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University of Zurich
The research delves into the complexities of the human brain, particularly how it processes social interactions and represents other people’s intentions. The central question is whether the brain’s complex cognitive processes can be described by mathematical equations. The research team developed a mathematical model that simulates how we represent others in our minds during social interactions. This model suggests that we start with a rudimentary mental representation of another person and refine it over time, based on sensory inputs and previous experiences. The team used magnetic resonance imaging to record brain activity and applied the mathematical model to interpret the data.
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.

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