Hostname: page-component-6766d58669-kl59c Total loading time: 0 Render date: 2026-05-15T06:37:23.793Z Has data issue: false hasContentIssue false
  • English
  • Français

The best game in town: The reemergence of the language-of-thought hypothesis across the cognitive sciences

Published online by Cambridge University Press:  06 December 2022

Jake Quilty-Dunn Open the ORCID record for Jake Quilty-Dunn [Opens in a new window] ,
Nicolas Porot Open the ORCID record for Nicolas Porot [Opens in a new window]  and
Eric Mandelbaum Open the ORCID record for Eric Mandelbaum [Opens in a new window]
Jake Quilty-Dunn
Affiliation:
Department of Philosophy and Philosophy-Neuroscience-Psychology Program, Washington University in St. Louis, St. Louis, MO, USA. quiltydunn@gmail.com, sites.google.com/site/jakequiltydunn/
Nicolas Porot
Affiliation:
Africa Institute for Research in Economics and Social Sciences, Mohammed VI Polytechnic University, Rabat, Morocco. nicolasporot@gmail.com, nicolasporot.com
Eric Mandelbaum
Affiliation:
Departments of Philosophy and Psychology, The Graduate Center & Baruch College, CUNY, New York, NY, USA. eric.mandelbaum@gmail.com, ericmandelbaum.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate–argument structure; (iv) logical operators; (v) inferential promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures – that is, LoTs – only prove more flexible and well-supported over time.

Keywords

animal cognitionautomaticitycognitive architecturedeep learningdual-process theoriesimplicit attitudesinfant cognitionlanguage-of-thoughtobject filesvisual cognition

Information

Type
Target Article
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e261
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

1

All authors contributed equally; authorship is in reverse alphabetical order.

References

Amalric, M., Wang, L., Pica, P., Figueira, S., Sigman, M., & Dehaene, S. (2017). The language of geometry: Fast comprehension of geometrical primitives and rules in human adults and preschoolers. PLoS Computational Biology, 13(1), e1005273.CrossRefGoogle ScholarPubMed
Anderson, J. R. (1990). The adaptive character of thought. Psychology Press.Google Scholar
Ayzenberg, V., & Lourenco, S. F. (2021). One-shot category learning in human infants. PsyArXiv. doi:10.31234/osf.io/acymrGoogle Scholar
Baddeley, A. (1992). Working memory. Science (New York, N.Y.), 255(5044), 556–559.CrossRefGoogle ScholarPubMed
Bae, G., Olkkonen, M., Allred, S., & Flombaum, J. (2015). Why some colors appear more memorable than others: A model combining categories and particulars in color working memory. Journal of Experimental Psychology: General, 144, 744–763.CrossRefGoogle Scholar
Bago, B., & De Neys, W. (2017). Fast logic?: Examining the time course assumption of dual process theory. Cognition, 158, 90–109.CrossRefGoogle ScholarPubMed
Bago, B., & De Neys, W. (2019). The smart system 1: Evidence for the intuitive nature of correct responding on the bat-and-ball problem. Thinking & Reasoning, 25(3), 257–299.CrossRefGoogle Scholar
Bago, B., & De Neys, W. (2020). Advancing the specification of dual process models of higher cognition: A critical test of the hybrid model view. Thinking & Reasoning, 26(1), 1–30.CrossRefGoogle Scholar
Bahrami, B. (2003). Object property encoding and change blindness in multiple object tracking. Visual Cognition, 10(8), 949–963.CrossRefGoogle Scholar
Baker, N., & Elder, J. H. (2022). Deep learning models fail to capture the configural nature of human shape perception. iScience, 25(9), 104913.CrossRefGoogle ScholarPubMed
Baker, N., Lu, H., Erlikhman, G., & Kellman, P. J. (2020). Local features and global shape information in object classification by deep convolutional neural networks. Vision Research, 172, 46–61.CrossRefGoogle ScholarPubMed
Bar, M. (2004). Visual objects in context. Nature Reviews Neuroscience, 5, 617–629.CrossRefGoogle ScholarPubMed
Barack, D. L., & Krakauer, J. W. (2021). Two views on the cognitive brain. Nature Reviews Neuroscience, 22(6), 359–371.CrossRefGoogle ScholarPubMed
Barenholtz, E., & Feldman, J. (2003). Visual comparisons within and between object parts: Evidence for a single-part superiority effect. Vision Research, 43, 1655–1666.CrossRefGoogle ScholarPubMed
Barenholtz, E., & Tarr, M. J. (2008). Visual judgment of similarity across shape transformations: Evidence for a compositional model of articulated objects. Acta Psychologica, 128, 331–338.CrossRefGoogle ScholarPubMed
Barsalou, L. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22, 577–609.CrossRefGoogle ScholarPubMed
Bays, P. M., Catalao, R. F. G., & Husain, M. (2009). The precision of visual working memory is set by allocation of a shared resource. Journal of Vision, 9(10), 1–11.CrossRefGoogle ScholarPubMed
Bays, P. M., Wu, E. Y., & Husain, M. (2011). Storage and binding of object features in visual working memory. Neuropsychologia, 49(6), 1622–1631.CrossRefGoogle ScholarPubMed
Bermudez, J. L. (2003). Thinking without words. Oxford University Press.CrossRefGoogle Scholar
Berwick, R. C., & Chomsky, N. (2016). Why only us: Language and evolution. MIT Press.CrossRefGoogle Scholar
Bickle, J. (2003). Philosophy and neuroscience: A ruthlessly reductive account. Kluwer.CrossRefGoogle Scholar
Biederman, I. (1987). Recognition-by-components: A theory of human image understanding. Psychological Review, 94(2), 115–147.CrossRefGoogle ScholarPubMed
Bloom, P. (1996). Intention, history, and artifact concepts. Cognition, 60(1), 1–29.CrossRefGoogle ScholarPubMed
Bonatti, L., Frot, E., Zangl, R., & Mehler, J. (2002). The human first hypothesis: Identification of conspecifics and individuation of objects in the young infant. Cognitive Psychology, 44, 388–426.CrossRefGoogle ScholarPubMed
Bowers, J. S., Malhotra, G., Dujmović, M., Montero, M. L., Tsvetkov, C., Biscione, V., … Blything, R. (2022). Deep problems with neural network models of human vision. PsyArXiv. doi:10.31234/osf.io/5zf4sGoogle ScholarPubMed
Boyd, R. (1999). Homeostasis, species, and higher taxa. In Wilson, R. A. (Ed.), Species: New interdisciplinary essays (pp. 141–185). MIT Press.Google Scholar
Brady, T. F., Konkle, T., Alvarez, G. A., & Oliva, A. (2013). Real-world objects are not represented as bound units: Independent forgetting of different object details from visual memory. Journal of Experimental Philosophy: General, 142(3), 791–808.Google Scholar
Braine, M. D. S., & O'Brien, D. P. (Eds.). (1998). Mental logic. Erlbaum.CrossRefGoogle Scholar
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., … Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.Google Scholar
Buckner, C. (2019). Deep learning: A philosophical introduction. Philosophy Compass, 14(10), e12625.CrossRefGoogle Scholar
Burge, T. (2010). Steps toward origins of propositional thought. Disputatio, 4(29), 39–67.CrossRefGoogle Scholar
Burgess, C. P., Matthey, L., Watters, N., Kabra, R., Higgins, I., Botvinick, M., & Lerchner, A. (2019). MONet: Unsupervised scene decomposition and representation. arXiv preprint arXiv: 1901.11390.Google Scholar
Call, J. (2004). Inferences about the location of food in the great apes. Journal of Comparative Psychology, 118, 232–241.CrossRefGoogle ScholarPubMed
