Prof. Dr. Thorsten Fehr
There is a variety of well-thought-out models explaining important aspects of complex social decision behaviour in humans. These models address biophysiological, genetical, contextual, socialisatory, internal trait and state conditions, and other potential modulators of social development, current status, and behavioural predictors. There is also some neuroscientific evidence that substantiate several model assumptions regarding the underlying functional neuroanatomy. The here presented series of work focused on both traditional approaches, such as topographical EEG power and ERP analyses at signal space level, and advanced approaches, such as the exploration of state and trait-related dynamics of source space data at neuronal generator level. As expected, EEG signal space data showed topographical differences between distinguishable emotional context entities during social decision making (i.e., neutral, social positive, and reactive aggressive interaction scenarios processed from a first person perspective) and behaviour (i.e., withdraw from the interaction or approach it). Early, middle, and late latency ERPs in different phases of the decision process (event- and response-locked) reflected widely distributed task condition related differences distributed over the scalp. Analogously, oscillatory brain topographies revealed task condition related differences during the decision generation phase in different oscillatory frequency ranges. To get deeper insights into the characteristics of the underlying spatio-temporal brain dynamics, a recently developed social decision source model was applied on band-pass filtered single trial data captured from the decision generation trial phases. Ad hoc appliance (seeding) of the source model revealed high amounts of variance explanation (> 95 percent) in all of the seven separately band-pass-filtered frequency ranges defined between 1.5 and 35 Hz. Quantification of emotion-category related follow-up characteristics (i.e., source dynamics) of the respectively seeded neural generator network (source model) and its relationships to different external variables (e.g., different aggression trait measures) will be shown and discussed with the audience.
