Semantic organisation and generalisation mechanisms of
abstract concepts

Dounia Lakhzoum

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To cite this version:

Dounia Lakhzoum. Semantic organisation and generalisation mechanisms of abstract concepts. Psychology. Université Clermont Auvergne; Laboratoire de Psychologie Sociale et Cognitive (LAPSCO), 2021. English. NNT: 2021UCFAL018. tel-03879839

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Université Clermont Auvergne, U.F.R. Psychologie, Sciences Sociales et Sciences de l'Éducation

Laboratoire de Psychologie Sociale et Cognitive (LAPSCO) – CNRS, UMR 6024

École Doctorale Lettres, Sciences Humaines et Sociales

# THÈSE DE DOCTORAT

## Semantic organisation and generalisation mechanisms of abstract concepts

Présentée par LAKHZOUM Dounia,

En vue de l'obtention du titre de Docteur en Psychologie.

### Sous la direction de :

**Marie IZAUTE** Professeure des Universités, Université Clermont Auvergne, Clermont-Ferrand

**Ludovic FERRAND** Directeur de Recherche CNRS, Université Clermont Auvergne, Clermont-Ferrand

### Membres du jury :

<table><tr><td>Simona BRAMBATI</td><td>Professeure de Psychologie, Université de Montréal</td><td>Rapporteure</td></tr><tr><td>Marc BRYSBAERT</td><td>Professeur de Sciences Cognitives, Ghent University</td><td>Rapporteur</td></tr><tr><td>Diane PECHER</td><td>Professeure de Psychologie, Erasmus University, Rotterdam</td><td>Examinatrice</td></tr><tr><td>René ZEELENBERG</td><td>Professeur Associé de Psychologie, Erasmus University, Rotterdam</td><td>Examineur</td></tr></table>
Abstract

The study of semantic representation of abstract concepts entails answering some
fundamental questions such as: (1) How is the meaning of the word represented? (2) How
does it relate to other words whether similar or associated? (3) How does meaning relate to
conceptual structure? (4) How is the meaning abstracted and generalised to all instances of the
concept?

In order to answer such question, researchers need to have access to normed stimuli.
The literature on abstract concepts still lacks from the availability of such stimuli. The first
experimental chapter (Chapter 2) introduces a database of semantically similar pairs of French
words with varying levels of abstractness. This database is to the best of our knowledge and at
the time of writing this manuscript, the first to introduce semantically similar pairs of abstract
words in the French language. Chapter 3 introduces another database for word associations
between concepts using the same words as in the previous database. Correlation analyses
revealed that cue words presented to the participants elicited response words of a similar level
of concreteness meaning that concepts are organised in the mental lexicon according to a
gradient of concreteness. Analyses from associative strength have shown that concrete
concepts elicit stronger associations compared to abstract concepts. The large amount of data
generated by the word association task allowed for the implementation of mathematical graph
analyses. Consequently, Chapter 4 introduces the first semantic network built from French
association data and the first to compare topological parameters for concrete and abstract
concepts. Results have shown that the French mental lexicon is structured according to a
small-world pattern characterised by a sparse density, a short average path length between
nodes and a high clustering coefficient. Comparison analyses between the networks for
concrete and abstract networks respectively have shown that concrete concepts are organised
in denser communities compared to abstract concepts. In addition, the concrete word nodes
are more influential in the network and can spread information better due to their position and
patterns of connectivity.

In Chapter 5, we explored the richness of abstract concepts in a picture-word priming
paradigm and compared the role of situational and intangible features in their processing.
Results have shown that even when compared with situational picture primes, extraction
mechanisms still occurred in the case of abstract pictures. We interpreted these results as
further evidence of the richness of abstract concepts which can be composed from features
other than lexical or situational, namely also abstract and intangible. These intangible features
could be construed as evidence of abstracted representations from varied exemplars of
concepts becoming generalised statistical traces in long-term memory. In Chapter 6, we
investigated the effect of similarity and contextual diversity on the ability to generalise the
meaning of abstract concepts to all instances. Results have shown that exposure to
contextually diverse exemplars enhanced performance in the testing phase compared to
similarity-based exposure.
We conclude this thesis by proposing a theoretical model based on our findings with the purpose of bridging the gap between levels of investigation of abstract concepts processing. Taken together, our findings describe a reversed pattern of processing with lower levels ruled by similarity-based processes while higher levels of processing are ruled by diversity-based mechanisms.

**Key-words:** abstract concepts processing, semantic similarity, verbal association, semantic network analysis, picture-word priming, abstraction, generalisation mechanisms.
Résumé – Long summary in French

L'étude de la représentation des concepts a depuis longtemps été centrale au domaine de la psycholinguistique. Si cette étude a longtemps été centrée sur les mécanismes de représentation des concepts concrets tels que des entités vivantes et des objets non vivants mais tangibles (e.g., chat, table), l'étude des concepts abstraits (e.g., justice, liberté) n'a bénéficiée que depuis récemment d'un regain d'intérêt. Cette étude de la représentation des concepts en général demande de répondre à des questions de recherche fondamentale telles que (1) quelles sont les modes de représentation des concepts en mémoire ? (2) Comment les mécanismes de similarité et d'association verbale influent-ils sur la représentation des concepts dans le lexique mental ? (3) En quoi cette organisation du lexique mental est influencée par les caractéristiques structurelles du réseau qui leur sert de matrice ? (4) Comment le sens est-il généralisé à tous les exemples d'application d'un concept ?

L'étude de phénomènes psycholinguistiques repose sur l'emploi de stimuli normés afin de
garantir le contrôle de potentielles variables confondues telles que la fréquence ou la longueur
des mots. En effet, il existe en psycholinguistique, un intérêt important pour les normes de
similarité en raison de leur utilisation dans des paradigmes d'amorçage sémantique qui
permettent l'étude de l'organisation des concepts dans le lexique mental. Par exemple pour la
langue anglaise, McRae et al. (2005) ont développé une base de données de traits sémantiques
pour 541 concepts représentant des entités vivantes et objets concrets. Vinson et Vigliocco
(2008) ont proposé une base de données similaire pour des objets concrets et verbes d'action.
Baroni (2011) a collecté les propriétés sémantiques de concepts relationnels en Allemand et
en Italien. En Espagnol, Moldovan et al. (2015) ont proposé 185 triplets de noms avec
variation de distance sémantique entre les membres d'un même triplet. L'une des principales
observations que nous avons pu faire concerne le manque de disponibilité de stimuli normés
pour l'étude rigoureuse des concepts abstraits dans la langue française. Au moment de
l'écriture de ce manuscrit, nous proposons la première base de données de paires de mots
abstraits sémantiquement liés en Français. Dans l'objectif de répondre à ce manque dans la
littérature, nous avons commencé par proposer une base de données de 630 paires de concepts
sémantiquement liées qui varient selon un gradient de concrétude de très abstraits à très
concrets dans 2 études indépendantes (voir Chapitre 1 partie expérimentale).

Nous avons fait le choix de joindre cette variable de concrétude afin de répondre aux
observations de Crutch et Warrington (2011) qui suggèrent que le lexique mental est organisé
selon un gradient de concrétude avec une organisation en fonction de la similarité sémantique
pour les concepts concrets tandis que les concepts abstraits seraient organisés selon
l'association verbale. La similarité sémantique signifie que les concepts d'une même paire
possèdent une signification proche (e.g., table-chaise) tandis que des concepts sont associés
verbalement lorsqu'ils sont fréquemment rencontrés ensemble dans le langage (e.g., miel-
abeille). Les mesures de similarité sémantique ont été collectées auprès de 900 participants
dans une tâche de jugement pour laquelle les participants devaient répondre sur une échelle en 7 points. Nos données ont par la suite été croisées avec des bases de données de psycholinguistique française permettant aux utilisateurs de contrôler les potentielles variables confondues. Pour cela, nos données ont été croisées avec Lexique (New et al., 2004), the French Lexicon Project (Ferrand et al., 2010), Wordlex (Gimenes & New, 2016), and MEGALEX (Ferrand et al., 2018). Notre variable de concrétude peut être utilisée sous deux formats : continu ou catégoriel. La variable de concrétude continue représente les moyennes des jugements des participants donnés sur l'échelle en 7 points. Pour obtenir une variable catégorielle de la concrétude, nous avons implémenté un algorithme k-means (Wang & Song, 2011) qui a mis en évidence une organisation des paires sémantiques selon 3 clusters de concrétude. Les paires sémantiques peuvent ainsi être filtrées par l'utilisateur en fonction de 3 niveaux de concrétude de très abstraites à très concrètes.

Dans une étude d'amorçage sémantique avec décision lexicale, on présente au participant un mot d'amorce suivi d'un mot cible (Meyer & Schvaneveldt, 1971 ; McNamara, 1992 ; Plaut, 1995). La tâche des participants consiste à déterminer si la cible est un mot. L'effet d'amorçage sémantique est un phénomène très robuste qui a pu être répliqué des centaines de fois et qui correspond à une facilitation de traitement caractérisée par des temps de réponse plus courts et un pourcentage d'erreurs plus faible lorsque l'amorce et la cible sont reliées. Ce lien peut être sémantique (poney-cheval) ou d'association verbale (selle-cheval ; Hutchison et al., 2008, 2013). De précédentes études ont mis en évidence l'importance de strictement différencier entre ces deux types de lien et ont montré qu'il est possible d'obtenir un amorçage purement sémantique non-associé ou purement associé non sémantique (McRae & Boisvert, 1998 ; Perea & Rosa, 2002 ; Thompson-Schill, Kurtz, & Gabrieli, 1998 ; Lucas, 2000 ; Hutchison, 2003 ; Ferrand & New, 2003). L'application de cette stricte distinction a été empiriquement vérifiée par Ferrand et New (2003) qui ont obtenu un effet d'amorçage sémantique pur en utilisant des paires sémantiquement liées sans association verbale (e.g., <baleine> et <dauphin> sont sémantiquement liés mais non associés verbalement).

Afin de répondre à cette nécessité de distinguer entre ces deux formes de lien entre les concepts, nous avons proposé une seconde base de données d'association verbale qui repose sur les mêmes mots utilisés pour former la précédente base de données de paires sémantiques. De la même façon, cette base de données d'association verbale regroupe les réponses des participants pour 1100 mots cibles isolés variant selon un degré de concrétude de très abstrait à très concret. Les réponses des participants ont été collectées dans une tâche d'association verbale dans laquelle on présente un mot cible au participant qui doit répondre avec le premier mot qui lui vient à l'esprit. Cette tâche à l'avantage d'être très économique dans son implémentation et de pouvoir générer un très grand nombre de données. Nous avons ainsi pu obtenir plus de 92 000 mots réponses sur la base de 1100 mots cibles et 1200 participants recrutés en ligne. Ces données permettent dans un premier temps d'obtenir un pourcentage d'association entre le mot cible et les réponses données par les participants. Plus une réponse a été donnée fréquemment et plus elle est fortement associée au mot cible. Dans un premier temps, cette variable d'association verbale permet un croisement avec la précédente base de données de similarité sémantique pour assurer la création de matériel purement sémantique, purement associé ou au contraire avec un recouvrement entre ces deux types de lien pour les chercheurs qui souhaiteraient étudier l'effet d'un tel recouvrement.
Deuxièmement, cette base de données d'association verbale permet par l'introduction de la concrétude, d'étudier l'organisation du lexique mental pour les concepts abstraits comparativement aux concepts concrets. Ainsi, à partir de ces données, nous avons pu établir que les mots cibles abstraits génèrent des réponses plus faiblement associées comparativement aux concepts concrets. Cela confirme la théorie de Paivio (1971) selon laquelle les concepts abstraits sont plus faiblement associés à un grand nombre de contextes tandis que les concepts concrets sont plus fortement associés à un nombre restreint de contextes.

De plus, les résultats ont révélé une forte corrélation du niveau de concrétude entre mots cibles et réponses générées par les participants. Ainsi, les mots cibles abstraits présentés aux participants génèrent des réponses abstraites. De la même façon, les mots cibles concrets génèrent des réponses concrètes. Ces résultats suggèrent une organisation du lexique mental selon un gradient de concrétude avec une proximité des concepts abstraits d'une part et des concepts concrets d'autre part.

Enfin, la quantité de données générées nous a permis d'employer des méthodes empruntées à la théorie des graphes en mathématiques afin de construire le premier réseau sémantique d'associations verbales françaises et le premier réseau comparant l'organisation structurale des concepts abstraits et concrets toutes langues confondues. Cet emprunt de techniques à la théorie des graphes en mathématiques a donné lieu à un nouveau champ disciplinaire appelé science des réseaux cognitifs (Baronchelli, Ferrer-i-Cancho, Pastor-Satorras, Chater, & Christiansen, 2013 ; voir Siew, Wulff, Beckage, & Kenett, 2019 pour une revue de la littérature). Cette discipline appliquée à l'étude du langage, permet de modéliser l'organisation structurelle du lexique mental sur une plus grande échelle comparativement à ce qu'il est possible de faire à partir de données comportementales obtenues sur la base de plans factoriels. Un réseau sémantique correspond à un graphe composé de nœuds liés entre eux par des liens qui indiquent la direction et la force de la connexion entre deux nœuds. Dans un réseau sémantique du lexique mental, les nœuds représentent des concepts (mots cibles et réponses des participants dans une tâche d'association verbale) et les liens indiquent une correspondance entre un mot cible et ses réponses. Ces liens sont également caractérisés par des poids qui représentent la fréquence d'association entre un mot cible et chacune de ses réponses. Plus une réponse est générée fréquemment par un grand nombre de participants et plus le poids du lien qui relie le mot cible à la réponse est élevé. L'analyse des réseaux sémantiques repose sur des paramètres d'intérêts tels que :

- Le diamètre du réseau qui correspond au chemin le plus long (en nombre de nœuds) qui relie deux nœuds du réseau ;

- Le chemin le plus court qui correspond au plus petit nombre de nœuds à parcourir pour passer d'un nœud à l'autre ;

- Le coefficient de clustering qui correspond à la probabilité que deux nœuds reliés entre eux possèdent également un nœud voisin en commun ;

- La densité du réseau qui correspond au nombre total de liens au sein du réseau. De précédentes études ont démontré par l'application de la science des réseaux aux données d'associations verbales que le lexique mental est caractérisé par une structure en « petit monde » ou « small-world » (Motter et al. 2002 ; Sigman & Cecchi 2002 ; de Jesus et al. 2004 ; Steyvers & Tenenbaum 2005 ; Bales & Johnson 2006 ; Borge-Holthoefer & Arenas 2010 ; Choudhury & Mukherjee 2009 ; Fukś & Krzemiński 2009 ; Veremyev, Semenov,
Pasilio & Boginski, 2019). Ce type de structure est caractérisé par une faible densité, un chemin plus court composé de peu de nœuds et un fort coefficient de clustering. Cela signifie que le lexique mental est structuré en clusters de nœuds fortement condensés qui permettent une distribution efficace de l'information (Borge-Holthoefer, Moreno, & Arenas, 2012 ; De Deyne et al. 2016). Notre étude a ainsi permis de construire le premier réseau sémantique du lexique mental français et de mettre en évidence sa structure en petit monde. Nous avons par la suite divisé ce réseau selon un gradient de concrétude avec un sous-réseau composé des concepts abstraits et un autre sous-réseau composé des concepts concrets. La comparaison des paramètres des deux réseaux suggère que le réseau des concepts abstraits est moins dense et plus diffus comparativement au réseau des concepts concrets. Une analyse supplémentaire des scores de diffusion de l'activation dans un réseau (voir Collins & Loftus, 1975) a démontré que les concepts abstraits ont un score de diffusion plus faible que les concepts concrets. Ces éléments permettent de mettre en évidence à un niveau structural des éléments de preuve selon lesquels les concepts abstraits sont organisés dans un pattern plus diffus comparativement aux concepts concrets. Ces éléments sont en accord avec de précédentes observations quand au déficit de traitement des concepts abstraits comparativement aux concepts concrets avec des concepts abstraits caractérisés par une lenteur de traitement et une apparente pauvreté de contenu dans les tâches de production de traits sémantiques (voir Recchia & Jones, 2012). Ceci coïncide également avec la proposition de Paivio (1971) selon laquelle les concepts abstraits sont faiblement connectés à une multitude de contextes tandis que les concepts concrets sont plus fortement connectés à un petit nombre de contextes.

Cette disparité structurelle dans l'organisation du lexique mental pour les concepts abstraits pose la question de la nature des traits sémantiques qui les composent et de leur mode de représentation (Barsalou, 2003 ; Borghi et al., 2017 ; Pecher, 2018 ; Lakhzoum et al., 2021). En effet, cette structure organisée dans un pattern plus diffus confirme la suggestion selon laquelle il n'existe pas de correspondance parfaite entre un concept abstrait et son référent. Les concepts abstraits font généralement référence à une multitude de situations complexes. Cette particularité des concepts abstraits a longtemps représenté un challenge pour l'étude de leur représentation d'un point de vue cognitif et cérébral dans la mesure où ils ne correspondent pas à des entités tangibles. Certaines études ont démontré que les concepts abstraits sont représentés dans des modalités situationnelles, émotionnelles, sociales, introspectives ou encore métacognitives (voir Borghi et al., 2017 pour une revue). De façon plus générale, ces études ont mis en évidence l'importance du contexte dans la représentation des concepts abstraits. D'autres part, plusieurs études ont exploré la représentation des concepts abstraits dans des tâches de production de listes de traits sémantiques (Barsalou & Wiemer-Hastings, 2005 ; Borghi et al., 2018 ; Kousta et al., 2011 ; Ferretti et al., 2001). Ces études suggèrent que les concepts abstraits peuvent eux-mêmes se décomposer en traits sémantiques abstraits. Pris ensemble, ces résultats semblent suggérer des implications contradictoires quand au mode de représentation des concepts abstraits avec certaines études suggérant un mode de représentation ancré dans des informations situationnelles tandis que d'autres suggèrent que les concepts abstraits se décomposent en traits sémantiques eux-mêmes abstraits.

Dans une étude récente McRae, Nedjadrasul, Pau, Pui-Hei et King (2018) ont utilisé un paradigme d'amorçage sémantique dans lequel des mots abstraits étaient amorcés par des
images illustrant chaque concept. Par exemple, le concept abstrait <discipline> était amorcé
par une image montrant des enfants en rang. Le protocole expérimental consistait à présenter
une image d'amorce puis un concept lexical abstrait dont le sens était soit lié sémantiquement
à l'image soit non relié. Les résultats ont montré une facilitation de traitement avec des temps
de réponse plus courts et un taux d'erreurs inférieur lorsque l'image d'amorce et le mot cible
étaient reliés comparativement à lorsque ceux-ci n'étaient pas reliés. Ces résultats semblent
confirmer une représentation des concepts abstraits selon un ancrage situationnel dans la
mesure où la présentation de situations imagées en amorce facilite le traitement du concept
abstrait lexical.

