Humboldt, W. F. von. (1836). *Über die Verschiedenheit des menschlichen Sprachbaues: und ihren Einfluss auf die geistige Entwicklung des Menschengeschlechts*. Druckerei der Königlichen Akademie der Wissenschaften.

Humphries, C., Binder, J. R., Medler, D. A., & Liebenthal, E. (2006). Syntactic and semantic modulation of neural activity during auditory sentence comprehension. *Journal of Cognitive Neuroscience*, 18(4), 665-679. https://doi.org/10.1162/jocn.2006.18.4.665

Hunt, R. H., & Aslin, R. N. (2001). Statistical learning in a serial reaction time task: Access to separable statistical cues by individual learners. *Journal of Experimental Psychology: General*, 130(4), 658-680. https://doi.org/10.1037/0096-3445.130.4.658

Inoue, M., & Mikami, A. (2006). Prefrontal activity during serial probe reproduction task: encoding, mnemonic, and retrieval processes. *Journal of Neurophysiology*, 95(2), 1008-1041. https://doi.org/10.1152/jn.00552.2005

Jäger, G., & Rogers, J. (2012). Formal language theory: refining the Chomsky hierarchy. *Philosophical Transactions of the Royal Society B: Biological Sciences*, 367(1598), 1956-1970. https://doi.org/10.1098/rstb.2012.0077

Johnson, S. P., Fernandas, K. J., Frank, M. C., Kirkham, N., Marcus, G., Rabagliati, H., & Slemmer, J. A. (2009). Abstract Rule Learning for Visual Sequences in 8- and 11-Month-Olds. *Infancy : the official journal of the International Society on Infant Studies*, 14(1), 2-18. https://doi.org/10.1080/15250000802569611

Kakishita, Y., Sasahara, K., Nishino, T., Takahasi, M., & Okanoya, K. (2009). Ethological data mining: an automata-based approach to extract behavioral units and rules. *Data Mining and Knowledge Discovery*, 18(3), 446-471. https://doi.org/10.1007/s10618-008-0122-1

Karlsson, F. (2007). Constraints on multiple center-embedding of clauses. *Journal of Linguistics*, 43(2), 365-392. https://doi.org/10.1017/S0022226707004616

Karlsson, F. (2010). Syntactic recursion and iteration. In H. van der Hulst (Éd.), *Recursion and Human Language* (p. 43-67). Berlin, Boston: De Gruyter Mouton. https://doi.org/10.1515/9783110219258

Kershenbaum, A., Bowles, A. E., Freeberg, T. M., Jin, D. Z., Lameira, A. R., & Bohn, K. (2014). Animal vocal sequences: not the Markov chains we thought they were.
*Proceedings of the Royal Society of London B: Biological Sciences*, 281(1792), 20141370. https://doi.org/10.1098/rspb.2014.1370

Kinsella, A. R. (2010). 10. Was recursion the key step in the evolution of the human language faculty? In *Recursion and Human Language*. Berlin, Boston: De Gruyter Mouton. https://doi.org/10.1515/9783110219258.177

Koechlin, E., & Jubault, T. (2006). Broca's Area and the Hierarchical Organization of Human Behavior. *Neuron*, 50(6), 963-974. https://doi.org/10.1016/j.neuron.2006.05.017

Lai, J., & Poletiek, F. H. (2011). The impact of adjacent-dependencies and staged-input on the learnability of center-embedded hierarchical structures. *Cognition*, 118(2), 265-273. https://doi.org/10.1016/j.cognition.2010.11.011

Langacker, R. W. (1987). *Foundations of Cognitive Grammar: Theoretical prerequisites*. Volume I. Stanford University Press.

Langacker, R. W. (1991). *Foundations of Cognitive Grammar: descriptive application*. Volume II. Stanford University Press.

Lany, J., & Gómez, R. L. (2008). Twelve-month-old infants benefit from prior experience in statistical learning. *Psychological Science*, 19(12), 1247-1252. https://doi.org/10.1111/j.1467-9280.2008.02233.x

Levelt, W. J. M. (2008). *Formal Grammars in Linguistics and Psycholinguistics: Volume 1: An Introduction to the Theory of Formal Languages and Automata*, Volume 2: *Applications in Linguistic Theory*, Volume 3: *Psycholinguistic Applications*. John Benjamins Publishing.

Lobina, D. J. (2011). "A Running Back" and Forth: A Review of Recursion and Human Language. *BIOLINGUISTICS*, 5(1-2), 151-169.

Lyn, H. (2017). The Question of Capacity: Why Enculturated and Trained Animals have much to Tell Us about the Evolution of Language. *Psychonomic Bulletin & Review*, 24(1), 85-90. https://doi.org/10.3758/s13423-016-1129-z

Maess, B., Koelsch, S., Gunter, T. C., & Friederici, A. D. (2001). Musical syntax is processed in Broca's area: an MEG study. *Nature Neuroscience*, 4(5), 540-545. https://doi.org/10.1038/87502

Makuuchi, M., Bahlmann, J., Anwander, A., & Friederici, A. D. (2009). Segregating the core computational faculty of human language from working memory. *Proceedings of the*
National Academy of Sciences, 106(20), 8362-8367.

https://doi.org/10.1073/pnas.0810928106

Malassis, R., Rey, A., & Fagot, J. (in press). Non-adjacent Dependencies Processing in Human and Non-Human Primates. *Cognitive Science*.

Manza, L., & Bornstein, R. F. (1995). Affective discrimination and the implicit learning process. *Consciousness and Cognition, 4*(4), 399-409.

https://doi.org/10.1006/ccog.1995.1047

Marcus, G. F. (2001). *The Algebraic Mind*. MIT Press.

Marcus, G. F., Brinkmann, U., Clahsen, H., Wiese, R., & Pinker, S. (1995). German inflection: the exception that proves the rule. *Cognitive Psychology, 29*(3), 189-256.

https://doi.org/10.1006/cogp.1995.1015

Marcus, G. F., Vijayan, S., Rao, S. B., & Vishton, P. M. (1999). Rule Learning by Seven-Month-Old Infants. *Science, 283*(5398), 77-80.

https://doi.org/10.1126/science.283.5398.77

Marcus, Gary F., Fernandes, K. J., & Johnson, S. P. (2007). Infant rule learning facilitated by speech. *Psychological Science, 18*(5), 387-391. https://doi.org/10.1111/j.1467-9280.2007.01910.x

Marks, L. E. (1968). Scaling of grammaticalness of self-embedded English sentences. *Journal of Verbal Learning and Verbal Behavior, 7*(5), 965-967.

https://doi.org/10.1016/S0022-5371(68)80106-9

Martins, M. D. (2012). Distinctive signatures of recursion. *Phil. Trans. R. Soc. B, 367*(1598),

2055-2064. https://doi.org/10.1098/rstb.2012.0097

Meguerditchian, A., Gardner, M. J., Schapiro, S. J., & Hopkins, W. D. (2012). The sound of one-hand clapping: handedness and perisylvian neural correlates of a communicative gesture in chimpanzees. *Proceedings of the Royal Society of London B: Biological Sciences, 279*(1735), 1959-1966. https://doi.org/10.1098/rspb.2011.2485

Meulemans, T., & Van der Linden, M. (1997). Associative chunk strength in artificial grammar learning. *Journal of Experimental Psychology: Learning, Memory, and Cognition, 23*, 1007-1028.
Miller, G.A. (1967). Project grammarama. In G.A. Miller (Éd.), *Psychology of communication*. New York: Basic Books.

Miller, George A., & Chomsky, N. (1963). Finitary Models of Language Users. In D. Luce (Éd.), *Handbook of Mathematical Psychology* (p. 2–419). John Wiley & Sons.

Miller, George A., & Isard, S. (1964). Free recall of self-embedded english sentences.
*Information and Control*, 7(3), 292–303. https://doi.org/10.1016/S0019-9958(64)90310-9

Milne, A. E., Mueller, J. L., Männel, C., Attaheri, A., Friederici, A. D., & Petkov, C. I. (2016). Evolutionary origins of non-adjacent sequence processing in primate brain potentials. *Scientific Reports*, 6, 36259. https://doi.org/10.1038/srep36259

Minier, L., Fagot, J., & Rey, A. (2016). The Temporal Dynamics of Regularity Extraction in Non-Human Primates. *Cognitive Science*, 40(4), 1019-1030.
https://doi.org/10.1111/cogs.12279

Misyak, J. B., & Christiansen, M. H. (2012). Statistical Learning and Language: An Individual Differences Study. *Language Learning*, 62(1), 302-331.
https://doi.org/10.1111/j.1467-9922.2010.00626.x

Mueller, J. L., Friederici, A. D., & Männel, C. (2012). Auditory perception at the root of language learning. *Proceedings of the National Academy of Sciences*, 109(39), 15953-15958. https://doi.org/10.1073/pnas.1204319109

Murphy, R. A., Mondragón, E., & Murphy, V. A. (2008). Rule Learning by Rats. *Science*, 319(5871), 1849–1851. https://doi.org/10.1126/science.1151564

Nazzi, T., Barrière, I., Goyet, L., Kresh, S., & Legendre, G. (2011). Tracking irregular morphophonological dependencies in natural language: evidence from the acquisition of subject-verb agreement in French. *Cognition*, 120(1), 119-135.
https://doi.org/10.1016/j.cognition.2011.03.004

Negishi, M. (1999). Do infants learn grammar with algebra or statistics? *Science* (New York, N.Y.), 284(5413), 435; author reply 436-437.

Nelson, M. J., Karoui, I. E., Giber, K., Yang, X., Cohen, L., Koopman, H., ... Dehaene, S. (2017). Neurophysiological dynamics of phrase-structure building during sentence processing. *Proceedings of the National Academy of Sciences*, 114(18), E3669-E3678.
https://doi.org/10.1073/pnas.1701590114
Nespor, M., Peña, M., & Mehler, J. (2003). On the Different Roles of Vowels and Consonants in Speech Processing and Language Acquisition. *Lingue e linguaggio*, (2/2003).
https://doi.org/10.1418/10879

Neubert, F.-X., Mars, R. B., Thomas, A. G., Sallet, J., & Rushworth, M. F. S. (2014).
Comparison of Human Ventral Frontal Cortex Areas for Cognitive Control and
Language with Areas in Monkey Frontal Cortex. *Neuron*, 81(3), 700-713.
https://doi.org/10.1016/j.neuron.2013.11.012

Nevins, A. I., Pesetsky, D., & Rodrigues, C. (2009). Pirahã Exceptionality: A Reassessment.
Consulté à l'adresse https://dash.harvard.edu/handle/1/3597237

Newport, E. L., & Aslin, R. N. (2004). Learning at a distance I. Statistical learning of non-adjacent dependencies. *Cognitive Psychology*, 48(2), 127-162.

Newport, E. L., Hauser, M. D., Spaepen, G., & Aslin, R. N. (2004). Learning at a distance II.
Statistical learning of non-adjacent dependencies in a non-human primate. *Cognitive
Psychology*, 49(2), 85-117. https://doi.org/10.1016/j.cogpsych.2003.12.002

Nissen, M. J., & Bullemer, P. (1987). Attentional requirements of learning: Evidence from
performance measures. *Cognitive Psychology*, 19(1), 1-32.
https://doi.org/10.1016/0010-0285(87)90002-8

O'Donnell, T. J., Hauser, M. D., & Fitch, W. T. (2005). Using mathematical models of
language experimentally. *Trends in Cognitive Sciences*, 9(6), 284-289.
https://doi.org/10.1016/j.tics.2005.04.011

Ojima, S., & Okanoya, K. (2014). The Non-Hierarchical Nature of the Chomsky Hierarchy-
Driven Artificial-Grammar Learning. *BIOLINGUISTICS*, 8(0), 163-180.

Onnis, L., Monaghan, P., Christiansen, M. H., & Chater, N. (2005). Variability is the spice of
learning, and a crucial ingredient for detecting and generalizing in nonadjacent
dependencies. In *PROCEEDINGS OF THE TWENTY-SIXTH ANNUAL
CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY*.

Onnis, Luca, Monaghan, P., Richmond, K., & Chater, N. (2005). Phonology impacts
segmentation in online speech processing. *Journal of Memory and Language*, 53(2),
225-237. https://doi.org/10.1016/j.jml.2005.02.011

Ouattara, K., Lemasson, A., & Zuberbühler, K. (2009). Campbell's monkeys concatenate
vocalizations into context-specific call sequences. *Proceedings of the National
Academy of Sciences, 106(51), 22026-22031.

https://doi.org/10.1073/pnas.0908118106

Pacton, S., & Perruchet, P. (2008). An attention-based associative account of adjacent and nonadjacent dependency learning. *Journal of Experimental Psychology. Learning, Memory, and Cognition, 34*(1), 80-96. https://doi.org/10.1037/0278-7393.34.1.80

Pacton, S., Sobaco, A., & Perruchet, P. (2015). Is an attention-based associative account of adjacent and nonadjacent dependency learning valid? *Acta Psychologica, 157*, 195-199. https://doi.org/10.1016/j.actpsy.2015.03.002

Pallier, C., Devauchelle, A.-D., & Dehaene, S. (2011). Cortical representation of the constituent structure of sentences. *Proceedings of the National Academy of Sciences, 108*(6), 2522-2527. https://doi.org/10.1073/pnas.1018711108

Peña, M., Bonatti, L. L., Nespor, M., & Mehler, J. (2002). Signal-driven computations in speech processing. *Science (New York, N.Y.)*, 298(5593), 604-607.
https://doi.org/10.1126/science.1072901

Perruchet, P., & Pacteau, C. (1990). Synthetic Grammar Learning: Implicit Rule Abstraction or Explicit Fragmentary Knowledge? *Journal of Experimental Psychology: General, 119*, 264-275. https://doi.org/10.1037/0096-3445.119.3.264

Perruchet, P., & Rey, A. (2005). Does the mastery of center-embedded linguistic structures distinguish humans from nonhuman primates? *Psychonomic Bulletin & Review, 12*(2), 307-313.

Perruchet, P., Tyler, M. D., Galland, N., & Peereman, R. (2004). Learning nonadjacent dependencies: no need for algebraic-like computations. *Journal of Experimental Psychology. General, 133*(4), 573-583. https://doi.org/10.1037/0096-3445.133.4.573

Petersson, K. M., Forkstam, C., & Ingvar, M. (2004). Artificial syntactic violations activate Broca's region. *Cognitive Science, 28*(3), 383-407.
https://doi.org/10.1016/j.cogsci.2003.12.003

Petersson, K.-M., Folia, V., & Hagoort, P. (2012). What artificial grammar learning reveals about the neurobiology of syntax. *Brain and Language, 120*(2), 83-95.
https://doi.org/10.1016/j.bandl.2010.08.003
Petkov, C. I., & Wilson, B. (2012). On the pursuit of the brain network for proto-syntactic learning in non-human primates: conceptual issues and neurobiological hypotheses. *Philosophical Transactions of the Royal Society B: Biological Sciences*, 367(1598), 2077-2088. https://doi.org/10.1098/rstb.2012.0073

Petrides, M., & Pandya, D. N. (2002). Comparative cytoarchitectonic analysis of the human and the macaque ventrolateral prefrontal cortex and corticocortical connection patterns in the monkey. *The European Journal of Neuroscience*, 16(2), 291-310.

Petrides, Michael, Cadoret, G., & Mackey, S. (2005). Orofacial somatomotor responses in the macaque monkey homologue of Broca's area. *Nature*, 435(7046), 1235-1238.
https://doi.org/10.1038/nature03628

Petrides, Michael, & Pandya, D. N. (2009). Distinct parietal and temporal pathways to the homologues of Broca's area in the monkey. *PLoS Biology*, 7(8), e1000170.
https://doi.org/10.1371/journal.pbio.1000170

Pinker, S. (1991). Rules of language. *Science* (New York, N.Y.), 253(5019), 530-535.

Pinker, Steven, & Jackendoff, R. (2005). The faculty of language: what's special about it?
*Cognition*, 95(2), 201-236. https://doi.org/10.1016/j.cognition.2004.08.004

Poletiek, F. H., Fitz, H., & Bocanegra, B. R. (2016). What baboons can (not) tell us about
natural language grammars. *Cognition*, 151, 108-112.
https://doi.org/10.1016/j.cognition.2015.04.016

Pothos, E. M. (2005). The rules versus similarity distinction. *The Behavioral and Brain Sciences*, 28(1), 1-14; discussion 14-49.

Procyk, E., Ford Dominey, P., Amiez, C., & Joseph, J. P. (2000). The effects of sequence structure and reward schedule on serial reaction time learning in the monkey. *Brain Research. Cognitive Brain Research*, 9(3), 239-248.

Ravignani, A., & Sonnweber, R. (2017). Chimpanzees process structural isomorphisms across sensory modalities. *Cognition*, 161, 74-79.
https://doi.org/10.1016/j.cognition.2017.01.005

Ravignani, A., Sonnweber, R.-S., Stobbe, N., & Fitch, W. T. (2013). Action at a distance: dependency sensitivity in a New World primate. *Biology Letters*, 9(6), 20130852.
https://doi.org/10.1098/rsbl.2013.0852
Ravignani, A., Westphal-Fitch, G., Aust, U., Schlumpp, M. M., & Fitch, W. T. (2015). More than one way to see it: Individual heuristics in avian visual computation. *Cognition*, **143**, 13-24. https://doi.org/10.1016/j.cognition.2015.05.021

Reber, A. (1969). Transfer of syntactic structure in synthetic languages. *Journal of Experimental Psychology*, **81**(1), 115-119. https://doi.org/10.1037/h0027454

Reber, A. S. (1967). Implicit learning of artificial grammars. *Journal of Verbal Learning and Verbal Behavior*, **6**(6), 855-863. https://doi.org/10.1016/S0022-5371(67)80149-X

Rey, A., Minier, L., Malassis, R., Bogaerts, L., & Fagot, J. (in press). Regularity extraction across species: associative learning mechanisms shared by human and non-human primates. *Topics in Cognitive Science*.

Rey, A., Perruchet, P., & Fagot, J. (2012). Centre-embedded structures are a by-product of associative learning and working memory constraints: evidence from baboons (Papio Papio). *Cognition*, **123**(1), 180-184. https://doi.org/10.1016/j.cognition.2011.12.005

Rilling, J. K., Glasser, M. F., Preuss, T. M., Ma, X., Zhao, T., Hu, X., & Behrens, T. E. J. (2008). The evolution of the arcuate fasciculus revealed with comparative DTI. *Nature Neuroscience*, **11**(4), 426-428. https://doi.org/10.1038/nn2072

Rogers, J., & Pullum, G. K. (2011). Aural Pattern Recognition Experiments and the Subregular Hierarchy. *Journal of Logic, Language and Information*, **20**(3), 329-342.
https://doi.org/10.1007/s10849-011-9140-2

Romanski, L. M., & Averbeck, B. B. (2009). The Primate Cortical Auditory System and Neural Representation of Conspecific Vocalizations. *Annual review of neuroscience*, **32**, 315-346. https://doi.org/10.1146/annurev.neuro.051508.135431

Romberg, A. R., & Saffran, J. R. (2013). All Together Now: Concurrent Learning of Multiple Structures in an Artificial Language. *Cognitive Science*, **37**(7), 1290-1320.
https://doi.org/10.1111/cogs.12050

Roy, A. C., & Arbib, M. A. (2005). The syntactic motor system. * Gesture*, **5**(1), 7-37.
https://doi.org/10.1075/gest.5.1.03roy

Saffran, J., Hauser, M., Seibel, R., Kapfhamer, J., Tsao, F., & Cushman, F. (2008). Grammatical pattern learning by human infants and cotton-top tamarin monkeys. *Cognition*, **107**(2), 479-500. https://doi.org/10.1016/j.cognition.2007.10.010
Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. *Science* (New York, N.Y.), 274(5294), 1926-1928.

Saffran, J. R., Johnson, E. K., Aslin, R. N., & Newport, E. L. (1999). Statistical learning of tone sequences by human infants and adults. *Cognition*, 70(1), 27-52.

