Classification and cognition
著者
書誌事項
Classification and cognition
（Oxford psychology series, no. 22）
Oxford University Press, 1994
- : pbk
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注記
Bibliography: p. [259]-272
Includes indexes
内容説明・目次
- 巻冊次
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ISBN 9780195073355
内容説明
Based on the well-known Fitts Lectures, this volume presents a core set of concepts and principles that propose a unified interpretation of memory categorization, and decision making.
目次
1: Introduction and Basic Concepts
1.1: Classification and Cognition: An Overview
1.2: The Array Model Framework
2: Category Structures and Categorization
2.1: Similarity in Theories of Classification
2.2: Predicting Categorization Performance
3: Models for Category Learning
3.1: The Exemplar-Similarity Model
3.2: Network-based Learning Models
4: Categorization and Memory Processing
5: On the Storage and Retrieval of Categorical Information
6: Extensions and Applications of the Exemplar-Similarity Model
7: Categorization and Recognition
8: Categorization and Cognition: Reprise
- 巻冊次
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: pbk ISBN 9780195109740
内容説明
Understanding classification is a major challenge for formulating theories of both human cognition and artificial intelligence. Classification is a human mental activity which covers such diverse things as forming concepts, categorizing medical patients, recognising an acquaintance, or discriminating phonetic components of a language. This book considers both previous theories and new research and comes up with a new core model which it proposes as the basis of all
forms of classification. Two versions of this model are developed, one based on symbol-processing, and one on connectionist architecture, and it is suggested that modules of each type of model coexist in the human cognitive system, competing for control of behavioural output.
目次
- 1. Introduction and Basic Concepts
- 2. Category Structures and Categorization
- 3. Models for Category Learning
- 4. Categorization and Memory Processing
- 5. On the Storage and Retrieval of Categorical Information
- 6. Extensions and New Applications of the Exemplar-Similarity Model
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