Dans une autre étude, Kuipers, Jones et Thierry (2018) ont utilisé un paradigme d'amorçage
similaire dans lequel des concepts lexicaux abstraits étaient amorcés par des images abstraites.
Ces auteurs ont cherché à explorer l'effet d'images abstraites dénuées de traits tangibles sur le
traitement de concepts lexicaux abstraits. De la même façon, le protocole expérimental
consistait à présenter une image d'amorce abstraite puis un concept lexical abstrait dont le
sens était soit lié sémantiquement à l'image soit non relié. Les résultats ont montré pour les
données comportementales un effet de facilitation avec des temps de réponse plus courts et un
taux d'erreurs plus faible lorsque l'image d'amorce abstraite et le mot cible étaient reliés
comparativement à lorsque ceux-ci n'étaient pas reliés. Les données d'EEG ont démontré une
plus faible amplitude de la N400 pour les paires reliées comparativement aux paires non-
reliées. Selon les auteurs, ces résultats suggèrent qu'il est possible d'activer des concepts
abstraits en mémoire sur la base d'images abstraites dénuées de traits tangibles. Cela suggère
donc que les concepts abstraits peuvent bénéficier d'un traitement sémantique même en
l'absence d'information situationnelle.

Afin d'approfondir ces résultats qui semblent se contredire quand au rôle des informations
situationnelles ou intangibles dans le traitement sémantique des concepts abstraits, nous avons
réalisé une étude dans laquelle nous avons directement comparé ces deux conditions. Nous
avons dans un premier temps répliqué l'étude de Kuipers et al. (2018) en Français afin de
vérifier la faisabilité du protocole expérimental dans un contexte plurilinguistique. Comme
Kuipers et al., (2018) nous avions formulé l'hypothèse selon laquelle nous devrions observer
un effet de facilitation pour les paires images-mots reliées comparativement aux paires non-
reliées. Les résultats ont confirmé ceux de Kuipers et al. (2018) et ont montré le même effet
de facilitation pour les concepts abstraits amorcés par des images abstraites sémantiquement
reliées comparativement aux paires non-reliées.

Dans un paradigme d'amorçage, nous avons présenté dans une première condition une série
de paires de concepts lexicaux amorcés par des images abstraites dénuées de traits tangibles et
dans une seconde condition, ces mêmes concepts lexicaux amorcés par des images riches en
informations situationnelles. Nous avions formulé l'hypothèse selon laquelle l'effet de
facilitation obtenu à partir des images situationnelles devrait être maintenu tandis que l'effet
des images intangibles devrait disparaître. Nous avions donc fait l'hypothèse d'une stratégie
de traitement préférentiel en faveur de traits situationnels comparativement à des traits
intangibles. Les résultats ont montré un effet de facilitation pour chaque condition avec des
temps de réponses plus courts et des taux d'erreurs plus faibles pour les paires images-mots
reliées comparativement aux paires non-reliées, ceci pour les images situationnelles ainsi que
pour les images abstraites. Cet effet de facilitation était plus fort pour les images
situationnelles comparativement aux images abstraites. Ces résultats ont permis d'approfondir les connaissances quand au rôle du type d'information permettant l'activation et le traitement des concepts abstraits. Ils suggèrent que même en présence d'informations situationnelles qui devraient stratégiquement faciliter le traitement des concepts dans la mesure où ils procurent un contexte d'ancrage, les participants ont continué d'extraire des traits sémantiques intangibles à partir des images abstraites. Une première interprétation de ces résultats au regard de l'étude de Kuipers et al. (2018) suggère que les concepts abstraits peuvent être décomposés en une série de traits qui peuvent être contextuels mais également abstraits. Cette première conclusion met ainsi l'accent sur la richesse des concepts abstraits et la variabilité de leurs modes de représentation qui va à l'encontre de précédentes suggestions concernant une pauvreté du contenu des concepts abstraits (voir Recchia & Jones, 2012).

Ces résultats peuvent également être interprétés en complément de ceux de Bolognesi et Vernillo (2019) qui proposent un traitement des concepts abstraits selon un mécanisme d'« abstraction par métonymie ». Celui-ci repose sur un processus d'abstraction des concepts partant de représentations tangibles vers des représentations plus abstraites. Nos résultats semblent suggérer un mécanisme opposé et complémentaire à celui-ci dans lequel les participants auraient extrait des traits sémantiques intangibles à partir desquels des représentations plus tangibles ont été inférées permettant l'activation des concepts lexicaux sémantiquement reliés.

Le traitement des concepts abstraits sur la base de ces traits intangibles pourrait également être interprété comme le résultat d'un processus de généralisation des concepts à partir d'une multitude d'exemples d'application dans des situations variées. En effet, les modèles de catégorisation cognitive décrivent la capacité des individus à faire abstraction des disparités situationnelles au travers de multiples exemples d'application situationnelle d'un concept afin d'en inférer un sens général. Cette capacité permet notamment de transférer le sens d'un concept à de nouvelles situations afin d'extraire les similitudes fondamentales dans la définition générale du concept. C'est grâce à cette aptitude que les individus sont capables de comprendre le sens d'un concept sans avoir besoin de faire l'expérience de toutes ses applications.

Dans le Chapitre 5, nous avons exploré ces capacités de généralisation du sens d'un concept à partir de la présentation d'une série d'exemples de situations d'application. La théorie de la catégorisation par l'exemplaire stipule que le sens des concepts est généralisé sur la base de la similarité entre un nouvel exemplaire et les exemplaires d'application stockés en mémoire. Par exemple, Gentner, Loewenstein et Hung (2007) ont montré que des enfants présentaient des facilitations de transfert de noms d'objets lorsqu'il existait une correspondance structurelle entre les exemplaires connus et le nouvel exemplaire d'objet à catégoriser. D'autres études ont toutefois remis en question cet effet de la similarité et suggèrent qu'elle pourrait porter atteinte à la capacité de transfert des caractéristiques communes d'une situation à une autre de par l'apprentissage de traits de surfaces idiosyncratiques (voir par exemple Braithwaite & Goldstone, 2015). Ces études préconisent l'effet de la diversité contextuelle lors de l'apprentissage d'un concept qui permettrait de faire abstraction de tels traits superficiels en faveur d'un traitement approfondi des bases communes aux exemplaires stockés en mémoire et le nouvel exemplaire à catégoriser. Par exemple, Braithwaite et Goldstone (2015) ont demandé aux participants d'apprendre une série de concepts
Acknowledgment

I would like to thank Prof. Simona Brambati, Prof. Marc Brysbaert, Prof. Diane Pecher and Dr. René Zeelenberg for accepting to be members of the jury. I am honoured to have the privilege of their expertise and am very pleased to have such a diverse jury from around the world.

This research made in the FACTOLAB framework has been sponsored by Michelin Tyres Manufacturer, by the French government research programs "Investissements d'Avenir" through the IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25) and the IMobS3 Laboratory of Excellence (ANR-10-LABX-16-01).
Publications

**Chapter 2 was published as** Lakhzoum, D., Izaute, M., & Ferrand, L. (2021). Semantic similarity and associated abstractness norms for 630 French word pairs. *Behavior Research Methods*, 53, 1166-1178. https://doi.org/10.3758/s13428-020-01488-z

Supplementary material: https://osf.io/qsd4v/

**Chapter 3 was submitted as:** Lakhzoum, D., Izaute, M., & Ferrand, L. (submitted). Word associations norms for 1,100 French words with varying levels of concreteness. *Journal of Psycholinguistic Research*.

Supplementary material: https://osf.io/dhuqs/

**Chapter 4 was submitted as the preprint** Lakhzoum, D., Izaute, M., & Ferrand, L. (2021). Semantic network analysis of abstract and concrete word associations. https://doi.org/10.31234/osf.io/epk47

Supplementary material: https://osf.io/4w69k/

**Chapter 5 was published as** Lakhzoum, D., Izaute, M., & Ferrand, L. (2021). Intangible features extraction in the processing of abstract concepts: Evidence from picture-word priming. *Plos One*, 16, e0251448. https://doi.org/10.1371/journal.pone.0251448

Supplementary material: https://osf.io/483tu/

**Chapter 6 is in preparation for submission as** Lakhzoum et al. (in preparation). Generalisation mechanisms in the processing of abstract concepts.

Supplementary material: https://osf.io/svm2p/
Table of content

<table><tr><td>Abstract</td><td>2</td></tr><tr><td>Résumé – Long summary in French</td><td>4</td></tr><tr><td>Acknowledgment</td><td>11</td></tr><tr><td>Publications</td><td>12</td></tr><tr><td>Foreword</td><td>21</td></tr><tr><td>Chapter 1. Theoretical Introduction</td><td>23</td></tr><tr><td>1. Models of semantic representation</td><td>23</td></tr><tr><td>Definition of semantic memory</td><td>23</td></tr><tr><td>Hierarchical and Network models</td><td>24</td></tr><tr><td>Feature models</td><td>26</td></tr><tr><td>2. Similarity, verbal association and semantic priming</td><td>29</td></tr><tr><td>3. Concepts concreteness</td><td>31</td></tr><tr><td>4. Distributional and grounded accounts of semantic representation</td><td>33</td></tr><tr><td>Distributed accounts of semantic representation</td><td>34</td></tr><tr><td>Grounded accounts of semantic representation</td><td>36</td></tr><tr><td>5. Abstraction and generalisation mechanisms in the processing of abstract concepts</td><td>40</td></tr><tr><td>Models of the prototype view of concept learning</td><td>41</td></tr><tr><td>Influential models of the exemplar theory</td><td>42</td></tr><tr><td>Challenges to the exemplar view</td><td>44</td></tr><tr><td>How does this relate to mechanisms of abstraction and generalisation in the processing of abstract concepts?</td><td>47</td></tr><tr><td>6. Network cognitive science</td><td>49</td></tr><tr><td>Historical account and classical models of network science</td><td>49</td></tr><tr><td>Network parameters and analysis</td><td>53</td></tr><tr><td>Cognitive network science approach to the mental lexicon</td><td>55</td></tr><tr><td>SYNTHESIS and INTRODUCTION TO EXPERIMENTAL CHAPTERS</td><td>59</td></tr><tr><td>Experimental Section</td><td>62</td></tr><tr><td>Chapter 2. Semantic similarity and associated abstractness norms for 630 French word pairs</td><td>64</td></tr><tr><td>ABSTRACT</td><td>65</td></tr><tr><td>INTRODUCTION</td><td>66</td></tr><tr><td>Accounts of semantic representation</td><td>66</td></tr><tr><td>Abstract concepts representation</td><td>70</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>Semantic priming and semantic similarity for concrete and abstract concepts</td>
      <td>71</td>
    </tr>
    <tr>
      <td>The present study: Semantic similarity norms for abstract words</td>
      <td>76</td>
    </tr>
    <tr>
      <td>METHOD</td>
      <td>77</td>
    </tr>
    <tr>
      <td>Participants</td>
      <td>77</td>
    </tr>
    <tr>
      <td>Stimuli</td>
      <td>77</td>
    </tr>
    <tr>
      <td>Procedure</td>
      <td>79</td>
    </tr>
    <tr>
      <td>RESULTS</td>
      <td>80</td>
    </tr>
    <tr>
      <td>DISCUSSION</td>
      <td>86</td>
    </tr>
    <tr>
      <td>CONCLUSION</td>
      <td>89</td>
    </tr>
    <tr>
      <td>REFERENCES</td>
      <td>89</td>
    </tr>
    <tr>
      <td>Chapter 3. Word associations norms for 1,100 French words with varying levels of concreteness</td>
      <td>99</td>
    </tr>
    <tr>
      <td>ABSTRACT</td>
      <td>100</td>
    </tr>
    <tr>
      <td>INTRODUCTION</td>
      <td>101</td>
    </tr>
    <tr>
      <td>Semantic and associative priming</td>
      <td>101</td>
    </tr>
    <tr>
      <td>Concreteness and organisation of the mental lexicon</td>
      <td>102</td>
    </tr>
    <tr>
      <td>Accounts of semantic representation</td>
      <td>103</td>
    </tr>
    <tr>
      <td>Word associations and normative datasets</td>
      <td>104</td>
    </tr>
    <tr>
      <td>The present study</td>
      <td>106</td>
    </tr>
    <tr>
      <td>METHOD</td>
      <td>107</td>
    </tr>
    <tr>
      <td>Participants</td>
      <td>107</td>
    </tr>
    <tr>
      <td>Stimuli</td>
      <td>107</td>
    </tr>
    <tr>
      <td>Procedure</td>
      <td>107</td>
    </tr>
    <tr>
      <td>RESULTS</td>
      <td>108</td>
    </tr>
    <tr>
      <td>DISCUSSION</td>
      <td>114</td>
    </tr>
    <tr>
      <td>REFERENCES</td>
      <td>117</td>
    </tr>
    <tr>
      <td>Chapter 4. Semantic network analysis of abstract and concrete word associations</td>
      <td>128</td>
    </tr>
    <tr>
      <td>ABSTRACT</td>
      <td>129</td>
    </tr>
    <tr>
      <td>INTRODUCTION</td>
      <td>130</td>
    </tr>
    <tr>
      <td>Network science and characteristics</td>
      <td>130</td>
    </tr>
    <tr>
      <td>Small-world structures and semantic networks</td>
      <td>132</td>
    </tr>
    <tr>
      <td>The present work</td>
      <td>134</td>
    </tr>
    <tr>
      <td>METHOD</td>
      <td>135</td>
    </tr>
  </tbody>
</table>
<table><tr><td>Material and procedures</td><td>135</td></tr><tr><td>RESULTS</td><td>135</td></tr><tr><td>DISCUSSION</td><td>141</td></tr><tr><td>REFERENCES</td><td>144</td></tr></table>

Chapter 5. Intangible features extraction in the processing of abstract concepts:

<table><tr><td>Evidence from picture-word priming</td><td>149</td></tr><tr><td>ABSTRACT</td><td>150</td></tr><tr><td>INTRODUCTION</td><td>151</td></tr><tr><td>Dimensions of abstract concepts</td><td>151</td></tr><tr><td>Theoretical accounts in the study of abstract concepts</td><td>152</td></tr><tr><td>Picture priming paradigms in the study of abstract concepts</td><td>154</td></tr><tr><td>The present study</td><td>154</td></tr><tr><td>Experiment 1</td><td>155</td></tr><tr><td>MATERIALS and METHODS</td><td>155</td></tr><tr><td>Participants</td><td>155</td></tr><tr><td>Materials</td><td>156</td></tr><tr><td>Procedure</td><td>156</td></tr><tr><td>RESULTS</td><td>157</td></tr><tr><td>Reaction times</td><td>157</td></tr><tr><td>Errors</td><td>159</td></tr><tr><td>DISCUSSION</td><td>159</td></tr><tr><td>Experiment 2</td><td>160</td></tr><tr><td>MATERIALS and METHODS</td><td>160</td></tr><tr><td>Participants</td><td>160</td></tr><tr><td>Materials</td><td>160</td></tr><tr><td>Procedure</td><td>160</td></tr><tr><td>RESULTS</td><td>162</td></tr><tr><td>Reaction times</td><td>162</td></tr><tr><td>Errors</td><td>164</td></tr><tr><td>DISCUSSION</td><td>164</td></tr><tr><td>GENERAL DISCUSSION</td><td>166</td></tr><tr><td>REFERENCES</td><td>168</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>Appendices: supplementary analyses</td>
      <td>172</td>
    </tr>
    <tr>
      <td>Chapter 6. Generalisation mechanisms in the processing of abstract concepts</td>
      <td>175</td>
    </tr>
    <tr>
      <td>INTRODUCTION</td>
      <td>175</td>
    </tr>
    <tr>
      <td>The prototype and exemplar accounts</td>
      <td>176</td>
    </tr>
    <tr>
      <td>The role of similarity and contextual diversity on abstraction and generalisation</td>
      <td>178</td>
    </tr>
    <tr>
      <td>Application to the generalisation of abstract concepts</td>
      <td>180</td>
    </tr>
    <tr>
      <td>The present work</td>
      <td>181</td>
    </tr>
    <tr>
      <td>EXPERIMENT 1a: selection of the NewAbstractConcepts database</td>
      <td>183</td>
    </tr>
    <tr>
      <td>METHOD</td>
      <td>183</td>
    </tr>
    <tr>
      <td>Participants</td>
      <td>183</td>
    </tr>
    <tr>
      <td>Materials and Procedure</td>
      <td>183</td>
    </tr>
    <tr>
      <td>RESULTS and DISCUSSION</td>
      <td>184</td>
    </tr>
    <tr>
      <td>EXPERIMENTS 1b and 1c: generating the NewAbstractConcepts database</td>
      <td>185</td>
    </tr>
    <tr>
      <td>METHOD</td>
      <td>186</td>
    </tr>
    <tr>
      <td>Participants</td>
      <td>186</td>
    </tr>
    <tr>
      <td>Materials and Procedure</td>
      <td>186</td>
    </tr>
    <tr>
      <td>RESULTS and DISCUSSION</td>
      <td>187</td>
    </tr>
    <tr>
      <td>METHOD</td>
      <td>188</td>
    </tr>
    <tr>
      <td>Participants</td>
      <td>188</td>
    </tr>
    <tr>
      <td>Materials and Procedure</td>
      <td>189</td>
    </tr>
    <tr>
      <td>RESULTS and DISCUSSION</td>
      <td>189</td>
    </tr>
    <tr>
      <td>EXPERIMENT 2</td>
      <td>192</td>
    </tr>
    <tr>
      <td>METHOD</td>
      <td>192</td>
    </tr>
    <tr>
      <td>Participants</td>
      <td>192</td>
    </tr>
    <tr>
      <td>Materials and Design</td>
      <td>192</td>
    </tr>
    <tr>
      <td>Procedure</td>
      <td>193</td>
    </tr>
    <tr>
      <td>RESULTS and DISCUSSION</td>
      <td>194</td>
    </tr>
    <tr>
      <td>GENERAL DISCUSSION</td>
      <td>198</td>
    </tr>
    <tr>
      <td>Chapter 7. General discussion and perspectives</td>
      <td>203</td>
    </tr>
    <tr>
      <td>Summary of results</td>
      <td>203</td>
    </tr>
    <tr>
      <td>A theoretical model and some implications for abstract concept processing</td>
      <td>207</td>
    </tr>
    <tr>
      <td>Implications for neural evidence</td>
      <td>212</td>
    </tr>
  </tbody>
</table>
<table><tr><td>Implications for natural language processing</td><td>217</td></tr><tr><td>Conclusions and future work</td><td>220</td></tr><tr><td>REFERENCES</td><td>222</td></tr></table>

# List of Figures

## Chapter 1

**Figure 1.** Illustration of (left) the models of Collins & Quillian (1969) and (right) Collins & Loftus (1975).

**Figure 2.** Semantic Features Model (adapted from Smith et al. (1974)).

**Figure 3.** Graphical representation of the semantic vector space for the concepts dog, cat, van and car from Lenci (2018).

