This talk was given at a local TEDx event, produced independently of the TED Conferences. Adequate representation of others’ intentions is the cornerstone of social interactions. This is particularly important when we have to make decisions based on someone else’s advice. Using ecologically valid, socially interactive games and mathematical models, we can capture from people’s responses how they learn about others’ intentions and decide to trust their expert advice.
This computational learning “fingerprint” reflects how any given individual builds and refines an internal model of another person. By fitting learning trajectories from this model to neuroimaging data, we captured where learning about socially-relevant information is represented in the brain. Furthermore, we found that individuals with a specific genetic predisposition, which governs the metabolism of the neuromodulator dopamine, represent intentions uniquely in the brain, in particular in situations when trust is broken. By combining neurocomputational models with genetics, I will highlight how we can obtain a deeper understanding of the neural mechanisms underlying social cognition, a domain where many psychiatric disorders are characterized by particularly salient deficiencies.
