Proceedings of the Annual Meeting of the Cognitive Science Society

We review the logic of neuropsychological inference, focusing on double dissociation, and present the results of an investigation into the dissociations observed when small artificial neural networks trained to perform two tasks are damaged. W e then consider how the dissociations discovered might scale up for more biologically and psychologically realistic networks. Finally, w e examine the methodological implications of this work for the cornerstone of cognitive neuropsychology: the inference from double dissociation to modularity of function.