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 -- A -- B -- C -- D -- E -- F -- G -- H -- I -- L -- M -- N -- O -- P -- R -- S -- T -- U -- W -- X.