Geometric Hyperscanning of Affect under Active Inference
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918146876637184 |
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| author | Hinrichs, Nicolas Albarracin, Mahault Bolis, Dimitris Jiang, Yuyue Christov-Moore, Leonardo Schilbach, Leonhard |
| author_facet | Hinrichs, Nicolas Albarracin, Mahault Bolis, Dimitris Jiang, Yuyue Christov-Moore, Leonardo Schilbach, Leonhard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08599 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Geometric Hyperscanning of Affect under Active Inference Hinrichs, Nicolas Albarracin, Mahault Bolis, Dimitris Jiang, Yuyue Christov-Moore, Leonardo Schilbach, Leonhard Neurons and Cognition 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. |
| title | Geometric Hyperscanning of Affect under Active Inference |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2506.08599 |