RELEARNING AFTER DAMAGE IN CONNECTIONIST NETWORKS - TOWARD A THEORY OF REHABILITATION

Connectionist modeling offers a useful computational framework for exp loring the nature of normal and impaired cognitive processes. The curr ent work extends the relevance of connectionist modeling in neuropsych ology to address issues in cognitive rehabilitation: the degree and sp eed of recovery through retraining, the extent to which improvement on treated items generalizes to untreated items, and how treated items a re selected to maximize this generalization. A network previously used to model impairments in mapping orthography to semantics is retrained after damage. The degree of relearning and generalization varies cons iderably for different lesion locations, and has interesting implicati ons for understanding the nature and variability of recovery in patien ts. Tn a second simulation, retraining on words whose semantics are at ypical of their category yields more generalization than retraining on more typical words, suggesting a counterintuitive strategy for select ing items in patient therapy to maximize recovery. In a final simulati on, changes in the pattern of errors produced by the network over the course of recovery is used to constrain explanations of the nature of recovery of analogous brain-damaged patients. Taken together, the find ings demonstrate that the nature of relearning in damaged connectionis t networks can make important contributions to a theory of rehabilitat ion in patients. (C) 1996 Academic Press, Inc.