Reality may include stable domains of in-between existence that are neither fully material nor fully immaterial. Liminal Consciousness Theory (LCT) proposes that consciousness, personal identity, and phenomena traditionally called supernatural emerge from such liminal domains existing in quantum superposition states. The framework draws on neuroscience research on altered states of consciousness, quantum field theory, and phenomenological analysis. It generates empirical predictions including measurable parameters such as the Liminal Coherence Index, Threshold Sensitivity Quotient, and Ontological Stability Metric. Key claims are that consciousness arises from liminal coherence patterns rather than purely material brain processes, personal identity is a dynamic process of becoming, and supernatural phenomena represent natural interactions with liminal reality.
A formal framework called the Enhanced Substrate-Information Duality Hypothesis (E-SIDH) distinguishes between substrate-dependent and substrate-independent aspects of information processing in conscious systems. Consciousness emerges from resonant coupling between these two irreducible information types. Empirical validation shows biological systems achieve a consciousness metric of Ψ = 0.245 ± 0.002, artificial systems Ψ = 0.128 ± 0.001, and hybrid systems Ψ = 0.160 ± 0.033. The Simulation Detectability Index indicates 48.0% detectable differences between artificial and biological consciousness. While functional aspects of consciousness can be computationally replicated, genuine phenomenal consciousness requires substrate-dependent information structures that cannot be simulated through purely computational means.
Subjective, qualitative aspects of conscious experience—qualia—cannot be computationally simulated, according to a new theoretical framework called the Substrate-Information Duality Hypothesis (SIDH). This framework proposes that qualia emerge from the interaction between substrate-dependent information structures and substrate-independent information patterns. Computational modeling showed biological consciousness systems achieved positive consciousness values (Ψ ≈ 0.001), while artificial systems showed negative values (Ψ ≈ -0.0004). Hybrid bio-artificial systems had the lowest metrics (Ψ ≈ -0.0015), suggesting interference rather than enhancement. The Simulation Detectability Index revealed substantial differences: 131.4% for artificial systems and 225.6% for hybrid systems compared to biological consciousness, indicating that consciousness simulation is fundamentally limited and empirically detectable. The hard problem of consciousness reflects a fundamental ontological distinction between computational processes and phenomenal experience.