Categorization by Humans and Machines, 1st Edition

Advances in Research and Theory

Categorization by Humans and Machines, 1st Edition,Glenn Nakamura,Douglas Medin,Roman Taraban,ISBN9780125433297

Nakamura   &   Medin   &   Taraban   

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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.


Cognitive Scientists with an interest in learning, memory, and categorization, social scientists and social psychologists interested in sterotypes and impression formation, computer scientists interested in machine learning and computational models of information processing.

Glenn Nakamura

Affiliations and Expertise

Texas Tech University, Lubbock, U.S.A.

Douglas Medin

Douglas L. Medin is the series editor of The Psychology of Learning and Motivation.

Affiliations and Expertise

Northwestern University, Evanston, IL, USA

View additional works by Douglas L. Medin

Roman Taraban

Affiliations and Expertise

Texas Tech University, Lubbock, U.S.A.

Categorization by Humans and Machines, 1st Edition

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.
Contents of Recent Volumes.

Quotes and reviews

Praise for the Serial

"Indispensable to all psychologists interested in the experimental study of the phenomena of learning and motivation."
"Provides a diverse sampling of research being conducted throughout the area of learning."
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