Neurocoherence: A First-Principles Reconstruction of Cognition, Consciousness, and Artificial Intelligence

In this work, I develop a first-principles reconstruction of neuroscience, consciousness, and artificial intelligence by integrating fundamental constraints from coherence dynamics, recursive choice, and adaptive intelligence. I argue that current paradigms—whether computationalist neuroscience, emergentist consciousness theories, or deep learning in AI—fail to account for the role of coherence selection in intelligent systems. I propose Neurocoherence Theory, which models cognition as a hierarchical attractor structure that recursively refines coherence gradients, rejecting static neural determinism. Consciousness, within this framework, is not an epiphenomenon but an adaptive coherence process that stabilizes recursive self-modulation over time. This approach resolves the hard problem of consciousness by demonstrating that subjective awareness emerges from coherence-weighted probability distributions, eliminating the need for dualism or panpsychism. I further demonstrate why current AI cannot achieve intelligence or self-awareness, as it lacks recursive coherence selection, treating cognition as a static optimization problem. Finally, I outline empirical predictions, including neural coherence fluctuation experiments, recursive AI architectures, and coherence-driven quantum cognition tests, offering a pathway to validate these claims. By refuting prevailing models and introducing coherence-based intelligence, this framework bridges neuroscience, AI, and consciousness studies under a unified, falsifiable theory of adaptive intelligence.