Chapter: Three Principles for Models of Category Learning

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Home > Categorization by Humans and Machines > Three Principles for Models of Category Learning

Categorization by Humans and Machines |

Glenn V. Nakamura; Roman Taraban; Douglas L. Medin

Academic Press

Three Principles for Models of Category Learning

Chapter Info

Authors

John K. Kruschke

ARI Id

1665585313459_17362894

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Not Available Free

Pages

57-90

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Chapters/Headings	Author(s)	Pages	Info
Series Page		iii
Copyright		iv
Contributors		xiii-xiv
Preface	Roman Taraban; Glenn V. Nakamura	xv-xvii
Introduction: A Coupling of Disciplines in Categorization Research	Roman Taraban	1-12
Part I Models of Data-Driven Category Learning and Processing
Models of Categorization and Category Learning	W.K. Estes	15-56
Three Principles for Models of Category Learning	John K. Kruschke	57-90
Exemplar Models and Weighted Cue Models in Category Learning	Roman Taraban; Joaquin Marcos Palacios	91-127
The Acquisition of Categories Marked by Multiple Probabilistic Cues	Janet L. McDonald	129-156
The Evolution of a Case-Based Computational Approach to Knowledge Representation, Classification, and Learning	Ray Bareiss; Brian M. Slator	157-186
Part II Data-Driven and Theory-Driven Processing and Processing Models
Integrating Theory and Data in Category Learning	Raymond J. Mooney	189-218
Categorization, Concept Learning, and Problem-Solving: A Unifying View	Douglas Fisher; Jungsoon Park Yoo	219-255
Processing Biases, Knowledge, and Context in Category Formation	Thomas B. Ward	257-282
Categorization and Rule Induction in Clinical Diagnosis and Assessment	Gregory H. Mumma	283-326
A Rational Theory of Concepts	Gregory L. Murphy	327-359
Part III Concepts, Category Boundaries, and Conceptual Combination
Concept Structure and Category Boundaries	Barbara C. Malt	363-390
Non-Predicating Conceptual Combinations	Edward J. Shoben	391-409
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