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Cogn Sci. 2008 Jan 2;32(1):108-54. doi: 10.1080/03640210701802071.

A rational analysis of rule-based concept learning.

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Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyDepartment of Psychology, Rutgers UniversityDepartment of Psychology, University of California, Berkeley.


This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space-a concept language of logical rules. This article compares the model predictions to human generalization judgments in several well-known category learning experiments, and finds good agreement for both average and individual participant generalizations. This article further investigates judgments for a broad set of 7-feature concepts-a more natural setting in several ways-and again finds that the model explains human performance.

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