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Is language-of-thought the best game in the town we live?

Published online by Cambridge University Press:  28 September 2023

Gary Lupyan*
Affiliation:
University of Wisconsin-Madison, Madison, WI, USA. [email protected]; http://sapir.psych.wisc.edu

Abstract

There are towns in which language-of-thought (LoT) is the best game. But do we live in one? I go through three properties that characterize the LoT hypothesis: Discrete constituents, role-filler independence, and logical operators, and argue that in each case predictions from the LoT hypothesis are a poor fit to actual human cognition. As a hypothesis of what human cognition ought to be like, LoT departs from empirical reality.

Type
Open Peer Commentary
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

The effort by Quilty-Dunn et al. to evaluate the language-of-thought hypothesis (LoTH) in light of what has been learned since Fodor's original formulation is commendable. But although it is possible to interpret some behaviors as being compatible with LoT, LoT remains a poor way to understand human cognition. If the target article is the “strongest article-sized empirical case for LoTH” (target article, sect. 1, para. 4), the case of LoT is rather weak.

Let us examine three properties of LoTH. For each, I will consider what we might expect if the property actually holds of human cognition and what we instead tend to find. The reasoning applies to the remaining three properties, but space prohibits further explication.

Discrete constituents: It is true that the English sentence “That is a pink square object” can be decomposed into constituents like “pink” and “square” that can be plugged into other sentences to convey something of the same meaning. Two problems. First, the authors are making a case for discrete constituents of thought, but support their core argument with examples from language. It is one thing to show that language has certain properties. It is quite another to show that these properties characterize thoughts (Lupyan, Reference Lupyan2016; Mahowald et al., Reference Mahowald, Ivanova, Blank, Kanwisher, Tenenbaum and Fedorenko2023; Malt & Majid, Reference Malt and Majid2013; Malt et al., Reference Malt, Gennari, Imai, Ameel, Saji, Majid, Margolis and Laurence2015). Supporting the latter would require showing that underlying our language use are discrete concepts (if one holds onto Fodor's extreme nativism, these concepts are also innate – an even higher bar). Evidence against such a view is too lengthy to review here (Levinson, Reference Levinson, Nuyts and Pederson1997; Lupyan & Zettersten, Reference Lupyan, Zettersten, Sera and Koenig2021; Malt & Majid, Reference Malt and Majid2013), but consider the fuzziness and context-dependence of even the easiest-to-define concepts like ODD, EVEN, and TRIANGLE (Lupyan, Reference Lupyan2013, Reference Lupyan2015). Second, even language may not be as discrete as is often assumed. To us, literate English-speaking scholars with a habit of reflecting on language as an external artifact, the idea that it is composed of discrete parts may seem self-evident. But this may speak more to what it can be than what it typically is. For example, literate, but not illiterate children can count words in a spoken sentence (Matute et al., Reference Matute, Montiel, Pinto, Rosselli, Ardila and Zarabozo2012; Olson, Reference Olson, Amsel and Byrnes2002) – a surprising result if natural language simply maps onto discrete constituents of thought.

Role-filler independence: John is the agent of “John loves Mary” in the same way that Mary is the agent of “Mary loves John.” Does this mean that role-filler independence is a characteristic property of our thoughts? Even if it were, this does not mean that role-filler independence is a core property of (nonlinguistic) cognition. But never mind that. Agent together with patient does indeed turn out to be perhaps the strongest example of role-filler independence (Rissman & Majid, Reference Rissman and Majid2019). However, Rissman and Majid go on to argue that evidence for the abstract nature of other seemingly basic roles like instrument and goal is rather mixed. Even for agent, role-filler independence is more subtle than it seems. In a nonlinguistic task requiring participants to categorize based on agent/patient relationships, a sizable minority (~40%) failed to induce it in the allotted time (Rissman & Lupyan, Reference Rissman and Lupyan2022). Those who did, generalized agency according to how similar the test items were to the items they saw at training as well as to the test item's similarity to agent prototypes (Dowty, Reference Dowty1991). It seems that not all agents are equally good agents, a surprising result if there is true role-filler independence.

The authors correctly point out that connectionist models “simulate compositionality, but fail to preserve identity of the original representational elements” (target article, sect. 2, para. 7). The authors do not consider the possibility that human compositionality may be simulated as well (Dekker, Otto, & Summerfield, Reference Dekker, Otto and Summerfield2022; Lahav, Reference Lahav1989).

