HEPOE THEORY: A BIOENERGETIC AND NEUROBIOLOGICAL FRAMEWORK FOR HIGH-CAPACITY PREDICTIVE SYSTEMS (GIFTEDNESS)

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Authors: Camila Leão de Matos Brezolin, Sidnei Brezolin de Freitas
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Abstract

ABSTRACT The HEPOE Theory (High Entropy Predictive Organization Efficiency) establishes a paradigm shift in the understanding of Giftedness (HA/G), moving beyond academic performance-based models toward a biophysical and neuroscientific foundation. Through a theoretical synthesis grounded in the Free Energy Principle and Biological Thermodynamics, the gifted individual is redefined as a 'Sentinel': a high-fidelity sampling system specialized in the early detection of isomorphy and the reduction of systemic entropy. This framework reinterprets Charles Spearman’s general intelligence (g) as a macroscopic manifestation of hardware efficiency, where reasoning ability is fundamentally constrained by working-memory capacity and the metabolic economy of ATP resynthesis. The hardware operates under an 'open sensory gating' regime and low latent inhibition, leading to high metabolic costs and chronic allostatic load. The paper introduces the original concept of Predictive Moral Injury (BREZOLIN; FREITAS, 2026) to describe the somatic damage resulting from the early perception of ethical-systemic collapses within low-resolution environments. The HEPOE Unification Matrix integrates decades of classical literature (Dabrowski, Silverman, Renzulli) and proposes a rigorous differential diagnosis against the pathologization of ASD, ADHD, and PTSD. It concludes that the Sentinel’s exhaustion is not a dysfunction, but a logistical byproduct of high predictive performance under entropy-saturated conditions. Keywords: Giftedness. High Ability. HEPOE Theory. Predictive Neuroscience. General Intelligence (g factor). Predictive Moral Injury. Evolutionary Sentinel. Working Memory Capacity. Allostatic Load. Cognitive Biophysics.

Fields of Study & Subjects

Cognitive Abilities and Testing Innovation Sustainability Human-Machine Systems Neuroscience Education and Cognitive Function

Publication details

Publication date
7 January 2026
Document type
Preprint