Many biological organisms interact with one another by transmitting and perceiving social signals. These include gaze, facial expression, and body pose that convey information about the individual’s internal state, including their focus of attention, intended actions, and emotional level. Despite the ubiquity and potential value of these social interactions, understanding how neural activity gives rise to social interactions is still a largely uncharted area. Past experiments were conducted either by subjective behavioral observations in natural social settings or by quantifiable methods such as neuroimaging in restricted social settings. This project will address these limitations by leveraging the project team's recently developed high resolution motion capture system, which can measure, detect, and quantify natural social behaviors and their corresponding neural activity. This research will open new opportunities to study early behavioral markers, such as those for at-risk children with autism spectrum disorder, schizophrenia, and obsessive-compulsive disorder.
The project team's main innovation is a new statistical model called social states, designed to encode the social context of joint behaviors. These social states will be associated with neurophysiological activities in two brain regions, the dorsolateral prefrontal cortex (dLPFC) and dorsal anterior cingulate cortex (dACC), both located in the prefrontal cortex. Using the neural correlates of social states, the project will develop a novel method to model the dynamics of social state transitions, facilitating an understanding of how these two brain regions are responsible for processing social signals. While the project will focus on specific brain regions, the planned research will provide a general computational foundation for understanding the complex social behaviors of macaques. The planned research will advance the computational understanding of cognitive and neural processes by learning from millions of neurobehavioral data points in real, unrestricted environments. Developing such a computational model is a complex, real world problem, requiring a new holistic, transformative, and integrative approach. The planned solution is built upon domain knowledge from multiple disciplines, including machine learning, primate physiology, neuroscience, and computer vision.
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.
