Proceedings of the Annual Meeting of the Cognitive Science Society

In this article I propose that categorization decisions are often made relative to causal models of categories that people possess. According to this causal-model theory of categorization, evidence of an exemplar's membership in a category consists of the likelihood that such an exemplar can be generated by the category's causal model. Bayesian networks are proposed as a representation of these causal models. Causal-model theory was fit to categorization data from a recent study, and yielded better fits than either the prototype model or the exemplar-based context model, by accounting, for example, for the confirmation and violation of causal relationships and the asymmetries inherent in such relationships.