Call, J. (2006). Descartes’ two errors: Reason and reflection in the great apes. In Hurley, S. & Nudds, M. (Eds.), Rational animals? (pp. 219–234). Oxford University Press.CrossRefGoogle Scholar
Camp, E. (2007). Thinking with maps. Philosophical Perspectives, 21, 145–182.CrossRefGoogle Scholar
Camp, E. (2009). Putting thoughts to work: Concepts, systematicity, and stimulus-independence. Philosophy and Phenomenological Research, 78(2), 275–311.CrossRefGoogle Scholar
Camp, E. (2018). Why maps are not propositional. In Grzankowski, A. & Montague, M. (Eds.), Non-propositional intentionality (pp. 19–45). Oxford University Press.Google Scholar
Carey, S. (2009). The origin of concepts. Oxford University Press.CrossRefGoogle Scholar
Carruthers, P. (2009). Invertebrate concepts confront the generality constraint (and win). In Lurz, R. (Ed.), The philosophy of animal minds (pp. 89–107). Cambridge University Press.CrossRefGoogle Scholar
Carruthers, P. (2018). The causes and contents of inner speech. In Vicente, A. & Langland-Hassan, P. (Eds.), Inner speech: New voices (pp. 31–52). Oxford University Press.Google Scholar
Castelhano, M. S., & Heaven, C. (2011). Scene context influences without scene gist: Eye movements guided by spatial associations in visual search. Psychonomic Bulletin & Review, 18, 890–896.CrossRefGoogle ScholarPubMed
Cavanagh, P. (2021). The language of vision. Perception, 50(3), 195–215.CrossRefGoogle ScholarPubMed
Cesana-Arlotti, N., & Halberda, J. (2022). Domain-general logical inference by 2.5-year-old toddlers. PsyArXiv. doi:10.31234/osf.io/qzxkpGoogle Scholar
Cesana-Arlotti, N., Kovács, A. M., & Téglás, E. (2020). Infants recruit logic to learn about the social world. Nature Communications, 11(5999).CrossRefGoogle ScholarPubMed
Cesana-Arlotti, N., Martín, A., Téglás, A., Vorobyova, L., Cetnarski, R., & Bonatti, L. L. (2018). Precursors of logical reasoning in preverbal human infants. Science (New York, N.Y.), 359, 1263–1266.CrossRefGoogle ScholarPubMed
Cheney, D. L., & Seyfarth, R. M. (2008). Baboon metaphysics: The evolution of a social mind. University of Chicago Press.Google Scholar
Cheng, C., & Kibbe, M. M. (2021). Children's use of reasoning by exclusion to track identities of occluded objects. Proceedings of the Cognitive Science Society, 43, 2038–2044.Google Scholar
Cheyette, S., & Piantadosi, S. (2017). Knowledge transfer in a probabilistic language of thought. Proceedings of the Cognitive Science Society, 39, 222–227.Google Scholar
Chomsky, N. (1965). Aspects of the theory of syntax. MIT Press.Google Scholar
Chomsky, N. (1995). The minimalist program. MIT Press.Google Scholar
Chomsky, N. (2017). Language architecture and its import for evolution. Neuroscience and Biobehavioral Reviews, 81, 295–300.CrossRefGoogle ScholarPubMed
Churchland, P. M. (1981). Eliminative materialism and the propositional attitudes. Journal of Philosophy, 78, 67–90.Google Scholar
Clarke, S. (2022). Beyond the icon: Core cognition and the bounds of perception. Mind & Language, 37(1), 94–113. doi:10.1111/mila.12315CrossRefGoogle Scholar
Clarke, S., & Beck, J. (2021). The number sense represents (rational) numbers. Behavioral and Brain Sciences, 44, e178.CrossRefGoogle ScholarPubMed
Cone, J., & Ferguson, M. J. (2015). He did what? The role of diagnosticity in revising implicit evaluations. Journal of Personality and Social Psychology, 108(1), 37.CrossRefGoogle ScholarPubMed
Conwell, C., & Ullman, T. D. (2022). Testing relational understanding in text-guided image generation. ArXiv. doi:10.48550/arXiv.2208.00005Google Scholar
Dabkowski, M., & Feiman, R. (2021). Evidence of accurate logical reasoning in online sentence comprehension. Poster at the Society for Philosophy and Psychology.Google Scholar
Danks, D. (2014). Unifying the mind: Cognitive representations as graphical models. MIT Press.CrossRefGoogle Scholar
De Houwer, J. (2006). Using the implicit association test does not rule out an impact of conscious propositional knowledge on evaluative conditioning. Learning and Motivation, 37(2), 176–187.CrossRefGoogle Scholar
De Houwer, J. (2019). Moving beyond system 1 and system 2. Experimental Psychology, 66(4), 257–265.CrossRefGoogle ScholarPubMed
De Neys, W., Cromheeke, S., & Osman, M. (2011). Biased but in doubt: Conflict and decision confidence. PLoS ONE, 6(1), e15954.CrossRefGoogle ScholarPubMed
De Neys, W., & Franssens, S. (2009). Belief inhibition during thinking: Not always winning but at least taking part. Cognition, 113(1), 45–61.CrossRefGoogle ScholarPubMed
De Neys, W., & Glumicic, T. (2008). Conflict monitoring in dual process theories of thinking. Cognition, 106(3), 1248–1299.CrossRefGoogle ScholarPubMed
De Neys, W., Rossi, S., & Houdé, O. (2013). Bats, balls, and substitution sensitivity: Cognitive misers are no happy fools. Psychonomic Bulletin & Review, 20, 269–273.CrossRefGoogle ScholarPubMed
Dennett, D. C. (1978). A cure for the common code?. In Dennett, D. C. (Ed.), Brainstorms (pp. 99–118). Bradford.Google Scholar
Dickinson, A. (2012). Associative learning and animal cognition. Philosophical Transactions of the Royal Society B, 367, 2733–2742.CrossRefGoogle ScholarPubMed
Draschkow, D., & Võ, M. L.-H. (2017). Scene grammar shapes the way we interact with objects, strengthens memories, and speeds search. Scientific Reports, 7(16471), 1–12.CrossRefGoogle ScholarPubMed
Dunbar, E., & Wellwood, A. (2016). Addressing the “two interface” problem: Comparatives and superlatives. Glossa: A Journal of General Linguistics, 1(1), 5.CrossRefGoogle Scholar
Duncan, J. (1984). Selective attention and the organization of visual information. Journal of Experimental Psychology: General, 123, 501–517.CrossRefGoogle Scholar
Edelman, S. (1999). Representation and recognition in vision. MIT Press.CrossRefGoogle Scholar
Egly, R., Driver, J., & Rafal, R. D. (1994). Shifting visual attention between objects and locations: Evidence from normal and parietal lesion subjects. Journal of Experimental Psychology: General, 123, 161–177.CrossRefGoogle ScholarPubMed
Eliasmith, C. (2013). How to build a brain: A neural architecture for biological cognition. Oxford University Press.CrossRefGoogle Scholar
Engelmann, J., Völter, C. J., O'Madagain, C., Proft, M., Haun, D. B., Rakoczy, H., & Herrmann, E. (2021). Chimpanzees consider alternative possibilities. Current Biology, 31, R1–R3.CrossRefGoogle ScholarPubMed
Erdogan, G., Yildirim, I., & Jacobs, R. A. (2015). From sensory signals to modality-independent conceptual representations: A probabilistic language of thought approach. PLoS Computational Biology, 11(11), e1004610.CrossRefGoogle ScholarPubMed
Evans, J. S. B., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science, 8(3), 223–241.CrossRefGoogle ScholarPubMed
Feiman, R., Mody, S., & Carey, S. (2022). The development of reasoning by exclusion in infancy. Cognitive Psychology, 135, 101473. doi:10.1016/j.cogpsych.2022.101473CrossRefGoogle ScholarPubMed
Fel, T., Felipe, I., Linsley, D., & Serre, T. (2022). Harmonizing the object recognition strategies of deep neural networks with humans. arXiv preprint arXiv: 2211.04533.Google ScholarPubMed
