Proceedings of the Annual Meeting of the Cognitive Science Society

Neuropsychological evidence has proved influential both in testing pre-existing cognitive theories and in developing new accounts. It has been argued that dissociations, and, in particular, double dissociation are particularly valuable in developing new theoretical accounts, since they may reveal the gross structure or "modularity" of cognitive processes. In this paper, we show that even fully distributed systems -i.e. systems with no modularity can give rise to double dissociations. W e give the example of a recurrent neural network which draws loops and spirals which shows a double dissociation between the two tasks when lesioned. This result suggests that the observation of a double dissociation implies little about the modularity of the underlying system. In the final section we argue that a dual task technique can give additional hints about the structure of the underlying system because the class of distributed systems we describe are not able, in general, to perform two tasks at the same time. Finally, we argue that neurobiology has to be taken into account in order to interpret purely behavioral data.