Category Number Impacts Rule-Based : A Reassessment of Evidence for Dissociable Category-Learning Systemsand: A Reassessment of Evidence for Dissociable Category-Learning Systems Information-Integration Category Learning: A Reassessment of Evidence for Dissociable Category-Learning Systems

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Abstract

Researchers have proposed that an explicit reasoning system is responsible for learning rule-based category structures and that a separate implicit, procedural-learning system is responsible for learning information-integration category structures. As evidence for this multiple-system hypothesis, researchers report a dissociation based on category-number manipulations in which rule-based category learning is worse when the category is composed of 4, rather than 2, response categories; however, information-integration category learning is unaffected by category-number manipulations. We argue that within the reported category-number manipulations, there exists a critical confound: Perceptual clusters used to construct the categories are spread apart in the 4-category condition relative to the 2-category one. The present research shows that when this confound is eliminated, performance on information-integration category learning is worse for 4 categories than for 2 categories, and this finding is demonstrated across 2 different information-integration category structures. Furthermore, model-based analyses indicate that a single-system learning model accounts well for both the original findings and the updated experimental findings reported here.

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