenstein J. (1981). Evolutionary Trees from DNA Sequences: A Maximum
Likelihood Approach. Journal of Molecular Evolution. 17: 368-376.
Hayata, T. (1999). Oncho no Taiporojii. Tokyo, Japan: Taishukan.
Labrune, Laurence. (2012). The phonology of Japanese. Oxford: Oxford
University Press.
McCawley, J. (1968). The Phonological Component of a Grammar of Japanese.
The Hague, Mouton.
Pagel M., Meade A. (2017). The deep history of the number words. Phil. Trans.
R. Soc. B 373: 20160517.
Utsugi, A. (2007). The interplay between lexical and postlexical tonal
phenomena and the prosodic structure in Masan / Changwon Korean, ICPhS
2007 Satellite Meeting: Workshop on "Intonational Phonology: Understudied orFieldwork Languages," Saarbrücken, Germany.
Uwano, Z. (2012). Three types of accent kernels in Japanese, Lingua, 122(13).
Takahashi T., Ihara Y. (2020). Quantifying the spatial pattern of dialect words
spreading from a central population. Journal of the Royal Society Interface 17,
20200335.

714

CUMULATIVE CULTURAL EVOLUTION, POPULATIONSTRUCTURE AND THE ORIGIN OF COMBINATORIALITY IN
HUMAN LANGUAGE
MONICA TAMARIZ*1, SIMON KIRBY2*Corresponding Author: m.tamariz@hw.ac.uk
Department of Psychology, Heriot Watt University, Edinburgh, UK
2Centre for Language Evolution, University of Edinburgh, UK

1

1. Introduction
The emergence of combinatoriality (or duality of patterning) in human language
is a central question for evolutionary linguistics. Combinatoriality is absent only
in a handful of natural languages, such as ABSL, an emergent sign language thatdeveloped in a village setting (Sandler et al. 2011). The emergence of anotherdesign feature of language, compositionality, has been explained as a trade-off
between learning biases for compressibility and communication pressure against
ambiguity operating over cultural transmission (Kirby et al. 2015). We test two
hypotheses: (H1) Combinatoriality also results from a similar trade-off. (H2)
Population structure explains differences in combinatoriality across languages.
2. Methods
We constructed minimal languages that could nevertheless show
combinatoriality. Each language had four atomic meanings, each mapped onto a
2-unit signal from the set {ac, bd, ad, bc, pr, ps, qr, qs}. The 4096 possible
languages included 8 Degenerate languages, with the same signal for all
meanings; 1632 Holistic languages with all distinct signals, and no reuse of units
across signals; and 48 Combinatorial languages with all distinct signals, andmaximum reuse of units across signals.
During learning, agents computed a Bayesian posterior probability distribution
over the set of possible languages. The compressibility bias was modelled as ahigher prior probability for languages with lower coding length. The
disambiguation pressure was modelled as a bias against ambiguous languages
during communication (Goodman & Frank 2016).

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

715

Two population dynamics were modelled. During a communicative interaction,with Vertical dynamics, akin to the situation in village sign language emergence,
agents learn from the oldest agent in the population. With Horizontal dynamics,
agents learn from other agents who are themselves learners. This is more like thesituation in deaf-community sign languages, such as the case of emergence inschools for the deaf (Meir et al., 2010). The oldest agent was removed and a newone added every 20 interactions.
3. Results and discussion

Figure 1. Posterior probability of each language type. In all cases agents have a bias forcompressibility. (a) Vertical dynamics, no disambiguation pressure (200 generations). (b) Vertical
dynamics, disambiguation pressure (200 generations). (c) Horizontal dynamics, disambiguationpressure (20 generations only).

With Vertical dynamics, under Compressibility bias alone (Fig. 1a), degenerate
languages spread in the population; however, under Compressibility plus
Disambiguation (Fig. 1b), combinatorial languages win out. This supports H1 thatcombinatoriality emerges as from a trade-off between compressibility anddisambiguation pressures.
When learning from the oldest agent, combinatoriality evolves much slower thanwhen agents learn from other learners. This supports H2 and suggests that
population dynamics modulates the rate of evolution of combinatoriality, offeringa new candidate explanation for why combinatoriality emerges more rapidly indeaf-community rather than in village sign languages.
References
Goodman, N.D., Frank, M.C. (2016) Pragmatic language interpretation asprobabilistic inference. Trends Cogn. Sci. 20, 818–829.
Kirby, S., Tamariz, M., Cornish, H., & Smith, K. (2015) Compression andcommunication in the cultural evolution of linguistic structure. Cognition,
141, 87–102.

716

Meir, I., Sandler, W., Padden, C. & Aronoff, M. 2010. Emerging sign languages.
In M. Marschark, P. E. Spencer & P. E. Nathan (Eds.), Oxford handbook of
Deaf studies, language, and education Vol. 2, 267–280. Oxford: Oxford
University Press.
Sandler, W., Aronoff, M., Meir, I., Padden, C. (2011) The gradual emergence ofphonological form in a new language. Nat. Lang. Ling. Theory, 29, 503–543.

717

THE PREVALENCE OF SYSTEMATICITY IN
INDO-EUROPEAN LANGUAGES
Nicole Tamer*1,2, Paul Widmer1,2*

Corresponding Author: Nicole.tamer@uzh.ch
Department of Comparative Language Science, University of Zurich, Zurich,
Switzerland
2
Center for the Interdisciplinary Study of Language Evolution, University of Zurich,
Zurich, Switzerland
1

Human language has long been thought to be largely arbitrary, meaning that no
intrinsic or logical connection exists between form and meaning (Hockett, 1960;
de Saussure, 1983). Recent studies have revealed new insights by applying novel
statistical methods, demonstrating the prevalence, and increasing relevance of
non-arbitrary forms. These forms are commonly divided into iconicity, theresemblance-based mapping of form and meaning, (Winter & Perlman, 2021;
Nielsen & Dingemanse, 2021) and systematicity, phonological cues predictive ofgrammatical categories (Raviv & Arnon, 2018; Pimentel et al., 2019; Nölle et al.,2018). An example of systematicity is the phonological distinction between open
and closed word classes, the former commonly accept new words, whereas thelatter does not, and within the open word class (Monaghan et al., 2007).
Because systematicity focuses on the relationships of large numbers of words toa small number of abstract categories, rather than the more frequent research on
the relationships of single words to simple referential meanings, it has received
less attention in the context of language evolution (Dingemanse et al., 2015).
Languages tend to only exhibit subtle aspects of iconicity and these tend to decline
over time in favour of more systematic or arbitrary forms (Little et al. 2017).
Understanding the prevalence and mechanisms of systematicity is crucial toreveal more about its roots (cognitive advantages such as ease of processing,learnability, and acquisition advantages: Raviv et al., 2021; Monaghan et al.,2012; Fitneva et al., 2009) and role in the emergence and evolution of large
grammatical and lexical inventories.
Corpus studies have revealed that some languages use subtle phonological andprosodic cues (e.g. stress, duration, voicing or phonotactics: Monaghan, et al.,2007) to differentiate between word classes and categories (Kelly 1992;
Monaghan et al., 2005). However, previous studies included only a small sample
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

718

heavily biased towards modern Western European languages. These factors
constrain the ability to generalise these findings to other languages which limits
broader inference about the role of systematicity in language evolution. To
address this research gap and carry out an in-depth analysis of the prevalence ofsystematicity, we collected grammatical information of 30 modern and ancientlanguages extracted from language-specific grammars, corpora, and dictionaries.
Specifically, we examined phonological cues towards grammatical categories ofthe first phoneme to capture the initial word recognition advantages provided bysystematicity (Tamariz, 2008; Dingemanse et al. 2015).
A simple Shannon entropy was calculated within each grammatical category. The
significance was tested by bootstrapping the original distribution of tokens tocreate new populations of entropy measures which provided information about
expected entropy and the uncertainty of the measure.

Figure 1. Entropy of the first word segment of closed and open word classes (left) with the highest
differences and between grammatical categories of the open word class in a sample of languages.

We found significant differences in entropy measures of phoneme variability
between open and closed word classes for every language across the IndoEuropean clades. No significant difference in variability was observed within the
open word class itself when, for example, nouns were compared to verbs or
adjectives with adverbs contrary to results of previous studies (Monaghan et al.,2007; Dingemanse et al., 2015).
We therefore conclude that a language-general pattern exists for differential
variability in first word segment phoneme distribution between open and closed
word classes. Intriguingly, this effect is not driven by variability between
grammatical categories within the open word class contrary to previous findings.
These findings demonstrate the critical and hitherto underestimated role thatsystematicity plays in the development and stability of certain grammaticalmacro-categories. Previous studies have not captured these aspects ofsystematicity, and these findings demonstrate how language behaves not only
across the Indo-European language family but also within grammatical categories
further highlighting the prevalence of non-arbitrariness in a sample of IndoEuropean languages.

719

References
De Saussure, F. (1983). Course in General Linguistics, Open Court.
Dingemanse, M., Blasi, D. E., Lupyan, G., Christiansen, M. H., & Monaghan, P.
(2015). Arbitrariness, iconicity, and systematicity in language. Trends in
cognitive sciences, 19(10), 603-615.
Fitneva, S. A., Christiansen, M. H., & Monaghan, P. (2009). From sound tosyntax: Phonological constraints on children's lexical categorization of newwords. Journal of child language, 36(5), 967-997.
Garrod, S., Fay, N., Lee, J., Oberlander, J., & MacLeod, T. (2007). Foundationsof representation: where might graphical symbol systems comefrom?. Cognitive science, 31(6), 961-987.
Hockett, C. F., & Hockett, C. D. (1960). The origin of speech. Scientific
American, 203(3), 88-97.
Kelly, P. (1992). Does the ear assist the eye in the long-term retention oflexis?. IRAL: International Review of Applied Linguistics in Language
Teaching, 30(2), 137.
Little, H., Rasilo, H., Van Der Ham, S., & Eryılmaz, K. (2017). Empiricalapproaches for investigating the origins of structure in speech. Interaction
Studies, 18(3), 330-351.
Monaghan, P., Mattock, K., & Walker, P. (2012). The role of sound symbolism
in language learning. Journal of experimental psychology: Learning,memory, and cognition, 38(5), 1152.
Monaghan, P., Christiansen, M. H., & Chater, N. (2007). The phonologicaldistributional coherence hypothesis: Cross-linguistic evidence in language
acquisition. Cognitive psychology, 55(4), 259-305.
Monaghan, P., Chater, N., & Christiansen, M. H. (2005). The differential role ofphonological and distributional cues in grammaticalcategorisation. Cognition, 96(2), 143-182.
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Meaning to form: Measuring systematicity as information. arXiv preprint
arXiv:1906.05906.
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easy to learn? A preregistered study on how systematic structure andcommunity size affect language learnability. Cognition, 210, 104620.
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Examining the emergence of linguistic structure in children and adults using
iterated learning. Cognition, 181, 160-173.

720

Tamariz, M. (2008). Exploring systematicity between phonological and contextcooccurrence representations of the mental lexicon. The Mental
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Winter, B., & Perlman, M. (2021). Iconicity ratings really do measure iconicity,and they open a new window onto the nature of language. Linguistics
Vanguard, 7(1).

721

INTEGRATIVE BRAIN ACTIVITY IN PROCESS OF VISUAL
AND AUDIAL RECOGNITION OF IMITATIVE WORDS ON
DIFFERENT DE-ICONIZATION STAGES
LIUBOV TKACHEVA*1, MARIA FLAKSMAN*2,3, YULIA LAVITSKAYA4, YULIASEDELKINA4, ELIZAVETA KOROTAEVSKAYA1, AND ANDREI NASLEDOV1*Corresponding Authors: tkachewa.luba@gmail.com, Mariia.Flaksman@anglistik.uni-

muenchen.de
1Department of Pedagogy and Pedagogical Psychology, Saint Petersburg State

University, Saint Petersburg, Russia
2Department for English and American Studies, Ludwig Maximilian

University, München, Germany
Department of Foreign Languages, St. Petersburg Electrotechnical University,
St. Petersburg, Russia
4Department of Foreign Languages and Linguo-Didactics, Saint Petersburg
State University, Saint Petersburg

3

1. Iconicity and de-iconization as a process of language evolution
Iconicity is a relationship of resemblance (Peirce, 1940). In the lexicon it
manifests itself in a form of imitative (onomatopoeic, sound-symbolic) words
and ideophones (Hinton et al, 1994; Voelz et al, 2001). It is believed to be adesign feature of the language (Voronin, 2005) that has played an important rolein the proto-language (Moreno-Cabrera, 2012; Voronin, 2005). Indeed, in theabsence of an established system of signs, it is likely that the first-ever elementsof ‘language’ could have been based on the ‘natural’ form-meaning similarity.
There is no possibility to study the proto-language in vivo to establish the role oflexical iconicity in the language origin and evolution. However, there is apossibility to conduct research on the imitative words existing in modern
languages and to study their evolution. Flaksman (2017) established that
imitative words undergo four de-iconization stages (SDs) and transform from‘vivid’ iconic interjections on SD-1 (ha-ha!, zzz!) to words on SD-4 which havecompletely lost the original form-meaning correlation under the influence ofThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

722

phonetic and changes (cliché, once a sound of molten metal). Thus, deiconization in a way can be considered as a model for evolution of a part of thelanguage’s lexicon. The study aims to study this process by means of EEG.
2. EEG markers of words recognition
There are several EEG markers connected with semantic processing, among
them early event-related potential (ERP) components in the range 100–200 ms
which are known to be sensitive to lexical frequency (Carreiras et al, 2005);
N250, which is sensitive to orthographic similarity (Carreiras et al, 2009a) andthe phonological status of the letters (Carreiras et al, 2009b); P300 component
reflects the processes of distribution of arbitrary attention and stimulus
categorization (Didoné et al., 2016); N400 component is associated with
lexical–semantic access (Laszlo, & Armstrong, 2013).
3. Research aims, material, and methods
The aim of this research was to conduct an experimental EEG study on Russianwords on different de-iconization stages and to establish whether there aredifferences in the brain activity while processing visual and audial stimuli ofdifferent nature.
3.1. Stimuli selection for the experiment
The material for the research were 15 sound imitative (SI) words equallydistributed into 3 groups according to the criterion of iconicity. The stimuli were
preselected by means of a lexical decision task. To investigate the degrees of
iconicity in word recognition the experiment by Sidhu et al. (2020) was partly
replicated. The results revealed typical representatives of each group: explicit SI
words – xlop (clap), čmok (smack), voj (howl), pisk (squeak), čix (sneeze);
implicit SI words - žuk (bug, beetle), zud (itch), pux (fluff), xrjak (boar), gusʹ(goose); and arbitrary, non-SI, words - vosk (wax), svod (vault), sypʹ (rash), taz(basin, bowl), trostʹ (cane).
3.2. Methods and procedure
110 Russian adult participants took part in this experiment after signing up
informed consent officially approved by ethical committee of S-Petersburg State
University. The basic experiment was preceded by a preparatory step, which
included the selection and validation of visual stimulus material for each lexicalstimulus. Each participant was randomly presented with 60 words, 30 visuallyand 30 audibly. The task of the subjects was to identify the word and choose the723

appropriate picture from the two proposed. EEG was recorded using Mitsar
electroencephalograph (bandwidth, 0.5–70 Hz) with a 250Hz sampling rate foreach channel. 19 monopolar leads were arranged symmetrically according to theInternational 10–20 System (Fp1, Fp2, Fp3, Fp4, F7, F8, С3, С4, Fz, Cz, Pz, Т3,
Т4, Т5, Т6, Р3, Р4, О1, and О2), EOG was recorded. Visual and auditory ERP
were calculated using Repeated Measures Analysis of Variance.
4. Results and discussion
It was found that there was no statistically significant difference between
visually presented explicit-, implicit-SI words and non-SI words. However,statistically significant differences were obtained for audibly presented explicit
SI words in contrast to implicit SI words at N400 ERP (p=0.014050) and
implicit SI words in contrast to non-SI words at P300 endogenous evoked
potential (p=0.043261). The first result corresponds to the fact that the N400 islarger for figurative language (Kutas, & Federmeier, 2011). The second one lets
us speculate that explicit SI words demand more cognitive resources in process
of audial recognition than non-SI words.
5. Conclusions
We assume that the results obtained indicate a specific brain response
associated with directed attention in the process of cognitive decisionmaking task regarding explicit and implicit SI words presented audibly,
which may reflect a higher level of cognitive complexity of identifying thistype of stimuli. Explicit SI words, thus, are not only defined as extrasystemic and ‘archaic’ by means of linguistics, but also stand out according
to our findings in EEG experiments.
Acknowledgements
The research was supported by RFBR grant No. 20-013-00575“Psychophysiological indicators of the perception of sound symbolic words innative and foreign languages”.
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position priming. Cerebral Cortex, 19(11): 2659–2670.

724

Carreiras, M., Gillon-Dowens, M., Vergara, M., & Perea, M. (2009b). Are
vowels and consonants processed differently? Event-related potential
evidence with a delayed letter paradigm. Journal of cognitive neuroscience,21(2): 275–288.
Carreiras, M., Vergara, M., & Barber, H. (2005). Early event-related potential
effects of syllabic processing during visual word recognition. Journal of
cognitive neuroscience, 17(11): 1803–1817.
Didoné, D. D., et al. (2016). Auditory evoked potential P300 in adults: reference
values. Einstein (Sao Paulo, Brazil), 14(2): 208–212.
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continuous onomatopoeic coinage. In M. Bauer, A. Zirker, O. Fischer & Ch.
Ljungberg (Eds.), Dimensions of Iconicity (pp. 15-38). Amsterdam: John
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meaning in the N400 component of the event-related brain potential (ERP).
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potentials to individual units in simulation of temporally extended ERP
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Moreno-Cabrera, J. C. (2012). The role of sound symbolism in protolanguage:
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Peirce, Ch. S. (1940). The Philosophy of Peirce: Selected Writings (ed. by J.
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Sidhu, D., Vigliocco, G., & Pexman, P. (2020). Effects of iconicity in lexicaldecision. Language and cognition 12(1): 164-181.
Voeltz, E. F. K. & Kilian-Hatz. Ch. (Eds.). (2001). Ideophones [TypologicalStudies in Language 44]. Amsterdam-Philadelphia: John Benjamins.
Voronin, S. V. (2005). Iconicity. Glottogenesis. Semiosis. St. Petersburg:
St. Petersburg University Press.

725

EMBEDDING PARALLELEPIPED IN CO-OCCURRENCE MATRIX:
SIMULATION AND EMPIRICAL EVIDENCE
Takuma Torii*1 , Akihiro Maeda1 , and Shohei Hidaka*1* Corresponding Authors: {tak.torii,shhidaka}@jaist.ac.jp

1 Japan Advanced Institute of Science and Technology, Ishikawa, Japan

Recent natural language processing technologies are based on the vector space models of language, in which each word is represented with a vector in high dimensional space. One of
the earliest successes using the vector space models is the four-term analogical reasoning task.
A certain quadruple of word vectors forms a “parallelogram” in the vector space, with which
the fourth word vector is inferred as an answer to a given triplet of query words. Despite thelarge body of successful applications of the vector space models, it has remained unknown what
the parallelogram means. This study aims to reveal this mystery. Our analysis suggested that
analogical reasoning is possible by decomposition of the bigram co-occurrence matrix, and we
demonstrated the formation of a parallelepiped by creating a miniature corpus and its word
vectors. This analysis and demonstration imply a sort of symmetry or exchangeability in wordword co-occurrence structure.

1. Introduction: Distributional Models of Language‘Evolution’ of the language processing capability of the machine is dramatic inthis decade. The accuracy of machine translation has reached the human level orperhaps more than educated non-native speakers. These successes of machinelearning language models have suggested how natural languages are organized.
Language is usually considered as an organized system that exhibits the capability of determining a class of words given the context of a word to be determined. This theoretical idea is called the distributional hypothesis (Harris, 1954).
The distributional hypothesis postulates that words that occur in similar contexts
tend to have similar meanings. For example, ‘an apple’ and ‘a banana’ both areallowed to appear in similar contexts, e.g., “she eats
every morning” and“is a fruit”. However, they may not appear in similar contexts of ‘a bus‘and ‘a train’, e.g., “she takes
home”. When we think of the fill-in-the-blankproblem “she eats
every morning”, the words that refer to something edible, women like, and common in breakfast would be coming up with, like thosespecified by the context of the blank to be filled.
One of the pervasive methods to implement the distributional hypothesis iscounting the co-occurrence of words in the pairs, triplets, or n-grams. Such naive
co-occurrence counting has, however, a few technical issues: the combinatorial
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

726

space of word pairs is too large to sample sufficiently (e.g., a bigram (pair) table has 1012 cells for 106 word types), and it causes underestimation on the cooccurrence probability. Thus, one needs further compressed representations of
the co-occurrence table, that compressed representations hopefully preserve the
distributional structure of the words in the table and the language. Latent Semantic Analysis (LSA) (Landauer & Dumais, 1997) is one of such earliest attempts.
The underlying idea of LSA is that a sparse co-occurrence matrix M can be approximated by vector representation of words, called word vectors. It has been
demonstrated that word-vector algorithms can solve semantic tasks, although theirperformances were limited (see Lenci (2018) for review).
More recently, Mikolov et al. (2013) discovered that four-term analogy problems can be solved accurately by their artificial neural network called skip-gram,which is an instance of the word2vec class of models. Four-term analogy problem questions “what is d to c as b is to a?” denoted by, a : b :: c : d. Formally,
the model needs to predict word d given the triple of query words a, b, and c.
, should be answered
For example, the question, man : woman :: king :with ‘queen’. Importantly, the word2vec was not optimized to solve the fourterm analogy questions, but it was optimized to predict the context words for eachword. However, with the learned word vectors, e.g., vking , vman , vwoman , one cananswer the analogy task by vector arithmetic vking − vman + vwoman ≈ vqueen . Since
analogical reasoning requires not only syntactic but also semantic aspects of language, their successes in the analogy task have been viewed as strong support forthe distributional hypothesis. And since analogy was considered to be ‘uniquely’
humans, this discovery gave a strong impact on a variety of research fields.
To solve such analogical questions, word2vec needs to successfully extract latent and distributional structures of the language, which is represented in the vector form. Since then, researchers of related fields have been attracted to resolve
this “mystery” of word2vec, e.g., (Levy et al., 2015; Arora et al., 2015; Hashimoto
et al., 2016). Most of them have concluded that the emergence of parallelograms
is due to the sophisticated learning algorithm. The current consensus (see, e.g.,(Lenci, 2018)) is the conclusion by (Levy et al., 2015) that the analogy performance of word2vec can be explained as a result of a factorization of the PPMI
(positive pointwise mutual information), one of the most popular preprocess of
co-occurrence matrix in natural language processing (NLP).
In this paper, we take a different approach to the mystery of word2vec. We
hypothesized that the word co-occurrence matrix itself, rather than some transformation of it such as PPMI, has sufficient information required to solve linguistic
tasks. Namely, we take one of the simplest forms of implementation of the distributional hypothesis. This approach has been rarely taken in the existing literature.
Connecting the co-occurrence matrix to analogical parallelograms directly
naturally leads constructive approach — simulation to test which type of cooccurrence may embed a parallelogram in the word vector space. Thus, we take727

the two types of approaches, data-driven analysis of co-occurrence matrix andconstructive simulation creating and manipulating a small corpus.
In what follows, we briefly introduce the word2vec model in Section 2, followed by an analysis of a co-occurrence matrix in Section 3, and the constructive
approach in Section 4. Lastly, we discuss future directions toward the understanding of the semantic nature of underlying word co-occurrence.
2. word2vec: The Word Embedding Algorithm
We briefly introduce the key ideas of word2vec, specifically of the skip-gram artificial neural network architecture. The skip-gram model consists of the threelayers, n input units, d hidden units, and n output units, where n is the vocabulary size. Initially, every word w in vocabulary W is represented by a so-called
one-hot vector ew of length n. Given a long sequence of words represented by
one-hot vectors, the goal of optimization is to obtain a d dimensional compressed
representation vw , called word vector, for every word w ∈ W , where d ≪ n.
Denote by wt a word at the position t in the corpus. The skip-gram seeks the
corpus to identify every subsequence (wt−k , . . . , wt−1 , wt , wt+1 , . . . , wt+k ), thek preceding and k following context words around the centre word wt . The skipgram model is trained, to optimize the latent word vectors {vw }w∈W , for each wt
to predict their all context words w⃗ t = (wt−k , . . . , wt−1 , wt+1 , . . . , wt+k ) simultaneously throughout the corpus. Mikolov et al. (2013) defined for the skip-grammodel the conditional probability of occurring y in the context of x as follows:exp(vy · vx ),w∈W exp(vw · vx )P (y|x) = P(1)

where vy · vx is the inner product of word vectors vx and vy .
Using a trained word2vec, Mikolov et al. (2013) demonstrated that it can solve
their four-term analogy questions. Consider, for example, the problem, man :woman :: king :, and the correct answer is ‘queen’. Given the word vectors
vman , vwoman , vking for the cue words, decide the most likely word y by calculatingv ·v
the cosine similarity measure cosine(vx , vy ) = ∥vxx∥ ∥vyy ∥ for all words x:
vy = argmax cosine(vman − vwoman + vking , vx ) .

