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Properties of LoTs: The footprints or the bear itself?

Published online by Cambridge University Press:  28 September 2023

Sam Whitman McGrath ,
Jacob Russin ,
Ellie Pavlick  and
Roman Feiman Open the ORCID record for Roman Feiman [Opens in a new window]
Sam Whitman McGrath
Affiliation:
Department of Philosophy, Brown University, Providence, RI, USA. sam_mcgrath1@brown.edu ellie_pavlick@brown.edu roman_feiman@brown.edu https://cs.brown.edu/people/epavlick/index.html https://sites.brown.edu/bltlab/ Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA
Jacob Russin
Affiliation:
Center for Neuroscience, University of California, Davis, Davis, CA, USA. jlrussin@ucdavis.edu https://jlrussin.github.io/
Ellie Pavlick
Affiliation:
Department of Philosophy, Brown University, Providence, RI, USA. sam_mcgrath1@brown.edu ellie_pavlick@brown.edu roman_feiman@brown.edu https://cs.brown.edu/people/epavlick/index.html https://sites.brown.edu/bltlab/ Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA
Roman Feiman
Affiliation:
Department of Philosophy, Brown University, Providence, RI, USA. sam_mcgrath1@brown.edu ellie_pavlick@brown.edu roman_feiman@brown.edu https://cs.brown.edu/people/epavlick/index.html https://sites.brown.edu/bltlab/ Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA
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Abstract

There are two ways to understand any proposed properties of language-of-thoughts (LoTs): As diagnostic or constitutive. We argue that this choice is critical. If candidate properties are diagnostic, their homeostatic clustering requires explanation via an underlying homeostatic mechanism. If constitutive, there is no clustering, only the properties themselves. Whether deep neural networks (DNNs) are alternatives to LoTs or potential implementations turn on this choice.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e284
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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Footnotes

*

Equal contribution.

References

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