Abstract
Learning and decision-making are deeply embedded in our social world. Our choices occur in the presence of others, are shaped by social expectations, and can have significant impacts on both our and others’ welfare. While these topics have been of interest to psychology and cognitive neuroscience for many years, recent efforts have incorporated model-based approaches to formalize the mechanisms through which our choices are influenced by social input. Combined with fMRI, our understanding of the role of neural circuits involved in reward-related behavior and social cognition in supporting such mechanisms has grown exponentially. In this chapter, we will first discuss the utility of incorporating computational approaches into studies of social learning and decision-making. Next, we will review recent evidence regarding how social outcomes can impact our choices and social learning. We will then discuss how social rejection can impact our desire for agency and social contact. We also discuss the importance of social sharing in real-time interactions and social media environments, and how this phenomenon fits into a framework of value-based choice that can shape social connection. Last, we will review recent work regarding how the value of social relationships shapes computational mechanisms underlying decisions to trust and take risks. We will conclude with future directions for the field, including merging computational approaches with individual differences, cutting-edge methodologies, and naturalistic paradigms to provide a more comprehensive picture of how the social world modulates choice and neural function.