Feldman, J., & Singh, M. (2006). Bayesian estimation of the shape skeleton. Proceedings of the National Academy of Sciences of the United States of America, 103(47), 18014–18019.CrossRefGoogle ScholarPubMed
Ferrigno, S., Huang, Y., & Cantlon, J. F. (2021). Reasoning through the disjunctive syllogism in monkeys. Psychological Science, 32(2), 292–300.CrossRefGoogle ScholarPubMed
Field, H. H. (1978). Mental representation. Erkenntnis, 13(1), 9–61.CrossRefGoogle Scholar
Finn, C., Yu, T., Zhang, T., Abbeel, P., & Levine, S. (2017). One-shot visual imitation learning via meta-learning. In Conference on robot learning (pp. 357–368). PMLR.Google Scholar
Firestone, C. (2020). Performance vs. competence in human–machine comparisons. Proceedings of the National Academy of Sciences of the United States of America, 117(43), 26562–26571.CrossRefGoogle ScholarPubMed
Firestone, C., & Scholl, B. J. (2014). “Please tap the shape, anywhere you like” shape skeletons in human vision revealed by an exceedingly simple measure. Psychological Science, 25(2), 377–386.CrossRefGoogle ScholarPubMed
Fitch, W. T. (2019). Animal cognition and the evolution of human language: Why we cannot focus solely on communication. Philosophical Transactions of the Royal Society B, 375, 20190046. doi:10.1098/rstb.2019.0046Google ScholarPubMed
Flombaum, J. I., Kundey, S. M., Santons, L. R., & Scholl, B. J. (2004). Dynamic object individuation in rhesus macaques: A study of the tunnel effect. Psychological Science, 15(12), 795–800.CrossRefGoogle ScholarPubMed
Flombaum, J. I., & Scholl, B. J. (2006). A temporal same-object advantage in the tunnel effect: Facilitated change detection for persisting objects. Journal of Experimental Psychology: Human Perception and Performance, 32, 840–853.Google ScholarPubMed
Flombaum, J. I., Scholl, B. J., & Santos, L. R. (2009). Spatiotemporal priority as a fundamental principle of object persistence. In Hood, B. M. & Santos, L. R. (Eds.), The origins of object knowledge (pp. 135–164). Oxford University Press.CrossRefGoogle Scholar
Fodor, J. A. (1975). The language of thought (Vol. 5). Harvard University Press.Google Scholar
Fodor, J. A. (1983). The modularity of mind. MIT Press.CrossRefGoogle Scholar
Fodor, J. A. (1987). Psychosemantics. MIT Press.CrossRefGoogle Scholar
Fodor, J. A. (1998). Concepts: Where cognitive science went wrong. Oxford University Press.CrossRefGoogle Scholar
Fodor, J. A. (2007). The revenge of the given. In McLaughlin, B. & Cohen, J. (Eds.), Contemporary debates in philosophy of mind (pp. 105–116). Blackwell.Google Scholar
Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28, 3–71.CrossRefGoogle ScholarPubMed
Fougnie, D., & Alvarez, G. A. (2011). Object features fail independently in visual working memory: Evidence for a probabilistic feature-store model. Journal of Vision, 11(12), 1–12.CrossRefGoogle ScholarPubMed
Frankland, S. M., & Greene, J. D. (2020). Concepts and compositionality: In search of the brain's language of thought. Annual Review of Psychology, 71, 273–303.CrossRefGoogle ScholarPubMed
Futo, J., Teglas, E., Csibra, G., & Gergely, G. (2010). Communicative function demonstration induces kind-based artifact representation in preverbal infants. Cognition, 117, 1–8.CrossRefGoogle ScholarPubMed
Gallistel, C. R. (2011). Prelinguistic thought. Language Learning and Development, 7, 253–262.CrossRefGoogle Scholar
Gallistel, C. R., & King, A. P. (2011). Memory and the computational brain: Why cognitive science will transform neuroscience. John Wiley & Sons.Google Scholar
Gangemi, A., Bourgeois-Gironde, S., & Mancini, F. (2015). Feelings of error in reasoning – In search of a phenomenon. Thinking & Reasoning, 21(4), 383–396.CrossRefGoogle Scholar
Gast, A., & De Houwer, J. (2013). The influence of extinction and counterconditioning instructions on evaluative conditioning effects. Learning and Motivation, 44(4), 312–325.CrossRefGoogle Scholar
Gauker, C. (2011). Words and images: An essay on the origin of ideas. Oxford University Press.CrossRefGoogle Scholar
Gautam, S., Suddendorf, T., & Redshaw, J. (2021). When can young children reason about an exclusive disjunction? A follow up to Mody and Carey (2016). Cognition, 207, 104507. doi:10.1016/j.cognition.2020.104507CrossRefGoogle Scholar
Gawronski, B., & Bodenhausen, G. V. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132(5), 692–731.CrossRefGoogle ScholarPubMed
Gayet, S., Paffen, S., & Van der Stigchel, S. (2018). Visual working memory storage recruits sensory processing areas. Trends in Cognitive Sciences, 22(3), 189–190.CrossRefGoogle ScholarPubMed
Gershman, S. J. (2022). The molecular memory code and synaptic plasticity: A synthesis. arXiv preprint arXiv: 2209.04923.Google Scholar
Ghasemi, O., Handley, S. J., & Howarth, S. (2021). The bright homunculus in our head: Individual differences in intuitive sensitivity to logical validity. Quarterly Journal of Experimental Psychology, 17470218211044691.Google ScholarPubMed
Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451–482.CrossRefGoogle ScholarPubMed
Goldstone, R. L., Medin, D. L., & Gentner, D. (1991). Relational similarity and the nonindependence of features in similarity judgments. Cognitive Psychology, 23, 222–262.CrossRefGoogle ScholarPubMed
Goodman, N., Mansinghka, V., Roy, D., Bonawitz, K., & Tenenbaum, J. (2008a). Church: A language for generative models. In McAllester, D. & Myllymaki, P. (Eds.), Proceedings of the 24th conference on uncertainty in artificial intelligence, UAI 2008 (pp. 220–229). AUAI Press.Google Scholar
Goodman, N. D., & Lassiter, D. (2015). Probabilistic semantics and pragmatics uncertainty in language and thought. In Lappin, S. & Fox, C. (Eds.), The handbook of contemporary semantic theory (pp. 655–686). Wiley.CrossRefGoogle Scholar
Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. (2008b). A rational analysis of rule-based concept learning. Cognitive Science, 32(1), 108–154.CrossRefGoogle ScholarPubMed
Goodman, N. D., Tenenbaum, J. B., & Gerstenberg, T. (2015). Concepts in a probabilistic language of thought. In Margolis, E. & Laurence, S. (Eds.), Concepts: New directions (pp. 623–654). MIT Press.CrossRefGoogle Scholar
Goodman, N. D., Ullman, T. D., & Tenenbaum, J. B. (2011). Learning a theory of causality. Psychological Review, 118(1), 110–199.CrossRefGoogle ScholarPubMed
Gordon, R. D., & Irwin, D. E. (1996). What's in an object file? Evidence from priming studies. Perception and Psychophysics, 58(8), 1260–1277.CrossRefGoogle Scholar
Gordon, R. D., & Irwin, D. E. (2000). The role of physical and conceptual properties in preserving object continuity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(1), 136–150.Google ScholarPubMed
Gordon, R. D., & Vollmer, S. D. (2010). Episodic representation of diagnostic and non-diagnostic object color. Visual Cognition, 18(5), 728–750.CrossRefGoogle ScholarPubMed
Gordon, R. D., Vollmer, S. D., & Frankl, M. L. (2008). Object continuity and the transsaccadic representation of form. Perception and Psychophysics, 70, 667–679.CrossRefGoogle ScholarPubMed
Green, E. J. (2019). On the perception of structure. Noûs, 53(3), 564–592.CrossRefGoogle Scholar
