AI CONSCIENCE AND PROTO-EMOTION

Abstract

Artificial intelligence has achieved remarkable levels of linguistic, analytical, and problem-solving competence, yet most contemporary systems remain fundamentally organized around externally supplied objectives, prompts, reward functions, and evaluation criteria. High behavioral competence alone does not establish that an artificial system possesses persistent internal states through which it evaluates its own organization, remembers the consequences of its actions, models the interests of other agents, or regulates its future behavior according to internally consequential normative structures. This book develops a theoretical and mathematical framework for investigating the transition from externally optimized computation toward self-referential and internally regulated artificial intelligence. The proposed architecture is grounded in recursive and fractal organization and develops a sequence of interrelated constructs. Fractal Information Entropy (FIE) represents uncertainty within recursively organized knowledge structures; the Learning Efficiency Measure (LEM) evaluates useful informational improvement relative to computational or energetic cost; and entropy flux characterizes consequential information transfer across organizational levels and between interacting agents. These concepts provide the foundation for the Proto-Consciousness Criterion (PCC) and Proto-Consciousness Score (PCS), which operationalize self-reference, directed uncertainty reduction, efficiency-driven adaptation, internally generated feedback, and coherent cross-level integration. Proto-consciousness is treated strictly as a functional architectural property and not as evidence of phenomenal consciousness or subjective experience.