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Combining meta-learned models with process models of cognition

Published online by Cambridge University Press:  23 September 2024

Adam N. Sanborn Open the ORCID record for Adam N. Sanborn [Opens in a new window] ,
Haijiang Yan  and
Christian Tsvetkov
Adam N. Sanborn*
Affiliation:
Department of Psychology, University of Warwick, Coventry, UK [email protected] [email protected] [email protected] https://go.warwick.ac.uk/adamsanborn
Haijiang Yan
Affiliation:
Department of Psychology, University of Warwick, Coventry, UK [email protected] [email protected] [email protected] https://go.warwick.ac.uk/adamsanborn
Christian Tsvetkov
Affiliation:
Department of Psychology, University of Warwick, Coventry, UK [email protected] [email protected] [email protected] https://go.warwick.ac.uk/adamsanborn
*
*Corresponding author.
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Abstract

Meta-learned models of cognition make optimal predictions for the actual stimuli presented to participants, but investigating judgment biases by constraining neural networks will be unwieldy. We suggest combining them with cognitive process models, which are more intuitive and explain biases. Rational process models, those that can sequentially sample from the posterior distributions produced by meta-learned models, seem a natural fit.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e163
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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