(2)

vx : x∈W

It is defined correct, if the word vector vy is vqueen . The overall percentage ofcorrect answers is about 66% for the 19,544 questions.
If any model answers correctly for a quadruple using Equation (2), these four
word vectors need to form a parallelogram in the vector space. Indeed, Mikolov
et al. (2013) graphically showed parallelograms in a lower-dimensional subspace.
3. Analogical Reasoning with Raw Co-occurrence Matrix
The past studies exploring the analogical reasoning based on the word2vec or others (Levy & Goldberg, 2014; Hashimoto et al., 2016; Arora et al., 2015) have es-728

sentially hypothesized and concluded that word2vec or other transformation suchas PPMI is crucial to have a good analogy performance. In this study, however,
we hypothesize that a raw co-occurrence matrix itself or its matrix decomposition
would be sufficient for analogical reasoning.
3.1. Method
To test our hypothesis, we directly counted the frequencies of pairwise cooccurrence of all words in the English Wikipedia dump corpus 20171001. The
text data contains approximately 7.9 billion words, of which 2.6 million words are
unique. The window size for word pair counting was k = 5. Although we counted
them all, algebraic operations using the full co-occurrence matrix were impossible
due to our computational power. Hence, for the analogical task, we only used the
sub-matrices composed of the top 1,000 (or 10,000) unique words, in addition to
the 905 unique words in the question set. Denote this co-occurrence matrix by
M ∈ Rn×n≥0 with vocabulary size n.
In NLP, it is commonly recognized that application of singular-value decomposition (SVD) to the co-occurrence matrix improves performance of linguistic
tasks. Technically, SVD is a method for decomposition of a real matrix M ofarbitrary finite size to the form M = U ΣV ⊤ , where matrix U and V are real
orthogonal matrices and the diagonal matrix Σ contains singular values in its diagonal elements. By taking the first d dimensions, the d dimensional word vectors
1/2
for n words are obtained as Ud Σd ∈ Rn×d . Since the word2vec was trained toconstruct 300 dimensional word vectors, d = 300 was used in this paper.
We trained our word2vec (skip-gram) model using the sample code of Python
library Gensim (Rehurek & Sojka, 2010). We used instead our own preprocessed
text data as described above. The window size k = 5 is the same. Only the words
that occur more than or equal to 100 times in the corpus were used for training themodel. The number of unique words was approximately 0.32 million.
3.2. Results
Figure 1 shows the performance for the four-term analogy task using the distributional models. As shown by Mikolov et al. (2013), the performance of word2vec
is 66%. We treat this as a benchmark. For the models freq, the rows of the cooccurrence frequency matrix M were directly used as word vectors. The models
showed accuracy below 5%. For the models logfreq, the logarithms of the rowsof M were used as word vectors. By taking the logarithms, the model performances got significantly increased about 40% and 35%. We think the logarithm
worked as a smoothing against the Zipf’s law. This partially supports our hypothesis that information required to solve linguistic tasks is inside the corpus data.
However, there is room for further improvement (could be) induced by word2vec.
To eliminate this possibility, we applied SVD, a classic word embedding method,

729

Figure 1.

Four-term analogy performances of distributional models

to the log-frequency matrices of M . Since SVD is linear, although the word2vec
is nonlinear, it would be helpful to resolve the mystery of word2vec. Surprisingly, the performances of the logfreq svd models are above 60% comparable toword2vec. This result supports the other half of our hypothesis that there is nolatent structure that can be discovered only when using word2vec.
3.3. Discussion: Why the Decomposed Co-occurrence Matrix Suffices
If the original word2vec (skip-gram) were successfully trained, the word vectors
V ∈ Rn×d determines conditional co-occurrence probability matrix P (y|x) inEquation (1). By taking logarithm, V V ⊤ ∈ Rn×n is extracted (the normalizing
term was ignored), and thus the skip-gram model could be viewed as an approximate matrix decomposition of the form V V ⊤ ≈ M̃ for unknown M̃ . Given
the results in Section 3.2, it suggests that “up-to-dth -rank matrix decompositionof the logarithm of M ” is essentially what the word2vec models do. This hypothesis differs from the previous study (Levy et al., 2015), which concluded thatword2vec is equivalent to the PPMI-like smoothing, or a matrix decomposition of
the PPMI-smoothed matrix of M . Our view, that word2vec as a co-occurrence
matrix decomposition, can be viewed as one of the simplest and most straightforward implementations of the distributional hypothesis (Harris, 1954).
4. Constructive Approach to the Parallelograms
The analysis has suggested that there is a subspace of the co-occurrence matrix, in
which a parallelogram is formed by a particular set of word vectors as each word
may have multiple aspects. For example, king is more similar with queen on theis-royal axis, but is more similar with man on the is-male axis. Such a multi-aspect
structure of the word king is supposed to be captured by a parallelepiped, rather

730

Figure 2. A hidden Markov model generating the 24 sentences in the toy corpus. Any hidden state X
other than the verbs generates the word X by probability 1. For example, the state “king” generates theword “king”. On the other hand, the two hidden states corresponding to the two verbs may generate
the same word. For example, both states “live in(R)” and “live in(C)” generate the word “live in”.

than a parallelogram. Although an analogy task tests a parallelogram, a collectionof analogy tasks would test a parallelepiped or more complex geometric object.
In this section, we take a constructive approach to address how this parallelepiped structure is involved with the syntactic or semantic nature of a language. Specifically, we construct a small toy corpus, that forms an idealized parallelepiped structure among the word vectors, and analyzed what condition would
be essential to form some parallelepiped of word vectors.
4.1. Demonstrating Parallelepiped Embedded in Co-occurrence Matrix
Toy Corpus. We created a corpus of 24 artificial sentences, which are not strictly
grammatical, but with a minimal syntactic and semantic structure. Each of thesentences in this corpus consists of four words in the form of Subject-Verb-ObjectAdverbial, such as “king live-in palace today”. There are eight subjects, threeverbs, six objects, and one adverb — in total 18 words. The corpus does not have
all the possible sentences out of these 18 words, 144 = 8 × 3 × 6, but it has only
24 sentences (Figure 2), which implicitly represents the hypothetical semantic
relationship between underlying concepts to which these words refer.
First, we analyzed the co-occurrence matrix constructed for the toy corpus
with each of the sentences generated by the equal probability 1/24. In this case,the co-occurrence matrix (up to scale and permutation similarity) canbe written08,8 C0with the two block matrices C0 ∈ R8×10 and C1 ∈ R10×10 by C = C ⊤ C1 .
0
Note that each row vector of the block matrix C0 is the non-zero part of word
vectors of the eight subject nouns. C0 has the rank 4, and it lives in 3 dimensional
affine space. Namely, there is some linearly independent basis of three vectors
b0 , b1 , b2 , b3 ∈ R8 , such that C0 = (b1 , b2 , b3 )A + b0 11,10 with a unique matrix
A ∈ R3×10 for each choice of the affine basis B = (b0 , b1 , b2 , b3 ). Let B̄ =

731

Figure 3. (Non-)parallelepipeds embedded in the co-occurrence matrix of (a) uniform toy corpus, (b)non-uniform toy corpus, and (c) a natural corpus.

(b1 , b2 , b3 ) has b1 , b2 , b3 ∈ R6 of non-zero vectors of the column vectors of C̄0 :=1
C0⊤ − 10
11,10 C0⊤ . Then the three dimensional coordinates of the 8 points are
given by the column vectors of (B̄ ⊤ B̄)−1 (B̄ ⊤ C̄0 ), in which a “parallelepiped” isembedded (Figure 3(a)). Thus, this uniform toy corpus gives a sufficient conditionor the existence of a way to embed a parallelepiped in the co-occurrence matrix.
4.2. Symmetry Breaker Against parallelepiped
It is also important to demonstrate on which condition the parallelepiped embedded in a co-occurrence matrix is broken, as such a demonstration gives a necessary
condition for the parallelepiped formation. To do so, we consider a variation of the
toy corpus, called non-uniform toy corpus, in which a certain randomly assigned
probability pi to sample the ith sentence to build the co-occurrence matrix. Figure3(b) shows the same set of the eight word vectors visualized in the same way as
Figure 3(a), for a set of non-uniform random probabilities pi . These eight word
vectors form neither a parallelepiped nor parallelograms. As the only difference
between the uniform and non-uniform toy corpus is their sampling probability,
this result suggests that a certain symmetric relationship in the probability distributions is needed to hold the parallelepiped.
4.3. A Parallelepiped in Natural Co-occurrence
The demonstration with the toy corpus above suggests that a certain class of word
vectors would form a parallelepiped relationship, if the class of vectors two or
more show independent syntactic-semantic statistical regularities on its word usage. We test this prediction by searching whether such a parallelepiped for a classof word vectors embedded in a natural co-occurrence matrix (logfreq svd, size1000). Figure 3(c) shows an example that we found in the set of 8-tuple word
vectors of the question words in the Family category (Mikolov et al., 2013) visualized by a two-dimensional subspace of the principal component analysis. Thisconfirms our prediction.

732

5. Conclusion
This study attempted to give a theoretical account of what the parallelogram meansin the vector space model. Our analysis of the co-occurrence matrix suggests a sortof co-occurrence matrix decomposition can give such a parallelogram useful for
analogical reasoning. This empirical observation leads us to a constructive approach to building a toy corpus that may or may not embed a parallelepiped in theco-occurrence matrix. This numerical simulation suggests that the parallelepiped
is tightly related to a certain class of the sentence probability distribution, perhaps
less restricted than uniform but more restricted than arbitrary.
The biolinguistic enterprise of seeking cognitive precursors to human language depends on hypotheses or views on the structure of language. Our ‘parallelotope hypothesis’ may provide yet another characterization of the structure oflanguage: word representations being structured are at least utilized for analogicalreasoning among words. This hypothesis makes a strong connection between themental representation of words of a language and relational reasoning on words.
This hypothesis may motivate comparative psychology research on precursors tolanguage in terms of the ability of relational reasoning.
Acknowledgements
This work is supported by JSPS KAKENHI JP 20H04994, JST PRESTO JPMJPR20C9, Japan.
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Rehurek, R., & Sojka, P. (2010). Software framework for topic modelling withlarge corpora. In Proceedings of the LREC Workshop (pp. 45–50).

733

GROUP SIZE AND LINGUISTIC STRUCTURE IN SILENTGESTURE
Ross Towns*1, Tessa Verhoef1*Corresponding Author: r.j.towns@liacs.leidenuniv.nl
1Creative Intelligence Lab, Leiden Institute for Advanced Computer Science (LIACS),

Leiden University, Netherlands

Evidence from natural languages suggests that properties of social environments
are instrumental in shaping linguistic features, with languages of smaller ormore isolated communities exhibiting more complexity (Lupyan & Dale, 2010,
Wray & Grace, 2007) and less regularity (Lupyan & Dale, 2010, Meir, Israel,
Sandler, Padden & Aronoff, 2012). In a recent artificial language study, Raviv,
Meyer and Lev-Ari (2019) found that participants communicating within larger
groups produced more systematic languages. They attributed this to the(initially) more variable input received by people in larger groups, which
produced cultural selection pressures favoring systematicity as a means of
reducing the cognitive strain of communicating with multiple partners.
We set out to extend this input variability hypothesis to be more readily
applicable to accounts of sign languages in relation to social structure (e.g., Meiret al., 2012). Manual communication offers considerable opportunities for iconic
signaling, which is associated with increased transparency of novel signals(Thompson et al., 2009) and may therefore counteract an effect of group size on
the emergence of systematic structure. To investigate this possibility, we asked
hearing non-signers to communicate using pantomime in groups of differing
sizes. Below we report the procedure and results for the first two rounds, sinceconditions were similar across all group sizes for these rounds only.
We conducted the experiment online, using the Jitsi video-conferencing
platform. Participants (n = 26) were all masters students taking part at home aspart of an introductory course on language evolution. Overall, we tested 3groups of 5 people, 2 groups of 4, and 1 group of 3. Participants took turns tocommunicate a selection of concepts, presented as typed English words for theirThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

734

groupmates to interpret. The stimulus set was adapted from Motamedi et al.
(2019), such that each item corresponded to a thematic category (e.g., Religion,
Music, Food) and one functional category (Person, Object, Location or Action).
Thus, the concept Singer corresponds to the categories of Music and Person,
Concert Hall corresponds to Music and Location, and so on.
Following Motamedi et al. (2019), recordings of participants’ gestures weremanually coded for shape and number of hands (e.g., 2hTakePhoto for gesturesin which both hands are used to mime taking a photograph) and use offunctional markers (gestures shared across items within a given functional
category that may be interpreted as indicating that category, e.g., pointing to
one’s chest to denote a person). Coded trials were then analyzed using two
measures of efficiency, namely utterance length and frequency of repetitions,and two measures of systematicity: entropy and proportion of functionallymarked utterances, both of which indicate the recombination of elements acrosstrials. Mixed effects linear regression models (with participant and round as
random effects) found a significant negative effect of group size on entropy ( =-0.32, SE = 0.14, t = -2.26, p = 0.032*), but no effect on the proportion offunctionally marked utterances. Marginally significant positive effects of groupsize were found for utterance length ( = 0.43, SE = 0.23, t = 1.88, p = 0.073)
and frequency of repetitions ( = 0.43, SE = 0.22, t = 2.00, p = 0.058).

Figure 1. Mean entropy of participants’ gestures. Error bars represent bootstrapped 95%
confidence intervals

These results thus offer tentative support for the claim that larger groups
produce more systematic gestures. However, the marginally significant results
for gesture length and repetition frequency may suggest that larger group size
hinders efficient communication. Though limited by our use of a small
convenience sample, which was partly determined by pedagogicalconsiderations, we argue that these findings provide a good basis for further
investigation.

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References
Dale, R., & Lupyan, G. (2012). Understanding the origins of morphologicaldiversity: The linguistic niche hypothesis. Advances in Complex Systems,
15(3–4).
Meir, I., Israel, A., Sandler, W., Padden, C. A., & Aronoff, M. (2012). The
influence of community on language structure. Linguistic Variation, 12(2),247–291.
Motamedi, Y., Schouwstra, M., Smith, K., Culbertson, J., & Kirby, S. (2019).
Evolving artificial sign languages in the lab: From improvised gesture tosystematic sign. Cognition, 192(April)
Raviv, L., Meyer, A., & Lev-Ari, S. (2019). Larger communities create more
systematic languages. Proceedings of the Royal Society B: Biological
Sciences, 286(1907), 20191262.
Thompson, R. L., Vinson, D. P., and Vigliocco, G. (2009). The link between
form and meaning in American sign language: lexical processing effects. J.
Exp. Psychol. Lang. Mem. Cogn. 35, 550–557.
Wray, A., & Grace, G. W. (2007). The consequences of talking to strangers:
Evolutionary corollaries of socio-cultural influences on linguistic form.
Lingua, 117(3), 543–578

736

THE CONDITION OF RECURSIVE COMBINATION IN THE
EVOLUTION OF REINFORCEMENT LEARNING AGENTS
GENTA TOYA*1, RIE ASANO2, and TAKASHI HASHIMOTO3*

Corresponding Author: toyagent@protonmail.com
Graduate School of Arts and Sciences, The University of Tokyo, Meguro, Japan2
Department of Systematic Musicology, University of Cologne, Cologne,
Germany

1School of Knowledge Science, Japan Advanced Institute of Scienceand Technology, Nomi, Japan

3

Language consists of multiple components with different evolutionary origins(Boeckx, 2013). Beside its central role in communication based on the capacity
for intention sharing (Tomasello, 1999), another important component oflanguage is its hierarchical structure. Human language requires not only asequential operation but also a recursive operation generating hierarchicalexpressions by combining two elements, e.g., X and Y, into a unit {X, Y} whichis again combined into another unit {Z, {X, Y}} (Chomsky, 1995). How and why
did the recursive combination (hereafter RC) evolve? RC is significantly observedin object manipulation by humans (Greenfield, 1991) as well as by captive
chimpanzees (Matsuzawa, 1991; Hayashi, 2007). Because object manipulation asa necessary skill for tool-use and -making can increase the fitness of an individual
without cooperating with others, it is hypothesized that the RC of objects or
actions evolved as a precursor of RC of lexical items or symbols (Fujita, 2017).
We pursue this hypothesis of the evolutionary scenario that RC in motor control
extended to RC in language in the course of human evolution.
In our simulation study, we model learning organisms as reinforcement
learning agents (Sutton & Barto, 2018). By using agent-based modeling andevolutionary simulation, we investigate how and why RC evolved in learningorganisms. In this simulation, agents, which are equipped with a reinforcement
learning algorithm, explore and learn the process of tool making. Tool-making is
implemented as a combination of elements through state transitions based on Qvalues. In addition, the hyperparameters (learning rate α, exploration rate ε, andtime discount rate γ) of the neural network encoding the reinforcement learningThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

737

and Q-values are explored by the genetic algorithm. The way agents make tools
can be classified into two categories. One is the non-RC type production, in whichelements are sequentially combined into a single object. The other is RC typeproduction, where the combined object is re-combined with another object using
a stack that can be acquired evolutionarily. The varieties of products that can bemade with either production method remain the same. We set the reward functionof the reinforcement learning algorithm so that the agents are rewarded more formaking novel products. This corresponds to the phenomena that the invention oftools allows access to new resources (Arthur, 2009). We define the fitness
function of the genetic algorithm so that the total rewards of an agent in a
generation is discounted by the depth of the stack as a cost. In other words, this isa more favorable setting for non-RC than for RC because RC is more costly due
to the additional use of the stack.
We found out that critical parameters for the emergence of RC include thecost of the stack, the reward discount rate, element types, and product length. If
the cost of the stack, which is necessary for RC, is low, RC emerges because using
RC in addition to the reinforcement learning is more advantageous than using
reinforcement learning only. If the reward discount rate of producing the same
product is low, it is less adaptive to produce as diverse products as possible. In
this case, RC emerges because reinforcement learning with a high exploration rate
ε is a less valuable strategy (Figure 1). If the types of elements are more and the
product length is short, RC emerges because it is more effective thanreinforcement learning with a low time discount rate γ.
In sum, in solving the exploration and exploitation problem, RC emerges inan environment where exploitation and exploration should be balanced.
Moreover, the environment, in which attending to multiple states (i.e., to both
workspace and stack) is not costly, facilitates the emergence of RC.

Figure 1. Reward discount rate dependency of the population share of RC users, hyperparameterof Q-learning (Alpha_QL, Gamma_QL, Epsiron_QL) at the 1,000th generation (average of 50
trials).

738

References
Arthur, B. W. (2009). The nature of technology: What it is and how it evolves.
The Free Press, NY: Simon & Schuster.
Boeckx, C. (2013). Merge: Biolinguistic considerations. English Linguistics 30,
463-484.
Chomsky, N. (1995). Bare Phrase Structure. In H. Campos. & P. Kempchinsky(Eds.), Evolution and Revolution in Linguistic Theory (pp. 1-15). Washington
DC: Georgetown University Press.
Fujita, K. (2017). On the parallel evolution of syntax and lexicon: A Merge-onlyview. The Journal of Neurolinguistics, 43, 178–192.
Greenfield, P. (1991). Language, tools and brain: The ontogeny and phylogeny ofhierarchically organized sequential behavior. Behavioral and Brain Science,
14, 531–595.
Hayashi, M. (2007). A New notation system of object manipulation in the nestingcup task for chimpanzees and humans, Cortex, 43, 308–318.
Matsuzawa, T. (1991). Nesting cups and metatools in chimpanzees. Behavioral
and Brain Science, 14: 570–571
Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction.
Cambridge, MA: The MIT press.
Tomasello, M. (1999). The cultural origins of human cognition. Cambridge, MA:
Harvard University Press.

739

CAN PHONOLOGICAL STRUCTURE EMERGE THROUGHITERATED IMITATIONS OF ENVIRONMENTAL SOUNDS?
Julia Trzeciakowska*1, Elizabeth Qing Zhang1, and Sławomir Wacewicz1*Corresponding Author: julietrz09@gmail.com
1Center For Language Evolution Studies, Faculty of Humanities, Nicolaus Copernicus

University, Toruń, Poland

1. Introduction
Experimental studies on the development of communication have tended to focus
on the role of the visual modality, and in particular the iconic potential of gesturesand other forms of bodily communication, for the de novo emergence ofcommunication systems in the laboratory (cf. Fay et al. 2014; Goldin-Meadow
2016). This line of enquiry has been relatively recently complemented with a newempirical approach to the potential for form-meaning iconicity in the vocalauditory modality. These empirical studies reveal that vocal iconicity is inherently
present in the lexicons of spoken languages (Johansson et al. 2021). Naïve
listeners are able to assign new iconic vocalizations to their ‘meanings’ in forcedchoice paradigms (Perlman et al. 2015), to a large extent even cross-culturally(Ćwiek et al. 2021). This has been taken to suggest that the iconic potential of the
vocal modality might have played a role in establishing the first forms oflanguage-like communication.
Further, existing research (Edmiston et al. 2018) with speakers of English
suggests that linguistic properties such as phonological words can emerge
spontaneously through repeated imitations of environmental sounds; however, theuniversality of these findings has not been tested beyond the native speakers of asingle language. Our study extends this approach to participants speaking natively
another three languages, from two language families, Indo-European (Polish andGerman) and Sino-Tibetan (Chinese). Also, four new onomatopoeic sounds were
included.

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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2. Methods
Data was collected in a classic iterated learning paradigm, in chains of 8participants per chain: first participant imitated a ‘seed’ environmental sound –such as glass, zipper or sneezing – and that participant’s output was played backas input to be imitated by the next participant in a chain, etc. (cf. ‘Chinese
whispers’). We collected imitations from native speakers of Polish (8 chains),
German (8 chains), and Chinese (8 chains). If imitations become gradually morelanguage-like with each iteration, then the last (8th) generation imitations shouldsound more wordlike (thus, be easier to transcribe) than the first generation
imitations. We asked native speakers of each language to convert the 1st and 8th
generation imitations into spelling (native orthography). In the case of Chinese,participants used pinyin, i.e., the official romanised spelling; we expected more
orthographic similarity (computed with the Ratcliff Obershelp algorithm)
between the transcriptions of the 8th than 1st generation imitations.
3. Results
Paired samples t-tests revealed a significant difference between the mean
similarity of transcriptions of sounds from the 1st and 8th generations in the
expected direction only for the sounds provided by Polish speakers (PL t (7)=-3.201,
p = 0.015 < 0.05), and no significant differences for the sounds produced by the
speakers of German (DE t(7) =-1.094, p =0.310 > 0.05) or Chinese (ZH t(7)= 0.392., p=0.707 > 0.05). In a battery of qualitative post-studies, we established that theresults heavily depended on the performance of the first participant in each chain(1st round imitator).
4. Discussion
We have been able to replicate the results of Edmiston et al. (2018) only for the
sample of Polish speakers, but not German or Chinese speakers. Minimally, thissuggests that care should be taken when generalizing results obtained fromspeakers of a single language, since these can reflect language or culture-specificphenomena rather than being informative of language-universal phenomena thatare the basis for formulating language-origins scenarios.

741

ReferencesĆwiek, A., Fuchs, S., Draxler, C. et al. Novel vocalizations are understood acrosscultures. Sci Rep 11, 10108 (2021). https://doi.org/10.1038/s41598-02189445-4
Edmiston, P., Perlman, M., & Lupyan, G. (2018). Repeated imitation makes
human vocalizations more word like. Proceedings of the Royal Society B,
285, 20172709.
Fay et al. (2014). Creating a communication system from scratch: gesture beats
vocalization hands down. Frontiers in Psychology, 5(345). ). https://doi.org/
10.3389/fpsyg.2014.00354.
Goldin-Meadow, S. (2016). What the hands can tell us about language emergence.
Psychonomic Bulletin & Review, 24(1), 1–6.
Johansson, E., J. & Carr, J., S. Kirby (2021). Cultural evolution leads to vocal
iconicity in an iterated learning task. Journal of Language Evolution, Volume
6, Issue 1, January 2021, 1–25, https://doi.org/10.1093/jole/lzab001
Kirby, S. & H. Cornish, K. Smith (2006) Cumulative cultural evolution in thelaboratory: An experimental approach to the origins of structure in humanlanguage, PNAS,
Perlman, M., Dale, R., & Lupyan, G. (2015). Iconicity can ground the creation of
vocal symbols. Royal Society Open Science, 2(8), 150152–16
Perniss P, Vigliocco G. (2014) The bridge of iconicity: from a world of experienceto the experience of language. Phil. Trans. R. Soc. B 369, 20130300.

