A Mixture of Experts in Associative Generalization

Jessica C Lee, Peter Lovibond, Brett Hayes, Stephan Lewandowsky

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

After learning that one stimulus predicts an outcome (e.g., an aqua-colored rectangle leads to shock) and a very similar stimulus predicts no outcome (e.g., a slightly greener rectangle leads to no shock), some participants generalize the predictive relationship on the basis of physical similarity to the predictive stimulus, while others generalize on the basis of the relational difference between the two stimuli (e.g., “higher likelihood of shock for bluer stimuli”). To date, these individual differences in generalization rules have remained unexplored in associative learning. Here, we present evidence that a given individual simultaneously entertains belief in both “similarity” and “relational” rules, and generalizes using a mixture of these strategies. Using a “mixture of experts” modelling framework constrained by participants self-reported rule beliefs, we show that considering multiple rules predicts generalization gradients better than a single rule, and that generalization behavior is better described as switching between, rather than averaging over, different rules.
Original languageEnglish
Title of host publicationProceedings of the Annual Meeting of the Cognitive Science Society
PublisherCognitive Science Society
Volume43
Publication statusPublished - 29 Jul 2021
Event43rd Annual Meeting of the Cognitive Science Society: "Comparative Cognition - Animal Minds" - Online
Duration: 26 Jul 202129 Jul 2021
https://cognitivesciencesociety.org/cogsci-2021/

Publication series

NameeScholarship
PublisherUniversity of California
Volume43
ISSN (Electronic)1069-7977

Conference

Conference43rd Annual Meeting of the Cognitive Science Society
Abbreviated titleCOGSCI 2021
Period26/07/2129/07/21
Internet address

Structured keywords

  • Cognitive Science

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