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On the hazards of relating representations and inductive biases

Published online by Cambridge University Press:  28 September 2023

Thomas L. Griffiths Open the ORCID record for Thomas L. Griffiths [Opens in a new window] ,
Sreejan Kumar  and
R. Thomas McCoy
Thomas L. Griffiths
Affiliation:
Departments of Psychology and Computer Science, Princeton University, Princeton, NJ, USA tomg@princeton.edu http://cocosci.princeton.edu/tom/
Sreejan Kumar
Affiliation:
Neuroscience Institute, Princeton University, Princeton, NJ, USA sreejank@princeton.edu http://sreejankumar.com
R. Thomas McCoy
Affiliation:
Department of Computer Science, Princeton University, Princeton, NJ, USA tom.mccoy@princeton.edu https://rtmccoy.com/
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Abstract

The success of models of human behavior based on Bayesian inference over logical formulas or programs is taken as evidence that people employ a “language-of-thought” that has similarly discrete and compositional structure. We argue that this conclusion problematically crosses levels of analysis, identifying representations at the algorithmic level based on inductive biases at the computational level.

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Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e275
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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