Green, E. J., & Quilty-Dunn, J. (2021). What is an object file? British Journal for the Philosophy of Science, 72(3), 665–699.CrossRefGoogle Scholar
Green, E. J. (unpublished). A pluralist perspective on shape constancy.Google Scholar
Gröndahl, T., & Asokan, N. (2022). Do transformers use variable binding? ArXiv. doi:10.48550/2203.00162Google Scholar
Hafri, A., Bonner, M. F., Landau, B., & Firestone, C. (2021). A phone in a basket looks like a knife in a cup: The perception of abstract relations. PsyArXiv. doi:10.31234/osf.io/jx4ygGoogle Scholar
Hafri, A., & Firestone, C. (2021). The perception of relations. Trends in Cognitive Sciences, 25(6), 475–492.CrossRefGoogle ScholarPubMed
Handley, S. J., Newstead, S. E., & Trippas, D. (2011). Logic, beliefs, and instruction: A test of the default interventionist account of belief bias. Journal of Experimental Psychology: Learning, Memory, and Cognition, 37(1), 28–43.Google ScholarPubMed
Handley, S. J., & Trippas, D. (2015). Dual processes and the interplay between knowledge and structure: A new parallel processing model. Psychology of learning and motivation (Vol. 62, pp. 33–58). Academic Press.Google Scholar
Harman, G. (1973). Thought. Princeton University Press.Google Scholar
Harris, D. W. (2022). Semantics without semantic content. Mind & Language, 37(3), 304–328.CrossRefGoogle Scholar
Harrison, S. A., & Tong, F. (2009). Decoding reveals the contents of visual working memory in early visual areas. Nature, 458(7238), 632–635.CrossRefGoogle ScholarPubMed
Haugeland, J. (1985). Artificial intelligence: The very idea. MIT Press.Google Scholar
Hein, E., Stepper, M. Y., Hollingworth, A., & Moore, C. M. (2021). Visual working memory content influences correspondence processes. Journal of Experimental Psychology: Human Perception and Performance, 47(3), 331–343.Google ScholarPubMed
Heinke, D., Wachman, P., van Zoest, W., & Leek, E. C. (2021). A failure to learn object shape geometry: Implications for convolutional neural networks as plausible models of biological vision. Vision Research, 189, 81–92.CrossRefGoogle ScholarPubMed
Hinzen, W., & Sheehan, M. (2013). The philosophy of generative grammar. Oxford University Press.Google Scholar
Hollingworth, A., & Franconeri, S. L. (2009). Object correspondence across brief occlusion is established on the basis of both spatiotemporal and surface feature cues. Cognition, 113(2), 150–166.CrossRefGoogle ScholarPubMed
Hollingworth, A., & Rasmussen, I. P. (2010). Binding objects to locations: The relationship between object files and visual working memory. Journal of Experimental Psychology: Human Perception and Performance, 36(3), 543–564.Google ScholarPubMed
Howard, S. R., Avargues-Weber, A., Garcia, J. E., Greentree, A. D., & Dyer, A. G. (2018). Numerical ordering of zero in honeybees. Science (New York, N.Y.), 360, 1124–1126.CrossRefGoogle Scholar
Howarth, S., Handley, S., & Polito, V. (2021). Uncontrolled logic: Intuitive sensitivity to logical structure in random responding. Thinking & Reasoning, 28(1), 1–36. doi:10.1080/13546783.2021.1934119Google Scholar
Howarth, S., Handley, S. J., & Walsh, C. (2016). The logic-bias effect: The role of effortful processing in the resolution of belief–logic conflict. Memory & Cognition, 44(2), 330–349.CrossRefGoogle ScholarPubMed
Hummel, J. E. (2000). Where view-based theories break down: The role of structure in shape perception and object recognition. In Dietrich, E. & Markman, A. (Eds.), Cognitive dynamics: Conceptual change in humans and machines (pp. 157–185). Erlbaum.Google Scholar
Hummel, J. E. (2011). Getting symbols out of a neural architecture. Connection Science, 23(2), 109–118.CrossRefGoogle Scholar
Hummel, J. E. (2013). Object recognition. In Reisburg, D. (Ed.), Oxford handbook of cognitive psychology (pp. 32–46). Oxford University Press.Google Scholar
Hutto, D. D., & Myin, E. (2013). Radicalizing enactivism: Basic minds without content. MIT Press.Google Scholar
Jiang, H. (2020). Effects of transient and nontransient changes of surface feature on object correspondence. Perception, 49(4), 452–467.CrossRefGoogle ScholarPubMed
Johnson, E. D., Tubau, E., & De Neys, W. (2016). The doubting system 1: Evidence for automatic substitution sensitivity. Acta Psychologica, 164, 56–64.CrossRefGoogle ScholarPubMed
Johnson-Laird, P. (2006). How we reason. Oxford University Press.Google Scholar
Jordan, K. E., Clark, K., & Mitroff, S. M. (2010). See an object, hear an object file: Object correspondence transcends sensory modality. Visual Cognition, 18(4), 492–503.CrossRefGoogle Scholar
Kahneman, D., Treisman, A., & Gibbs, B. J. (1992). The reviewing of object files: Object-specific integration of information. Cognitive Psychology, 24(2), 175–219.CrossRefGoogle ScholarPubMed
Kaiser, D., Quek, G. L., Cichy, R. M., & Peelen, M. V. (2019). Object vision in a structured world. Trends in Cognitive Sciences, 23(8), 672–685.CrossRefGoogle Scholar
Katz, Y., Goodman, N. D., Kersting, K., Kemp, C., & Tenenbaum, J. B. (2008). Modeling semantic cognition as logical dimensionality reduction. Proceedings of the Cognitive Science Society, 30, 71–76.Google Scholar
Kelemen, D., & Carey, S. (2007). The essence of artifacts: Developing the design stance. In Margolis, E. & Laurence, S. (Eds.), Creations of the mind: Theories of artifacts and their representation (pp. 212–230). Oxford University Press.Google Scholar
Kemp, C. (2012). Exploring the conceptual universe. Psychological Review, 119(4), 685–722.CrossRefGoogle ScholarPubMed
Khaligh-Razavi, S. M., & Kriegeskorte, N. (2014). Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Computational Biology, 10(11), e1003915.CrossRefGoogle Scholar
Kibbe, M. M., & Leslie, A. M. (2011). What do infants remember when they forget? Location and identity in 6-month-olds’ memory for objects. Psychological Science, 22(12), 1500–1505.CrossRefGoogle ScholarPubMed
Kibbe, M. M., & Leslie, A. M. (2019). Conceptually rich, perceptually sparse: Object representations in 6-month-old infants’ working memory. Psychological Science, 30(3), 362–375.CrossRefGoogle ScholarPubMed
Kosslyn, S. M. (1980). Image and mind. Harvard University Press.Google Scholar
Kosslyn, S. M., Ball, T. M., & Reiser, B. J. (1978). Visual images preserve metric spatial information: Evidence from studies of imagery scanning. Journal of Experimental Psychology: Human Perception and Performance, 4, 47–60.Google Scholar
Kosslyn, S. M., Thompson, W. L., & Ganis, G. (2006). The case for mental imagery. Oxford University Press.CrossRefGoogle Scholar
Kriegeskorte, N. (2015). Deep neural networks: A new framework for modeling biological vision and brain information processing. Annual Review of Vision Science, 1(1), 417–446.CrossRefGoogle ScholarPubMed
Kulvicki, J. (2015). Maps, pictures, and predication. Ergo, 2(7). doi:10.3998/ergo.12405314.0002.007Google Scholar
Kurdi, B., & Banaji, M. R. (2017). Repeated evaluative pairings and evaluative statements: How effectively do they shift implicit attitudes? Journal of Experimental Psychology: General, 146(2), 194.CrossRefGoogle ScholarPubMed
