A Rational Analysis of Rule-Based Concept Learning
Version of Record online: 10 FEB 2010
2008 Cognitive Science Society, Inc.
Volume 32, Issue 1, pages 108–154, January-February 2008
How to Cite
Goodman, N. D., Tenenbaum, J. B., Feldman, J. and Griffiths, T. L. (2008), A Rational Analysis of Rule-Based Concept Learning. Cognitive Science, 32: 108–154. doi: 10.1080/03640210701802071
- Issue online: 10 FEB 2010
- Version of Record online: 10 FEB 2010
- Concept learning;
- Bayesian induction;
- Probabilistic grammar;
- Language of thought
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.