Abstract
Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalizing and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.
Highlights
Learning about actions and outcomes fundamentally shapes social cognition and behaviour
How do we form associations between actions and outcomes when they occur in a social context? And are the brain areas involved in social learning uniquely ‘social’ or do they reflect domain-general processing shared with other cognitive faculties? One of the most important influences on psychology, neuroscience and economics has come from associative or reinforcement learning theory that precisely and mathematically describes how decisions are paired with outcomes over time (Sutton and Barto, 1998; Dayan and Balleine, 2002)
Future studies using a reinforcement learning approach might help to further understand the difference in brain areas tracking social vs non-social prediction errors
Summary
Learning about actions and outcomes fundamentally shapes social cognition and behaviour. Another example from parametric reinforcement learning fMRI studies is that responses to prediction errors in ventral striatum appear to be non-specific, that is, responses in this area track PEs in both social and non-social contexts when directly compared (Behrens et al, 2008; Burke et al, 2010; Sul et al, 2015; Lockwood et al, 2016, 2018) (Figure 2C).
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