Causal Categorization with Bayes Nets

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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Bob Rehder


A theory of categorization is presented in which knowledge of causal relationships between category features is represented as a Bayesian network. Referred to as causal-model theory, this theory predicts that objects are classified as category members to the extent they are likely to have been produced by a categorys causal model. On this view, people have models of the world that lead them to expect a certain distribution of features in category members (e.g., correlations between feature pairs that are directly connected by causal relationships), and consider exemplars good category members when they manifest those expectations. These expectations include sensitivity to higher-order feature interactions that emerge from the asymmetries inherent in causal relationships.

Research on the topic of categorization has traditionally focused on the problem of learning new categories given observations of category members. In contrast, the theory-based view of categories emphasizes the influence of the prior theoretical knowledge that learners often contribute to their representations of categories [1]. However, in contrast to models accounting for the effects of empirical observations, there have been few models developed to account for the effects of prior knowledge. The purpose of this article is to present a model of categorization referred to as causal-model theory or CMT [2, 3]. According to CMT, people 's know ledge of many categories includes not only features, but also an explicit representation of the causal mechanisms that people believe link the features of many categories.

In this article I apply CMT to the problem of establishing objects category membership. In the psychological literature one standard view of categorization is that objects are placed in a category to the extent they have features that have often been observed in members of that category. For example, an object that has most of the features of birds (e.g., wings, fly, build nests in trees, etc.) and few features of other categories is thought to be a bird. This view of categorization is formalized by prototype models in which classification is a function of the similarity (i.e. , number of shared features) between a mental representation of a category prototype and a to-be-classified object. However , a well-known difficulty with prototype models is that a features contribution to category membership is independent of the presence or absence of other features. In contrast , consideration of a categorys theoretical influence which combinations of features make for knowledge acceptable category members. For example , people believe that birds have nests in trees because they can fly , and in light of this knowledge an animal that doesnt fly