742

ENVIRONMENTAL AND SOCIAL FACTORS IN THE
EMERGENCE OF SPATIAL LANGUAGE
Kristian Tylén*1,2, Zahra Alinam3, and Cordula Vesper1,2*Corresponding Author: kristian@cc.au.dk
1Deparment of Linguistics, Cognitive Science and Semiotics,

Aarhus University, Aarhus, Denmark
2The Interacting Minds Centre, Aarhus University, Aarhus, Denmark
3Architecture & Urbanism Faculty, Tabriz Islamic Art University, Tabriz, Iran

Abstract
Languages across the world vary in how they construe simple spatial relations(Majid et al., 2004). It is, however, far from clear what the source of this variation
is. One suggestion is that variation is driven first and foremost by cultural andhistorical factors such as language contact, transmission, and stochastic drift. As
languages are passed down from generation to generation, cognitive biases forstructure, compressibility and expressibility shape the emergence of linguistic
structure and conventions (Christiansen & Chater, 2008; Kirby et al., 2015). In
turn, when a speaker acquires a particular natural language, the structure of that
language can influence how she will distribute attention and conceptualize her
surrounding giving rise to linguistic relativity effects (Bowerman, 1996;
Levinson, 2003; Roberson et al., 2005).
Another suggestion is that variation evolves contingent on ecological andenvironmental factors (Lupyan & Dale, 2016; Winters et al., 2015). The structureof our surrounding environment (affordances) can thus come to bias which
conceptual solutions are experientially salient and thus influence which linguistic
structures are stabilized (Nölle et al., 2020; Tylén et al., 2013). Here, we present
a model that attempts to integrate the two elements (linguistic relativity and the
linguistic niche hypothesis) in one dynamical system and provide experimental
evidence to test its predictions. The model assumes dynamical circular causality

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

743

between environmental affordances and cognitive construal enhanced throughcultural transmission (Alinam et al., 2021).
In a preregistered experiment, we used Virtual Reality to study the interactive
processes shaping these affordances and their implications focusing on the caseof urban environments. Seventy-two participants performed a navigation task first
following, and later creating, instructions to find a target object in an urban
environment. In each session, a participant would search a virtual urban space fora target, return and make written instructions for the next participant about where
to go and look for the target. The same procedure repeated in a transmission chainof 6 generations, where the instructions of the previous participant was passed on
to the next. In order to investigate the contingencies between environmental
affordances, conceptualization and linguistic interaction, half of the participants
did the task in an urban space characterized by windy streets and salient colorfulbuilding textures (henceforth the “Barcelona” condition), while the other half did
the task in an urban space characterized by straight perpendicular streets andbuildings with desaturated color/texture (the “Manhattan” condition). Number ofhouses, street crossings and their relative position was kept constant between thecondition.
In support of our predictions, we find that route descriptions differed between
conditions. Descriptions in the Barcelona condition were generally more reliant
on local salient vertical landmarks while participants in the Manhattan condition
relied more on horizontal and cardinal cues. This suggest that participants are
susceptible to the visuo-spatial affordances on their local environment when
preparing their instructions supporting the linguistic niche hypothesis. However,more interestingly, these tendencies are enhanced over generations in thetransmission chains. Reading the verbal instructions of the previous participant
seems to bias the attention of the current participant towards particular cues in theenvironment. Searching for the target, participants pay more attention to thosedimensions of the space profiled in the instructions. And in turn, when later
writing their own instructions for the next participant, they seem to not only
reproduce description expressing the attentional profile of received instructions,but to even enhance such biases over generations. This is indicative of a linguistic
relativity effect (at least according to a weak version of this).
Together, our results point to the mutually enhancing influences of (nonlinguistic) environmental affordances and language in guiding attention andconceptualization, and can inform discussions of how linguistic structure emerge
contingent on environmental structure.

744

Acknowledgements
The authors would like to thank Astrid Endrup Iversen, Amalie Lysgaard
Andersen, and Annamaya Halskov-Jensen for assistance with data collections.
This study was supported by seed funding from the Interacting Mind Centre.

References
Alinam, Z., Tylén, K., Pirbabaei, M. T., & Gharehbaglou, M. (2021). CognitiveCultural Looping Mechanism of Urban Space Conceptualization.
Integrative Psychological and Behavioral Science, 1–19.
Bowerman, M. (1996). The origins of children’s spatial semantic categories:
Cognitive versus linguistic determinants. In J. J. Gumperz Levinson, S.
C. (Ed.), Rethinking Linguistic Relativity. Cambridge University Press.
Christiansen, M., & Chater, N. (2008). Language as shaped by the brain.
Behavioral and Brain Sciences, 31(05), 1–70.
Kirby, S., Tamariz, M., Cornish, H., & Smith, K. (2015). Compression andcommunication in the cultural evolution of linguistic structure.
Cognition, 141, 87–102.
Levinson, S. C. (2003). Space in Language and Cognition. Cambridge
University Press.
Lupyan, G., & Dale, R. (2016). Why Are There Different Languages? The Roleof Adaptation in Linguistic Diversity. Trends in Cognitive Sciences,20(9), 649–660.
Majid, A., Bowerman, M., Kita, S., Haun, D. B. M., & Levinson, S. C. (2004).
Can language restructure cognition? The case for space. Trends in
Cognitive Sciences, 8(3), 108–114.
Nölle, J., Fusaroli, R., Mills, G. J., & Tylén, K. (2020). Language as shaped by
the environment: Linguistic construal in a collaborative spatial task.
Palgrave Communications, 6(1), 1–10.
Roberson, D., Davidoff, J., Davies, I. R. L., & Shapiro, L. R. (2005). Color
categories: Evidence for the cultural relativity hypothesis. Cognitive
Psychology, 50(4), 378–411.
Tylén, K., Fusaroli, R., Bundgaard, P. F., & Østergaard, S. (2013). Making
sense together: A dynamical account of linguistic meaning-making.
Semiotica, 2013(194), 39–62.
Winters, J., Kirby, S., & Smith, K. (2015). Languages adapt to their contextual
niche. Language and Cognition, 7(3), 415–449.

745

INITIATING LEARNED VOCALIZATION IS PRECEDED BY
LONG RAMPING IN NEURAL ACTIVITY IN BASAL GANGLIAIN A SPECIES OF SONGBIRDSACHIO UMEMOTO*1,2, SHIN YANAGIHARA1,3, and KAZUO OKANOYA 1,3,4*Corresponding Author: umemoto@m.u-tokyo.ac.jp
1 Graduate School of Arts and Sciences, The University of Tokyo, Japan
2 Center for Disease Biology and Integrative Medicine, Graduate School of

Medicine, The University of Tokyo, Japan.
3 Advanced Comprehensive Research Organization, Teikyo University, Japan
4 RIKEN Center for Brain Science, RIKEN, Japan

Learning is essential to speak language. Humans are highly motivated to speak
and improve it especially in their childhood. After critical period of language
learning, they can still acquire new vocabulary, and even second language. It
enables humans to sophisticate abstract and hierarchical language and contributes
to communication among or between groups of large size. As well as the capacityof learning, motivation to learn language during their lifetime is needed on
improvement of speaking language without being praised or rewarded, especiallyin their adulthood. It suggests that language learning should be motivated byinternal system, and investigating mechanism of motivation of learning would
help to understand how human language is maintained and evolved. In theperspective of motivation to maintain learned vocalization, vocal-learners are
expected to have common system to vocalize frequently. As well as humans, malesongbirds conduct vocal learning both as a child and an adult. Although their songis used to attract females, they also sing undirected song (US), and it has a role inmaintain their syntax and acoustic features (Sakata & Brainard, 2006). It suggeststhat male songbirds are good model to investigate internal system to motivate
vocalization. Anterior Forebrain Pathway (AFP), a cortico-basal gangliathalamo-cortical loop in songbird, is suggested to learn and maintain US(Brainard, 2004; Ölveczky et al., 2005; Hoffmann et al., 2016; Gadagkar et al.,2016). In AFP, neural activity in Area X, basal ganglia in songbird, is expected toreflect both preparatory activity and motivational signal because it receives
premotor and dopaminergic input, and was reported on dopamine concentration
during singing US (Sasaki et al., 2006). In order to investigate neural activity
reflecting motivation in learned vocalization, present study recorded preparatory

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

746

activity in basal ganglia of male songbirds during US, and compared duration of
the activity change in US and compared it to that in vocalizing innate call.
Male Java sparrows (Lonchura oryzivora) (n = 4) were used for neural recording.
Manually-driven electrode was implanted in their Area X, and neural activity wasrecorded while they vocalized US and innate calls in a sound-proof box. Neuralactivity was calculated by firing rate (FR) of sorted unit from recorded data. USbout was composed of motif (main body), and preceding vocalizations. FR inseven 1 second bins before the onsets and one bin after the onsets were used forstatistical analysis. FR in 7-6 seconds before the onset was regarded asspontaneous firing rate (SFR), and song-related neurons were decided byWilcoxon’s sign rank test between FR in the bin after onset and SFR. Ramping
duration of preparatory activity was decided by the number of continuous bins
significantly higher in FR than SFR from the onset. As to innate calls, trill calls,
specific long call in Java sparrow, was used in activity analysis. FR in three 1
second bins before the onsets and one bin after the onsets were used for statisticaltest. 24 neurons were recorded, 16 neurons showed US related activity and 6
showed trill call related activity. Ramping duration of preparatory activity was
2.25 ± 1.56 seconds in US (bout: 2.58 ± 1.55 seconds, motif: 1.25 ± 1.09 seconds),
and 1.33 ± 0.47 seconds in trill call. The results shows that longer activity change
in basal ganglia before learned vocalization than innate call in songbird. It
suggests that initiation of learned vocalization may cause longer change ininternal state, and motivate activity increase in various area, which may enables
individuals complex and skilled vocalization.

Figure: (Top) Spectrogram and waveform of US. (Middle) Raster plot of neuralactivity initiating US. (Bottom) Histogram of activity prior to US.

747

References
Brainard, M. (2004). Contributions of the Anterior Forebrain Pathway to Vocal
Plasticity. Annals of the New York Academy of Sciences, 1016(1), 377-394.
Gadagkar, V., Puzerey, P. A., Chen, R., Baird-Daniel, E., Farhang, A. R., &Goldberg, J. H. (2016). Dopamine Neurons Encode Performance Error in
Singing Birds. Science, 354(6317), 1278-1282.
Hoffmann, L. A., Saravanan, V., Wood, A. N., He, L., & Sober, S. J. (2016).
Dopaminergic contributions to vocal learning. Journal of Neuroscience, 36(7),2176-2189.
Ölveczky, B. P., Andalman, A. S., & Fee, M. S. (2005). Vocal experimentationin the juvenile songbird requires a basal ganglia circuit. PLoS biology, 3(5),e153.
Sakata, J. T., & Brainard, M. S. (2006). Real-time contributions of auditory
feedback to avian vocal motor control. Journal of Neuroscience, 26(38), 96199628.
Sasaki, A., Sotnikova, T. D., Gainetdinov, R. R., & Jarvis, E. D. (2006). Social
context-dependent singing-regulated dopamine. Journal of Neuroscience,
26(35), 9010-9014.

748

IMPLICIT COMMUNICATION OF WORD MEANING THROUGH
CO-OCCURRENCE
Jeroen van Paridon*1 and Gary Lupyan*1* Corresponding Author: vanparidon@wisc.edu

1 Department of Psychology, University of Wisconsin-Madison, USA

Communication requires people to align, at least in part, on what words mean. To accomplish
this, language learners can observe what a word refers to. However, many words have referents
that are abstract or otherwise hard to observe. In addition, many concrete words also have more
abstract denotations. In the absence of direct referents, language learners can align word meanings to those of their language community by observing how words are used in context. Thismechanism is underappreciated as a driver of semantic alignment across large language communities in which individual speakers are unlikely to ever interact directly. In three experiments,
we demonstrate alignment between blind and sighted speakers for the semantic associationsof color terms – whose direct referents can only be observed by the sighted participants – anddemonstrate how a word embedding model can achieve this alignment by learning from word
co-occurrence patterns.

Experiments in language evolution often focus on the transmission of structure rather than the transmission of semantics. For example, Kirby, Cornish, andSmith (2008) write that “each utterance has a dual purpose, carrying semantic
content but also conveying information about its own construction. Upon hearing
a sentence, a language learner uses the structure of that sentence to make newinferences about the language that produced it”. This is a foundational claim in
the field of language evolution, and it has been repeatedly demonstrated empirically through, e.g., iterated learning experiments. In addition to conveying pragmatic meaning and information about its own construction, however, language
also carries implicit information about its own lexical semantics in the form of
word co-occurrences. For example, we might learn that “odd”, “strange”, and“weird” are related because because they are used in similar contexts. This is anunderappreciated mechanism for language evolution, as it serves to align lexical
semantics across speakers in a language community, which is vital for developing
and maintaining a mutually intelligible lexicon.
Of course learning what words mean involves more than than tracking cooccurrences. Often, there is a direct referent present that the learner can observe.
In some cases however, the language itself is the only source of information about
lexical semantics that a language learner has access to. Blind people, for example,
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

749

can only learn about the meanings of color words through language. We would
expect, therefore, that they acquire those aspects of color word semantics that are
implicitly conveyed in spoken and written language.
Recently, Saysani, Corballis, and Corballis (2021) showed that blind people’s
judgments resemble those of sighted people when asked to place color words
along various dimensions, for example indicating where “red” and “green” fallon cold-hot, unripe-ripe and fast-slow continua. Given that blind people cannot
directly observe that hot objects sometimes glow red or that unripe fruits and vegetables tend to be green, it perhaps seems obvious that any color associations they
do have, they must learn from language (cf. Kim, Aheimer, Manrara, & Bedny,2021). However, how color semantics are represented in spoken and written language – and to what extent language, rather than perception, can align semantic
representations of colors between individuals – is not obvious. Are color semantics conveyed explicitly, e.g. through generic statements such as “green fruits areunripe”? Are they conveyed through simple co-occurrences, when a color word
occurs adjacent to another word, e.g. “red hot coals”? Or are color semantics
encoded in more complex semantic structures – a web of associations from which
we can derive semantics of color terms?
Experiment 1: Reanalysis of Saysani et al. (2021) data
MethodParticipants
Saysani et al. recruited 32 native speakers of New Zealand English, 20 of whom
had normal, trichromatic vision and 12 of whom were congenitally blind with noresidual vision. We recruited 130 additional sighted participants from the student
participant pool at a large public university, speakers of American English.
Design and procedure
Participants were asked to rate each of nine color terms (red, orange, yellow, green, blue, brown, purple, black, and white) on 17 semantic dimensions,each defined by two antonyms placed at the poles of a seven-point Likert scale(happy–sad, calm–angry, submissive–aggressive, relaxed–tense, exciting–dull,
selfless–jealous, active–passive, like–dislike, alive–dead, fast–slow, new–old, unripe–ripe, soft–hard, light–heavy, fresh–stale, clean–dirty, and cold–hot).
Results
The main finding reported by Saysani et al. was that multidimensional scaling
solutions were more variable between blind participants than between sighted participants. When we compared intraclass correlations (ICC) for the blind (.35, 95%
CI [.29, .42]) and the sighted (.49, 95% CI [.43, .55]) groups, blind participants

750

were indeed more variable than the sighted participants. At the same time, theresponses of sighted and blind participants were remarkably similar (see Figure1).

red is hot

red is aggressive

blue is hard
red is dull

red is tensewhite is clean

blue is cold

Figure 1. Blind and sighted participants’ color-adjective association ratings from Experiment 1.
Points on the diagonal from bottom-left to top-right represent perfect agreement between blind andsighted participants.

To understand how this aspect of color semantics may be represented in language, we relied on a fastText word embedding model (Bojanowski, Grave, Joulin,& Mikolov, 2017) trained on the fiction subcorpus of the Corpus of Contemporary American English (COCA-fiction). We projected the vector-representation ofeach color word onto a semantic dimension formed by the antonym pairs, e.g., hotand cold, and computing the cosine similarity between the color word vector andthe axis vector. The projection for e.g. the color blue on the dimension cold-hot#« # « # «is then given by cos (hot − cold, blue) (see Grand, Blank, Pereira, & Fedorenko,
2018, for a discussion of this projection method). This provides us with a relative
measure of word similarity, taken along the semantic dimension’s axis, that we
can use to predict human ratings of color associations.
Using a Bayesian linear mixed-effects model with weakly regularizing pri-751

ors (Capretto et al., 2020), we regressed word embedding projections onto participants’ color-adjective association ratings while adjusting for frequency andconcreteness of the words forming each dimension. Color-adjective ratings (e.g.,
placing yellow closer to ripe than unripe) were predicted by word embedding projections, with a standardized effect size of .40 (95% CI [.37, .43]) for sighted
participants and .33 (95% CI [.24, .41]) for blind participants.
Discussion
Language is produced by people, most of whom have direct experiences of color.
What is remarkable however, is that color information then becomes embeddedin the statistics of language, enabling – in principle – someone who has no direct
experience of color whatsoever to build up meaningful color semantics that can
produce judgments quite similar to that of sighted people.
Experiment 2: Where in language are color associations coming from?
So where do the embeddings “learn” their color semantics? One way of findingout is to remove the critical signal from the training corpus so that the resulting
word embeddings no longer predict human judgments. In this experiment, we
examined four potential sources of color-adjective associations:(a) First-order co-occurrences: The occurrence of a color word and a semantic
dimension word in the same sentence (e.g. “the fire was red hot”; color
associations in these sentences can be explicit, but often are not).
(b) Second-order co-occurrences: The occurrence of color words and semantic
dimension words in similar contexts (i.e. color words and semantic dimension words may not co-occur, but share words that they co-occur with, e.g.
“Southern cooking uses green tomatoes” and “Southern cooking uses unripe
tomatoes”). These sentences encompass nearly the entire corpus becausesome words (e.g. many function words) co-occur with every other word,
which made removing all of them from the training corpus infeasible. More
importantly, it rules out a strong form of the second-order co-occurrence
hypothesis (i.e. all second-order co-occurrence relationships are informative), but it does not preclude a weaker form, where some second-order cooccurrences (e.g. the psychologically salient words from hypothesis (d)) are
central to learning color-adjective associations.
(c) Co-occurrences between color words and words in the same semantic neighborhood as semantic dimension words: For example in “The forest was whitewith snow”, snow is in the same semantic neighborhood as cold, which mightlead to an association between white and cold). We identified semantic neighborhood words using cosine similarity between word embeddings and removed sentences containing any of the ten nearest neighbors of each colorand dimension word from the corpus.

752(d) Mediation by psychologically salient words: It is possible that color-adjective
associations are mediated by specific words. For example, when placing
yellow on the unripe-to-ripe dimension, people may think of a yellow andripe banana. We do not know a priori which words mediate color-adjective
associations, but we presented participants with color-adjective pairs (e.g.,yellow-ripe, white-cold) and asked them to provide a word they associatewith the pair. We then take the most common word for each pair and remove
sentences containing those words from the training corpus.
Note that these sources of semantic information need not be mutually exclusive;words captured by (c) and (d) may overlap, and all of these words may be a subsetof the words described by (b).
MethodParticipants
We recruited 100 sighted participants from the student participant pool at a large
public university who did not participate in previous color-adjective rating studies.
Participants were presented with the color-adjective pairs and asked to generate aword that they associate with both. These associates were taken to be psychologically salient mediator words from hypothesis (d).
Design and procedure
To test each potential source of color-adjective associations, we removed it fromthe training corpus and then tested the predictive efficacy of embedding projections trained on the filtered corpus by using them to model the association ratingsfrom Experiment 1.
Results
Removing first-order co-occurrences did not meaningfully reduce the effect sizeof the word embedding predictions. Removing nearest neighbors and especiallyremoving participant-generated labels for color-adjective associations had a measurable impact however (see Figure 2 for estimated effect sizes).
Discussion
It is tempting to think that knowledge that blue is cold may come from sentencessuch as ”His lips were blue with cold”. However, removing such first-order cooccurrences had no measurable effect on the model’s ability to pull out human-likeassociations. In contrast, removing sentences containing psychologically salient
mediators (e.g., ”ice” for cold-blue) reduced the signal substantially. This is especially surprising because the number of labels generated by at least two participants (the threshold for inclusion in our corpus filtering procedure) was only 242;
on average less than one label per color-adjective pair.

753

95% CIs for embedding projections
after filtering corpora
sightedblind

COCA-fiction projection
COCA-fiction without
1st order co-occ. (-1%)
COCA-fiction withoutnearest neighbors (-80%)
COCA-fiction without
common mediators (-31%)
0.1 0.0 0.1 0.2 0.3 0.4 0.5

effect size (standardized coefficient)Figure 2. Estimated effects of word embedding projections in predicting blind and sighted participants’ color-adjective association ratings. Percentage of training corpus removed by each manipulation
is listed in parentheses.

Experiment 3
To better understand what kinds of sentences were contributing to learning humanlike color-adjective associations, we modified an embedding model to record
color-adjective embedding projections at every single training step. This allows
us to rank training examples in order of the impact they have on specific coloradjective projections (e.g. “blue” on the axis “hot”-“cold”).
MethodTo measure the impact each individual sentence in the training corpus had on theembedding projections, we modified the word2vec (Mikolov, Chen, Corrado, &Dean, 2013) implementation included in the gensim Python package (Řehůřek &Sojka, 2010). The modified word2vec implementation computes and logs the embedding projections of interest after every training cycle (i.e. reading a training
sentence, computing and back-propagating the error, and computing the updated
embeddings). We then used the final embedding projections (after training is completed) as a reference and calculated how much each training sentence reduced therelative distance (between the previous projection and the final projection).
Results
The sentences that most informed the final embeddings projections were (1) likely
to contain either a dimension word (e.g., cold) or a color word, and (2) were likely
to contain a color-adjective mediator produced in Experiment 2. For example, ahighly informative sentence for moving “blue” toward “cold” is“The cold seaside

754

air here has both a fishy and a piney sniff to it”. We can count up the occurrencesof color and dimension words in the top 1000 most informative sentences forthe “blue” and “hot”-“cold” pairing. We find 447 occurrences of “cold”, 326
occurrences of “hot”, and 303 occurrences of “blue”. Every sentence in the top
1000 contained at least one of these words, and only a few contained more thanone. This suggests that the associations that underpin the projections are learned
from specific second-order co-occurrences.
The most informative of these second-order co-occurrences are disproportionately mediated by words that participants in Experiment 2 named as salient labels
for specific color associations (e.g. the association between ”yellow” and ”ripe”
is mediated by salient label ”banana”). The top 1% of informative training sentences contains 2%–6% of the participant-provided mediator words in the training
corpus, when aggregated by color.
Discussion
Our results are strongly consistent with the model learning the color-adjective
associations that inform the projections from second-order co-occurrence relationships. The higher prevalence of participant-proved mediator words for eachcolor-adjective pair in the most informative training sentences demonstrates thatparticipants were able to articulate with some degree of success which indirect(second-order) co-occurrence relationships are informative for the relationship between each given color and adjective (e.g. “white” and “cold”, mediated by theword “snow”).
General discussion
In a language community where word meanings are always changing and where
speakers cannot observe many words’ referents directly, how does a language
learner align their understanding of word meanings to those of other speakers andthe community at large?
One example of word meanings that have to be aligned without observing direct referents is blind people’s knowledge of color words. Blind people cannot
directly perceive colors in their visual contexts, yet we found that their understanding of color associations is broadly aligned with that of sighted people, andthat the color associations of both groups of participants could be predicted fromword embedding projections. That these color associations can be learned froma corpus of written text by a model that learns from distributional information
demonstrates how media, both spoken and written, could serve to align lexical
semantics across a large language community in which most members never interact directly with each other. Communicating word meaning implicitly throughco-occurrence also allows a language community to incrementally develop themeanings of abstract words–for which speakers cannot make use of referents–byscaffolding them on top of more concrete words.

755

Here, we used an adapted word embedding model to demonstrate exactly how
co-occurrence information can be used by an associative learner to learn aspectsof word meaning. We show that the core signal lies in second-order linkages mediated by a third word, e.g., the link between “ripe” and “red” being mediated by“tomato”. Large-scale semantic alignment and the mechanisms underpinning it
are an under-explored topic in language evolution, but we believe that any comprehensive theory of language change needs to account for how language communities can maintain mutual intelligibility in the face of changing word meanings
and varied access to direct perceptual information.
Acknowledgements
This research was supported by NSF BCS grant 2020969, awarded to Gary
Lupyan. We would like to thank Armin Saysani and Michael and Paul Corballis for making available the raw data used in Experiment 1.
References
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching word
vectors with subword information. arXiv [Preprint]. (https://arxiv.
org/abs/1607.04606)
Capretto, T., Piho, C., Kumar, R., Westfall, J., Yarkoni, T., & Martin, O. A. (2020).
Bambi: A simple interface for fitting Bayesian linear models in Python.
arXiv [Preprint]. (https://arxiv.org/abs/2012.10754)
Grand, G., Blank, I. A., Pereira, F., & Fedorenko, E. (2018). Semantic projection: Recovering human knowledge of multiple, distinct object featuresfrom word embeddings. arXiv [Preprint]. (https://arxiv.org/
abs/1802.01241)
Kim, J. S., Aheimer, B., Manrara, V. M., & Bedny, M. (2021). Shared understanding of color among sighted and blind adults. Proceedings of the National
Academy of Sciences, 118(33).
Kirby, S., Cornish, H., & Smith, K. (2008). Cumulative cultural evolution in
the laboratory: An experimental approach to the origins of structure in human language. Proceedings of the National Academy of Sciences, 105(31),10681–10686.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimationof word representations in vector space. arXiv [Preprint]. (https://
arxiv.org/abs/1301.3781)
Řehůřek, R., & Sojka, P. (2010). Software Framework for Topic Modelling with
Large Corpora. In Proceedings of the LREC 2010 Workshop (pp. 45–50).
(http://is.muni.cz/publication/884893/en)
Saysani, A., Corballis, M. C., & Corballis, P. M. (2021). Seeing colour throughlanguage: Colour knowledge in the blind and sighted. Visual Cognition,
1–9.

756

ON SCALING AND MODELS OF LANGUAGE EVOLUTIONREMI VAN TRIJP*1* Corresponding Author: remi.vantrijp@sony.com

1 Sony Computer Science Laboratories Paris, Paris, France

Computational models of language evolution offer important insights for explaining the emergence and evolution of human languages. However, such models have recently been criticized
for being computationally intractable. The goal of this paper is to show that this criticism ismisleading because it reduces all models of language evolution to only a specific subset ofmodels that assume that the basic unit of cultural transmission is the language itself, whichleads to astronomically large hypothesis spaces. In fact, there is already decades worth of computational modelling using the Language Game paradigm that has successfully addressed theissue of scaling by treating language as a complex adaptive system that spontaneously evolves
as the side-effect of local communicative interactions. This paper explains why the Language
Game method scales so well, and how it incorporates insights from constructivist usage-basedlearning and Relevance theory. It will illustrate the method through a Naming Game, supported
by open-source code that readers can download, test and reuse for their own work.