Saffran, Jenny R. (2001). The Use of Predictive Dependencies in Language Learning. *Journal of Memory and Language*, 44(4), 493-515. https://doi.org/10.1006/jmla.2000.2759

Saffran, Jenny R., Newport, E. L., & Aslin, R. N. (1996). Word Segmentation: The Role of Distributional Cues. *Journal of Memory and Language*, 35(4), 606-621.
https://doi.org/10.1006/jmla.1996.0032

Saffran, Jenny R., & Thiessen, E. D. (2007). Domain-General Learning Capacities. In E. Hoff & rilyn Shatz (Éd.), *Blackwell Handbook of Language Development* (p. 68-86).
Blackwell Publishing Ltd. https://doi.org/10.1002/9780470757833.ch4

Sandoval, M., & Gómez, R. L. (2013). The development of nonadjacent dependency learning in natural and artificial languages. *Wiley Interdisciplinary Reviews: Cognitive Science*, 4(5), 511-522. https://doi.org/10.1002/wcs.1244

Santelmann, L. M., & Jusczyk, P. W. (1998). Sensitivity to discontinuous dependencies in language learners: evidence for limitations in processing space. *Cognition*, 69(2), 105-134. https://doi.org/10.1016/S0010-0277(98)00060-2

Santi, A., & Grodzinsky, Y. (2007). Working memory and syntax interact in Broca's area. *NeuroImage*, 37(1), 8-17. https://doi.org/10.1016/j.neuroimage.2007.04.047

Santolin, C., Rosa-Salva, O., Regolin, L., & Vallortigara, G. (2016). Generalization of visual regularities in newly hatched chicks (Gallus gallus). *Animal Cognition*, 19(5), 1007-1017. https://doi.org/10.1007/s10071-016-1005-2

Santolin, C., & Saffran, J. R. (2018). Constraints on Statistical Learning Across Species. *Trends in Cognitive Sciences*, 22(1), 52-63. https://doi.org/10.1016/j.tics.2017.10.003

Savage-Rumbaugh, E. S., & Lewin, R. (1996). *Kanzi: the ape at the brink of the human mind*. Wiley.

Schel, A. M., Tranquilli, S., & Zuberbühler, K. (2009). The alarm call system of two species of black-and-white colobus monkeys (Colobus polykomos and Colobus guereza).
*Journal of Comparative Psychology* (Washington, D.C.: 1983), 123(2), 136-150.
https://doi.org/10.1037/a0014280

Schenker, N. M., Hopkins, W. D., Spocter, M. A., Garrison, A. R., Stimpson, C. D., Erwin, J. M., ... Sherwood, C. C. (2010). Broca's Area Homologue in Chimpanzees (Pan troglodytes): Probabilistic Mapping, Asymmetry, and Comparison to Humans.
*Cerebral Cortex*, 20(3), 730-742. https://doi.org/10.1093/cercor/bhp138

Schlenker, P., Chemla, E., Arnold, K., Lemasson, A., Ouattara, K., Keenan, S., ...
Zuberbühler, K. (2014). Monkey semantics: two 'dialects' of Campbell's monkey alarm calls. *Linguistics and Philosophy*, 37(6), 439-501.
https://doi.org/10.1007/s10988-014-9155-7

Schlenker, P., Chemla, E., Schel, A. M., Fuller, J., Gautier, J. P., Kuhn, J., ... Zuberbühler, K. (2016a). Formal monkey linguistics : the debate. http://dx.doi.org/10.1515/tl-2016-0010

Schlenker, P., Chemla, E., Schel, A. M., Fuller, J., Gautier, J.-P., Kuhn, J., ... Zuberbühler, K. (2016b). Formal monkey linguistics. *Theoretical Linguistics*, 42(1-2), 1-90.
https://doi.org/10.1515/tl-2016-0001

Schlenker, P., Chemla, E., & Zuberbühler, K. (2016). What Do Monkey Calls Mean? *Trends in Cognitive Sciences*, 20(12), 894-904. https://doi.org/10.1016/j.tics.2016.10.004

Seidenberg, M. S., & Elman, J. L. (1999). Do Infants Learn Grammar with Algebra or Statistics? *Science*, 284(5413), 433-433.
https://doi.org/10.1126/science.284.5413.433f

Seidenberg, M. S., MacDonald, M. C., & Saffran, J. R. (2002). Neuroscience. Does grammar start where statistics stop? *Science* (New York, N.Y.), 298(5593), 553-554.
https://doi.org/10.1126/science.1078094

Seki, Y., Suzuki, K., Osawa, A. M., & Okanoya, K. (2013). Songbirds and humans apply different strategies in a sound sequence discrimination task. *Frontiers in Psychology, 4*. https://doi.org/10.3389/fpsyg.2013.00447

Servan-Schreiber, E., & R. Anderson, J. (1990). Learning Artificial Grammars With Competitive Chunking. *Journal of Experimental Psychology. Learning, Memory, and Cognition, 16*(4), 592-608. https://doi.org/10.1037/0278-7393.16.4.592
Seyfarth, R. M., & Cheney, D. L. (2014). The evolution of language from social cognition. *Current Opinion in Neurobiology*, 28, 5-9. https://doi.org/10.1016/j.conb.2014.04.003

Shannon, C. E. (1948). A Mathematical Theory of Communication. *The Bell System Technical Journal*, 27, 623-656.

Shima, K., Isoda, M., Mushiake, H., & Tanji, J. (2007). Categorization of behavioural sequences in the prefrontal cortex. *Nature*, 445(7125), 315-318.
https://doi.org/10.1038/nature05470

Shultz, T. R., & Bale, A. C. (2001). Neural Network Simulation of Infant Familiarization to Artificial Sentences: Rule-Like Behavior Without Explicit Rules and Variables. *Infancy*, 2(4), 501-536. https://doi.org/10.1207/S15327078IN0204_07

Siegelman, N., Bogaerts, L., Christiansen, M. H., & Frost, R. (2017). Towards a theory of individual differences in statistical learning. *Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences*, 372(1711).
https://doi.org/10.1098/rstb.2016.0059

Siegelman, N., & Frost, R. (2015). Statistical learning as an individual ability: Theoretical perspectives and empirical evidence. *Journal of Memory and Language*, 81, 105-120.
https://doi.org/10.1016/j.jml.2015.02.001

Soderstrom, M., White, K. S., Conwell, E., & Morgan, J. L. (2007). Receptive Grammatical Knowledge of Familiar Content Words and Inflection in 16-Month-Olds. *Infancy*, 12(1), 1-29. https://doi.org/10.1111/j.1532-7078.2007.tb00231.x

Sonnweber, R., Ravignani, A., & Fitch, W. T. (2015). Non-adjacent visual dependency learning in chimpanzees. *Animal Cognition*, 18(3), 733-745.
https://doi.org/10.1007/s10071-015-0840-x

Spierings, M. J., & ten Cate, C. (2016). Budgerigars and zebra finches differ in how they generalize in an artificial grammar learning experiment. *Proceedings of the National Academy of Sciences of the United States of America*, 113(27), E3977-3984.
https://doi.org/10.1073/pnas.1600483113

Stadler, M. A., & Frensch, P. (Éd.). (1998). *Handbook of Implicit Learning*. Sage Publications.
Steedman, M. J. (1984). A Generative Grammar for Jazz Chord Sequences. *Music Perception: An Interdisciplinary Journal*, 2(1), 52-77.
https://doi.org/10.2307/40285282

Steiper, M. E., Young, N. M., & Sukarna, T. Y. (2004). Genomic data support the hominoid slowdown and an Early Oligocene estimate for the hominoid–cercopithecoid divergence. *Proceedings of the National Academy of Sciences of the United States of America*, 101(49), 17021–17026. https://doi.org/10.1073/pnas.0407270101

Stobbe, N., Westphal-Fitch, G., Aust, U., & Fitch, W. T. (2012). Visual artificial grammar learning: comparative research on humans, kea (Nestor notabilis) and pigeons (Columba livia). *Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences*, 367(1598), 1995-2006. https://doi.org/10.1098/rstb.2012.0096

Suge, R., & Okanoya, K. (2010). Perceptual chunking in the self-produced songs of Bengalese finches (Lonchura striata var. domestica). *Animal Cognition*, 13(3), 515-523. https://doi.org/10.1007/s10071-009-0302-4

Suzuki, R., Buck, J. R., & Tyack, P. L. (2006). Information entropy of humpback whale songs. *The Journal of the Acoustical Society of America*, 119(3), 1849-1866.

Suzuki, T. N., Wheatcroft, D., & Griesser, M. (2016). Experimental evidence for compositional syntax in bird calls. *Nature Communications*, 7, 10986.
https://doi.org/10.1038/ncomms10986

Takahasi, M., Yamada, H., & Okanoya, K. (2010). Statistical and Prosodic Cues for Song Segmentation Learning by Bengalese Finches (Lonchura striata var. domestica). *Ethology*, 116(6), 481-489. https://doi.org/10.1111/j.1439-0310.2010.01772.x

ten Cate, C. (2017). Assessing the uniqueness of language: Animal grammatical abilities take center stage. *Psychonomic Bulletin & Review*, 24(1), 91-96.
https://doi.org/10.3758/s13423-016-1091-9

ten Cate, C., & Ballintijn, M. R. (1996). Dove Coos and Flashed Lights: Interruptibility of « Song » in a Nonsongbird. *Journal of Comparative Psychology*, 110(3), 267-275.
https://doi.org/10.1037/0735-7036.110.3.267

ten Cate, C., Lachlan, R., & Zuidema, W. (2013). *Analyzing the structure of bird vocalizations and language: finding common ground*.
ten Cate, C., & Okanoya, K. (2012). Revisiting the syntactic abilities of non-human animals: natural vocalizations and artificial grammar learning. *Philosophical Transactions of the Royal Society of London B: Biological Sciences*, 367(1598), 1984-1994.
https://doi.org/10.1098/rstb.2012.0055

Terrace, H. S., Petitto, L. A., Sanders, R. J., & Bever, T. G. (1979). Can an ape create a sentence? *Science* (New York, N.Y.), 206(4421), 891-902.

Todt, D., & Hultsch, H. (1998). How songbirds deal with large amounts of serial information: retrieval rules suggest a hierarchical song memory. *Biological Cybernetics*, 79(6), 487-500. https://doi.org/10.1007/s004220050498

Tomalin, M. (2007). Reconsidering recursion in syntactic theory. *Lingua*, 117(10), 1784-1800. https://doi.org/10.1016/j.lingua.2006.11.001

Tomasello, M. (2003). *Constructing a Language: A Usage-Based Theory of Language Acquisition*. Harvard University Press.

Toro, J. M., Nespor, M., Mehler, J., & Bonatti, L. L. (2008). Finding words and rules in a speech stream: functional differences between vowels and consonants. *Psychological Science*, 19(2), 137-144. https://doi.org/10.1111/j.1467-9280.2008.02059.x

Toro, J. M., & Trobalón, J. B. (2005). Statistical computations over a speech stream in a rodent. *Perception & Psychophysics*, 67(5), 867-875.
https://doi.org/10.3758/BF03193539

Truswell, R. (2017). Dendrophobia in Bonobo Comprehension of Spoken English. *Mind & Language*, 32(4), 395-415. https://doi.org/10.1111/mila.12150

Turing, A. M. (1937). On Computable Numbers, with an Application to the Entscheidungsproblem. *Proceedings of the London Mathematical Society*, s2-42(1), 230-265. https://doi.org/10.1112/plms/s2-42.1.230

Turk-Browne, N. B., Jungé, J., & Scholl, B. J. (2005). The automaticity of visual statistical learning. *Journal of Experimental Psychology. General*, 134(4), 552-564.
https://doi.org/10.1037/0096-3445.134.4.552

Uddén, J., Folia, V., Forkstam, C., Ingvar, M., Fernandez, G., Overeem, S., ... Petersson, K. M. (2008). The inferior frontal cortex in artificial syntax processing: an rTMS study. *Brain Research*, 1224, 69-78. https://doi.org/10.1016/j.brainres.2008.05.070
Uddén, J., Ingvar, M., Hagoort, P., & Petersson, K. M. (2012). Implicit acquisition of grammars with crossed and nested non-adjacent dependencies: investigating the push-down stack model. *Cognitive Science*, 36(6), 1078-1101.
https://doi.org/10.1111/j.1551-6709.2012.01235.x

Uhrig, L., Dehaene, S., & Jarraya, B. (2014). A hierarchy of responses to auditory regularities in the macaque brain. *The Journal of Neuroscience: The Official Journal of the Society for Neuroscience*, 34(4), 1127-1132. https://doi.org/10.1523/JNEUROSCI.3165-13.2014

van Heijningen, C. A. A., Chen, J., van Laatum, I., van der Hulst, B., & ten Cate, C. (2013). Rule learning by zebra finches in an artificial grammar learning task: which rule? *Animal Cognition*, 16(2), 165-175. https://doi.org/10.1007/s10071-012-0559-x

van Heijningen, C. A. A. van, Visser, J. de, Zuidema, W., & ten Cate, C. (2009). Simple rules can explain discrimination of putative recursive syntactic structures by a songbird species. *Proceedings of the National Academy of Sciences*, pnas.0908113106.
https://doi.org/10.1073/pnas.0908113106

van Heugten, M., & Johnson, E. K. (2010). Linking infants’ distributional learning abilities to natural language acquisition. *Journal of Memory and Language*, 63(2), 197-209.
https://doi.org/10.1016/j.jml.2010.04.001

van Kampen, A., Parmaksiz, G., Van de Vijver, R., & Hoehle, B. (2008). Metrical and statistical cues for word segmentation: the use of vowel harmony and word stress as a cue to word boundaries by 6- and 9-month-old Turkish learners.

Versace, E., Spierings, M. J., Caffini, M., ten Cate, C., & Vallortigara, G. (2017). Spontaneous generalization of abstract multimodal patterns in young domestic chicks. *Animal Cognition*, 20(3), 521-529. https://doi.org/10.1007/s10071-017-1079-5

Vokey, J. R., & Brooks, L. R. (1992). Salience of item knowledge in learning artificial grammar. *Journal of Experimental Psychology. Learning, Memory, and Cognition*, 18(2), 328-344. https://doi.org/10.1037/0278-7393.18.2.328

Vouloumanos, A., & Werker, J. F. (2004). Tuned to the signal: the privileged status of speech for young infants. *Developmental Science*, 7(3), 270-276.
Vouloumanos, A., & Werker, J. F. (2007). Listening to language at birth: evidence for a bias for speech in neonates. *Developmental Science*, 10(2), 159-164.
https://doi.org/10.1111/j.1467-7687.2007.00549.x

Vries, M. de, Christiansen, M., & Petersson, K. M. (2011). Learning Recursion: Multiple Nested and Crossed Dependencies. *BIOLINGUISTICS*, 5(1-2), 010-035.

Vuong, L. C., Meyer, A. S., & Christiansen, M. H. (2016). Concurrent Statistical Learning of Adjacent and Nonadjacent Dependencies. *Language Learning*, 66(1), 8-30.
https://doi.org/10.1111/lang.12137

Wang, L., Uhrig, L., Jarraya, B., & Dehaene, S. (2015). Representation of Numerical and Sequential Patterns in Macaque and Human Brains. *Current Biology*, 25(15), 1966-1974. https://doi.org/10.1016/j.cub.2015.06.035

Wang, M. D. (1970). The role of syntactic complexity as a determiner of comprehensibility. *Journal of Verbal Learning and Verbal Behavior*, 9(4), 398-404.
https://doi.org/10.1016/S0022-5371(70)80079-2

Watumull, J., Hauser, M. D., Roberts, I. G., & Hornstein, N. (2014). On recursion. *Language Sciences*, 4, 1017. https://doi.org/10.3389/fpsyg.2013.01017

Wilson, B., Kikuchi, Y., Sun, L., Hunter, D., Dick, F., Smith, K., ... Petkov, C. I. (2015). Auditory sequence processing reveals evolutionarily conserved regions of frontal cortex in macaques and humans. *Nature Communications*, 6, 8901.
https://doi.org/10.1038/ncomms9901

Wilson, B., Marslen-Wilson, W. D., & Petkov, C. I. (2017). Conserved Sequence Processing in Primate Frontal Cortex. *Trends in Neurosciences*, 0(0).
https://doi.org/10.1016/j.tins.2016.11.004

Wilson, B., Slater, H., Kikuchi, Y., Milne, A. E., Marslen-Wilson, W. D., Smith, K., & Petkov, C. I. (2013). Auditory Artificial Grammar Learning in Macaque and Marmoset Monkeys. *The Journal of Neuroscience*, 33(48), 18825-18835.
https://doi.org/10.1523/JNEUROSCI.2414-13.2013

Wilson, B., Smith, K., & Petkov, C. I. (2015). Mixed-complexity artificial grammar learning in humans and macaque monkeys: evaluating learning strategies. *The European Journal of Neuroscience*, 41(5), 568-578. https://doi.org/10.1111/ejn.12834
Wright, A. A., Rivera, J. J., Katz, J. S., & Bachevalier, J. (2003). Abstract-concept learning and list-memory processing by capuchin and rhesus monkeys. *Journal of Experimental Psychology. Animal Behavior Processes, 29*(3), 184-198.

Yang, C. (2013). Ontogeny and phylogeny of language. *Proceedings of the National Academy of Sciences of the United States of America, 110*(16), 6324-6327.
https://doi.org/10.1073/pnas.1216803110

Zaccarella, E., & Friederici, A. D. (2016). The neurobiological nature of syntactic hierarchies.
*Neuroscience and Biobehavioral Reviews*.
https://doi.org/10.1016/j.neubiorev.2016.07.038

Zimmerer, V. C., Cowell, P. E., & Varley, R. A. (2011). Individual behavior in learning of an artificial grammar. *Memory & Cognition, 39*(3), 491-501.
https://doi.org/10.3758/s13421-010-0039-y

Zuberbühler, K. (2002). A syntactic rule in forest monkey communication. *Animal Behaviour, 63*(2), 293-299. https://doi.org/10.1006/anbe.2001.1914
ANNEXES

<figure><img src="image_79.png" /></figure>
# I. Etude IV: Supplementary Information

1 2 3 4
5 6 7 8
9 10 11 12
13 14 15 16

**Figure S1** Locations on the touchscreen. Note that letters are given for illustrative purposes but were not displayed during the experiment.

