Connectionist models of neuropsychological disorders

Artificial neural networks ('connectionist models') embody aspects of real neuronal systems. But does studying the breakdown of performance in such models help us to understand cognitive impairments in humans following brain damage? Here we review recent attempts to capture different neuropsychological disorders using connectionist models with simulated lesions. We show how such lesion studies can be used to evaluate some of the standard assumptions made in neuropsychological research, concerning both double dissociations and associations between patterns of impairment. We also illustrate how lesioned models, like humans, can sometimes be more impaired on the easier of two tasks and demonstrate that connectionist models can incorporate forms of internal structure. Finally we discuss the utility of the models for understanding and predicting the effectiveness of different rehabilitation strategies. Future questions concern the role and possible development of internal structure within these models, whether the models can be generalized to larger-scale simulations, and whether they can accommodate higher-order linguistic disorders.