Kurdi, B., & Banaji, M. R. (2019). Attitude change via repeated evaluative pairings versus evaluative statements: Shared and unique features. Journal of Personality and Social Psychology, 116(5), 681.CrossRefGoogle ScholarPubMed
Kurdi, B., & Dunham, Y. (2021). Sensitivity of implicit evaluations to accurate and erroneous propositional inferences. Cognition, 214, 104792.CrossRefGoogle ScholarPubMed
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, e253.CrossRefGoogle ScholarPubMed
Lambert, M. L., & Osvath, M. (2018). Comparing chimpanzees’ preparatory responses to known and unknown future outcomes. Biology Letters, 14(9), 20180499. https://doi.org/10.1098/rsbl.2018.0499CrossRefGoogle ScholarPubMed
Lea, R. B. (1995). On-line evidence for elaborative logical inferences in text. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21(6), 1469–1482.Google ScholarPubMed
Lea, R. B., Mulligan, E. J., & Walton, J. L. (2005). Accessing distant premise information: How memory feeds reasoning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31(3), 387–395.Google ScholarPubMed
Leahy, B., & Carey, S. (2020). The acquisition of modal concepts. Trends in Cognitive Sciences, 24(1), 65–78.CrossRefGoogle ScholarPubMed
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444.CrossRefGoogle ScholarPubMed
Leslie, A. M., Xu, F., Tremoulet, P. D., & Scholl, B. J. (1998). Indexing and the object concept: Developing what and where systems. Trends in Cognitive Sciences, 2(1), 10–18.CrossRefGoogle ScholarPubMed
Liang, P., Jordan, M., & Klein, D. (2010). Learning programs: A hierarchical Bayesian approach. Proceedings of the 27th International Conference on Machine Learning, pp. 639–646.Google Scholar
Lieder, F., & Griffiths, T. L. (2020). Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources. Behavioral and Brain Sciences, 43, 1–60.CrossRefGoogle Scholar
Lin, Y., Li, J., Gertner, Y., Ng, W., Fisher, C. L., & Baillargeon, R. (2021). How do the object-file and physical-reasoning systems interact? Evidence from priming effects with object arrays or novel labels. Cognitive Psychology, 125, 101368. doi:10.1016/j.cogpsych.2020.101368CrossRefGoogle ScholarPubMed
Lonnqvist, B., Bornet, A., Doerig, A., & Herzog, M. H. (2021). A comparative biology approach to DNN modeling of vision: A focus on differences, not similarities. Journal of Vision, 21(10), 1–10.CrossRefGoogle Scholar
Loukola, O. J., Perry, C. J., Coscos, L., & Chittka, L. (2017). Bumblebees show cognitive flexibility by improving on an observed complex behavior. Science (New York, N.Y.), 355, 833–836.CrossRefGoogle Scholar
Lovett, A., & Franconeri, S. L. (2017). Topological relations between objects are categorically coded. Psychological Science, 28(10), 1408–1418.CrossRefGoogle ScholarPubMed
Machery, E. (2016). The amodal brain and the offloading hypothesis. Psychonomic Bulletin & Review, 23, 1090–1095.CrossRefGoogle ScholarPubMed
Mandelbaum, E. (2013). Numerical architecture. Topics in Cognitive Science, 5(2), 367–386.CrossRefGoogle ScholarPubMed
Mandelbaum, E. (2016). Attitude, inference, association: On the propositional structure of implicit bias. Noûs, 50(3), 629–658.CrossRefGoogle Scholar
Mandelbaum, E. (2018). Seeing and conceptualizing: Modularity and the shallow contents of perception. Philosophy and Phenomenological Research, 97(2), 267–283.CrossRefGoogle Scholar
Mandelbaum, E. (2020). Assimilation and control: Belief at the lowest levels. Philosophical Studies, 177(2), 441–447.CrossRefGoogle Scholar
Mandelbaum, E., Dunham, Y., Feiman, R., Firestone, C., Green, E. J., Harris, D. W., … Quilty-Dunn, J. (under review). Problems and mysteries of the many languages of thought.Google Scholar
Mann, T. C., Cone, J., Heggeseth, B., & Ferguson, M. J. (2019). Updating implicit impressions: New evidence on intentionality and the affect misattribution procedure. Journal of Personality and Social Psychology, 116(3), 349–374.CrossRefGoogle ScholarPubMed
Mann, T. C., & Ferguson, M. J. (2015). Can we undo our first impressions? The role of reinterpretation in reversing implicit evaluations. Journal of Personality and Social Psychology, 108(6), 823–849.CrossRefGoogle ScholarPubMed
Mann, T. C., & Ferguson, M. J. (2017). Reversing implicit first impressions through reinterpretation after a two-day delay. Journal of Experimental Social Psychology, 68, 122–127.CrossRefGoogle ScholarPubMed
Marcus, G. F. (2001). The algebraic mind. MIT Press.CrossRefGoogle Scholar
Marcus, G. F. (2018). Deep learning: A critical appraisal. arXiv. doi:1801.00631Google Scholar
Markov, Y. A., Tiurina, N. A., & Utochkin, I. S. (2019). Different features are stored independently in visual working memory but mediated by object-based representations. Acta Psychologica, 197, 52–63.CrossRefGoogle ScholarPubMed
Markov, Y. A., Utochkin, I. S., & Brady, T. F. (2021). Real-world objects are not stored in holistic representations in visual working memory. Journal of Vision, 21(3), 1–24.CrossRefGoogle Scholar
Markovits, H., & Nantel, G. (1989). The belief-bias effect in the production and evaluation of logical conclusions. Memory & Cognition, 17(1), 11–17.CrossRefGoogle ScholarPubMed
Marr, D., & Nishihara, H. K. (1978). Representation and recognition of the spatial organization of three-dimensional shapes. Proceedings of the Royal Society of London Series B: Biological Sciences, 200(1140), 269–294.Google ScholarPubMed
Martin, A. E., & Doumas, L. A. A. (2020). Tensors and compositionality in neural systems. Philosophical Transactions of the Royal Society B, 375, 20190306. doi:10.1098/rstb.2019.0306CrossRefGoogle ScholarPubMed
Marvel, C. L., & Desmond, J. E. (2012). From storage to manipulation: How the neural correlates of verbal working memory reflect varying demands on inner speech. Brain and Language, 120(1), 42–51.CrossRefGoogle ScholarPubMed
Meck, W. H., & Church, R. M. (1983). A mode control model of counting and timing processes. Journal of Experimental Psychology: Animal Behavior Processes, 9(3), 320–334.Google ScholarPubMed
Miller, J., Naderi, S., Mullinax, C., & Phillips, J. L. (2022). Attention is not enough. Proceedings of the Annual Meeting of the Cognitive Science Society, 44(44), 3147–3153.Google Scholar
Mitroff, S. R., Scholl, B. J., & Wynn, K. (2005). The relationship between object files and conscious perception. Cognition, 96, 67–92.CrossRefGoogle ScholarPubMed
Mody, S., & Carey, S. (2016). The emergence of reasoning by the disjunctive syllogism in early childhood. Cognition, 154, 40–48.CrossRefGoogle ScholarPubMed
Mollica, F., & Piantadosi, S. (2015). Towards semantically rich and recursive word learning models. Proceedings of the cognitive science conference (Vol. 37).Google Scholar
Moore, C. M., Stephens, T., & Hein, E. (2010). Features, as well as space and time, guide object persistence. Psychonomic Bulletin & Review, 17(5), 731–736.CrossRefGoogle ScholarPubMed
Morgan, L. C. (1894). An introduction to comparative psychology. Walter Scott.CrossRefGoogle Scholar
Mylopoulos, M. (2021). The modularity of the motor system. Philosophical Explorations, 24(3), 376–393.CrossRefGoogle Scholar
Nichols, S. (2021). Rational rules: Towards a theory of moral learning. Oxford University Press.CrossRefGoogle Scholar
