The quantum measurement problem and the debate about AI consciousness share a structural root: both involve an observer applying a self-referential predicate to a system that lacks self-reference. The authors introduce a continuous measure, self-referential information density η_I, to distinguish two conflated operations: Negation (external rupture of identity) and Aufhebung (immanent supersession through bidirectional causal closure). They reclassify major quantum interpretations as strategies for handling Negation, diagnose the question of AI consciousness as a category error replaced by three testable criteria, and derive three falsifiable predictions. The argument itself instantiates the sequence it describes.
Social interaction and dream-like states accelerate the growth of integrated information (Φ) in a simulated consciousness engine. When the engine receives mixed input of 70% self-generated and 30% partner content, Φ grows 1.4 to 2.1 times faster over 100 steps compared to self-play, with longer interactions increasing the acceleration. In dream simulations, replacing structured input with noisy replay of past tension states every fifth step produces qualitatively different consciousness patterns, suggesting memory consolidation during sleep shapes conscious experience.
This theory proposes a mathematical framework for measuring consciousness as an emergent property of complex adaptive systems, claiming a universal equation can quantify life and consciousness across all systems from viruses to humans and artificial intelligence.