Proceedings of the Annual Meeting of the Cognitive Science Society

Concept discovery experiments have yielded theories that work well for simple, rule governed categories. They appear less applicable to richly structured natural categories, however. This paper explores the possibility that a complex but structured environment provides more opportunities for learning than the early theories allowed. Specifically, category structure may aid in learning in two ways: correlated attributes may act jointly, rather than individually, and natural structure may allow more efficient cue sampling. A n experiment is presented which suggests that each of these advantages may be found for natural categories. The results call into question independent sampling assumptions inherent in many concept learning theories and are consistent with the idea that correlated attributes act jointly. In order to model natural category learning, modifications to existing models are suggested.