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.

Causal models describe some part of the world to allow an information system to perform complex tasks such as diagnosis. However, as many researchers have discovered, such models are rarely complete or consistent. As well, the world may change slightly, making a previously complete model incomplete. A computational theory of the use of causal models must allow for completion and correction in the face of new evidence. This paper discusses these issues with respect to the evolution of a causal model in a diagnosis task. The reasoner's goal is to diagnose a fault in a malfunctioning automobile, and it improves its diagnostic model by comparing it with an instructor's. A general process model is presented with two implementations. Related work in explanation based learning and in incorrect causal models is discussed