Word recognition in developmental dyslexia: A connectionist interpretation

We present a study of the accuracy, consistency, and speed of word naming in a dyslexic boy, JM, who has severe impairments in the ability to use sub-lexical, phonological reading strategies. For words that he can recognise, JM's naming latencies do not differ from those of control subjects matched for reading age, and he is generally consistent from one occasion to the next. He can also match printed homophones with their definitions--a skill that requires access to well-specified orthographic representations. The data are interpreted as evidence for the creation of efficient recognition devices for words within JM's sight vocabulary. However, he shows a continuing inability to use phonological decoding strategies to deal with words that he cannot recognize by sight. Overall we argue our results pose problems for stage models of reading development, and that they may best be interpreted within a connectionist framework of the development of word recognition skills.     

Perceptual Categorization: Connectionist Modelling and Decision Rules

Although it is currently popular to model human associative learning using connectionist networks, the mechanism by which their output activations are converted to probabilities of response has received relatively little attention. Several possible models of this decision process are considered here, including a simple ratio rule, a simple difference rule, their exponential versions, and a winner-take-all network. Two categorization experiments that attempt to dissociate these models are reported. Analogues of the experiments were presented to a single-layer, feed-forward, delta-rule network. Only the exponential ratio rule and the winner-take-all architecture, acting on the networks' output activations that corresponded to responses available on test, were capable of fully predicting the mean response results. In addition, unlike the exponential ratio rule, the winner-take-all model has the potential to predict latencies. Further studies will be required to determine whether latencies produced under more stringent conditions conform to the model's predictions.