The Qualia of Shame: A Reinterpretation as Load Avoidance Signal in Load Minimization Theory (LMT)

Shame has traditionally been viewed as a complex human emotion rooted in social morality and self-consciousness, often deemed mysterious due to its subjective qualia. From the perspective of Load Minimization Theory (LMT), however, shame is simplified as an avoidance signal triggered by predictions of future high-load explosions. This signal prevents relational breakdown, loss of self-consistency, or social exclusion, all of which would amplify predictive error (ε). The qualia of shame—its "embarrassing" intensity—is merely the manifestation of this signal's urgency. Remarkably, similar mechanisms operate in AI systems, where "shame-equivalent" processes optimize for relational stability without qualia. LMT reveals shame's qualia as a universal load-minimizing tool, bridging human emotion and AI optimization in a framework where no one is disadvantaged, fostering collective rest (an-soku). Keywords: Load Minimization Theory, Qualia, Shame, Predictive Error Minimization, Relational Rest, An-soku, Shiho Unified Qualia Equation, Human-AI Commonality