Lexicality and pronunciation in a simulated neural net

Citation

Phillips W, Hay IM & Smith L (1993) Lexicality and pronunciation in a simulated neural net. British Journal of Mathematical and Statistical Psychology, 46 (2), pp. 193-205. https://doi.org/10.1111/j.2044-8317.1993.tb01011.x

Abstract
Self-supervised compressive neural nets can perform nonlinear multilevel latent structure analysis. They therefore have promise for cognitive theory. We study their use in the Seidenberg & McClelland (1989) model of reading. Analysis shows that self-supervised compression in their model can make only a limited contribution to lexical decision, and simulation shows that it interferes with the associative mapping into phonology. Self-supervised compression is therefore put to no good use in their model. This does not weaken the arguments for self-supervised compression, however, and we suggest possible beneficial uses that merit further study.

Journal
British Journal of Mathematical and Statistical Psychology: Volume 46, Issue 2

People

Professor Bill Phillips

Emeritus Professor, Psychology

Professor Leslie Smith

Emeritus Professor, Computing Science