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.
Original language | English |
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Pages (from-to) | 761-771 |
Number of pages | 11 |
Journal | Social Cognitive and Affective Neuroscience |
Volume | 16 |
Issue number | 8 |
Early online date | 30 Mar 2020 |
DOIs | |
Publication status | Published - Aug 2021 |
Keywords
- computational modelling
- reinforcement learning
- social
- reward
- model fitting
- model selection