On the Generalization of Simple Alternating Category Structures

A fundamental question in the study of human cognition is how people learn to predict the category membership of an example from its properties. Leading approaches account for a wide range of data in terms of comparison to stored examples, ions capturing statistical regularities, or logical rules. A...

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Bibliographic Details
Published inCognitive science Vol. 45; no. 4; pp. e12972 - n/a
Main Authors Kurtz, Kenneth J., Wetzel, Matthew T.
Format Journal Article
LanguageEnglish
Published United States Wiley Subscription Services, Inc 01.04.2021
Subjects
Categorization
Cognition
Generalization
Neural networks
Relational categories
Rules
Similarity
Relational categories
Similarity
Generalization
Neural networks
Categorization
Rules
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Summary:A fundamental question in the study of human cognition is how people learn to predict the category membership of an example from its properties. Leading approaches account for a wide range of data in terms of comparison to stored examples, ions capturing statistical regularities, or logical rules. Across three experiments, participants learned a category structure in a low‐dimension, continuous‐valued space consisting of regularly alternating regions of class membership (A B A B). The dependent measure was generalization performance for novel items outside the range of the training space. Human learners often extended the alternation pattern––a finding of critical interest given that leading theories of categorization based on similarity or dimensional rules fail to predict this behavior. In addition, we provide novel theoretical interpretations of the observed phenomenon.
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ISSN:0364-0213
1551-6709
DOI:10.1111/cogs.12972