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Probabilistic programming versus meta-learning as models of cognition

Published online by Cambridge University Press:  23 September 2024

Desmond C. Ong Open the ORCID record for Desmond C. Ong [Opens in a new window] ,
Tan Zhi-Xuan Open the ORCID record for Tan Zhi-Xuan [Opens in a new window] ,
Joshua B. Tenenbaum Open the ORCID record for Joshua B. Tenenbaum [Opens in a new window]  and
Noah D. Goodman
Desmond C. Ong*
Affiliation:
Department of Psychology, University of Texas at Austin, Austin, TX, USA [email protected] https://cascoglab.psy.utexas.edu/desmond/
Tan Zhi-Xuan
Affiliation:
Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA [email protected] [email protected] https://ztangent.github.io/ https://cocosci.mit.edu/
Joshua B. Tenenbaum
Affiliation:
Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA [email protected] [email protected] https://ztangent.github.io/ https://cocosci.mit.edu/
Noah D. Goodman
Affiliation:
Department of Psychology, Stanford University, Stanford, CA, USA [email protected] https://cocolab.stanford.edu/ Department of Computer Science, Stanford University, Stanford, CA, USA
*
*Corresponding author.
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Abstract

We summarize the recent progress made by probabilistic programming as a unifying formalism for the probabilistic, symbolic, and data-driven aspects of human cognition. We highlight differences with meta-learning in flexibility, statistical assumptions and inferences about cogniton. We suggest that the meta-learning approach could be further strengthened by considering Connectionist and Bayesian approaches, rather than exclusively one or the other.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e158
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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