Connectionist Models in Cognitive Psychology is a state-of-the-art review of neural network modelling in core areas of cognitive psychology including: memory and learning, language (written and spoken), cognitive development, cognitive control, attention and action. The chapters discuss neural network models in a clear and accessible style, with an emphasis on the relationship between the models and relevant experimental data drawn from experimental psychology, neuropsychology and cognitive neuroscience. These lucid high-level contributions will serve as introductory articles for postgraduates and researchers whilst being of great use to undergraduates with an interest in the area of connectionist modelling.
Table of Contents
George Houghton, Introduction. Section 1: Learning. David R. Shanks, Connectionist Models of Basic Human Learning Processes. John A. Bullinaria, Connectionist Neuropsychology. John K. Kruschke, Learning Involves Attention. Section 2: Memory. Randall C. O'Reilly, The Division of Labor between the Neocortex and Hippocampus. E. Charles Leek, Category-specific Semantic Memory Impairments: What Can Connectionist Simulations Reveal about the Organisation of Conceptual Knowledge? Mike Page, Connectionist Models of Short-term Memory for Serial Order. David W. Glasspool, Serial Order in Behaviour: Evidence from Performance Slips. Section 3: Attention and Cognitive Control. Dietmar Heinke and Glyn W. Humphreys, Computational Models of Visual Selective Attention: A Review. Richard P. Cooper, The Control of Routine Action: Modelling Normal and Impaired Functioning. Section 4: Language Processes. Morten H. Christiansen and Suzanne Curtin, Integrating Multiple Cues in Language Acquisition: A Computational Study of Early Infant Speech Segmentation. Gary S. Dell, Language Production, Lexical Access, and Aphasia. Marco Zorzi, Computational Models of Reading.
George Houghton - University of Wales, Bangor, UK