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Statistical prediction alone cannot identify good models of behavior

Published online by Cambridge University Press:  06 December 2023

Nisheeth Srivastava
Affiliation:
Department of Cognitive Science, Indian Institute of Technology Kanpur, Kalyanpur, UP, India nsrivast@iitk.ac.in sanjali@iitk.ac.in nsrini@iitk.ac.in https://www.cse.iitk.ac.in/users/nsrivast/ https://sites.google.com/site/ammuns68/
Anjali Sifar
Affiliation:
Department of Cognitive Science, Indian Institute of Technology Kanpur, Kalyanpur, UP, India nsrivast@iitk.ac.in sanjali@iitk.ac.in nsrini@iitk.ac.in https://www.cse.iitk.ac.in/users/nsrivast/ https://sites.google.com/site/ammuns68/
Narayanan Srinivasan
Affiliation:
Department of Cognitive Science, Indian Institute of Technology Kanpur, Kalyanpur, UP, India nsrivast@iitk.ac.in sanjali@iitk.ac.in nsrini@iitk.ac.in https://www.cse.iitk.ac.in/users/nsrivast/ https://sites.google.com/site/ammuns68/

Abstract

The dissociation between statistical prediction and scientific explanation advanced by Bowers et al. for studies of vision using deep neural networks is also observed in several other domains of behavior research, and is in fact unavoidable when fitting large models such as deep nets and other supervised learners, with weak theoretical commitments, to restricted samples of highly stochastic behavioral phenomena.

Information

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

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