**Figure 4.** The hub-and-spoke model of semantic processing. Modality-specific spokes represent motion (yellow), colours (dark blue), shapes (green), names (orange), actions (light blue) and tasks (purple) which converge towards the amodal “hub” (red) represented in the anterior temporal lobe (ATL; Adapted from Patterson et al., 2007).

**Figure 5.** Schematic illustration of a category structure to explain the workings of the GCM

**Figure 6.** The two-step procedure to construct a category representation: (1) partition the category into clusters and (2) construct the centroid for each cluster. In this way, it is possible to construct the prototype representation (panel a), the exemplar representation (panel B), and a set of intermediate representations, one of which is illustrated in panel C (reproduced from Vanpaemel & Storms, 2008).

**Figure 7.** Euler's schema from 'Solutio problematis ad geometriam situs pertinentis'.

**Figure 8.** Illustration of the Small-World Problem from Milgram (1967).

**Figure 9.** Small-world structure from Watts & Strogatz (1998). The small-world model was obtained by interpolation of a regular ring lattice and a random network, without altering the number of nodes or edges in the graph.

**Figure 10.** Three graphs of 20 nodes each generated with the Barabasi-Albert model (1990). The increasing value m represents the parameter of attachment. A high value of m signifies that the more connected a node is, the more it is susceptible to receive more links.

**Figure 11.** Proposed large-scale structures for semantic networks from Tenenbaum and Steyvers (2005): (a), a tree-structured hierarchy (e.g., Collins & Quillian, 1969); (b), an
arbitrary, unstructured graph (e.g., Collins & Loftus, 1975); (c), a scale-free, small-world graph (e.g. Tenebaum & Steyvers, 2005).

**Figure 12.** Graphical representation of the scope of the present thesis.

## Chapter 3

**Figure 1.** Frequency spectrum on a logarithmic scale representing the number of types ($V_m$) as a function of frequency class ($m$). The class corresponding to $m = 1$ corresponds to the hapax legomena.

**Figure 2.** Vocabulary Growth Curve (VGC) representing the increase of cue-response pairs as new responses are collected by comparing the VGC of our dataset (grey curve) to an empirical VGC obtained from the fZM model red line). The vertical lines indicate the total number of observed tokens for our sample.

## Chapter 4

**Figure 1.** Visual representation of the whole network. Each point represents a word. The colour gradient from grey to red represents the distance between connected nodes the greyer the farther and the redder the closer (built with the software Gephi; Bastian, Heymann & Jacomy, 2009).

**Figure 2.** Visualisation of a portion of the network with a representation of the nodes degree were the larger and the darker green a node is and the more connection it has. The node with the most number of connection on this portion is the French concept “amour” or love in English. One of the nodes with the smallest degree is the French concept “ennui” or boredom in English (built with the software Gephi; Bastian, Heymann & Jacomy, 2009).

## Chapter 5

**Figure 1.** Trial procedure for the concept royalty (“royauté” in French).

**Figure 2.** Mean correct latencies (in ms with standard error bars) for Type of Image Prime and Type of Target Word (R = related pairs; NR = non-related pairs; **p = 0.01; ***p < 0.001) in both Experiments 1 and 2.
**Figure A.1.** Results of the Bayes factor replication test applied to our replication attempt based on Kuipers et al. (2018).

# Chapter 6

**Figure 1.** Percentage of response for each choice.

**Figure 2.** Mean ratings for the similar and dissimilar conditions in Experiment 1c (* p < 0.001).

**Figure 3.** 2-dimensional graphical representation of the psychological space for concept SONDER. Each point represents an exemplar with coordinates $x_{sonder_s}$ as the exemplar's mean similarity rating in the similar dimension and $x_{sonder_d}$ as the exemplar's mean similarity rating in the dissimilar dimension.

**Figure 4.** Mean similarity judgments (on a scale from 0 to 100) as a function of reading phase exposure (similar vs. dissimilar exemplars; p<0.001)

**Figure 5.** Prior and Posterior distribution for the paired samples t-test comparing the similar and dissimilar conditions.

**Figure 6.** Graphical model implementation of the GCM

# Chapter 7

**Figure 1.** Schematic representation of our theoretical model. Level I represents abstract concepts and features at the word levels. Concepts and features are activated following a threshold of activation. Thicker traits represent more influential nodes which possess a higher activation potential compared to thinner traits. Level II represents exemplars in memory as instances of concepts. Level III represents the generalised concepts based on the processing of contextually diverse exemplars which allow for the recognition of novel instances.

**Figure 2.** Representation of the hub-and-spoke model with spoke regions which code for modality-specific components of concepts while the ATL is a semantic hub which aggregates across modalities and form amodal conceptual representations (see text for more details).

From Lambon et al. (2017).

**Figure 3.** Advances in NLP research cross 3 major era following the syntactics, semantics and pragmatics curves (From Cambria & White, 2014).
# List of Tables

## Chapter 2

**Table 1.** Semantic similarity for word pairs and associated concreteness for prime and target words.

**Table 2.** Descriptive and behavioural data for target words.

**Table 3.** Descriptive and behavioural data for prime words.

**Table 4.** Correlation matrix between concreteness levels and lexical variables with significance levels (*** p < 0.001; ** p < 0.01; * p < 0.05).

**Table 5.** Correlations of the present concreteness variable measures with those provided by other databases.

## Chapter 3

**Table 1.** Descriptive and behavioural data for cue words.

**Table 2.** Mean frequency and reaction times for response words

**Table 3.** Response frequencies and latencies according to cue concreteness

## Chapter 4

**Table 1.** Description of network characteristics used throughout the paper.

**Table 2.** Network statistics for the directed and undirected associative networks for the whole, concrete and abstract datasets.

## Chapter 5

**Table A.2.** Results of the Bayesian Replication Test.

## Chapter 6

**Table 1.** Examples of scenarios and exemplars for the concept SONDER.

**Table 2.** All combinations for the concept SONDER
# Foreword

"When I use a word," Humpty Dumpty said, in rather a scornful tone, "it means just what I choose it to mean—neither more nor less."

"The question is," said Alice, "whether you can make words mean so many different things."

Alice in Wonderland, Lewis Carroll (1865).

It is often said that language is the cornerstone of human cognition. This uniquely human trait requires assimilating mental representations or concepts that can be manipulated to make sense of the world. This feat has understandably fascinated philosophers and scientists alike for centuries who have pondered the question of how the mind generates such flexible yet stable representations from encountering very few positive examples. The mind can in the space of milliseconds understand a complex utterance and in turn produce words that will automatically generate an idea, an emotion in the mind of a listener or a reader. This ability is nothing short of miraculous as evidenced by an infant's capacity to learn such an elaborate skill with virtually no formal teaching or training. What transpires is that the mind is a wired organ destined to learn and manipulate language. Some researchers posit that we develop in early infancy a common-sense core of knowledge built on intuitive theories about physics (forces, masses, gravity...) and psychology (desires, beliefs, intents...; see for example Gerstenberg & Tenenbaum, 2017). This core matrix is common to other species such as non-human primates but benefits from an enrichment and sophistication through language that is specific to humans. Intuitive theories are the tenets of featural patterns organised into a conceptual system from which semantic concepts can arise. The stability and flexibility of the semantic conceptual system is demonstrated by the mind's ability to understand a statement even when intuitive theories are violated. To illustrate this point let us turn to the writer and mathematician Lewis Carroll (1832-1898) and his mastery of the nonsense literature, which
operates on its own intrinsic realm governed by the assumption that nothing exists outside the text. In *Alice's Adventures in Wonderland* and *Through the Looking Glass and What Alice Found There*, Alice enters a world of logical fallacies that ignore the laws of physics (e.g., "Either the well was very deep, or she fell very slowly [...]"), common sense (e.g., "Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!"), semantic truths (e.g., "I've had nothing yet," Alice replied in an offended tone "so I can't take more. "You mean you can't take less," said the Hatter: "it's very easy to take more than nothing") and semantic fallacies (e.g., "That's the reason they're called lessons," the Gryphon remarked: "because they lessen from day to day") which implies a new meaning for the word lesson given by a phonological neighbour. Almost instantaneously, the reader is able to let go of mental representations acquired at the cost of many experiences and create new nonsensical ones in order to enjoy the story. This ability illustrates the complexity of the mental lexicon as a dynamic organ with constant interactions between its levels of processing from core featural properties to the understanding of complex statements.

Attempts to understand such semantic abilities have become the focal point for
behavioural, computational and neurological investigations since the early phases of the
cognitive revolution. The present thesis is a study of such semantic phenomena centered on
abstract concepts from the organisation of concepts at the word-level in the mental lexicon to
the mechanisms underlying our ability to generalise the meaning of concepts to novel
instances.
# Chapter 1. Theoretical Introduction

Psycholinguistics has a long tradition of studying the representation of semantic concepts.
This tradition has mainly been focused on the representation of concrete objects but in recent
years, the focus has shifted to include the representation of abstract concepts. What does it
entail to know the meaning of the word *owl* or *justice*? Answering this question raises some
fundamental issues: (1) How is the meaning of the word represented? (2) How does it relate to
other words whether similar or associated? (3) How does meaning relate to conceptual
structure? (4) How is the meaning abstracted and generalised to all instances of the concept?
The first part of this chapter is a review of semantic models from early hierarchal, network
and feature models that have been the centre of attention staring from the 1960-70s to the
more contemporary debate between the distributional and embodied accounts of semantic
representation and their application to the challenge posed by abstract concepts. Next, we
introduce the literature on abstraction and generalisation mechanisms of semantic concepts.
Finally, the last section introduces the field of cognitive network science and its application to
the issue of conceptual structure of the mental lexicon.

## 1. Models of semantic representation

### Definition of semantic memory

Concepts are the building blocks of higher-order cognition. They are the basis for our
ability to form complex thoughts and generate abstract ideas. As such, philosophers and
scientists have pondered the question of their representation in the mind and brain for
millennia. During the last century, many influential models have been dedicated to the study
of semantic concepts and the representation of meaning. In particular, Tulving (1972) built on
Fodor's (1950) modular account of the cognitive system to propose a distinction between
episodic and semantic memory that differ on the nature of the information they store.
Semantic memory is defined as a storage system for general knowledge of the world and their corresponding abstract symbols (e.g., what is a chair?). In contrast, episodic memory stores individual experiences tied to time, space and sensory impressions (e.g., a favourite reading chair). Early models of semantic memory can be divided in two categories: models that are centered on network representations of concepts and models that are centered on a featural (or decompositional) view of concepts (Collins & Loftus, 1975, see also Vigliocco & Vinson, 2005).

## Hierarchical and Network models

Semantic network theories draw on Fodor's (1980) holistic argument against the decomposition of concepts into a set of defining features and posit that the meaning of a concept is determined by its relationships with other concepts. Accordingly, this suggests that for every element of the world –be it an object, an event or property, etc. - there is a symbolic lexical equivalent that acts as a referent in the conceptual system of the mind (Fodor, Garrett, Walker & Parkes, 1980; Berg & Levelt, 1990; Roelofs, 1997; Levelt, Roelofs & Meyer, 1999). This was motivated by the work of Quillian (1962, 1967) who built a computer simulation of semantic search in a dictionary-like network where concepts were represented as nodes linked by hierarchical relations (Collins & Quillian, 1969 see Figure 1). Quillian's theory suggests that accessing a concept activates the whole network of related nodes, which gives the full meaning of the concept (Quillian, 1962, 1967). For instance, accessing the concept <machine> activates in turn the concepts <manufactured>, <composed of moving parts> and <a car> is an example of a machine. This computational view of semantic representation illustrates the complex structure of the conceptual system and the rich number of concepts that people must possess. However, Quillian was aware of the theoretical constraints put on the model due to its computational nature. Collins and Quillian (1969) tested the cognitive plausibility of this model in a series of True-False reaction-time experiments. Participants were presented with words and
properties and were asked to identify whether these properties belonged to the meaning of the
word (for e.g. "A canary can fly", "An ostrich is an animal").

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Illustration of (left) the models of Collins & Quillian (1969) and (right) Collins & Loftus (1975).</figcaption></figure>

Results confirmed the hierarchical structure of semantic memory with an organisation
comprising superordinate categories, which could account for a distance effect with longer
latencies when more levels needed to be verified. The theory later influenced the development
of several global theories of semantic network representation such as Schank's conceptual
dependency (Schank, 1972), the active structural networks of Norman and Rumelhart (1975)
and Anderson and Bower's associative network theory (Anderson & Bower 1983).

Collins and Loftus (1975) built upon this model and proposed a mechanism of semantic
access by implementing their spreading of activation theory in a network. The theory posits that
when a concept is processed, it activates the path between related nodes at a speed proportional
to the strength of their similarity. This model corrected some of the hierarchical constraints of
Collins and Quillian (1969) at the expanse of less typical concepts (Carey, 1985; Rips, Shoben, & Smith, 1973; Sloman, 1998). The notion of semantic similarity is central to the model. The more properties two concepts have in common, the more links their nodes share and the closer their meaning. This assumption of semantic similarity in the spreading of activation theory accounts for both the strength of the link between nodes and the ensuing dynamics of activation for related concepts. For instance, accessing the concept *fire* would activate related nodes with decreasing levels of activation from the node *red* to the node *street* (see Figure 1). The theory was successful in explaining many empirical results such as free word association (Freedman & Loftus, 1971; Loftus, 1973a, 1973b), multiple category (Juola & Atkinson, 1971), sentence-verification (Conrad, 1972), categorization, semantic relatedness, typicality (Holyoak & Glass, 1975; Rips et al., 1973; Rosch, 1973; Smith et al., 1974), sentence processing (Traxler, Foss, Seely, Kaup, & Morris, 2000), errors in sentence production (Dell, 1986) and semantic priming effects (Balota & Lorch, 1986; de Wit & Kinoshita, 2015). The spreading of activation theory is still very influential today and has fuelled other disciplines that use networks to represent complex systems such as cognitive network science (see Siew, Wulff, Beckage & Kenett, 2019).

This one-to-one mapping between concepts and lexical referents that lay at the core of hierarchical and network models contrasts with the decompositional view of feature models.

## Feature models

The featural view posits that concepts can be decomposed into a set of properties or features that represent a diversity of semantic dimensions (e.g., Jackendoff, 1992; Vigliocco et al., 2004). The *Feature Comparison Model* developed by Smith, Shoben and Rips (1974) suggests two types of features: defining features referring to the core properties of a concept and characteristic features referring to informational but non-essential features (see Figure 2).
For instance, the concept *bird* would be decomposed into defining features such as <have wings> and <lay eggs> and characteristic features such as <can fly> because most but not all birds can fly.

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Semantic Features Model (adapted from Smith et al. (1974)).</figcaption></figure>

The notion of similarity is also important for feature models according to which the similarity between two concepts is given by the number of their overlapping features. The model assumes a two-stage process of comparison between two concepts. At stage 1, concepts are compared with respect to both feature types. If the first stage is not sufficient to produce a similarity judgment, a second stage reiterates the comparison based on defining features only. Despite the model's ability to account for categorisation data, the strict distinction between feature types represents an impractical constraint. For instance, based on a strict application of
the 2-stage process, a bird that would have had its wings removed would no longer be considered a bird (Collins & Loftus 1975).

This notion that meaning could be reduced to a definite set of defining features was heavily criticised for its impracticality (Fodor & Garrett, 1975; Fodor et al., 1980). Critics pointed that people seldom have knowledge of a concept's defining features, which calls into question the ecological validity of the model (Collins & Loftus, 1975). It can be argued that the model would also struggle to account for abstract concepts representation given that abstract concepts are more likely to be defined in relation with other concepts rather than as a set of features (Paivio, 1968; Crutch & Warrington, 2010).

Smith et al. (1974) argued that feature models could better explain empirical data compared to network models. Collins and Loftus (1975) expressed their opposition to this argument particularity due to Hollan's successful implementation of a feature model in a network (Hollan, 1975). This opposition between hierarchical network and feature representation models has yet to be resolved despite the accumulation of data mainly due to confounding variables such as familiarity and associations that have produced mixed results. More recently, Murphy, Hampton and Milovanovic (2012) have tested the validity of these models in a series of experiments based on novel materials to avoid such confounding variables. Their results provided evidence in favour of feature models and suggested that a hierarchical organisation of concepts is plausible but not the preferred option.

Despite early criticism, the featural assumption remains an elegant and effective model to account for semantic representation, which has benefited from a renewed interest for its ability to explain neuropsychological data (Allport, 1985). Warrington and McCarthy (1983) described the case of a patient who had difficulty identifying non-living things compared to living things while Warrington and Shallice (1984) described the case of a patient who
exhibited the opposite pattern. Consequently, it was possible to infer the existence of two
dimensions of conceptual features that are the living vs. non-living dimensions (McRae et al.,
2005).

The notion of similarity provides a consensus for both network and feature models.
This explains the prevalence of semantic similarity in psycholinguistics as evidenced by its
long tradition for developing semantic similarity norms and semantic priming paradigms.

## 2. Similarity, verbal association and semantic priming

To gain insight into the nature of semantic processing, two main relationships between
concepts have been extensively studied, namely semantic similarity and word association.
Semantic similarity refers to the overlap between the defining features of concepts (for
example <bee> and <wasp> are semantically similar) which is measured using a feature
generation task or a similarity judgment of word pairs. Word association refers to the co-
occurrence of words across language (for example <bee> and <honey>; McNamara, 1992;
Plaut, 1995; Ferrand & Alario, 1998) which are obtained through a word association task.