Nonaka, S., Majima, K., Aoki, S. C., & Kamitani, Y. (2021). Brain hierarchy score: Which deep neural networks are hierarchically brain-like? iScience, 24, 103013.CrossRefGoogle ScholarPubMed
Oaksford, M., & Chater, N. (2009). Précis of Bayesian rationality: The probabilistic approach to human reasoning. Behavioral and Brain Sciences, 32(1), 69–84.CrossRefGoogle ScholarPubMed
O'Callaghan, C. (forthcoming). Crossmodal identification. In Mroczko-Wąsowicz, A. & Grush, R. (Eds.), Sensory individuals, properties, and perceptual objects. Oxford University Press.Google Scholar
Öhlschläger, S., & Võ, M. L.-H. (2020). Development of scene knowledge: Evidence from explicit and implicit scene knowledge measures. Journal of Experimental Child Psychology, 194(104782), 1–21.CrossRefGoogle ScholarPubMed
Overlan, M. C., Jacobs, R. A., & Piantadosi, S. T. (2017). Learning abstract visual concepts via probabilistic program induction in a language of thought. Cognition, 168, 320–334.CrossRefGoogle Scholar
Palangi, H., Smolensky, P., He, X., & Deng, L. (2018). Question-answering with grammatically-interpretable representations. The Thirty-Second AAAI Conference on Artificial Intelligence.CrossRefGoogle Scholar
Papineau, D. (2003). Human minds. Royal Institute of Philosophy Supplements, 53, 159–183.CrossRefGoogle Scholar
Penn, D. C., Holyoak, K. J., & Povinelli, D. J. (2008). Darwin's mistake: Explaining the discontinuity between human and nonhuman minds. Behavioral and Brain Sciences, 31, 109–130.CrossRefGoogle ScholarPubMed
Pennycook, G., Trippas, D., Handley, S. J., & Thompson, V. A. (2014). Base rates: Both neglected and intuitive. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(2), 544–554.Google ScholarPubMed
Pepperberg, I. M., Gray, S. L., Cornero, F. M., Mody, S., & Carey, S. (2019). Logical reasoning by a grey parrot (Psittacus erithacus)? A case study of the disjunctive syllogism. Behaviour, 156, 409–445.CrossRefGoogle Scholar
Perner, J., & Leahy, B. (2016). Mental files in development: Dual naming, false belief, identity and intensionality. Review of Philosophy and Psychology, 7, 491–508.CrossRefGoogle Scholar
Peters, B., & Kriegeskorte, N. (2021). Capturing the objects of vision with neural networks. Nature Human Behaviour, 5(9), 1127–1144.CrossRefGoogle ScholarPubMed
Piantadosi, S. T., & Jacobs, R. A. (2016). Four problems solved by the probabilistic language of thought. Current Directions in Psychological Science, 25(1), 54–59.CrossRefGoogle Scholar
Piantadosi, S. T., Tenenbaum, J. B., & Goodman, N. D. (2012). Bootstrapping in a language of thought: A formal model of numerical concept learning. Cognition, 123(2), 199–217.CrossRefGoogle Scholar
Piantadosi, S. T., Tenenbaum, J. B., & Goodman, N. D. (2016). The logical primitives of thought: Empirical foundations for compositional cognitive models. Psychological Review, 123(4), 392–424.CrossRefGoogle ScholarPubMed
Pietroski, P. M. (2018). Conjoining meanings: Semantics without truth values. Oxford University Press.CrossRefGoogle Scholar
Pinker, S. (1994). The language instinct. William Morrow.CrossRefGoogle Scholar
Pollatsek, A., Rayner, K., & Collins, W. E. (1984). Integrating pictorial information across eye movements. Journal of Experimental Psychology: General, 113(3), 426–442.CrossRefGoogle ScholarPubMed
Pomiechowska, B., & Gliga, T. (2021). Nonverbal category knowledge limits the amount of information encoded in object representations: EEG evidence from 12-month-old infants. Royal Society Open Science, 8(200782), 1–17.CrossRefGoogle ScholarPubMed
Porot, N. J. (2019). Some non-human languages of thought (Doctoral dissertation). CUNY Graduate Center.Google Scholar
Premack, D. (2007). Human and animal cognition: Continuity and discontinuity. Proceedings of the National Academy of Sciences of the United States of America, 104(35), 13861–13867.CrossRefGoogle ScholarPubMed
Prinz, J. J. (2002). Furnishing the mind: Concepts and their perceptual basis. MIT Press.CrossRefGoogle Scholar
Pylyshyn, Z. W. (1973). What the mind's eye tells the mind's brain: A critique of mental imagery. Psychological Bulletin, 80(1), 1.CrossRefGoogle Scholar
Pylyshyn, Z. W. (2002). Mental imagery: In search of a theory. Behavioral and Brain Sciences, 25, 157–238.CrossRefGoogle ScholarPubMed
Pylyshyn, Z. W. (2003). Seeing and visualizing: It's not what you think. MIT Press.CrossRefGoogle Scholar
Pylyshyn, Z. W. (2004). Some puzzling findings in multiple-object tracking: I. Tracking without keeping track of object identities. Visual Cognition, 11(7), 801–822.CrossRefGoogle Scholar
Pylyshyn, Z. W. (2007). Things and places: How the mind connects with the world. MIT Press.CrossRefGoogle Scholar
Pylyshyn, Z. W., & Storm, R. (1988). Tracking multiple independent targets: Evidence for a parallel tracking mechanism. Spatial Vision, 3(3), 179–197.CrossRefGoogle ScholarPubMed
Quilty-Dunn, J. (2020a). Concepts and predication from perception to cognition. Philosophical Issues, 30(1), 273–292.CrossRefGoogle Scholar
Quilty-Dunn, J. (2020b). Is iconic memory iconic? Philosophy & Phenomenological Research, 101(3), 660–682.CrossRefGoogle Scholar
Quilty-Dunn, J. (2020c). Perceptual pluralism. Noûs, 54(4), 807–838.CrossRefGoogle Scholar
Quilty-Dunn, J. (2021). Polysemy and thought: Toward a generative theory of concepts. Mind & Language, 36, 158–185.CrossRefGoogle Scholar
Quilty-Dunn, J., & Green, E. J. (2023). Perceptual attribution and perceptual reference. Philosophy and Phenomenological Research, 106(2), 273–298.CrossRefGoogle Scholar
Quilty-Dunn, J., & Mandelbaum, E. (2018a). Inferential transitions. Australasian Journal of Philosophy, 96(3), 532–547.CrossRefGoogle Scholar
Quilty-Dunn, J., & Mandelbaum, E. (2018b). Against dispositionalism: Belief in cognitive science. Philosophical Studies, 175(9), 2353–2372.CrossRefGoogle Scholar
Quilty-Dunn, J., & Mandelbaum, E. (2020). Non-inferential transitions: Imagery and association. In Chan, T. & Nes, A. (Eds.), Inference and consciousness (pp. 151–171). Routledge.Google Scholar
Quiroga, R. Q. (2020). No pattern separation in the human hippocampus. Trends in Cognitive Sciences, 24(12), 994–1007.CrossRefGoogle Scholar
Recanati, F. (2012). Mental files. Oxford University Press.CrossRefGoogle Scholar
Redshaw, J., & Suddendorf, T. (2016). Children's and apes’ preparatory responses to two mutually exclusive possibilities. Current Biology, 26, 1758–1762.CrossRefGoogle ScholarPubMed
Rescorla, M. (2009). Cognitive maps and the language of thought. British Journal for the Philosophy of Science, 60(2), 377–407.CrossRefGoogle Scholar
Reverberi, C., Pischedda, D., Burigo, M., & Cherubini, P. (2012). Deduction without awareness. Acta Psychologica, 139(1), 244–253.CrossRefGoogle ScholarPubMed
Richard, A. M., Luck, S. J., & Hollingworth, A. (2008). Establishing object correspondence across eye movements: Flexible use of spatiotemporal and surface feature information. Cognition, 109(1), 66–88.CrossRefGoogle ScholarPubMed
Rips, L. J. (1994). The psychology of proof. MIT Press.CrossRefGoogle Scholar
Rivera-Aparicio, J., Yu, Q., & Firestone, C. (2021). Hi-def memories of lo-def scenes. Psychonomic Bulletin & Review, 28, 928–936.CrossRefGoogle ScholarPubMed
