Abstract
Face memory abilities are at the core of human social interaction, yet there is great degree of variance in the population in face memory capabilities, ranging from congenital prosopagnosia at one end, to “super-recognizers” on the other. Face processing is among the most studied subjects in cognitive neuroscience, but this effort has mostly focused on the well-described ventral visual face regions and on their relation to face perception. In the context of face memory, we found that although the nodes of the face system are tightly coupled at rest, the strength of these correlations was not predictive of performance on a face memory task (measured by the Cambridge Face Memory Test, CFMT). Given these results, the nature of the face memory task, and the social context in which it takes place, we were interested in exploring how the collaboration between different networks outside the face network (measured through resting state connectivity) might better predict performance on the CFMT. Our data revealed that face recognition memory was dependent on multiple connections between the face patches and regions of the medial temporal lobe memory system (including the hippocampus), and the social processing system. Moreover, this network was selective for memory for faces, and did not predict memory for other visual objects, such as cars. These findings suggest that in the general population, variability in face memory is dependent on how well the face processing system interacts with other processing networks, with interaction among the face patches themselves accounting for little of the variance in performance.