On the importance of feedback for categorization: Revisiting category learning experiments using an adaptive filter model

Nicolás Marchant, Sergio E. Chaigneau

Research output: Contribution to journalArticlepeer-review

Abstract

Associative accounts of category learning have been, for the most part, abandoned in favor of cognitive explanations (e.g., similarity, explicit rules). In the current work, we implement an Adaptive Linear Filter (ALF) closely related to the Rescorla and Wagner learning rule, and use it to tackle three learning tasks that pose challenges to an associative view of category learning. Across three computational simulations, we show that the ALF is in fact able to make the predictions that seemed problematic. Notably, in our simulations we use exactly the same model and specifications, attesting to the generality of our account. We discuss the consequences of our findings for the category learning literature. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Original languageEnglish
Pages (from-to)295-306
Number of pages12
JournalJournal of experimental psychology. Animal learning and cognition
Volume48
Issue number4
DOIs
StatePublished - 1 Oct 2022
Externally publishedYes

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