A computational understanding of social information processing: How do we combine individual- and group-knowledge to predict the behavior of others?


Successful social interaction depends on our ability to predict the behavior of others. These predictions are generated by internal mental models that can reflect knowledge about a specific individual (i.e., individual-knowledge) or about the social group that the individual is a member of (i.e., group-knowledge). While it is known that relying on group-knowledge has great implications for social interactions, little is known about the underlying computational and neural mechanisms. We will combine a recently developed paradigm for studying social decision making with computational modeling and neuroimaging to understand how people predict the actions of others based on individual and group-knowledge and the interaction between these sources of information. First, we will unravel the computational mechanisms of generating predictive models that combine individual-knowledge and group-knowledge in an optimal way by developing and validating a hierarchical Bayesian computational framework. Secondly, we will identify the neural pathways implementing the model's computations in the human brain. Finally, we will identify how people update group-level knowledge based on observing different individuals in various environments. Taken together, this project will result in an overarching framework explaining how social behavior is shaped at a mechanistic, neuronal and behavioral level.





Dr. R.B. Mars

Verbonden aan

Radboud Universiteit Nijmegen


M. Braunsdorf


01/04/2018 tot 04/02/2022


€ 234.974