Causal Learning:173--189 (2010)

David Danks
Carnegie Mellon University
Many different, seemingly mutually exclusive, theories of categorization have been proposed in recent years. The most notable theories have been those based on prototypes, exemplars, and causal models. This chapter provides “representation theorems” for each of these theories in the framework of probabilistic graphical models. More specifically, it shows for each of these psychological theories that the categorization judgments predicted and explained by the theory can be wholly captured using probabilistic graphical models. In other words, probabilistic graphical models provide a lingua franca for these disparate categorization theories, and so we can quite directly compare the different types of theories. These formal results are used to explain a variety of surprising empirical results, and to propose several novel theories of categorization.
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DOI 10.1093/acprof:oso/9780195176803.003.0012
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Bayesian Cognitive Science, Unification, and Explanation.Stephan Hartmann & Matteo Colombo - 2017 - British Journal for the Philosophy of Science 68 (2).
Not Different Kinds, Just Special Cases.David Danks - 2010 - Behavioral and Brain Sciences 33 (2-3):208-209.

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