Mind, Motion and Multiscale Emergence: Exploring the Thermodynamics of Cognitive Experiences

This article presents a novel theoretical framework that reimagines cognitive dynamics through dynamical systems theory and thermodynamic principles. Beginning with an intuitive pixel-experience analogy, a mathematical approach based on attractor dynamics, Lyapunov functions, and weak/strong Markov blankets is then used to describe how cognitive states continuously stabilize representations under metabolic constraints, while dynamically updating beliefs through prediction-driven attractor shifts, ensuring adaptive inference. The framework unifies perception, memory, and emotion within a non-Bayesian predictive model that formalizes the emergence of both computational and linguistic capabilities via recursive self-organization. By embedding cognition within thermodynamic and geometric principles, this work bridges neural mechanics with phenomenological experience, providing new insights into consciousness, learning, and pathological states, while suggesting testable hypotheses for theoretical neuroscience and AI.