By Glenn V. Nakamura, Douglas L. Medin and Roman Taraban (Eds.)

The target of the sequence has continually been to supply a discussion board during which major individuals to a space can write approximately major our bodies of study during which they're concerned. The working strategy has been to ask contributions from attention-grabbing, energetic investigators, after which enable them basically unfastened rein to give their views on vital learn difficulties. the results of such invites over the last twenty years has been collections of papers which include considerate integrations delivering an summary of a specific clinical challenge. The sequence has a good culture of top of the range papers and is commonly learn via researchers in cognitive and experimental psychology.

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3. It may be seen that the error scores vary widely, reflecting rapid learning for the problems of simplest structure but slow and incomplete learning for the problems of most complex structure. One of the purposes of Shepard et al. (1961) was to determine whether a principle of stimulus generalization, combined with assumptions drawn from associative learning theory, could account for the relationship between difficulty of a categorization problem and its logical structure. Their conclusion was that the answer is negative.

Context theory of classification learning. Psychological Review, 85, 207-238. Murphy, G. (1992). Theories and concept formation. In 1. Van Mechelen, J. Hampton, R. Michalski, & P. ), Categories and concepts: Theoretical views and inductive data analysis (pp. 173-200). London: Academic Press. , & Medin, D. (1985). The role of theories in conceptual coherence. Psychological Review, 92, 289-316. Nakamura, G. (1985). Knowledge-based classification of ill-defined categories. Memory & Cognition, 13, 377-384.

Pazzani, & P. ), Concept formation: Knowledge and experience in unsupervised learning (pp. 237-278). San Mateo, CA: Morgan Kaufmann. PART I MODELS OF DATA-DRIVEN CATEGORY LEARNING AND PROCESSING This Page Intentionally Left Blank MODELS OF CATEGORIZATION AND CATEGORY LEARNING W . K . Estes I. ’ The answer is not hard to come by if we look at the variety of research and theory represented in the conference, all bearing on the common theme of how we bring the results of past experience to bear on new situations in domains ranging from everyday life to artificial intelligence.

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