Brain-to-brain dynamical Coupling: A New framework for the communication of social knowledge

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Project Summary Social interaction in real-life contexts necessitates dynamical interactions among two or more brains as individuals listen, speak, act, and mutually adapt to one another to reach shared understanding. Elucidating how brains forge such shared understanding requires shifting from a “one-brain” to a “multiple-brain” frame of reference, as well as from artificial laboratory conditions to natural, real-life settings. Here we outline a novel framework to identify inter-brain dynamical coupling that underpins knowledge about social phenomena (e.g., mental states, social norms, emotions, etc.), henceforth referred to as "social knowledge". The long-term goal of our laboratories is to understand how neural networks couple, within and across brains, to create and share information that enables social understanding and connectedness. We intend to develop a novel brain-to-brain coupling framework to better understand how social concepts are represented in the brain and how they are transferred across brains to achieve group cohesiveness. The overall objective of this application is to identify mechanisms that facilitate coupling across a speaker and a listener during real–life interaction. This approach is innovative because it uses new experimental paradigms optimize for studying social interactions in naturalistic contexts, employs both fMRI and fNIRS methods (single scanning and hyperscanning), and includes development of novel analysis methods for modeling shared brain responses to complex, natural social stimuli in real-life contexts. This contribution is significant because the proposed research will provide new insights into a central function of the human brain: the ability to connect with others by dynamically and interactively creating and sharing social knowledge. The work proposed in this application will advance knowledge of how brains process and share information in ways that promote social understanding and will produce new approaches for detecting and diagnosing communication and developmental disorders.