Connectionist Modeling of Relearning and Generalization in Acquired Dyslexic Patients

Abstract

Connectionist models implement cognitive processes in terms of cooperative and competitive interactions among large numbers of simple, neuron-like processing units. Such models provide a useful computational framework in which to explore the nature of normal and impaired cognitive processes. The current work extends the relevance of connectionist modeling in neuropsychology to address issues in cognitive rehabilitation: the degree and speed of recovery through retraining, the extent to which improvement on treated items generalizes to untreated items, and how treated items are selected to maximize this generalization. A network previously shown to model impairments in mapping orthography to semantics was retrained after damage. The degree of relearning and generalization depended on the location of the lesion and had interesting implications for understanding the nature and variability of recovery in patients. In a second simulation, retraining on words whose semantics are atypical of their category yielded more generalization than retraining on more typical words, suggesting a counterintuitive strategy for selecting items in patient therapy to maximize recovery. Taken together, the findings demonstrate that the nature of relearning in damaged connectionist networks can make important contributions to a theory of rehabilitation in patients.