Neurocomputational mechanism of real-time distributed learning on social networks.

Social networks shape our decisions by constraining what information we learn and from whom. Yet, the mechanisms by which network structures affect individual learning and decision-making remain unclear. Here, by combining a real-time distributed learning task with functional magnetic resonance imaging, computational modeling and social network analysis, we studied how humans learn from observing others' decisions on seven-node networks with varying topological structures.

Reading between the lines: Listener's vmPFC simulates speaker cooperative choices in communication games.

Humans have a remarkable ability to understand what is and is not being said by conversational partners. It has been hypothesized that listeners decode the intended meaning of a communicative signal by assuming speakers speak cooperatively, rationally simulating the speaker's choice process and inverting it to recover the speaker's most probable meaning. We investigated whether and how rational simulations of speakers are represented in the listener's brain, by combining referential communication games with functional neuroimaging.