Models of human category learning have predominately assumed that both the structure in the world and the analogous structure of the internal cognitive representations are best modeled by hierarchies of disjoint categories. Strict taxonomies do, in fact, capture important structure of the world. However, there are realistic situations in which systems of overlapping categories can engender more accurate inferences than can taxonomies. Two preliminary models for learning overlapping categories are presented and their benefit is illustrated. The models are discussed with respect to their potential implications for theory-based category learning and conceptual combination.
