BEACON Seminar: Some neuro-computational models of social decision-making and learning

If you would like to chat with the speaker on the day, please do get in touch with Matthew Rushworth at


In social interactions, humans often resort to heuristic decision-making and learning strategies. These strategies can be described with reinforcement learning models and can be linked to parts of the medial prefrontal cortex (MPFC).

First, I will present a series of studies that specify how humans combine optimal and heuristic solutions to maximize rewards for themselves and for others in multistep decision scenarios. Model-based analyses of fMRI data suggest a role of the MPFC in the computation of the employed policies and of the uncertainty associated with relying on these policies.

Second, I will describe experiments showing how humans learn about other people’s character traits. The best-fitting models combine principles derived from reinforcement learning algorithms with participants’ world knowledge about the distributions and interrelations of different character traits. I will present an fMRI study testing if these interrelations between character traits are represented as “grid-like code” in the MPFC.
Taken together, the to-be-presented projects aim at providing neuro-computational accounts of the trade-offs in complex social decision-making and learning processes. I will briefly outline ongoing and future research projects that build on these insights to elucidate how social decision-making and learning can go awry in psychiatric populations.