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Most humans have the good fortune to live their lives embedded in richlystructured social groups. Yet, it remains unclear how humans acquire knowledgeabout these social structures to successfully navigate social relationships.Here we address this knowledge gap with an interdisciplinary neuroimaging studydrawing on recent advances in network science and statistical learning.Specifically, we collected BOLD MRI data while participants learned thecommunity structure of both social and non-social networks, in order to examinewhether the learning of these two types of networks was differentiallyassociated with functional brain network topology. From the behavioral data inboth tasks, we found that learners were sensitive to the community structure ofthe networks, as evidenced by a slower reaction time on trials transitioningbetween clusters than on trials transitioning within a cluster. From theneuroimaging data collected during the social network learning task, weobserved that the functional connectivity of the hippocampus andtemporoparietal junction was significantly greater when transitioning betweenclusters than when transitioning within a cluster. Furthermore, temporoparietalregions of the default mode were more strongly connected to hippocampus,somatomotor, and visual regions during the social task than during thenon-social task. Collectively, our results identify neurophysiologicalunderpinnings of social versus non-social network learning, extending ourknowledge about the impact of social context on learning processes. Morebroadly, this work offers an empirical approach to study the learning of socialnetwork structures, which could be fruitfully extended to other participantpopulations, various graph architectures, and a diversity of social contexts infuture studies.

Functional brain network architecture supporting the learning of social networks in humans