Theoretical Training

A mixture of keynote speakers and instructors will offer insight into how computational models can be used to test different questions in social cognition research, how they can be combined with neuroimaging and psychopharmacological measures, and how they can be used to test between different populations.

To advance the conceptual understanding of computational modelling, attendees can engage in theory-focused lectures covering a broad range of topics including:

• Reinforcement learning models and their applications to social cognition

• Theory of Mind and models of strategic social interactions

• Social motivation and models of effort-discounting

• Modelling social preferences and misinformation

Additional sessions will also be dedicated to group work among participants.

See our Program page to review the confirmed speakers and instructors.

Attendees will engage in several practical sessions led by faculty and supported by demonstrators with significant modelling expertise. A mixture of demonstrations and sessions where attendees work directly on code will allow them to develop their coding skills and implement a range of different models. Topics will include:

• Reinforcement learning approaches to social decisions

• Bayesian hierarchical modelling and its application in social cognition

• Drift-diffusion models and their application to social decisions

• A conceptual understanding of what computational models can and can’t offer, how to optimise experimental design for model-based analysis, and gold-standard model selection (e.g. simulation of parameter, parameter recovery)

• An understanding of a range of different computational models (e.g. learning, effort-discounting, theory of mind, Bayesian models)

• Hands-on practical code implementation and understanding, leaving the school with working scripts