Acoustic-Driven Coherence and Attractor Transitions in High-Dimensional Potential Landscapes: A Free Energy Principle Approach to Biological and Cognitive Self-Optimization

Abstract:Understanding the mechanisms of self-optimization, cognitive state transitions, and observer-driven stabilization in complex biological systems remains a fundamental challenge in theoretical biophysics and cognitive neuroscience. This paper proposes a rigorous mathematical framework modeling biological and cognitive systems as non-equilibrium thermodynamic and information-geometric systems governed by Langevin dynamics and the Free Energy Principle. We formalize cognitive and psychological distress (e.g., systemic stagnation, hallucinations, and local minima entrapment) as high topological noise and deep local attractors within a high-dimensional latent potential landscape. To drive systemic self-optimization, we introduce a dual-mechanism model: (1) an external geometric reference frame functioning as an optimal universal attractor template, and (2) a forced acoustic-mechanical resonance input (vocal vibration) that injects controlled thermodynamic fluctuations while phase-locking internal neural/cellular oscillations. We demonstrate mathematically that the synergistic coupling of focused observational intent and acoustic coherence effectively minimizes the variational free energy, allowing the system to escape dysfunctional local minima and execute a robust phase transition toward a high-order coherent equilibrium state. Open-source simulation code implementing this dynamic state transition is provided as an appendix.