<table><caption>**Table S2** Lists of all the sequences used in this study. Numbers denote different locations on the touchscreen as shown in Figure S1.</caption><thead><tr><th>Set</th><th>Mirror</th><th>Copy</th><th>Set</th><th>Mirror</th><th>Copy</th></tr></thead><tbody><tr><td rowspan="20">4-target<br>baseline<br>sequences</td><td>4.1.1.4</td><td>1.4.1.4</td><td rowspan="8">6-target<br>baseline<br>sequences<br>(Test 1)</td><td>15.2.1.1.2.15</td><td>1.2.15.1.2.15</td></tr><tr><td>11.1.1.11</td><td>1.11.1.11</td><td>13.10.2.2.10.13</td><td>2.10.13.2.10.13</td></tr><tr><td>12.1.1.12</td><td>1.12.1.12</td><td>1.15.3.3.15.1</td><td>3.15.1.3.15.1</td></tr><tr><td>15.1.1.15</td><td>1.15.1.15</td><td>3.1.10.10.1.3</td><td>10.1.3.10.1.3</td></tr><tr><td>1.2.2.1</td><td>2.1.2.1</td><td>10.3.13.13.3.10</td><td>13.3.10.13.3.10</td></tr><tr><td>7.2.2.7</td><td>2.7.2.7</td><td>2.13.15.15.13.2</td><td>15.13.2.15.13.2</td></tr><tr><td>8.2.2.8</td><td>2.8.2.8</td><td></td><td></td></tr><tr><td>9.2.2.9</td><td>2.9.2.9</td><td>11.3.1.1.3.11</td><td>1.3.11.1.3.11</td></tr><tr><td>1.3.3.1</td><td>3.1.3.1</td><td>6.4.1.1.4.6</td><td>1.4.6.1.4.6</td></tr><tr><td>4.3.3.4</td><td>3.4.3.4</td><td>4.6.1.1.6.4</td><td>1.6.4.1.6.4</td></tr><tr><td>5.3.3.5</td><td>3.5.3.5</td><td>12.10.1.1.10.12</td><td>1.10.12.1.10.12</td></tr><tr><td>11.3.3.11</td><td>3.11.3.11</td><td>3.11.1.1.11.3</td><td>1.11.3.1.11.3</td></tr><tr><td>6.4.4.6</td><td>4.6.4.6</td><td>10.12.1.1.12.10</td><td>1.12.10.1.12.10</td></tr><tr><td>11.4.4.11</td><td>4.11.4.11</td><td>12.8.2.2.8.12</td><td>2.8.12.2.8.12</td></tr><tr><td>14.4.4.14</td><td>4.14.4.14</td><td>8.12.2.2.12.8</td><td>2.12.8.2.12.8</td></tr><tr><td>16.4.4.16</td><td>4.16.4.16</td><td>11.1.3.3.1.11</td><td>3.1.11.3.1.11</td></tr><tr><td>4.5.5.4</td><td>5.4.5.4</td><td>1.11.3.3.11.1</td><td>3.11.1.3.11.1</td></tr><tr><td>10.5.5.10</td><td>5.10.5.10</td><td>6.1.4.4.1.6</td><td>4.1.6.4.1.6</td></tr><tr><td>13.5.5.13</td><td>5.13.5.13</td><td>1.6.4.4.6.1</td><td>4.6.1.4.6.1</td></tr><tr><td>16.5.5.16</td><td>5.16.5.16</td><td>14.9.4.4.9.14</td><td>4.9.14.4.9.14</td></tr><tr><td>1.6.6.1</td><td>6.1.6.1</td><td>16.11.4.4.11.16</td><td>4.11.16.4.11.16</td></tr><tr><td>3.6.6.3</td><td>6.3.6.3</td><td>9.14.4.4.14.9</td><td>4.14.9.4.14.9</td></tr><tr><td>11.6.6.11</td><td>6.11.6.11</td><td>11.16.4.4.16.11</td><td>4.16.11.4.16.11</td></tr><tr><td>16.6.6.16</td><td>6.16.6.16</td><td>16.7.5.5.7.16</td><td>5.7.16.5.7.16</td></tr><tr><td>5.7.7.5</td><td>7.5.7.5</td><td>7.16.5.5.16.7</td><td>5.16.7.5.16.7</td></tr><tr><td>6.7.7.6</td><td>7.6.7.6</td><td>4.1.6.6.1.4</td><td>6.1.4.6.1.4</td></tr><tr><td>10.7.7.10</td><td>7.10.7.10</td><td>1.4.6.6.4.1</td><td>6.4.1.6.4.1</td></tr><tr><td>14.7.7.14</td><td>7.14.7.14</td><td>16.5.7.7.5.16</td><td>7.5.16.7.5.16</td></tr><tr><td>5.8.8.5</td><td>8.5.8.5</td><td>5.16.7.7.16.5</td><td>7.16.5.7.16.5</td></tr><tr><td>9.8.8.9</td><td>8.9.8.9</td><td>12.2.8.8.2.12</td><td>8.2.12.8.2.12</td></tr></tbody></table>
<table><tr><td>12.8.8.12</td><td>8.12.8.12</td></tr><tr><td>15.8.8.15</td><td>8.15.8.15</td></tr><tr><td>4.9.9.4</td><td>9.4.9.4</td></tr><tr><td>7.9.9.7</td><td>9.7.9.7</td></tr><tr><td>12.9.9.12</td><td>9.12.9.12</td></tr><tr><td>13.9.9.13</td><td>9.13.9.13</td></tr><tr><td>1.10.10.1</td><td>10.1.10.1</td></tr><tr><td>8.10.10.8</td><td>10.8.10.8</td></tr><tr><td>12.10.10.12</td><td>10.12.10.12</td></tr><tr><td>14.10.10.14</td><td>10.14.10.14</td></tr><tr><td>5.11.11.5</td><td>11.5.11.5</td></tr><tr><td>10.11.11.10</td><td>11.10.11.10</td></tr><tr><td>14.11.11.14</td><td>11.14.11.14</td></tr><tr><td>16.11.11.16</td><td>11.16.11.16</td></tr><tr><td>2.12.12.2</td><td>12.2.12.2</td></tr><tr><td>3.12.12.3</td><td>12.3.12.3</td></tr><tr><td>6.12.12.6</td><td>12.6.12.6</td></tr><tr><td>13.12.12.13</td><td>12.13.12.13</td></tr><tr><td>2.13.13.2</td><td>13.2.13.2</td></tr><tr><td>3.13.13.3</td><td>13.3.13.3</td></tr><tr><td>8.13.13.8</td><td>13.8.13.8</td></tr><tr><td>10.13.13.10</td><td>13.10.13.10</td></tr><tr><td>2.14.14.2</td><td>14.2.14.2</td></tr><tr><td>8.14.14.8</td><td>14.8.14.8</td></tr><tr><td>9.14.14.9</td><td>14.9.14.9</td></tr><tr><td>15.14.14.15</td><td>14.15.14.15</td></tr><tr><td>2.15.15.2</td><td>15.2.15.2</td></tr><tr><td>6.15.15.6</td><td>15.6.15.6</td></tr><tr><td>7.15.15.7</td><td>15.7.15.7</td></tr><tr><td>13.15.15.13</td><td>15.13.15.13</td></tr><tr><td>3.16.16.3</td><td>16.3.16.3</td></tr><tr><td>7.16.16.7</td><td>16.7.16.7</td></tr><tr><td>9.16.16.9</td><td>16.9.16.9</td></tr><tr><td>15.16.16.15</td><td>16.15.16.15</td></tr></table>

<table><tr><td>2.12.8.8.12.2</td><td>8.12.2.8.12.2</td></tr><tr><td>15.13.8.8.13.15</td><td>8.13.15.8.13.15</td></tr><tr><td>13.15.8.8.15.13</td><td>8.15.13.8.15.13</td></tr><tr><td>14.4.9.9.4.14</td><td>9.4.14.9.4.14</td></tr><tr><td>4.14.9.9.14.4</td><td>9.14.4.9.14.4</td></tr><tr><td>12.1.10.10.1.12</td><td>10.1.12.10.1.12</td></tr><tr><td>1.12.10.10.12.1</td><td>10.12.1.10.12.1</td></tr><tr><td>3.1.11.11.1.3</td><td>11.1.3.11.1.3</td></tr><tr><td>1.3.11.11.3.1</td><td>11.3.1.11.3.1</td></tr><tr><td>16.4.11.11.4.16</td><td>11.4.16.11.4.16</td></tr><tr><td>4.16.11.11.16.4</td><td>11.16.4.11.16.4</td></tr><tr><td>10.1.12.12.1.10</td><td>12.1.10.12.1.10</td></tr><tr><td>8.2.12.12.2.8</td><td>12.2.8.12.2.8</td></tr><tr><td>2.8.12.12.8.2</td><td>12.8.2.12.8.2</td></tr><tr><td>1.10.12.12.10.1</td><td>12.10.1.12.10.1</td></tr><tr><td>15.8.13.13.8.15</td><td>13.8.15.13.8.15</td></tr><tr><td>8.15.13.13.15.8</td><td>13.15.8.13.15.8</td></tr><tr><td>9.4.14.14.4.9</td><td>14.4.9.14.4.9</td></tr><tr><td>4.9.14.14.9.4</td><td>14.9.4.14.9.4</td></tr><tr><td>13.8.15.15.8.13</td><td>15.8.13.15.8.13</td></tr><tr><td>8.13.15.15.13.8</td><td>15.13.8.15.13.8</td></tr><tr><td>11.4.16.16.4.11</td><td>16.4.11.16.4.11</td></tr><tr><td>7.5.16.16.5.7</td><td>16.5.7.16.5.7</td></tr><tr><td>5.7.16.16.7.5</td><td>16.7.5.16.7.5</td></tr><tr><td>4.11.16.16.11.4</td><td>16.11.4.16.11.4</td></tr></table>
<table><thead><tr><th rowspan="2">Set</th><th colspan="2">Mirror</th><th colspan="2">Copy</th></tr><tr><th>Consistent</th><th>Inconsistent</th><th>Consistent</th><th>Inconsistent</th></tr></thead><tbody><tr><td rowspan="24">Pre-test 1</td><td>1.14.14.1</td><td>1.14.1.14</td><td>1.14.1.14</td><td>1.14.14.1</td></tr><tr><td>1.9.9.1</td><td>1.9.1.9</td><td>1.9.1.9</td><td>1.9.9.1</td></tr><tr><td>10.3.3.10</td><td>10.3.10.3</td><td>10.3.10.3</td><td>10.3.3.10</td></tr><tr><td>10.6.6.10</td><td>10.6.10.6</td><td>10.6.10.6</td><td>10.6.6.10</td></tr><tr><td>11.12.12.11</td><td>11.12.11.12</td><td>11.12.11.12</td><td>11.12.12.11</td></tr><tr><td>11.13.13.11</td><td>11.13.11.13</td><td>11.13.11.13</td><td>11.13.13.11</td></tr><tr><td>12.11.11.12</td><td>12.11.12.11</td><td>12.11.12.11</td><td>12.11.11.12</td></tr><tr><td>12.15.15.12</td><td>12.15.12.15</td><td>12.15.12.15</td><td>12.15.15.12</td></tr><tr><td>13.11.11.13</td><td>13.11.13.11</td><td>13.11.13.11</td><td>13.11.11.13</td></tr><tr><td>13.7.7.13</td><td>13.7.13.7</td><td>13.7.13.7</td><td>13.7.7.13</td></tr><tr><td>14.1.1.14</td><td>14.1.14.1</td><td>14.1.14.1</td><td>14.1.1.14</td></tr><tr><td>14.16.16.14</td><td>14.16.14.16</td><td>14.16.14.16</td><td>14.16.16.14</td></tr><tr><td>15.12.12.15</td><td>15.12.15.12</td><td>15.12.15.12</td><td>15.12.12.15</td></tr><tr><td>15.4.4.15</td><td>15.4.15.4</td><td>15.4.15.4</td><td>15.4.4.15</td></tr><tr><td>16.14.14.16</td><td>16.14.16.14</td><td>16.14.16.14</td><td>16.14.14.16</td></tr><tr><td>16.2.2.16</td><td>16.2.16.2</td><td>16.2.16.2</td><td>16.2.2.16</td></tr><tr><td>2.16.16.2</td><td>2.16.2.16</td><td>2.16.2.16</td><td>2.16.16.2</td></tr><tr><td>2.5.5.2</td><td>2.5.2.5</td><td>2.5.2.5</td><td>2.5.5.2</td></tr><tr><td>3.10.10.3</td><td>3.10.3.10</td><td>3.10.3.10</td><td>3.10.10.3</td></tr><tr><td>3.7.7.3</td><td>3.7.3.7</td><td>3.7.3.7</td><td>3.7.7.3</td></tr><tr><td>4.15.15.4</td><td>4.15.4.15</td><td>4.15.4.15</td><td>4.15.15.4</td></tr><tr><td>4.8.8.4</td><td>4.8.4.8</td><td>4.8.4.8</td><td>4.8.8.4</td></tr><tr><td>5.2.2.5</td><td>5.2.5.2</td><td>5.2.5.2</td><td>5.2.2.5</td></tr><tr><td>5.9.9.5</td><td>5.9.5.9</td><td>5.9.5.9</td><td>5.9.9.5</td></tr><tr><td>6.10.10.6</td><td>6.10.6.10</td><td>6.10.6.10</td><td>6.10.10.6</td></tr><tr><td>6.8.8.6</td><td>6.8.6.8</td><td>6.8.6.8</td><td>6.8.8.6</td></tr><tr><td>7.13.13.7</td><td>7.13.7.13</td><td>7.13.7.13</td><td>7.13.13.7</td></tr><tr><td>7.3.3.7</td><td>7.3.7.3</td><td>7.3.7.3</td><td>7.3.3.7</td></tr><tr><td>8.4.4.8</td><td>8.4.8.4</td><td>8.4.8.4</td><td>8.4.4.8</td></tr><tr><td>8.6.6.8</td><td>8.6.8.6</td><td>8.6.8.6</td><td>8.6.6.8</td></tr><tr><td>9.1.1.9</td><td>9.1.9.1</td><td>9.1.9.1</td><td>9.1.1.9</td></tr><tr><td>9.5.5.9</td><td>9.5.9.5</td><td>9.5.9.5</td><td>9.5.5.9</td></tr></tbody></table>
<table><thead><tr><th rowspan="2">Set</th><th colspan="2">Mirror</th><th colspan="2">Copy</th></tr><tr><th>Consistent</th><th>Inconsistent</th><th>Consistent</th><th>Inconsistent</th></tr></thead><tbody><tr><td rowspan="24">Pre-test<br>2</td><td>1.14.14.1</td><td>1.14.14.16</td><td>1.14.1.14</td><td>1.14.1.9</td></tr><tr><td>1.9.9.1</td><td>1.9.9.5</td><td>1.9.1.9</td><td>1.9.1.14</td></tr><tr><td>10.3.3.10</td><td>10.3.3.7</td><td>10.3.10.3</td><td>10.3.10.6</td></tr><tr><td>10.6.6.10</td><td>10.6.6.8</td><td>10.6.10.6</td><td>10.6.10.3</td></tr><tr><td>11.12.12.11</td><td>11.12.12.15</td><td>11.12.11.12</td><td>11.12.11.13</td></tr><tr><td>11.13.13.11</td><td>11.13.13.7</td><td>11.13.11.13</td><td>11.13.11.12</td></tr><tr><td>12.11.11.12</td><td>12.11.11.13</td><td>12.11.12.11</td><td>12.11.12.15</td></tr><tr><td>12.15.15.12</td><td>12.15.15.4</td><td>12.15.12.15</td><td>12.15.12.11</td></tr><tr><td>13.11.11.13</td><td>13.11.11.12</td><td>13.11.13.11</td><td>13.11.13.7</td></tr><tr><td>13.7.7.13</td><td>13.7.7.3</td><td>13.7.13.7</td><td>13.7.13.11</td></tr><tr><td>14.1.1.14</td><td>14.1.1.9</td><td>14.1.14.1</td><td>14.1.14.16</td></tr><tr><td>14.16.16.14</td><td>14.16.16.2</td><td>14.16.14.16</td><td>14.16.14.1</td></tr><tr><td>15.12.12.15</td><td>15.12.12.11</td><td>15.12.15.12</td><td>15.12.15.4</td></tr><tr><td>15.4.4.15</td><td>15.4.4.8</td><td>15.4.15.4</td><td>15.4.15.12</td></tr><tr><td>16.14.14.16</td><td>16.14.14.1</td><td>16.14.16.14</td><td>16.14.16.2</td></tr><tr><td>16.2.2.16</td><td>16.2.2.5</td><td>16.2.16.2</td><td>16.2.16.14</td></tr><tr><td>2.16.16.2</td><td>2.16.16.14</td><td>2.16.2.16</td><td>2.16.2.5</td></tr><tr><td>2.5.5.2</td><td>2.5.5.9</td><td>2.5.2.5</td><td>2.5.2.16</td></tr><tr><td>3.10.10.3</td><td>3.10.10.6</td><td>3.10.3.10</td><td>3.10.3.7</td></tr><tr><td>3.7.7.3</td><td>3.7.7.13</td><td>3.7.3.7</td><td>3.7.3.10</td></tr><tr><td>4.15.15.4</td><td>4.15.15.12</td><td>4.15.4.15</td><td>4.15.4.8</td></tr><tr><td>4.8.8.4</td><td>4.8.8.6</td><td>4.8.4.8</td><td>4.8.4.15</td></tr><tr><td>5.2.2.5</td><td>5.2.2.16</td><td>5.2.5.2</td><td>5.2.5.9</td></tr><tr><td>5.9.9.5</td><td>5.9.9.1</td><td>5.9.5.9</td><td>5.9.5.2</td></tr><tr><td>6.10.10.6</td><td>6.10.10.3</td><td>6.10.6.10</td><td>6.10.6.8</td></tr><tr><td>6.8.8.6</td><td>6.8.8.4</td><td>6.8.6.8</td><td>6.8.6.10</td></tr><tr><td>7.13.13.7</td><td>7.13.13.11</td><td>7.13.7.13</td><td>7.13.7.3</td></tr><tr><td>7.3.3.7</td><td>7.3.3.10</td><td>7.3.7.3</td><td>7.3.7.13</td></tr><tr><td>8.4.4.8</td><td>8.4.4.15</td><td>8.4.8.4</td><td>8.4.8.6</td></tr><tr><td>8.6.6.8</td><td>8.6.6.10</td><td>8.6.8.6</td><td>8.6.8.4</td></tr><tr><td>9.1.1.9</td><td>9.1.1.14</td><td>9.1.9.1</td><td>9.1.9.5</td></tr><tr><td>9.5.5.9</td><td>9.5.5.2</td><td>9.5.9.5</td><td>9.5.9.1</td></tr></tbody></table>
<table><thead><tr><th rowspan="2">Set</th><th colspan="2">Mirror</th><th colspan="2">Copy</th></tr><tr><th>Consistent</th><th>Inconsistent</th><th>Consistent</th><th>Inconsistent</th></tr></thead><tbody><tr><td rowspan="20">Test 1</td><td>11.14.5.5.14.11</td><td>11.14.5.5.11.14</td><td>11.12.8.11.12.8</td><td>11.12.8.11.8.12</td></tr><tr><td>11.7.6.6.7.11</td><td>11.7.6.6.11.7</td><td>11.8.12.11.8.12</td><td>11.8.12.11.12.8</td></tr><tr><td>12.16.14.14.16.12</td><td>12.16.14.14.12.16</td><td>12.5.6.12.5.6</td><td>12.5.6.12.6.5</td></tr><tr><td>12.8.11.11.8.12</td><td>12.8.11.11.12.8</td><td>12.6.5.12.6.5</td><td>12.6.5.12.5.6</td></tr><tr><td>14.11.5.5.11.14</td><td>14.11.5.5.14.11</td><td>14.12.16.14.12.16</td><td>14.12.16.14.16.12</td></tr><tr><td>14.7.9.9.7.14</td><td>14.7.9.9.14.7</td><td>14.16.12.14.16.12</td><td>14.16.12.14.12.16</td></tr><tr><td>16.12.14.14.12.16</td><td>16.12.14.14.16.12</td><td>16.4.9.16.4.9</td><td>16.4.9.16.9.4</td></tr><tr><td>16.5.8.8.5.16</td><td>16.5.8.8.16.5</td><td>16.9.4.16.9.4</td><td>16.9.4.16.4.9</td></tr><tr><td>4.9.16.16.9.4</td><td>4.9.16.16.4.9</td><td>4.6.8.4.6.8</td><td>4.6.8.4.8.6</td></tr><tr><td>4.9.7.7.9.4</td><td>4.9.7.7.4.9</td><td>4.8.6.4.8.6</td><td>4.8.6.4.6.8</td></tr><tr><td>5.16.8.8.16.5</td><td>5.16.8.8.5.16</td><td>5.11.14.5.11.14</td><td>5.11.14.5.14.11</td></tr><tr><td>5.6.12.12.6.5</td><td>5.6.12.12.5.6</td><td>5.14.11.5.14.11</td><td>5.14.11.5.11.14</td></tr><tr><td>6.5.12.12.5.6</td><td>6.5.12.12.6.5</td><td>6.11.7.6.11.7</td><td>6.11.7.6.7.11</td></tr><tr><td>6.8.4.4.8.6</td><td>6.8.4.4.6.8</td><td>6.7.11.6.7.11</td><td>6.7.11.6.11.7</td></tr><tr><td>7.11.6.6.11.7</td><td>7.11.6.6.7.11</td><td>7.4.9.7.4.9</td><td>7.4.9.7.9.4</td></tr><tr><td>7.14.9.9.14.7</td><td>7.14.9.9.7.14</td><td>7.9.4.7.9.4</td><td>7.9.4.7.4.9</td></tr><tr><td>8.12.11.11.12.8</td><td>8.12.11.11.8.12</td><td>8.16.5.8.16.5</td><td>8.16.5.8.5.16</td></tr><tr><td>8.6.4.4.6.8</td><td>8.6.4.4.8.6</td><td>8.5.16.8.5.16</td><td>8.5.16.8.16.5</td></tr><tr><td>9.4.16.16.4.9</td><td>9.4.16.16.9.4</td><td>9.14.7.9.14.7</td><td>9.14.7.9.7.14</td></tr><tr><td>9.4.7.7.4.9</td><td>9.4.7.7.9.4</td><td>9.7.14.9.7.14</td><td>9.7.14.9.14.7</td></tr></tbody></table>
<table><thead><tr><th rowspan="2">Set</th><th colspan="2">Mirror</th><th colspan="2">Copy</th></tr><tr><th>Consistent</th><th>Inconsistent</th><th>Consistent</th><th>Inconsistent</th></tr></thead><tbody><tr><td rowspan="15">Test 2</td><td>10.13.5.5.13.10</td><td>10.13.5.5.10.13</td><td>10.14.8.10.14.8</td><td>10.14.8.10.8.14</td></tr><tr><td>13.10.5.5.10.13</td><td>13.10.5.5.13.10</td><td>10.8.14.10.8.14</td><td>10.8.14.10.14.8</td></tr><tr><td>13.3.12.12.3.13</td><td>13.3.12.12.13.3</td><td>12.13.3.12.13.3</td><td>12.13.3.12.3.13</td></tr><tr><td>14.8.10.10.8.14</td><td>14.8.10.10.14.8</td><td>12.3.13.12.3.13</td><td>12.3.13.12.13.3</td></tr><tr><td>15.2.14.14.2.15</td><td>15.2.14.14.15.2</td><td>14.15.2.14.15.2</td><td>14.15.2.14.2.15</td></tr><tr><td>16.3.6.6.3.16</td><td>16.3.6.6.16.3</td><td>14.2.15.14.2.15</td><td>14.2.15.14.15.2</td></tr><tr><td>2.15.14.14.15.2</td><td>2.15.14.14.2.15</td><td>15.6.7.15.6.7</td><td>15.6.7.15.7.6</td></tr><tr><td>3.13.12.12.13.3</td><td>3.13.12.12.3.13</td><td>15.7.6.15.7.6</td><td>15.7.6.15.6.7</td></tr><tr><td>3.16.6.6.16.3</td><td>3.16.6.6.3.16</td><td>2.7.9.2.7.9</td><td>2.7.9.2.9.7</td></tr><tr><td>4.5.3.3.5.4</td><td>4.5.3.3.4.5</td><td>2.9.7.2.9.7</td><td>2.9.7.2.7.9</td></tr><tr><td>5.4.3.3.4.5</td><td>5.4.3.3.5.4</td><td>3.4.5.3.4.5</td><td>3.4.5.3.5.4</td></tr><tr><td>6.7.15.15.7.6</td><td>6.7.15.15.6.7</td><td>3.5.4.3.5.4</td><td>3.5.4.3.4.5</td></tr><tr><td>7.6.15.15.6.7</td><td>7.6.15.15.7.6</td><td>5.10.13.5.10.13</td><td>5.10.13.5.13.10</td></tr><tr><td>7.9.2.2.9.7</td><td>7.9.2.2.7.9</td><td>5.13.10.5.13.10</td><td>5.13.10.5.10.13</td></tr><tr><td>8.14.10.10.14.8</td><td>8.14.10.10.8.14</td><td>6.16.3.6.16.3</td><td>6.16.3.6.3.16</td></tr><tr><td>9.7.2.2.7.9</td><td>9.7.2.2.9.7</td><td>6.3.16.6.3.16</td><td>6.3.16.6.16.3</td></tr><tr><td rowspan="16">Test 3<br>&<br>Control</td><td>1.13.7.7.13.1</td><td>1.13.7.7.1.13</td><td>1.5.9.1.5.9</td><td>1.5.9.1.9.5</td></tr><tr><td>1.16.8.8.16.1</td><td>1.16.8.8.1.16</td><td>1.9.5.1.9.5</td><td>1.9.5.1.5.9</td></tr><tr><td>10.4.15.15.4.10</td><td>10.4.15.15.10.4</td><td>10.16.2.10.16.2</td><td>10.16.2.10.2.16</td></tr><tr><td>12.15.5.5.15.12</td><td>12.15.5.5.12.15</td><td>10.2.16.10.2.16</td><td>10.2.16.10.16.2</td></tr><tr><td>13.1.7.7.1.13</td><td>13.1.7.7.13.1</td><td>10.3.9.10.3.9</td><td>10.3.9.10.9.3</td></tr><tr><td>15.12.5.5.12.15</td><td>15.12.5.5.15.12</td><td>10.9.3.10.9.3</td><td>10.9.3.10.3.9</td></tr><tr><td>15.9.11.11.9.15</td><td>15.9.11.11.15.9</td><td>11.15.9.11.15.9</td><td>11.15.9.11.9.15</td></tr><tr><td>16.1.8.8.1.16</td><td>16.1.8.8.16.1</td><td>11.9.15.11.9.15</td><td>11.9.15.11.15.9</td></tr><tr><td>16.2.10.10.2.16</td><td>16.2.10.10.16.2</td><td>15.10.4.15.10.4</td><td>15.10.4.15.4.10</td></tr><tr><td>2.16.10.10.16.2</td><td>2.16.10.10.2.16</td><td>15.4.10.15.4.10</td><td>15.4.10.15.10.4</td></tr><tr><td>3.9.10.10.9.3</td><td>3.9.10.10.3.9</td><td>5.12.15.5.12.15</td><td>5.12.15.5.15.12</td></tr><tr><td>4.10.15.15.10.4</td><td>4.10.15.15.4.10</td><td>5.15.12.5.15.12</td><td>5.15.12.5.12.15</td></tr><tr><td>5.9.1.1.9.5</td><td>5.9.1.1.5.9</td><td>7.1.13.7.1.13</td><td>7.1.13.7.13.1</td></tr><tr><td>9.15.11.11.15.9</td><td>9.15.11.11.9.15</td><td>7.13.1.7.13.1</td><td>7.13.1.7.1.13</td></tr><tr><td>9.3.10.10.3.9</td><td>9.3.10.10.9.3</td><td>8.1.16.8.1.16</td><td>8.1.16.8.16.1</td></tr><tr><td>9.5.1.1.5.9</td><td>9.5.1.1.9.5</td><td>8.16.1.8.16.1</td><td>8.16.1.8.1.16</td></tr></tbody></table>
## II. Etude sur la Prévalence des Informations Ordinales et Relationnelles : Données Comparatives chez le Babouin et l'Humain