1. Introduction
Computational models of language evolution have played an important role in
exploring the origins and evolution of human languages ever since the late 1980s
and early 1990s (e.g. Hurford, 1989; Steels, 1995). However, a recent complexity
analysis by Woensdregt et al. (2021) suggests that models of language evolution,
at least in their current formulation, are computationally intractable so they cannot
be scaled up to more ecological scales involving tens of thousands of words.
While complexity analysis can offer useful insights for scaling a model, thecriticism of Woensdregt et al. (2021) is misleading because it reduces all modelsof language evolution to one specific kind of iterated model based on Bayesianinference (e.g. Griffiths & Kalish, 2007). Other kinds of models are not only
ignored, but also simply discarded based on the following two arguments:“[I]t is not clear that other models [...] would not run into the samewall of intractability. Moreover, the Bayesian formalism has thevirtue of being able to model agents’ epistemic states and transitions while remaining agnostic about the precise implementing mechanisms” (Woensdregt et al., 2021 p. 6).
The goal of this paper is to refute this conclusion by showing that there already exists decades worth of research using the Language Game methodology
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

757(Steels, 1995, 2000) which has successfully addressed issues of scale by treating language as a complex adaptive system, and by drawing inspiration fromconstructivist usage-based language learning (Bybee, 2006; Goldberg, 2011) andRelevance Theory (Sperber & Wilson, 1986). This paper will explain why theLanguage Game method scales so well through a Naming Game (Steels, 1995;
Baronchelli, Felici, Loreto, Caglioti, & Steels, 2006). Readers who are interested in running the Naming Game on their own computers can download thepaper’s supporting code for free as open-source software,1 as well as the opensource framework Babel2 (Loetzsch, Wellens, De Beule, Bleys, & van Trijp,
2008) that has been used for the implementation, which can be downloaded athttps://gitlab.ai.vub.ac.be/ehai/babel-core.
2. Illustrating the Problem of Scalability
Before turning to language games, it is important to understand the argument ofcomputational intractability. Woensdregt et al. (2021) assume a Bayesian iterated
learning model of cultural transmission in the style of Griffiths and Kalish (2007),
in which an adult language user produces a number of utterances that are observed
by a child learner, who forms hypotheses about which language could produce
such utterances. Each hypothesis h is a language, so the hypothesis space consistsof all of the possible languages. At the end of a cycle, there is a generational
turnover in which the child becomes the adult and a new learner is introduced.
Woensdregt et al. (2021) provide a complexity analysis that shows that suchmodels are computationally intractable. They illustrate the idea with the following example: suppose that a language is a set of one-to-one mappings between
signals and referents, then the hypothesis space of all possible languages consistsof all possible signal-referent mappings, which amounts to 2#signals×#ref erents .
Woensdregt et al. (2021) write that even for a toy language in which 50 signals
exist for 25 referents, “learners need to consider all 250×25 , about 1.9 × 10376 ,
possible languages” (p. 1). Increasing the number of signals and referents thus
leads to a combinatorial explosion in the hypothesis space, which makes scaling
impossible. In other words, such a model cannot be salvaged by faster computersor better implementations, because they face “a deeper theoretical issue” (p. 1).
Woensdregt et al. (2021) do not identify what exactly that deeper theoreticalis, but the culprit seems obvious: the learner needs to consider an astronomicallylarge hypothesis space, which is due to the fact that they have to consider the
probabilities of all possible languages. This is a side-effect of the model’s implicit
assumption that the basic unit of cultural transmission is the language itself, as
opposed to utterance-based models of cultural transmission (e.g. Croft, 2000) thatmake the learning task much more manageable.
1 https://github.com/SonyCSLParis/Naming-Game

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Population

world
AGENT_7

AGENT_8

1
CONTEXTAGENT_6

AGENT_9
Object-3

Object-17

Object-21

3

AGENT_5

AGENT_2

AGENT_4

AGENT_1

AGENT_3

AGENT_10

Object-3
Object-17
Object-21

Object-3
Object-17
Object-21

2
ProducerComprehender2

Figure 1. In the Language Game paradigm, two agents are drawn from a population of peers in orderto engage in a locally situated communicative interaction.

3. The Naming GameLet us now turn to the Language Game methodology, which is illustrated in Figure
1. A language game experiment typically involves a population of multiple agents,organized according to a network topology that represents the population’s social
structure. At each time step of the experiment, two agents are drawn from the
population to play a language game with each other, which is a locally situatedinteraction that is private to the participating agents. Since all agents are peers,
each agent can take on the role of producer or comprehender. During a language
game, agents only worry about achieving their communicative goals with respect
to the current situation, so they are not preoccupied by learning “the” community
language. In fact, as will be demonstrated below, the community language spontaneously emerges and evolves through self-organization in very much the same
way an ant path or other complex systems are formed in nature.
The simplest Language Game experiment is the Naming Game, which was
first introduced by Steels (1995) and which has well-understood mathematicalproperties (Baronchelli et al., 2006; De Vylder & Tuyls, 2006). The Naming
Game involves a population of N agents that need to self-organize a shared lexicon L for referring to a number M of objects present in their world. Eachagent is endowed with an associative lexicon La that consists of a list of lexical constructions, here operationalized as signal-referent associations (in order tobe consistent with the example of Woensdregt et al., 2021) that are assigned apreference score that represents the strength of an association. More formally:
La = {hs1 , r1 , σ1 i, ...} where si is a possible signal (a string), ri a possible referent (a unique symbol), and where 0.0 ≤ σi ≤ 1.0 is the preference score withas initial value 0.5. All agents start with an empty lexicon at time step t = 0,
but gradually invent and learn new constructions as they interact with each otheraccording to the following scenario:

759

1
situation model

Object-3
Object-17
Object-21

Object-3
Object-17
Object-21

2
situation model

Select
Topic

Producer

lexicon

Produce
Signal
Production
FailedInventconstruction

InventConstruction

Production
Succeeded

Transmit
SignalComprehender
Observe
SignalIndicate
FailurePoint toObjectComprehend
Utterance

lexiconComprehension
FailedComprehension
Succeeded

Figure 2. This Figure shows the mental operations that the producer goes through in order to produce
an utterance, and the operations that the comprehender performs to comprehend the utterance.

1. Each time step t, a situated context is initialized, as illustrated in Figure 1.
(a) A context consists of n objects randomly selected from the “world.”(b) One agent is randomly selected to act as the producer, and a comprehender is selected among the agents that are directly connected to the
producer’s social network (shown on the left of Figure 1).
2. The agents start interacting. Figure 2 illustrates their linguistic actions:(a) Both agents maintain a situation model of the current context, in which
they keep track of the objects they perceive.
(b) The producer randomly selects one of the objects as the topic.
(c) The producer transmits a signal for referring to the topic to the comprehender:• If the producer does not know an appropriate signal yet for the
topic, they will invent one. Here, they will randomly generate
a string according to the template “CVCVCVCVCV” (e.g. “kebekobola”);• If the producer knows more than one signal that associated withthe topic (“competitors”), they will choose one according to theinventory dynamics of the experiment. In this paper, the agent
chooses the construction that has the highest preference score.
(a) The comprehender tries to comprehend the producer’s signal.
• If the comprehender knows a construction that maps the signal
onto one of the referents in their situation model, comprehension
succeeded. They will point to that object.
• If not, the comprehender will indicate failure to the producer.

760

3. The producer gives feedback to the comprehender. They will signal success if the comprehender pointed to the correct object, and signal failure
otherwise. In the latter case they also point to the intended topic, so the thecomprehender can learn a new signal-referent association.
4. The agents update their linguistic inventories based on the success or failureof the game according to the experiment’s inventory dynamics.
As can be inferred from the above, the inventory dynamics of an experiment
determine how the agents cope with variation in the population and how they
update their linguistic inventories after each usage event (see Baronchelli, 2018
for a primer on suitable strategies for achieving consensus). In the experiments ofthis paper, agents update their inventories using lateral inhibition, which meansthat they will increase the preference scores of lexical constructions that led tosuccess while at the same time punishing competitors by lowering their preference
scores, which is compatible with more recent proposals on statistical preemption
in constructivist language learning (Goldberg, 2011). Agents will also punish
constructions that led to communicative failure. The experiments in this paper
adopt the score updating rule of De Beule, De Vylder, and Belpaeme (2006),
which has been proven successful in prior research.
4. Experimental Results and Discussion
To remain close to the example of Woensdregt et al. (2021), a Naming Game experiment was set up with a population size of N = 10 and a number of objects
M = 25. In order to test whether the population succeeds at self-organizing a lexicon for referring to these 25 objects, 100 independent simulations were executedwith each 6.000 time steps, which amounts to an average of 1.200 interactions per
agent per simulation.
Figure 3 shows the most important results averaged over the hundred simulations, with error bars indicating the variability between each run. The measure
Communicative Success is a running average of the past 10 interactions in which a
failed game counts as 0 and a successful game as 1. As can be seen, the agents already reach success after about 2.000 time steps (about 400 interactions per agent
or 16 interactions per object). Communicative success however doesn’t meanthat agents have reached consensus about which signal to use for which referent, because each agent might simply have learned all of the signals in order tounderstand the others, but keep using their own preferences. The consensus ofa signal-referent association is measured as the inverse of normalized (Shannon)
entropy, as formally defined in the supporting code. Global consensus simply averages over these individual consensus scores. As can be seen in the left graph of
Figure 3, consensus quickly follows communicative success, with maximal consensus after about 4.000 interactions, an average of 800 interactions per agent, or
32 interactions per referent.

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1

80

communicative
success

0.8

LearnedConstructions

70

consensus

number of constructions

60

0.6

50

Active Constructions

40

0.4

30
20

0.2

10

0

0

1000

2000

3000
time steps

4000

5000

6000

0

0

1000

2000

3000
time steps

4000

5000

Figure 3. Naming Game with N = 10 and M = 25. Results average over 100 simulations. Left:
Agents reach persistent communicative success after about 2.000 time steps and a global consensus
after about 5.000 time steps. Right: Agents have to learn on average 75 words, but their alignment
strategy allows them to reduce their active lexicon to an optimal size of one signal per referent.

The graph on the right shows more information about the emergent lexicon.
Each agent learns about 75 different constructions for referring to 25 objects.
However, through lateral inhibition the agents succeed in reducing their lexicons
to an optimal size of 25 active constructions. Constructions are considered to be
active as long as their preference score exceeds a threshold σi ≥ 0.2, otherwise
they become dormant. Hidden from the graph is the actual variation in the population: every time a producer communicates about an object for the first time,they invent a new construction. Given that the producer is randomly selected, on
average half of the population N/2 will invent a competitor for the same object,leading to an average of five competitors per referent or a total of 125 words for
25 referents circulating in the population. If the agents would have to consider
the space of all possible languages, as in the formalization of Woensdregt et al.
(2021), they would have to entertain 2125×25 , or about 5.2 × 10939 , possible languages. Yet, as readers who test the code can verify, it takes only a couple ofseconds to run the simulations on a present-day laptop (in fact, it takes more timeto produce the graphs than running the simulations themselves).
5. Discussion and Conclusion
The previous sections explained a simple Naming Game, but how does it scale?
Prior research has provided mathematical proof that the Naming Game always converges (De Vylder & Tuyls, 2006), and Baronchelli et al. (2006) andBaronchelli (2006) have examined how the model behaves when scaling the population size, reporting simulations up to N = 100000. These experiments revealthat the model displays similar behavior as natural language dynamics, most notably that there is an S-shaped curve with sharp transition towards population-wide
convergence. In fact, the transition becomes sharper and sharper as the size of thelanguage increases. Baronchelli et al. (2006) conclude that this “surprising result[...] explains why human language can scale up to very large populations.”

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Other experiments have shown how the Language Game method can also successfully apply to more complex languages where there is no one-to-one mapping between signal and referent, including large lexicons and meaning spaces(Wellens, Loetzsch, & Steels, 2008), or experiments on grammatical structures
such as argument structure constructions (see Steels, 2012 for a collection of experiments, and the open-access book series Computational Models of Language
Evolution at Language Science Press).
Why doesn’t the Language Game method hit the wall of computational intractability? The answer is that the agents never exhaustively search the hypothesis space, but instead only consider what is relevant for achieving communicative
success in their local interactions. For example, when a learner observes a particular signal, they will not try to update their entire lexicon, but only those constructions that were involved in the language game: the constructions that wereused and their competitors. In the current setup, an agent knows on average threecompetitors for one referent, so they will on average never make more than three
local adjustments (i.e. update their preference scores) at each time step instead ofrecalculating the probabilities of all possible mappings. Global consensus is nevertheless achieved as a side-effect in the same way as ant paths are spontaneously
formed as the side-effect of local behaviour. Language can therefore be seen as acomplex adaptive system that is constantly shaped and reshaped through language
usage (Steels, 2000).
In sum, when Woensdregt et al. (2021) posed the challenge of scaling, theywrongly equated “models of language evolution” with a particular kind of modelin which a language must be learned as a whole. Other models that operationalize language learning as a much more manageable task, such as utterance-basedmodels of language evolution (Croft, 2000) or the Language Game paradigm, do
not run into the problem of computational intractability; and have in the lattercase already been demonstrated to scale to more realistic settings. And just likethe Bayesian iterated learning models, the Language Game method is a generalframework that is agnostic to the specifics of implementation, as can be gleaned
from the breadth of techniques and phenomena that have already been investigatedwith this method (Steels, 2012).
Acknowledgements
The work reported in this paper has been funded by the Sony Computer Science
Laboratories Paris. The author wishes to thank Luc Steels for his invaluable feedback, as well as Martina Galletti and Inès Blin, and the many researchers who
contributed through discussions and collaborations on the Babel software suite,
particularly Katrien Beuls (Université de Namur) and Paul Van Eecke (VUB –KULeuven). Special thanks also go to Hiroaki Kitano, Vittorio Loreto and Peter
Hanappe for creating such a superb work environment.

763

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Baronchelli, A. (2018). The emergence of consensus: A primer. Royal SocietyOpen Science, 5(172189).
Baronchelli, A., Felici, M., Loreto, V., Caglioti, E., & Steels, L. (2006). Sharp
transition towards shared vocabularies in multi-agent systems. Journal of
Statistical Mechanics, P06014.
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Language, 82(4), 711–733.
Croft, W. (2000). Explaining language change: An evolutionary approach. Harlow, Essex: Longman.
De Beule, J., De Vylder, B., & Belpaeme, T. (2006). A cross-situational learningalgorithm for damping homonymy in the guessing game. In L. M. Rocha,
L. S. Yaeger, M. A. Bedau, D. Floreano, R. L. Goldstone, & A. Vespignani(Eds.), Artificial life x (pp. 466–472). MIT Press.
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convergence for the naming game. Journal of Theoretical Biology, 242(4),818–831.
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Cognitive Linguistics, 22(1), 131–154.
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Loetzsch, M., Wellens, P., De Beule, J., Bleys, J., & van Trijp, R. (2008). The
babel2 manual (Tech. Rep. No. AI-Memo 01-08). Brussels: AI-Lab VUB.
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Cambridge, MA: Harvard University Press.
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Parallel problem solving from nature (pp. 17–28). Berlin: Springer-Verlag.
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John Benjamins.
Wellens, P., Loetzsch, M., & Steels, L. (2008). Flexible word meaning in embodied agents. Connection Science, 20(2–3), 173–191.
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M. (2021). Why is scaling up models of language evolution hard?
PsyArXiv. (https://doi.org/10.31234/osf.io/d2h5c, version of May 2021)

764

EVO-DEVO FRAMEWORK: CONTEXTUALIZING LANGUAGE
ONTOGENY IN LANGUAGE EVOLUTION
OLGA VASILEVA*1 and ANTONIO BENITEZ-BURRACO2*Corresponding Author: ovasileva@langara.ca
1 Psychology Department, Langara College, Vancouver, Canada
2Linguistics Department, University of Seville, Seville, Spain

Language development in human infants has long been of interest to the field of
language evolution. Topics of investigation include (but are not limited to)arguments regarding “language fossils” and potential parallels between ontogeny
and phylogeny of language development (e.g. Bickerton, 1984; Wray, 1998),
co``mparative research with language-trained primates (Savage-Rumbaugh et al.,1993) and arguments regarding language nativism (Berwick, Chomsky & PiatelliPalmarini, 2013). Perhaps surprisingly, despite such a long-standing interest of
the field, to this day there is no systemic approach to the question of language
ontogeny in language evolution. This seems crucial since evolution depends onchanges in ontogenetic patterns. Given advancements of relevant disciplines(evolutionary, developmental and systems biology) we argue that the time is ripefor developing such an approach. Specifically, we propose that translating theachievements of the Evolutionary Developmental Biology (Evo-Devo) to the fieldof language evolution is a particularly promising avenue for future research. EvoDevo approach stresses that development is central to evolutionarily processes bybecoming the source of phenotypic plasticity on which selection operates andoptimizing trait alignment with the adaptive landscape (Jamnickzky et al., 2010;
Michel et al., 2018). Moreover, due to similarities in molecular foundations ofgene networks expression across distant species (i.e. deep homology), analogous
or divergent pattern of the same developmental program unfolding in different
species and even taxa can be informative for understanding specific selection
pressures and convergent and divergent evolution, respectively (Fitch, 2012).
Thus, the premise of Evo-Devo approach unites ontogeny and phylogeny onseveral levels going beyond simplistic claims of recapitulation and in the context

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

765

of language emergence beyond simplistic parallels between processes of language
acquisition by a child and its emergence in phylogeny. Ultimately, Eco-Evo-Devotheories support the view that organisms evolve as a result of the interactions
between their genes, their developmental paths, and the environments in which
they live. In the context of human language evolution this Eco-Evo-Devoframework opens the door to views of language evolution that see the origins ofmodern language as the result of both a biological process that changed our brain
and behavior, and a cultural process that enabled the emergence of distinctive
aspects of human languages through transmission and use.
In the talk, we will outline how application of this framework provides insights inthe field of language evolution. First, in line with Evo-Devo approaches, we will
advocate for a systemic approach to language acquisition where language istreated as a multicomponent system (Fitch, 2012). Instead of trying to single out
the component of language that provides major evolutionary novelty either on theinternal (syntax) or external (vocal learning) levels, we will support the view that
it is more productive and accurate to investigate development of systems, bothcognitive and behavioural, along with their interaction with the environment. This
is in line with current comparative research, that is no more interested in finding
the trait that could explain language, but how language results from a unique
combination of many shared abilities (Fitch, 2020; Vasileva, 2019). Second, we
will argue that since systems develop over the lifespan, constantly being underselection due to changing adaptive resources of an organism (Bateson 2014), thefocus should be thus shifted towards the analysis of the emergence and interactionof varied components (e.g. vocabulary and ToM) over the life span, without
limiting on early ontogeny. Finally, because development can be conceptualized
as continuous bidirectional interactions between different levels from themolecular-DNA to the behavior - psychological ones, leading to trait variability
and stability (Lickliter & Honeycutt, 2013; West-Ebenard, 2003), we will defend
the view that it is key to investigate systemic patterns of variation in both typicaland abnormal cognitive-linguistic development (Barceló-Coblijn, BenitezBurraco & Irurtzun 2015), with a special emphasis on the variation indevelopmental patterns, rather than capturing trait association at a single timepoint become crucial (Michel et al., 2018). Four major processes - heterochrony,heterotypy, heterometry and heterotopy - described in the Evo-Devo that can beapplied to the study of language ontogeny to contextualize language ontogeny inlanguage evolution and deserve a close examination.

766

References
Barceló-Coblijn, LI., Benítez-Burraco, A., & Irurtzun, A. (2015). Syntactic
networks as an endophenotype of developmental language disorders: An evodevo approach to clinical linguistics. Biolinguistics, 9, 043-049.
Bateson, P. (2014). New thinking about biological evolution. Biological Journalof the Linnean Society, 112(2), 268-275.
Berwick, R. C., Chomsky, N., & Piattelli-Palmarini, M. (2013). Poverty of thestimulus stands: Why recent challenges fail. Rich languages from poor inputs,
19-42.
Bickerton, D. (1984). The language bioprogram hypothesis. Behavioral and brain
sciences, 7(2), 173-188.
Fitch, W. T. (2012). Evolutionary developmental biology and human language
evolution: constraints on adaptation. Evolutionary biology, 39(4), 613-637.
Fitch, W. T. (2020). Animal cognition and the evolution of human language: why
we cannot focus solely on communication. Philosophical Transactions of theRoyal Society B, 375(1789), 20190046.
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ways to investigate the generation and modulation of phenotypic variation.
Bioessays, 32(7), 553-558.
Lickliter, R., & Honeycutt, H. (2013). A developmental evolutionary frameworkfor psychology. Review of General Psychology, 17(2), 184-189.
Michel, G. F., Babik, I., Nelson, E. L., Campbell, J. M., & Marcinowski, E. C.
(2018). Evolution and development of handedness: An Evo–Devo approach.
Progress in brain research, 238, 347-374.
Savage-Rumbaugh, E. S., Murphy, J., Sevcik, R. A., Brakke, K. E., Williams, S.
L., Rumbaugh, D. M., & Bates, E. (1993). Language comprehension in ape
and child. Monographs of the society for research in child development, i-252.
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Language & communication, 18(1), 47-67.

767

LANGUAGE DEVELOPMENT AND COGNITIVE ONTOGENY:AN IMPORTANT LINKOLGA VASILEVA*1 and MICHAEL PLEYER2*Corresponding Author: ovasileva@langara.ca
1Psychology Department, Langara College, Vancouver, Canada
2CLES, Nicolaus Copernicus University, Torun, Poland

The field of language of evolution is inherently interdisciplinary, attracting
specialists from varied disciplines spanning from linguistics and psychology tocomputer science and biology. Although this interdisciplinary nature is incredibly
productive and should be celebrated, it poses additional challenges to the field.
Among such challenges is the existence of somewhat neglected topics that are
crucial for understanding language evolution and yet are not sufficiently
investigated, such as, for example, animal cognition (Fitch, 2020). Recent
advancements in the field call for a more holistic approach to language by treating
it first and foremost as a multi component system (Benítez-Burraco & Boeckx,
2014; Fitch, 2012; 2020) that is intimately related to human cognition. It is thistight link between organisms’ communicative system and cognitive abilities thatis unmatched in other species and makes human language particularly “stand out”.
Although the very idea of the role of language in the formation of humancognition is not novel (tracing back to Vygotsky (2012), it has received
surprisingly little attention from the field of language evolution.
Language ontogeny is frequently invoked in language evolution discussions. At
the same time, it is a research area that highlights the sharp contrast between two
approaches focusing on the communicative and “purely linguistic” aspects oflanguage. While the former investigate communicative aspects of language andother parameters that likely interact with the language system, such as social
cognition (Tomasello at al., 2005), the latter focuses on the development of thelinguistic system (e.g. syntax or vocabulary). It is thus possible to say that theseapproaches investigate either the formation of the linguistic system and its internalThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

768“gear” (e.g. syntax) or language precursors or the external aspects of language(e.g. speech production), with insufficient interaction between the two. Thissituation in turn results in a quite fragmented (if not reductionist) approach wherechild development is studied not as a unified phenomenon, but rather specificaspects of it at a time point. An Evolutionary-Developmental Linguistics remains
quite underdeveloped as a research field.
In our opinion, language development provides one of the most convincing typesof evidence of the connection between language and cognition in humans. We
briefly outline two lines of research demonstrating that in human children
language development 1) corresponds with simultaneous and inter-dependent
emergence of varied cognitive abilities and 2) results in qualitative changes inbehavior. From the first line of research examples of language association withother domains include: object permanence (Gopnik & Meltzoff, 2021),
categorization (Novack et al., 2021), and ToM (Tomasello et al., 2005). Researchon the interaction of the motor domain and language suggests that developing
linguistic system allows children to perform more complex hierarchical goaloriented behaviors (Greenfield, 1991; Michel et al., 2013) as well as engage incomplex play behavior (Pleyer, 2020).
Summarized results of decades long research programs with primates trained tocommunicate with language-like systems suggests that primates are overallcapable of using symbolic communicative systems (Savage-Rumbaugh et al.,2018). Additionally, results of comparative studies demonstrate the presence ofcognitive abilities in other animals remarkable both in their complexity andabundance (Andrews, 2020; Zentall, 2020). Animals can represent theirenvironment mentally, operate abstract concepts and perform goal-oriented
behavior (Godfrey-Smith, 2016). However, to this day, there is no convincing
evidence the development of the communicative system has drastic effects on thedevelopment of cognitive abilities and qualitative changes in the behavior in otherspecies (Novack & Waxman, 2020). While absence of evidence, by all means,cannot be equated with evidence of absence, this notion requires careful
theoretical and empirical investigation. Underestimation of the importance of thelink between human cognition and language in ontogeny in our opinion leads tolimitations in developing language evolution research programs. We suggest that
it is important not only to compare and contrast animal communication andcommunicative functions in human language but to explore differences incognitive ontogeny as well as pose the question of why and how the tight link
between language and cognition emerged in humans.

