Categorization by Humans and Machines: Advances in Research and Theory by Glenn V. NakamuraCategorization by Humans and Machines: Advances in Research and Theory by Glenn V. Nakamura

Categorization by Humans and Machines: Advances in Research and Theory

byGlenn V. NakamuraEditorDouglas L. Medin, Roman Taraban

Hardcover | September 16, 1993

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The objective of the series has always been to provide a forum in which leading contributors to an area can write about significant bodies of research in which they are involved. The operating procedure has been to invite contributions from interesting, active investigators, and then allow them essentially free rein to present their perspectives on important research problems. The result of such invitations over the past two decades has been collections of papers which consist of thoughtful integrations providing an overview of a particular scientific problem. The series has an excellent tradition of high quality papers and is widely read by researchers in cognitive and experimental psychology.

About The Author

Douglas L. Medin is the series editor ofThe Psychology of Learning and Motivation .
Categorization by Humans and Machines: Advances in Research and Theory
Categorization by Humans and Machines: Advances in Research and Theory

by Glenn V. Nakamura

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Title:Categorization by Humans and Machines: Advances in Research and TheoryFormat:HardcoverDimensions:552 pages, 9 × 6 × 0.98 inPublished:September 16, 1993Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0125433298

ISBN - 13:9780125433297

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Table of Contents

R. Taraban, Introduction: A Coupling of Disciplines in Categorization Research.
Models of Data Driven Category Learning and Processing:
W.K. Estes, Models of Categorization and Category Learning.
J.K. Kruschke, Three Principles for Models of Category Learning.
R. Taraban and J.M. Palacios, Exemplar Models and Weighted Cue Models in Category Learning.
J.L. McDonald, The Acquisition of Categories Marked by Multiple Probabilistic Cues.
R. Bareiss and B.M.Slator, The Evolution of a Case-Based Computational Approach to Knowledge Representation, Classification, and Learning.
Data-Driven And Theory-Driven Processing And Processing Models
R.J. Mooney, Integrating Theory and Data in Category Learning.
D. Fisher and J.P. Yoo, Categorization, Concept Learning, and Problem-Solving: A Unifying View.
T.B. Ward, Processing Biases, Knowledge, and Context in Category Formation.
G.H. Mumma, Categorization and Rule Induction in Clinical Diagnosis and Assessment.
G.L. Murphy, A Rational Theory of Concepts.
Concepts, Category Boundaries, And Conceptual Combination:
B.C. Malt, Concept Structure and Category Boundaries.
E.J. Shoben, Non-Predicating Conceptual Combinations.
A.C. Graesser, M.C. Langston, and W.B. Baggett, Exploring Information About Concepts by Asking Questions.
E.W. Averill, Hidden Kind Classifications.
T.J. van Gelder, Is Cognition Categorization?
W.F. Brewer, What are Concepts?
Issues of Representation and Ontology.
Index.
Contents of Recent Volumes.

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