Fagot, J., Malassis, R., & Medam, T. (2018). The processing of positional information in a two-item sequence limits the emergence of symmetry in baboons (*Papio papio*), but not in humans (*Homo sapiens*). *Learning & Behavior*, 46(1), 67-78.
https://doi.org/10.3758/s13420-017-0290-1

Cette étude est issue d'une collaboration au sein de l'équipe. La question abordée ne traitait pas directement des régularités non-adjacentes et l'extraction de structure, mais portait sur la prévalence de certains modes d'encodage des séquences sur d'autres, lorsque plusieurs stratégies sont disponibles. Nous avons utilisé l'adaptation de la tâche SRT visuelle que j'ai développé pour la deuxième étude de ma thèse.

Nous avons cherché à quantifier l'importance relative pour les babouins des propriétés ordinales (e.g. A apparaît en 1er, B en 2ème) et relationnelles (e.g. A est associé à B) caractérisant un set de séquences. Après une période d'exposition à trois séquences constituées chacune de deux formes visuelles associées (notées AB, CD, EF), nous avons présenté aux babouins des réarrangements de ces séquences (BA, ED, FC). Dans ces séquences test, les informations ordinales et relationnelles étaient manipulées de manière orthogonale :

<table><thead><tr><th>Exposition</th><th>Test</th><th>Relation</th><th>Position</th></tr></thead><tbody><tr><td>A-B</td><td>B-A</td><td>+</td><td>-</td></tr><tr><td>C-D<br>E-F</td><td>E-D</td><td>-</td><td>+</td></tr><tr><td>C-D<br>E-F</td><td>F-C</td><td>-</td><td>-</td></tr></tbody></table>
L'examen des temps de réponse des babouins sur le deuxième élément de ces séquences test a indiqué que les babouins réagissaient davantage aux modifications de la *position* des stimuli dans la séquence qu'aux modifications des *relations* entre ces stimuli. La même expérience menée chez l'humain dans des conditions similaires d'apprentissage révèle que ceux-ci réagissent de manière similaire aux deux types de modifications. Ces résultats suggèrent que lorsque des séquences de stimuli sont caractérisées à la fois par des régularités ordinales et relationnelles, les babouins donnent priorité à ces premières.
<figure><img src="image_80.png" /></figure>

# The processing of positional information in a two-item sequence limits the emergence of symmetry in baboons (*Papio papio*), but not in humans (*Homo sapiens*)

Joël Fagot<fnref n="1" />,<fnref n="2" /> • Raphaelle Malassis<fnref n="1" />,<fnref n="2" /> • Tiphaine Medam<fnref n="1" />,<fnref n="2" />

© Psychonomic Society, Inc. 2017

**Abstract** When trained to associate Stimulus A to Stimulus B, humans can derive the untrained symmetrical B to A relation while nonhuman animals have much more difficulties. Urcuioli (2008, *Journal of the Experimental Analysis of Behavior*, 90, 257–282; 2015, *Conductal*, 3, 4–25) proposed that the apparent difficulty of animals in symmetry testing reflects their double encoding of the information on the stimuli (identity and relation) and their positional (i.e., spatial and temporal/ordinal) characteristics. This comparative study tested the emergence of symmetry in humans and baboons in a task in which the position of the stimuli was manipulated independently of their relation. Humans and baboons initially learned to associate pairs of visual shapes on a touch screen in a specific order. Three pairs of (A-B, C-D, and E-F) stimuli were used in training. After training, the two species were tested with the B-A, F-C, and E-D pairs. The B-A pairs preserved the association initially learned with A-B but reversed the positional information relative to training. The F-C pair neither preserved the association nor the positional information of the training pairs, and positional information were the only cues preserved in the E-D pair. Humans showed a response time advantage for B-A, suggesting symmetry, but also for E-D, suggesting that they also process positional information. In baboons, the advantage was found only for E-D, suggesting that they only process positional information. These

results confirm that the processing of stimulus pairs differ between nonhuman animals to humans.

**Keywords** Associative learning · Equivalence classes · Language · Categorization

Derived relational responding is a cognitive phenomenon in which trained relations between arbitrary stimuli result in the spontaneous emergence of untrained relations. This cognitive phenomenon is best demonstrated in humans by experiments on stimulus equivalence (Sidman & Tailby, 1982). In such experiments, the participants must typically learn by trial and error that the visual sample Stimulus A must be associated in a conditional matching-to-sample task to the comparison Stimulus B. They must also learn that the same B stimulus now used as sample in the matching task must be associated to the comparison Stimulus C. After such (A-B, B-C) training, the subjects demonstrate stimulus equivalence when they spontaneously exhibit the relation of reflexivity, symmetry, and transitivity. Reflexivity implies the ability to spontaneously associate each stimulus to itself, for instance, to indicate that A goes with A. The relation of symmetry implies that any given association learned during the training phase can be reversed. In other words, if the subject has learned that A goes to B, he or she should be able to indicate without training that B also goes to A. The relation of transitivity involves that the stimuli that have common associates go together. In other words, after being trained with A-B and B-C, the subject showing transitivity should be able to match A to C and C to A. Stimulus equivalence, as defined by the ability to process all these three relations, was initially theorized and demonstrated by Sidman and collaborators using this kind of matching-to-sample protocols (Sidman, 1971; Sidman & Tailby, 1982), and the fact that our species has this ability

<figure><img src="image_81.png" /></figure>

<footnote>Joël Fagot
joel.fagot@univ-amu.fr</footnote>

<footnote n="1">¹ Laboratory of Cognitive Psychology, Aix Marseille University, CNRS, Marseille, France</footnote>

<footnote n="2">² Brain and Language Research Institute, Aix Marseille University, CNRS, Aix-en-Provence, France</footnote>
has been largely confirmed since their initial studies (e.g., Adams, Fields, & Verhave, 1993; Carp & Petursdottir, 2015; Devany, Hayes, & Nelson, 1986). One reason for which stimulus equivalence is interesting is that it appears to be closely linked with human language. First, our flexible use of words implies a bidirectional (i.e., symmetrical) relation between the word and its referent: the word refers to the object (e.g., in requesting contexts) as the object evokes the word (e.g., in naming contexts). Second, stimulus equivalence emerges at approximately 2 years of age (e.g., Boelens, van den Broek, & van Klarenbosch, 2000; Lipkens, Hayes, & Hayes, 1993) and is thus concurrent with language learning. Third, children with language disabilities or other clinical groups with linguistic deficiencies (e.g., children with autism) often express difficulties in tasks of stimulus equivalence (Devany et al., 1986; Eikeseth & Smith, 1992; O'Donnell & Saunders, 2003; but see Carr, Wilkinson, Blackman, & McIlvane, 2000). The apparent relations between language and stimulus equivalence call for comparative studies on stimulus equivalence in non-linguistic species.

From a comparative point of view, stimulus equivalence has been an elusive phenomenon when tested in nonhuman animals, such as pigeons (e.g., R. Lipkens, Kop, & Matthijs, 1988) and nonhuman primates (e.g., rhesus monkeys and baboons; Sidman, Rauzin, et al., 1982). Positive results have been repeatedly reported regarding reflexivity (Barros, Galvão, & McIlvane, 2002; Sweeney & Urcuioli, 2010) and transitivity (D'Amato, Salmon, Loukas, & Tomie, 1985; Kuno, Kitadate, & Iwamoto, 1994; Zentall, Clement, & Weaver, 2003), but the large majority of the studies have reported negative results regarding the symmetry relation. Lionello-DeNolf (2009) reviewed 24 investigations of symmetry in nonhuman animals and concluded that only three of these studies found clear evidence of symmetry in their subjects (pigeons: Frank & Wasserman, 2005; sea lions: Kastak, Schusterman, & Kastak, 2001; Schusterman & Kastak, 1993). Since this review has been published, a few additional papers (e.g., Urcuioli, 2015) have confirmed that symmetry is in the scope of pigeons. Nevertheless, the general picture of the existing literature remains that symmetry seems an exceptionally difficult feat in nonhuman species. The main goal of our research was to identify which factor may best explain the general difficulties expressed by nonhuman animals during symmetry testing.

Urcuioli (2008, 2015) proposed that the difficulty experienced by pigeons in symmetry testing would result from their mode of encoding of two types of information that are (1) the information on the identity of the nominal stimuli and their relation and (2) the information on their position in the spatial and temporal dimensions. According to Urcuioli, a Stimulus A appearing as sample in a fixed spatial position #1 (e.g., typically, the center response key) would be encoded as Stimulus A in temporal/ordinal position #1 at spatial position

#1, while a Stimulus B appearing as a comparison in a fixed spatial position #2 (e.g., left or right response key) would be encoded as Stimulus B in temporal/ordinal position #2 at spatial location #2. This mode of encoding would prevent generalization in symmetry trials because Stimulus A in temporal/ordinal position #1 and spatial position #1 would be treated as functionally different from the same stimulus presented in the temporal/ordinal position #2 and spatial position #2.

The idea that nonhuman animals focus too strongly their attention on positional information during the learning of associations between pairs of items is mostly supported by Frank and Wasserman's (2005) and Urcuioli's (2008, Experiment 3) studies in pigeons. These studies had two interesting distinguishing features that may explain their positive results in symmetry trials. First, they both used a successive matching-to-sample task rather than the two-alternative matching tasks (e.g., Lipkens et al., 1988). In their successive matching task, the sample and comparison stimuli are shown on the same location on the screen, and the pigeons have to peck (go response) or not (no-go response) the comparison stimulus depending on the identity of the sample. This aspect of their task contrasts strongly with the two-alternative matching-to-sample task in which the sample and comparisons are shown on different locations on the displays (see Lionello-DeNolf, 2009, for a review of negative findings obtained in this task; see also Urcuioli, 2008, Experiments 1 and 2). Second, both Frank and Wasserman (2005) and Urcuioli (2008, Experiment 3) mixed identity (A-A, B-B) and symbolic matching trials (A-B) during the training phase. One consequence of this intermixing of the identity and symbolic matching trials is that each stimulus has also been presented in the first or second temporal/ordinal position during training. This counterbalancing might have reduced the importance given to temporal/ordinal information in the pigeons' mode of encoding, allowing a stronger focus on relational information at the origin of a successful performance in symmetry trials. Said differently, the two most convincing lines of evidence of symmetry in nonhuman animals were obtained with a test design that minimized the possibility to encode positional (spatial and temporal/ordinal) information in pigeons. This hypothesis may also explain why symmetry training, involving exposure to the reversal of the initially learned associations, seems to promote generalized symmetry (e.g., Pepperberg & Gordon, 2005; Schusterman & Kastak, 1993).

Our study aimed at testing further the general idea that nonhuman animals encode in priority the positional information to the detriment of the relational information in A-B types of sequences, and that this mode of encoding is the main source of failures in symmetry tests involving B-A types of trials. We developed for that purpose a novel prediction task, inspired from the serial response time task (Misyak, Christiansen, & Bruce Tomblin, 2010; Nissen & Bullemer, 1987), which was given to both humans (*Homo sapiens*) and
Guinea baboons (*Papio papio*). The main advantage of this procedure is to offer fine-grained measures of participants’ expectations on critical trials rather than merely reflecting the result of a decision process in a binary choice task, as in the aforementioned studies. Our task required participants to learn in a training phase the association between items in three pairs of shapes. A test phase then presented some novel pairings of the stimuli used during the training phase. One test condition preserved positional information but disrupted relational information. One other test condition preserved relational information but disrupted positional information. A last test condition disrupted both kinds of information. We predicted that the baboons will demonstrate an advantage for the stimulus pairs preserving positional information, while humans would demonstrate an advantage for the stimulus pair preserving the relations. To our knowledge, this study is the first to extend Urcuioli’s line of research with nonhuman primate species.

# Method

## Participants

Data were collected on a total of 14 Guinea baboons (*Papio papio*; age range: 4–12 years; seven females) living in a larger social group of 24 individuals located at the CNRS Station de Primatologie, Rousset-sur-Arc, France. This group of baboons is maintained within a 700 m² outdoor enclosure connected to an indoor area used at night, and two test rooms in which the test systems were presented ad libitum (see below and Fagot & Bonté, 2010, for a more detailed description of their living conditions). The baboons were all marked with a radio-frequency identification (RFID) microchip implanted in each arm. Water was provided ad libitum during the experiment. Animals were not food deprived. They received their regular portion of food (monkey chow and fruits) every day at 5 p.m. All baboons had previously participated to numerous computerized experiments using the ALDM test systems and had already been involved in an unpublished experiment that used the same prediction task as in this study. However, this previous experiment never trained or tested our baboons with symmetrical pairs. Note also that some of our subjects have already been involved in Medam, Marzouki, Montant, and Fagot’s (2016) study on the effect of category training on symmetry. The use of a two-alternative matching-to-sample procedure in this study failed to reveal symmetry in these individuals.

The human data were collected on a total of 21 adults (age range: 19–28 years, 16 females) who were recruited at our university and participated for payment. These participants were all unaware of the purpose of the research and have never been involved in any of our research programs.