769

References
Andrews, K. (2020). The animal mind: An introduction to the philosophy ofanimal cognition. Routledge.
Benítez-Burraco, A., & Boeckx, C. (2014). Universal Grammar and biologicalvariation: an EvoDevo agenda for comparative biolinguistics. Biological
Theory, 9(2), 122-134.
Godfrey-Smith, P. (2016). Other minds: The octopus and the evolution of
intelligent life. London: William Collins.
Gopnik, A., & Meltzoff, A. N. (2021). Early semantic developments and theirrelationship to object permanence, means-ends understanding, andcategorization. In Children’s language (pp. 191-212). Psychology Press.
Fitch, W. T. (2012). Evolutionary developmental biology and human language
evolution: constraints on adaptation. Evolutionary biology, 39(4), 613-637.
Fitch, W. T. (2020). Animal cognition and the evolution of human language: why
we cannot focus solely on communication. Philosophical Transactions of theRoyal Society B, 375(1789), 20190046
Novack, M. A., Brentari, D., Goldin-Meadow, S., & Waxman, S. (2021). Sign
language, like spoken language, promotes object categorization in young
hearing infants. Cognition, 215, 104845.
Novack, M. A., & Waxman, S. (2020). Becoming human: human infants linklanguage and cognition, but what about the other great apes?. PhilosophicalTransactions of the Royal Society B, 375(1789), 20180408.
Pleyer, M. (2020). Pretend play as a scaffold for language evolution. In A.
Ravignani, C. Barbieri, M. Martins, M. Flaherty, Y. Jadoul, E. Lattenkamp,
H. Little, K. Mudd, & T. Verhoef (Eds.). The evolution of language:
Proceedings of the 13th international conference (EvoLang13).
https://doi.org/10.17617/2.3190925
Savage-Rumbaugh, S., Roffman, I., Lingomo, S., & Pugh, E. (2018). The fully
conscious ape. International Journal of Comparative Psychology.
Tomasello, M., Carpenter, M., Call, J., Behne, T., & Moll, H. (2005).
Understanding and sharing intentions: The origins of cultural cognition.
Behavioral and brain sciences, 28(5), 675-691.
Vygotsky, L. S. (2012). Thought and language. MIT press.
Zentall, T. R. (2020). Animal intelligence. In R. J. Sternberg (Ed.), The
Cambridge handbook of intelligence (pp. 397–427). Cambridge University
Press.

770

LANGUAGE EVOLUTION WITHOUT TRUSTTILL NIKOLAUS VON HEISELER*1, ANNE AERTS14, KAI SHAMAN2,CHRISTOPHER DROPPA2, DAPHNE PRESTON-KENDAL3, FRANZISKAEBERHARDT5, MAURICE PFLUG2, ANNA MAASER3, CLAUDIO FABBRONI2,
VICTORIA CHARLOTT UHLICH2

1*
Corresponding Author: formatlabor.net@gmail.com
Department of Philosophy, 2Berlin School of Mind and Brain, 3Department of German
Studies and Linguistics, 4Department of Social Sciences, 5Institute for Biology,
Humboldt University, Berlin, Germany

Most researchers in the field of language evolution share the assumption that
before the evolution of language could even begin, a complex social development
must have taken place, establishing new forms of collaboration and trust between
the members of a group, not seen among any other apes. The reason for this is that
linguistic communication requires trust since it is typically not backed up by direct
evidence. According to Dor, Knight, and Lewis (2014, p. 4), virtually all
researchers agree that an unprecedented level of collective cooperation and trust
was needed for the first steps toward the evolution of language to occur. This
paper aims to show that this dominant assumption is unnecessary.
We support our claim through a methodological suggestion: the investigation
into language evolution should not begin with the traditional assumptions about
language, but with the use of contemporary evolutionary theory to inform us how
language should be conceptualized to be evolutionary explainable. One wellsupported view is the function-first approach (von Heiseler, 2020), where a new
function is initially fulfilled by a behavioral shift that exploits an already existing
structure (Mayr, 2001, pp. 224-229). Since in a novel configuration, the new
behavior is adaptive, the elements that were used for this behavior will adapt by
natural selection to fulfill the new function. This process usually begins with achange in the evolutionary configuration (an arrangement of the physiological,
environmental, and social components and constraints that jointly constitute thedevelopment and sustainment of an adaption by natural selection), making the
new function adaptive.

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771

We apply the function-first approach to the evolution of language by suggestingthat the proper evolutionary function of language is to convey propositions,especially about displaced actions. There are two mutually exclusive and jointly
exhaustive classes of signs: natural and non-natural signs (Grice, 1957). In a world
without non-natural signs (such as mimetic or conventional symbols), only natural
signs (if anything) can refer to a displaced action. An example of such referential
displacement can be found in the display of objects that imply a past action of thecommunicator, e.g., communicating a past kill by displaying the hunted animal asa trophy. Such use of natural signs could be reproductively beneficial for anindividual by enhancing their attractiveness to potential partners for cooperation,or mating, or social status in general.
The function-first approach predicts that language evolved from such display
behavior. This proposal has the potential to solve both major problems oflanguage evolution—the problem of the graduality of the emergence of syntax andthe bootstrapping problem of language and cooperativeness/trust. The problem
of graduality is solved by identifying a structural similarity between
understanding the display of an indexical object implying a past action—such asa trophy—and the simplest linguistic syntactic patterns—that of the simple
transitive sentence: the presenter of the trophy constitutes the agent, the trophy
represents the patient, and the verb is implied by the state of the patient (vonHeiseler, 2019). By integrating a single mimetic gesture that represents an action
by reenactment and which marks the thematic roles by being directed from theagent to the patient, a simple proposition is expressed. Moreover, since thisapproach suggests that the first language-like communication included indexical
objects as evidence, the problem of trust disappears. We show how the functionof referring to displaced actions could have evolved further in various niches oftrust, such as in mother-child communication and teaching (kin selection; Fitch,
2004, Gärdenfors & Högberg, 2022), displaying performative qualities (a quality
in which individuals vary naturally; Mithen, 2005), and bonding (building
reciprocal relations; Dunbar, 2011). Thus, as these two problems, which are
intractable in the dominant view of language evolution, are solvable under our
proposal, we suggest that the best course of action in language evolution researchis to search for evolutionary configurations in which referential displacement andpropositional communication became adaptive (see e.g., Bickerton & Szathmáry,
2011, von Heiseler, 2022). We conclude with the hope that we have shown thatthe hypothesis that an unprecedented level of collective cooperation was needed
before the first step towards the evolution of language could have occurred, is not
only unnecessary but is also unwarranted.

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References
Bickerton, D., & Szathmáry, E. (2011). Confrontational scavenging as a possible
source for language and cooperation. BMC Evolutionary Biology.
Dor, D., Knight, C., & Lewis, J. (2014). Introduction. In D. Dor, C. Knight, & J.
Lewis (Eds.), The Social Origins of Language. Oxford: Oxford
University Press.
Dunbar, R. (2011). Gossip and the social origins of language. In M.Tallerman,& S.Gibson (Eds.), Oxford Handbook of Language Evolution. Oxford:
Oxford University Press.
Fitch, W. T. (2004). Kin selection and mother tongues: a neglected component
in language evolution. In U. Griebel, & D. K. Oller (Eds.), Evolution ofcommunication systems: a comparative approach (pp. 275–296).
Cambridge, Massachusetts: MIT Press.
Gärdenfors, P., & Högberg, A. (2022). Evolution of intentional teaching. In N.
Gontier, A. Lock, & C. Sinha, The Oxford Handbook of Human
Symbolic Evolution. Oxford.
Grice, P. H. (1957). Meaning. The Philosophical Review, 66, pp. 377 - 388.
Mayr, E. (2001). What Evolution Is. New York: Basic Books.
Mithen, S. (2005). The Singing Neanderthals: the origins of music, language.
Harvard University Press.
von Heiseler, T. N. (2019). Syntax of Testimony: Indexical Objects, Syntax, andLanguage or How to tell a story without words. Frontier in Psychology.
von Heiseler, T. N. (2020). The Social Origin of the Concept of Truth – How
Statements Are Built on Disagreements. Frontiers in Psychology.
von Heiseler, T. N. (2022). How Language and Human Altruism Evolved Hand
in Hand — The Backchannel Hypothesis. Frontiers in Psychology.

773

UNPACKING THE PROXIMATE FUNCTIONS OFFUNCTIONALLY REFERENTIAL SIGNALS: A TEST OF 3
HYPOTHESES
Stuart K Watson*1,2,3, Andrea Müller3, and Marta Manser2,3*Corresponding Author: swatso88@gmail.com
1 Department of Comparative Language Science, University of Zurich, Switzerland
2 Center for the Interdisciplinary Study of Language Evolution, University of

Zurich, Switzerland
3 Department of Evolutionary Biology and Environmental Studies, University of
Zurich, Zurich, Switzerland

Decades of research have demonstrated a broad range of species possess vocal
signals which are produced only in the presence of specific stimuli, such as aparticular predator type. Crucially, conspecific receivers are able to decode thesestimulus-specific associations and use them to choose an appropriate behavioural
response to the eliciting cause (Arnold & Zuberbühler, 2013; Dawson Pell et al.,2018; R. Seyfarth et al., 1980). Because of the surface-level resemblance of thesecall-types to referential language, these have been termed “functionallyreferential” vocalisations, and have been argued to present a likely evolutionary
precursor to semantics (R. M. Seyfarth & Cheney, 2017). However, plausible
competing explanations for the underlying function of these calls have also beenpresented. For example, stimulus-specific calls may be a reflexive expression of
the internal arousal state brought about by a particular stimulus, which receivers
have simply learned to associate with the eliciting cause (Wheeler & Fischer,2012). Alternatively, they may be more akin to ‘imperative’ statements inlanguage, functioning primarily to direct a specific behavioural change inreceivers (Schamberg et al., 2018). Determining the proximate function of thesesignals is therefore key to understanding both animal communication systems andthe evolutionary foundations of language. Unfortunately, the underlying functionof these calls from a producer perspective is not trivial to determine. Playback
experiments are a powerful tool for revealing how receivers interpret and extract
information from a given call, but cannot directly inform us about the motivationof callers themselves. Here, we use a novel model predator demonstration
experiment to shed light on the proximate function of aerial-specific alarm callproduction in meerkats (Suricata suricatta), a cooperatively breeding species ofmongoose with a complex communication system (Manser et al., 2014).
1This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

774

Meerkats have an expansive vocal repertoire, one item of which is a functionallyreferential alarm call produced only in response to aerial predators (typicallymartial eagles) (Manser et al., 2002). In the Kalahari Desert, we simulated
predation events for 7 groups of wild meerkats (4-22 individuals per group) usinga model predator, a kite painted to imitate a martial eagle. Demonstrations took
place in two behavioural contexts: i) while the majority of the group was safe andclose (<5m) to their burrows (N = 3 trials per group), and ii) while the majorityof the group was vulnerable, foraging at a distance (>10m) from their burrows (N= 3 trials per group). All trials elicited strong predator-avoidance response
behaviours from the meerkats. We recorded all calling bouts produced in responseto these predator demonstrations and coded whether they contained an aerial
alarm call. Contextual information was also recorded, including: the distance ofthe caller to shelter, the proportion of the group who were close to shelter, and the
distance of the kite from the group. The association between contextual variables
and the production of aerial alarm calls was then used to test three hypotheses
regarding the underlying function of these calls (Table 1).
Table 1. Summary of hypotheses for the underlying function of meerkat aerial alarm calls and theircorresponding empirical predictions.
Hypothesis
Predictions
Produced to share contextual
Production will not vary according to theReferential
information with receivers
relative danger towards the caller or group
Produced to elicit a specific
Production is more likely when the group
Imperative
behaviour in receivers(i.e. receivers) are further from safety
Produced reflexively as a
Production is more likely when the caller is
Emotional
product of arousal state
further from safety / closer to danger

To determine which of these three hypotheses best explained the observed data,
we fit a suite of Bayesian generalised linear mixed-effects models with effectstructures corresponding to each of these hypotheses. The out-of-sample
predictive power of each of these models was then compared using WatanabeAkaike information criterion. We found that the model which best fit the data was
one in which no fixed effects corresponding to the relative safety of the caller orgroup influenced the likelihood of an individual producing an aerial alarm call,supporting the referential hypothesis.
This finding indicates that the production of aerial alarm calls in meerkats
primarily serves a referential function, i.e., to inform receivers about the presenceof a particular predator type in their immediate surroundings. This novel approachprovides the first insights into the cognition and motivations underlying the
production of these functionally referential signals Broader application of thisapproach to the functionally referential signals of other species will determine
whether this is a general feature of this class of signal, or if there is variation
within and between species.
2

775

Acknowledgements
SKW and MM were funded by NCCR Evolving Language, Swiss National
Science Foundation Agreement #51NF40_180888.
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Experimentally Altered Contextual Information to Disambiguate theCause of Male Alarm Calls. PLOS ONE, 8(6), e65660.
https://doi.org/10.1371/journal.pone.0065660
Dawson Pell, F. S. E., Potvin, D. A., Ratnayake, C. P., Fernández-Juricic, E.,
Magrath, R. D., & Radford, A. N. (2018). Birds orient their heads
appropriately in response to functionally referential alarm calls ofheterospecifics. Animal Behaviour, 140, 109–118.
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Manser, M. B., Jansen, D. A. W. A. M., Graw, B., Hollén, L. I., Bousquet, C. A.
H., Furrer, R. D., & le Roux, A. (2014). Vocal Complexity in Meerkats
and Other Mongoose Species. In Advances in the Study of Behavior(Vol. 46, pp. 281–310). Elsevier. https://doi.org/10.1016/B978-0-12800286-5.00006-7
Manser, M. B., Seyfarth, R. M., & Cheney, D. L. (2002). Suricate alarm calls
signal predator class and urgency. Trends in Cognitive Sciences, 6(2),55–57. https://doi.org/10.1016/S1364-6613(00)01840-4
Schamberg, I., Wittig, R. M., & Crockford, C. (2018). Call type signals caller
goal: A new take on ultimate and proximate influences in vocal
production. Biological Reviews, 93(4), 2071–2082.
https://doi.org/10.1111/brv.12437
Seyfarth, R., Cheney, D., & Marler, P. (1980). Monkey responses to threedifferent alarm calls: Evidence of predator classification and semantic
communication. Science, 210(4471), 801–803.
https://doi.org/10.1126/science.7433999
Seyfarth, R. M., & Cheney, D. L. (2017). The origin of meaning in animal
signals. Animal Behaviour, 124, 339–346.
https://doi.org/10.1016/j.anbehav.2016.05.020
Wheeler, B. C., & Fischer, J. (2012). Functionally referential signals: A
promising paradigm whose time has passed. Evolutionary
Anthropology: Issues, News, and Reviews, 21(5), 195–205.
https://doi.org/10.1002/evan.21319

3

776

USING MACHINE LEARNING TO SHOW CHIMPANZEE (PAN
TROGLODYTES SCHWEINFURTHII) LIP-SMACK RHYTHMVARIATION: IMPLICATIONS FOR THE STUDY OF HUMAN
SPEECH EVOLUTION.
CHARLOTTE WILTSHIRE*1, ANDRÉ PEREIRA2, GAL BADIHI1, ADRIANSOLDATI1,3, KATIE SLOCOMBE4, KLAUS ZUBERBUEHLER1,3, ADRIANO REISE LAMEIRA5, and CATHERINE HOBAITER1*Corresponding Author: cw266@st-andrews.ac.uk

1School of Psychology and Neuroscience, University of St Andrews, St Andrews, UK
2
School of Psychology, University of Exeter, Exeter, UK
3
School of Biology, University of Neuchâtel, Neuchâtel, Switzerland
4
School of Psychology, University of York, York, UK
5
School of Psychology, University of Warwick, Coventry, UK

1. Introduction
Despite the incredible diversity of sound and structure, all human languages
have an open-close mouth rhythm of 2-7Hz (Chandrasekaran et al., 2009) that isessential for speech perception (Shannon et al., 1995; Smith et al., 2002).
Chimpanzee lip-smacks are an affiliative signal recently found to match therhythm of human speech (Pereira et al., 2020) and, unlike other primate signals
displaying this rhythm (Ghazanfar et al., 2012; Toyoda et al., 2017; Terleph etal., 2018), show substantial variation between and within populations. Humanspeech rhythm also varies within the 2-7Hz range between languages(Abercrombie, 1967; Ordin and Polyanskaya, 2015) and within them dependingon the addressee (Wynn et al., 2018). The recent evidence that chimpanzee lipsmacks vary within the 2-7Hz band suggests that this important step-change in
flexibility occurred before our split from other hominids. Whilst suggesting
important similarities to human speech, it remains unclear whether lip-smack
rhythm variation described (Periera et al. 2020) in chimpanzees was underindividual control, and/or shaped by genetic or environmental factors. Here we
further explore variation in chimpanzee lip smacks and assess the impact offeatures of individual (identity, age, rank), partner (identity, relative rank), andgroup identity. To increase sample size and improve measurement reliability, we
employed a novel machine learning approach using DeepLabCut (Mathis andMathis, 2020).

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

777

2. Methods
We recorded videos of lip-smacking from 2007-2020 in two communities within
the Budongo population of East-African chimpanzees. We extracted 161 lipsmacking bouts from 26 individuals (19 males, 7 females, age 9-45 years) We
created a bespoke chimpanzee facial tracker, to track 10 key-points using
DeepLabCut (Mathis and Mathis, 2020). The effect of an individual’s community,
identity, age, sex, relative rank to their grooming partner or the sex of theirgrooming partner on bout rhythm was tested with a GLMM.
3. Results
Lip-smack rhythm varied from 1.52hz-9.49hz, with a mean frequency of 2.9hz (±1.67Hz). The Waibira community showed a mean of 2.9hz (± 1.67Hz) and Sonso
a mean of 2.87hz (±1.57Hz). We found no effect of the test variables or theirinteractions on rhythm. No two bouts displayed by the same individual showed
the same rhythm, even within a grooming event with the same partner.
4. Discussion
We employed novel machine learning techniques to generate substantial sample
sizes and reveal a new-found degree of flexibility in chimpanzee lip-smack
rhythm. Rhythm falls within the range of speech, averaging towards the lower endof the range. This supports previous findings that primate rhythmic facial signals
may be a precursor to speech (Morrill et al. 2012; Periera et al. 2020) rather thanbeing dictated by other rhythmic mouth movements, such as chewing. This isfurther supported by substantial within-individual variation, which may havepresented itself as within- and between-group variation in previous studies withsmall datasets (see: Periera et al. 2020).
Rhythm varied within a grooming bout, where multiple context factors areunlikely to have changed. Like speech, lip-smack rhythm is not fully explainedby an individual’s identity, sex, age, community, relative rank, or their partner’s
sex. Instead, they appear highly flexible, potentially reflecting context specific,
rapidly changing features within a grooming bout. Controlling for context
changes occurring during a grooming bout may offer further insight into what is
driving variation. Within-bout variation in rhythm may suggest high levels offine motor control, allowing chimpanzees to quickly change the timing of their
lip-smacking during its production, as human’s can to aid their communication
during speech (Wynn et al., 2018).

778

Acknowledgements
This research was funded by the European Research Council under grant
agreement number 802719 and the St Andrews Restarting Research fund. We
thank the field assistants and staff of the Budongo Conservation Field Stationand the Greater Mahale Ecosystem Research Conservation station for theirsupport. We thank the Ugandan Wildlife Authority, the Ugandan National
Council for Science and Technology, the Tanzanian Wildlife Research Institute,and the Tanzanian Commission for Science and Technology for permissions to
conduct research.
References
Abercrombie, D. (1967). Elements Of General Phonetics. Edinburgh: Edinburgh
University Press, pp. 112-146.
Chandrasekaran, C., Trubanova, A., Stillittano, S., Caplier, A., and Ghazanfar,
A.A. (2009). The natural statistics of audio-visual speech. PLoS
Computational
Biology,5(7),e1000436,
DOI:
https://doi.org/10.1371/journal.pcbi.1000436.
Ghazanfar, A.A., Takahashi, D.Y., Mathur, N. and Fitch, T.W. (2012).
Cineradiography of monkey lip-smacking reveals putative precursors ofspeech dynamics. Current biology, 22, pp. 1176-1182.
Mathis, M.W. and Mathis, A. (2020). Deep learning tools for the measurement ofanimal behavior in neuroscience. Current Opinion in Neurobiology, 60, pp. 111.
Morrill, R.J., Paukner, A., Ferrari, P.F. and Ghazanfar, A.A. (2012). Monkey Lipsmacking Develops Like the Human Speech Rhythm. Developmental
Science, 15(4), pp. 557-568.
Ordin, M. and Polyanskaya, L. (2015). Acquisition of speech rhythm in a second
language by learners with rhythmically different native languages. The
Journal of the Acoustical Society of America, 138(2), pp. 533-544.
Pereira, A.S., Kavanagh, E., Hobaiter, C., Slocombe, K.E. and Lameira, A.R.
(2020). Chimpanzee lip-smacks confirm primate continuity for speechrhythm evolution. Biology Letters, 16(5), 20200232, DOI:
http://dx.doi.org/10.1098/rsbl.2020.0232.
Shannon, R.V., Zeng, F.G., Kamath, V., Wygonski, J., Ekelid, M. (1995). Speech
recognition with primarily temporal cues, Science, 270(5234), pp. 303-304.
Smith, Z.M., Delgutte, B., Oxenham, A.J. (2002). Chimaeric sounds reveal
dichotomies in auditory perception, Nature, 416(6876), pp. 87-90.
Terleph, T.A., Malaivijitnond, S., Reichard, U.H. (2018). An analysis of whitehanded gibbon male song reveals speech-like phrases. American Journal of
Physical Anthropology, 166(3), pp. 649-660.
Toyoda, A., Maruhasi, T., Malaivijitnond, S. and Koda, H. (2017). Speech-likeorofacial oscillations in stump-tailed macaque (Macaca arctoides) facial and779

vocal signals. American Journal of Physical Anthropology, 164(2), pp. 435439.
Wynn, C.J., Borrie, S.A. and Sellers, T.P. (2018). Speech rate entrainment inchildren and adults with and without autism spectrum disorder. American
Journal of Speech-Language Pathology, 27(3), pp. 965-974.

780

EVOLUTIONARY DYNAMICS OF MULTILINGUAL COMPETITIONUNDER INTERVENTION
Zhijun Wu
Department of Mathematics, Iowa State University, Ames, Iowa, USA
Email: zhijun@iastate.edu

An evolutionary dynamics model is investigated for the evolution of multilingual populations.
The model consists of two different parts, formulated as two different evolutionary games. The
first part accounts for the selection of languages based on the competition for popularity andsocial or economic advantages. The second part relates to the circumstance when the selectionis altered, for better or worse, by forces other than competition such as public policies, education, or family influences. By combining competition with intervention, the model shows how amultilingual population may evolve under these two different sources of influences, and the languages may co-exist in evolutionarily stable multilingual forms with appropriate interventional
measures. This is in contrast with the predictions from previous studies that the co-existenceof languages is unstable in general, and one language will eventually dominate while all others
become extinct.

1. Introduction
While many languages are in danger of extinction, multilingualism is being
adopted as a common practice for communication among different language
groups, and is playing a unique role in preserving language and cultural diversities (Gabszewicz et al., 2011; Grosjean, 2012). Languages compete and spreadamong their speakers, as genes are inherited and passed down to biological generations, where some are selected while others become extinct (Pinker & Bloom,1990). Genes may be carried over in mixed forms. So are languages by multilingual speakers. In this paper, a mixed population of multilingual speakers andbilingual speakers in particular is considered, with multilingual defined broadly
as zero, limited, or full uses of multiple languages or dialects, and an evolutionary
dynamics model for its evolution is proposed, similar to that for genetic evolution(Burger, 2000).
Unlike genetic evolution though, the uses of languages are not only dependentof competition, but also subject to various societal interventions, common in social
or cultural evolution. The proposed model consists of two different parts accordingly, formulated as two different evolutionary games, respectively. The first part
accounts for the selection of languages based on the competition for popularity
and social or economic advantages. The second part relates to the circumstanceThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

781

when the selection of languages is altered, for better or worse, by forces other thancompetition such as public policies, education, or family influences.
Much work has been done on modeling language competition, although not
specifically for the evolution of multilingualism. A well known model was proposed by Abrams & Strogatz, 2003 for the study of language death. The model
was later extended to more general and complex cases by several other groups(Mira & Paredes, 2005; Patriaca & Heinsalu, 2009; Vazquez et al., 2010; Fujie
et al., 2013). The models along this line focus mainly on language competition
for popularity and social or economic advantages, and predict that one language
will eventually dominate while all others become extinct, and the co-existence of
languages is unstable and hard to sustain.
While successfully applied to some language populations, the previous models have not explicitly distinguished language competition from possible societal
interventions that may reverse the course of language changes. By combining
language competition with possible societal interventions, this paper shows how
a multilingual population may evolve under these two different sources of influences. It shows in particular that the co-existence of languages can be madestable and language extinction can be prevented with appropriate interventional
measures, as seen in many multilingual communities across the world (Shin &Kominski, 2010; Grosjean, 2012; Batalova & Zong, 2016; China, 2017).
2. The Evolutionary Dynamics Model
Consider a bilingual population, the simplest yet the most common multilingual
population. Assume it is large and well mixed, i.e., every individual speaker caninteract with all others in the population. If an individual speaker uses two languages A and B with frequencies xA and xB , respectively, this individual is calledan (xA , xB )-speaker, where 0 ≤ xA , xB ≤ 1, and xA + xB = 1. Likewise, if language A and B are used with frequencies yA and yB in average in the whole population, this population is called a (yA , yB )-population, where 0 ≤ yA , yB ≤ 1,
and yA + yB = 1.
First, consider a competition-only population. Let PA (yA ) and PB (yB ) be
the payoff functions for A and B speakers in a (yA , yB )-population, respectively,
defined in terms of the population sizes yA and yB and some other parameters forsocial or economic impacts, with PA increasing in yA and PB in yB , meaningthat the larger the population size of a language, the more benefit the language
provides for its speakers. Then, the payoff function for an (xA , xB )-speaker in a(yA , yB )-population can be defined in terms of the average use of A and B by thisspeaker:π((xA , xB ), (yA , yB )) = xA PA (yA ) + xB PB (yB ).

