Geometric Hyperscanning under Active Inference
Journal:
arXiv
Published Date:
Jun 10, 2025
Abstract
Second-person neuroscience holds social cognition as embodied meaning
co-regulation through reciprocal interaction, modeled here as coupled active
inference with affect emerging as inference over identity-relevant surprise.
Each agent maintains a self-model that tracks violations in its predictive
coherence while recursively modeling the other. Valence is computed from
self-model prediction error, weighted by self-relevance, and modulated by prior
affective states and by what we term temporal aiming, which captures affective
appraisal over time. This accommodates shifts in the self-other boundary,
allowing affect to emerge at individual and dyadic levels. We propose a novel
method termed geometric hyperscanning, based on the Forman-Ricci curvature, to
empirically operationalize these processes: it tracks topological
reconfigurations in inter-brain networks, with its entro-py serving as a proxy
for affective phase transitions such as rupture, co-regulation, and
re-attunement.