## Apparatus

The baboons had an unrestricted access during the experiment to 10 automated learning devices for monkeys (ALDMs; Fagot & Bonté, 2010; Fagot & Paleressompoulle, 2009), which were installed in two trailers directly accessible from their outside enclosure. Each ALDMs consisted of a 70 cm × 70 cm × 80 cm testing chamber with a view port and two hand ports. The view port allowed to see a 19-in. touch screen (1939L Open-Frame Touchmonitor, Elo Touch Solutions) installed in front of the chamber, and the hand port was used to manipulate it. The RFID microchip implanted in the arm of each subject served to automatically identify each baboon once it introduced one of its arms through the hand port. The baboon could use the ALDM test system of its choice, whenever it wanted to use it, because the automatic reading of the subject identity was used to prompt the program to resume the trial list at the appropriate place for that subject. The ALDMs automatically dispensed several grains of dry wheat inside the test chamber once a correct response had been registered. Incorrect responses were not food rewarded but gave rise to a 3-s time-out indicated by a green screen. The experiment was programmed using E-Prime (Version 2.0, Psychology Software Tools, Pittsburgh, PA) software.

The apparatus for humans consisted of a 19-in. computer touch screen (ET1915L, Elo Touch) installed on a table in a quiet room. Correct responses in the task were indicated by a short 1-s black screen; incorrect responses were followed by a 3-s time-out with a green screen.

## Stimuli

This experiment used a total of six different yellow visual shapes that served to create three different pairs of stimuli (hereafter referred to as pair A-B, C-D, and E-F). The individual stimuli were shown on a 1024 × 768 pixel background and measured approximately 180 × 180 pixels on the screen. The stimuli were drawn for the purpose of the experiment, with the sole constraint that the discrimination of the items of the set should be easy for the two species.

## General design of the task

The two species were tested in an adapted version of the serial reaction time task, in which the subjects had to select and touch two stimuli consecutively. The procedure is illustrated in Fig. 1. Each trial began with a display of two items presented one above the other (locations balanced) on the left side of the screen. These two stimuli were first presented during 200 ms, after which one of them disappeared for a short period of 80 ms. This short disappearance of the stimulus gives the impression that the stimulus blinks once. The stimulus disappearing for 80 ms was the positive stimulus (S+) that
<figure><img src="image_82.png" /><figcaption>**Fig. 1** Illustration of the experimental procedure used in baboons. This same procedure was also used in humans, except that humans did not receive a food reward after each successful response</figcaption></figure>

the subject had to touch in this first display. A total of 4,700 ms was allowed for touching this stimulus. A correct touch of S+ at this stage caused the display to disappear immediately to be replaced by the second display. An incorrect selection of the negative stimulus (S−) triggered a 3-s green screen, and the trial was aborted and scored as incorrect. Once the S+ had been touched, the subject saw the second display containing two other stimuli that were presented one above the other (location balanced) on the right part of the screen. Again, one of these two stimuli disappeared for 80 ms, and the subject then had to touch this S+ stimulus, while avoiding S−. Again, 4,700 ms were allowed for responding at this stage. A correct selection of S+ on this second display triggered a positive feedback (a black screen and food reward for monkey, and black screen only for humans), and an incorrect response produced the 3-s green time-out screen, indicating that an error has been made. Trials in which the participants failed to touch the screen during the time window allotted for responding to the first or second display were systematically aborted. These aborted trials were represented immediately.

The procedure outlined above was used in a first training phase and in the following test phase. The pairs used in each phase are indicated in Table 1. The training phase aimed to teach the subjects that the stimuli were organized in pairs. In the training trials, each first stimulus of a given pair (e.g., Stimulus A in the A-B pair) served as S+ in the first display of the training trials using this pair, and the second stimulus of the pair (i.e., B) served as S+ in the second display. The first and second stimuli of another pair (e.g., C-D or E-F pair) served as S− in the first and the second displays, respectively.

The test sessions presented the same stimuli as in training sessions but used the three stimulus pairings indicated in Table 1. The first one (i.e., B-A) tested for the emergence of symmetry, because this pairing is a reversal of the A-B pairing of the training phase. The remaining two pairings (i.e., F-C and E-D) assessed the effect of stimulus ordering, because the position of these stimuli was either preserved (E-D) or reversed (F-C) in comparison to training. Within a trial, the first and second stimulus of a given pair (e.g., Stimuli B and A, in the B-A pair) served as S+ in the first and second display, respectively, while the first and second stimulus of another pair (e.g., E-D or F-C pair) served as S− in the first and the second displays, respectively. The dependent variables used for statistical analyses were the number of correct responses to the first and the second display of each kind of trial, as well as the response times to S+ in the first (hereafter referred to as

<table><caption>**Table 1** Experimental design in the training and test phase</caption><thead><tr><th>Training Pairs</th><th>Test Pairs</th><th>Associative cue</th><th>Positional cue</th></tr></thead><tbody><tr><td>A-B</td><td>B-A</td><td>+</td><td>-</td></tr><tr><td>C-D</td><td>E-D</td><td>-</td><td>+</td></tr><tr><td>E-F</td><td></td><td></td><td></td></tr><tr><td>C-D</td><td>F-C</td><td>-</td><td>-</td></tr><tr><td>E-F</td><td></td><td></td><td></td></tr></tbody></table>

*Note.* Each letter corresponds to one stimulus in the set. For each pair, the first letter indicates the stimuli used for the first display, and the second letter indicates the stimulus used for second display. Bold characters indicate the stimulus of the training pairs under scrutiny in the corresponding test pair. A “+” sign indicates that the associative (third column) and/or positional cues (fourth column) present in the training pair were preserved in the test pairs. A “-” sign indicates that it was not.
RT1) and second display of the trials (RT2). However, because very few errors were recorded in the task, our attention was mostly focused on response times variations that provided fine-grained information on learning processes and on the effect of the different tested factors. With this test design, comparison of the response to the second display of the B-A and F-C test pairs should reveal if the participants process the trained associations as if they are symmetrical. If they do so, then they should express a better accuracy on the second object and/or faster RT2 for the A shape than for the C shape. In addition, a comparison of the response to the second display of the F-C and E-D pairs should also indicate if the subject process the stimulus position, independently of the trained associations. In that case, they should express a better accuracy and/or faster RT2 in D than in C.

## Training procedure

During training, the human and baboon participants received blocks of 96 counterbalanced training trials, each containing 32 trials with the A-B, C-D, and E-F stimulus pairs. Each pair was presented in an identical number of trials as the S+ or the S− pair within a block. The ordering of the trials was randomized at the beginning of each block and therefore differed from one block to the next. The baboons received a total of 20 such blocks (1,920 trials) during the training phase. To avoid boredom or inattention to the task, the human participants only received five training blocks (480 trials).

Before the experiment, the baboons received two preliminary training phases, which both used different stimuli from those of the next training and testing phase. These two preliminary training phases were not presented to humans, who rapidly discovered by themselves that they had to touch the stimuli that blinked in the displays. Thus, in a first preliminary phase, the baboons were taught to touch the stimulus among two which disappeared for 80 ms from the screen. Once they reached 80% correct with two individual shapes, we proceeded to the second preliminary training phase. This phase introduced two displays in succession, which were presented following the same procedure as for the training trials (see above), but which used different stimuli from those of the final training and test phase. To obtain a reward, the baboon had to touch the stimulus that blinked once in each display, once again until they reached 80% correct with this design. The stimuli were organized in three pairs in this second preliminary training phases, to teach the subjects that the task involved some associations between stimuli. The training phase involving the three A-B, C-D, and E-F pairs was introduced immediately after these two preliminary training phases. The instruction given to humans prior to the experiments only explained that they had to touch some stimuli on the screen, and that response errors are indicated by a green screen.

## Test procedure

During the test phase, the baboons received 10 test blocks of 96 trials each (960 trials). Each block involved 32 trials with each of the three (B-A, F-C, and E-D) test pairs. Human participants received five test blocks (480 trials). Importantly, the stimulus taking the role of the A, B, C, D, E, and F stimulus was counterbalanced across subjects. This counterbalancing of the visual shapes guaranteed that the variations in accuracy or response time are not tied to individual stimuli.

At the end of the test phase, the experimenter asked the human subjects to report verbally their understanding of the task. Thus, all the subjects were first asked, “What were you supposed to do?” Then, if the free report of the subjects did not include the following information, the subject was asked to answer the following questions in the following order: (1) “Which forms had to be touched?” (2) “Did you notice some relationships between and among the stimuli?” (3) “Did you notice any change during the experiment?” and (4) “Did you use verbal names to refer mentally to the stimuli?”

## Data filtering

A filtering procedure was applied for the analysis of correct response times to neglect the outlier trials with excessively long response times that may reflect transient inattention to the task. Thus, we first discarded from the data set all the trials in which RT1 and/or RT2 was greater than 3 s. This first layer of filtering removed .11% of the trials for humans and .30% for the baboons. The second filter additionally removed all the trials for which RT2 was greater than two standard deviations from the RT2 mean (computed for each individual and block). This second filtering procedure removed 3.62% of the remaining trials for humans and 4.32% for the baboons. Statistical analyses considered separately the data from the two species because of an uneven number of training and test blocks in humans and baboons.

## Results

### Humans

The human participants performed consistently high in the task, with an average percentage of correct responses of 96.97% (SD = 17%) in the training phase, and 96.4% (SD = 18.64%) in the test phase. This performance precludes detailed statistical analyses on accuracy due to potential ceiling effects.

A preliminary inspection of the data indicated that some participants showed an acceleration in RT2 throughout the training phase, while others did not. These different response profiles suggest that only a subset of humans—those for
which the RT2 declined with training—could predict their response to the second item of the pair depending on the identity of the first item. We therefore analyzed the postexperimental verbal reports of the subjects, and split our sample of participants in two groups. The first group (n = 10, hereafter named AS+), includes all the participants who have reported verbally during the debriefing that the stimuli were organized in pairs. The second group (n = 11, hereafter named the group AS−) includes all the subjects who failed to report any association between or among the stimuli.

**Training phase** Figure 2 represents the RT1 and RT2 training data for the two groups of participants.

A two-way analysis of variance (ANOVA) was computed for each group, using the stimulus pair (A-B, C-D, and E-F) as well as the training block (1–5) as independent variables, and RT1 as a dependent variable. None of the main effects or the interactions emerged as significant, in either group (all *ps* > .05). We found in particular no reliable effect of stimulus pair for RT1, whatever the group considered: AS+, *F*(2, 18) = .17, *p* > .10, ηₚ² = .02; AS−, *F*(2, 20) = 1.71, *p* > .10, ηₚ² = .15. This finding is important because it demonstrates that the response to the first item of each pair did not differ in terms of perceptual or motor constraints.

The same ANOVAs conducted on the RT2 revealed no reliable main effects or interaction when the AS− group was considered (all *ps* > .10). In the case of the AS+ group, neither the effect of stimulus pair nor the interaction were significant, *F*(2, 18) = 2.16, *p* > .1, ηₚ² = .19; and *F*(8, 72) = 1.07, *p* > .1, ηₚ² = .11, respectively. However, the main effect of block was statistically significant for this AS+ group, *F*(4, 36) = 12.59, *p* < .001, ηₚ² = .58. As shown in the top right panel of Fig. 2, this main effect of block corresponds to a reduction of response

<figure><img src="image_83.png" /><figcaption>**Fig. 2** Mean correct response times on the first (RT1) and the second (RT2) display obtained for each training pair, in the five training blocks presented to the human participants of the AS+ (top) and AS− (bottom) group. Note that the y-axes have different scales in the two groups and display only a subset of the range. Error bars indicate the standard errors centered on each participant mean RT</figcaption></figure>

times with repeated training. One potential explanation of this decrease in RT2 is that it reflects a general improvement of performance for pointing the stimuli on the screen, but this hypothesis is ruled out because our analyses of RT1 showed that they remained stable across the training blocks. These findings, along with the verbal reports of the subjects, therefore suggest that AS+ is the sole group that learned that the stimuli were organized by pairs. For these individuals, the learning of the stimulus pairing that occurred across the training blocks gave rise to reduced RT2 because the identity of the second stimulus became progressively predictable with a continuous exposure to the training stimulus pairs.

**Test phase** Figure 3 represents the RT2 data obtained during the test phase in the AS+ and AS− groups.

**Analyses for the AS+ group** With our test procedure, we expected that the effects of symmetry (if any) will emerge in the AS+ group immediately after the different pairs were recombined, therefore during the first test trials. Two-tailed paired *t* tests compared the mean RT2 for the first 10 trials of each pair. These analyses revealed that the response times were shorter on average for the B-A pair (mean = 584 ms, *SD* = 57 ms) than for the F-C pair (620 ms, *SD* = 60 ms), *t*(9) = 3.22, *p* < .05, Cohen's *d* = .81. Importantly, the stimuli composing the B-A and F-C test pairs all changed their position in comparison to training, and in these two pairs, only B-A preserved the association learned during the training phase. The shorter RT2 obtained for B-A, in comparison to F-C, can therefore be explained by the fact that the subject expected to see an A in the second display of the symmetrical B-A pairs, while no similar prediction was possible in the F-C pairs. This

<figure><img src="image_84.png" /><figcaption>**Fig. 3** Mean correct response times on the second display (RT2) obtained in the AS+ and AS− group for each test block and stimulus pair. Note that the y-axes have different scales in the two groups and display only a subset of the range. Error bars indicate the standard errors centered on each participant mean RT</figcaption></figure>
result suggests that the human participants from the AS+ group processed the relation of symmetry.

Data analyses also revealed shorter RT2 on average for the E-D pair (mean = 543 ms, *SD* = 32 ms), compared to both the B-A, *t*(9) = -2.55, *p* < .05, Cohen's *d* = .47, and the F-C pairs, *t*(9) = -4.66, *p* < .01, Cohen's *d* = .49. This effect indicates that the subjects from the AS+ group also processed the position of the stimuli, in addition to their processing of symmetry reported above.

Another question is if these effects could persist in AS+ with repeated exposure to the five test blocks. This question was addressed with a stimulus pair (B-A, E-D, F-C) by test block (1–5) two-way ANOVA on RT2. This analysis revealed that the main effect of test block was significant, *F*(4, 36) = 4.59, *p* < .01, ηₚ² = .33. It showed that the RT2 decreased on average with repeated testing. The effect of stimulus pair approached significance, *F*(2, 18) = 3.53, *p* < .06, ηₚ² = .28, and the stimulus pair by test block interaction was not significant, *F*(8, 72) = 1.12, *p* > .10, ηₚ² = .11. The effect of stimulus pair, albeit not significant, is shown in Fig. 3 by systemically shorter RT2 in the five test blocks for B-A than for both F-C and E-D. The fact that these RT2 remained systematically shorter during the five test blocks suggests a relative persistence of the effect of symmetry in our group of participants. Conversely, the initial advantage in RT2 found for the E-D pair during the analysis of the 10 first test trials disappeared rapidly. Figure 3 indeed indicates that RT2 for the E-D pair is shorter than the RT2 for the F-C pair in the first test block only. The effect of position seems therefore more transitory in humans of the AS+ group than the effect of stimuli symmetry.

**Analyses for the AS- group** The same set of analyses conducted on the AS- group revealed shorter response times in the 10 first trials on average for the E-D pair (mean = 642 ms, *SD* = 96 ms) than for the F-C pair (mean = 679 ms, *SD* = 76 ms), *t*(10) = 2.36, *p* = .04. This difference suggests an encoding of the position within the first 10 trials which is confirmed by a nonsignificant trend for shorter RT2 on average for E-D than for B-A (mean = 673 ms, *SD* = 76 ms), *t*(10) = 2.05, *p* = .068. No reliable difference emerged between B-A and F-C, *t*(10) = 0.39, *p* > .10.

The ANOVA performed on the five blocks of the test phase revealed a main effect of block, *F*(4, 40) = 5.07, *p* < .01, ηₚ² = .37, but no reliable effect of condition, *F*(2, 20) = 0.14, *p* > .100, ηₚ² = .01, and no significant interaction between these two factors, *F*(8, 80) = 1.10, *p* > .100, ηₚ² = .10. Taken together, results of the AS- group confirm that participants from the AS- group did not learn that the item that blinks once in the first display predicts a specific item in the second display. It seems that these participants encoded only the positional properties of the individual stimuli. However, as for the AS+ group, this encoding seems transitory as it only appeared in

the first 10 test trials, but disappeared when the entire test phase was considered.

**Verbal reports** All the subjects from the AS+ group, but none of the AS- group, reported verbally after the experiment that they had noticed a change during the experiment (i.e., the changes between the training and testing phases). Within AS+, three subjects indicated that one association (i.e., for A-B) was preserved after the change. These data suggest variations among the AS+ subjects, some of them seem to process the symmetry relation implicitly, while the three subjects who noticed that the A-B association was preserved, processed it more explicitly. Despite these variations, all the AS+ subjects reported that they have given labels to the object stimuli.

## Baboons

The baboons performed 76.30% correct on average during the training phase (*SD* = 42.48%) and therefore made more errors than humans. On that basis, we report below statistical analyses on both response times and accuracy.

**Response times of the training phase** We found earlier that the human subjects who noticed that the stimuli were organized in pairs during the training phase (AS+ participants) showed a progressive decline in RT2 during that phase. Because this decline in RT2 is in humans an indicator of learning, our first analyses of the baboons' data distinguished two groups of baboons. The first group (referred to below as the AS+ group) was composed of the individuals who showed reduced RT2 with training, and the second group (referred to below as the AS- group) was composed of the individuals who did not show this effect. This was achieved by way of one-tailed *t* tests (*p* < .05) comparing the mean RT2 obtained, for each individual, during the first and last blocks of the training phase. From these analyses, eight baboons were assigned to the AS+ group, the remaining six subjects were assigned to the AS- group.

Figure 4 represents the mean RT1 and RT2 obtained in the AS+ and AS- groups during training. Because the number of errors was greater in baboons than in humans, reducing the number of RT measures per block, statistical analyses of correct RT1 and RT2 considered blocks of 192 trials made from the grouping of two initial contiguous 96-trial blocks. RT1 and RT2 data were analyzed independently for the AS- and AS+ groups, using two-way ANOVAS, considering the stimulus pair (A-B, C-D, E-F) and the training block (ten 196-trial blocks) as independent variables.

For the AS+ group, the two-way ANOVA on RT1 revealed a main effect of block, *F*(9, 63) = 3.40, *p* < .002, ηₚ² = .33. The main effect of stimulus pair and the interaction between the pair and block factors were not significant (all *ps* > .10).
<figure><img src="image_85.png" /><figcaption>**Fig. 4** Mean RT1 and RT2 obtained during the training phase for the AS+ and AS− groups of baboons. The y-axes have different scales in the two groups and display only a subset of the range. Error bars represent the standard errors centered on each participant mean RT</figcaption></figure>

Similar results were obtained in AS+ for RT2, with a reliable effect of block being the only significant effect, *F*(9, 63) = 12.37, *p* < .001, ηₚ² = .64.

For the AS− group, the two-way ANOVA conducted on RT1 showed a main effect of block, *F*(9, 45) = 2.69, *p* < .02, ηₚ² = .35, but no reliable effect of stimulus pair and no stimulus pair by block interaction (all *ps* > .1). When computed on RT2, the same ANOVA revealed no reliable main effect or interaction in AS− (all *ps* > .10).

In sum, the individuals from the AS+ groups showed an effect of block for both RT1 and RT2. Regarding RT1, we can safely assume that the effect of block indicates a general effect of practice, reducing the response times with continuous training. Regarding RT2, the question arises of whether block effect only corresponds to a general effect of practice, as per RT1, or can also reflect the learning of the associations between the stimuli belonging to each pair. To address this question, we ran a complementary two-way ANOVA on the response times in which the training block (1–10) and the target of the response (Object 1 vs. Object 2) were used as factors. This ANOVA revealed a general effect of block, *F*(9, 63) = 10.36, *p* < .001, ηₚ² = .60, as well as a main effect of target, *F*(1, 7) = 13.44, *p* < .01, ηₚ² = .66. Of the most importance for our purpose, these two factors interacted significantly, *F*(9, 63) = 2.54, *p* < .05, ηₚ² = .27. As shown in Fig. 4, training has a stronger effect on the time to respond to the second than to the first object of the stimulus pairs. This result is a strong indicator that the subjects of the AS+ group have learned that the stimuli are organized in pairs. By contrast, no decrease in RT2 was observed in AS−. This effect confirms that the AS+ and AS− groups behaved differently in the task, regarding their response to the second display. We will see below that these differences in behavior correspond to cognitive differences during the test phase.

