Consciousness Science (State Space Theory)

January 1, 2024

This is my primary theoretical research program.

The State Space Theory (SST) of consciousness proposes that consciousness is constituted by hierarchical delay coordinate embedding in plastic recurrent neural networks. The core ideas require explanation. Delay coordinate embedding is a technique from dynamical systems theory: you take a time series and arrange time-delayed copies of the signal into a higher-dimensional space, reconstructing the geometry of the underlying attractor. Takens’ theorem guarantees that this reconstruction preserves the topology of the original system. SST holds that biological neural networks perform this operation on their own activity, and that the resulting reconstructed state space is the physical substrate of conscious experience.

I published the core theory in the Journal of Consciousness Studies in 2025. A unification paper extending SST and connecting it to existing frameworks (IIT, Global Workspace Theory, Higher-Order Theories) is published in Nonlinear Dynamics, Psychology, and Life Sciences (2026).

Computational Dynamic Monism

SST requires a metaphysics. Computational Dynamic Monism (CDM) provides it. CDM holds that computational and physical descriptions of neural processes are two aspects of a single substrate, unified through the mathematics of dynamical systems. This avoids both substance dualism and eliminativism by grounding mental properties in the topological and geometric structure of neural state spaces. The CDM paper has been submitted to Synthese.

Mathematical Foundations

With Alessandro Selvitella at Purdue Fort Wayne, I work on the formal underpinnings of SST. The central question: under what conditions do smooth embeddings exist in contracting recurrent networks? This is not an abstract exercise. SST makes a specific claim about what kinds of dynamical systems can support consciousness, and the math has to back it up. We characterize the class of systems capable of delay coordinate reconstructions under biologically plausible constraints: contraction, plasticity, finite dimensionality. The preprint is on arXiv.

The Unfolding Argument

With Selvitella and Aaron Schurger (Chapman University; INSERM-CEA, Paris), I address the unfolding argument against computational theories of consciousness. Doerig et al. (2019) claimed that any feedforward network can replicate the input-output mapping of a recurrent network, and therefore computational structure cannot distinguish conscious from non-conscious systems. We show this argument fails when plasticity and temporal dynamics are taken into account. Recurrent networks with plasticity generate state space structures that feedforward unfoldings cannot reproduce. The unfolding argument assumes away exactly the features that matter.

Chinese Rooms

I analyze Searle’s Chinese Room argument through the lens of SST and dynamical systems theory. Searle’s thought experiment conflates static symbol manipulation with the temporally extended, geometrically structured computation that SST identifies as constitutive of understanding. The Chinese Room, as described, lacks the recurrent dynamics and plasticity required to generate the relevant state space embeddings. It is not a counterexample to computational theories of mind; it is a counterexample to a theory nobody holds.

Publications and Preprints

Full publication list