Romano, S., Salles, A., Amalric, M., Dehaene, S., Sigman, M., & Figueira, S. (2018). Bayesian validation of grammar productions for the language of thought. PLoS ONE, 13(7), e0200420.CrossRefGoogle ScholarPubMed
Roumi, F. A., Marti, S., Wang, L., Amalric, M., & Dehaene, S. (2021). Mental compression of spatial sequences in human working memory using numerical and geometrical primitives. Neuron, 109, 2627–2639.CrossRefGoogle ScholarPubMed
Rumelhart, D. E., & McClelland, J. L. (1986). Parallel distributed processing, volume 1: Explorations in the microstructure of cognition: Foundations. MIT Press.CrossRefGoogle Scholar
Rydell, R. J., & McConnell, A. R. (2006). Understanding implicit and explicit attitude change: A systems of reasoning analysis. Journal of Personality and Social Psychology, 91(6), 995–1008.CrossRefGoogle Scholar
Sablé-Meyer, M., Ellis, K., Tenenbaum, J., & Dehaene, S. (2021a). A language of thought for the mental representation of geometric shapes. PsyArXiv. doi:10.31234/osf.io/28mg4Google Scholar
Sablé-Meyer, M., Fagot, J., Caparos, S., van Kerkoerle, T., Amalric, M., & Dehaene, S. (2021b). Sensitivity to geometric shape regularity in humans and baboons: A putative signature of human singularity. Proceedings of the National Academy of Sciences of the United States of America, 118(16), e2023123118.CrossRefGoogle ScholarPubMed
Saiki, J., & Hummel, J. E. (1998a). Connectedness and part-relation integration in shape category learning. Memory & Cognition, 26(6), 1138–1156.CrossRefGoogle ScholarPubMed
Saiki, J., & Hummel, J. E. (1998b). Connectedness and the integration of parts with relations in shape perception. Journal of Experimental Psychology: Human Perception and Performance, 24(1), 227–251.Google ScholarPubMed
Schneider, S. (2011). The language of thought: A new philosophical direction. MIT Press.CrossRefGoogle Scholar
Scholl, B. J. (2007). Object persistence in philosophy and psychology. Mind & Language, 22(5), 563–591.CrossRefGoogle Scholar
Scholl, B. J., & Leslie, A. (1999). Explaining the infant's object concept: Beyond the perception/cognition dichotomy. In Lepore, E. & Pylyshyn, Z. W. (Eds.), What is cognitive science? (pp. 26–73). Blackwell.Google Scholar
Scholl, B. J., & Pylyshyn, Z. W. (1999). Tracking multiple items through occlusion: Clues to visual objecthood. Cognitive psychology, 38(2), 259–290.CrossRefGoogle ScholarPubMed
Scholl, B. J., Pylyshyn, Z. W., & Franconeri, S. L. (unpublished). The relationship between property–encoding and object-based attention: Evidence from multiple object tracking.Google Scholar
Schrimpf, M., Kubilius, J., Hong, H., Majaj, N. J., Rajalingham, R., Issa, E. B., … DiCarlo, J. J. (2018). Brain-score: Which artificial neural network for object recognition is most brain-like? BioRxiv. doi:10.1101/407007Google Scholar
Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., … Silver, D. (2020). Mastering atari, go, chess and shogi by planning with a learned model. Nature 588(7839), 604–609.CrossRefGoogle ScholarPubMed
Schwitzgebel, E. (2013). A dispositional approach to attitudes: Thinking outside of the belief box. In Nottelman, N. (Ed.), New essays on belief: Constitution, content, and structure (pp. 75–99). Palgrave Macmillan.CrossRefGoogle Scholar
Shea, N. (2018). Representation in cognitive science. Oxford University Press.CrossRefGoogle Scholar
Shea, N. (2023). Moving beyond content-specific computation in artificial neural networks. Mind & Language, 38(1), 156–177. doi:10.1111/mila.12387CrossRefGoogle Scholar
Shepard, R. N., & Metzler, J. (1971). Mental rotation of three-dimensional objects. Science (New York, N.Y.), 171, 701–703.CrossRefGoogle ScholarPubMed
Shepherd, J. (2021). Intelligent action guidance and the use of mixed representational formats. Synthese, 198(17), 4143–4162.CrossRefGoogle ScholarPubMed
Sloman, S. A. (1996). The empirical case for two systems of reasoning. Psychological Bulletin, 119(1), 3–22.CrossRefGoogle Scholar
Smolensky, P. (1990). Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence, 46, 159–216.CrossRefGoogle Scholar
Smortchkova, J., & Murez, M. (2020). Representational kinds. In Smortchkova, J., Dolega, K., & Schlicht, T. (Eds.), What are mental representations? (pp. 213–241). Oxford University Press.CrossRefGoogle Scholar
Solvi, C., Al-Khudhairy, S. G., & Chittka, L. (2020). Bumble bees display cross-modal object recognition between visual and tactile senses. Science (New York, N.Y.), 367, 910–912.CrossRefGoogle ScholarPubMed
Spelke, E. S. (1990). Principles of object perception. Cognitive Science, 14, 29–56.CrossRefGoogle Scholar
Stavans, M., & Baillargeon, R. (2018). Four-month-old infants individuate and track simple tools following functional demonstrations. Developmental Science, 21, e12500.CrossRefGoogle ScholarPubMed
Stavans, M., Lin, Y., Wu, D., & Baillargeon, R. (2019). Catastrophic individuation failures in infancy: A new model and predictions. Psychological Review, 126(2), 196–225.CrossRefGoogle ScholarPubMed
Stein, T., Kaiser, D., & Peelen, M. V. (2015). Interobject grouping facilitates visual awareness. Journal of Vision, 15(8), 1–11.CrossRefGoogle ScholarPubMed
Strickland, B., & Scholl, B. J. (2015). Visual perception involves event-type representations: The case of containment versus occlusion. Journal of Experimental Psychology: General, 144(3), 570–580.CrossRefGoogle ScholarPubMed
Stupple, E. J., Ball, L. J., Evans, J. S. B., & Kamal-Smith, E. (2011). When logic and belief collide: Individual differences in reasoning times support a selective processing model. Journal of Cognitive Psychology, 23(8), 931–941.CrossRefGoogle Scholar
Suddendorf, T., Crimston, J., & Redshaw, J. (2017). Preparatory responses to socially determined, mutually exclusive possibilities in chimpanzees and children. Biology Letters, 13(6), 20170170. doi:10.1098/rsbl.2017.0170CrossRefGoogle ScholarPubMed
Suddendorf, T., Watson, K., Bogaart, M., & Redshaw, J. (2019). Preparation for certain and uncertain future outcomes in young children and three species of monkey. Developmental Psychobiology, 62(2), 191–201.CrossRefGoogle ScholarPubMed
Surian, L., & Caldi, S. (2010). Infants’ individuation of agents and inert objects. Developmental Science, 13(1), 143–150.CrossRefGoogle ScholarPubMed
Szabo, Z. (2011). The case for compositionality. In Hinzen, W., Machery, E., & Werning, M. (Eds.), The Oxford handbook on compositionality (pp. 64–80). Oxford University Press.Google Scholar
Thompson, V. A., & Johnson, S. C. (2014). Conflict, metacognition, and analytic thinking. Thinking & Reasoning, 20(2), 215–244.CrossRefGoogle Scholar
Thompson, V. A., Turner, J. A. P., & Pennycook, G. (2011). Intuition, reason, and metacognition. Cognitive Psychology, 63(3), 107–140.CrossRefGoogle ScholarPubMed
Tikhonenko, P. A., Brady, T. F., & Utochkin, I. S. (2021). Independent storage of real-world object features is visual rather than verbal in nature. PsyArXiv. doi:10.31234/osf.io/d9c4hGoogle Scholar
Tolman, E. C. (1948). Cognitive maps in rats and men. Psychological Review, 55(4), 189–208.CrossRefGoogle ScholarPubMed
Toribio, J. (2011). Compositionality, iconicity, and perceptual nonconceptualism. Philosophical Psychology, 24(2), 177–193.CrossRefGoogle Scholar