**Accuracy of the training phase** Figure 5 reports the mean accuracy, for each group and training conditions. Following the same logic as above, the accuracy to the first object (Object 1) and second (Object 2) objects of the stimulus pairs were analyzed separately with block (1–10) by stimulus pairs (A-B, C-D, E-F) two-way ANOVAs computed independently for the two groups of baboons.

For the AS+ groups, the two-way ANOVA on Object 1 only found a reliable effect of block, *F*(9, 63) = 4.41, *p* < .01, ηₚ² = .39. A similar result was obtained when the response to Object 2 was considered, with the effect of block being the only significant effect, *F*(9, 63) = 5.63, *p* < .001, ηₚ² = .43. Because the effect of block was significant in AS+ for both Objects 1 and 2, the question arises of whether the increased accuracy with blocks for Object 2 can be accounted for only by a general effect of practice, as observed for Object 1, or if this effect can also be explained by the learning of the associations between the first and second stimulus of each pair. To assess this idea, we ran an additional block (1–10) by object (Object 1 vs. Object 2) two-way ANOVA, using accuracy as the dependent variable. This analysis indicated no main effect of object (*p* > .10) but a main effect of block, *F*(9, 63) = 6.65, *p* < .001, ηₚ² = .49, showing that the performance increased with training. More importantly for our purpose, the Object × Block interaction was also significant, *F*(9, 63) = 2.68, *p* < .02, ηₚ² = .28, indicating that the effect of block was stronger for Object 2 than for Object 1. These findings therefore converge perfectly with those reported above on both RT1 and RT2. They confirm that the baboons from AS+ progressively learned the associations between the first and second objects of each stimulus pair.

<figure><img src="image_86.png" /><figcaption>**Fig. 5** Mean accuracy obtained during the training phase in baboons, depending on the group of baboons (AS+ and AS−), 196-trial test block, and stimulus pair. The y-axes have different scales in the two groups and display only a subset of the range. Error bars represent the standard errors</figcaption></figure>
For the AS- group, the two-way ANOVA using the accu-
racy of the response to Object 1 revealed no significant main
effect or interaction (all ps > .05), and these results were rep-
licated in the analyses of the response to Object 2 (all ps >
.05). Taken together, these data reinforce the idea that the
baboons from AS- did not learn that the stimuli were orga-
nized in pairs and were therefore unable to predict the identity
of Object 2 depending on the identity of Object 1.

**Response times of the test trials** Figure 6 represents the mean response times obtained during the test in AS+ and AS- groups, for each block and stimulus pair. With our procedure, we first expected that the effects of symmetry (if any) will be of maximal amplitude during the first test trials. These effects might eventually decrease in amplitude or even disappear because of the learning of the novel stimulus pairing that may occur with the repetition of the test blocks.

**Analyses for the AS+ group** To analyze the response times obtained for AS+ at the onset of the test, we computed two-tailed paired *t* tests comparing the mean RT2 obtained in the first 10 trials for the different stimulus pairs. These analyses indicated shorter RT2 for the stimulus pair E-D (mean = 674 ms, *SD* = 54 ms) than for F-C (mean = 768 ms, *SD* = 77 ms), *t*(7) = -4.17, *p* < .01, Cohen's *d* = 0.58. The differences between B-A (mean = 727 ms, *SD* = 136 ms) and E-D, or between B-A and F-C were not statistically significant (*ps* > .10). These results suggest that the AS+ baboons processed stimulus positional information. However, they provide no indication that these baboons were able to process the relation of symmetry.

To analyze how RT2 evolved with the repetition of the test
blocks, we ran a block (1–5) by stimulus pair (B-A, F-C, E-D)
two-way ANOVA on RT2 using blocks of 192-trials made
from the grouping of two initial 96-trial blocks. This analysis
showed a reliable effect of block, indicating that the RT2 de-
creased through continuous training, *F*(4, 28) = 3.73, *p* < .02,
*η*<sub>p</sub><sup>2</sup> = .35, but no effect of stimulus pair, *F*(2, 14) = 2.60, *p* >
.10, and no reliable interaction *F*(8, 56) = 1.24, *p* > .10. Note
the lack of effect of stimulus pair departs from the data obtain-
ed from the analysis of the 10 first trials. This discrepancy
suggests that the effect of the preservation of the position of
the stimuli in the training pairs declined with repeated testing.

**Analyses for the AS- group** The same analyses conducted on the AS- group revealed no difference in mean RT2 between the stimulus pairs when the first 10 test trials were considered (B-A: mean = 656 ms, *SD* = 81 ms; F-C: mean = 664 ms, *SD* = 38 ms; E-D = 668 ms, *SD* = 76 ms; all *ps* > .10). In addition, the two-way ANOVA showed no reliable main effect of stimulus pair or block, and interaction between these two factors (*ps* > .10).

<figure><img src="image_87.png" /><figcaption>**Fig. 6** Mean responses times on the second object (RT2) obtained in the AS+ and AS− groups, for each stimulus pair and test block. The y-axes differ between the two groups. *Error bars* represent the standard errors centered on each participant mean RT</figcaption></figure>

**Accuracy of the test trials** Figure 7 represents how the accu-
racy varied across the test blocks and stimulus pairs. Accuracy
analyses followed the same logic as for the analyses of RT2.
First, paired two-tailed *t* tests compared the mean performance
on Object 2 obtained during the first 10 test trials of each
stimulus pair. The next analyses focused our attention on all
the test blocks.

**Analyses for the AS+ group** This analysis revealed a trend for a greater performance at the onset of the test phase with E-D (mean = 92% correct, SD = 11%) than with both F-C (mean = 71%, *SD* = 19%), *t*(7) = 2.35, *p* = .05, Cohen's *d* = -0.37, and B-A (mean = 66%, *SD* = 30%), *t*(7) = 2.20, *p* = .064, Cohen's *d* = -1.16. No difference was found between F-C and

<figure><img src="image_88.png" /><figcaption>**Fig. 7** Mean accuracy obtained in the two groups of baboons for each stimulus pair and test block. The y-axes differ between the two groups. *Error bars* represent the standard errors</figcaption></figure>
B-A ($p > .10$). The results obtained from the analyses of the accuracy and mean RT2 were therefore highly consistent as the advantage found above for E-D in terms of response times converge with an accuracy advantage. Because the E-D pair is the sole test pair for which the order of the stimulus was preserved in comparison to training, this advantage for E-D confirms a processing of the positional information by the baboons from the AS+ group.

The second set of analyses on the five test blocks revealed a main effect of block, $F(4, 28) = 6.71, p < .001$, $\eta_p^2 = .49$, as well as a significant block by pair interaction, $F(8, 56) = 2.34, p < .05$, $\eta_p^2 = .25$. No main effect of stimulus pair was found ($p > .10$). Post hoc analyses revealed that the baboons' performance with the E-D pair was higher compared to the B-A pair in the first test block, but that no difference was observed for the next blocks. These results therefore converge with those reported above on RT2. Altogether, these results indicate that the initial advantage for E-D in terms of RT and accuracy progressively disappeared during the test.

**Analyses for the AS- group** The analyses conducted on the data from the AS- group confirmed that no difference in accuracy was observed for this group during the first 10 test trials between the B-A (mean = 93% correct, SD = 6%), F-C (mean = 91% correct, SD = 9%), and E-D (mean = 87% correct, SD = 15%) pairs ($p_s > .10$). The two-way ANOVA considering the five blocks of tests showed no reliable main effect nor interaction ($p_s > .10$).

Discussion

This comparative human/baboon experiment examined the encoding of pairs of stimuli in sequences to identify potential species differences in their processing of the symmetry relation. A serial response time task involving pairs of objects was developed and presented to the two species for that purpose. In our task, we presumed that the individuals of the two species who learned the association between the objects to be touched on the displays should demonstrate an RT2 acceleration with training. We distinguished two groups of subjects on that basis and categorized all the individuals showing an RT2 acceleration during training in the AS+ group; all the remaining subjects were categorized in the AS- group.

The results of the test phase confirmed our expectation that the baboon and human subjects of the AS- groups did not process the associations between the target stimuli. In the AS- baboons, we obtained no reliable effect of stimulus pair and no Block × Stimulus Pair interaction in the test phase. We propose that the baboons of the AS- group only processed the blink in each display but did not process the fact that the stimuli which blinked once within a trial were organized in pairs. In the AS- humans, the only effect emerging from the

test trials is a response time advantage for the E-D pair occurring at the onset of the test. This advantage suggests that the AS- human subjects encoded at least minimally the identity of the stimuli and their position. The processing of the positional information might therefore occur in the AS- human group along with the processing of the information on the blink mentioned above for the baboons.

Test trials identified the cognitive strategy employed by the members of the AS+ groups of each species. In humans, we found shorter RT2 in the B-A than in the F-C pair. The shorter RT2 obtained for B-A in Test Block 1, in comparison to F-C, can be explained by the preservation of the associative link between the stimuli in the B-A pair only. We take this result as demonstrating that humans from the AS+ group processed the associations between the stimuli, and furthermore processed A-B and B-A as symmetrical relations in our study. Importantly, humans from this AS+ group also demonstrated a RT2 advantage for E-D over B-A. This advantage for E-D suggests that this group also processed positional information, along with the information of the associations.

These results in humans contrast sharply with those obtained in baboons from the AS+ group. In these baboons, the stimulus pair giving rise to the shortest RT2 was E-D, not B-A, as found in humans. We also found a reliable difference between E-D and F-C. The advantage for the stimulus pair E-D, which emerged in the first 10 trials, is confirmed by a better accuracy for E-D, in comparison to both F-C and B-A. Critically, neither E-D nor F-C preserved the relations learned during the training phase, and the positional information is preserved in E-D only. Therefore, while baboons from the AS+ group processed the associations between the stimuli in the training phase, they did not processed A-B and B-A as symmetrical relations in our study. Therefore, our results suggest an encoding of positional information in baboons of the AS+ group. This encoding of the positional information is further confirmed by a lack of statistical difference between B-A and F-C, which are the two stimulus pairs reversing their position in comparison to training. In turn, results suggest that the baboons from the AS- group have neither encoded the positional nor the relational information. Apparently, these subjects only paid attention to the blink cue, as discussed above.

The fact that the baboons from the AS+ group give priority to the positional information converges with previous findings and conclusions from Urcuioli (2015). Our results also add to the literature this idea that prioritization of positional over relational information is a pervasive effect that can be replicated in pigeons and monkeys, at least within the nonhuman animal species. The demonstration that nonhuman animals give priority to perceptual cues over relational ones is not new in the comparative literature. Such effects were already found with relational stimuli of different types (hierarchical stimuli: Fagot & Deruelle, 1997; artificial relational stimuli:
Fagot, Kruschke, Dépy, & Vauclair, 1998; Wasserman, Fagot, & Young, 2001; ABA/ABB sequential patterns: Chen, van Rossum, & ten Kate, 2015). Nevertheless, our study makes it even more striking, following Urcuioli (2015), that such prioritization has major impacts on the nonhuman animals' ability to both processes symmetry and to form equivalence classes based on symmetrical relations.

The current research has practical consequences for further studies on symmetry and equivalence class formation in animals. As already suggested by Urcuioli (2015), it confirms that the search for symmetry in animals requires a strict control of the positional cues that might be available in the task. One limitation of our study is that spatial and temporal/ordinal information covaried during the training and test phases, because the first stimulus of each pair was always presented on the left side of the screen, the second one being presented on the right side. Our study therefore provides no information to distinguish the exact influence of these two factors (but see Lionello-DeNolf, 2009, for a discussion of this issue). Nevertheless, the current results as well as those of previous research all point to the same conclusion: They suggest that this kind of perceptual cues are very important to consider in future experimental designs. Note, however, that such effects do not rule out the possibility that other factors also intervene. For instance, Medam et al. (2016) proposed that experimental designs mixing symbolic and identity matching trials (as in Frank & Wasserman, 2005) promote cognitive flexibility, because the subjects can learn that the stimuli can have different roles in the task. We suggest that a more flexible encoding of the stimulus pair should also be favored in future experimental designs.

The cognitive ability to solve symmetry problems seems highly adaptive, and experts of primate cognition can easily mention situations in which animals apparently treat relations between items as symmetrical. One classic example of this kind is the case of vervet monkeys, who can both produce alarm calls in response to leopards (leopard → alarm call) and can meanwhile show specific behavioral responses when hearing these calls, suggesting that they also treat the call → leopard symmetrical association (Seyfarth, Cheney, & Marler, 1980). Although this kind of situations probably occurs in nature, they are neither as variable nor as frequent as found during interpersonal verbal communication in humans involving a continuous shift between speech production and speech perception. When communicating verbally with others, our comprehension of others' speech requires that the words we perceived are mapped to their referent, while speech production requires the reversed referent to word mapping. In other words, verbal communication is an exercise for which it is central to treat the associations/relationships between and among items as being bidirectional. This importance of symmetry for our communicative system might explain previous demonstrations of a strong relationship between the ability to

develop equivalence classes and language skills (e.g., Devany et al., 1986). An important result in our study is that the AS+ humans who used labels to code the stimuli demonstrated symmetry. By contrast, the AS+ baboons and AS- humans who did not use labels to code the stimuli failed to process the stimulus pairing and relied exclusively on positional cues to solve the task. These results suggest an effect of stimulus labeling on the processing of the task. More experimental work will be needed to better understand this effect.

Most of the associations occurring in the environment are unidirectional. This is true in both humans and nonhuman animals (e.g., a horn predicts a car in humans, the animal keeper predicts food delivery in baboons, and the reverse associations are not true), but only humans have extra training with symmetrical relations via language. Other animal species that are missing a human-like communication system, and thus the extensive bidirectional training that humans have with their exposure to language, may require specific training and test conditions to learn that the stimuli go together in a bidirectional way; to neglect information such as the stimuli position, which is irrelevant to the task; and to be able to derive untrained (symmetrical) relations from the trained ones.

**Acknowledgements** The authors thank the staff of the CNRS primate center (Rousset-sur-Arc, France) as well as Julie Gullstrand for technical supports. This research was supported by the project Premilang2 (#ANR-13-BSH2-0002), labex BLRI (ANR-11-LABX-0036), and the Convergence ILCB institute.

## Compliance with ethical standards

**Ethical standard** This research conformed to the Standard of the American Psychological Association's Ethical Principles of Psychologist and Code of Conduct, and received ethical approval from the French Ministry of Education (approval APAFIS#2717-2015111708173794 v3).

## References

Adams, B. J., Fields, L., & Verhave, T. (1993). Formation of generalized equivalence classes. *Psychological Record*, *43*, 553–566.

Barros, R. S., Galvão, O. F., & McIlvane, W. J. (2002). Generalized identity matching-to-sample in *Cebus apella*. *The Psychological Record*, *52*, 441–460.

Boelens, H., van den Broek, M., & van Klarenbosch, T. (2000). Symmetric matching to sample in 2-year-old children. *Psychological Record*, *50*, 293–304.

Carp, C. L., & Petursdottir, A. L. (2015). Intraverbal naming and equivalence class formation in children. *Journal of the Experimental Analysis of Behavior*, *104*, 223–240.

Carr, D., Wilkinson, K. M., Blackman, D., & McIlvane, W. J. (2000). Equivalence classes in individuals with minimal verbal repertoires. *Journal of Experimental Analysis of Behavior*, *74*, 101–114.

Chen, J., van Rossum, D., & ten Cate, C. (2015). Artificial grammar learning in zebra finches and human adults: XXY versus XXY. *Animal Cognition*, *18*(1), 151–164. doi:10.1007/s10071-014-0786-4
D'Amato, M. R., Salmon, D. P., Loukas, E., & Tomie, A. (1985). Symmetry and transitivity of conditional relations in monkeys (*Cebus apella*) and pigeons (*Columba livia*). *Journal of the Experimental Analysis of Behavior* **44**, 35–47.

Devany, J. M., Hayes, S. C., & Nelson, R. O. (1986). Equivalence class formation in language-able and language-disabled children. *Journal of the Experimental Analysis of Behavior* **46**, 243–257.

Eikeseth, S., & Smith, T. (1992). The development of functional and equivalence classes in high-functioning autistic children: The role of naming. *Journal of Experimental Analysis of Behavior* **58**, 123–133.

Fagot, J., & Bonté, E. (2010). Automated testing of cognitive performance in monkeys: Use of a battery of computerized test systems by a troop of semi-free ranging baboons. *Behavior Research Methods* **42**, 507–516.

Fagot, J., & Deruelle, C. (1997). Processing of global and local visual information and hemispheric specialization in humans (*Homo sapiens*) and baboons (*Papio papio*). *Journal of Experimental Psychology: Human Perception and Performance*, **23**, 429–442.

Fagot, J., Kruschke, J. K., Dépy, D., & Vauclair, J. (1998). Associative learning in humans (*Homo sapiens*) and baboons (*Papio papio*): Species differences in learned attention to features. *Animal Cognition*, **1**, 123–133.

Fagot, J., & Paleressompoulle, D. (2009). Automatic testing of cognitive performance in baboons maintained in social groups. *Behavior Research Methods*, **41**, 396–404.

Frank, A. J., & Wasserman, E. A. (2005). Associative symmetry in the pigeon after successive matching-to-sample training. *Journal of the Experimental Analysis of Behavior*, **84**, 147–165.

Kastak, C. R., Schusterman, R. J., & Kastak, D. (2001). Equivalence classification by California sea lions using class-specific reinforcers. *Journal of the Experimental Analysis of Behavior*, **76**, 131–158.

Kuno, H., Kitadate, T., & Iwamoto, T. (1994). Formation of transitivity in conditional matching to sample by pigeons. *Journal of the Experimental Analysis of Behavior*, **62**, 399–408.

Lionello-DeNolf, K. M. (2009). The search for symmetry: 25 years in review. *Learning & Behavior*, **37**, 188–203.

Lipkens, G., Hayes, S. C., & Hayes, L. J. (1993). Longitudinal study of derived stimulus relations in an infant. *Journal of Experimental Child Psychology*, **56**, 201–239.

Lipkens, R., Kop, P. F., & Matthijs, W. (1988). A test of symmetry and transitivity in the conditional discrimination performances of pigeons. *Journal of the Experimental Analysis of Behavior*, **49**, 395–409.

Medam, T., Marzouki, Y., Montant, M., & Fagot, J. (2016). Categorization does not promote symmetry in Guinea baboons (*Papio papio*). *Animal Cognition*, **19**, 987–998.

Misyak, J. B., Christiansen, M. H., & Bruce Tomblin, J. (2010). Sequential expectations: The role of prediction-based learning in language. *Topics in Cognitive Science*, **2**(1), 138–153.

Nissen, M. J., & Bullemer, P. (1987). Attentional requirements of learning: Evidence from performance measures. *Cognitive Psychology*, **19**(1), 1–32.

O'Donnell, J., & Saunders, K. J. (2003). Equivalence relations in individuals with language limitations and mental retardation. *Journal of Experimental Analysis of Behaviour*, **80**, 131–157.

Pepperberg, I. M., & Gordon, J. D. (2005). Number comprehension by a grey parrot (*Psittacus erithacus*), including a zero-like concept. *Journal of Comparative Psychology*, **119**, 197–209.

Schusterman, R. J., & Kastak, D. (1993). A California sea lion (*Zalophus californianus*) is capable of forming equivalence relations. *Psychological Record*, **43**, 823–839.

Seyfarth, R. M., Cheney, D. L., & Marler, P. (1980). Monkey responses to three different alarm calls: Evidence of predator classification and semantic communication. *Science*, **210**, 801–803.