(1)

Now consider the situation where the use of languages is influenced by somesocietal decisions. Assume that the societal interventions are implemented to782

counter the arbitrary increase or decrease of either language. Let P̄A (yA ) andP̄B (yB ) be the payoff functions for A and B speakers in a (yA , yB )-population,
respectively, defined in terms of the population sizes yA and yB and some parameters for language reversing, with P̄A decreasing in yA and P̄B in yB , meaning thatthe smaller the population size of a language, the more incentive or less penalty forthe speakers of the language. Then, the payoff function for an (xA , xB )-speakerin a (yA , yB )-population can be defined in terms of the average use of A and B
by this speaker:π̄((xA , xB ), (yA , yB )) = xA P̄A (yA ) + xB P̄B (yB ).

(2)

The above two types of payoff functions can be combined to obtain an evolutionary dynamics model for bilingual competition under societal intervention:ẏA = yA yB (P̃A (yA ) − P̃B (yB ))ẏB = yB yA (P̃B (yB ) − P̃A (yA )),(3)P̃A (yA ) = λPA (yA ) + (1 − λ)P̄A (yA ),

(4)

{
where

P̃B (yB ) = λPB (yB ) + (1 − λ)P̄B (yB ).

(5)

where 0 ≤ λ ≤ 1. The model is reduced to competition-only when λ = 1, and tointervention-only when λ = 0.
Based on evolutionary game theory (Weibull, 1995; Hofbauer & Sigmund,1998), the equations in (3) form a so-called system of replicator equations, whichcorresponds to an evolutionary game, with the Nash equilibrium being a strategy(x∗A , x∗B ) such thatπ̃((x∗A , x∗B ), (x∗A , x∗B )) ≥ π̃((xA , xB ), (x∗A , x∗B )) for all (xA , xB ),(6)

where π̃ is the payoff function for the game, and for an (xA , xB )-speaker in a(yA , yB )-population(7)π̃((xA , xB ), (yA , yB ))= λπ((xA , xB ), (yA , yB )) + (1 − λ)π̄((xA , xB ), (yA , yB )).

In general, PA and PB can be some increasing functions and P̄A and P̄B besome decreasing functions. However, in this study, they are defined using thefollowing empirical functions similar to those in Abrams & Strogatz, 2003:
α−1
α−1
PA (yA ) = cyAsA , PB (yB ) = cyB
sB , 1 < α ≤ 2,

ᾱ−1P̄A (yA ) = c̄yA
s̄A ,

ᾱ−1
P̄B (yB ) = c̄yBs̄B ,0 ≤ ᾱ < 1,

(8)
(9)

783

where c and c̄ are scaling constants, α, ᾱ, sA , sB , s̄A , s̄B are all parameters,
0 ≤ sA , sB , s̄A , s̄B ≤ 1. The parameters α, ᾱ determine the order of dependencyof the payoffs on the population sizes. Since 1 < α ≤ 2, the payoffs from PA
and PB increase with increasing population sizes. On the other hand, since 0 ≤ᾱ < 1, the payoffs from P̄A and P̄B decrease with increasing population sizes.
The parameters sA , sB are used to define the payoffs from competition. They are
indicators of social or economic impacts on the payoffs. The larger these values,
the more benefits for the corresponding language groups. The parameters s̄A , s̄B
are used to define the payoffs from intervention. They are rates for language
reversing due to interventions. The larger these values, the faster the reversing
rates.
3. Dynamics Analysis
Several theoretical results can immediately be established following the model
given in the previous section: (i) Languages A and B can co-exist when the payoffs for speaking A and B are balanced. (ii) The co-existence is evolutionarily
stable under certain conditions. (iii) Interventional conditions can be found tomaintain an evolutionarily stable multilingual population. The following are moredetailed analysis.
∗∗
Without loss of generality, let λ = 0.5 and c = c̄ = 1. Consider a (yA
, yB
)∗
∗
population, yA , yB 6= 0, i.e., languages A and B co-exist in the population. Then,∗∗) to be an equi, yB
it is easy to see that a necessary and sufficient condition for (yAlibrium solution to the equations in (3) or in other words, an equilibrium strategy∗∗), i.e.,) = P̃B (yB
for the game in (6) is P̃A (yA∗ α−1∗ ᾱ−1∗ α−1∗ ᾱ−1(yA)
sA + (yA)
s̄A = (yB)
sB + (yB
)
s̄B .

(10)

For a specific population, for example, for α = 3/2 and ᾱ = 0, it can be simplified
to
∗ 1/2
∗ −1
∗ 1/2
∗ −1
(yA
) sA + (yA) s̄A = (yB) sB + (yB) s̄B .

(11)∗∗
In addition, a sufficient condition for (yA, yB) to be evolutionarily stable is∗ ᾱ−2∗ ᾱ−2∗ α−2∗ α−2(1 − ᾱ)[(yA)
s̄A + (yB)
s̄B ] > (α − 1)[(yA)
sA + (yB)
sB ]. (12)For a specific population, for example, for α = 3/2 and ᾱ = 0, the condition canbe simplified to:
∗ −2
∗ −2
∗ −1/2∗ −1/2(yA) s̄A + (yB) s̄B > [(yA)
sA + (yB)
sB ]/2.

(13)

∗
∗
∗
∗
Assume that (yA
, yB
), yA
, yB
6= 0, is an equilibrium solution to the systemof equations in (3) satisfying the equilibrium condition in (10). Then, several

784interventional conditions can be obtained to make the solution to be evolutionarily
stable when the interventional parameters s̄A and s̄B fall in certain ranges:
Condition 1: If 1 − ᾱ ≥ α − 1, i.e., α + ᾱ ≤ 2, the stability conditionin (12) can be satisfied easily when the reversing rates s̄A and s̄B are in certain∗ ᾱ−2∗ α−2∗ ᾱ−2∗ α−2ranges: Since ᾱ − 2 < α − 2, (yA
)
> (yA
)
and (yB
)
> (yB
)
, and
therefore, the stability condition in (12) is satisfied if s̄A and s̄B are sufficiently
large, say s̄A = tsA and s̄B = tsB , where 1 ≤ t ≤ min{1/sA , 1/sB }. With such∗∗a choice of s̄A and s̄B , one can prove that (yA, yB) is also unique (see an example
in Figure 1 (a)).
Condition 2: If in particular, 1 − ᾱ = α − 1, i.e., α + ᾱ = 2, and s̄A = tsB
and s̄B = tsA for any 1 ≤ t ≤ min{1/sA , 1/sB }, then it is easy to verify that
∗
∗
∗
∗
∗
∗
PA (yA) = PB (yB) and P̄A (yA) = P̄B (yB) if and only if P̃A (yA) = P̃B (yB),∗∗
which implies that (yA , yB ) is an equilibrium solution to the system of equationsin (3) for competition-only and intervention-only and both combined. For the∗∗
combined one, one can prove that (yA, yB) is also evolutionarily stable.
∗∗∗∗
Condition 3: In general, given a desired solution (yA
, yB
), yA
, yB
6= 0, it ispossible to make it to be an equilibrium solution if the reversing rates s̄A and s̄Bsatisfy the following conditions: For 1 − ᾱ ≥ α − 1,
∗ 1−ᾱ ∗ α−1∗ 1−ᾱ ∗ α−1s̄A = (yA)(yB )sB , s̄B = (yB
)
(yA )
sA .

(14)

∗
∗
∗
∗
∗
Then, P̄A (yA) = PB (yB) and P̄B (yB) = PA (yA). It follows that P̃A (yA
) =
∗
∗
∗
◦
P̃B (yB ), and (yA , yB ) becomes an equilibrium solution. In addition, let yA
=
∗
∗
∗
≥
) is selected such that yA, yB
1/(1 + (sA /sB )1/(α−1) ), and assume that (yA
∗
◦
∗
∗
◦
}. Then, the condition in (12) is satisfied at, yB≤ min{yA} or yA, yB
max{yA∗∗), and the solution is also evolutionarily stable (see an example in Fig, yB(yA
ure 1 (b)).

(a)

(b)

Figure 1. Dynamic Behaviors with Competition and Intervention. Payoff functions P̃A and P̃B are
plotted against yA and the changing directions of yA are pointed with arrows. In (a), α = 3/2∗ = 0.8315 and y ∗ = 0.1685.
and ᾱ = 0, s̄A = sA = 0.75 and s̄B = sB = 0.25, and yA
B∗ = 0.8 and y ∗ = 0.2, and s̄
In (b), α = 3/2 and ᾱ = 0, sA = 0.6 and sB = 0.4, yAA =B∗ )(y ∗ )1/2 s = 0.1431 and s̄ = (y ∗ )(y ∗ )1/2 s = 0.1073.
(yA
B
B
A
B
B
A

785

4. Computer Simulation
A dynamic simulation is carried out to track the changes of the bilingual level of a
population across time and space. A 2D torus-shaped lattice of n × n cells is constructed first, with each cell assumed to be occupied by an individual speaker. An
individual can then be selected repeatedly from the lattice, and a game is played
for the individual against the population of the lattice: Let (xA , xB ) be the current strategy for the individual, and (yA , yB ) the strategy for the population. Let
pA = P̃A (yA ) and pB = P̃B (yB ) be the payoffs for A and B speakers, respectively. Then, the payoff for the individual, π = xA P̃A (yA ) + xB P̃B (yB ), is
computed. If pA > π, xA is increased by setting xA = yA if xA < yA . On theother hand, if pA < π, xA is reduced by setting xA = yA if xA > yA .
Initially, each individual is assigned with a random strategy. The game isplayed n2 times for the population to complete a generation. The game is repeated for 100 generations to make sure the population reaches its equilibrium.
In general, the game can be played in a neighborhood of each selected individual.
Let the neighborhood be an m×m sub-lattice, with the selected individual locatedat the center. Then, the game can be carried out for each selected individual only
against the population in its neighborhood of this size, with the population strategy (yA , yB ) computed from the population in the neighborhood. Such a game
may in fact be more realistic, as people usually interact only with a small groupof others around them.
Figure 2 demonstrates a typical set of results obtained from the simulation
for a given population. The population is distributed on a 75 × 75 lattice, with
α = 2 and ᾱ = 0, and s̄A = sB = 0.75 and s̄B = sA = 0.25. The simulation
is done three times with the neighborhood size equal to 75 × 75, 25 × 25, and5 × 5, respectively. The final distribution of the individual frequency x∗A in the
population is displayed for each simulation in the corresponding order.
From left to right, the first graph in Figure 2 shows the result from the simulation with the neighborhood size equal to 75 × 75, when each individual interacts∗
with all others in the whole population. The equilibrium frequency yAof the population in this case is approximately equal to 0.75, which agrees with the direct
prediction from the model described in previous sections. In addition, the distribution of the individual frequency x∗A in the population is very homogeneous, withx∗A ≈ 0.75 across the board, suggesting that language A and B co-exist in the population in an evenly distributed bilingual form. The second graph shows the result
from the simulation with the neighborhood size equal to 25 × 25, when the inter∗actions among individual speakers are restricted. The equilibrium frequency yAof the population remains about the same, approximately equal to 0.75. However,
the individual frequency x∗A becomes less constant. Some regions have higher
individual frequencies than others, and local groups are formed with varying individual frequencies, as shown in the graph. The third graph shows the result

786

from the simulation with the neighborhood size further reduced to 5 × 5. While∗
the population frequency yA
is not significantly changed, the individual frequency∗
xA shows even bigger variations, with even smaller local spots formed with higher
or lower individual frequencies than average. The dynamic behaviors shown fromthese simulations agree with the general experience in language development: Indeed, when communications are restricted to local groups, language variations
often remain.

Figure 2. Dynamic Simulation Results. The distributions of color-coded A speaking frequencies in
the 2D lattice at equilibrium are displayed in graphs from left to right, with corresponding neighborhood sizes equal to 75 × 75, 25 × 25, and 5 × 5. Each of the graphs is a 75 × 75 2D lattice. The x-axis
and y-axis of the graph represent the 75 units of the lattice in the horizontal and vertical directions.
The population is assumed to have α = 2 and ᾱ = 0, s̄A = sB = 0.75 and s̄B = sA = 0.25.

5. Conclusion
An evolutionary dynamics model for multilingual competition with societal intervention is proposed and analyzed. The model consists of two separate parts
corresponding to two evolutionary games, one for the evolution of multilingualism with “natural” competition, and the other for the evolution with “artificial”
intervention. Both games may have a multilingual co-existing equilibrium state,
but the one for the competition-only game is evolutionarily unstable, which leads
to the conclusion that multiple languages cannot co-exist, and one of them will
eventually dominate while all others become extinct, as stated in many previous
studies.
However, multiple languages do exist in many language communities, often
in multilingual forms. By combining competition with intervention, the proposedmodel provides a more general theoretical framework for the study of language
competition than those previously investigated. The model shows how multiple
languages may co-evolve when appropriate interventions are introduced, and why
they may co-exist in stable equilibrium states, at least in theory. The computer
simulation on the dynamic behaviors of bilingual populations further validates themodel, and also demonstrates how local bilingual groups may be formed when the787interactions among the speakers are restricted.
Some experimental work needs to be done to connect the theory to the reality: The parameters in the model need to be refined, denoted, and determinedwith real-world language data, while their values may vary with varying language
populations. For simplicity, the model is defined and discussed only for bilingual
populations, but it can in fact be extended to populations with more than two languages, although the analysis may be more mathematically involved (Wu, 2020).
References
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Batalova, J., & Zong, J. (2016). Language diversity and English proficiency in
the United States. Migration Information Source.
Burger, R. (2000). The mathematical theory of selection, recombination, andmutation. New York, NY: John Wiley & Sons.
China, P. R. (2017). China National Language Development Commission: Zhongguo yuyan wenzi shiye fazhan baogao. Beijing, China: The Commercial
Press.
Fujie, R., Aihara, K., & Masuda, N. (2013). A model of competition among morethan two languages. Journal of Statistical Physics, 151, 289–303.
Gabszewicz, J., Ginsburgh, V., & Weber, S. (2011). Bilingualism and communicative benefits. Annals of Economics and Statistics, 101, 271–286.
Grosjean, F. B. (2012). Bilingual: Life and realty. Cambridge, MA: Harvard
University Press.
Hofbauer, J., & Sigmund, K. (1998). Evolutionary games and population dynamics. Cambridge, UK: Cambridge University Press.
Mira, J., & Paredes, A. (2005). Interlinguistic similarity and language death
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Pinker, S., & Bloom, P. (1990). Natural language and natural selection. Behavioral and Brain Sciences, 13, 707–784.
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Wu, Z. (2020). Why multilingual, and how to keep it – an evolutionary dynamics
perspective. PLoS ONE, 15, e0241980.

788

ROLE OF MIDBRAIN DOPAMINERGIC SYSTEM IN SOCIAL
ENHANCEMENT OF VOCAL LEARNING IN SONGBIRDSHIN YANAGIHARA*1, MAKI IKEBUCHI2, CHIHIRO MORI3, RYOSUKE O.
TACHIBANA4, KAZUO OKANOYA2,4*

Corresponding Author: yanagihara.shin.al@teikyo-u.ac.jp

1Advanced Comprehensive Research Organization, Teikyo University, Tokyo, Japan
2 RIKEN Center for Brain Science, Saitama, Japan
3 Department of Molecular Biology, Faculty of Pharmaceutical Sciences,

Teikyo University, Tokyo, Japan
4 Graduate School of Arts and Sciences, The University of Tokyo, Tokyo,

Japan

There are many similarities between avian vocal learning and human language
learning, and in particular, social interaction is known to have a significant impact
on vocal learning in both. For example, social interaction is known to facilitate
language learning in humans (Kuhl, 2010). Similarly, social interactions between
tutor and tutee are also known to enhance vocal learning in songbirds (Chen,
Matheson, & Sakata, 2016; Eales, 1989). Juvenile birds learn to sing the same
song by listening to the tutor song. In order to learn songs accurately, juvenile
birds need to hear the songs directly from their tutor. On the other hand, theycannot learn to imitate songs well by passively listening to songs presented froma speaker. These behavioral studies highlight the importance of social interactions
in vocal learning, but the neural mechanisms underlying the social enhancementof vocal learning remain unclear. In this study, we aim to understand theevolutionary origin of language learning by clarifying the neural basis of social
enhancement of vocal learning in songbirds. We tested the hypothesis that direct
listening to songs from the tutor bird enhances midbrain dopaminergic activity asa social reward for juvenile birds, leading to accurate song memory formation.
We measured neural activity in the ventral tegmental area (VTA) and substantia
nigra pars compacta (SNc) of freely behaving juvenile zebra finches. We

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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examined whether auditory responses to the tutor songs changed depending on
the presence or absence of a live tutor bird. As a result, we found that a group of
neurons in the VTA/SNc showed auditory responses to the tutor song presented
from the speaker, and that these responses were markedly enhanced in thepresence of the tutor. Moreover, similar enhanced auditory responses wereobserved when a juvenile listened to a song from a live tutor. These results suggestthat midbrain dopaminergic system is involved in the process of juvenile birds
learn songs from their tutor.
Next, we tried to find out where in the brain dopamine acts to enhance vocal
learning. Previously, it has been suggested that memory of songs learned from atutor bird is formed in the higher auditory cortex (NCM) (Gobes & Bolhuis, 2007;
London & Clayton, 2008; Yanagihara & Yazaki-Sugiyama, 2016). In addition,
dopamine receptors are abundant in the higher auditory cortex (Kubikova, Wada,& Jarvis, 2010). Thus, dopamine released in the brain when a juvenile bird hears
the song from a tutor may enhance auditory responses in the higher auditory
cortex. To test this, we examined whether dopamine modulates auditory
responses in the higher auditory cortex. While measuring neural activity in thehigher auditory cortex, we found that local administration of dopamine near therecording site markedly enhanced auditory responses to the songs presented froma speaker. These results suggest that dopamine is released into the higher auditory
cortex by directly listening to the tutor song, which enhances the auditory
responses in the higher auditory cortical neurons and leads to successful songmemory formation. In conclusion, we show that dopamine is a key molecule forsocial enhancement of vocal learning in songbirds. This study provides a newresearch direction to explore the origin of language through social interaction anddopamine.

Acknowledgements
This work is supported by MEXT/JSPS KAKENHI 17H06380 (#4903),
17H01015, 20H00105 to K.O. and 17K07066, 20K06907 to S.Y., and ACRO
Incubation Grants of Teikyo University.
References
Chen, Y., Matheson, L. E., & Sakata, J. T. (2016). Mechanisms underlying thesocial enhancement of vocal learning in songbirds. Proc Natl Acad Sci
U S A, 113(24), 6641-6646. doi:10.1073/pnas.1522306113

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Eales, L. A. (1989). The Influences of Visual and Vocal Interaction on Song
Learning in Zebra Finches. Animal Behaviour, 37, 507-508. doi:Doi
10.1016/0003-3472(89)90097-3
Gobes, S. M., & Bolhuis, J. J. (2007). Birdsong memory: a neural dissociation
between song recognition and production. Curr Biol, 17(9), 789-793.
doi:10.1016/j.cub.2007.03.059
Kubikova, L., Wada, K., & Jarvis, E. D. (2010). Dopamine receptors in a songbird
brain. J Comp Neurol, 518(6), 741-769. doi:10.1002/cne.22255
Kuhl, P. K. (2010). Brain mechanisms in early language acquisition. Neuron,67(5), 713-727. doi:10.1016/j.neuron.2010.08.038
London, S. E., & Clayton, D. F. (2008). Functional identification of sensory
mechanisms required for developmental song learning. Nat Neurosci,
11(5), 579-586. doi:10.1038/nn.2103
Yanagihara, S., & Yazaki-Sugiyama, Y. (2016). Auditory experience-dependent
cortical circuit shaping for memory formation in bird song learning. Nat
Commun, 7, 11946. doi:10.1038/ncomms11946

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HOMO REGULARIS: LANGUAGE AS A CULTURAL INVENTION OF
THE RULE-OBSESSED SPECIES
Masato YOSHIKAWA*1* myshkw@gmail.com

1 Faculty of Letters, Keio University, Tokyo, Japan

As is often pointed out, human beings are quite distinctive in their astonishingability to find patterns and rules based on the observation of recurrent phenomena,
static sequences and so forth. The ability is so peculiar that they can even learn
rules that are only assumed to exist but actually do not (e.g., as seen in the resultof so-called iterated learning experiments: Kirby & Hurford, 2002). Even moreunique is, however, their extraordinary inclination to obey the rules they find: notonly are they good at detecting recurrent patterns from observation and memorize
them, but also enforcing themselves (and others) to follow the patterns, which ischaracterized as the norm psychology (e.g., Henrich, 2015).
In this sense, human beings can be called Home regularis, man of rules: they
are unique species in that they can hardly help finding, learning, and obeying rules.
This study explores the rule-obsessed nature of human beings and tries to describe
the nature of human language as what Homo regularis evolved to have during thecourse of cultural evolution. Specifically, the “language of Homo regularis” view,the LHR view for short, assumes that 1) language acquisition in the course of
ontological development especially concerning grammatical rules is based on theprocess of rule generation, as opposed to rule learning; 2) the basic mechanism ofrule generation is abductive reasoning, or abduction, rather than induction, whichis performed by forming possible hypotheses, namely rules, based on input data
even if the data are sporadic; 3) the computational engine of rule generation is
not a combinatorial operation of minimum units (e.g., words) such as Merge (e.g.,
Hauser, Chomsky, & Fitch, 2002), but exemplar-based analogy.
The best strategy to guess what linguistic rules as social conventions or norms
are shared within a community would not be to simply generalize relevant input data, because rules are implicit and inputs are concrete while target structure,
namely rules, are, or at least can be, abstract. This is a part of the so-called povertyof stimulus (POS) argument (e.g., Chomsky, 1980), and as a possible solution to
the POS problem, this study assumes that rules are not learned through generalizing input data, but generated with the inference from input data. As describedabove, humans as Homo regularis have a strong tendency to see rules in recurrent
This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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phenomena even when there are no such rules, which means what they usually dowhen they think they learn rules would actually be rule generation.
This assumption could reconcile the long-standing nature-nurture dispute ofgrammatical knowledge/ability. The LHR view presupposes an innate capacity
to perform grammatical operation, for example, ordering linguistic units in a semantically consistent way, as evidenced by emergent grammatical patterns foundin home signs (e.g., Goldin-Meadow & Feldman, 1977) and the emergence of a
new language (e.g., Nicaraguan Sign Language). The view, however, puts moreemphasis on the inheritance of grammatical conventions shared and maintained in
a community throughout the history, so the self-generation of grammatical rules,
which seems actually possible, is of second-hand importance. In the course of cultural evolution, humans can generate grammatical rules even when they think they
do replicate what the former generation does, as shown in the so-called iterated
learning experiments (e.g., Kirby, Cornish, & Smith, 2008). From this it follows
that, under the LHR view, language evolution as a cultural evolution is considered
to be the repeated process of rule-generation by pseudo-learners.
If the language acquisition process is best characterized as rule generation,
what mechanism makes the process possible? The answer the LHR view provides to this question is abductive reasoning, or abduction, which enables us tobuild a hypothesis about a possible rule or cause generating a result, only basedon the observation of the result. Rule-generation could also be equated with theprocess of inductive learning as in, for example, Kirby and Hurford (2002), but
inductive process might need a lot more assumptions of learning mechanisms or
algorithms than abductive reasoning. Although abduction as the basic mechanismof language acquisition is critically discussed by Chomsky (1968/2006), the objection could be refuted by combining the rule generation process discussed above
and the so-called norm psychology. Humans are sensitive to others’ behaviors andwould-be rules behind them but great apes such as chimpanzees do not seem tobe (e.g., Haun, Rekers, & Tomasello, 2014). Abductively constructed rules or hypotheses may, therefore, well be effectively rejected by observing others’ actions
and reactions, resulting in moderately homogeneous behavior and mildly different
but largely common sets of generated rules.
If the abduction-based rule generation is the basic mechanism of human language acquisition, how can we construct specific rules based on observed data?
Under the LHR view, the computation of rule generation is based on the operation of exemplar-based analogy (Cf. Skousen, Lonsdale, & Parkinson, 2002).
This marks a stark contrast with the view presupposing as a computational engineof grammar a combinatorial operation such as Merge (e.g., Hauser et al., 2002).
The LHR view considers that exemplar-based analogy is more cost-effective inthe context of computational load than combinatorial operation, in that it does notneed full segmentation and can avoid the possible risk of combinatorial explosion(see the supplementary material).