Lastly, logical operators such as AND, IF, and OR are a “hallmark of LoT architectures” (target article, sect. 2, para. 10). Yet children under the age of about five have a notoriously difficult time learning categories based on even the simplest logical rules (Rabi, Miles, & Minda, Reference Rabi, Miles and Minda2015; Rabi & Minda, Reference Rabi and Minda2014). Adults are better (and certainly better than other animals!), but arguably rule-based reasoning is far more difficult than it should be if such logical operators actually underlie much of our perception and reasoning (Goldwater, Don, Krusche, & Livesey, Reference Goldwater, Don, Krusche and Livesey2018; Lupyan, Reference Lupyan2013; Mercier & Sperber, Reference Mercier and Sperber2017).

It is true that at least for stimuli composed of easy-to-verbalize and recombine features such as circles and triangles of various colors used by Piantadosi, Tenenbaum, and Goodman (Reference Piantadosi, Tenenbaum and Goodman2016) adults can do well, showing patterns of behavior well-explained by the use of logical operators. However, such behavior is fragile in ways unexpected if these operators underlie our everyday cognition. Formally simple operations like XOR are notoriously difficult for people (Shepard, Hovland, & Jenkins, Reference Shepard, Hovland and Jenkins1961). Even on simple rules like IF A, performance strongly depends on factors like verbal nameability of the constituents (Zettersten & Lupyan, Reference Zettersten and Lupyan2020).

Ironically, Piantadosi, cited in support of hard-coded logical connectives (Piantadosi et al., Reference Piantadosi, Tenenbaum and Goodman2016) was explicit that their data concern adults (“our results are not about children,” p. 22) making the claim that logical operators underlie our core cognitive processes suspect. He later went on to argue that “primitives” like AND and OR need not in fact be primitives and can be learned (Piantadosi, Reference Piantadosi2021). I would add that such learning may be supported in part by natural language (Lupyan & Bergen, Reference Lupyan and Bergen2016).

To be fair, not all the evidence the authors use in support of the LoTH is linguistic. A considerable weight is placed on the construct of object files that are somehow meant to explain perception in terms of LoTH. Although object files may be a useful construct for understanding certain perceptual generalizations, there is good reason why research in perception treats visual representations as analog/iconic representations (Block, Reference Block, McLaughlin and Cohenforthcoming).

In a town inhabited by highly educated people with a Western philosophical bent, LoTH is a sensible starting point in thinking about how cognition works. In towns inhabited by the rest of us, it is a curious game that some learn to play. The most fun games are often those that transport us to imagined worlds. The world of the LoT hypothesis is likely one of these.

Financial support

This study was supported by NSF-PAC 2020969.

Competing interest

None.

