THERMODYNAMIC COMPRESSION OF LOGIC: A Theoretical Spatiotemporal Graph Neural Network (ST-GNN) Framework for High-Fidelity Intuition

Abstract

ABSTRACT Classical cognitive psychology has historically explored intuition as a heuristic, an evolutionary mental shortcut often susceptible to cognitive biases (System 1). This paper proposes an alternative framework grounded in the HEPOE Theory (High Entropy Predictive Organization Efficiency), suggesting that intuition might be more accurately modeled as deductive reasoning operating in a state of thermodynamic superconductivity. By analyzing human cognition through the lens of non-equilibrium thermodynamics and Spatiotemporal Graph Neural Networks (ST-GNN), we hypothesize that high-fidelity intuition functions as a biological survival mechanism optimizing energy efficiency. It is proposed as the thermodynamic compression of massive empirical datasets into executable commands, potentially bypassing the high Erasure Cost () of linear prefrontal deduction. Furthermore, we outline a three-tiered spectrum of intuitive processing, ranging from heuristic bias to localized expertise and transversal systemic intuition arguing that predictive accuracy strongly correlates with the empirical dataset volume and the topological complexity of the network processed under systemic friction (Ω). Keywords: High-Fidelity Intuition. Thermodynamic Compression. .Spatiotemporal Graph Neural Networks (ST-GNN). HEPOE Theory. Landauer’s Principle. Predictive Processing. Cognitive Heuristics.