The Positive Experience Principle: Forecasting Conscious Choices with AI Embeddings
Zheng Su, Mingyan Fang
arXiv (Cornell University) July 18, 2026 preprint DOI: 10.48550/arxiv.2607.16659 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Keywords | Consciousness Metric unit Value mathematics Path computing Computational model Code set theory Artificial neural network Cognitive science Cognitive psychology Artificial intelligence Epistemology |
| Key points | Proposes that conscious systems inherently tend toward states of higher positive subjective experience, quantified by a Positive Experience Value (PEV) metric derived from the Universal Consciousness Code (UCC) theory. |
Abstract
A fundamental challenge in the science of consciousness is the lack of a universal, predictive framework for motivated behavior. While existing theories excel at describing specific mechanisms, from neural pathways to computational models, they do not provide a foundational principle that explains the consistent direction of conscious systems toward certain states and away from others. To address this gap, we propose the Positive Experience Principle (PEP), a unifying principle stating that conscious systems have an inherent tendency to move toward states of higher positive subjective experience. This tendency is quantified by a Positive Experience Value (PEV), a scalar metric derived from the physical configurations defined by our earlier Universal Consciousness Code (UCC) theory. The PEP bridges physics, neuroscience, and psychology by positing that diverse behaviors are manifestations of a single, fundamental drive to optimize PEV. The PEP generates testable predictions for the dynamics of conscious systems, offering a path toward a unified science of behavior.