title: Evidence for a procedural-learning-based system in perceptual category learning.
abstract: The consistency of the mapping from category to response location was investigated to test the hypothesis that abstract category labels are learned by the hypothesis testing system to solve rule-based tasks, whereas response position is learned by the procedural-learning system to solve information-integration tasks. Accuracy rates were examined to isolate global performance deficits, and model-based analyses were performed to identify the types of response strategies used by observers. A-B training (consistent mapping) led to more accurate responding relative to yes-no training (variable mapping) in the information-integration category learning task. Model-based analyses indicated that the yes-no accuracy decline was due to an increase in the use of rule-based strategies to solve the information-integration task. Yes-no training had no effect on the accuracy of responding or distribution of best-fitting models relative to A-B training in the rule-based category learning tasks. These results both provide support for a multiple-systems approach to category learning in which one system is procedural-learning-based and argue against the validity of single-system approaches.
authors.affiliation: University of Texas, Department of Psychology, Austin, TX 78712, USA. maddox@psy.utexas.edu
authors.forename: W Todd
authors.initials: WT
authors.lastname: Maddox
authors.forename: Corey J
authors.initials: CJ
authors.lastname: Bohil
authors.forename: A David
authors.initials: AD
authors.lastname: Ing
mesh_terms: D006801:Humans; D007858:Learning; D010465:Perception; D013997:Time Factors
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