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Abstract:
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
category's 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.
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