The nature of categories and their acquisition is one of the central open questions in Cognitive Science. We suggest that categories are represented via structured descriptions and formed by a process of progressive abstraction, through successive comparison with incoming exemplars. This paper describes how SEQL (Skorstad, Gentner, & Medin, 1988), a computer model for category learning, which is based on SME (Falkenhainer et al 1986, 1989; Forbus et al 1994) can be used to simulate a recent categorization experiment (Ramscar & Pain, 1996), using a new algorithm, Generalization and Exemplar Learning (GEL). We demonstrate that SEQL produces behavior consistent with human subjects. Introduction Similarity is often viewed as central to categorization. For instance, prototype theories of categorization posit that categorization decisions are made on the basis of the similarity of an entity to the prototypical member of that category (Rosch 1975). However, similarity-based accou...