In a feature generation task, participants are given a series of target words for which
they are asked to list as many features of the word as possible. The more features two words
have in common, the more similar they are (McRae, Cree, Seidenberg, & McNorgan, 2005;
McRae, de Sa, & Seidenberg, 1997; Sánchez-Casas, Ferré, García-Albea & Guasch, 2006;
Vigliocco, Vinson, Lewis & Garrett, 2004; Vinson & Vigliocco, 2008). However, the
procedure can be very demanding due to the previously mentioned constraint regarding
people's lack of systematic knowledge to decompose words into defining features as well as
for its time-consuming nature (see McRae et al., 2005 for a discussion of these limitations).
Researchers have more often resolved to generate semantically similar pairs of words that are
presented to participants and rated in a similarity judgment task (e.g., truck-car; Ferrand & New, 2003; Perea & Rosa, 2002; Ferrand & New, 2003; Sánchez-Casas et al., 2006).

In a word association task, participants are presented with a word (the cue) and asked to generate the first word that comes to mind (the response). In addition to providing normed stimuli to use in factorial designs, the task can unveil shared mental representations, language patterns and mechanisms of meaning extraction (Szalay & Deese, 1978; Prior & Bentin, 2008; Mollin, 2009, De Deyne et al., 2008, 2018). The amount of data generated also allows for the application of computationally rich models such as distributional statistics and network representation that provide insight into the structure of the mental lexicon (Steyvers & Tenenbaum, 2005; De Deyne et al., 2008, 2016, 2018). Both semantic similarity and word association have been used in semantic priming designs which are regarded as the gold standard for studying how semantic knowledge is organised (see Hutchinson, Balota, Cortese & Watson, 2008; Hutchison et al., 2013; Pulvermüller, 2013; Mandera, Keuleers & Brysbaert, 2017).

In a semantic priming study, participants are presented with a prime word followed by a target word (Meyer & Schvaneveldt, 1971; McNamara, 1992; Plaut, 1995). Semantic priming designs are often used with a lexical decision task for which participants must decide whether the target is a word. The semantic priming effect refers to the robust result, which has been replicated hundreds of times, showing that participants respond faster for related primes and targets –either semantically or verbal associates– compared to unrelated ones (Hutchison et al., 2008, 2013). Early studies of semantic priming have shown some discrepancies due to an overlap between semantic similarity and verbal association (for example *cat* and *dog* are semantically similar but also verbal associates; see Thompson-Schill et al., 1998, but also Hutchison, 2003). The importance of strictly differentiating between associative and semantic relations when studying the mental lexicon has long been established (for example *whale* and *dolphin*; McRae & Boisvert, 1998; Perea & Rosa, 2002; Thompson-Schill, Kurtz, &
Gabrieli, 1998; Lucas, 2000; Hutchison, 2003; Ferrand & New, 2003). Ferrand and New (2003) obtained a pure semantic priming effect by using stimuli that were semantically similar while controlling for verbal association (e.g., <dolphin> and <whale>). In reverse, they obtained a pure associative priming effect by using strongly associated stimuli that had no semantic similarity (e.g., <spider> and <web>). Consequently, word association norms are used either to select associated word pairs or to check that semantically similar pairs are not also verbally associated. This robustness of semantic and associative priming reported in the literature reflects fundamental rules of semantic organisation in the mental lexicon.

To focus only on similarity and verbal association does not provide a full picture of the
organisation of semantic memory however as recent studies on concreteness have shown.

**3. Concepts concreteness**

Concreteness is a prevalent lexical variable in psycholinguistics and refers to the
degree of tangibility of a conceptual entity denoted by a word referent (Coltheart, 1980;
Brysbaert, Warriner & Kuperman, 2014). According to the Dual Coding Theory (Paivio,
Yuille & Madigan, 1968) concrete concepts refer to tangible entities that are perceptible via
the senses, whereas abstract concepts are intangible. According to the Context Availability
Theory (Schwanenflugel, Harnishfeger & Stowe, 1988) while concrete concepts refer to a
definite number of contexts, abstract concepts are connected to varied contexts. The
importance of this variable lies in its ability to predict the hemispheric lateralisation of
semantic effects. Neuropsychological studies have shown a concreteness effect in the
processing of concepts with a processing facilitation in the right hemisphere for abstract
concepts compared to concrete concepts (e.g., Oliveira, Perea, Ladera & Gamito, 2013). Other
studies have shown that concrete concepts are easier to retrieve from memory compared to
abstract concepts (Newton & Barry, 1997; Hanley, Hunt, Steed, & Jackman, 2013) and have a
higher emotional valence (Kousta, Vigliocco, Vinson, Andrews, & Del Campo, 2011; Paivio,
2013). In recent years, new elements of definition have emerged to describe abstract concepts
in terms of intangible features such as events, social contexts and introspective states (e.g.,
Barsalou & Wiemer-Hastings, 2005; Harpaintner, Trumpp & Kiefer, 2018; see Borghi et al.,
2017 for a review). The importance of the concreteness variable is further evidenced by the
development of concreteness rating databases (Coltheart, 1981; Brysbaert, Warriner &
Kuperman, 2014; Bonin et al., 2018) to use in semantic priming designs for instance.

Despite the robustness of the semantic priming effect with concrete concepts, results
have been inconsistent with abstract concepts. Crutch (2005; Crutch, Conell & Warrington,
2009; Crutch & Warrington, 2010) showed that while concrete concepts are organized
according to semantic similarity, abstract concepts are organized according to verbal
association. Several studies tried to replicate these results but revealed discrepancies (e.g.,
Hamilton & Coslett, 2008; Duñabeitia, Avilés, Afonso, Scheepers & Carreiras, 2009; Geng &
Schnur, 2015). Indeed, these studies have attempted to replicate the results according to which
concrete and abstract concepts have different dependencies upon semantic similarity and
associative strength. They have failed however to find any such difference in the organization
of concrete and abstract concepts. A more recent study found that both semantic similarity
and verbal association elicited a priming effect for concrete concepts whereas for abstract
concepts it was only found with verbal association (Ferré, Guasch, García-Chico & Sánchez-
Casas, 2015). Crutch and Jackson (2011) suggested the relationship between concreteness and
association type could explain these disparities. They presented evidence based on data from
healthy and neuropsychological patients showing that when presented with triplets of low,
middle and high-levels of concreteness, the effect of semantic similarity increased with
concreteness while the effect of verbal association decreased with concreteness. Furthermore,
they suggested that concreteness be used as a graded variable rather than a binary one
especially when studying its effect on the organization of semantic memory.
In recent years, the representation of abstract concepts has gained extra interest and became the focus of the debate between two seemingly opposite accounts of the nature of semantic representation, namely the distributional and the embodiment accounts. They differ from each other in respect to the information used to represent meaning. While distributional semantics relies on symbolic and linguistic features, embodiment relies on perceptual and sensory-motor states. According to this definition, the distributional account can easily explain the representation of abstract concepts while embodiment struggles to account for their representation due to their intangible nature. Both the distributional and embodied assumptions can account for priming effects, which results from the activation of features shared between the prime and the target (Mahon & Caramazza, 2008; Dove, 2009; Andrews, Frank & Vigliocco, 2014; Carota, Kriegeskarte, Nili & Pulvermüller, 2017). Where these accounts differ, however, is in the nature of the features. The distributional account suggests the priming effect results from the activation of linguistic features, the embodied account that it results from the activation of shared sensorimotor states. Recently, a hybrid account has emerged as a consensus to suggest that both linguistic and perceptual features are responsible for this phenomenon.

**4. Distributional and grounded accounts of semantic representation**

The symbolic or experiential nature of conceptual features is a question that dates back to
ancient philosophers. On the *rationalist* end of the debate, philosophers such as Descartes,
Leibnitz and Kant -building on ideas from Platon- had developed the notion that concepts are
detached from sensory matters and reside in the mind as symbolic entities. On the *empiricist*
end of the debate, philosophers such as Hume and Locke building on assumptions from
Aristotle had suggested that concepts represent impressions of the sensory environment (see
Markie, 2008, Machery, 2009; 2016). This opposition has fuelled the current debate between distributional and grounded accounts of semantic representation.

## Distributed accounts of semantic representation

The distributional hypothesis originated from the field of computational linguistics that represents meaning in a semantic vector space (Lenci, 2018; see Figure 3). According to models of distributional semantics, meaning is the result of the statistical distribution of words across written and spoken language (Andrew, Frank & Vigliocco, 2014; Lenci, 2018; Lund & Burgess, 1996; Landauer & Dumais, 1997; Griffiths, Steyvers & Tenenbaum, 2007; Mandera, Keuleers & Brysbaert, 2017). The core tenet of this hypothesis lies in Harris’ principle according to which words occurring in similar contexts have similar meanings (Harris, 1954; see also Firth, 1957). Similarity lies at the core of the model and the meaning of concepts is given by the co-occurrence of its vector across similar contexts. For instance, Figure 3 shows that the concepts <cat> and <dog> are represented by close coordinates in a vector space because they occur in similar contexts and therefore have similar meanings. The meaning of words is therefore defined in relation to other words, depending on their shared symbolic and linguistic features.

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Graphical representation of the semantic vector space for the concepts dog, cat, van and car from Lenci (2018).</figcaption></figure>
This hypothesis later became influential in cognitive psychology as distributional
analysis methods were considered a suitable foundation to represent semantic similarity
(Miller, 1967). Miller and Charles (1991) formulated a definition of semantic similarity
within the distributional framework and described it as a "function of the contexts in which
words are used" (Miller & Charles, 1991, p.3). The reliance of this framework on large text
corpora to extract semantic representations across written language provided much insights
into the linguistic environment from which the mental lexicon is acquired and expanded
(McRae & Jones, 2013; Jones et al., 2015). In contrast, the use of distributional analysis on
behavioural data such as word associations established a framework for modelling the impact
of linguistic environment on the mental lexicon. This allowed for the modelling of various
distributional phenomena such as word frequency and centrality in the mental lexicon as well
as vocabulary growth defined as the probability of generating novel words as the number of
responses in a word association task increases (Herdan, 1964; Baroni, 2006; Evert & Baroni,
2007).

Distributional models of semantics are defined according to two main dimensions: the
type of context used and the learning algorithm for distributional vectors (see Bullinaria &
Levy, 2007; Riordan & Jones, 2011 for reviews). Accordingly, three major models have
proved to be very influential in computational linguistics and cognitive psychology alike:
LSA (Latent Semantic Analysis; Landauer & Dumais, 1997), HAL (Hyperspace Analogue of
Language; Burgess 1998) and the Topic models (Griffiths, Steyvers & Tenenbaum, 2007).
The LSA and HAL algorithms describe the statistical distribution of concepts according to
their frequency of co-occurrence with other words. In addition, these models produced close
performances when comparing with behavioural data. This suggests that they are able, to
some extent, to mimic the extraction of semantic representation from language (see Andrews
et al., 2009; Binder, Conant, Humphries, Fernandino, Simons, Aguilar & Desai, 2016). In
particular, the distance values between similar vectors obtained from the LSA and HAL
models were able to predict reaction times for semantic priming effects and synonym
recognition tests respectively. More recently, Griffiths and colleagues (2007) developed a
Bayesian model of distributional statistics, which outperformed the LSA and HAL models on
predicting a series of semantic phenomena such as semantic similarity and word associations.

This account of semantic representation based on linguistic features finds no issue in
explaining the representation of abstract concepts. This prevalence of linguistic features is
further evidenced by the late age of acquisition of abstract concepts, which suggests that they
are more likely to be acquired through language exposure (Della Rosa et al., 2010). In
addition, neuropsychological data have shown that the processing of abstract words shows
higher levels of activation in the left hemisphere associated with linguistic processing (Binder
et al., 2005; Binder, Desai, Graves, & Conant, 2009; Wang et al., 2010). This view of
distributional semantics using amodal linguistic symbols as a proxy for representing meaning
has been under fire, particularly from researchers subscribing to the theory of embodiment,
for its lack of grounding in perceptual and motor states.

## Grounded accounts of semantic representation

According to the embodied account of semantic representation, concepts gain their
meaning from internal simulation of the perceptual and sensorimotor features that define them
(Barsalou, 1999; 2012; Borghi et al., 2017; Gallese & Lakoff, 2005; Glenberg, 1997; Zwaan,
2004; Kiefer & Pulvermüller, 2012; Meteyard, Cuadrado, Bahrami & Vigliocco, 2012). For
example, the concept <lemon> acquires its meaning from a re-enactment of features such as
the colour yellow, the texture of the peel and the acidic taste. Pulvermüller, Shtyrov, and
Ilmoniemi (2005) used brain-imaging techniques to show that brain areas responsible for
motor actions of the face and leg are activated when action words such as kick or lick are
processed. This evidence struggles, however, to explain the grounding mechanisms for
abstract concepts such as *justice* or *freedom* which do not refer to direct perceptual features or
sensory-motor states (see Borghi & Pecher, 2011; Borghi, Binkofski, Castelfranchi, Cimatti,
Scorolli & Tummolini, 2017, Pecher, 2018 for reviews; Dove, 2009, 2011, 2014; Machery,
2016). However, several hypotheses, from strongly to weakly embodied, have been developed
as explanations for the grounding mechanisms of abstract concepts.

According to strong embodiment assumptions abstract concepts are as grounded and reliant on sensory-motor states as concrete concepts are (e.g., Glenberg & Kaschak, 2002; see Borghi et al., 2017 for a review). For instance, the conceptual metaphor theory describes the grounding of abstract concepts according to ‘image schemas’ corresponding to mental representations (e.g., Lakoff & Johnson, 1980; Gallese & Lakoff, 2005). More precisely, studies have shown that abstract concepts of valence and power are grounded in schemas described by spatial vectors using vertical vectors to represent positions of power while horizontal vectors represent valence concepts (see Pecher, 2018 for a review). As these schemas establish a mapping between abstract concepts and metaphors the Conceptual Metaphor Theory is limited by the availability of such metaphors to represent the richness of abstract concepts.

According to weak embodiment assumptions abstract concepts are grounded via both
sensorimotor and linguistic modes of representation. Several studies have shown that abstract
concepts activate social and introspective aspects of situations (Barsalou & Wiemer-Hastings,
2005), emotional features (Kousta, Vigliocco, Vinson, Andrews & Del Campo, 2011; Lenci,
Lebani & Passaro, 2018), information about events and thematic roles (Ferretti, McRae &
Hatherell, 2001). They have also found that linguistic information can act as a shortcut to
conceptual simulation (Barsalou, Santos, Simmons & Wilson, 2008). Such results suggest that
abstract concepts are grounded according to mechanisms which place a great emphasis on the
context in which they are used (e.g., Barsalou, 1999; 2003; Wiemer-hasting and Xu, 2005).

Contrary to strongly embodied accounts, these assumptions are sufficiently general to apply to a variety of abstract concepts.

The strongly and weakly embodied accounts represent the extremes of a theoretical continuum. Middle-ground theories, have also been formulated to account for the representation of abstract concepts which consider that both linguistic and grounded features contribute to their representation. For instance, Dove's representation pluralism (Dove 2009, 2011, 2014; see also Borghi et al., 2017; Pecher, 2018) is a hybrid account, according to which abstract concepts activate both linguistic and modality-specific features. Andrews, Vigliocco and Vinson (2009) integrated both distributional and experiential data in a probabilistic model which learned semantic representations in an attempt to reconcile the distributional and embodied accounts. The model revealed an interdependency of linguistic and sensory information. In another study, Lenci, Lebani and Passaro (2018) showed a similar statistical co-occurrence between linguistic and affective information in the representation of abstract words by applying distributional analyses on norming data.

Finally, the hub-and-spoke model provides some neurological evidence in accordance with hybrid accounts of conceptual representation (Patterson et al., 2007, 2015; Mahon & Caramazza, 2009; Lambon Ralph et al., 2010; Martin et al., 2014; Martin, 2016). The hub-and-spoke model posits that semantic concepts are processed in a distributed network of modality-specific areas called spokes connected through cortical routes to hub regions that integrate all modalities (Figure 4).
<figure><img src="image_6.png" /></figure>

**Figure 4.** The hub-and-spoke model of semantic processing. Modality-specific spokes represent motion (yellow), colours (dark blue), shapes (green), names (orange), actions (light blue) and tasks (purple) which converge towards the amodal “hub” (red) represented in the anterior temporal lobe (ATL; Adapted from Patterson et al., 2007).

Neuropsychological data from patients with semantic dementia (SD) identify the ATL (anterior temporal lobe, see Figure 4) as a semantic hub region (see for example Patterson et al., 2007) due to a cross-modality pattern of semantic impairment found in SD patients. Consequently, this deficit has been construed as evidence of the amodal nature of the ATL and its role as a convergence zone for amodal conceptual representations (Patterson et al., 2007, Rogers et al., 2004; Rogers & McClelland, 2004). Few studies have investigated the role of the ATL in the representation of abstract concepts specifically. However, recently, Hoffman, Binney and Lambon Ralph (2015) found an activation of the ATL for both concrete and abstract concepts with a graded shift as regards to the conceptual information relevant to each type. Accordingly, concrete concepts elicited stronger patterns of activation for ventromedial areas closely associated with visual regions while abstract concepts elicited a stronger activation in dorsolateral areas associated with auditory-verbal regions. Taken together, these patterns of activation suggest that the ATL is a hub for both abstract and concrete concepts across all modalities with stronger connections to related regions according to conceptual content.

Conclusion
Since the early research of the 1960-70s, psycholinguistics has tried to establish a pattern of organisation of the mental lexicon first as a computational prospect with network models and later to make sense of neuropsychological and behavioural data. Major advances have been made over the decades as researchers established that similarity, whether computed as the overlap of features or co-occurrence in similar contexts plays a key role in the organisation of conceptual knowledge. In recent years, the nature of the conceptual features has been at the centre of attention in order to answer the challenge posed by abstract concept representation. Neuropsychological, computational and behavioural evidence have shown that the nature of features can be linguistic as well as grounded in modality-specific information. Neural evidence has also suggested the existence of hubs that abstract specific features in favor of similar core components of concepts (Patterson & Lambon Ralph, 2016). Evidence from the amodal hubs suggests a mechanism of abstraction which allows for the generalisation of conceptual meaning beyond encoded idiosyncratic features. As a result, the question of how concepts are represented is impossible to treat without considering how meaning is abstracted and generalised to all instances of a concept.

**5. Abstraction and generalisation mechanisms in the processing of abstract concepts**

Mechanisms of categorisation address a core tenet of conceptual knowledge in exploring how the meaning of concepts is generalised from isolated exemplars to all instances. Generalisation refers to the process of extracting features across multiple instances of a concept so that common features are given more weight in the representation of meaning compared to idiosyncratic features which become abstracted (see Murphy, 2002; Glenberg, 2006; Altmann, 2017; Yee, 2019). This process is a cognitive feat without which it would not be possible to reason, make inferences and more generally make sense of the world. Many similarities exist in the respective literatures for concept representation and generalisation
which suggests that both rely on related mechanisms. As Murphy (2013) observed, this literature is still set in the prototype vs. exemplar debate as it has been for decades and without the perspective of a clear resolution in sight.

