Proceedings of the Annual Meeting of the Cognitive Science Society

Methodological advances have made it possible to generatefMRI predictions for cognitive architectures, such as ACT-R, thus expanding the range of model predictions and mak-ing it possible to distinguish between alternative models thatproduce otherwise identical behavioral patterns. However, fortasks associated with relatively brief response times, fMRI pre-dictions are often not sufficient to compare alternative models.In this paper, we outline a method based on effective connec-tivity, which significantly augments the amount of informationthat can be extracted from fMRI data to distinguish betweenmodels. We show the application of this method in the caseof two competing ACT-R models of the Stroop task. Althoughthe models make, predictably, identical behavioral and BOLDtime-course predictions, patterns of functional connectivity fa-vor one model over the other. Finally, we show that the samedata suggests directions in which both models should be re-vised.