Neural mechanisms underlying the computation of socially inferred rewards

Abstract

No one knows everything. Therefore, it is often not enough to rely solely on one’s own knowledge, nor to indiscriminately follow advice from others. The current work examines the neural systems that support the human ability to capitalize on imperfect social information to support decision-making. Participants completed an fMRI task where they could choose to stay with an option of known value or switch to a hidden option, while receiving advice from an advisor who had access to both options, no options, or only the option that was hidden from participants. First, we find that value-guided regions (including dorsal striatum, dMPFC) preferentially track the expected value of the hidden option when it is the only option the advisor can access. Second, the advisor’s knowledge state is represented in regions that support social reasoning (precuneus, vMPFC). Our results suggest that neural systems that support social cognition and value-based decision-making support computations that enable humans to harness social information to vicariously explore the value of latent options.