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References
Chomsky, N. (1968/2006). Language and mind (Third ed.). Cambridge: Cambridge University Press.
Chomsky, N. (1980). Rules and representations. New York: Columbia University
Press.
Goldin-Meadow, S., & Feldman, H. (1977). The development of language-likecommunication without a language model. Science, 197(4301), 401–403.
Haun, D. B., Rekers, Y., & Tomasello, M. (2014). Children conform to the behavior of peers; other great apes stick with what they know. Psychologicalscience, 25(12), 2160–2167.
Hauser, M. D., Chomsky, N., & Fitch, T. W. (2002). The faculty of language:
What is it, who has it, and how did it evolve? Science, 298, 1569–1579.
Henrich, J. (2015). The secret of our success: How culture is driving human
evolution, domesticating our species, and making us smarter. Princeton:
Princeton University Press.
Kirby, S., Cornish, H., & Smith, K. (2008). Cumulative cultural evolution in
the laboratory: An experimental approach to the origins of structure in human language. Proceedings of the National Academy of Sciences, 105(31),10681–10686.
Kirby, S., & Hurford, J. R. (2002). The emergence of linguistic structure: An
overview of the iterated learning model. In A. Cangelosi & D. Parisi (Eds.),
Simulating the evolution of language (pp. 121–147). London: Springer.
Skousen, R., Lonsdale, D., & Parkinson, D. B. (Eds.). (2002). Analogical modeling: An exemplar-based approach to language. Amsterdam/Philadelphia:
John Benjamins Publishing.

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YOUR THEORY OF LANGUAGE EVOLUTION ALSO
DEPENDS ON YOUR VIEW OF EVOLUTION
THIAGO MACEK GONÇALVES ZAHN*1*Corresponding Author: thimacek@gmail.com
1Departamento de Linguística, Universidade de São Paulo, São Paulo, Brazil

One exciting but often puzzling aspect of the field of Language Evolution is thevariety of theories and approaches it includes. These not only embrace different,
sometimes contradictory assumptions, but also often seek explanations todifferent questions. An interesting topic is thus how different theories/ approaches
are related, i.e. how different assumptions may lead to disparate questions andviews, and how contradictory different assumptions made in the field in fact are.
In an important contribution, Jackendoff (2010) argued that “what there is for atheory of language to explain” depends on one’s theory of “what language is”,
discussing how different views on the “innate language capacity” and on how
domain-specific it is lead to different theories on how that language capacity
might have evolved. As Jackendoff mentions, some defend that little or nothing
special (i.e. domain-specific) is needed for the evolution of language, making it
essentially a “cultural phenomenon”. In the same article, however, he states that
“if that (“disparate languages” passed down through cultural transmission) is all
there is to language, a theory of the evolution of language has nothing at all to
explain”. Therefore, although Jackendoff speaks of cultural evolution, he sees aclear contrast between this and language evolution, which is apparently equatedwith biological evolution and deemed more important.
Others distinguish even more clearly between evolution as a specificallybiological phenomenon, and language change: Berwick & Chomsky (2016, p. 92)
explicitly say “Languages change, but they do not evolve. (…) nonbiologicalevolution (…) is not evolution at all”; and Andersen (2006) has a whole chapter
arguing “that there is no chance of explaining language change by themechanisms of evolutionary theory”. For some (e.g. Berwick & Chomsky), thisThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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distinction is taken as given - perhaps reflecting the Chomskyan view that
linguistic variation and change are strongly constrained by a shared Universal
Grammar. Others, like Andersen (2006) and Itkonen (1999), have discussed atsome length what they see as disanalogies that would disallow the use ofevolutionary thinking to explain cultural linguistic phenomena.
Although researchers who advocate for a cultural evolutionary approach tolanguage change and evolution (i.e. origin of modern human languages) haveprovided some answers to such criticisms (e.g. Dediu et al, 2013, p. 305-307;
Steels, 2017), little conversation seems to occur between both sides of the divide.
In part, this may reflect different conceptions of language, as pointed byJackendoff (2010), mirroring the Formalist-Functionalist divide common in
linguistics. However, it seems clear that another aspect underlying the divide aredifferent understandings of evolution – including what counts as evolution, what
evolution can/ should explain and related questions. This suggests that some ofthe ongoing debates in the field are inherently theoretical, and thus cannot be
solved solely empirically, since new evidence may be interpreted differently and/or given different weights depending on one’s assumptions.
Discussion on “what is evolution” and on whether it is possible (or productive) to
expand evolutionary thinking beyond biology has a prolific history in philosophy(e.g. Lewontin, 1970; Hull, 1988; Godfrey-Smith, 2007, 2009, 2012; Baraghith& Feldbacher-Escamilla, 2021; also Price, 1995[1971]; Frank, 2012; Luque,2017). Assuming an ontology based on a ‘general selection theory’ (e.g. Croft,
2000; Clark, 2010; Gong, 2012; Steels & Szathmáry, 2018) allows linguisticchanges to be studied alongside or independently of genetic changes in speakers,
and I suggest it may solve some incommensurabilities between approaches. In
fact, models based on such assumptions have shown how cultural language
evolution could have worked alongside biological evolution to shape modern
human language(s) (see e.g. Steels, 2010; Kirby, 2013). Meanwhile, taking‘evolution’ as limited to genetic changes raises questions about what is inherently
different in systems of cultural change, and whether phenomena at that level
might not have long-term effects in the evolution of human language(s).
Taking as a “general model” Kirby (2017, p.125)’s idea that language involves
the interaction of three dynamical systems (individual learning, cultural evolution
and biological evolution), important differences between theories may berecognized by considering which of these systems in fact involve evolution, which
are deemed more important, and how they influence each other in each approach.
It is perhaps not surprising that one’s theory of Language Evolution depends on
one’s theories of language and evolution; thus, clearing assumptions in bothregards may go a great way in building more constructive exchanges in the field.

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Acknowledgments
The author would like to thank João Figueiredo Nobre Cortese, Thomas Finbow,
Paulo Chagas de Souza, Joana Bortolini Franco, Wellington Santos da Silva,
Mariana Payno and Vitória Lima for constructive discussions about the nature oflanguage and language evolution, and CAPES (Coordenação de Aperfeiçoamento
de Pessoal de Nível Superior) for financial support which was essential to allow
the development of this research.
References
Andersen, H. (2006) Synchrony, Diachrony, and Evolution. In: O.N. Thomsen(Ed.) Competing models of linguistic change – Evolution and Beyond (pp. 5990). Amsterdam/ Philadelphia: John Benjamins Publishing Company.
Baraghith, K, & Feldbacher-Escamilla, C. (2021). The Many Faces ofGeneralizing the Theory of Evolution. American Philosophical Quarterly
58(1): 35-50.
Berwick, R., & Chomsky, N. (2016). Why only Us: Language and evolution.
Cambridge, MA: MIT Press.
Clark, B. (2010). Evolutionary Frameworks for Language Change: The Price
Equation Approach. Language and Linguistics Compass 4(6): 363-376.
Croft, W. (2000). Explaining Language Change: An Evolutionary Approach.
London: Longman.
Dediu, D., Cysouw, M., Levinson, S.C., Baronchelli, A., Christiansen, M.H.,
Croft, W., Evans, N., Garrod, S., Gray, R.D., Kandler, A., Lieven, E. (2013)
Cultural Evolution of Language. In: P.J. Richerson and M.H. Christiansen(Eds.) Cultural Evolution: Society, Technology, Language, and Religion. (pp.
303-332). Cambridge, MA: MIT Press.
Frank, S.A. (2012). Natural selection. IV. The Price equation. Journal of
Evolutionary Biology 25: 1002-1019.
Godfrey-Smith, P. (2007). Conditions for Evolution by Natural Selection. Journalof Philosophy 104: 478-516.
Godfrey-Smith, P. (2009). Darwinian populations and natural selection. New
York, NY: Oxford UP.
Godfrey-Smith, P. (2012). Darwinism and cultural change. PhilosophicalTransactions of the Royal Society B 367: 2160-2170.
Gong, T., Shuai, L., Tamariz, M., Jäger, G. (2012). Studying Language Change
Using Price Equation and Pólya-urn Dynamics. PLoS ONE 7(3): e33171.
Hull, D.L. (1988). A mechanism and its metaphysics: An evolutionary account of
the social and conceptual development of science. Biology and Philosophy
3(2): 123-155.
Itkonen, E. (1999). Functionalism yes, biologism no. Zeitschrift für
Sprachwissenschaft, 18(2): 219-221.

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Jackendoff, R. (2010). Your theory of language evolution depends on your theoryof language. In: R.K. Larson, V. Déprez, H. Yamakido (Eds.) The Evolutionof Human Language – Biolinguistic Perspectives (pp. 63-72). Cambridge:
Cambridge University Press.
Kirby, S. (2013). Transitions: The Evolution of Linguistic Replicators. In: Binde,
P.-M.; Smith, K. (Eds). The Language Phenomenon: Human Communicationfrom Milliseconds to Millennia. (pp. 121-138). Heidelberg: Springer.
Kirby, S. (2017). Culture and biology in the origins of linguistic structure.
Psychonomic Bulletin and Review 24: 118-137.
Lewontin, R.C. (1970). The units of selection. Annual Review of Ecology and
Systematics 1: 1-18.
Luque, V.J. (2017). One equation to rule them all: a philosophical analysis of thePrice equation. Biology & Philosophy 32(1): 97-125.
Price, G.R. (1995[1971]). The Nature of Selection. Journal of Theoretical Biology
175: 389-396.
Steels, L. (2010). Can Evolutionary Linguistics Become a Science? Journal for
Evolutionary Linguistics 1(1): 1-34.
Steels, L. (2017). Do languages evolve or merely change? Journal of
Neurolinguistics 43(B): 199-203.
Steels, L., Szathmáry, E. (2018). The evolutionary dynamics of language.
Biosystems 164: 128-137.

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IS REDUNDANCY USEFUL IN LANGUAGE? AGENTRECIPIENT DISAMBIGUATION IN ENGLISH AND DUTCHEVA ZEHENTNER*1 and DIRK PIJPOPS2*Corresponding Author: eva.zehentner@es.uzh.ch
1English Department, University of Zurich, Zurich, Switzerland
2Department of Modern Languages, University of Liège, Liège, BelgiumThis paper discusses the competing evolutionary motivations of efficiency versus
robustness in language processing and learning (MacWhinney et al. 2014), bothfrom a typological and diachronic perspective. Specifically, we assess thepotential benefits or costs of redundancy in morphosyntactic marking ofparticipant roles, comparing and testing two opposing hypotheses:
On the one hand, following the most crucial tenet in usage-based linguistics
that language use affects – or even determines – grammar (Bybee 2010), we
assume that language is organised in a way that facilitates efficient usage (e.g.
Gibson et al. 2019). On this account, redundant marking should be dispreferred.
Well-known typological ‘trade-off’ distributions and diachronic trajectories
between word order and morphological case marking seem to support this point(Fedzechkina et al. 2017). Furthermore, prepositional marking is often only
applied in contexts where it comes with some added processing benefit (cf.
Pijpops et al. 2018 on the impact of complexity on Dutch transitive object
marking, or Tal et al. 2020, Levshina 2021 on ambiguity/atypicality in differential
object marking).
On the other hand, however, we pursue Van de Velde's (2014) argument thata certain amount of redundancy – or rather, ‘degenerate’ marking (involving
many-to-many relationships) – is in fact beneficial from a usage perspective:
redundancy constitutes an indispensable component of any degenerative Complex
Adaptive System, and thus also of language (Steels 2000; Beckner et al. 2009).
Such redundancy/degeneracy comes with two important advantages, viz.
robustness and evolvability: most importantly for the present paper, the former
entails that redundant marking offers protection against information loss in the
noisy language channel, even though it may be less efficient. Redundancy isfurthermore assumed to increase learnability, particularly in more complex
situations (e.g. Tal et al. 2021).
Our case study to assess the plausibility of what we call the ‘strict-efficiency’
versus the ‘robustness’ account is participant role marking in ditransitive clauses
in Present Day Dutch and English, for a comparative perspective, as well as
historical English for a diachronic view. More precisely, we investigate theinteraction between strategies used to distinguish agents and recipients in transferevents, e.g. with verbs of giving as in (1) and (2).

This paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

807(1) TheyAGENT give a book to the studentRECIPIENT.
(2) Ze AGENT geven een boek aan de studentRECIPIENT.
Since both agents and recipients in ditransitive clauses are prototypically animate(sentient) and volitional (e.g. Newman 1998; Naess 2007; Haspelmath 2015),
disambiguating these roles based on semantic-pragmatic information is usuallydifficult if not impossible. Morpho-syntactic cues are hence indispensable indetermining ‘who gave what to whom’. Among the strategies language users haveat their disposal are (i) constituent order (e.g. SVO in Present Day English), (ii)case marking/ formal differentiation (e.g. subject vs object pronoun forms in
PDE), (iii) subject-verb agreement, and (iv) prepositional marking. Employing
multiple strategies at the same time constitutes redundant marking; for example,in (1) all four disambiguation strategies are given. Meanwhile in (3), none areused, resulting in an ambiguous sentence.
(3) Mijn baas kan je niet zomaar een uitbrander geven.
‘You can’t just give my boss a telling-off’ or ‘My boss can’t just give
you a telling off.’
In our study, we make use of the Sonar Corpus of Written Dutch (Oostdijk et al.
2013), a pre-compiled dataset of ditransitives from the ICE-GB (Röthlisberger2018) and the Penn Parsed Corpus of Middle English (PPCME2; Kroch et al.
2000). Instances of ditransitive clauses with give are extracted from the corpora,and coded for the strategies instantiated by them. Following the ‘strict-efficiency’
account, we then expect language users to prefer employing a single strategy foreach instance. By contrast, based on the degeneracy/ robustness account, we
anticipate sentences that simultaneously instantiate multiple strategies to be mostcommon, and cases where only one strategy is at work to be rare. Our results
indicate that even though the precise strategies and their disambiguation power
differ between Dutch and English, both languages show substantial redundancy
to be the default. Still, redundancy seems to operate within limits, with four-foldstrategy use being rare, and two simultaneous strategies being most common. Our
diachronic results are in line with this conclusion: We find that English appears
to have moved towards more redundant marking over time, but that after a shortperiod of ‘exuberant’ redundancy, double redundancy is settled on as the norm.
In a final step, we assess the question of whether redundant marking isparticularly frequent in complex environments, here measured as sentence length
in words (excluding the subject and object arguments of the respective ditransitive
patterns). Our findings are again mixed: for Dutch and historical English,
complexity emerges as an influential predictor; in Present Day English, however,no significant effect can be observed. We interpret this outcome of our study inlight of the differing degrees of variability of strategies in the languages/ stages.

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References
Beckner, C., Blythe, R., Bybee, J., Christiansen, M., Croft, W., Ellis, N., Holland,J., Ke, J., Larsen‐Freeman, D., & Schoenemann, T. (2009). Language is aComplex Adaptive System: Position paper. Language Learning, 59(1), 1-26.
Bybee, J. (2010). Language, usage and cognition. Cambridge: Cambridge
University Press.
Gibson, E., Futrell, R., Piantadosi, S.P., Dautriche, I., Mahowald, K., Bergen, L.
& Levy, R. (2019). How efficiency shapes human language. Trends in
Cognitive Sciences, 23(5), 389-407.
Fedzechkina, M., Newport, E., & Jaeger, F. (2017). Balancing effort andinformation transmission during language acquisition: Evidence from word
order and case marking. Cognitive Science, 41(2), 416-446.
Haspelmath, M. (2015). Ditransitive constructions. Annual Review of Linguistics
1, 19-41.
Kroch, A., Taylor, A., & Santorini, B. (2000-). The Penn-Helsinki Parsed Corpusof Middle English (PPCME2). Second edition, release 4.
MacWhinney, B, Malchukov, A., & Moravcsik, E. (eds.). (2014). Competing
motivations in grammar and usage. Oxford: Oxford University Press.
Naess, A. (2007). Prototypical transitivity. Amsterdam: Benjamins.
Newman, J. (1998). Recipients and ‘give’ constructions. In W. van Langendonckand W. Van Belle (Eds.), The dative, Vol. 2: Theoretical and contrastive
studies (pp. 1-28). Amsterdam: Benjamins.
Oostdijk, N., Reynaert, M., Hoste, V., & Schuurman, I. (2013). The constructionof a 500-million-word reference corpus of contemporary written Dutch. In P.
Spyns and J. Odijk (Eds.), Essential speech and language technology for
Dutch, Theory and applications of Natural Language Processing (pp. 219247). Heidelberg: Springer.
Pijpops, D., Speelman, D., Grondelaers, S., & Van de Velde, F. (2018).
Comparing explanations for the Complexity Principle. Evidence fromargument realization. Language and Cognition, 10(3), 514-543.
Röthlisberger, M. (2018). The dative dataset of World Englishes. KU Leuven.
Steels, L. (2000). Language as a Complex Adaptive System. In M. Schoenauer,
K. Deb, G. Rudolph, X. Yao, E. Lutton, J. Merelo and H.-P. Schwefel (Eds.),
Proceedings of PPSN VI (pp. 17-26). Berlin: Springer.
Tal, S., E. Grossman, H. Rohde & Arnon, I. (2021). Speakers use more redundantreferents with language learners: Evidence for communicatively-efficientreferential choice. Preprint. PsyArXiv.
Tal, S., Smith, K., Culbertson, J., Grossman, E., & Arnon, I. (2020). The impact
of information structure on the emergence of differential object marking: An
experimental study. Preprint. PsyArXiv.
Van de Velde, F. (2014). Degeneracy: the maintenance of constructional
networks. In R. Boogaart, T. Colleman and G. Rutten (Eds.), Extending thescope of Construction Grammar (pp. 141-179). Berlin: Mouton de Gruyter.

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CATEGORICAL PERCEPTION AND DISCREETNESS: AN
EVOLUTIONARY AND NEUROCOGNITIVE PERSPECTIVE
ELIZABETH QING ZHANG 1*1, EDWARD RUOYANG SHI 22*Corresponding Author: zqelizabeth@gmail.com
1 Center for Language Evolution Studies, University Centre of Excellence IMSErt,

Nicolaus Copernicus University, Torun, Poland
2 Department of Catalan Philology and General Linguistics, University ofBarcelona, Barcelona, Spain
As one of the design features of human language, discreteness is present at bothphonological and syntactic levels. We hypothesize that categorical perception (CP) could
have paved the way for discreteness across domains including language. Evidence fromcomparative and neurobiological perspectives are provided in this paper. Comparative
studies on CP suggest that it has a phylogenetic root and is a combination of nature andnurture. Neurobiological studies suggest that the connection between auditory cortex and(pre)motor cortex, and between (pre)motor cortex and the basal ganglia could play animportant role in CP and further discreteness. This paper discusses how domain generalperceptual ability—categorical perception—could have contributed to discreteness,
highlighting the role of sensory-motor system in the evolution of language.

1. Introduction
In this paper, we propose that Categorical Perception (CP) could have played afoundational role for discreteness of language, which is a prominent feature ofhuman language (Hockett, 1960). We will approach discreteness from domain
general perspective and highlight how it is salient in language. Then by reviewing
CP of sounds in nonhuman animals, we argue that CP has its phylogenetic roots
in terms of evolution. Following this, we explicate how CP could have been thebasis for discreteness with neurological evidence focusing on auditory cortex,(pre)motor cortex and the basal ganglia. The current work discusses the role ofperception in language evolution, which provides new avenue to explore theevolution of human language from sensory-motor system.
2. DiscretenessDiscreteness is an essential concept in several areas, such as quantum physics andchemistry. As Hauser et al. (2002) pointed out, life is constructed on separate andThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

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unblendable components (genes). Besides, an example in our daily lives is thattime is calculated as discrete in the units of seconds. In terms of human cognition,
it is generally established that there exist discrete units of mathematics, such as
integers, and discrete units of music, such as melody and rhythm. In the domainof human language, as Hockett (1960) pointed out, discrete and compositional
language differs from continuous and holistic nonhuman animal communication
signals, which he considered as one of the design features of human language.
But what Hockett has emphasized is phonological structure of language, namely
syllables are made up of separate vowels and consonants at the phonological level.
However, discreteness is also observed at syntactic levels. Through merging
lexical items into grammatical phrases or sentences at the syntactic level, lexicalelements are discretely dispersed and organized into grammatical phrases orsentences. These discrete atoms at various levels of language combine to form
bigger structures based on the rules at that level. For example, you may find 6and 15-word sentences, but not 7.5-word ones. Just as Searle (2008; p 176)
defined, discreteness is "the property through which syntactical elements keep
their identity when subjected to diverse syntactical processes".
3. Categorical perception and Discreteness: Comparative evidence
Categorical perception (CP) is a psychophysical process in which continuous
inputs are perceived discretely across modalities (Harnad, 1987). The interactions
between low-level perception and high-level cognition are revealed by this
implicit segmentation of continuous physical inputs (Goldstone & Hendrickson,2010). The mechanism of CP has been investigated utilizing psychophysicalapproaches such as labeling and discerning continuous stimulus paradigms(Harnad, 1987). The phenomenon of CP was initially identified in sound modality
in human speech sounds (Liberman et al., 1957), where listeners were prone toperceive the b-d-g continuum into three distinct groups. Furthermore, research on
infants has revealed that they appear to be endowed with the ability todiscriminate different sounds in all languages (which is the nature part), but asthey have more contact with one (or more for multilinguals) ambient language(s),they tend to group sounds that are not contrastive in their native language(s). For
instance, after 8-10 months, Japanese newborns can no longer distinguishbetween /l/ and /r/, despite the fact that these two sounds are acoustically distant.
Statistical learning (Maye et al., 2002) has been proposed to achieve such
postnatal shaping CP of phonemes, which appears to be reliant on the requirementthat speech sounds are discrete. Statistical learning is the process of learning thestatistical distribution of environmental inputs via sheer exposure to the stimuli

811

without instruction (Romberg & Saffran, 2010). The categories boundaries of thestimuli could be changed as a result of statistical learning. The finding of Japanese
newborns no longer discriminating between /l/ and /r/ might be due to implicit
statistical learning of the non-contrastive distribution of /l/ and /r/ in Japanese.
Statistical learning may also be seen in the process of segmenting words fromspeech streams (Saffran et al., 1996), which underlies CP in the sense that discreteelements are extracted from continuous inputs. It is also worth mentioning thatstatistical learning is used in a variety of cognitive domains other than language(Bogaerts et al., 2020).
It is also worth emphasizing that CP is not exclusive to speech sounds, butrather a phenomenon observed in a variety of domains and modalities. If ourhypothesis that discreteness arises from CP is correct, the discovery of CP in
nonhuman animals shows that discreteness of language may have been derived
from a preserved trait, namely CP. Comparative studies in nonhuman animals
reveal that CP is phylogenetically anchored in early invertebrates and appears to
be a combination of nature and nurture (Zhang et al., 2021). The psychophysicalparadigm was also employed to investigate CP in nonhuman animals, and theparticipants were instructed to discriminate and categorize the stimuli, much as it
did in human research. We present examples of CP in the sound modality innonhuman animals.
Crickets have been observed to be able to distinguish between
communication calls and predator ultrasounds at a sharp border when it comes tonaturally produced noises (Wyttenbach et al., 1996). Female tungara frogs
respond to mating sounds in a categorical manner (Baugh, Akre & Ryan, 2008).
These findings imply that sound CP is linked to reproduction and survival. In
addition, early research looked at how nonhuman animals categorize humanspeech sounds, which demands a high level of auditory learning capacity.
Nonhuman primates were shown to be able to discriminate both consonants andvowels (e,g. Sinnott & Mosteller, 2001). Furthermore, the CP of sounds in birds
was studied from the standpoints of both conspecific calls and human speechsounds. It was discovered as early in the 1980s that budgerigars not only had alow discrimination frequency corresponding to their contact calls, but also had asimilar range of voice onset time to humans in the perceptual change of bilabial,
alveolar, and velar continua (Dooling et al. 1987). The budgerigars used the samecues as humans to distinguish between vowel groups (Dooling & Brown, 1990).
This shows that CP of speech sounds is related to auditory perception in general.
Moreover, the vocal learning continuum hypothesis, with the most widelydistributed species having auditory learning ability and a small number of species

812

having vocal production learning ability, is in some ways paralleled by the largerange distribution of categorical perception in sound modality in diverse animals(Jarvis, 2019).
How might CP of sounds be linked to the discreteness of language? To
perceive the world in a discrete way seems to be a prerequisite for discrete
production. Then how is the discreteness of sounds related to the discreteness oflexical items or words? In this context, one of the most intriguing and contentious
questions is how lexical items or words emerged in evolution. Two scenarios havebeen considered: One is that words evolved directly from animal communication;and the other one is that words evolved from some other cognitive ability. Some
researchers have defended that, if language developed from a propositional andholistic animal communication signal, there must have been a period whenhumans successfully extracted pieces from certain expressions that could bereused in other expressions (Tallerman, 2007), gradually forming
compositionality. Another possible scenario is that words developed throughvocal imitation of natural sounds. Word learning, according to Hauser et al.
(2002), necessitates the ability of vocal imitation, which has been observed in
nonhuman species such as songbirds (p. 1574). Recent research found thatgenerations of vocal imitation of some natural sounds can lead to word-like
outputs with category labels, implying that vocal imitation may be a major factor
in the emergence of first words (Edmiston et al., 2018). Returning to ourhypothesis that CP prepared the basis for discreteness, it has been shown that thediscreteness of sounds and the discreteness of words are inextricably intertwined.
When it comes to CP of speech sounds, or how to discriminate and categorize
speech sounds in a given language, it appears that sequential statistics manifested
in words (Transitional Probability reference), rather than acoustic features of thesounds, drive discrimination and categorization of the speech sounds (Saffran etal., 1996). Furthermore, data in infants suggest that perceptual statistical learning
also plays a key role in word segmentation from speech streams (Romberg &Saffran, 2010). In this sense, the key of discreteness offered at the phoneme levelappears to be influenced by how sound sequences in words are organized(Bidelman & Lee, 2015). This shows that discreteness may have been founded on
categorical perception.
4. Categorical perception and Discreteness: Neurocognitive considerations
Neurobiological studies on CP and discreteness of language also support our
proposal. Using birds as an example, HVCx (HVC is a letter-based term) cells inswamp sparrows have been demonstrated to respond robustly to auditory

