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Classification and Cognition [Mīkstie vāki]

(Professor, Department of Psychology, Harvard University)
  • Formāts: Paperback / softback, 296 pages, height x width x depth: 226x167x17 mm, weight: 431 g, line figures, tables
  • Sērija : Oxford Psychology Series 22
  • Izdošanas datums: 09-Jan-1997
  • Izdevniecība: Oxford University Press Inc
  • ISBN-10: 0195109740
  • ISBN-13: 9780195109740
Citas grāmatas par šo tēmu:
  • Mīkstie vāki
  • Cena: 65,11 €
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  • Formāts: Paperback / softback, 296 pages, height x width x depth: 226x167x17 mm, weight: 431 g, line figures, tables
  • Sērija : Oxford Psychology Series 22
  • Izdošanas datums: 09-Jan-1997
  • Izdevniecība: Oxford University Press Inc
  • ISBN-10: 0195109740
  • ISBN-13: 9780195109740
Citas grāmatas par šo tēmu:
Based on Estes' important Fitts Lectures, this volume details a set of psychological concepts and principles that offers a unified interpretation of a wide variety of memory, categorization, and decision-making phenomena. These phenomena are explained via two families of models established by the author: a storage-retrieval model and an adaptive network model. Estes considers whether the models are competing or complementary, offering cogent and instructive arguments for both perspectives. Estes' theory is then applied to two large-scale series of studies on category learning and recognition, providing an integrated understanding of seemingly disparate phenomena. This book is the culmination of the author's more than ten years of research in the field, and stands as a great achievement by one of this century's eminent psychologists. It will be indispensable to a wide variety of behavioral scientists, including mathematical and cognitive psychologists.

Recenzijas

...astoundingly wide sphere of application. * Science *

1. Introduction and Basic Concepts1.1. Classification and Cognition: An Overview1.2. The Array Model Framework2. Category Structures and Categorization2.1. Similarity in Theories of Classification2.2. Predicting Categorization Performance3. Models for Category Learning3.1. The Exemplar-Similarity Model3.2. Network-based Learning Models4. Categorization and Memory Processing5. On the Storage and Retrieval of Categorical Information6. Extensions and Applications of the Exemplar-Similarity Model7. Categorization and Recognition8. Categorization and Cognition: Reprise