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Abstract: A connectionist approach to word reading, based on the principles of distributed representation, graded learning of statistical structure, and interactivity in processing, has led to the development of explicit computational models which account for a wide range of data on normal skilled reading and on patterns of reading impairment due to brain damage. There have, however, been recent empirical challenges to these models, and the approach in general, relating to the influence of orthographic length on the naming latencies of both normal and dyslexic readers. The current work presents a simulation which generates sequential phonological output in response to written input, and which can refixate the input when encountering difficulty. The normal model reads both words and nonwords accurately, and exhibits an effect of orthographic length and a frequency-by-consistency interaction in its naming latencies. When subject to peripheral damage, the model exhibits an increased length effect which interacts with word frequency, characteristic of letter-by-letter reading in pure alexia. Although the model is far from a fully adequate account of all the relevant phenomena, it suggests how connectionist models may be extended to provide deeper insight into sequential processes in reading.

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