## Models of the prototype view of concept learning

What is the nature of the mental representation that comes to mind when we think of a category word such as furniture or emotion? According to the prototype theory of Rosch (1975; Rosch & Mervis, 1975), such mental representations require a summary description of the core properties which define a category. Concepts are categorised according to their similarity with the most prototypical member of the category. In a series of priming experiments, Rosch (1975) found a facilitation effect for members of categories that presented a pattern with many properties in common with other members of the category and few properties in common with other categories. Consequently, researchers have adopted an interpretation of the prototype view according to which categories are represented by a set of features with some features being more important than others are (Posner & Keele, 1968; Hampton, 1979; Rosch & Mervis, 1975; Smith & Medin, 1981; Murphy, 2002; 2016). In these terms, the prototype view is in accordance with the featural view of concept representation and distributional approaches including feature-based models (e.g., Rogers et al., 2004; Rosch & Mervis, 1975; Smith, Rips, & Shoben, 1974) and hierarchical network models (e.g., Collins & Quillian, 1969). According to this account, concepts are defined as an unstructured list of features with associated weights called schemata (plural of schema; Cohen & Murphy, 1984; Smith & Osherson, 1984). Rumelhart and Ortony (1977) described schemata as data structures that represent generalised concepts underlying objects, situations, events and network relations with other concepts (see Murphy, Hampton & Milovanovic, 2012, for example).
This prototype view became dominant after challenging the classical “all-or-none
view” of concepts according to which concepts are categorized based on necessary and
sufficient rules of membership (see Katz, 1972 for example). Despite its initial success, this
account fell out of favour to the benefit of an exemplar view of concept learning which
offered a better fit of empirical data (see Nosofsky, 1992 for a review). The major argument
against the prototype view lies in its failure to represent the variety of correlated features that
compose the examples encountered during concept learning (Fried & Holyoak, 1984; Medin,
Altom, Edelson & Freko, 1982; Rips, 1989).

The exemplar theory of categorisation is an alternative to the prototype view according
to which such rules of membership are not necessary. The exemplar theory represents
category membership according to stored situations where the concept applies (Medin &
Schaffer, 1978; Blair & Homa, 2001; Medin & Schwanenflugel, 1981; Murphy, 2002; 2005).
The generalisation of a category label from encountering a set of examples is dependent on
the similarity of the examples and each member of the category (Medin & Schaffer, 1978;
Nosofsky, 1986). For instance, a child that would encounter three instances of a bird and
recognise it as such would have formed three “bird” exemplars that can be generalised to all
instances and serve for later classifications (Barsalou, Huttenlocher, & Lamberts, 1998;
Nosofsky, 1988). Contrary to the prototype theory, exemplar models suggest that all
exemplars are stored and retrieved in concept learning.

**Influential models of the exemplar theory**

One of the most influential models of the exemplar account is the Generalised Context
Model (GCM) of Nosofsky (1986) which posits that people store and classify concepts based
on a vast collection of exemplars. New elements are stored and classified on the basis of their
similarity to previously encountered exemplars. The model uses multidimensional scaling to
represent similarity (MDS; see Shepard, 1958; 1987) which defines exemplars as points in a
psychological space. Similarity is measured as the distance between exemplar points. The shorter the distance between points, the more similar the exemplars they represent are. Figure 5 illustrates the conceptual idea behind the model. Each point represents an exemplar which can vary along two main dimensions A and B. Exemplar A2 and B4 are positioned very closely in the dimensional space compared to exemplars A5 and B2. According to the illustrated case, A2 and B4 would be computed as highly similar compared to A5 and B2 that would be computed as highly dissimilar. The model can compute the categorisation of a novel item *i* by summing up its similarity to all exemplar in category A and in category B to determine its category membership.

<figure><img src="image_7.png" /><figcaption>**Figure 5.** Schematic illustration of a category structure to explain the workings of the GCM (adapted from Nosofsky, 2012).</figcaption></figure>

The GCM model theoretically derives from the Medin-Schaffer context model (1978) which considered exemplars based on two dimensions only. In contrast, the GCM offers a more varied account of exemplars domains represented in the multidimensional scale. This flexibility largely contributed to the model's influence as evidenced by the numerous extensions that have been proposed over the years. For example, Kruschke's (1992) highly influential ALCOVE (attention-learning covering map) model implemented building blocks of the GCM such as the exemplar-based dimensionality and similarity principles in a connectionist framework. In this way, classification mechanisms are not only modelled but
rather learned on a trial-by-trial basis. The compelling account of the GCM model is illustrated in the number of extensions that have been proposed over the years which include Nosofsky and Palmeri's (1997) exemplar based random walk (EBRW) and Lambert's (2000) extended generalised context model for response times (EGCM-RT) which all provide predictive data. However, like the prototype view of concept learning, the exemplar view did not remain unchallenged.

## Challenges to the exemplar view

The main challenges to the exemplar view concern the issue of cognitive economy (Rosch, 1978). Exemplar accounts posit that every encountered exemplar is stored and retrieved from memory. Both assumptions seem impractical as they violate the economical strategies of cognitive processes (Feldman, 2003). More recently, Murphy (2016) reviewed the literature on exemplar models and concluded that despite their ability to explain data from many concept-learning experiments, it is not adequate to describe real-world category and concept learning. Murphy (2016) objected to the idea that category learning experiments can faithfully provide an ecological representation of the cognitive mechanisms involved in concept learning. In contrast with the prototype view, the exemplar view cannot account for the hierarchical structures of semantic networks (Quillian & Loftus, 1969; Collins & Loftus, 1975). To account for such hierarchies would require that every exemplar be encoded along with its category membership which would contradict the cognitive economy principle. However, the exemplar view has an advantage over the prototype theory in its ability to garner a sufficient amount of information to represent and learn concepts that would be lost in a prototypical account.

Taken in isolation, neither the prototype nor the exemplar theory can provide a compelling account of concept learning. The major debate opposes the loss of information in
a prototype account to the violation of cognitive economy in the exemplar view (Komatsu, 1992; Vanpaemel & Ons, 2005; see Vanpaemel & Storms, 2008 for a discussion).

To answer these challenges, Vanpaemel and Storms (2008) proposed the Varying Abstraction Model (VAM) that combines both the information of the exemplar account and the cognitive economy of the prototype account. They viewed the debate between exemplar and prototype accounts as a debate on abstraction mechanisms. The VAM model posits that the prototype and the exemplar accounts exist on a continuum of abstraction where exemplars represent minimal abstraction whereas prototypes represent maximal abstraction. The model describes a two-steps procedure with varying levels of abstraction. On Figure 6, panel A represents a mechanism of abstraction based on the prototype end of the continuum while panel B represents a mechanism based on the exemplar end of the spectrum. Finally, panel C represents a middle-ground position that consists of subprototypes obtained from the abstraction of a subset of exemplars.

<figure><img src="image_8.png" /><figcaption>**Figure 6.** The two-step procedure to construct a category representation: (1) partition the category into clusters and (2) construct the centroid for each cluster. In this way, it is possible to construct the prototype representation (panel a), the exemplar representation (panel B), and a set of intermediate representations, one of which is illustrated in panel C (reproduced from Vanpaemel & Storms, 2008).</figcaption></figure>
Despite the emergence of models to find a middle ground between these two views,
the debate has yet to be resolved and no consensus has been reached. More recently,
Ambridge (2020) argued against stored abstractions and proposed instead a radical exemplar
model based on a strict dichotomy between the exemplar view and the process of abstraction.
This dichotomy is rooted in the original debate on prototype and exemplar theories which
defined prototypes as abstracted core features that represent a category while exemplar
accounts assumed a position against abstraction based on the storage of every exemplar. This
exemplar vs. abstraction dichotomy has been explored in studies on concept learning in
children. For instance, Ambridge (2016) took position in favour of the prototype view and
explained that children form and store a semantic construction prototype. This position has
been challenged by recent models and experiments which suggest that the assumptions of
storing exemplars and abstraction are not contradictory. Studies have shown that children
develop more abstract representations as their language grows until they are able to generalise
across common features of meaning (Savage, Lieven, Theakston & Tomasello, 2006;
Rowland, Chang, Ambridge, Pine & Lieven, 2012). In addition, hybrid models posit that
abstracted representation are composed of stored exemplars (Abbot-Smith & Tomasello,
2006; Goldberg, 2006; Langacker, 1988). Even Ambridge (2020) reversed his position and
proposed that word meanings are composed of exemplars, rather than represented as
prototypes based on a central meaning. Most of the literature on concept learning and
categorisation has been focused on concrete objects and generic concepts (e.g., triangles,
faces, etc). These assumptions can also be applied to the study of abstraction mechanisms in
the learning of abstract concepts.
How does this relate to mechanisms of abstraction and generalisation in the
processing of abstract concepts?

In recent years, much work has been focused on solving the challenge posed by
abstract concepts (see Borghi et al., 2017; Pecher, 2011, 2018 for reviews) especially in light
of their variety and complexity which make them difficult to classify in well-defined
categories. Little is known about how abstract concepts are learned, categorised and
generalised to all instances of a particular concept. Studies have shown that the preferred
mechanism of abstraction depends on the structure of the category. According to Minda and
Smith (2001), the prototype account is better suited for large categories whereas Feldman
(2003) suggests that the exemplar account is more adequate for complex categories. More
broadly, the literature on concept learning explores the mechanisms of abstraction that
aggregate individual instances of a concept such that common features gain prominence while
idiosyncratic features carry little weight (see Yee, 2019 for a review).

Abstraction mechanisms are discussed in terms of categorisation levels from lower
levels to higher levels of abstraction (e.g., robin -> bird -> animal). This hierarchy is more
difficult to establish for abstract concepts that can themselves be decomposed into a set of
abstract features and generally refer to complex situations (Borghi et al., 2017; Recchia &
Jones, 2012). How are the concepts of joy, fantasy or justice categorised for instance? The
literature on abstract concepts has described several large domains to define abstract concepts
rather than the categories used to classify concrete objects. These domains can broadly be
described as number concepts, morals and aesthetics, the social and cultural dimension as well
as emotion. These domains can be grounded in dimensions of sociality, language,
interoception, perception and action, and finally metacognition (see Borghi, Barca, Binsofsky
& Tummolini, 2018 for a review of these domains and dimensions of abstract concepts). This
complexity of abstract concepts mean that little is known about their mechanisms of
abstraction.

In recent years, Gentner and Asmuth (2017) have proposed a process of abstraction
they call Career of Metaphor which describes how abstract meaning can be generalised
through figurative language. For example, they consider the abstract relational meaning of the
concept <anchor> that can be abstracted through figurative expressions such as <religion is an
anchor>. Repeated exposure to such figurative instances would highlight the common features
between the concepts <anchor> and <religion> by abstracting the meaning of the metaphor in
favour of an underlying common relational structure which can be extended to other situations
that share similar structures. The major limitation to the metaphorical account for abstraction
is similar to the one found in the Conceptual Metaphor Theory for the grounding of abstract
concepts which is that there cannot be a one-to-one mapping between each abstract concept
and relational metaphors (see Pecher, 2018 for a discussion).

Conclusion

The literature on concept learning is made complex by a lack of consensus between
the prototype and exemplar views on the one hand and the exemplars and abstraction
dichotomy on the other hand. This literature is made even more complex for psycholinguistics
research due to a lack of studies that have used semantic concepts in favour of more generic
concepts such as lines, triangles, faces which are easier to generate, to norm and to diversify
along chosen dimensions. The combination of word learning experiments and concept
learning provides a powerful framework for studying how semantic concepts are abstracted
and generalised to all instances. While this framework has been successfully applied to
studying the abstraction mechanisms of concrete objects, very little is known about how
abstract concepts are abstracted and generalised. This is mainly due to the fact that the
literature on abstract concepts is rather recent and the majority of studies and models have
been dedicated to their representation and grounding mechanisms that have always
represented a challenge. The complexity of abstract concepts and their lack of clear-cut
categories make them even more of a challenge. The previous sections of this theoretical
chapter have introduced the behavioural and neural evidence on the representation of abstract
concepts. Although cognitive and neuroscientific approaches often have little to say about
how concepts are structured, the newly prolific class of cognitive network models are an
important exception.

**6. Network cognitive science**

Networks are used to explore the intrinsic structural design of many real-world
phenomena. The last two decades have generated a wider interest for their application to study
complex systems across all domains of science. This section is divided in three parts. The
first part retraces the historical roots of network science and introduces the three most
commonly encountered models of networks: the Erdös-Renyi model (1959), the Watts-
Strogatz model (1998) and the Barabasi-Albert model (1999). The second part exposes the
main mathematical parameters that allow for inferential network analyses. Finally, the third
part is an overview of cognitive network science or the application of graph theory to the
study of human cognition with an emphasis on the mental lexicon.

**Historical account and classical models of network science**

Network science stems from the mathematical graph theory but its historical roots are much
older. The earliest known paper written by the mathematician Euler (1735) provided a
solution to the famous Königsberg bridge problem. In this paper, Euler transcribed the map of
the Prussian town of Königsberg into what is now known as a network to prove that it was not
possible to cross each bridge of the town exactly once (Figure 7). This solution described for
the first time the notions of nodes –or vertices– and edges that are central to the field of
network science today. These notions are the basis to a powerful quantitative method with
nodes that can represent any entity of a complex system and edges that symbolise patterns of
connectivity.

<figure><img src="image_9.png" /><figcaption>**Figure 7.** Euler's schema from 'Solutio problematis ad geometriam situs pertinentis'.</figcaption></figure>

The generalisation of Euler’s solution found many applications in every domain of science
and led to the discovery of fundamental principles of networks. One such principle is the
randomness of natural networks described by the mathematician Erdös in the 1950s. The
**Erdös-Renyi model (1959)** states that for any two random nodes in a network it is possible to
compute the shortest path -or the least number of jumps- that it would take to go from one
node to the other. According to the model, natural random networks are characterised by
small path lengths meaning that it takes very few jumps to move from one node to another
regardless of the size of the network.

These later developments in the formulation of network principles have given rise to a
number of applications in domains other than discrete mathematics. For instance, Milgram
(1967) tested this property of shortest path in a social experiment (see Figure 8). He asked a
group of participants from Omaha, Nebraska to send a letter to a stockbroker in Boston,
Massachusetts using their own social connections. Results showed that on average, only six
intermediary social connections were needed for the letter to reach its destination. This model
so named because it illustrates the saying we live in a small world lies at the origin of the six-
degree-separation principle (Guare, 1990). Milgram formulated the following small-world
problem: "[...] starting with any two people in the world, what is the probability that they will
know each other? ...while persons X and Z may not know each other, they may share a
mutual acquaintance- that is a person who knows both of them." Milgram (1967, p.4).

<figure><img src="image_10.png" /><figcaption>**Figure 8.** Illustration of the Small-World Problem from Milgram (1967).</figcaption></figure>

This principle became very influential in most fields that use network science for its ability to model real world phenomena. **Watts and Strogatz (1998)** explored the dynamics of self-organising systems further and established that most complex systems such as biological, technological or social networks present structural characteristics that lie between regular networks and random networks. In a regular network, every node is connected to every neighbour while a random network is characterised by a random pattern of connections between nodes. These middle-ground models can be highly clustered -with a high number of connections between nodes- like regular networks yet have small path lengths like random networks (Figure 9). A combination of high clustering and small path lengths became the
hallmark of a small-world structure based on an analogy with Milgram's small world phenomenon.

<figure><img src="image_11.png" /><figcaption>**Figure 9.** Small-world structure from Watts & Strogatz (1998). The small-world model was obtained by interpolation of a regular ring lattice and a random network, without altering the number of nodes or edges in the graph.</figcaption></figure>

Later studies confirmed these small-world properties in social networks (e.g., Watts & Strogatz, 1998; Kitsak et al., 2010), epidemiology of infectious diseases (e.g., Keeling & Rohani, 2008), spread of computer viruses (e.g., Pastor-Satorras & Vespignani, 2001), protein interaction (e.g., Bork et al., 2004), gene expression (e.g., van Noort, Snel & Huynen, 2004), neural (e.g., Basset & Bullmore, 2017) and semantic networks (e.g., Steyvers & Tenenbaum, 2005).

The benefit of using network representation for complex systems lies in their ability to represent not only structural characteristics but also dynamic processes of growth over time.

For instance, according to the **Barabasi-Albert model (1999)**, every time a node is added to a network, it connects either to an existing hub of influential nodes or to a community of nodes that are close to each other following a mechanism of preferential attachment (Figure 10).
<figure><img src="image_12.png" /><figcaption>**Figure 10.** Three graphs of 20 nodes each generated with the Barabasi-Albert model (1999). The increasing value of m represents the parameter of attachment. A high value of m signifies that the more connected a node is, the more it is susceptible to receive links.</figcaption></figure>

These characteristics generate self-organising systems with a high level of information transfer and provide powerful structural and dynamic models to explain real world phenomena from the World Wide Web to human cognition. Network models also provide powerful mathematical parameters based on graph theory to draw quantitative inferences about structural and growth mechanisms.

## Network parameters and analysis

A network is a graph composed of connected nodes also called vertices. The links between nodes can provide three types of information: (1) the existence of a connection between two given nodes for which case, the link is called an edge in an undirected network, (2) the direction of this connection from the source node to the destination node for which case, the link is represented by an arrow called an arc in a directed network, and (3) the weight of this connection. Nodes can be made to represent any entity of a complex system such as grids, airports, people, protein, etc. In the same way, the weights correspond to any value of the complex systems such as distance between locations, force of interaction, etc. These characteristics provide the fundamental topology of a network. Network modelling proposes
parameters and measures that are used to draw inferences. Inferences are not based on
statistical significance as it is usually the case when manipulating behavioural data but rather
draws on the comparison of the network of interest with a comparable random network that
can be generated with the Erdös-Renyi model (1959).

The macrostructure of the network is given by its diameter (D) measured as the longest path between two nodes. The average shortest path length (ASPL) refers to the average minimum number of steps from one node to another. The Erdös-Renyi (1959) and Watts-Strogatz (1998) models have established the importance of the ASPL for random and small-world networks which is often used to assess the small-world structure of a network. On a more empiric viewpoint, the ASPL has been used in semantic networks to model lexical parameters such as semantic similarity and verbal association (see Kenett, Levi, Anaki & Faust, 2017). The density (d) is defined as a ratio of the number of edges in the network to the number of all possible edges.

