Understanding normal and impaired word reading: Computational principles in quasi-regular domains.

We develop a connectionist approach to processing in quasi-regular domains, as exemplified by English word reading.A consideration of the shortcomings of a previous implementation (Seidenberg & McClelland, 1989, Psych.Rev.) in reading nonwords leads to the development of orthographic and phonological representations that capture better the relevant structure among the written and spoken forms of words.In a number of simulation experiments, networks using the new representations learn to read both regular and exception words, including low-frequency exception words, and yet are still able to read pronounceable nonwords as well as skilled readers.A mathematical analysis of the effects of word frequency and spelling-sound consistency in a related but simpler system serves to clarify the close relationship of these factors in influencing naming latencies.These insights are verified in subsequent simulations, including an attractor network that reproduces the naming latency data directly in its time to settle on a response.Further analyses of the network's ability to reproduce data on impaired reading in surface dyslexia support a view of the reading system that incorporates a graded division-of-labor between semantic and phonological processes.Such a view is consistent with the more general Seidenberg and McClelland framework and has some similarities with-but also important differences from-the standard dual-route account.