Research Summary: CARDIO-Affect: A Hamiltonian-Variability Framework for Spatio-Temporal Emotional Pattern Recognition with Manifold-Based Individual and Group Profiling

A theoretical framework named CARDIO-Affect has been developed for the analysis of long-term emotional dynamics within defined social groups. This framework addresses the complexities of naturalistic emotion, which often includes multi-stable attractors, weak chaos, long-range memory, and sparse heterogeneous coupling, features not captured by conventional short-clip facial emotion analysis. It models individual emotion as a multi-stable nonlinear stochastic dynamical system and group emotion as an emergent property of a sparsely coupled network.

Why it matters

Understanding complex, long-term emotional dynamics within groups is critical for fields requiring nuanced human interaction analysis, such as organizational behavior, public safety, and consumer sentiment. This framework offers a scientific approach to quantifying and predicting these dynamics, moving beyond superficial, short-term analyses to provide deeper insights into group cohesion, stability, and potential emergent behaviors.

Key insights

• CARDIO-Affect is a complex-systems theoretical framework for long-term emotional dynamics in bounded social groups.
• It incorporates explicit uncertainty quantification at every layer of analysis.
• Long-period naturalistic emotion in stable small groups exhibits complex system characteristics such as multi-stable attractors, weak chaos, long-range memory, and sparse heterogeneous coupling.
• Conventional short-clip facial-emotion analysis is insufficient to capture these complex emotional dynamics.
• The framework treats individual emotion as a multi-stable nonlinear stochastic dynamical system.
• Group emotion is conceptualized as an emergent macrostate within a sparsely-coupled network.
• The framework is formalized through six propositions and four foundational pillars, grounded in statistical mechanics with neural-parameterised Hamiltonian Stochastic Differential Equations (SDEs).

Source

arXiv — Computers and Society — https://arxiv.org/abs/2510.16046