Causal Learning:173--189 (2010)
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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Citations of this work BETA
Learning to Learn Causal Models.Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum - 2010 - Cognitive Science 34 (7):1185-1243.
Bayesian Cognitive Science, Unification, and Explanation.Stephan Hartmann & Matteo Colombo - 2015 - British Journal for the Philosophy of Science:axv036.
The Heterogeneity of Knowledge Representation and the Elimination of Concept.Edouard Machery - 2010 - Behavioral and Brain Sciences 33 (2-3):231-244.
Not Different Kinds, Just Special Cases.David Danks - 2010 - Behavioral and Brain Sciences 33 (2-3):208-209.
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