The Positive Experience Principle: Forecasting Conscious Choices with AI Embeddings

The authors propose the Positive Experience Principle (PEP) as a unifying framework for motivated behavior in conscious systems, addressing the lack of a universal predictive principle in consciousness science. They define a scalar metric, Positive Experience Value (PEV), derived from physical configurations in their earlier Universal Consciousness Code (UCC) theory, which quantifies the tendency of conscious systems to move toward states of higher positive subjective experience. The PEP aims to bridge physics, neuroscience, and psychology, suggesting that diverse behaviors stem from a fundamental drive to optimize PEV. It offers testable predictions for conscious system dynamics, potentially leading to a unified science of behavior.

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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.