Using computational, parallel distributed processing networks to model rehabilitation in patients with acquired dyslexia: An initial investigation

Abstract

Background : Traditional cognitive neuropsychological models are good at diagnosing deficits but are limited when it comes to studying recovery and rehabilitation. Parallel distributed processing (PDP) models have more potential in this regard as they are dynamic and can actually learn. However, to date very little work has been done in using PDP models to study recovery and rehabilitation. Aims : This study seeks to demonstrate how a PDP model of acquired dyslexia can be extended to provide a computational framework that is capable of making predictions about the relative effectiveness of therapeutic interventions. Methods & Procedures : A replication of Plaut, McClelland, Seidenberg, and Patterson's (1996, simulation 2) model of word reading was trained and then damaged. This damaged network was then retrained in a number of different ways designed to model both natural (spontaneous) recovery and recovery that can be attributed to a specific therapeutic intervention. Outcomes & Results : Interventions that used regular words were more effective than interventions based on inconsistent words. Early intervention (during the period of spontaneous recovery) was more effective than late intervention. Conclusions : These results suggest that this technique has the potential to provide a useful input to the therapeutic arena. The potential opportunities for further work are discussed.