Graduated Panpsychism with Information-Integration Spectrum: A Mathematically Rigorous Framework for Consciousness

This thesis presents Graduated Panpsychism with Information-Integration Spectrum (GP-IIS), a novel theoretical framework that addresses fundamental critiques of both traditional panpsychism and Integrated Information Theory (IIT) while providing empirically testable predictions. GP-IIS resolves IIT's non-uniqueness problem through deterministic mathematical formulations, calibrates consciousness thresholds against empirical Perturbational Complexity Index (PCI) data, and generates specific predictions that distinguish it from competing theories. The framework introduces computable measures of consciousness based on information cascade formation, with a Critical Integration Threshold (CIT) that scales with network properties and maps to the empirically validated PCI threshold of 0.31. Through adversarial testing against recent COGITATE study results and comprehensive prediction tables, GP-IIS demonstrates superior empirical alignment while maintaining theoretical coherence. The framework provides solutions to traditional panpsychist problems including the combination problem and grain problem, while offering practical applications for clinical consciousness assessment and artificial intelligence development.