The group Social Educational Neuroscience (SENSe) & Artificial Intelligence (AI) focuses on how we interact with other human beings and exploit current and explore missing knowledge to foster educational learning. Neuroscience and Artificial Intelligence are integrated to understand, model, and foster learning. Our theoretical starting position is that our brain is a prediction machine. We investigate the way the brain exploits predictions based on an internal model to make sense of incoming signals and using them to guide perception, thought, learning, and action. 

The basic research is organized in two lines; social interaction and education. The social line includes communication (i.e., how do we communicate and/or recognize intentions through actions), and social categorization (i.e., how do we make predictions on the basis of knowledge about a specific individual or about the social group that the individual is a member). The education line focuses on understanding education and learning based on internal models, with a particular focus on language. For example, how predictions within the first language (i.e., how semantic priming affects the perception of faces), and in learning a second language (i.e., how similarities at an orthographic, phonological and semantic level), influence the learning of a language. 

Towards this end, we utilize a cognitive neuroscience approach; measuring on-line, dynamic eye and hand movements as well as accompanying brain activation (e.g., employing MEG, fMRI, TMS). Based on the neuroscience measurements, we build computational models to simulate the underlying processes. Lastly, we use the insights and models to foster social interaction and learning in (digital) educational tools. In this way, we attempt to bridge the full gap from fundamental scientific understanding to wider societal value.