Computational Models of Social Learning and Decision Making

This project aimed to develop and test computational models of learning that quantify how people learn under social uncertainty .We used a combination of methods, which included: hierarchical Bayesian model of learning, model-based fMRI and DCMs. The poster on this project description summaries the main results of this project. Our results showed the importance of the neurotransmitter dopamine in updating prediction errors in the social context."