Abstract

This paper introduces the state space theory of consciousness, positing that the cortex processes information through delay coordinate embedding operationalized by recurrent neural network engines. This leverages the power of Takens' theorem, giving rise to representations of reality as points within state space. Consciousness is posited to arise at the highest order engines amongst hierarchical and parallel engine pathways. Consciousness is cast as a dynamic process rather than as a neuronal state, reconciling dualist intuitions with a monist perspective. Neuronal representations develop uniquely in each individual due to history-dependent training of these engines, accounting for the privacy of qualia while also addressing cortical plasticity and the heuristic nature of cortical processing. Posited engines exhibit non-linear dynamics that are sensitive to initial conditions, explaining phenomena such as ambiguous figure interpretation and offering a pathway to explaining free will. The state space theory aligns with and expands upon major theories (e.g.higher-order theories, global workspace theories, integrated information theory), essentially providing a computational mechanism that unifies elements of these theories. Future work will explore neural mechanisms and validate predictions.