813

categorical changes in note duration (Prather et al., 2009). In birds, HVC projects
to many brain regions, with HVCx being the projection to AreaX (striatal area x)
among others. HVC is a premotor nucleus that has been proposed to be analogous
to Broca's area in humans and serve sensory-motor functions (Prather et al., 2017).
As a result, HVCx could be analogous to the premotor-striatal connection, which
has been linked to beat perception in humans (Grahn & Rowe, 2009). Furthermore,
the auditory nuclei field L projecting to CLM (caudolateral mesopallium) andNCM (the caudal part of the medial nidopallium), both projecting to CMM (thecaudal part of the medial mesopallium), present an analogous hierarchy to humans
in which physical information is processed at the lower level while abstract
concepts are encoded at the higher level in European Starlings (Jeanne et al.,2011). It is worth noting that NCM and CMM, which are similar to human
auditory cortex, have also been documented for auditory memory (Bolhuis &Gahr, 2006). Moreover, Lampen et al. (2017) discovered that NCM and CMM inzebra finches are more responsive to rhythmic than arrhythmic songs, indicatingthat NCM and CMM are involved in auditory detection and discrimination.
The auditory cortex is also shown to be involved in CP of speech sounds inprimates. Spiking activity from the superior temporal gyrus (STG) in rhesus
monkeys were recorded (Tsunada et al., 2011). In humans, however, CP of sounds
was mediated by not only the STG and superior temporal sulcus (STS) (Harinen& Rinne, 2013), but also the premotor and primary motor cortex (Chevillet et al.,2013) comparable to those found in the NCM and CMM of birds. Furthermore,
Hickok & Peoppel (2004) proposed a dual-stream theory, with a ventral stream
dealing with phoneme and lexical recognition and lexical combinations, and adorsal stream dealing with sensorimotor transformation in speech output. The
dorsal stream connects the STG with the premotor cortex, both regions werereported to be where CP takes place. If CP is the basis for discreteness, then suchdorsal connection could have played a role in discreteness. The dorsal pathway
has been shown to be necessary for vocal imitation, which has been shown to be
one of the key factors for word emergence (Edmiston et al., 2018). In this sense,
discreteness seems to rely on CP in both phonological level, namely phonemes,
and syntactic level, namely words.
Beyond cortex, recent evidence has shown that the basal ganglia also relate
to cognitive functions beyond motor domain which includes perceptual
categorization (e.g., Ashby et al., 1998; Hochstenbach et al., 1998; Seger, 2008,
for reviews). Though basal ganglia-mediating category learning and speech
perception and learning have been largely studied independently, as Lim et al.
(2014) reviewed, these separate lines of research share commonalities and there814

is great potential in bridging efforts to understand speech perception and learningwith general cognitive neuroscience approaches and neurobiological models of
Learning. Due to the fact that speech perception required the integration of
acoustic cues across various dimensions, explicit attempts to the properties ofspeech are difficult. It has been shown that explicit/directed attention to acoustic
features are engaged in the process of directed categorization training (Logan etal., 1991). Further, in an fMRI study, Tricomi et al. (2006) showed that category
training of non-native speech categories engages the basal ganglia (i.e., thestriatum). These results suggest that the basal ganglia learning system are
involved in promoting adult speech category learning, which is the nurture partof CP. This is also in parallel with the finding in swamp sparrow mentioned abovethat Area X is also involved in CP of note duration.
5. Conclusion
In this paper, we come up with the hypothesis that categorical perception (CP)
could have laid the foundation for discreteness, one of the design features oflanguage. By reviewing comparative studies on CP in nonhuman animals andhumans, we found that CP has a phylogenetic root dating back to invertebrates
which is closely related to reproduction and survival, and seems to be a
combination of innateness and experience. In addition, by reviewing
neurobiological studies, we show that tasks of CP activate cortical and subcorticalareas including auditory and (pre)motor cortex as well as the basal ganglia, theconnection between which could be insightful for locating domain generaldiscreteness. The current work provides additional evidence for the important roleof sensory-motor system in language evolution.
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CROSS-LINGUISTIC EVIDENCE FOR DEPENDENCE
BETWEEN THE PERCEPTUAL DISPERSION AND SALIENCE
OF VOWEL SYSTEMSMENGHAN ZHANG*1,2,3,4 and TAO GONG5*Corresponding Author: mhzhang@fudan.edu.cn
1Institute of Modern Languages and Linguistics, Fudan University, Shanghai, China
2Ministry of Education Key Laboratory of Contemporary Anthropology,

Department of Anthropology and Human Genetics, School of Life Sciences,
Fudan University, Shanghai, China
3Key Innovation Group of Digital Humanities Resource and Research,
Shanghai Normal University, Shanghai, China
4
School of Foreign Languages, Zhejiang University of Finance & Economics,
Hangzhou, China
5Google, 111 8th Avenue, NY 10011

Investigating typological variations of languages worldwide and generalizing
their universality is an important research goal in linguistics. In general, theevolution of spoken language is carried out toward the functional goal of
improving communicative effectiveness between speakers and listeners. During
this process, several functional pressures from perceptual dispersion (Liljencrants
and Lindblom, 1972), perceptual salience (Schwartz et al., 2007), and articulatory
costs (Evans & Levinson, 2009; Hammarström, 2016; Chirkova & Gong, 2014)
have been proposed as the intrinsic driving forces to lead to the universal patternsof sound systems of human speech, especially vowel systems. Following theprinciple of clarity (Zuidema & de Boer, 2006), the dispersion theory states thatthe sound systems of human speech are shaped predominantly by perceptual
distinctiveness between sounds (Liljencrants and Lindblom, 1972). Moreover, thedispersion-focalization theory integrates both perceptual dispersion and salienceof vowels to investigate the general formation mechanism underlying the global
structural variation of vowel systems (Schwartz et al., 2007). Moreover, both
perceptual dispersion and salience of vowels can be measured based on theformant frequencies of vowels which are related to the tongue movements andThis paper is distributed under a Creative Commons BY-NC-ND 3.0 license.

818

opening/closing of the jaw (Oudeyer, 2006; Bradlow, 1995). In addition, theQuantal theory (Stevens, 1972) and the articulatory complexity theory (Lindblom& Maddieson, 2006; Kingston, 2007) characterize articulatory difficulty in speech
production where the auditory pressures of maximizing perceptual distance andminimizing articulatory effort cohesively shape the articulatory subspace of asound system.
Despite these theories being proposed independently, they imply the
association between perceptual dispersion and salience under articulatory effort
minimization. In this study, we conduct a cross-linguistic comparison using aworldwide phonetic database (Becker-Kristal, 2010). The database contains
formant frequencies of each vowel for 532 individual samples covering 357
languages from 36 language families. We adopt two properties named Effective
Dispersion estimate (EDE) and Focalization estimate (FE) to capture thestructural crowdedness of a vowel system and the spectral salience of intra-vowel
sounds, respectively. EDE is specifically a composite property of the classic
Dispersion estimate divided by articulatory space area. All properties arecalculated on the psychoacoustic domain.
Using a linear mixed effects model (Quené & Bergh, 2008), we identify thatthe association between perceptual dispersion and salience follows a power-lawbased dependence across vowel systems of worldwide languages. We also
demonstrate that such dependence was a language-universal tendency,
independent of geographic regions, language families, and linguistic affiliations.
Using the phylogenetic methods such as PIC (Felsenstein, 1985; Paradis, 2012)
and PGLS (Paradis, 2012), we take Indo-European languages as an example toelaborate that the dependence resulted from correlated evolutions of the two
structural properties. The results show the correlated evolution could follow anadaptation process under the stabilizing selection pressures. These results also
indicate that the evolution of vowel systems could be a dynamic process ofadaptive organization of their structures and optimization capacities. The
phylogenetic analysis also shows that the dependence should proceed in apunctuated equilibrium. In other words, the correlated evolution of EDE and FE
is not a gradual process. It suggests an evolution scenario: vowel systems tend to
slowly accumulate structural changes till reaching a threshold, and then, the wholesystem is rapidly restructured. To extend the scope of early explanations oflanguage universals, we provide our understanding of these findings from four
aspects: language change dynamics, self-organizing criticality of systemcomplexity, and the principle of the least effort in human behavior.

819

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Author Index
Abner, Natasha, 168

Bi, Yanchao, 469

Adornetti, Ines, 220

Bickel, Balthasar, 91, 101, 278,

Aerts, Anne, 770

302, 495

Akita, Kimi, 325

Blasi, Damián E., 413, 669

Alinam, Zahra, 742

Blundell, Zachary, 203

Allassonnière-Tang, Marc, 112,

Blythe, Richard, 256

624

Boeckx, Cedric, 2Altché, Florent, 109

Boehm, Irene, 84

Altenhof, Aja, 28

Boruta, Monika, 87, 672

Anselme, Rémi, 31

Boschker, Remko, 498

Arias, Pablo, 558

Bosshard, Alexandra, 91

Arita, Takaya, 699

Bouckaert, Remco, 413

Arnon, Inbal, 34, 450

Bouziane, Siham, 69

Asano, Rie, 736

Bowman, Jacob, 606

Atanasova, Tanja, 445

Boë, Louis-Jean, 447

Audebert, Nicolas, 447

Brinkmann, Levin, 536
Brochhagen, Thomas, 583

Badihi, Gal, 185, 776

Burchardt, Lara Sophie, 217

Bailes, Rachael, 36

Burgess, Danielle, 95

Barbieri, Chiara, 579

Burkart, Judith Maria, 11, 91

Barceló-Coblijn, Lluís, 39
Baumann, Andreas, 47, 50, 528
Bayne, Ted, 58

Cahuana, Candy, 78

Becker, Yannick, 69

Cai, Zhenguang, 262

Beeck, Veronika C., 72

Call, Josep, 681

Bellmann, Oliver Tab, 75

Cambier, Nicolas, 98

Benetos, Emmanouil, 569

Caparos, Serge, 368

Beniamine, Sacha, 635

Capirci, Olga, 678

Benítez-Burraco, Antonio, 78,

Carlson, Daryll, 154

81, 98, 220, 606, 764

Carugati, Filippo, 229

821

Cathcart, Chundra, 101, 351,

Ellison, T. Mark, 189, 203

579, 624

Erben Johansson, Niklas, 192

Chaabouni, Rahma, 109

Esher, Louise, 635

Chacon, Thiago, 112

Espree-Conaway, Deandre, 194

Chandia, Bosco, 456

Everett, Caleb, 196

Chen, Sihan, 78
Cheung, Ching Yat, 115

Fabbroni, Claudio, 770

Childers, Jane, 123

Fallon, Brittany, 650

Clay, Zanna, 13

Fay, Nicolas, 203

Coulson, Seana, 453

Fedurek, Pawel, 681

Coupé, Christophe, 115

Feeney, Andrew, 206

Cronhamn, Sandra, 192

Ferdinand, Vanessa, 209

Cross, Catharine, 650

Fernandez, Ahana Aurora, 217

Culbertson, Jennifer, 131, 297

Ferrer-i-Cancho, Ramon, 650

Cuskley, Christine, 36, 134, 390,

Ferretti, Francesco, 220
Fishbein, Adam, 174

685

Fitch, W. Tecumseh, 259, 492
Danel, Dariusz, 573

Flaherty, Molly, 168, 223

Darquenne, Chantal, 196

Flaksman, Maria, 721

Davis, Emily, 143, 405

Friard, Olivier, 229

Davoodi, Elnaz, 109

Fryns, Caroline, 653

de Boer, Bart, 151, 157, 525

Fröhlich, Marlen, 226

De Gregorio, Chiara, 229

Fujita, Koji, 16

De Kleine, Naomi, 203

Fujiwara, Masayuki, 566

de Reus, Koen, 154
de Vos, Connie, 525, 539

Gallot, Quentin, 506

Dediu, Dan, 31, 368

Gamba, Marco, 229

Dekker, Peter, 157

Garcia, Maxime, 154

Dezecache, Guillaume, 681

Garde, Murray, 203

Dingemanse, Mark, 160

Garrod, Oliver G. B., 558

Do, Monica, 168

Gast, Volker, 275, 669

Dona, Loïs, 171

Giacoma, Cristina, 229

Dooling, Robert, 174

Gil, David, 78

Droppa, Christopher, 770

Ginsburg, Jason, 237

Du, Fiona, 354

Giraud, Anne-Lise, 579

Dunn, Michael, 357

Goldin-Meadow, Susan, 168,
203, 223

Déaux, Éloïse, 579

Gong, Tao, 817
Eberhardt, Franziska, 770

Gopalkrishnan, Akila, 123

Ekström, Axel G., 177

Graham, Kirsty, 245, 272

Eleuteri, Vesta, 185

Grandjean, Didier, 579

822

Gray, Russell D., 399, 413, 669

Ihara, Yasuo, 20, 711

Green, Kirsty, 248

Iizuka, Hiroyuki, 317

Greenhill, Simon J., 399, 413,

Ikebuchi, Maki, 788

669

Imai, Mutsumi, 325

Gross, Stephanie, 154

Inoue, Naoki, 791

Grosz, Patrick, 272

Inoue, Yoichi, 333

Guerrero Montero, Juan

Irurtzun, Aritz, 340

Antonio, 256

Irvine, Elizabeth, 616

Gutowski, Adam, 489, 672

Ishizuka, Masayuki, 348

Gökgöz, Kadir, 387

Ivani, Jessica K, 351

Hagoort, Peter, 294

Jack, Rachael E., 558

Haiduk, Felix, 259

Jacobson, Sarah L., 606

Hashimoto, Takashi, 19, 566,

Jee, Hana, 354

736

Jing, Yingqi, 357

Haslett, David, 262

Johansson, Sverker, 360

Hayashi, Misato, 270

Jordan, Fiona, 616

Haynie, Hannah J., 669

Josserand, Mathilde, 368

Hedvig, Skirgård, 669
Heesen, Raphaela, 650

Kabbach, Alexandre, 371

Heilmann, Gunnar, 72

Kali, Aniket, 379

Heim, Johannes, 134

Kan, U§ur, 387

Heintz, Christophe, 656

Karjus, Andres, 256, 390

Henderson, Matthew, 185, 272

Kataoka, Hiroki, 566

Herbelot, Aurelie, 371

Katsu, Noriko, 393

Herbst, Lina, 275

Kauhanen, Henri, 396

Herce, Borja, 101, 278

Kazuo, Okanoya, 613

Hermann, Aymeric, 399

Kidd, Celeste, 469

Hickok, Gregory, 281

King, Benedict, 399

Hidaka, Shohei, 725

Kirby, Simon, 131, 168, 297,
402, 685, 714

Hillert, Dieter, 285
Hirata, Soichiro, 699

Kita, Sotaro, 325

Hobaiter, Catherine, 185, 245,

Klamer, Marian, 157
Kluender, Robert, 405

272, 632, 650, 776
Hoeksema, Nienke, 294

Knörnschild, Mirjam, 217

Hofmann, Klaus, 47

Kobayashi, Harumi, 22Holtz, Annie, 297

Koda, Hiroki, 517

Hosaka, Michio, 300

Koile, Ezequiel, 413

Huber, Eva, 302

Kol, Kayla, 416

Huisman, John, 306

Kondoh, Sotaro, 556

Hussain, Shumon T., 309

Konno, Takeshi, 424

823

Korotaevskaya, Elizaveta, 721

Mann, Stephen Francis, 635

Koshevoy, Alexey, 427

Manser, Marta, 773

Kouwenhoven, Tom, 434

Manseld, John, 189

Krahmer, Emiel, 539

Margiotoudi, Konstantina, 475

Kratschmer, Alexandra R., 309,

Martins, Pedro Tiago, 478
Martí, Louis, 469

437
Kreiliger, Giuachin, 495

Mathewson, Kory, 109

Kuroyanagi, Jiei, 569

Matsumoto, Daiki, 481

Kühnert, Denise, 275

Matzinger, Theresa, 489, 492
Mazara, Jekaterina, 495, 688

Laganaro, Marina, 445

Mbeju Moreno, Alvaro, 643

Lalani, Sneh, 123

McBride, John, 569

Lameira, Adriano, 514

McLean, Bonnie, 306

Lameira, Adriano R., 776

Meguerditchian, Adrien, 69

Lancheros, Monica, 445

Merlo, Paola, 91, 302

Laporte, Marion, 447, 653

Meyer, Martin, 562

Lavi-Rotbain, Ori, 450

Miaretsoa, Longondraza, 229

Lavitskaya, Yulia, 721

Miceli, Luisa, 189

Lazaridou, Angeliki, 109

Michaelis, Susanne Maria, 669

LeFloch, Auriane, 506

Miletitch, Roman, 98

Leonardis, Eric, 453

Mills, Gregory, 498, 500

Leroux, Maël, 456

Minamii, Marika, 503

Lev-Ari, Shiri, 458

Miton, Helena, 427

Lewandowski, Mariusz, 489, 672

Moran, Steven, 506, 514

Lewandowski, Zdzisªaw, 573

Mori, Chihiro, 788

Liesenfeld, Andreas, 160

Morin, Olivier, 427

Lieven, Elena, 688

Morita, Junya, 791

Linden, Josh, 461

Morita, Takashi, 517

Lindsay-Smith, Emily, 635

Motamedi, Yasamin, 402, 539

Lipowska, Dorota, 465

Mudd, Katie, 525

Lister, Casey, 203

Muhumuza, Geresomu, 681

Liu, Ella, 469

Mühlenbernd, Roland, 50, 528

Liu, Fang, 569

Müller, Andrea, 773

Lowry, Alice, 154

Müller, Thomas, 536

Lupyan, Gary, 469, 691, 748
Lyn, Heidi, 472, 675

Naegeli, Danielle, 539

Lynch, Vincent J., 606

Nagy, Martina, 217
Nakajima, Kensuke, 270

Maaser, Anna, 770

Nakano, Ryosuke, 424

Maeda, Akihiro, 725

Nakata, Seiya, 542

Maiolini, Marco, 506, 514

Namatame, Miki, 325

824

Namboodiripad, Savithry, 223

Pitarch, Lucía, 583

Nasledov, Andrey, 721

Placi«ski, Marek, 87, 489, 601,
705

Nasukawa, Kuniya, 550

Pleyer, Michael, 586, 590, 708,

Nedelcu, Vlad, 553

767

Ng, Yen, 601
Nitta, Jun, 556

Pleyer, Monika, 590

Noguchi, Wataru, 317

Plotnik, Joshua M., 606

Norvik, Miina, 357

Podlipniak, Piotr, 598

Nölle, Jonas, 558

Poniewierska, Aleksandra, 601,

Oderbolz, Chantal, 562

Preston-Kendal, Daphne, 770

Ohba, Masato, 325

Progovac, Ljiljana, 78, 81, 220

Ohira, Akari, 542

Proutskouva, Polina, 569

Okanoya, Kazuo, 3, 333, 393,

Pulvermüller, Friedemann, 475

705, 708

503, 517, 556, 702, 745,
Raimondi, Teresa, 229

788
Okuda, Jiro, 566

Rapha, Emilie, 69

Onohara, Ayaka, 711

Ravignani, Andrea, 154, 229

Ota, Ryusei, 424

Raviv, Limor, 25, 98, 606

Ozaki, Yuto, 569

Redeker, Gisela, 500

Ozyurek, Asli, 678

Reifegerste, Jana, 78
Renaud, Luc, 69

Padden, Carol, 6

Reznikova, Zhanna, 609

Patel, Aniruddh, 23

Riseru, Koshiishi, 613

Patel-Grosz, Pritty, 272

Ritt, Nikolaus, 84, 492

Peeters, David, 539

Roberts, Gareth, 28Pellegrino, François, 31, 368

Roberts, Sean, 616

Penn, Gerald, 379

Rochant, Neige, 624

Perea Garcia, Juan, 573

Rodrigues, Evelina Daniela, 632

Pereira, André, 776

Rolnik, Marc, 405

Pereira, Joana, 632

Round, Erich, 635

Perlman, Marcus, 248, 576

Rubio-García, Ana, 154

Pescetelli, Niccolò, 536

Ryabko, Daniil, 643Pug, Maurice, 770
Pfordresher, Peter, 569

Safryghin, Alexandra, 650

Phelipon, Romane, 69

Saito, Yumi, 503

Piantadosi, Steven, 469

Saji, Noburo, 325

Piette, Theophane, 579

Salazar-Casals, Anna, 154

Pijpops, Dirk, 806

Saldana, Carmen, 278

Pika, Simone, 8, 416

Santos, António José, 632

Piot, Bilal, 109

Sasaki, Taiki, 317

825

Szczepa«ska, Aleksandra, 601,

Savage, Patrick, 569

705, 708

Schel, Anne, 456

Sümer, Beyza, 387

Schick, Johanna, 653
Schouwstra, Marieke, 171, 539

Tachibana, Ryosuke O., 517,

Schyns, Philippe G., 558

556, 788

Scott-Phillips, Thom, 656
Sedelkina, Yulia, 721

Takahashi, Takuya, 711

Seifert, Marva, 196

Takamura, Maki, 270

Sein, Julien, 69

Takezawa, Masanori, 542
Tallec, Corentin, 109

Seki, Yoshimasa, 659

Tamariz, Monica, 354, 714

Semenuks, Arturs, 453, 661, 665

Tamer, Nicole, 717

Senghas, Ann, 168

Tamyürek, Elvan, 387

Shaman, Kai, 770

Tanabe, Keiko, 270

Shcherbakova, Olena, 669

Tanaka, Shin-ichi, 550

Shi, Edward Ruoyang, 809

Tarassov, Eugene, 109

Shillcock, Richard, 354

Tatarinova, Tatiana, 78

Shufaniya, Amir, 34

Teghipco, Alex, 281

Sibierska, Marta, 87, 601, 672

Tieleman, Olivier, 109

Sims, Greyson, 453

Tierney, Adam, 569

Sinun, Waidi, 333

Tkacheva, Liubov, 721

Slade, Jonathan, 196

Torii, Takuma, 725

Slagle Luenser, Rebecca, 675

Torti, Valeria, 229

Slocombe, Katie, 456, 776

Towns, Ross J., 434, 733

Slonimska, Anita, 678

Townsend, Simon W., 91, 456,

Smith, Kenny, 143, 256, 402,

653

553

Toya, Genta, 736

Soldati, Adrian, 185, 506, 681,

Trzeciakowska, Julia, 739

776

Tu, Vicky, 174

Sommer, Kees, 685

Tumminello, Ryan, 196

Specker, Eva, 492

Tylén, Kristian, 742

Spike, Matt, 635
Stark, Elisabeth, 562

Uhlich, Victoria Charlott, 770

Stennette, Katie, 174

Umeda, Yusuke, 270

Stoeger, Angela S., 72

Umemoto, Sachio, 745

Stoll, Sabine, 495, 653, 688

Uno, Ryoko, 348

Strub, Florian, 109Sull, Ellise, 691

Valente, Daria, 229

Suzuki, Reiji, 699

van de Weijer, Joost, 672

Suzuki, Yuka, 702

van Paridon, Jeroen, 469, 691,

Szala, Anna, 705

748

826

van Schaik, Carel P., 226, 653

Wu, Chieh-Hsi, 306

Van Soom, Marnix, 525

Wu, Yichen, 558

van Trijp, Remi, 756

Wu, Zhijun, 780

Vasileva, Olga, 764, 767
Velly, Lionel, 69

Yakpo, Ko, 115

Venezia, Jonathan, 281

Yamada, Takemi, 270

Veppo, Flávia, 632

Yamamoto, Masahito, 317

Verhoef, Tessa, 434, 685, 733

Yanagihara, Shin, 702, 745, 788

Vernes, Sonja, 294

Yano, Soma, 791

Vesakoski, Outi, 357

Yates, Mark, 472
Yoshikawa, Masato, 799

Vesper, Cordula, 742

Yosida, Shigeto, 333

von Heiseler, Till Nikolaus, 770

Young, Marissa, 123

Wacewicz, Sªawomir, 489, 573,

Zahn, Thiago Macek Gonçalves,

601, 705, 708, 739

802

Walker, Bradley, 203

Zakharko, Taras, 351

Wallenberg, Joel, 36

Zehentner, Eva, 806

Walworth, Mary, 275, 399

Zhang, Elizabeth Qing, 586,

Wamberg, Jacob, 437

739, 809

Warkentin, Emily, 123

Zhang, Menghan, 817

Watson, Stuart, 773

Zlatev, Jordan, 672

Wegdell, Franziska, 653

Zuberbühler, Klaus, 185, 456,

Widmer, Paul, 717

506, 653, 681, 776

Wilke, Claudia, 456
Wiltshire, Charlotte, 776

Özyürek, Asl, 387

Winters, James, 536
Woensdregt, Marieke, 160

ywiczy«ski, Przemysªaw, 87,

Wolters, Lucie, 402

489, 601, 672, 708

827

In this proceedings, we collected 195
papers and abstracts from the Joint
Conference on Language Evolution(JCoLE), held in Kanazawa, Japan, in
September 2022.
.
Scientiﬁcresearchonlanguageevolution has been making remarkableprogress for some decades now,
leading to a rapid growth ofinterdisciplinary research communities
withdiversethematic
andmethodological foci. In 2022, for the ﬁrst
time, we held a joint conference coorganized by Evolang, Protolang, andEvolinguistics. This conference provided
an unprecedented opportunity to bring
together all the language evolution
research communities, enabling a
global platform for interdisciplinary
discussion.