Connectionist Models of Learning, Development and Evolution

Jacques P. Sougne

ISBN 10: 1852333545 / ISBN 13: 9781852333546

Editore: Springer London, Springer London Mär 2001, 2001

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This item is printed on demand - Print on Demand Titel. Neuware -Connectionist Models of Learning, Development and Evolution comprises a selection of papers presented at the Sixth Neural Computation and Psychology Workshop - the only international workshop devoted to connectionist models of psychological phenomena.With a main theme of neural network modelling in the areas of evolution, learning, and development, the papers are organized into six sections:The neural basis of cognitionDevelopment and category learningImplicit learningSocial cognition EvolutionSemanticsCovering artificial intelligence, mathematics, psychology, neurobiology, and philosophy, it will be an invaluable reference work for researchers and students working on connectionist modelling in computer science and psychology, or in any area related to cognitive science.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 340 pp. Englisch.

Codice articolo 9781852333546

Dati bibliografici

• Titolo: Connectionist Models of Learning, Development and Evolution
• Editore: Springer London, Springer London Mär 2001
• Data di pubblicazione: 2001
• Lingua: Inglese
• ISBN 10: 1852333545
• ISBN 13: 9781852333546
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 19 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 19 millimetri di profondità
• Peso: 517 grammi

Descrizione articolo

Riassunto

Connectionist Models of Learning, Development and Evolution comprises a selection of papers presented at the Sixth Neural Computation and Psychology Workshop - the only international workshop devoted to connectionist models of psychological phenomena.
With a main theme of neural network modelling in the areas of evolution, learning, and development, the papers are organized into six sections:
The neural basis of cognition
Development and category learning
Implicit learning
Social cognition Evolution
Semantics
Covering artificial intelligence, mathematics, psychology, neurobiology, and philosophy, it will be an invaluable reference work for researchers and students working on connectionist modelling in computer science and psychology, or in any area related to cognitive science.

Contenuti

SECTION I: Neural Basis of Cognition: 1. Applying Neuroanatomical Distinctions to Connectionist Cognitive Modelling; 2. Pseudopatterns and Dual-network Memory Models: Advantages and Shortcomings; 3. A Learning Algorithm for Synfire Chains; 4. Towards a Spatio-temporal Analysis Tool for fMRI Data: An Application to Depth-from-Motion Processing in Humans; 5. A Simple Model Exhibiting Scalar Timing; Modularity and Specialized Learning in the Organization of Behaviour; 6. Modelling Modulatory Aspects in Association Processes; 8. Recognition of Novelty Made Easy: Constraints of Channel Capacity on Generative Networks; 9. A Biologically Plausible Maturation of an ART Network.- SECTION II: Development and Category Learning: 10. Developing Knowledge about Living Things: A Connectionist Investigation; 11. Paying Attention to Relevant Dimensions: A Localist Approach; 12. Coordinating Multiple Sensory Modalities While Learning to Reach; 13. Modelling Cognitive Development with Constructivist Neural Networks; 14. Learning Action Affordances and Action Schemas; 15. A Three-layer Configural Cue Model of Category Learning Rtes; 16. A Revival of Turing's Forgotten Connectionist Ideas: Exploring Unorganized Machines; 17. Visual Crowding and Category-specific Deficits: a Neural Network Model.- SECTION III: Implicit Learning: 18. Implicit Learning of Regularities in Western Tonal Music by Self-organization; 19. Rules vs. Statistics in Implicit Learning of Biconditional Grammars; 20. Hidden Markov Model Interpretations of Neural Networks.- SECTION IV: Social Cognition: 21. A Connectionist Model of Person Perception and Stereotype Formation; 22. Learning about an Absent Cause: Discounting and Augmentation of Positively and Independently Related Causes; SECTION V: Evolution. 23. Exploring the Baldwin Effect in Evolving Adaptable Control Systems; 24. Borrowing Dynamics from Evolution: Association using Catalytic Network Models; 25. Evolving Modular Architectures for Neural Networks; 26. Evolution, Development and Learning - a Nested Hierarchy?- SECTION VI: Semantics: 27. Learning Lexical Properties from Word Usage Patterns: Which Context Words Should be Used?; 28. Associative Computation and Associative Prediction; 29. The Development of Small-world Semantic Networks; 30. What is the Dimensionality of Human Semantic Space?