At the microscopic level, nodes are characterised by their degree or number of connections. In a directed network, each node has an in-degree ($k^{in}$) that is the number of incoming arcs and an out-degree ($k^{out}$) that is the number of outgoing arcs. An undirected network only has degree $k$ corresponding to the number of edges of a node. The average degree $<k>$ describes the overall connectivity of the network. The clustering coefficient (CC) is the probability that two nodes are interconnected knowing they both are connected to a common neighbour (Table 1). The microscopic description of a network can assess the effectiveness of flow of information in the network. Studies have shown the impact of the clustering coefficient on word recognition (Chan & Vitevitch, 2009; Yates, 2013; Siew, 2018) and recall (Vitevitch, Chan & Roodenrys, 2012).
<table><caption>**Table 3.** Network parameters and description</caption><thead><tr><td>network characteristics</td><td>Description</td></tr></thead><tbody><tr><td>D</td><td>the diameter of the network</td></tr><tr><td>ASLP</td><td>the average length of shortest path</td></tr><tr><td>CC</td><td>the clustering coefficient</td></tr><tr><td>&lt;k&gt;</td><td>average degree</td></tr><tr><td>k, k<sup>in</sup>, k<sup>out</sup></td><td>the degree, in-degree, and out-degree</td></tr><tr><td>d</td><td>density of the network</td></tr></tbody></table>

At the mesoscopic level, nodes can be organised into sub-network communities. Communities are defined as a group of nodes that are tightly interconnected but have few connections with the rest of the network. This community structure is best captured by the modularity measure established by Newman (2006). A network with a high modularity measure is formed of an important number of communities.

The described network parameters have been applied to uncover structural principles of human cognition. More particularly, semantic network analysis has been used to great extent in the seminal work of Tenenbaum and Steyvers (2005) to establish the small-world structure of semantic networks.

## Cognitive network science approach to the mental lexicon

Cognitive science has a long tradition of exploring the structural substrates of human cognition as evidenced by the development of symbolic, connectionist and neural models (Anderson, 1996; McClelland, McNaughton and O'Reilly, 1995; Dell, Chang & Griffin, 1999). It has emerged in recent years as a new mathematical framework to analyse behavioural and neural data (Baronchelli, Ferrer-i-Cancho, Pastor-Satorras, Chater, & Christiansen, 2013; see Siew, Wulff, Beckage, & Kenett, 2019 for a review). Much of this approach has been dedicated to modelling the structural principles and growth mechanisms of the mental lexicon (see Tenenbaum & Steyvers, 2005).
Network representation of semantic memory is rooted in Collins and Quillians' seminal work (1969) which predates this new interest for cognitive network science. Collins and Quillian (1969) suggested that semantic memory is organised in a hierarchical tree-like structure with categories and features represented as nodes (see Figure 11a). Their model stated that the retrieval efficiency of the semantic fact <a canary can fly> for example lies in the generalisation of stored features such as <a canary is a bird> and <birds can fly>. This model puts much constraint on lexical access based on hierarchical relations at the expanse of less typical concepts (Carey, 1985; Rips, Shoben, & Smith, 1973; Sloman, 1998). Collins and Loftus (1975) proposed an alternative to this hierarchical network structure (Figure 11b).

Their model represented a large-scale unstructured network where nodes correspond to words or concepts linked by patterns of similarity. The model described a retrieval mechanism based on spreading activation able to predict many lexical phenomena such as retrieval from memory, priming and interference effects (Deese, 1965; Collins & Loftus, 1975; Anderson, 2000; Nelson, McKinney, Gee & Janczura, 1998). Despite the efficiency of the model of Collins and Loftus (1975), it fails to render the dynamic processes governing the organisation and growth of the mental lexicon.

More recently, Tenenbaum and Steyvers (2005) studied the structural principles of human semantic knowledge using semantic network analysis on word association data. They found that semantic networks have a small-world structure (Figure 11c).
<figure><img src="image_13.png" /><figcaption>**Figure 11.** Proposed large-scale structures for semantic networks from Tenenbaum and Steyvers (2005): (a), a tree-structured hierarchy (e.g., Collins & Quillian, 1969); (b), an arbitrary, unstructured graph (e.g., Collins & Loftus, 1975); (c), a scale-free, small-world graph (e.g. Tenebaum & Steyvers, 2005).</figcaption></figure>

In a word association task, participants are presented with a series of cue-words to which they must respond with the first word that comes to mind. In a network based on association data, the nodes correspond to words connected by an undirected edge or a directed arc. A directed network provides additional information about the direction of the association from cue to response words. The weights of the arcs and edges represent the frequency or strength of association.

Tenenbaum and Steyvers (2005) constructed a directed network from word association data collected by Nelson, McEvoy and Schreiber (1999) comprising 5,000 cue-words. Their analyses focused on the number of connections per word or degree ($<k>$, $k^{in}$, $k^{out}$), the average shortest path length (ASPL) between two words and the clustering coefficient (CC). When comparing the word association network to a random network generated with the **Erdös-Renyi model (1959)**, the word association network showed a combination of highly clustered nodes and a small average shortest path length characteristic of a small-world structure described by Milgram (1967) and **Watts and Strogatz (1998)**. In addition, a focus on the mesoscopic level showed the presence of many hubs of highly interconnected nodes that share few connections with the rest of the network. This pattern of connectivity testifies
to the nature of growth mechanism taking place in the network over time. It suggests a scale-
free organisation in which new nodes tend to join already existing hubs and communities
following the same mechanism of preferential attachment described by **Barabasi and Albert**
**(1999)**.

De Deyne and Storms (2008) later confirmed the small-world properties of semantic networks using a continuous word association task. They found that the centrality of nodes within the network related to the age of acquisition of the word with a denser pattern of connection for words acquired early. This supports the view of how the mental lexicon grows over time suggested by the model of Tenenbaum and Steyvers (2005). More recently, De Deyne, Navarro and Storms (2013) constructed a network based 12,000 cues using a multiple-response procedure that covered a large portion of the mental lexicon and which allowed for better prediction of lexical access and semantic similarity (see also De Deyne, Navarro, Perfors, Brysbaert & Storms, 2019).

**Conclusion**

The network modelling approach has proven its ability to provide additional insights by
representing language processing on a larger and more dimensional scale, which was not
possible with previous methods and models. This approach aims to provide a complementary
account of the organisation and dynamic of the mental lexicon. It proves to be a powerful tool
that empirically outperforms the prediction ability of more linear databases. Finally, network
science can be applied to study brain connectivity using the same parameters (Siew et al.,
2019 for a review). This behavioural account of the mental lexicon could therefore be
completed with network analyses of brain patterns of connectivity to link the behavioural data
to the neural substrates that generated the structure and dynamic of the mental lexicon.
SYNTHESIS and INTRODUCTION TO EXPERIMENTAL

CHAPTERS

Research on abstract conceptual knowledge has recently become the focus of interest and benefitted from a long tradition of studying psycholinguistic phenomena which translates into a diversity of approaches, models and theories. In the present thesis, we set out to explore fundamental questions about abstract conceptual knowledge spanning from the processing of the word-level to the higher levels of processing which allow for the representation of generalised abstract concepts. Figure 12 provides an overview of the scope of the levels of investigation that we explored.

<figure><img src="image_14.png" /><figcaption>**Figure 12.** Graphical representation of the scope of the present thesis.</figcaption></figure>
The second chapter draws from the longest psycholinguistic tradition and provides a much-needed database for 630 semantically-similar word pairs in French. The main incentive for the development of this database was methodological and practical as it aims to bridge the gap in the French literature as regards to the availability of normed stimuli for abstract semantic word pairs. Similarly, Chapter 3 provides a complementary database comprising the same isolated words from the previous database which were presented in a word association task. The aim for this database was to provide users with the ability to have control over the nature of crafted stimuli with respect to purely semantic, purely associated or with an overlap in the relationship between chosen stimuli. These two chapters focus on the representation of semantic abstract concepts at the word-level. As such, they provide insights as to the linguistic variables that can impact the processing of lexical concepts but are rather limited when it comes to the structural organisation of the mental lexicon. The rich amount of data generated by the word association task allowed for the use of methods borrowed from corpus linguistics and the mathematical graph theory. In Chapter 4, we used these data to build the first semantic network of the French mental lexicon at the time of writing this manuscript which provides novel insights as to the difference in structural organisation for abstract concepts compared to concrete concepts.

During the last two decades, the interest for the representation of abstract concepts has intensified. In the past however, assumptions had been made as to an assumed paucity of content characterising abstract concepts compared to concrete concepts. In Chapter 5, we explored the richness of abstract concepts and the nature of their features. We used a novel type of stimuli composed of abstract pictures devoid of tangible features in a well-established picture-word priming paradigm. This allowed us to study the role of situational information compared to intangible information in the processing of abstract concepts. This chapter
provides insights as to the featural nature of abstract concepts beyond their situational and
linguistic components.

In chapter 6, we explored a higher level of processing focused on the mechanisms that
come into play in the generalisation of meaning to novel instances of abstract concepts. We
introduced a database of 42 novel abstract concepts each illustrated by 9 exemplars of
scenarios. The database was presented to participants who rated the similarity between
exemplars. The database was then used in a word learning experiment where participants were
introduced to a series of exemplars of each abstract concept that were either similar or
dissimilar to each other. The combination of word learning experiments and concept learning
provided a powerful framework for studying how abstract semantic concepts are generalised
to all instances of meaning.

Finally, we conclude this thesis by proposing a theoretical model based on our
findings with the purpose of bridging the gap between levels of investigation of abstract
concepts processing. Taken together, our findings describe a reversed pattern of processing
with lower levels ruled by similarity-based processes while higher levels of processing are
rules by diversity-based mechanisms.
# Experimental Section
The study of the representation of concepts at the word-level requires access to normed stimuli to insure the control of potentially confounded variables. This study of semantic concepts has long been centered on the representation of concrete concepts.

However, in recent years, this focus has shifted to the representation of abstract concepts. This means that abstract concepts have not benefitted from the long tradition of developing psycholinguistic norms to the same extent that concrete concepts have. Consequently, there is a gap in the literature as regards to the availability of normed stimuli for abstract concepts. In order to address this gap in the literature, the following chapter introduces a database of semantically similar pairs of French words with varying levels of abstractness. This database is to the best of our knowledge and at the time of writing this manuscript, the first to introduce semantically similar pairs of abstract words in the French language. In addition, we collected semantic similarity and concreteness judgments to address the suggestion of Crutch and Warrington (2011) about different patterns of organisation in the mental lexicon for concrete and abstract concepts. The database was cross-referenced with other known psycholinguistic databases to allow for the control of lexical variables in the crafting of experimental stimuli.

A first cluster analysis has shown that the word pairs could be organised according to three levels of concreteness. Further descriptive and correlational results have shown that the prime and target words shared the same level of concreteness and were well-matched across all lexical variables. The entire database is available on the OSF framework.
# Chapter 2. Semantic similarity and associated abstractness norms for 630 French word pairs

Dounia Lakhzoum, Marie Izaute, and Ludovic Ferrand

Université Clermont Auvergne, CNRS, LAPSCO, F-63001 Clermont-Ferrand, France

**Chapter 2 was published under the reference:** Lakhzoum, D., Izaute, M., & Ferrand, L. (2021). Semantic similarity and associated abstractness norms for 630 French word pairs.

*Behavior Research Methods, 53, 1166-1178.*

Supplementary material: https://osf.io/qsd4v/

Running-title: Semantic similarity and associated abstractness norms

**Corresponding author:** Dounia Lakhzoum or Ludovic Ferrand, Université Clermont Auvergne, CNRS LAPSCO, 34 avenue Carnot TSA 60401-63001 Clermont-Ferrand Cedex 1, France

Phone: + 33 4 73 40 62 57 – Fax: + 33 4 73 40 64 82

Email: dounia.lakhzoum@uca.fr ; ludovic.ferrand@uca.fr

## Author's note

This research was financed by the French government IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25).

## ACKNOWLEDGMENT

This work was sponsored by a public grant overseen by the French National Research Agency as part of the "Investissements d'Avenir" through the IDEX-ISITE initiative CAP 20-25 (ANR-16-IDEX-0001).
**ABSTRACT**

The representation of abstract concepts remains a challenge, justifying the need for further
experimental investigation. To that end, we introduce a normative database for 630
semantically-similar French word pairs and associated levels of abstractness for 1260 isolated
words based on data from 900 subjects. The semantic similarity and abstractness norms were
obtained in two studies using 7-points scales. The database is organized according to word-
pair semantic similarity, abstractness, and associated lexical variables such as word length (in
number of letters), word frequency, and other lexical variables to allow for matching of
experimental material. The associated variables were obtained by cross-referencing our
database with other known psycholinguistic databases such as Lexique (New et al., 2004), the
French Lexicon Project (Ferrand et al., 2010), Wordlex (Gimenes & New, 2016), and
MEGALEX (Ferrand et al., 2018). We introduced sufficient diversity to allow researchers to
select pairs with varying levels of semantic similarity and abstractness. In addition, it is
possible to use these data as continuous or discrete variables. The full data are available in the
supplementary materials as well as on OSF (https://osf.io/qsd4v/).

**Keywords:** Semantic similarity norms, Concreteness, Normative ratings, Abstract concepts, French word pairs.
# INTRODUCTION

Conceptual representation has been the focus of much study and debate for decades in the fields of semantics and psycholinguistics. In contrast with the holistic and non-decompositional view held by Collins and Loftus (1975), at the heart of the debate now are two seemingly opposite accounts of semantic representation: the distributional and the embodied accounts of conceptual representation (Harris, 1954; Firth, 1957; see Lenci, 2008; Andrews, Vigliocco & Vinson, 2009; Andrews, Frank & Vigliocco, 2014; Bruni, Tran & Baroni, 2014; Lenci, 2018 for reviews of the distributional account. See Glenberg, 1997; Barsalou, 1999; Zwaan, 2004; Meteyard, Cuadraro, Bahrami & Vigliocco, 2012; Pulvermüller, 2013; Ostarek & Huettig, 2019 for reviews of the embodied account). Both these accounts consider that feature and property overlap play a major role in the processing of meaning (see Vigliocco & Vinson, 2007; Vigliocco, Meteyard, Andrews & Kousta for reviews). Indeed, there is much evidence of this from semantic priming studies, widely regarded as the gold standard for studying semantic representation in the mind and brain (e.g., Hitchison et al., 2013; Kim, Yap & Goh, 2019). However, for both sides of the spectrum the representation of abstract concepts remains a challenge, hence the need for a database of source material enabling us to further our understanding of abstract concepts representation.

## Accounts of semantic representation

### Holistic view and spreading of activation

According to the holistic view of semantic representation, for every element of the world – be it an object, an event, property, etc. – there is an abstract and symbolic lexical equivalent that acts as a referent in the conceptual system of the mind (Fodor, Garrett, Walker & Parkes, 1980; Berg & Levelt, 1990; Roelofs, 1997; Levelt, Roelofs & Meyer, 1999). In this view of one-to-one mapping, each referent represents a single node in a semantic network, with nodes linked
according to their semantic similarity. For instance, the concept *fire* would be represented by a
single node linked to related concepts or properties, such as *red*, also represented by a single
node. Collins and Loftus (1975) described the mechanisms of semantic processing based on
their theory of the spreading of activation in a network, according to which a concept, when it
is processed, activates the path between related nodes at a speed proportional to the strength of
the link between them. The assumption of semantic similarity in the spreading of activation
theory accounts for both the strength of the link between nodes and the ensuing dynamics of
activation for related concepts. Given that in this holistic view, each property or feature of a
concept is represented by a single node, it is a view which contrasts with the decompositional
or featural view.

***Featural view***

According to the featural view of semantic representation, words can be decomposed into
a set of defining features or properties reflecting the meaning of the concept to which they relate
(Smith, Shoben & Rips, 1974). For instance, the concept *fire* would be decomposed according
to its defining features such as <is hot> and <is red>. As with the holistic view, at the core of the
featural view is semantic similarity, but in this case it is measured by the number of features
two concepts have in common (Plaut, 1995; McRae, de Sa & Seidenberg, 1997; Cree, McRae
& McNorgan, 1999; Vigliocco, Vinson, Lewis & Garrett, 2004; Kiefer & Pulvermüller, 2012).
The more features they share, the more semantically similar they are. In recent years, two
seemingly opposite accounts of this featural view have dominated the debate on the nature of
semantic representation, namely the distributional and the embodiment accounts. They differ
from each other in respect of the information used to represent meaning. While distributional
semantics relies on symbolic and linguistic features, embodiment relies on perceptual and
sensory-motor states.
According to models of distributional semantics, meaning is the result of the statistical distribution of words across written and spoken language (see Andrew, Frank & Vigliocco, 2014; Lenci, 2018 for reviews of this account, see also Lund & Burgess, 1996; Landauer & Dumais, 1997; Griffiths, Steyvers & Tenenbaum, 2007; Mandera, Keuleers & Brysbaert, 2017). The meaning of words is therefore defined in relation to other words, depending on their shared symbolic and linguistic features. According to the distributional hypothesis, words occurring in similar contexts have similar meanings (Harris, 1954). This use of intralinguistic relationships was successfully implemented in computational models of semantics (e.g., Hoffman, McClelland & Lambon Ralph, 2018). Motivation for using algorithms such as LSA (latent semantic analysis; Landauer & Dumais, 1997) is the notion that meaning can be extracted by computing semantic similarities between concepts (Louwerse, 2008; 2011; Louwerse & Jeuniaux, 2008; 2010; Rogers & McClelland, 2004; Kintsch, McNamara, Dennis & Landauer, 2007). In addition, the close performance between computational models and human behaviour suggests these models are able, to some extent, to mimic the extraction of semantic representation from language (see Andrews et al., 2009; Binder, Conant, Humphries, Fernandino, Simons, Aguilar & Desai, 2016).

This view of distributional semantics using amodal linguistic symbols as a proxy for representing meaning has been under fire, particularly from researchers subscribing to the theory of embodiment, for its lack of grounding in perceptual and motor states.

The embodied account of semantic representation defines meaning as grounded in perceptual and motor states derived from an individual's sensory experience (Barsalou, 1999; Glenberg, 1997; Zwaan, 2004; Kiefer & Pulvermüller, 2012; Meteyard, Cuadrado, Bahrami & Vigliocco, 2012). For instance, Pulvermüller, Shtyrov, and Ilmoniemi (2005) used brain-imaging techniques to show that brain areas responsible for motor actions of the face and leg are activated when action words such as *kick* or *lick* are processed. Evidence like this struggles,
however, to explain the grounding mechanisms for abstract concepts, where there are no
physical and sensory features (see Borghi & Pecher, 2011; Borghi, Binkofski, Castelfranchi,
Cimatti, Scorolli & Tummolini, 2017 for reviews).

