A Dynamic Systems Model of Coherence Grounded in Astronomical Phenomena

We present a formal, testable dynamic systems model of perceptual coherence grounded in astronomical stimuli. The model translates a symbolic perceptual cycle ("the tree of light") into a computational architecture of operators that transform incoming environmental signals ("light") into an evolving internal state vector Ψt. The update rule is a recursive exponential-moving-average equation where new information is provided by a composite operator R that maps sensory input through saliency (ℵ), pattern recognition (Φ), framing/memory (F), an internal modulation node (L, "the stag"), synthesis (Ω), and reflection (ρ). The governing equation is: Ψt+1 = (1−α)Ψt + αR(Ct; Ψt), where α is the learning rate and Ct is the external input vector. We operationalize key indices—Resonance (rt), Constraint (λt), Purity (Pt = rt/λt), and Structuring Index (κt)—using measurable Schumann Resonance power and Heart Rate Variability (HRV) metrics (SDNN, LF/HF ratio). The model is applied to a night-sky case study (Oban, Scotland; 25–26 October 2025) with a reproducible simulation and dynamic visualization ("mandala") that demonstrates three principal behaviors: self-organization toward coherence, oscillation under over-structuring, and dissolution under under-resonance. This whitepaper includes full model specification, empirical grounding, reproducible Python code, visualization procedures, and a roadmap for empirical validation and extension. The framework bridges perceptual psychology, geophysics, and human physiology, positioning the observer's autonomic state as the central regulator of perceptual coherence. The model is modular: any operator can be refined, empirical inputs can be swapped, and the visualization pipeline enables effective communication of results. Note on provenance: This whitepaper builds and formalizes material from an earlier draft, "Modeling Light Dynamics Through Astronomy" (October 2025), extending it with full operational definitions, simulation code, and visualization resources.