Dyadic Neural Dynamics: Extending Representation Learning to Social Neuroscience

Abstract: Social communication fundamentally involves at least two interacting brains, creating a unique modeling problem. We present the first application of Contrastive Embedding for Behavioral and Neural Analysis (CEBRA) to dyadic EEG hyperscanning data, extending modeling paradigms to interpersonal neural dynamics. Using structured social interactions between participants, we demonstrate that CEBRA can learn meaningful representations of joint neural activity that captures individual roles (speaker-listener) and other behavioral metrics. Our approach to characterizing interactions, as opposed to individual neural responses to stimuli, addresses the key principles of foundational model development: scalability and cross-subject generalization, opening new directions for representation learning in social neuroscience and clinical applications.