Connectionist modelling: Implications for cognitive neuropsychology

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Abstract
We review here the logic of neuropsychological inference in the context of connectionist modelling, focusing on the inference from double dissociation to modularity of function. The results of an investigation into the effects of damage on a range of small artificial neural networks that have been trained to perform two distinct mappings (rules vs exceptions), suggest that a double dissociation is possible without modularity. However, when these studies are repeated using sufficiently larger and more distributed networks, which are presumably more psychologically and biologically relevant, double dissociations are not observed. Further analysis suggests that double dissociation between performance on rule-governed and exceptional items is only found when the contribution of individual units to the overall network performance is significant, and hence that such double dissociations are merely artefacts of scale. In large, fully distributed systems, a wide range of damage produces only a single dissociation in which the main regularities are selectively preserved. Thus, in this context, connectionism appears to create no additional problems for the traditional neuropsychological inference.