Social species, and specifically human and non-human primates, rely heavily on conspecifics for survival. Considerable time is spent watching each other’s behavior because this is often the most relevant source of information for preparing adaptive social responses. The project RELEVANCE aims to understand how the brain evolved special structures to process highly relevant social stimuli like bodies and to reveal how social vision sustains adaptive behavior. This requires a novel way of thinking about biological information processing, currently among the brains’ most distinctive and least understood characteristic that accounts for the biggest difference between brains and computers.

The project will develop a mechanistic and computational understanding of the visual processing of bodies and interactions and show how this processing sustains higher abilities such as understanding intention, action and emotion. Relevance will accomplish this by integrating advanced methods from multiple disciplines. Crosstalk between human and monkey methods will establish homologies between the species, revealing cornerstones of the theory. Physiologically inspired neural and deep neural network models will help to understand the dynamic mechanisms of neural processing of complex social stimuli and its task-dependent modulation.

RELEVANCE aims at uncovering novel principles of the processing of social stimuli that might inspire novel diagnostic and treatment approaches. We aim to model the visual recognition of bodies, actions and interactions. The goal is to use different computational approaches to ultimately predict and explain experimental data. In neuropsychiatry, and which might inspire Nobel architectures for the processing of socially relevant information in computer and robotic systems.

This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement n° 856495).