Connectionist modelling and neural network applications had become a major sub-field of cognitive science by the mid-1990s. In this ground-breaking book, originally published in 1995, leading connectionists shed light on current approaches to memory and language modelling at the time.

Preface. Acknowledgements. Contributors. Section 1: Memory 1. David W. Glasspool Competitive Queuing and the Articulatory Loop 2. Dimitrios Bairaktaris Temporal Chunking and Synchronization Using a Modular Recurrent Network Architecture 3. Gordon D. A. Brown, Tim Preece, Charles Hulme Learning to Learn in a Connectionist Network: The Development of Associative Learning 4. Noel E. Sharkey and Amanda J.C. Sharkey Interference and Discrimination in Neural Net Memory 5. Jacob M.J. Murre Transferof Learning in Back-propagation and in Related Neural Network Models 6. Joseph P. Levy and Dimitrios Bairaktaris Interactions Between Short- and Long-term Weights: Applications for Cognitive Modelling Section 2: Reading 7. Robert I. Damper Self-learning and Connectionist Approaches to Text-Phoneme Conversion 8. David C. Plaut, James L. McClelland Mark S. Seidenberg Reading Exception Words and Pseudowords: Are Two Routes Really Necessary? 9. John A. Bullinaria Neural Network Models of Reading: Solving the Alignment Problem without Wickelfeatures Section 3: Computation and Statistics 10. Robert W. Kentridge Cortical Neurocomputation, Language and Cognition 11. Nick Chater Neural Networks: The New Statistical Models of Mind 12. Steve Finch, Nick Chater, Martin Redington Acquiring Syntactic Information from Distributional Statistics Section 4: Speech and Audition 13. Leslie S. Smith Onset/Offset Filters for the Segmentation of Sound 14. Mukhlis Abu-Bakar and Nick Chater Time-warping Tasks and Recurrent Neural Networks 15. Paul Cairns, Richard Shillcock, Nick Chater, Joseph P. Levy Bottom-up Connectionist Modelling of Speech 16. Trevor A. Harley and Siobhan B.G. MacAndrew. Index.