CRCNS US-French Research Proposal: Computational and Neural Mechanisms of Social Learning in Social Networks

In addition to experiential learning, where people learn from the outcomes of their own choices, people also acquire a substantial amount of knowledge by observing friends, relatives, and others within their social network, a phenomenon referred to as social learning. Not surprisingly, social networks play a crucial role in transmission of information across society.

This project examines the relationship between social and experiential learning to shed light on mechanisms by which the brain learns from interactions with others in a social network. In addition to the research, the project also includes interdisciplinary experiences for trainees and outreach to K-12 students from underrepresented backgrounds.

A deeper understanding of the neurocomputational mechanisms of social learning within social networks requires identifying both fundamental computational principles and the brain systems that perform these computations. This project aims to establish a basis for such understanding by leveraging computational modeling and model-based neuroimaging experiments to explore the similarities and differences between one-shot averaging typically assumed in social learning and error-driven reinforcement learning.

Specifically, the project examines how the uncertainty and volatility of the environment, along with the topology of the social network, influence the preference for one-shot averaging versus error-driven updating. It also aims to identify brain areas involved in these learning processes and their arbitration. By doing so, it provides a unique link between the fields of cognitive, behavioral, and social neuroscience that could fundamentally advance our understanding of social cognition.

A companion project is being funded by the French National Research Agency (ANR).

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.