This article presents a theory of categorization that accounts for the effects of causal knowledge that relates the features of categories. According to causal-model theory, people explicitly represent the probabilistic causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught causal knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches. For the last several decades, research on the topic of categorization has focused on the problem of learning new categories via examples of category members, that is, from empirical observations. The result has been a host of categorization models that are based on representational ideas such as central prototypes, stored exemplars, and variabilized rules, and on processing principles such as similarity, that have considerable explanatory power and experimental support. More recently, the influence of the prior “theoretical ” knowledge that learners often contribute to their representations of categories has also been a topic of study (Carey,

this article. We also thank Denise Hatton, Tisha Baldwin, Joshua Nathan, Helen Sullivan, and Julia Wenzlaff for collecting data. Some of the stimulus materials used in Experiments 1 and 2 are adapted from the stimulus materials used in Rehder and Hastie (1997) and we thank them for inspiring many of the features and objects used in these studies. This project was supported by a National Science Foundation Grant (NSF-SBR 9515085) and a National Institute of Mental Health Grant (RO1 MH57737) given to Woo-kyoung Ahn, a National Science Foundation Graduate Fellowship to Nancy Kim, and a National Institute of Mental Health Postdoctoral Fellowship (MH10888-01A1) to Mary Lassaline

Ahn and Lassaline [Ahn, W., Lassaline, M.E., 1995. Causal structure in categorization.

The theory-based model of categorization posits that concepts are represented as theories rather than as feature lists. Thus, it is particularly interesting that the DSM-IV (American Psychiatric Association, 1994), establishes a set of atheoretical guidelines for diagnosis in the domain of mental disorders. Five experiments investigated how clinicians handle an atheoretical nosology. Clinical psychologists' causal theories for DSM-IV disorders and their responses on diagnostic and memory tasks were measured. Participants were more likely to diagnose a hypothetical patient with a disorder if that patient had causally central rather than causally peripheral symptoms according to their theory of the disorder. They also showed biased memory for the causally central symptoms. Clinicians are cognitively driven to form and apply theories despite decades of training and practice with the DSM's atheoretical guidelines. Clinical Psychologists' Theory-Based Representations of Mental Disorders Predict their Diagnostic Reasoning and Memory The theory-based view of categorization proposes that concepts are represented as theories or causal explanations. Murphy and Medin (1985) suggested that our nave theories about the world hold the features of a concept together in a cohesive package. For instance, a layperson's concept of anorexia not only contains the features "fear of becoming fat" and "refuses to maintain minimal body weight," but also the notion that the fear of becoming fat helps cause the refusal to maintain minimal body weight (Kim & Ahn, 2002). Indeed, a growing body of evidence supports the notion that the human mind constantly seeks out rules and explanations that make sense of incoming data concerning its surroundings, and forms concepts based on its theories about the ...

Probabilistic Causation 2 In this study preschool-aged children made predictions for a set of salient probabilistic causes. Of interest was whether they viewed outcomes of familiar causes of illness as definite or as probabilistic. In Experiment 1, children judged that a common cause would affect all members of a group in the same way. In Experiment 2 children believed they could definitely predict illness outcomes in a single case. These judgments contrasted with adults ' variable and uncertain predictions. Children did recognize uncertainty in outcomes dependent on voluntary choices. A third experiment presented both high and low potency causes of illness. Children treated all causes of illness as non-probabilistic. These results are discussed in the context of children's understanding of causal relations and the sources of variability. There is now intense interest in young children's beliefs about causal relations (see Wellman & Gelman, in press, and 1992, for reviews). Representations of causal relations

Available online at www.sciencedirect.com