The dichotomy between abstract and concrete concepts is not clear-cut (Della Rosa et al., 2010). The most commonly invoked criterion is tangibility, with concrete concepts referring to tangible entities that are perceptible via the senses, whereas abstract concepts are intangible. According to the Dual Coding Theory (Paivio, Yuille & Madigan, 1968), concrete concepts trigger processing based on two informational systems, one visual, the other verbal, whereas abstract concepts are processed only in the verbal system. The Context Availability Theory (Schwanenflugel, Harnishfeger, Stowe, 1988) posits that while concrete concepts refer to a definite number of contexts, abstract concepts are connected to varied contexts. Although true, this distinction can be considered reductive and contributes to the view that abstract concepts are poor in terms of features. More recently, with the interest shown in abstract concepts by grounded cognition, new elements of definition have emerged according to which abstract concepts refer to intangible features such as emotions, events, social contexts and introspective states (e.g., Barsalou & Wiemer-Hastings, 2005; Harpainter et al., 2018; see Borghi, 2017 for a review). This latter definition reflects a new interest in their grounding mechanisms and semantic representation.

The tangibility criterion is best represented by the concreteness variable defining the
distinction between concrete and abstract concepts based on the dual coding and context
availability theories. It plays a key role in psycholinguistic research, as well as providing an
explanation for many phenomena, such as hemispheric lateralisation in the processing of
concrete and abstract concepts (Oliveira, Perea, Ladera, & Gamito, 2013), or ease of retrieval
of concrete words compared to abstract ones (Mate, Allen, & Baques, 2012; Nishiyama,
2013).
The importance of the concreteness variable is further borne out by the development of
several widely used databases containing concreteness rating norms (Coltheart, 1981) and,
more recently, 40 000 words in English (Brysbaert, Warriner & Kuperman, 2014) and 1659
words in French (Bonin et al., 2018).

Abstract concepts representation

The embodied account has yet to propose a unified theory for the representation of
abstract concepts such as *justice* or *freedom* which do not refer to direct perceptual features or
sensory-motor states (Dove, 2009, 2011, 2014; Machery, 2016; see Pecher, 2018 for a review).
However, several hypotheses, ranging from strongly to weakly embodied, have been put
forward as explanations for the grounding mechanisms of abstract concepts. The strong
embodiment assumptions make no allowance for multiple representations and consider abstract
concepts to be as grounded and reliant on sensory-motor systems as concrete concepts are (e.g.,
Glenberg & Kaschak, 2002; see Borghi et al., 2017 for a review). For instance, according to the
conceptual metaphor theory, abstract concepts are grounded through image schemas
corresponding to mental representations (e.g., Lakoff & Johnson, 1980; Gallese & Lakoff,
2005). Several studies have shown that abstract concepts of *valence* and *power* are grounded in
two-dimensional spatial schema with the higher point of a vertical vector representing positions
of power while the left-hand side of a horizontal vector represents negative concepts (see
Pecher, 2018 for a review). However, the need for one-to-one mapping between abstract
concepts and concrete metaphors means there are limits to the availability of such metaphors
for every type of abstract concept.

At the other end of the spectrum, according to weak embodiment assumptions abstract
concepts are grounded via multiple representations of meaning with the involvement of both
sensorimotor and linguistic processing. These grounding mechanisms place a greater emphasis
on the context in which abstract concepts are used (e.g., Barsalou, 1999; 2003; Wiemer-hasting
and Xu, 2005). Several studies have shown that abstract concepts activate social and
introductory aspects of situations (Barsalou & Wiemer-Hastings, 2005), emotional features
(Kousta, Vigliocco, Vinson, Andrews & Del Campo, 2011; Lenci, Lebani & Passaro, 2018),
information about events, and thematic roles (Ferretti, McRae & Hatherell, 2001), and, more
generally, linguistic information acting as a shortcut to conceptual simulation (Barsalou,
Santos, Simmons & Wilson, 2008). Such assumptions have the advantage of being sufficiently
general to apply to a variety of abstract concepts.

Whether seen from the distributional or embodied end of the spectrum, all accounts agree
on the importance of relationships between concepts for the organisation of semantic
knowledge. Two kinds of relationships have been widely investigated: semantic similarities
(theft-burglary) and verbal association (theft-prison), and a lot of effort has gone into creating
databases of material to use in semantic priming studies regarded as the gold standard for
studying how semantic knowledge is organised (see Hutchinson, Balota, Cortese & Watson,
2008; Hutchison et al., 2013; Pulvermüller, 2013; Mandera, Keuleers & Brysbaert, 2017).

Semantic priming and semantic similarity for concrete and abstract concepts

Semantic priming

In a semantic priming study, participants are presented with a prime word followed by a target
word (Meyer & Schvaneveldt, 1971). The relationship between the two words is one of either
semantic similarity, where the two words belong to the same superordinate category (e.g.,
prime: *eagle*; target: *owl*), or verbal association, where the two words are frequently found
together across spoken and written language (e.g., prime: *fireman*; target: *truck*; McNamara,
1992; Plaut, 1995). In a lexical decision task, participants make a decision on the target by
indicating whether or not it is a word. The semantic priming effect refers to the robust result, which has been replicated hundreds of times, showing that participants respond faster for related primes and targets compared to unrelated ones (Hutchison et al., 2008, 2013). This phenomenon has been widely studied as it provides considerable insight into the organisation and mechanisms of semantic knowledge. In actual fact, each theoretical view discussed above can account for this priming effect. According to the holistic view (Fodor et al., 1980; Berg & Levelt, 1990; Roelofs, 1997), the priming effect is the result of spreading activation from the prime to the target along strongly linked nodes, whereas according to the distributional, embodied and hybrid accounts, it results from the activation of features shared between the prime and the target (Mahon & Caramazza, 2008; Dove, 2009; Andrews, Frank & Vigliocco, 2014; Carota, Kriegeskarte, Nili & Pulvermüller, 2017). Where these last accounts differ, however, is in the nature of the features. The distributional account suggests the priming effect results from the activation of linguistic features, the embodied account that it results from the activation of shared sensorimotor states, and the hybrid account that both linguistic and perceptual features are responsible for this phenomenon.

Despite the robustness of the semantic priming effect with concrete concepts, with abstract concepts results have been inconsistent. Crutch (2005; Crutch, Conell & Warrington, 2009; Crutch and Warrington, 2010) showed that while concrete concepts are organized according to semantic similarity, abstract concepts are organized according to verbal association. Several studies tried to replicate these results but revealed discrepancies (e.g., Hamilton & Coslett, 2008; Duñabeitia, Avilés, Afonso, Scheepers & Carreiras, 2009; Geng & Schnur, 2015). Indeed, these studies have attempted to replicate the results according to which concrete and abstract concepts have different dependencies upon semantic similarity and associative strength. They have however failed to find any such difference in the organization of concrete and abstract concepts. A more recent study found that both semantic similarity and verbal
association elicited a priming effect for concrete concepts whereas for abstract concepts it was
only found with verbal association (Ferré, Guasch, García-Chico, & Sánchez-Casas, 2015).
Crutch and Jackson (2011) suggested the relationship between concreteness and association
type could explain these disparities. They presented evidence based on data from healthy and
neuropsychological patients showing that when presented with triplets of low, middle and high-
levels of concreteness, the effect of semantic similarity increased with concreteness while the
effect of verbal association decreased with concreteness. Furthermore, they suggested that
concreteness be used as a graded variable rather than a binary one especially when studying its
effect on the organization of semantic memory. Accordingly, this calls for a shift in the way
abstract concepts are studied, to place more emphasis on the type and associated level of
concreteness for selected abstract concepts. Two different procedures are used to generate
material for semantic similarity and priming studies: feature generation tasks and semantic
similarity ratings.

***Semantic similarity: feature generation and semantic pairs***

In a feature generation task, participants are given a list of words for which they are required to
provide a list of features defining each word. The procedure provides measures of semantic
similarity by comparing the feature overlap between two words. The more features two words
have in common, the more similar they are (McRae, Cree, Seidenberg, & McNorgan, 2005;
McRae, de Sa, & Seidenberg, 1997; Sánchez-Casas, Ferré, García-Albea & Guasch, 2006;
Vigliocco, Vinson, Lewis and Garrett, 2004; Vinson & Vigliocco, 2008). However, it is a
procedure which is highly time-consuming and which has limitations (see McRae et al., 2005
for a discussion of these limitations). For instance, in feature naming, participants may provide
only a linguistic approximation of conceptual content. It is fair to assume, therefore, that some
parts of the concepts would be lost in verbalisation. This criticism appears to be particularly
relevant in the case of abstract concepts which may themselves be decomposed into abstract
features. Indeed, many authors have suggested that, compared to concrete concepts, abstract concepts appear to be semantically impoverished, with their representation requiring associations with other concepts or grounding simulations in introspective and social states (Barsalou et al., 2008; Borghi, Scorolli, Caligiore, Baldassare & Tummolini, 2013; Borghi, Barca, Binkofski, Castelfranchi, Pezzulo & Tummolini, 2019, see also Recchia & Jones, 2012).

On the other hand, Wiemer-Hastings and Xu (2005) suggested that this apparent paucity of features for abstract concepts is due mainly to the instructions given to participants during a feature generation task. In the original method, Wiemer-Hastings and Xu (2005) asked participants only to generate features defining the concept, whereas later they instructed them to provide context features. The results showed that the difference between abstract and concrete concepts in terms of semantic richness disappeared when participants were encouraged to provide context features. By using the same method of property listing as Wiemer-Hastings and Xu (2005), Harpainter, Trumpp and Kiefer (2018) gathered properties for close to 300 abstract concepts. By doing so, they further demonstrated the richness and heterogeneity of abstract concepts showing that they can elicit affective, introspective, social and sensory-motor properties. This heterogeneity of abstract concepts was further investigated by Villani, Lugli, Liuzza and Borghi (2019) who evaluated more than 400 abstract concepts on 15 dimensions. Their results provided further support for a multiple representation view of abstract concepts.

In addition, Bolognesi, Pilgram and van den Heerik (2017) adapted Wu and Barsalou's taxonomy (2009) to include 20 feature categories belonging to four main dimensions (concept properties, situation properties, introspections, and taxonomic properties) that must be distinguished to convey the full semantic richness of concepts. Recchia and Jones (2012) have not, however, been able to determine whether such distinctions in respect of semantic features could benefit abstract concept representation. They invoked the shallowness of lexical decision
tasks in semantic processing. Consequently, future studies will need to reach conclusions on
the role of feature categories for abstract concept representation.

Another, less costly, way of creating material for semantic representation studies is to generate
semantically similar word pairs. This option relies on a similarity-rating task where participants
are presented with pairs of words formed by the researcher with a view to obtaining concepts
either belonging to the same category or being similar in meaning (e.g., truck-car; Ferrand &
New, 2003; Perea & Rosa, 2002). Participants must rate the semantic similarity of the pairs on
a scale (Ferrand & New, 2003; Sánchez-Casas et al., 2006). Studies have shown that the pairs
rated as being highly similar produced a strong priming effect (e.g., McRae & Boisvert, 1998;
Plaut & Booth, 2000; Hutchison, 2003; Andrews, Lo & Xia, 2017). In addition, studies have
shown a strong correlation between the measures from similarity-rating tasks and feature
generation ensuring the legitimacy of this latter technique (e.g., McRae, De Sa & Seidenberg,
1997). More recently, Maki, Krimsky and Muñoz (2006) used a semantic rating task to show
that ratings were a good predictor of feature overlap for existing semantic feature norms.

*Normative databases for semantic similarity*

Given the importance of carefully crafted material for studying semantic representation, much
effort has been directed towards building normative databases to provide the research
community with the material it needs. The most commonly found datasets gather English
feature norms. McRae and collaborators (2005), for instance, provides feature norms for 541
living and non-living concepts. Subsequently, Buchanan, Holmes, Teasley and Hutchison
(2013) built a searchable web portal based on the work of McRae and collaborators (2005),
facilitating the search for experimental stimuli in their dataset. Buchanan, Valentine and
Maxwell (2019) expanded previous databases and provided features for more that 4000 words.
Vinson and Vigliocco (2008) provided an interesting dataset based on concrete object nouns
and verb events that allow semantic representation to be studied beyond the usual focus on concrete concepts. Devereux, Tyler, Geertzen, and Randall (2014) built on McRae and colleagues' work by adding features produced by at least two participants compared to McRae and collaborator's (2005) 5-feature threshold for inclusion. In other languages, De Deyne and Storms (2008) and De Deyne and colleagues (2008) collected normative features among Dutch participants. Lebani, Bondielli and Lenci (2015) collected thematic role features to study the semantic content of Italian verbs. Also in Italian, Lenci, Baroni, Cazzolli and Marotta (2013) collected semantic features from congenitally-blind and sighted participants, making it possible to study the role of perceptual information in concept processing. Kremer and Baroni (2011) collected properties and semantic relation types for German and Italian. More recently, Vivas, Vivas, Comesaña, Coni and Vorano (2017) published the first Spanish semantic feature production norms for living and non-living concepts.

Researchers have used similarity-rating tasks to a lesser extent to produce such norms. Buchanan and collaborators (2013) compiled a English dataset comprising 1 808 words paired according to semantic similarity. In Spanish, Moldovan, Ferré, Demestre, and Sánchez-Casas (2015) collected normative ratings for 185 Spanish noun triplets with variation of semantic distance within each triplet. However, much of the effort in developing databases has been focused on concrete concepts. To the best of our knowledge, the present work offers the first database of semantically-similar abstract word pairs in French.

**The present study: Semantic similarity norms for abstract words**

The present work introduces a dataset comprising semantic similarity ratings for abstract word
pairs obtained from French participants. We have added a measure of the concreteness of each
word from each pair to allow for the selection of abstract concepts in line with Crutch and
Jackson's (2011) suggestion that there is a relationship between graded levels of concreteness
and semantic organisation. To provide a dataset of experimental stimuli according to the
significant lexical variables and lexical latencies previously discussed, we have combined our
list of words with existing databases such as the French Lexicon Project (FLP, Ferrand et al.,
2010), Lexique (New et al., 2001, 2004, 2007), MEGALEX (Ferrand et al., 2018), and Wordlex
(Gimenes & New, 2016).

METHOD

Participants

Both the similarity- and concreteness-rating tasks were presented as online questionnaires.
Participants for the two studies were all French native speakers and between 18 and 45 years
old. We collected data from 373 participants (334 women; M<sub>age</sub> = 26.43; SD = 8.34) for the
similarity-rating task, and 529 (486 women; M<sub>age</sub> = 29.7; SD = 9.03) for the concreteness-rating
task. Participants volunteered in response to an announcement posted on Facebook group walls
and no compensation was paid. Participants took part in only one of the tasks in an attempt to
ensure their ratings were not influenced by previous exposure to the items which are common
to both tasks. Both studies obtained the approval of the Université Clermont Auvergne Research
Ethics Committee.

Stimuli

To have some guarantee of the level of abstractness<fnref n="1" /> of our material before collecting our own
ratings, we selected 1020 words having a low level of concreteness (range between 100 and
600) from Coltheart's (1981) concreteness norms. We then translated the selected words into
French following a back-translation procedure (Sperber, Devellis & Boehlecke, 1994),

<footnote n="1">¹ Both “abstractness” and “concreteness” words are used across this paper. The use of these notions is not arbitrary. Abstractness is a central notion to the present work as it aimed at introducing abstract stimuli whereas concreteness is used to refer to other studies that introduce or deal with the concreteness variable.</footnote>
following which 174 words were excluded. We also added the material from Ferrand (2001)
comprising 260 French abstract words.

Based on our linguistic intuition, we then formed semantically similar pairs (e.g., joie-bonheur; [joy-happiness]). To the best of our ability (see below), we ensured that the semantic pairs were non-associates (according to McRae & Boisvert, 1998), and were not linked by either a super/supra-ordinate, part/whole or antonym relationships. The material was then divided into 6 lists of pairs, and 30% of fillers (unrelated pairs, e.g., défaut-frisson; [flaw-chill]) were added per list. So that the participants would be sensitive to the abstractness of the pairs, we also added concrete words from Ferrand and Alario (1998) and formed semantic pairs. Accordingly, we were able to form 628 semantically related pairs (460 noun pairs, 99 adjective pairs and 69 verb pairs). Both prime and target words had the same grammatical status within each semantically-similar pair. To ensure the pairs were semantically similar and not associated, we translated the target words back into English and checked for forward strength in the Small World of Words database<fnref n="2" /> (SWOW, De Deyne, Navarro, Perfors, Brysbaert & Storms, 2019). We identified all pairs for which the prime and target presented a forward associative strength of higher than 10%. Seventy pairs were identified as both associated and semantically similar (e.g., anxiety-fear). We kept them in the main database with the possibility to filter them out. In addition, we created a secondary database containing only the semantically similar and associated word pairs. As suggested by De Deyne et al. (2019), association data are not to be discarded and provide a strong indication of meaning similarity.

For the concreteness-rating task, the pairs were separated, and the lists of individual words were
presented in another experiment. Given the added material from Ferrand and Alario (1998),

<footnote n="2">² We used the Small World of Words database because there are no databases large enough in French.</footnote>
participants were presented with stimuli ranging from abstract to concrete, thereby ensuring their sensitivity to the task and avoiding learned response patterns.

## Procedure

The stimuli (fillers included) were randomly divided into 6 lists of word pairs and 10 lists of isolated words respectively for the similarity-rating and concreteness-rating tasks. The motivation for dividing the pairs into different lists was twofold. Firstly, we wanted to keep the experiment concise so as to not overwhelm participants. Secondly, some words appear several times in different pairs, which is why we used semi-randomization to ensure that participants never saw pairs with the same words. The pairs and words were presented one by one on the screen in a randomized order. The experiment was conducted online using the Qualtrics software (2020). The design of the interface for this experiment allowed participants to complete the task on either a computer or smartphone.

