Publisher's Synopsis
In this provocative book, leading linguists and computer scientists consider the challenges that computational innovations pose to current rule-based phonological theories and speculate about the advantages of phonological models based on artificial neural networks and other computer designs. The authors offer new conceptions of phonological theory for the 1990s, the most radical of which proposes that phonological processes cannot be characterized by rules at all, but arise from the dynamics of a system of phonological representations in a high-dimensional vector space of the sort that a neural network embodies. This new view of phonology is becoming increasingly attractive to linguists and others in the cognitive sciences because it answers some difficult questions about learning while drawing on recent results in philosophy, psychology, artificial intelligence, and neuroscience.
The contributors are John A. Goldsmith, Larry M. Hyman, George Lakoff, K. P. Mohanan, David S. Touretzky, and Deirdre W. Wheeler.