Relearning after Damage in Connectionist Networks: Implications for Patient Rehabilitation

Connectionist modeling is applied to issues in cognitive rehabilitation, concerning the degree and speed of recovery through retraining, the extent of generalization to untreated items, and how treated items are 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 considerably for diierent lesion locations , and has 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 yields more generalization than retraining on more prototypical words, suggesting a surprising strategy for selecting items in patient therapy to maximize recovery.