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.

This paper examines performance on an intelligent tutoring system designed to teach students in a first-year algebra class to solve simple linear equations. W e emphasize the effects of requiring students to complete low-level arithmetic operations on higher-level strategic decisions. On aX + b = c problems, students who were required to perform arithmetic became less likely to solve such problems by first dividing by a than students who were not required to perform the arithmetic required to carry out the ojjeration. The shift away from this strategy is in keeping with the predictions of ACT-R. W e discuss these results in terms of the educational implications of providing computational tools to students learning basic mathematics.