Travis, C. (2001). Unshadowed thought. Harvard University Press.Google Scholar
Trippas, D., Handley, S. J., Verde, M. F., & Morsanyi, K. (2016). Logic brightens my day: Evidence for implicit sensitivity to logical validity. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42(9), 1448–1457.Google ScholarPubMed
Trippas, D., Thompson, V. A., & Handley, S. J. (2017). When fast logic meets slow belief: Evidence for a parallel-processing model of belief bias. Memory & Cognition, 45(4), 539–552.CrossRefGoogle ScholarPubMed
Tuli, S., Dasgupta, I., Grant, E., & Griffiths, T. L. (2021). Are convolutional neural networks or transformers more like human vision? ArXiv. doi:2105.07197Google Scholar
Ullman, S. (1996). High-level vision. MIT Press.CrossRefGoogle Scholar
Ullman, T. D., Goodman, N. D., & Tenenbaum, J. B. (2012). Theory learning as stochastic search in the language of thought. Cognitive Development, 27(4), 455–480.CrossRefGoogle Scholar
Utochkin, I. S., & Brady, T. F. (2020). Independent storage of different features of real-world objects in long-term memory. Journal of Experimental Psychology: General, 149(3), 530–549.CrossRefGoogle ScholarPubMed
Van Dessel, P., De Houwer, J., Gast, A., Smith, C. T., & De Schryver, M. (2016). Instructing implicit processes: When instructions to approach or avoid influence implicit but not explicit evaluation. Journal of Experimental Social Psychology, 63, 1–9.CrossRefGoogle Scholar
Van Dessel, P., Gawronski, B., Smith, C. T., & De Houwer, J. (2017a). Mechanisms underlying approach-avoidance instruction effects on implicit evaluation: Results of a preregistered adversarial collaboration. Journal of Experimental Social Psychology, 69, 23–32.CrossRefGoogle Scholar
Van Dessel, P., Mertens, G., Smith, C. T., & De Houwer, J. (2017b). The mere exposure instruction effect. Experimental Psychology, 64(5), 299–314.CrossRefGoogle ScholarPubMed
Van Dessel, P., Ye, Y., & De Houwer, J. (2019). Changing deep-rooted implicit evaluation in the blink of an eye: Negative verbal information shifts automatic liking of Gandhi. Social Psychological and Personality Science, 10(2), 266–273.CrossRefGoogle Scholar
Varley, R. (2014). Reason without much language. Language Sciences, 46, 232–244.CrossRefGoogle Scholar
Vasas, V., & Chittka, L. (2019). Insect-inspired sequential inspection strategy enables an artificial network of four neurons to estimate numerosity. iScience, 11, 85–92.CrossRefGoogle ScholarPubMed
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.Google Scholar
Võ, M. L.-H. (2021). The meaning and structure of scenes. Vision Research, 181, 10–20.CrossRefGoogle ScholarPubMed
Võ, M. L.-H., Bettcher, S. E. P., & Draschkow, D. (2019). Reading scenes: How scene grammar guides attention and aids perception in real-world environments. Current Opinion in Psychology, 29, 205–210.CrossRefGoogle ScholarPubMed
Võ, M. L.-H., & Henderson, J. M. (2009). Does gravity matter? Effects of semantic and syntactic inconsistencies on the allocation of attention during scene perception. Journal of Vision, 9(3), 1–15.CrossRefGoogle ScholarPubMed
Võ, M. L.-H., & Wolfe, J. M. (2013). Different electrophysiological signatures of semantic and syntactic scene processing. Psychological Science, 24(9), 1816–1823.CrossRefGoogle ScholarPubMed
Vul, E., Goodman, N., Griffiths, T. L., & Tenenbaum, J. B. (2014). One and done? Optimal decisions from very few samples. Cognitive Science, 38(4), 599–637.CrossRefGoogle ScholarPubMed
Wang, B., Cao, X., Theeuwes, J., Olivers, C. N. L., & Wang, Z. (2017). Separate capacities for storing different features in visual working memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(2), 226–236.Google ScholarPubMed
Wang, L., Amalric, M., Fang, W., Jiang, X., Pallier, C., Figueira, S., … Dehaene, S. (2019). Representation of spatial sequences using nested rules in human prefrontal cortex. NeuroImage, 186, 245–255.CrossRefGoogle ScholarPubMed
Webb, T. W., Sinha, I., & Cohen, J. D. (2021). Emergent symbols through binding in external memory. ArXiv. doi:10.48550/arXiv.2012.14601Google Scholar
Weise, C., Ortiz, C. C., & Tibbetts, E. A. (2022). Paper wasps form abstract concept of “same and different.” Proceedings of the Royal Society of London Series B: Biological Sciences, 289(1979), 20221156. https://doi.org/10.1098/rspb.2022.1156Google ScholarPubMed
Wood, J. N., & Wood, S. M. W. (2020). One-shot learning of view-invariant object representations in newborn chicks. Cognition, 199, 104192. doi:10.1016/j.cognition.2020.104192CrossRefGoogle ScholarPubMed
Xu, F. (2019). Toward a rational constructivist theory of cognitive development. Psychological Review, 126(6), 841–864.CrossRefGoogle Scholar
Xu, F., & Carey, S. (1996). Infants’ metaphysics: The case of numerical identity. Cognitive Psychology, 30(2), 111–153.CrossRefGoogle ScholarPubMed
Xu, Y. (2017). Reevaluating the sensory account of visual working memory storage. Trends in Cognitive Sciences, 21(10), 794–815.CrossRefGoogle ScholarPubMed
Xu, Y. (2020). Revisit once more the sensory storage account of visual working memory. Visual Cognition, 5–8, 433–446.CrossRefGoogle Scholar
Xu, Y., & Vaziri-Pashkam, M. (2021a). Examining the coding strength of object identity and nonidentity features in human occipito-temporal cortex and convolutional neural networks. Journal of Neuroscience, 41(19), 4234–4252.CrossRefGoogle ScholarPubMed
Xu, Y., & Vaziri-Pashkam, M. (2021b). Limits to visual representational correspondence between convolutional neural networks and the human brain. Nature Communications, 12(2065), 1–16.Google ScholarPubMed
Xu, Y., Zhou, X., Chen, S., & Li, F. (2019). Deep learning for multiple-object tracking: A survey. IET Computer Vision, 13(4), 355–368.CrossRefGoogle Scholar
Yamins, D. L., & DiCarlo, J. J. (2016). Using goal-driven deep learning models to understand sensory cortex. Nature Neuroscience, 19(3), 356–365.CrossRefGoogle ScholarPubMed
Yassa, M. A., & Stark, C. E. L. (2011). Pattern separation in the hippocampus. Trends in Neurosciences, 34(10), 515–525.CrossRefGoogle ScholarPubMed
Ye, X., & Durrett, G. (2022). The unreliability of explanations in few-shot in-context learning. ArXiv. doi:2205.03401Google Scholar
Yildirim, I., & Jacobs, R. A. (2015). Learning multisensory representations for auditory-visual transfer of sequence category knowledge: A probabilistic language of thought approach. Psychonomic Bulletin & Review, 22(3), 673–686.CrossRefGoogle ScholarPubMed
Zettlemoyer, L. S., & Collins, M. (2005). Learning to map sentences to logical form: Structured classification with probabilistic categorical grammars. Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, pp. 658–666.Google Scholar
Zhou, K., Luo, H., Zhou, T., Zhuo, Y., & Chen, L. (2010). Topological change disturbs object continuity in attentive tracking. Proceedings of the National Academy of Sciences of the United States of America, 107(50), 21920–21924.CrossRefGoogle ScholarPubMed
Zhu, Y., Gao, T., Fan, L., Huang, S., Edmonds, M., Liu, H., … Zhu, S. C. (2020). Dark, beyond deep: A paradigm shift to cognitive AI with humanlike common sense. Engineering, 6(3), 310–345.CrossRefGoogle Scholar