Semantic Parasitism: The Bio-Energetic Cost of High-Entropy Abstract Information in Human Neural Architectures

Modern psychiatry frequently encounters patients experiencing profound apathy, lethargy, and a complete loss of motivation. Traditionally, this is diagnosed through the lens of psychological distress or neurochemical imbalance. In this paper, I propose an alternative, computationally grounded framework: the Semantic Parasitism Hypothesis. I argue that what we often label as "depression" is actually a systemic, protective metabolic shutdown caused by the thermodynamic tax of processing ungrounded abstract information. By integrating Predictive Processing and Neural Energetics, I demonstrate how the human brain—an organ evolutionarily optimized for spatial-motor navigation—becomes trapped in infinite computational loops when trying to resolve abstract data that lacks physical anchors. When these non-terminating loops threaten cellular energy reserves, the organism initiates a biological circuit breaker to prevent mitochondrial damage. This framework reframes profound mental fatigue not as a psychological failure, but as a predictable mechanical response to computational overload, while offering testable, somatic-based interventions.