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Summary: Delayed Feedback Effects on Rule-Based and Information-Integration
Category Learning
W. Todd Maddox
University of Texas at Austin
F. Gregory Ashby
University of California, Santa Barbara
Corey J. Bohil
University of Texas at Austin
The effect of immediate versus delayed feedback on rule-based and information-integration category
learning was investigated. 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.
Feedback delay had no effect on the accuracy of responding or on the distribution of best fitting models
in the rule-based category-learning task. However, delayed feedback led to less accurate responding in
the information-integration category-learning task. Model-based analyses indicated that the decline in
accuracy with delayed feedback was due to an increase in the use of rule-based strategies to solve the
information-integration task. These results provide support for a multiple-systems approach to category
learning and argue against the validity of single-system approaches.
Categorization is fundamental to the survival of all organisms
(Ashby & Maddox, 1998). Every day people make thousands of
categorization judgments and are often remarkably accurate. An
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