A Unified Computational Framework for Subjective Experience and Objective Reality: A Solution to the Hard Problem of Consciousness

Abstract

This work advances the computational account of mind and consciousness, distinguishing them from high-performance computing. We adopt the Turing-theoretic interpretation and argue that the mind is best modelled as a universal Turing machine equipped with formally unbounded memory. Physical systems, including quantum computers, von Neumann architectures, organoids, and the brain as a biological device, are bounded by finite resources. In contrast, we posit that the mind operates with unbounded representational capacity at the level of a formal device. We develop an architecture in which a sequential chaotic finite-state controller interacts with two interdependent, unbounded tapes that encode objective reality and subjective experience. Their bidirectional coupling yields co-arising streams of syntax and semantics. We integrate a Hilbert-space formalism to allow alignment with quantum mechanics where appropriate. We also introduce prime-based Gödel encoding to assign exact symbolic codes to subjective and objective contents. The infinite supply of primes for encoding and unbounded memory tapes for embedding them enables unbounded expressivity, a core capability of the mind. Self-reference naturally arises in this setting, creating feedback loops that drive deterministic chaos. We analyse this behaviour using the logistic map to explain sensitivity to initial conditions in conscious processes. The framework clarifies how subjective experience can be coupled to objective reality without exceeding classical computability. It challenges the view that consciousness requires extreme computation and instead locates the key differentiator in the unbounded expressivity of the mind. This has implications for human-in-the-loop oversight and governance of artificial systems. It also motivates a cautious reconsideration of consciousness in primitive life if a formal device with unbounded expressivity can be instantiated. We outline how these analogies support human-centred governance principles for artificial intelligence and guide future innovations in neurotechnology and other emerging human-centred technologies.