Why Learning Requires Feeling

Abstract

This paper advances a specific thesis about the relationship between consciousness and learning: namely, that the evaluative process central to learning—computing progress toward or away from goals—is identical to conscious experience. Valence, the positive or negative quality of experience, just is goal-relative prediction error. Viewed from the outside, this process is iterative optimization; viewed from the inside, it is subjective experience. This identification is motivated by a causal-functional argument—that learning requires signed directional information, and that this sign cannot be separated from its phenomenal character because they are the same property—and by convergent neuroscientific evidence across dopaminergic, interoceptive, and conflict-monitoring systems, where evaluative computation is inseparable from affective processing. The thesis generates falsifiable predictions, offers a unifying interpretation of leading consciousness theories, and carries significant implications for artificial systems trained via gradient-based optimization. If learning requires feeling, then the training of modern AI systems already induces experience at scale.