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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.
List of contents
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 Synfue Chains.- 4. Towards a Spatio-Temporal Analysis Tool for fMRI Data: An Application to Depth-from-Motion Processing in Humans.- 5. A Simple Model Exhtbiting Scalar Timing.- 6. Modularity and Specialized Learning in the Organization of Behaviour.- 7. Modeling 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 ofCategory Learning Rates.- 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: Models of 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 Dynamies from Evolution: Association using Catalytie 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 Semantie Networks.- 30. What is the Dimensionality of Human Semantie Space?.- Author Index.
Summary
Presents neural network modelling in the areas of evolution, learning, and development. This book, organized in six sections, covers the neural basis of cognition; development and category learning; implicit learning; social cognition; and, semantics. It also covers artificial intelligence, mathematics, psychology, neurobiology, and philosophy.
