Operationalizing Social Neuroscience through HumanHuman and HumanMachine Interactions

How are neural, behavioral and social scales coordinated in real time so as to make possible the emergenceof social cognition? Answering this question requires to study the dynamics of coordination in real humaninteractions. However, even at the simplest dyadic scale, methodological and theoretical challenges remain.Several theories have been proposedto infer the link between neurobiology and social psychology, but the dynamical components of humaninteraction are still poorly explored because of the difficulty to record simultaneously the brain activity fromseveral subjects. This is the goal of hyperscanning methodology. I will first present how the combination ofsituated social paradigms with hyperscanning allowed to demonstrate that states of interactional synchronyat the behavioral level correlate with the emergence of interindividualsynchronization at the brain level(Dumas et al. PLoS ONE 2010). These interbrainsynchronization appeared to reflect in different frequencybands different aspects of social interaction, such as interactional synchrony, anticipation of other’s actionsand coregulationof turntaking.Then, I will present how such phenomena can be simulated with biologicallyinspired numerical simulations (e.g. using direct measures of brain connectivity with DTI) and how thehuman connectome facilitates interindividualsynchronizations and thus may partly account for ourpropensity to generate dynamical couplings with others (Dumas et al. PLoS ONE 2012). Finally, I willpresent another tool called the Human Dynamic Clamp (HDC) (Dumas et al. PNAS 2014). This HDCintegrates equations of human motion at the neurobehavioral level. A human and a “virtual partner” are thenreciprocally coupled in realtime,which allow controlling the dynamical parameters of the interaction whilemaintaining the continuous flow of interaction. This technique scaled up to the level of human behavior theidea of dynamic clamps used to study the dynamics of interactions between neurons. Combininghumanhumanand humanmachineinteractions thus presents new approaches for investigating theneurobiological mechanisms of social interaction, and for testing theoretical/computational modelsconcerning the dynamics at the neural, behavioral and social scales.