A causal-model theory of categorization

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 o f 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. Several investigators have argued that category learning and categorization are strongly influenced by the theoretical, explanatory, and causal knowledge that people bring to bear (Murphy & Medin, 1985; Murphy, 1993; Heit, 1998). For example, manipulations of stimulus materials affect category learning by eliciting different aspects of people's background knowledge (e.g., Pazzani, 1989; Murphy & Allopenna, 1994). Performance on a variety of tasks has been correlated with the amount of relevant domain knowledge individuals possess (Keil, 1989; Medin, Lynch, Coley, & Atran, 1997). However, there has been relatively little development of this "theory-based " view of categories in terms of detailed theory and computational models (c.f. Heit, 1994). This state of affairs arises in part because of the uncertainty surrounding exactly what knowledge participants deploy in an experimental task. A few recent studies have addressed this problem by employing novel domains and teaching participants "background " knowledge as part of the experimental session (e.g., Ahn & Lassaline, 1996; Rehde