References

Block, N. (forthcoming). Let's get rid of the concept of an object file. In McLaughlin, B. & Cohen, J. (Eds.), Contemporary debates in philosophy of mind (2nd ed., pp. 494516). Wiley. https://philarchive.org/rec/BLOLGRGoogle Scholar
Dekker, R. B., Otto, F., & Summerfield, C. (2022). Curriculum learning for human compositional generalization. Proceedings of the National Academy of Sciences of the United States of America, 119(41), e2205582119. https://doi.org/10.1073/pnas.2205582119CrossRefGoogle ScholarPubMed
Dowty, D. (1991). Thematic proto-roles and argument selection. Language, 67(3), 547619. https://doi.org/10.2307/415037CrossRefGoogle Scholar
Goldwater, M. B., Don, H. J., Krusche, M. J. F., & Livesey, E. J. (2018). Relational discovery in category learning. Journal of Experimental Psychology. General, 147(1), 135. https://doi.org/10.1037/xge0000387CrossRefGoogle ScholarPubMed
Lahav, R. (1989). Against compositionality: The case of adjectives. Philosophical Studies: An International Journal for Philosophy in the Analytic Tradition, 57(3), 261279.CrossRefGoogle Scholar
Levinson, S. C. (1997). From outer to inner space: Linguistic categories and non-linguistic thinking. In Nuyts, J. & Pederson, E. (Eds.), Language and conceptualization (pp. 1345). Cambridge University Press.CrossRefGoogle Scholar
Lupyan, G. (2013). The difficulties of executing simple algorithms: Why brains make mistakes computers don't. Cognition, 129(3), 615636. https://doi.org/10.1016/j.cognition.2013.08.015CrossRefGoogle ScholarPubMed
Lupyan, G. (2015). The paradox of the universal triangle: Concepts, language, and prototypes. Quarterly Journal of Experimental Psychology, 70(3), 389412. https://doi.org/10.1080/17470218.2015.1130730CrossRefGoogle Scholar
Lupyan, G. (2016). The centrality of language in human cognition. Language Learning, 66(3), 516553. https://doi.org/10.1111/lang.12155CrossRefGoogle Scholar
Lupyan, G., & Bergen, B. (2016). How language programs the mind. Topics in Cognitive Science, 8(2), 408424. https://doi.org/10.1111/tops.12155CrossRefGoogle ScholarPubMed
Lupyan, G., & Zettersten, M. (2021). Does vocabulary help structure the mind?. In Sera, M. D. & Koenig, M. A. (Eds.), Minnesota symposia on child psychology (pp. 160199). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119684527.ch6CrossRefGoogle Scholar
Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2023). Dissociating language and thought in large language models: A cognitive perspective. arXiv: 2301.06627. https://doi.org/10.48550/arXiv.2301.06627CrossRefGoogle Scholar
Malt, B. C., Gennari, S., Imai, M., Ameel, E., Saji, N., & Majid, A. (2015). Where are the concepts? What words can and can't reveal. In Margolis, E. & Laurence, S. (Eds.), Concepts: New directions (pp. 291326). MIT Press.CrossRefGoogle Scholar
Malt, B. C., & Majid, A. (2013). How thought is mapped into words. Wiley Interdisciplinary Reviews: Cognitive Science, 4(6), 583597. https://doi.org/10.1002/wcs.1251Google ScholarPubMed
Matute, E., Montiel, T., Pinto, N., Rosselli, M., Ardila, A., & Zarabozo, D. (2012). Comparing cognitive performance in illiterate and literate children. International Review of Education, 58(1), 109127. https://doi.org/10.1007/s11159-012-9273-9CrossRefGoogle Scholar
Mercier, H., & Sperber, D. (2017). The enigma of reason. Harvard University Press.Google Scholar
Olson, D. R. (2002). What writing does to the mind. In Amsel, E. & Byrnes, J. P. (Eds.), Language, literacy, and cognitive development (pp. 153165). Erlbaum.Google Scholar
Piantadosi, S. T. (2021). The computational origin of representation. Minds and Machines, 31(1), 158.CrossRefGoogle ScholarPubMed
Piantadosi, S. T., Tenenbaum, J., & Goodman, N. (2016). The logical primitives of thought: Empirical foundations for compositional cognitive models. Psychological Review, 123(4), 392424.CrossRefGoogle ScholarPubMed
Rabi, R., Miles, S. J., & Minda, J. P. (2015). Learning categories via rules and similarity: Comparing adults and children. Journal of Experimental Child Psychology, 131, 149169. https://doi.org/10.1016/j.jecp.2014.10.007CrossRefGoogle ScholarPubMed
Rabi, R., & Minda, J. P. (2014). Rule-based category learning in children: The role of age and executive functioning. PLoS ONE, 9(1), e85316. https://doi.org/10.1371/journal.pone.0085316CrossRefGoogle ScholarPubMed
Rissman, L., & Lupyan, G. (2022). A dissociation between conceptual prominence and explicit category learning: Evidence from agent and patient event roles. Journal of Experimental Psychology. General, 151(7), 17071732. https://doi.org/10.1037/xge0001146CrossRefGoogle ScholarPubMed
Rissman, L., & Majid, A. (2019). Thematic roles: Core knowledge or linguistic construct? Psychonomic Bulletin & Review, 26(6), 18501869. https://doi.org/10.3758/s13423-019-01634-5CrossRefGoogle ScholarPubMed
Shepard, R. N., Hovland, C. I., & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75(13), 142. https://doi.org/10.1037/h0093825CrossRefGoogle Scholar
Zettersten, M., & Lupyan, G. (2020). Finding categories through words: More nameable features improve category learning. Cognition, 196, 104135. https://doi.org/10.1016/j.cognition.2019.104135CrossRefGoogle ScholarPubMed