Sidman, M. (1971). Reading and auditory-visual equivalences. *Journal of Speech and Hearing Research*, **14**, 5–13.

Sidman, M., Rauzin, R., Lazar, R., Cunningham, S., Tailby, W., & Carrigan, P. (1982). A search for symmetry in the conditional discriminations of rhesus monkeys, baboons, and children. *Journal of the Experimental Analysis of Behavior*, **37**, 23–44.

Sidman, M., & Tailby, W. (1982). Conditional discrimination vs. matching to sample: An expansion of the testing paradigm. *Journal of the Experimental Analysis of Behavior*, **37**, 5–22.

Sweeney, M. M., & Urcuioli, P. J. (2010). Reflexivity in pigeons. *Journal of the Experimental Analysis of Behavior*, **94**, 267–282.

Urcuioli, P. J. (2008). Associative symmetry, antisymmetry, and a theory of pigeons’ equivalence-class formation. *Journal of the Experimental Analysis of Behavior*, **90**, 257–282.

Urcuioli, P. J. (2015). A successful search for symmetry. *Conductal*, **3**, 4–25.

Wasserman, E. A., Fagot, J., & Young, M. E. (2001). Same-different conceptualization by baboons (*Papio papio*): The role of entropy. *Journal of Comparative Psychology*, **115**, 42–52.

Zentall, T. R., Clement, T. S., & Weaver, J. E. (2003). Symmetry training in pigeons can produce functional equivalences. *Psychonomic Bulletin & Review*, **10**, 387–391.
### III. Extraction de Régularités Séquentielles : Données Comparatives chez le Babouin et l'Humain

Rey, A., Minier, L., Malassis, R., Bogaerts, L., & Fagot, J. (sous presse). Regularity extraction across species: associative learning mechanisms shared by human and non-human primates. *Topics in Cognitive Science*.

Regularity extraction across species:

associative learning mechanisms shared by human and non-human primates

Arnaud Rey, Laure Minier, Raphaëlle Malassis,

Louisa Bogaerts, and Joël Fagot

Laboratoire de Psychologie Cognitive

CNRS & Aix-Marseille Université, Marseille, France
Abstract

Extracting the regularities of our environment is a core cognitive ability in human and non-human primates. Comparative studies may provide information of strong heuristic value to constrain the elaboration of computational models of regularity learning. The current study illustrates this point by testing human and non-human primates (Guinea baboons, *Papio papio*) with the same experimental paradigm, using a novel online learning measure. For *local co-occurrence regularities*, we found similar patterns of regularity extraction in baboons and humans. However, only humans extracted the more *global sequence structure*. It is proposed that only the first result that is common to both species should be used to constrain models of regularity learning. The second result indicates that the extraction of global regularities cannot be accounted for by mere associative learning mechanisms and suggests that humans probably benefit from their language recoding abilities for extracting these regularities. We propose to use a comparative approach to address a series of remaining theoretical questions, which will contribute to the development of a general theory of regularity learning.

**Keywords:** regularity extraction; statistical learning; implicit learning; associative learning; verbal recoding
Statistical learning – the ability to extract and encode environmental regularities – appears as a key feature of human cognitive systems and has been a domain of intensive research over the last 20 years (see (R. Frost, Armstrong, Siegelman, & Christiansen, 2015b), and Thiessen, Kronstein & Hufnagle, 2013, for reviews, but also e.g., Estes, 1950; Restle, 1970, for earlier pioneer work). It is thought to play a key role in the segmentation of continuous input, prediction, discrimination and categorization, shaping our basics representations of the environment (Frost et al., 2015).

Historically, before the ‘statistical learning’ wave, similar issues were addressed in the field of ‘implicit learning’. The latter term captures the observation that some forms of learning occur after the mere repeated exposure to regularities (or rule systems, as in artificial grammar learning experiments, e.g., Reber, 1967), with no intention of learning and no clear awareness of what has been learned (Cleeremans, Destrebecqz, & Boyer, 1998; Shanks, 2005). Some authors, such as Conway and Christiansen (2006; see also Christiansen, this issue), have proposed to merge the two terms (i.e., ‘implicit statistical learning’) to cover the general learning ability to extract and encode different forms of environmental regularities.

Others, like Perruchet (2005; see also Perruchet & Poulin-Charronnat, 2012), have argued that ‘statistical learning’ is nothing more than a new terminology corresponding, in fact, to the old domain of associative learning (e.g., Hebb, 1961; Mitchell & Le Pelley, 2010; Shanks, 1995). In this view, the extraction of regularities is understood as the formation of new larger units also called ‘chunks’. This process is supported by associative learning mechanisms, which itself is considered a by-product of the concurrent attentional processing of multiple elements (or small units). Learning can also be qualified as ‘implicit’ as it doesn’t require awareness of what is learnt nor the intention to learn (see also Perruchet & Pacton, 2006, Perruchet & Vinter, 2002)<fnref n="37" />. In that perspective, implicit statistical learning and associative learning are considered as two sides of the same coin, the former having a broader scope (including issues related to various aspects of language processing) compared to the latter that was more concerned by the formation of simple associations or by operant conditioning behaviors.

Basic associative (or implicit statistical) learning mechanisms are certainly shared by human and non-human primates (e.g., Wilson, Marslen-Wilson, & Petkov, 2017), and possibly

<footnote n="37">³⁷ In Perruchet and Vinter’s (2002) theory, learning involves attention or, more precisely, attentional processing of the learned information. In that sense, attention or attentional processing are equivalent to consciousness, but not awareness. According to this view, one can learn a regularity by paying attention to that regularity while being unaware of that regularity (i.e., not being able to *report* the exact content of that regularity).</footnote>
by all species equipped with a nervous system (e.g., chicks: Santolin, Rosa-Salva, Regolin, & Vallortigara, 2016; songbirds: Chen, van Rossum, & ten Cate, 2015; rodents: Toro, Nespor & Gervain, 2016). This raises the question which aspects of the human ability for regularity extraction can be related to those in non-human primates. The present study aims to address this question by a direct behavioral comparison between humans and Guinea baboons (*Papio papio*) in the same experimental paradigm. This paradigm provides an online measure of regularity extraction at different levels of integration. We argue that the use of an online learning measure on the one hand and the study of non-human primates on the other hand form a valuable approach to overcome two difficulties that characterize the existing literature.

The first difficulty is that the temporal dynamics of regularity extraction is frequently not considered. The seminal study of Saffran, Aslin, and Newport (1996) is often considered as the critical starting point in the study of statistical learning, and several theoretical accounts and computational models of this phenomenon have been proposed (e.g., Frank, Goldwater, Griffiths, & Tenenbaum, 2010; French, Addyman, & Mareschal, 2011; Perruchet & Vinter, 1998; Pothos, 2007). The empirical evidence used to elaborate these models is mainly derived from studies using the same artificial language learning paradigm. In this task, participants are exposed to an artificial language composed of few polysyllabic nonsense words (e.g., 4) that are auditorily presented without any pause between words. After this exposure phase, participants are asked to indicate which one of two test sequences looks more familiar. The main dependent variable used to test models is therefore an offline measure that informs us about which kind of regularities can be learned but that does not provide any information regarding the temporal dynamics of learning (see e.g., Siegelman, Bogaerts, & Frost, 2017, for a detailed discussion of the problems of offline learning measures). In contrast to offline measures, online measures are obtained by recording the performance of participants during the exposure to the regularities and can therefore provide information on the temporal dynamics of regularity extraction. The serial response time task (Nissen & Bullemer, 1987) is one good example of such online measure. In this task, participants respond to sequences of stimuli that appear one-by-one at various locations on a computer screen. If the sequences include regularities (allowing for the prediction of upcoming locations), then participants speed their response times (RTs) if they manage to extract these regularities (see also Batterink, 2017; Christiansen, this issue; Misyak, Christiansen, & Tomblin, 2010; Siegelman, Bogaerts, Kronenfeld, & Frost, 2017, for other examples of online learning measures).
A second difficulty is paradoxically the strong focus on human (adult) participants.
Indeed, during the exposure phase, participants may develop strategies or explicit learning
procedures (like repeating internally some sequences of syllables) and their resulting
performance is certainly influenced and biased by various forms of explicit recoding processes
(Batterink, Reber, Neville, & Paller, 2015; Siegelman et al., 2017). Testing human infants
without any advanced language and reasoning abilities (like the original Saffran et al. study did,
followed by many others, e.g., Bulf, Johnson, & Valenza, 2011; Singh, Reznick, & Xuehua,
2012) could be a solution to overcome that difficulty. However, the study of infants also brings
other procedural difficulties because the measures that can be recorded for this population (for
example, the head-turn procedure) are indirect and much noisier (i.e., less reliable). Testing
non-human primates allows us to study regularity extraction in the absence of any bias due to
language experience and the related ability to verbally recode regularities.

Previous studies that adopted a comparative approach to study statistical learning were mainly concerned with discovering differences between human and non-human primates (e.g., Fitch & Hauser, 2004; Wang, Uhrig, Jarraya, & Dehaene, 2015). That logic is derived from a research tradition that searched for a critical distinctive feature that could distinguish animal communication systems from human language. For instance, Hauser, Chomsky, and Fitch (2002) put forward the ability to process and produce recursive structures as a fundamental distinctive feature that sets humans apart from other species. However, as argued by Pinker and Jackendoff (2005), there are certainly many other critical differences between human and non-human primates that can account for their distinct cognitive trajectories (see also, Fagot, Malassis, Medam, & Montant, 2017).

Here we propose an alternative perspective stating that we can increase our
understanding of the precise dynamics of regularity learning processes by looking at the
*common* patterns produced by human and non-human primates in online experimental
paradigms. As mentioned above, it is obvious that human adults can make use of both implicit
associative learning mechanisms and explicit recoding strategies based on their language
abilities to perform regularity extraction tasks. Non-human primates miss the language tools
that humans can use to explicitly recode regularities. One can therefore safely assume that non-
human primates mainly rely on associative learning mechanisms in these tasks, and that these
mechanisms are shared with humans. Therefore, any common pattern of performance between
human and non-human primates should reflect common fundamental properties of these
learning mechanisms. Alternatively, any difference could be related to the use of language
recoding abilities and should therefore be considered with caution for constraining the
development of computational models.

We now illustrate this proposition by comparing the performance of human and non-
human primates in a novel task that has been designed to combine the serial response time task
and the artificial language paradigm (Franco & Destrebecqz, 2012). Human adults and baboons
had to touch a red circle appearing successively at nine different locations of a computer screen.
The dot to follow moved on the screen along paths containing statistical regularities. The use
of this task allowed us (1) to test the two populations with the exact same experimental
procedure, (2) to provide detailed information on the temporal dynamics of regularity
extraction, and (3) to compare humans and baboons at different levels of integration, at the local
level within the sequence (i.e., co-occurrences between individual elements), or at the level of
the global sequence structure (see the Method section below for an operational definition of the
distinction between local and more global regularities). Note that part of the primate data was
reported in Minier, Fagot, and Rey (2015).

Methods

Participants were ten Guinea baboons (*Papio papio*, age range 3–15.5 years) from the CNRS primate facility in Rousset (France) and 5 human adults (age range 20–24 years) from Aix-Marseille University.

Baboons live within a larger group of 26 individuals, within a 700 m2 outdoor enclosure and had a permanent access to ten Automated Learning Devices for Monkeys (ALDM, for a detailed description, see (Joël Fagot & Bonté, 2010b; Joël Fagot & Paleressompoulle, 2009) equipped with a 19-inch touch screen and a food dispenser. The main feature of ALDM equipment is that a radio frequency identification reader (RFID) identifies each baboon via a microchip implanted in each arm. The baboons can therefore participate to the research at will, without being captured, as the test programs recognize them automatically. All baboons had previously participated to numerous computerized experiments using the ALDM test systems. The experiment was controlled by the E-Prime software (Version 2.0, Psychology Software Tools, Pittsburgh).

A trial began with the presentation of a fixation cross at the bottom of a touch screen. The cross stayed on the screen till it was pressed. After pressing on it, the fixation-cross disappeared and immediately 9 crosses representing locations were displayed on the screen,
one of them being replaced by the target, a red circle. When the target was touched, it disappeared and was replaced by the cross. The next cross/location in the sequence was then replaced by the red circle until the end of the sequence. Reward (a small pellet) was provided at the end of a sequence of nine touches.

If participants touched an inappropriate location (incorrect trial), or failed to touch the screen within 5,000ms after the red circle's appearance (aborted trial), a green screen was displayed for 3000ms (baboons) / 1000ms (humans) as a marker of failure. Aborted trials were not counted as trials and were therefore presented again, while incorrect trials were not. The time elapsed between the appearance of the red circle and the participant's touch on this circle was recorded as the response time (RT). To learn the task, baboons initially received trials that were rewarded after one touch, after which the number of touches in a trial was progressively increased up to nine. Humans were simply instructed to touch the red circle as quickly as possible. They were also informed that each trial began by the presentation of the yellow cross at the bottom of the screen followed by a sequence of nine touches on the moving red circle.

To equate for the motor difficulty of the sequences to be produced, each participant was first tested on 5 blocks (baboons) / 4 blocks (humans) of 200 random trials (called “Random I phase”), each composed of a random ordering of nine locations, without immediate repetition. A baseline measure for all possible transitions from one location to another was computed by calculating mean RTs for each transition at the group level (i.e., the entire group of baboons on the one hand and of human participants on the other hand).

After this Random I phase, participants were exposed to trials including statistical regularities (hereafter, “Pattern phase”). Three independent regularities, each composed of three fixed locations were constructed ($R_1$ = ‘$A_1B_1C_1$’; $R_2$ = ‘$A_2B_2C_2$’; $R_3$ = ‘$A_3B_3C_3$’). To study the extraction dynamic of these regularities, we carefully selected these three patterns so that the mean RTs for the initial transition (‘$A_iB_iC_i$’) and final transition (‘$B_iC_i$’) would not be statistically different considering the baseline measurements obtained for these transitions during the random phase. By matching these *local* adjacent dependencies at the onset of the Pattern phase, it was possible to study the fine-grained evolution of these RTs that provide a direct behavioral index of regularity extraction.

Moreover, to study the extraction dynamic at a more complex and *global* level, the three regular patterns appeared in each sequence of nine touches, increasing the predictability of each pattern in the sequence (1/3, 1/2, 1). For example, if participants are exposed to the regular patterns $R_3$ followed by $R_1$, then the last regular pattern will be $R_2$. If they manage to extract
such a complex regularity, we should then observe a decrease in RTs for the first element of
the last displayed regular pattern (in our example, for A₂).

During the Pattern phase, participants performed 10 (baboons) / 7 (humans) blocks of
200 trials each, one trial being composed of a random combination of the three regular
patterns<fnref n="38" />. Finally, participants performed a last series of 5 (baboons) / 1 (humans) random
blocks ("Random II phase") that had the same structure as the Random I phase (see Figure 1).

<figure><img src="image_89.png" /><figcaption>**Figure 1** Experimental procedure for baboons and humans across the whole experiment (A). Experimental procedure at the block- and trial level (B). Patterns of locations on the touch screen used with humans (C).</figcaption></figure>

Results

<footnote n="38">³⁸ The number of blocks in humans and baboons are not exactly the same, because if the baboons could do the task when they wanted, humans had to complete the blocks in one long session. That is the reason why the number of blocks for humans has been slightly reduced.</footnote>
Incorrect trials were removed from the dataset (7.6% for baboons and 5.9% for humans), as well as RTs greater than two standard deviations from the mean (computed for each subject and each block, 4.2% for baboons and 3.6% for humans).

The first analysis concerned regularity extraction at the local level. Minier et al. (2015) reported an advantage of the second transition (i.e., BC) over the first one (i.e., AB) in baboons, with RTs on C decreasing faster than RTs on B. We therefore ran the same 2 (Transitions) * 7 (Block) repeated measures ANOVA on the mean RTs of humans. Mauchly's test indicated a violation of the sphericity assumption for the block factor ($\chi^2(44)=71.7, p<0.05$), so Greenhouse-Geisser estimates ($\epsilon=0.11$) were used to correct for degrees of freedom. We found a significant effect of Transition (F(1,9)=14.9, p<0.01, $\eta^2_p=0.62$), first transition RTs being slower than those of second transitions, and a significant effect of Block (F(2.88, 25.9)= 36.5, p<0.001, $\eta^2_p=0.80$) as the response latencies decreased through learning. Finally, the Transition * Block interaction was also significant (F(9,81)=4.9, p<0.001, $\eta^2_p=0.33$). Contrasts between first and second transitions for each block revealed a significant difference in blocks 1 to 8, but not in blocks 9 and 10. As shown in Figure 2, the human results match those of baboons with a faster decrease of RTs on C than on B.
<figure><img src="image_90.png" /><figcaption>Figure 2 Evolution of mean RTs for the first (AB) and second (BC) transitions over blocks for baboons (on the left) and humans (on the right).</figcaption></figure>

The second analysis concerned regularity extraction at a more global level, specifically, the predictability of the third pattern in the sequence given the first and second. As shown in Figure 3 (upper panel), the predictability within regular patterns is always equal to 1. Indeed, in each regular sequence, $A_i$ was always followed by $B_i$ that was always followed by $C_i$. However, the predictability of A varied across patterns depending on their position in the sequence of 9 touches. The first A of the first ABC regular pattern could occur with a probability of 1/3, the second A with a probability of 1/2, and the last A with a probability of 1. Of course, these probabilities depend on the ability of participants to extract the global statistical structure of the experiment.

We thus considered RTs for the three patterns on the three different positions in the sequence. Because humans and baboons did not receive the same number of blocks, we ran two different ANOVAs, both including Place in the pattern (first = A, second = B or third = C), Blocks (1 to 10 for baboons and 1 to 7 for humans) and Position of the patterns in the sequence (first, second or third) as within-subject factors.
For baboons, Mauchly's test indicated a violation of the assumption of sphericity for the Block factor ($\chi^2(44)=67.5, p<0.05$) as well as for the Position factor ($\chi^2(2)=6.3, p<0.05$), so Greenhouse-Geisser estimates ($\epsilon=0.31$ and $\epsilon=0.62$, respectively) were used to correct for degrees of freedom. We found a significant effect of Position ($F(1.23, 11.2)=8.0, p<0.013, \eta_p^2=0.47$), and a significant effect of Block ($F(2.8, 25.2)=3.7, p<0.001, \eta_p^2=0.29$). The interaction between Block and Position were found to be significant as well ($F(18,162)=1.8, p=0.026, \eta_p^2=0.17$). Helmert contrasts showed that the effect of Position was between the first position and the last two ($F(1,9)=8.9, p=0.02, \eta_p^2 = 0.5$) but not between the second position and the third ($F(1,9)=1.74, p=0.22, \eta_p^2 =0.16$). Another ANOVA ran on the mean RTs of the first place of the patterns (i.e., A) with only the last learning block and Position as a within-participant factor with 3 levels (1st, 2nd and 3rd) showed a significant effect ($F(2,18)=4.9, p<0.05, \eta_p^2 =0.35$). Helmert contrasts confirmed that this effect was significant between the first and two last positions ($F(1,9)=6.2, p=0.04, \eta_p^2 =0.40$) but not between the second and the third positions ($F(1,9)=0.67, p=0.43, \eta_p^2 =0.07$).

