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...

questions of Cognitive Science. Recently Marcus et al.

ssing. We suggest that some types of similarity are determined automatically. When the cognitive system recognizes similarities, they influence cognitive processing, even when the person does not intend for their processing to be affected by similarities. In order to support this claim, we first outline three different approaches to similarity. Then, we examine how similarity can influence both low-level processes like attention and memory retrieval and higher cognitive processes like analogical reasoning and decision making. Next, we explore a number of examples in which cognitive processing is influenced by the presence of similarities in a stimulus set. Finally, we broaden the discussion to include similarities in more deliberate cognitive processes. Three Approaches to Similarity Representation and Similarity When a person makes a similarity comparison, the result is typically both a judgment of similarity and also some awareness of the commonalities and differences of the pair com