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Causal LearningPsychology, Philosophy, and Computation$
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Alison Gopnik and Laura Schulz

Print publication date: 2007

Print ISBN-13: 9780195176803

Published to Oxford Scholarship Online: April 2010

DOI: 10.1093/acprof:oso/9780195176803.001.0001

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Theory Unification and Graphical Models in Human Categorization

Theory Unification and Graphical Models in Human Categorization

Chapter:
(p.173) 11 Theory Unification and Graphical Models in Human Categorization
Source:
Causal Learning
Author(s):

David Danks

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195176803.003.0012

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.

Keywords:   categorization, prototype models, exemplar models, causal models, graphical models, Bayesian networks, Markov random fields

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