Reactivation and abstraction of self-related social information during sleep

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The learning of social information from complex and highly dynamic social interactions is a central human capacity. To navigate the social environment, humans need to abstract information from others’ behaviors and adapt their own actions based on incoming feedback. Extracting the gist of self-related information from complex social processes thereby has an essential function in the regulation of social behavior. The overarching goal of this proposal is to study the functional role of reactivation during sleep for the abstraction of gist from self-related social information. We hypothesize that the reactivation of self-related information during sleep is a central mechanism for the abstraction of self-related beliefs. We further hypothesize that during learning negative information is encoded to a stronger degree, whereas during sleep more positive, and thus self-beneficial, information is reactivated and consequently abstracted. Thereby, sleep adaptively counteracts an otherwise negative dynamic. We will test these hypotheses in healthy humans (1) by decoding spontaneous reactivation of learning-related neural patterns during sleep with fMRI and (2) by externally manipulating reactivations during sleep with targeted memory reactivation (TMR). Finally, we will explore whether and how these processes differ in sub-clinical populations with a high risk for developing mental disorders (e.g., low scores in self-esteem). The findings of this project will deepen our understanding of sleep-based mechanisms in the abstraction of self-related information from social interactions and will open new avenues for sleep-based interventions in individuals showing maladaptive social information processing.