Computational modelling of social cognition and behaviour—a reinforcement learning primer

Patricia L Lockwood*, Miriam C Klein-Flügge

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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 languageEnglish
Pages (from-to)761-771
Number of pages11
JournalSocial Cognitive and Affective Neuroscience
Volume16
Issue number8
Early online date30 Mar 2020
DOIs
Publication statusPublished - Aug 2021

Keywords

  • computational modelling
  • reinforcement learning
  • social
  • reward
  • model fitting
  • model selection

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