On the Importance of Feedback for Categorization: Revisiting Category Learning Experiments Using an Adaptive Filter Model

被引：1

作者：

Marchant, Nicolas ^{[1
]}

Chaigneau, Sergio E. ^{[1
,2
]}

机构：

[1] Univ Adolfo Ibanez, Ctr Social & Cognit Neurosci, Sch Psychol, Ave Presidente Errazuriz 3328, Santiago 7550313, Chile

[2] Univ Adolfo Ibanez, Ctr Cognit Res CINCO, Sch Psychol, Santiago, Chile

来源：

JOURNAL OF EXPERIMENTAL PSYCHOLOGY-ANIMAL LEARNING AND COGNITION | 2022年 / 48卷 / 04期

关键词：

Rescorla and Wagner; association; category learning; adaptive filter; computational simulation; POLYMORPHOUS CONCEPTS; SELECTIVE ATTENTION; LINEAR SEPARABILITY; MULTIPLE SYSTEMS; CONTEXT THEORY; CLASSIFICATION; SIMILARITY; PROTOTYPE; EXEMPLAR; IDENTIFICATION;

D O I：

10.1037/xan0000339

中图分类号：

B84 [心理学];

学科分类号：

04 ; 0402 ;

摘要：

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.

引用

页码：295 / 306

页数：12