The application of computational models to social neuroscience: promises and pitfalls.

Interactions with conspecifics are key to any social species. In order to navigate this social world, it is crucial for individuals to learn from and about others. From learning new skills by observing parents perform them to making complex collective decisions, understanding the mechanisms underlying social cognitive processes has been of considerable interest to psychologists and neuroscientists. Here, we review studies that have used computational modelling techniques, combined with neuroimaging, to shed light on how people learn and make decisions in social contexts. As opposed to standard social neuroscience methods, the computational approach allows one to directly examine where in the brain particular computations, as estimated by models of behavior, are implemented. Findings suggest that people use several strategies to learn from others: vicarious reward learning, where one learns from observing the reward outcomes of another agent; action imitation, which relies on encoding a prediction error between the expected and actual actions of the other agent; and social inference, where one learns by inferring the goals and intentions of others. These computations are implemented in distinct neural networks, which may be recruited adaptively depending on task demands, the environment and other social factors.