Classification and cognition / W.K. Estes.

Format:

Book

Author/Creator:

Estes, William K. (William Kaye)

Series:

Oxford psychology series ; no. 22.

Oxford psychology series ; no. 22

Language:

English

Subjects (All):

Categorization (Psychology).

Recognition (Psychology).

Cognitive learning theory.

Physical Description:

xii, 282 p. : ill.

Edition:

1st ed.

Place of Publication:

New York : Oxford University Press, 1994.

Summary:

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.

Contents:

Intro

Contents

1. INTRODUCTION AND BASIC CONCEPTS

1.1 Classification and cognition: an overview

1.1.1 Concepts and categories

1.1.2 Approaches to categorization: two theoretical traditions

1.1.3 Categorization and induction

1.1.4 Remarks on theoretical style

1.2 The array model framework

1.2.1 Representation: attributes, dimensions, and features

1.2.2 The problem of access to memory

1.2.3 Comparison and similarity

1.2.4 The product rule for patterns of binary-valued attributes

1.2.5 The core model for classification

Appendix 1.1 Union and intersection rules for computation of pattern similarity

Appendix 1.2 Attentional learning in the exemplar model

2. CATEGORY STRUCTURES AND CATEGORIZATION

2.1 Similarity in theories of classification

2.1.1 The core model applied to a natural category

2.1.2 From similarity to response probability

2.1.3 An alternative measure of similarity: the contrast model

2.2 Predicting categorization performance

2.2.1 The simplest categorization model in the array framework

2.2.2 On category structures and conceptual levels

3. MODELS FOR CATEGORY LEARNING

3.1 The exemplar-similarity model

3.1.1 Augmentations of the core model

3.1.2 Categorization and identification

3.1.3 Similarity and cognitive distance

3.1.4 Status of the exemplar-similarity model

3.2 Network-based learning models

3.2.1 A simple adaptive network model

3.2.2 The similarity-network model

3.2.3 Pattern to feature transfer

Appendix 3.1 Categorization probability for the exemplar model in relation to initial memory load

Appendix 3.2 Similarity-network output and learning functions for standard four-pattern categorization

Appendix 3.3 Additional details of Experiment 3.1 procedure

4. CATEGORIZATION AND MEMORY PROCESSING

4.1 Concurrent categorizations.

4.2 Categorization with constraints on memory

4.2.1 Categorization with constrained repetition lags

4.2.2 Categorization based on short-term memory

4.2.3 Analyses of response frequency data

4.2.4 Analyses of reaction times

4.3 A modular view of exemplar and network models

Appendix 4.1 Method of Experiment 4.1

Appendix 4.2 Learning about invalid cues in concurrent categorizations: Experiment 4.2

Appendix 4.3 Method of Experiment 4.4: categorization in short-term memory

5. ON THE STORAGE AND RETRIEVAL OF CATEGORICAL INFORMATION

5.1 Standard versus observational training procedures

5.1.1 Method for comparison of training procedures

5.1.2 Results for comparisons of training procedures

5.2 Learning on the basis of average or configural prototypes

5.3 Inducing prototypes

5.4 Predicting features from categories

5.4.1 Feature-frequency estimates based on long-term memory

5.4.2 Feature-frequency estimates based on shorter-term memory

5.5 Pattern completion

Appendix 5.1 Induction of prototypes in a correlated-feature category structure: Experiment 5.1

Appendix 5.2 Induction of prototypes in an independent-feature category structure: Experiment 5.2

Appendix 5.3 Method and results of Gluck (1984) study

Appendix 5.4 Derivations of predictions from exemplar and similarity-network models for MacMillan (1987) study

6. EXTENSIONS AND NEW APPLICATIONS OF THE EXEMPLAR-SIMILARITY MODEL

6.1 Processing assumptions

6.2 Extensions and applications

6.2.1 Confusable categories

6.2.2 Transfer between categorizations

6.2.3 The burden of the past: interference in learning successive categorizations or discriminations

6.2.4 Learning multiple categorizations

6.2.5 Category learning in relation to number of categories

6.2.6 Effects of category size

6.2.7 Causal relations.

6.2.8 The "dilution effect

6.3 From categorization to recall

6.3.1 Simple cued recall of paired associates

6.3.2 Ordered recall

6.3.3 The crossover effect in sentence memory

6.3.4 Recall without response cueing

6.3.5 A comment on direct recall

6.4 On the extendability of the exemplar-similarity model

Appendix 6.1 Similarity computations for application of the array model to causal inference problems

7. CATEGORIZATION AND RECOGNITION

7.1 Recognition: a window to memory?

7.1.1 Experimental paradigms

7.1.2 Aspects of recognition theory

7.2 Recognition in the array framework

7.2.1 Forced-choice recognition

7.2.2 Old/new recognition

7.2.3 The role of memory load in recognition

7.2.4 Effects of item repetition

7.2.5 Word-frequency and the "mirror effect

7.3 Recognition in the similarity-network model

7.4 Short-term memory search

7.5 On recognition as a measure of memory

Appendix 7.1 Application of signal detectability theory to recognition

Appendix 7.2 Array-model computations for list-length/list-strength design in old/new recognition

Appendix 7.3 Summary of study of memory load in old/new recognition: Experiment 7.1

Appendix 7.4 Summary of repetition study: Experiment 7.2

8. CATEGORIZATION AND COGNITION: REPRISE

8.1 The basis of classification in memory

8.1.1 The new faces of instance memory

8.1.2 Summary of the assumptions of the array-similarity model

8.2 Concepts and categories

8.3 The place of rules in a memory-based theory

8.4 Representation and structure

8.4.1 The issue of independence of attributes

8.4.2 Array versus network representations

8.5 Alternative theoretical approaches

8.5.1 The ACT framework

8.5.2 Hintzman's Minerva model

8.5.3 The general recognition model

8.5.4 Multilayer connectionist networks.

8.5.5 On differential tests of models

References

Author Index

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

R

S

T

U

V

W

Y

Z

Subject Index

X.

Notes:

Includes bibliographical references (p. [259]-272) and indexes.

Description based on publisher supplied metadata and other sources.

ISBN:

9786610473793

9780195360882

0195360885

9780199867899

0199867895

9780195109740

0195109740

9781280473791

1280473797

9781601298669

1601298668

9781423764717

1423764714

OCLC:

922952737