The brain computations engaged in social learning have started to be investigated in dyadic interactions and in small groups1-7. Yet, little is known about the mechanisms used by the brain when individuals interact in social networks. First, I will present a taxonomy of different types of computations used by the brain for learning and inferences made during social interactions. I will illustrate how this taxonomy is useful to understand the computations underlying four types of social interactions. In particular, I will show recent model-based functional MRI results showing how the human brain adapts to fluctuating intentions of others when the nature of the interactions (to cooperate or compete) is not explicitly and truthfully signaled. Second, I will present recent theoretical and experimental work shedding light on the computations people use to integrate and transmit information in social networks. I will also show how the brain decides whether to share extra information with others, depending upon one’s own confidence about the reliability of information and upon one’s beliefs concerning the preferences of receivers. Together, these results pave the way towards developing a mechanistic understanding of social learning mechanisms used by networked individuals interacting dynamically in real time, that shape collective performance and consensus.
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