Emotional Physics: A Dynamical Systems Model of Affective Energy, Awareness, and Interaction

Neuro-Emotional Field Theory (NEFT) introduces a physics-inspired framework for understanding human emotions as measurable, dynamical processes. Instead of treating affect as vague or metaphorical “energy,” NEFT formalizes it as a low-dimensional state vector evolving under the influence of inertia, damping, appraisal potentials, awareness, and stochastic drives. At its core, NEFT proposes an “emotional Newton’s law” — a second-order dynamical equation governing how emotions shift in response to stimuli, self-regulation, and noise. Different potential landscapes capture homeostasis, multi-stable moods, or resonance with rhythmic inputs such as music or thought-waves. Extending further, NEFT defines an interpersonal coupling model that predicts emotional entrainment and synchronization between people. The framework is empirically testable: biosignals such as EEG, heart-rate variability, electrodermal activity, facial EMG, and voice prosody provide observable proxies for latent emotional states. A stability proof via Lyapunov energy, resonance frequency-response predictions, and canonical experiments (recovery, resonance sweeps, hysteresis, dyadic coupling) give NEFT a falsifiable scientific backbone. NEFT aims to unify affective science, dynamical systems, and human–AI interaction, offering both theoretical insight and practical tools for affective computing, therapeutic design, and emotionally intelligent companions.