The neural mechanisms that underlie and promote social interaction and communication remain elusive, despite a large number of evidence-based studies in psychology and psychiatry. This is partially due to the lack of instruments and technology that can directly measure natural human interactions. The proposed project intends to develop a dyadic functional magnetic resonance imaging (dfMRI) system to directly observe the brains of pairs of individuals while they are interacting with each other visually and by touch. The project also hopes to develop a computer model to quantify these interactions. Developing this technology will improve the level of knowledge about how brains work in complex environments. Findings from this project will serve the national interest by assisting in the design of social robots, which will interact more naturally with humans. Project findings may also help in identifying the neural underpinnings of mental disorders, particularly those that affect communication.

This project will leverage a pioneering dfMRI technology and methodology that can simultaneously scan two people in one commercial MRI scanner, and has generated successful feasibility tests in observing and analyzing brain-to-brain coupling in live face-to-face communication. The first aim will be to develop a state-of-the-art dfMRI technology that will significantly improve temporal resolution and imaging quality by implementing local static magnetic field shimming and parallel imaging. The second aim will be to use the new system to address two fundamental hypotheses in brain-to-brain interactions: (1) Face-to-face communication (measured by dfMRI) recruits more brain faculty that the Internet-based communications (measured by MR hyperscanning); therefore, it conveys more social and affective information. (2) A multi-channel cerebral network model - derived from dfMRI data - may be trained to deduce some basic social attribution processes in dyadic interaction; thus, a causal relationship between social behaviors and brain network can be identified. Overall, this is a transformative, high-risk, high-payoff interdisciplinary proposal integrating engineering and social and behavioral science to enable new technology and methods to study human social interaction, mental disorders, and social robots.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Graduate Education (DGE)
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Ellen Carpenter
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Columbia University
New York
United States
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