The Contrast-Field Account of Conscious Experience: How Self-Model Depth and the Learning–Automation Cycle Shape Consciousness
Zenodo (CERN European Organization for Nuclear Research) April 13, 2026 Praveen Gali
Consciousness emerges under novelty, fades with familiarity, and returns when expectations are disrupted. The Predictive Self-Model Consciousness framework proposes that the quality of conscious experience depends on both the magnitude of prediction error and the depth of an organism's automated self-model in the domain where the error occurs. A deeper self-model provides a richer background against which disruptions register, producing more textured, self-involving experience. Consciousness functions as a temporary adaptive mode modulated by a cycle of novelty, learning, and automation. The framework formalizes self-model depth through three proxies and specifies experimental protocols to test its predictions against competing accounts.