Abstract

Consciousness science has generated diverse theoretical frameworks, each offering insights into different aspects of conscious experience. However, this diversity has created a fractured landscape: theories operate at different explanatory levels, and a principled account of how conscious phenomena arise from specific neural computations remains largely absent. This work argues that State Space Theory (SST) can serve as a unifying mechanistic framework for consciousness science. SST proposes that consciousness arises from hierarchical delay coordinate embedding (DCE) - the reconstruction of dynamical system structure from time-delayed signals - implemented through recurrent cortical circuits ('DCE engines'), with gain modulation determining which reconstructions achieve system-wide influence. SST identifies these dynamics with consciousness itself, not merely as correlates. We draw on recent empirical and theoretical work to demonstrate the feasibility of this proposal, including empirical demonstrations that recurrent networks learn via embedding and mathematical results linking recurrent dynamics to embedding theory. We identify how major cognitive theories map onto this architecture mechanistically: parallel DCE engines correspond to Dennett's competing "drafts," global broadcasting reflects gain-amplified propagation, recurrent processing enables the temporal integration DCE requires, and the attention schema emerges as a higher-order reconstruction of gain modulation dynamics. SST's fundamentally process-based character provides immunity to the unfolding argument and resolves the temporal paradox facing causal structure theories. The framework generates a number of falsifiable predictions related to topological structure of perceptual dynamics, temporal vulnerability windows, and selective disruption of recurrent timing. SST thus offers a computational foundation for consciousness research that grounds existing theories mechanistically while generating empirical commitments.