Categorization by Humans and Machines

Abstract

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.