Once they had given their consent and registered their demographic information, participants were randomly assigned to one of the lists. Their task was to judge the similarity between the two words presented for the similarity ratings and whether the words were more abstract or concrete for the concreteness ratings. Both tasks used a 7-point Likert-like scale ranging from 1 = “not at all similar” (“*pas du tout similaires*” in French) to 7 = “totally similar” (“*tout à fait similaires*”) for the similarity-rating tasks and from 1 = “very abstract” (“*très abstrait*”) to 7 = “very concrete” (*très concret*) for the concreteness-rating task. The words appeared one by one on the screen and were replaced as soon as participants had rated them. They were presented in the middle of the screen in Arial 12 font against a white background. We provided examples of items and their possible ratings in the instructions. No training was given before the tasks started. Both studies were self-paced, with no time limit for either the stimulus presentation
(word pair or isolated pair) or participant's answer. Both tasks took about 12 minutes to complete.

## RESULTS

We first computed general statistics for the entire dataset. The general statistics collected for the semantic similarity and concreteness variable are shown in Table 1³. Tables 2 and 3 provide the means for associated lexical variables computed by crossing our dataset with the Lexique (New et al., 2004), FLP (Ferrand et al., 2010), MEGALEX (Ferrand et al., 2018) and Wordlex (Gimenes & New, 2016) databases.

<table><caption>**Table 1.** Semantic similarity for word pairs and associated concreteness for prime and target words.</caption><thead><tr><th></th><th>Pair Semantic<br>Similarity</th><th>Prime Word<br>Concreteness</th><th>Target Word<br>Concreteness</th></tr></thead><tbody><tr><td>Mean</td><td>4.43</td><td>4.41</td><td>4.40</td></tr><tr><td>SD</td><td>1.63</td><td>1.48</td><td>1.54</td></tr><tr><td>Min</td><td>1.13</td><td>1.63</td><td>1.51</td></tr><tr><td>Max</td><td>6.93</td><td>6.92</td><td>6.95</td></tr><tr><td>Median</td><td>4.65</td><td>4.43</td><td>4.47</td></tr><tr><td>Range</td><td>5.80</td><td>5.29</td><td>5.44</td></tr><tr><td>Skewness</td><td>-0.52</td><td>-0.31</td><td>-0.35</td></tr><tr><td>Q1</td><td>3.37</td><td>3.44</td><td>3.38</td></tr><tr><td>Q3</td><td>5.60</td><td>5.40</td><td>5.46</td></tr></tbody></table>

It is apparent from the general statistics in Table 1 that the semantic similarity ratings range from 1.13 to 6.93 on a 7-points scale. This shows participants used the full range of the scale but also reflects the diversity of the word pairs in terms of semantic similarity. Separating very similar (M = 5.13; SD = 0.41) and less similar (M = 3.67; SD = 0.59) pairs based on the median revealed a significant effect of semantic similarity [t(300) = 35.78, p < 0.001, d = 2.06]. This

<footnote n="3">³The semantic similarity variable reported in Table 1 corresponds to the mean similarity ratings for word pairs. The concreteness variable corresponds to the mean concreteness for the prime word and target word separately as ratings were obtained on individual words for concreteness and on pairs of words for semantic similarity.</footnote>
effect is particularly large, given that Cohen's d suggests the difference is greater than 2 standard deviations. This will allow for the use of semantic similarity either as a continuous or categorical variable for researchers who would wish to study the effect of variation in semantic similarity. Concerning the concreteness variable, the means for prime and target are very close to one another, showing a good concreteness match within each pair (Mean prime concreteness = 4.41; Mean target concreteness = 4.40). A paired-samples T-test showed no significant difference between the mean concreteness ratings for prime and target words (t(628) = 0.27, p = 0.80 ns). This close match is further demonstrated in the correlation we computed between prime and target words with a strong and highly significant correlation [r = .87, t(628) = 44.50, p < 0.001].

<table><caption>**Table 2.** Descriptive and behavioural data for target words.</caption><thead><tr><th></th><th>min</th><th>max</th><th>M</th><th>SD</th></tr></thead><tbody><tr><td>Movie Subtitles Frequency</td><td>0.00</td><td>2751.99</td><td>56.21</td><td>186.30</td></tr><tr><td>Book Frequency</td><td>0.00</td><td>4696.15</td><td>64.51</td><td>286.62</td></tr><tr><td>Blog Frequency</td><td>0.06</td><td>3095.93</td><td>84.85</td><td>279.23</td></tr><tr><td>Twitter Frequency</td><td>0.00</td><td>4070.02</td><td>77.11</td><td>309.32</td></tr><tr><td>Newspaper Frequency</td><td>0.00</td><td>3782.26</td><td>78.54</td><td>295.09</td></tr><tr><td>N-letters</td><td>3.00</td><td>13.00</td><td>6.88</td><td>2.03</td></tr><tr><td>N-orthographic Neighbours</td><td>0.00</td><td>23.00</td><td>2.52</td><td>3.73</td></tr><tr><td>OLD20</td><td>1.00</td><td>5.40</td><td>2.06</td><td>0.62</td></tr><tr><td>Reaction Times (FLP)</td><td>515.54</td><td>915.85</td><td>653.09</td><td>62.71</td></tr><tr><td>Reaction Times (MEGALEX)</td><td>473.48</td><td>747.84</td><td>549.10</td><td>43.01</td></tr></tbody></table>

Note. OLD20: orthographic Levenshtein distance (Yarkoni, Balota, & Yap, 2008); Movie subtitles frequency (from Lexique; New et al., 2004).
<table><caption>**Table 3.** Descriptive and behavioural data for prime words.</caption><thead><tr><th></th><th>min</th><th>max</th><th>M</th><th>SD</th></tr></thead><tbody><tr><td>Movie Subtitles Frequency</td><td>0.00</td><td>986.59</td><td>31.02</td><td>81.43</td></tr><tr><td>Book Frequency</td><td>0.00</td><td>835.47</td><td>35.36</td><td>81.71</td></tr><tr><td>Blog Frequency</td><td>0.15</td><td>2866.60</td><td>53.28</td><td>150.60</td></tr><tr><td>Twitter Frequency</td><td>0.07</td><td>4070.02</td><td>46.79</td><td>189.88</td></tr><tr><td>Newspaper Frequency</td><td>0.00</td><td>1556.37</td><td>46.37</td><td>118.08</td></tr><tr><td>N-letters</td><td>3.00</td><td>14.00</td><td>6.99</td><td>2.00</td></tr><tr><td>N-orthographic Neighbours</td><td>0.00</td><td>20.00</td><td>2.43</td><td>3.58</td></tr><tr><td>OLD20</td><td>1.00</td><td>4.55</td><td>2.10</td><td>0.60</td></tr><tr><td>Reaction Times (FLP)</td><td>515.54</td><td>1005.15</td><td>657.94</td><td>67.18</td></tr><tr><td>Reaction Times (MEGALEX)</td><td>473.92</td><td>738.03</td><td>553.11</td><td>44.78</td></tr></tbody></table>

Note. OLD20: orthographic Levenshtein distance (Yarkoni, Balota, & Yap, 2008); Movie subtitles frequency (from Lexique; New et al., 2004).

Table 2 and 3 display the lexical characteristics for the primes and targets composing our word pairs. The statistics presented in Table 2 and 3 were obtained by cross-referencing our dataset with Lexique (New et al., 2001, 2004, 2007), the French Lexicon Project (Ferrand et al., 2010), Wordlex (Gimenes & New, 2016), and MEGALEX (Ferrand et al., 2018). Movie Subtitles Frequency corresponds to the freqfilms2 variable from Lexique and refers to word frequency based on movie subtitles. The other frequencies were computed from books (Lexique: New et al., 2004), blog posts, Twitter and Newspapers (Wordlex: Gimenes & New, 2016).

We also computed correlations between semantic similarity for the pair and lexical variables as well as concreteness levels for the prime and target respectively. Such correlations were all non-significant except for the correlation between semantic similarity and concreteness. Indeed, the concreteness level of the prime and target respectively was negatively and moderately correlated to the semantic similarity of the pair ($R_{prime_concreteness} = -0.26$; $R_{target_concreteness} = -0.28$, $p<0.001$), suggesting that the higher the semantic similarity, the lower the level of concreteness. However, the mean concreteness is not as different for highly similar pairs ($M_{concreteness} = 4.14$;
$SD$ = 1.59) as for less similar pairs ($M_{concreteness}$ = 4.78; $SD$ = 1.39). This means researchers using the present database will be able to study phenomena of semantic similarity and their relationship with graded levels of concreteness without having to worry that the concreteness variable and the lexical variables might act as confounding variables.

In addition, we computed correlations between the concreteness variable and other lexical variables. It is clear from Table 4 that the concreteness variable shows a negative correlation to frequencies based on blog posts and Twitter. Such correlations are rather weak ($r$ = -0.10), however, and should not be cause for concern as regards potential confounding variables. The concreteness variable is also moderately and negatively correlated with the number of letters and orthographic similarity, but positively correlated with the number of orthographic neighbours. All lexical variables are significantly intercorrelated, a result which replicates previous findings from the psycholinguistic norms literature. Indeed, upon comparing the correlations shown in Table 4 with those reported in MEGALEX (Ferrand et al., 2018), we found that the correlations between lexical variables were similar in size and significance levels, which further validates our dataset. For example, and among the most widely used, word frequencies computed from books are highly correlated to other word frequencies computed from subtitles ($r$=.78), blogposts ($r$=.73), Twitter ($r$=.63) and newspapers ($r$=.68, see Table 4).

We computed correlations between our concreteness variable and those collected by Bonin et al. (2018) in French and Brysbaert, Warriner and Kuperman (2014) and Coltheart (1981) in English. Table 5 shows that the correlations are strong and highly significant, ensuring thus the validity of the concreteness variable we collected.
<table><caption>**Table 4.** Correlation matrix between concreteness levels and lexical variables with significance levels (*** p < 0.001; ** p < 0.01; * p < 0.05).</caption><thead><tr><th></th><th>Concreteness</th><th>Movie Subtitles Frequency</th><th>Book Frequency</th><th>Blog Frequency</th><th>Twitter Frequency</th><th>Newspaper Frequency</th><th>N-letters</th><th>N-orthographic neighbours</th><th>OLD20</th></tr></thead><tbody><tr><td>Concreteness</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Movie Subtitles Frequency</td><td>-0.03</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Book Frequency</td><td>-0.003</td><td>0.78***</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Blog Frequency</td><td>-0.10**</td><td>0.73***</td><td>0.73***</td><td>1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Twitter Frequency</td><td>-0.10**</td><td>0.63***</td><td>0.45***</td><td>0.87***</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>Newspaper Frequency</td><td>-0.06</td><td>0.68***</td><td>0.78***</td><td>0.89***</td><td>0.66***</td><td>1</td><td></td><td></td><td></td></tr><tr><td>N-letters</td><td>-0.24***</td><td>-0.23***</td><td>-0.20***</td><td>-0.23***</td><td>-0.22***</td><td>-0.19***</td><td>1</td><td></td><td></td></tr><tr><td>N-orthographic neighbours</td><td>0.18***</td><td>0.24***</td><td>0.24***</td><td>0.23***</td><td>0.24***</td><td>0.19***</td><td>-0.59***</td><td>1</td><td></td></tr><tr><td>OLD20</td><td>-0.21***</td><td>-0.19***</td><td>-0.17***</td><td>-0.18***</td><td>-0.18***</td><td>-0.16***</td><td>0.79***</td><td>-0.68***</td><td>1</td></tr></tbody></table>
<table><caption>**Table 5.** Correlations of the present concreteness variable measures with those provided by other databases.</caption><thead><tr><th rowspan="2">Language</th><th rowspan="2">Study</th><th colspan="2">Correlation with the present study</th></tr><tr><th>r</th><th>N</th></tr></thead><tbody><tr><td>French</td><td>Bonin et al. (2018)</td><td>.95</td><td>257</td></tr><tr><td>English</td><td>Coltheart (1981)</td><td>.91</td><td>1256</td></tr><tr><td>English</td><td>Brysbaert et al. (2014)</td><td>.91</td><td>1256</td></tr></tbody></table>

Note. N: number of items in common. All correlations are significant at the p < .0001 level

Finally, to investigate the concreteness variable further, we implemented the package Ckmeans.1d.dp in R studio, an unsupervised learning algorithm for clustering univariate data (Wang & Song, 2011). Based on a Bayesian information criterion, the algorithm suggested the concreteness variable be split according to 3 clusters of abstractness, with cluster 1 the most abstract and cluster 3 the least abstract. The cluster variable is particularly important in relation to the previously discussed need to control the concreteness variable when manipulating semantic similarity. It is a variable which will therefore allow experimenters to select stimuli with matching concreteness levels. We have provided the cluster variable in the supplementary material.

## Availability of the database

The dataset for the present study is available in excel format on the BRM and OSF websites (https://osf.io/qsd4v/). The main database is organized according to the following variables: Word Pairs in French, word-pairs translation in English, word-pair mean concreteness, cluster variable based on word-pair mean concreteness, verbal association strength based on the SWOW, and mean pair similarity with associated general statistics (SD, min, max, median, range, skewness, Q1, Q3). The rest of the database is divided according to Prime word and Target word for the following variables: Mean concreteness and associated general statistics, lexical variables (grammatical category, number of letters, phonemes, orthographic and
phonemic neighbours), reaction times (based on FLP and MEGALEX) and frequencies per
million (movie subtitles, books, blogs, Twitter and newspapers). The secondary database is
organized following the same variables but contains only the 70 word pairs that are semantically
similar as well as verbally associated.

DISCUSSION

The present study aimed to produce French norms of semantic similarity for abstract concepts. Based on our statistical analyses, we can provide material with varying levels of semantic similarity. In addition, based on our collection of concreteness ratings and the implementation of the k-means clustering algorithm (Wang & Song, 2011), we organized the semantic pairs according to three clusters of abstractness. Our ultimate aim is for this database to be used to design material for studies such as semantic priming studies and other language-based paradigms (see, for example, Hutchison et al., 2013). The cross-references we computed with previously mentioned lexical databases allow stimuli to be matched on the basis of frequencies and other lexical variables. The analysis based on this cross-referencing also provides information about the potentially confounding variables that could create noise in an experimental design.

The comparison of prime and target words across the concreteness and lexical variables produced highly significant correlations, thus ensuring a good match within each pair. Further comparisons between semantic similarity and lexical variables resulted, however, in either very weak or non-significant correlations. This suggests there is no need to be particularly careful to avoid confounding lexical variables when using the similarity ratings. The strong and significant correlation in concreteness levels within word pairs, along with the cluster variable we introduced were aimed at addressing Crutch and Jackson's (2011) suggestion that discrepancies found when studying the organization of semantic memory according to similarity or association might be due to a binary, rather than graded definition of concreteness
levels. Indeed, when considering the organization of semantic memory at the extremes of concrete vs. abstract concepts, we lose substantial evidence for the concepts in-between these two extremes. This limitation can be addressed by considering graded levels of concreteness. Previous findings have shown that concepts are organized according to semantic similarity when concreteness increases and according to verbal association when abstractness increases.

Finally, we suggest that, when creating materials, researchers pay attention to the moderate but significant correlation between semantic similarity and the concreteness variable, insofar as results have shown that more abstract pairs are also perceived as more similar than concrete pairs.

The aim of this database was also to fill a gap in the French literature regarding norms for abstract concepts. We therefore consider the present work to be a good starting point for developing other French-language databases focusing on abstract concepts such as verbal association.

Indeed, studies using word stimuli tend to focus primarily on pairing stimuli according to word frequency, word length, and age of acquisition. However, such variables fail to capture fully the effect of word processing by the human mind, as best illustrated by the percentage of variance explained in norming studies and megastudies, which stagnates between .20 and .50 (Balota, Yap, Hutchison, Cortese, Kessler, Loftis & Treiman, 2007; Keuleers, Brysbaert & New, 2010; Ferrand, New, Brysbaert, Bonin & Pallier, 2010; Brysbaert, Mandera & Keuleers, 2018). Newly-developed variables have therefore been introduced with a view to capturing more of the word-processing phenomena. For instance, Brysbaert, Mandera, McCormick and Keuleers (2019) introduced the word prevalence variable (the proportion of people who know a particular word), first in Dutch (Brysbaert, Stevens, Mandera, and Keuleers, 2016; Keuleers, Stevens, Mandera, & Brysbaert, 2015), and then in English (Brysbaert et al., 2019). This
variable was shown to explain an additional 6-10 percent of the variance in response latencies
in a lexical decision task.

In addition, we consider that most norming studies have focused mainly on concrete concepts,
although, as shown by Recchia and Jones (2012), abstract concepts have a richness of their own
which warrants further study. For instance, Chedid, Brambati, Bedetti, Rey, Wilson and Vallet
(2019) recently introduced a perceptual strength variable for Canadian French, which aims to
identify auditory and visual involvement in conceptual knowledge. In addition, the Sensory
Experience ratings variable (SER, Juhasz & Yap, 2013; Bonin et al., 2015; 2018) was
introduced as a measure of the extent to which a word can elicit sensory and perceptual
experiences. The correlation analyses between our concreteness variable and the SER variable
based on the 257 items in common is .33. This rather low correlation goes to show that the SER
variable cannot capture the same psycholinguistic phenomena as the concreteness variable, thus
ensuring the relevance of the latter. We also computed the correlation between our concreteness
variable and the Perceptual Strength variable (Chedid, Brambati, Bedetti, Rey, Wilson & Vallet,
2019) and found that r=.80 based on 507 items in common. Although this correlation value
appears rather high, it is consistent with the findings of Chedid and colleagues, who reported a
correlation value of r=.76 between Perceptual Strength and Bonin and colleagues' concreteness
variables. According to Chedid et al. (2019), however, this new variable cannot be regarded as
another form of concreteness since it made an independent contribution to the prediction of
word latencies in word processing.

Until recently, grounding has mainly been studied in concrete concepts, owing to a previous
consensus that abstract concepts are not grounded. However, several studies have shown that
abstract concepts can be grounded in perceptual situations and events. In addition, Connell,
Lynott and Banks (2018) consider interoception a forgotten modality for abstract concepts and
report a facilitation effect of interoceptive strength. Future work will therefore focus on
developing norms that capture these modalities for abstract concepts to further our knowledge
about their representation.

CONCLUSION

The present study aimed to provide French semantic similarity norms for 630 word pairs with varying levels of similarity and associated concreteness. The database is organized in such a way that semantic similarity and concreteness may be used as either continuous or categorical variables. The continuous variables correspond to the ratings we collected, whereas the categorical variables correspond to the cluster variable we computed for concreteness and the median for semantic similarity. The database also provides frequency and lexical variables for matching pairs in stimuli set design. We anticipate that it will be very useful for researchers working on memory and language, especially given the growing interest for studying abstract concepts representation.

Open Practices Statement

In line with an open data policy, all data discussed in this article are freely available on our web
site on the Open Science Framework web site (https://osf.io/qsd4v/).

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