Neurocomputational mechanisms of seeking information about oneself and other people

Humans rely on information, but with so much available we must constantly choose to seek or avoid it. These decisions have critical importance for global issues and can be linked to precise neural and computational mechanisms. Existing research has focused on how we seek information about ourselves but overlooked a crucial aspect: information about other people. Here, I will quantify (i) the computational mechanisms, (ii) the spatiotemporal neural trajectory, and (iii) the causal neural modulators that drive information-seeking. For each, I will compare social and non-social information to characterise commonalities and mechanisms specific to information about other people. Across four experiments, I will combine novel tasks with state-of-the-art methods. Computational modelling quantifies how the nature of information (positive/negative, level of uncertainty) determines willingness to seek it, and whether the same computations underlie seeking information about other people. Bringing together diverse techniques establishes neural representations in space and time then tests causal mechanisms, through deep brain stimulation and dopamine manipulations in a unique sample of Parkinson's disease patients. Together, these results will provide a shift in understanding of when and why people choose to find out or avoid information about themselves and others, with vital implications for physical and mental health.