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Is evidence of language-like properties evidence of a language-of-thought architecture?

Published online by Cambridge University Press:  28 September 2023

Nuhu Osman Attah  and
Edouard Machery Open the ORCID record for Edouard Machery [Opens in a new window]
Nuhu Osman Attah
Affiliation:
Department of History and Philosophy of Science, University of Pittsburgh, Pittsburgh, PA, USA nuo2@pitt.edu, www.nuhuosmanattah.com
Edouard Machery
Affiliation:
Department of History and Philosophy of Science, University of Pittsburgh, Pittsburgh, PA, USA nuo2@pitt.edu, www.nuhuosmanattah.com Center for Philosophy of Science, University of Pittsburgh, Pittsburgh, PA, USA machery@pitt.edu, www.edouardmachery.com African Centre for Epistemology and Philosophy of Science, University of Johannesburg, Johannesburg, South Africa
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Abstract

We argue that Quilty-Dunn et al.'s commitment to representational pluralism undermines their case for the language-of-thought hypothesis as the evidence they present is consistent with the operation of the other representational formats that they are willing to accept.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e264
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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References

Bansal, K., Loos, S., Rabe, M., Szegedy, C., & Wilcox, S. (2019). HOList: An environment for machine learning of higher order logic theorem proving. Proceedings of the 36th international conference on machine learning (Vol. 97, pp. 454–463).Google Scholar
Clark, A. (1993). Associative engines: Connectionism, concepts, and representational change. MIT Press.CrossRefGoogle Scholar
Clark, K., Khandelwal, U., Levy, O., & Manning, C. D. (2019). What does BERT look at? An analysis of BERT's attention. Proceedings of the 2019 ACL workshop BlackboxNLP: Analyzing and interpreting neural networks for NLP (pp. 276–286). Florence, Italy: Association for Computational Linguistics.Google Scholar
Dai, W. Z., Xu, Q., Yu, Y., & Zhou, Z. H. (2019). Bridging machine learning and logical reasoning by abductive learning. Proceedings of the 33rd international conference on neural information processing systems (pp. 2811–2822). Red Hook, NY: Curran Associates Inc.Google Scholar
Eliasmith, C. (2013). How to build a brain: A neural architecture for biological cognition. Oxford University Press.CrossRefGoogle Scholar
Eliasmith, C., & Anderson, C. H. (2003). Neural engineering: Computation, representation and dynamics in neurobiological systems. MIT Press.Google Scholar
Irving, G., Szegedy, C., Alemi, A. A., Eén, N., Chollet, F., & Urban, J. (2016). DeepMath-deep sequence models for premise selection. In Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, I., & Garnett, R. (Eds.), Advances in neural information processing systems (Vol. 29, pp. 2235–2243).Google Scholar
Machery, E. (2014). In defense of reverse inference. The British Journal for the Philosophy of Science, 65, 251–267.CrossRefGoogle Scholar
Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data?. Trends in Cognitive Sciences, 10(2), 59–63.CrossRefGoogle ScholarPubMed
Smolensky, P. (1990). Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence, 46(1–2), 159–216.CrossRefGoogle Scholar
Smolensky, P. (1991). Connectionism, constituency, and the language of thought. In Loewer, B. M. & Rey, G. (Eds.), Meaning in mind: Fodor and his critics (pp. 201–227). Oxford: Blackwell.Google Scholar
Stoianov, I., & Zorzi, M. (2012). Emergence of a “visual number sense” in hierarchical generative models. Nature Neuroscience, 15(2), 194–196.CrossRefGoogle Scholar
Tenney, I., Das, D., & Pavlick, E. (2019). BERT rediscovers the classical NLP pipeline. Proceedings of the 57th annual meeting of the association for computational linguistics (pp. 4593–4601). Florence, Italy: Association for Computational Linguistics.Google Scholar