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Geometric Hyperscanning of Affect under Active Inference

Nicolas Hinrichs, Mahault Albarracin, Dimitris Bolis, Yuyue Jiang, Leonardo Christov-Moore, Leonhard Schilbach

Active Inference. IWAI 2025. Communications in Computer and Information Science, vol 2857. Springer, Cham, 2026
arXiv: 2506.08599v4 - DOI (q-bio.NC)
12 pages excl. references, 2 figures, and 2 appendixes. Accepted at the 6th International Workshop on Active Inference

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.

Submitted to arXiv on 10 Jun. 2025