For humans, we found a significant effect of Position ($F(2,8)=36.83, p<0.001, \eta_p^2=0.90$), and a significant effect of Block ($F(6, 24)=7.0, p<0.001, \eta_p^2=0.64$). The interaction between Position and Block was also significant ($F(12,48)=13.57, p<0.001, \eta_p^2=0.77$). Helmert comparisons showed that the effect of Position was between the first position and the two last ($F(1,4)=51.5, p=0.02, \eta_p^2=0.98$), and between the second position and the third position ($F(1,4)=8.03, p=0.047, \eta_p^2=0.67$). A final ANOVA considered mean RTs of the first place in the patterns (i.e., A) in the last learning block only. Position was entered as within-participant factor with 3 levels (1st, 2nd and 3rd). This analysis showed a significant effect ($F(2,8)=36.7, p<0.0001, \eta_p^2=0.90$). Helmert contrasts indicated that this effect was significant between the first and two last positions ($F(1,4)=68.0, p<0.001, \eta_p^2=0.94$) and between the second and the third position as well ($F(1,4)=14.3, p=0.02, \eta_p^2=0.78$). The evolution of mean RTs for baboons and humans over each block, each position within the sequence, and each place within the regular pattern is provided in Figure 3 (lower panels). A clear difference between baboons and humans appears for the first element of the triplet when it occurred in Position 3 of the sequence (i.e., $A_3$): RTs decreased across learning blocks for humans while no decrease was observed for baboons.
<figure><img src="image_91.png" /><figcaption>**Figure 3** Upper central panel: Predictability of each location in the sequence of nine touches. Lower panels: Evolution of mean RTs over blocks (1-10 for baboons and 1-7 for humans), positions (1, 2, or 3) and places within the regular patterns (A, B, or C) for baboons (on the left) and humans (on the right).</figcaption></figure>

Discussion

Two main findings were obtained in the present comparative study in which the performance of human adults and baboons were collected in the same regularity learning task. For regularities at the *local* level, human and non-human primates displayed a similar statistical learning dynamic: RTs decreased faster on the third location of a regular pattern (i.e., C) than on the second (i.e., B). At a more *global* level, we found different results for humans and baboons. In humans, RTs for the first element (i.e., A) of the third displayed regular pattern (i.e., the 7th element in the sequence, which should become predictable over the course of learning) decreased gradually across learning blocks while no such improvement was observed for baboons.

The first result, already reported for baboons by Minier et al. (2015), indicates that the two species had a similar dynamic of regularity extraction. In both species, RTs decreased faster on C than on B in ABC regular patterns, suggesting that it is a general property that should be captured by current computational models. As argued previously by Minier et al. (2015), this pattern of results is inconsistent with theoretical propositions resting on the notion of
transitional probabilities between adjacent elements. Indeed, in our task, the transitional probabilities from A to B and from B to C are all equal to one and are therefore insufficient to account for the present data. Similarly, the TRACX model by French et al. (2011) predicts that RTs should decrease faster on B compared to C given that the model is based on a left-to-right acquisition of statistical regularities. Only the SRN model (Elman, 1990) predicts the learning advantage of C over B. SRN assumes that learning is based on the development of associations between the successive elements of the input that are frequently and consistently encountered together. SRN encodes also the contextual information that precedes the successive elements, and therefore C benefits from richer contextual information (i.e., the preceding systematic sequence AB) while B benefits only from the systematic occurrence of A.

Our second result indicates that only humans capture the more complex regularity related to the predictability of the third regular pattern in the larger sequence. In that situation, following the logic that we proposed, the absence of a common behavioral pattern of results between human and non-human primates suggests that this kind of regularity cannot be extracted by mere associative learning mechanisms. Computational models are therefore not expected to account for this phenomenon that may require language recoding abilities.

Following a different line of reasoning, one may argue that the present results point to a possible limitation of the learning capacities in non-human primates. The extraction of these global regularities might indeed require a more complex learning mechanism or a larger memory span for preceding elements. One would need however to specify what is the precise nature of this learning mechanism and what would be the role of short-term memory span in facilitating the extraction of such global regularities.

## Conclusions and avenues for future research

The comparative approach taken in the current paper suggests that the mechanisms underlying regularity extraction can be further understood by looking at patterns of performance that are common to human and non-human primates. Based on the present results we suggest that shared associative learning mechanisms can account for the extraction of *local* regularities (i.e., co-occurrences) and that a general theory of regularity learning could benefit from the large existing literature on associative learning (Mitchell & Le Pelley, 2010; Shanks, 1995). Moreover, the temporal dynamics of co-occurrence learning we observed in both species suggests a learning advantage for the final stimulus of a repeated (triplet) pattern, providing an
important empirical constraint for models of regularity extraction. Sensitivity to *global* regularities at the level of the entire sequence was observed for humans only and might call for a theory that accounts for the interaction between basic associative learning mechanisms and verbal recoding strategies.

Several important issues remain to be addressed to reach a general theory of associative learning mechanisms and their role for regularity extraction in different situations. We propose that the use of a comparative approach can be a fruitful avenue for future research to address a series of remaining theoretical questions, which we outline in more detail below.

A first series of questions concerns the role of the *timing* of co-occurrences in the sensory input. Suppose that A and B are two sources of information that co-occur systematically, A being always followed by B. Assuming no interfering information between A and B, how does the dynamic of regularity extraction change as a function of the delay between the presentation of A and the presentation of B? How many times A-B has to be repeated in order to observe an extraction of the co-occurrence? What is the role of the delay between two repetitions of A-B?

A second series of questions is related to the *sources of information surrounding* the A-B co-occurrence to be extracted. A general theory of regularity learning should be able to specify the role of factors such as the number and nature of interfering information between two repetitions of A-B, the role of the similarity between the interfering information and each element of the co-occurrence (A and B), the role of forward and backward inconsistencies (i.e., the role of the statistical co-occurrence of A with another source of information, e.g., C, and the interference of A-C on the extraction of A-B or, respectively, the co-occurrence of B with a preceding source of information, e.g., D, and the interference of D-B on the extraction of A-B), and finally, the effect of the presence of other co-occurrences (e.g., like E-F) appearing between two repetitions of A-B.

A third series of questions concerns the *number of co-occurring elements*. What is the regularity extraction dynamic when A-B is systematically followed by another information C (an issue that has started to be addressed in the present study)? Similarly, what will happen if we increase the chain of co-occurring elements? For ABCD sequences, for example, will we observe an even faster decrease of RTs on D compare to C (because of the richer context) or is the role of contextual information limited to a restricted time-window? And for longer sequences, what is the effect of repeating a previously encountered element (e.g., like in the sequence ABCAD)?
A fourth set of questions is related to the *learning of non-adjacent co-occurrences*
(Gómez, 2002; Pacton & Perruchet, 2008; Pacton, Sobaco, & Perruchet, 2015; Wilson,
Spierings, Ravignani, Mueller, Mintz, Wijnen, van der Kant, Smith, Rey, submitted). Under
which conditions can we extract the co-occurrence of A and B when other sources of
information are placed between A and B? Clearly, if co-occurrence learning is supported by
associative learning mechanisms, the presence of interfering information between A and B will
strongly decrease the probability to extract that regularity. However, baboons have shown some
ability to process long-distance dependencies (Rey, Perruchet, & Fagot, 2012) indicating that
the learning of non-adjacent regularities should certainly be accounted for by current
computational models of regularity learning.

To conclude, we have argued that the domains of statistical learning and implicit
learning, recently chunked under the term ‘implicit statistical learning’ (e.g., Christiansen, this
issue), are fundamentally rooted in the domain of associative learning. We have also proposed
an alternative way of using comparative studies to better comprehend the fundamental
mechanisms supporting regularity extraction and the temporal dynamics of associative learning
mechanisms. Rather than focusing on the differences between species, we suggest focusing on
common patterns of performances to determine the empirical evidence that should constrain
the elaboration of computational models. By employing online measures, we can generate fine-
grained behavioral results that can inform models about the precise dynamics of regularity
extraction mechanisms. We have suggested that this approach can be quite powerful in testing
the predictions of computational models. Finally, we have outlined a list of questions that would
need to be addressed to reach a deeper understanding of associative learning mechanisms in the
context of regularity extraction.
Acknowledgments

This work, carried out within the Labex BLRI (ANR-11-LABX-0036) and the Institut
Convergence ILCB (ANR-16-CONV-0002), has benefited from support from the French
government, managed by the French National Agency for Research (ANR) and the Excellence
Initiative of Aix-Marseille University (A*MIDEX). This research was also supported by the
Premilang2 ANR-project (#ANR-13-BSH2-0002) and the Chunked ANR-project (#ANR-17-
CE28-0013-02).
Batterink, L. J. (2017). Rapid Statistical Learning Supporting Word Extraction From Continuous Speech. *Psychological Science*, 28, 921-928.
doi:10.1177/0956797617698226

Batterink, L.J., Reber, P.J., Neville, H.J. & Paller, K.A. (2015). Implicit and explicit contributions to statistical learning. *Journal of Memory and Language*, 83, 62-78.

Bulf, H., Johnson, S. P., & Valenza, E. (2011). Visual statistical learning in the newborn infant. *Cognition*, 121(1), 127-132.

Chen, J., van Rossum, D., & ten Cate, C. (2015). Artificial grammar learning in zebra finches and human adults: XYX versus XXY. *Animal Cognition*, 18(1), 151-164.
doi:10.1126/science.284.5416.875a

Cleeremans, A., Destrebecqz, A., & Boyer, M. (1998). Implicit learning: News from the front. *Trends in Cognitive Sciences*, 2(10), 406-416. doi:10.1016/S1364-6613(98)01232-7

Christiansen, M. H. (this issue). Implicit Statistical Learning: A Tale of Two Literatures. *Topics in Cognitive Science*.

Conway, C. M., & Christiansen, M. H. (2006). Statistical learning within and between modalities: Pitting abstract against stimulus-specific representations. *Psychological Science*, 17(10), 905-912.

Elman, J. L. (1990). Finding structure in time. *Cognitive science*, 14(2), 179-211.

Estes, W. K. (1950). Toward a statistical theory of learning. *Psychological Review*, 57(2), 94-107. doi:10.1037/h0058559

Fagot, J., & Bonté, E. (2010). Automated testing of cognitive performance in monkeys: Use of a battery of computerized test systems by a troop of semi-free-ranging baboons (Papio papio). *Behavior Research Methods*, 42(2), 507-516.

Fagot, J., & Paleressompoulle, D. (2009). Automatic testing of cognitive performance in baboons maintained in social groups. *Behavior Research Methods*, 41(2), 396-404.

Fagot, J., Malassis, R., Medam, T. & Montant, M. (2017). Comparing human and nonhuman animal performance on domain-general functions: Towards a multiple bottleneck scenario of language evolution. in *Origins of human language: continuities and splits*
with nonhuman primates (pp 329-365). Editors: Louis-Jean Boë, Joël Fagot, Pascal Perrier, Jean-Luc Schwartz, Peter lang: Frankfurt.

Fitch, W. T., and Hauser, M. D. (2004). Computational constraints on syntactic processing in a nonhuman primate. *Science*, **303**, 377-380.

Franco, A., & Destrebecqz, A. (2012). Chunking or not chunking? How do we find words in artificial language learning? *Advances in Cognitive Psychology*, **8**(2), 144.

Frank, M. C., Goldwater, S., Griffiths, T., & Tenenbaum, J. B. (2010). Modeling human performance in statistical word segmentation. *Cognition*, **117**, 107-125.

French, R. M., Addyman, C., & Mareschal, D. (2011). TRACX: A Recognition-Based Connectionist Framework for Sequence Segmentation and Chunk Extraction. *Psychological Review*, **118**(4), 614-636.

Frost, R., Armstrong, B. C., Siegelman, N., & Christiansen, M. H. (2015). Domain generality versus modality specificity: The paradox of statistical learning. *Trends in Cognitive Sciences*, **19**(3), 117-125. doi:10.1016/j.tics.2014.12.010

Gómez, R. L. (2002). Variability and detection of invariant structure. *Psychological Science*, **13**, 431–436.

Hauser, M. D., Chomsky, N., and Fitch, W. T. (2002). The faculty of language: what is it, who has it, and how did it evolve? *Science*, **298**, 1569-1579.

Hebb, D. O. (1961). Distinctive features of learning in the higher animal. In J. F. Delafresnaye (Ed.), *Brain mechanisms and learning* (pp. 37–46). Oxford, UK: Blackwell.

Minier. L., Fagot, J., & Rey, A. (2015). The temporal dynamics of regularity extraction in nonhuman primates. *Cognitive Science*. doi:10.1111/cogs.12279

Misyak, J. B., Christiansen, M. H., & Tomblin, J. B. (2010). On-line individual differences in statistical learning predict language processing. *Frontiers in Psychology, 1*, 31. doi.org: 10.3389/fpsyg.2010.00031

Mitchell, C. J., & Le Pelley, M. E. (Eds.). (2010). *Attention and associative learning: From brain to behaviour*. Oxford University Press, USA.

Nissen, M. J., & Bullemer, P. (1987). Attentional requirements of learning: Evidence from performance measures. *Cognitive Psychology*, **19**(1), 1-32.
Pacton, S., & Perruchet, P. (2008). An attention-based associative account of adjacent and nonadjacent dependency learning. *Journal of Experimental Psychology: Learning, Memory and Cognition, 34*, 80-96.

Pacton, S., Sobaco, A., & Perruchet, P. (2015). Is an attention-based account of adjacent and nonadjacent dependency learning valid? *Acta Psychologica, 157*, 195-199.

Perruchet, P. (2005). Statistical approaches to language acquisition and the self-organizing consciousness: A reversal of perspective. *Psychological Research, 69*, 316-329.

Perruchet, P., & Pacton, S. (2006). Implicit learning and statistical learning: One phenomenon, two approaches. *Trends in Cognitive Sciences, 10*(5), 233-238.
doi:10.1016/j.tics.2006.03.006

Perruchet, P., & Poulin-Charronnat, B. (2012). Word segmentation: Trading the (new, but poor) concept of statistical computation for the (old, but richer) associative approach.
In Rebuschat, P., & Williams, J. N. (Eds.), *Statistical learning and language acquisition* (pp. 119-143). Berlin: De Gruyter Mouton.

Perruchet, P., & Vinter, A. (1998). PARSER: A model for word segmentation. *Journal of Memory and Language, 39*(2), 246-263.

Perruchet, P., & Vinter, A. (2002). The self-organizing consciousness. *Behavioral and Brain Sciences, 25*, 297-388. doi:10.1017/S0140525X02000067

Pinker, S., & Jackendoff, R. (2005). The faculty of language: what's special about it?.
*Cognition, 95*(2), 201-236.

Pothos, E. M. (2007). Theories of artificial grammar learning. *Psychological Bulletin, 133*(2),
227-244. doi: 10.1037/0033-2909.133.2.227

Reber, A. S. (1967). Implicit learning of artificial grammars. *Journal of Verbal Learning and Verbal Behavior, 6*(6), 855-863. doi: 10.1016/S0022-5371(67)80149-X

Restle, F. (1970). Theory of serial pattern learning: Structural trees. *Psychological Review, 77*(6), 481-495. doi: 10.1037/h0029964

Rey, A., Perruchet, P., & Fagot, J. (2012). Centre-embedded structures are a by-product of associative learning and working memory constraints: Evidence from baboons (Papio Papio). *Cognition, 123*(1), 180-184.

Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. *Science, 274*(5294), 1926-1928.
Santolin, C., Rosa-Salva, O., Regolin, L., & Vallortigara, G. (2016). Generalization of visual regularities in newly hatched chicks (Gallus gallus). *Animal Cognition, 19*(5), 1007-1017. doi:10.1007/s10071-016-1005-2

Shanks, D. R. (1995). *The Psychology of Associative Learning*. Cambridge: Cambridge University Press.

Shanks, D. R. (2005). Implicit learning. In: Lamberts, K., and Goldstone, R., (eds.) *Handbook of Cognition*, pp. 202–220. London: Sage.

Siegelman, N., Bogaerts, L., & Frost, R. (2017). Measuring individual differences in statistical learning: Current pitfalls and possible solutions. *Behavior research methods, 49*(2), 418-432. doi:10.3758/s13428-016-0719-z

Siegelman, N., Bogaerts, L., Kronenfeld, O., & Frost, R. (2017). Redefining “Learning” in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities? *Cognitive Science*. doi:10.1111/cogs.12556

Singh, L., Steven Reznick, J., & Xuehua, L. (2012). Infant word segmentation and childhood vocabulary development: a longitudinal analysis. *Developmental Science, 15*(4), 482-495. doi: 10.1111/j.1467-7687.2012.01141.x

Thiessen, E. D., Kronstein, A. T., & Hufnagle, D. G. (2013). The extraction and integration framework: A two-process account of statistical learning. *Psychological bulletin, 139*(4), 792-814. doi:10.1037/a0030801

Toro, J. M., Nespor, M., & Gervain, J. (2016). Frequency-based organization of speech sequences in a nonhuman animal. *Cognition, 146*, 1-7.
doi:10.1016/j.cognition.2015.09.006

Wang, L., Uhrig, L., Jarraya, B., & Dehaene, S. (2015). Representation of Numerical and Sequential Patterns in Macaque and Human Brains. *Current Biology*, 25, 1966-1974.

Wilson, B., Marslen-Wilson, W. D., & Petkov, C. I. (2017). Conserved sequence processing in primate frontal cortex. *Trends in Neurosciences*, 40, 72-82.
doi:10.1016/j.tins.2016.11.004
Résumé

Une hypothèse dominant actuellement les théories sur l'évolution des capacités syntaxiques est celle d'une spécificité humaine pour le traitement des grammaires supra-régulières. Cette hypothèse tend à être confirmée par les données comparatives actuellement disponibles, qui ne fournissent pas de démonstration non ambiguë de cette capacité chez une autre espèce que l'humain. Dans cette thèse, nous avons adopté une nouvelle approche consistant à étudier les prérequis cognitifs pour le traitement de ce type de grammaires. Notre hypothèse était que ces échecs précédents puissent être attribués à un biais chez ces espèces vers l'exploitation de régularités locales et des difficultés pour l'extraction de régularités plus distantes, plutôt qu'à une incapacité à traiter ces grammaires. Pour tester cette hypothèse, nous avons mené une série de quatre études chez le babouin de guinée (Papio papio) et l'humain. Dans une première étude, nous avons montré que le babouin nécessitait une quantité d'exposition beaucoup plus importante que l'humain pour apprendre des associations non-adjacentes. Dans une seconde étude, les babouins ont été capables de généraliser des patterns basés sur une répétition adjacente ou non-adjacente d'un élément, mais ils se sont montrés davantage sensibles aux répétitions adjacentes. Une troisième étude, corrélationnelle, révèle que les babouins parvenant se montrant sensibles aux régularités non-adjacentes dans différentes tâches ne sont pas ceux obtenant les meilleures performances pour l'apprentissage de dépendances adjacentes. Une dernière étude suggère que les babouins sont sensibles à une structure en miroir (impliquant des dépendances centrées-emboitéées), mais pas à une structure en copie (dépendances croisées). Cet ensemble de résultats met au jour une continuité des capacités syntaxiques au sein de la lignée des primates, mais révèle également des différences interspécifiques importantes dans les contraintes mnésiques pesant sur celles-ci.

Mots-clés : apprentissage de grammaires artificielles, régularités séquentielles, primates, cognition, psychologie comparée

Abstract

A current dominant hypothesis on the evolution of syntactic abilities propose that the processing of supra-regular grammars is a unique human capacity. In support of this hypothesis, artificial grammar learning studies conducted so far do not provide unambiguous demonstration of this capacity in a non-human species. In this thesis, we adopted a new approach by studying cognitive prerequisites for supra-regular grammar processing. Our hypothesis was that these previous failures could be attributed to a bias in these species towards the exploitation of local regularities and difficulties for processing more distant relationships, rather than an inability to master supra-regular grammars. We conducted a series of experiments in Guinea baboons (Papio papio) and humans to assess this hypothesis. In a first experiment, we show that baboons need much more exposure than humans to learn non-adjacent associations. In a second study, we show that baboons can generalize patterns involving an adjacent or a non-adjacent repetition of an element, but that they are more sensitive to the former. A third, correlational, study reveal that baboons succeeding to extract non-adjacent regularities are not those showing the best performance in learning local ones. A last study suggest that baboons are sensitive to a mirror structure (involving center-embedded dependencies), but not to a copy structure (crossed dependencies). Overall, our results reveal a stronger continuity in grammar processing capacities within the primate order than previously thought, but also highlight important species differences in memory constraints.

Keywords: artificial grammar learning, sequential regularities, primates, cognition, comparative
psychology