From Differentiation to Cognition: UTD as a Model of Recursive Awareness

This paper introduces the Universal Theory of Differentiation (UTD) as a foundational framework for modeling cognition through structured acts of distinction. Instead of treating mental content as primitive, UTD posits that cognition emerges from recursive differentiations within a categorical hierarchy ∆ₙ, where each level represents structured differences between prior acts. Key cognitive functions—perception, memory, attention, and self-awareness—are expressed as stable fixpoints of differentiation, formalized via recursive morphisms Dₙ₊₁(δ, δ) = Iₙ. We demonstrate how UTD reframes existing theories such as Integrated Information Theory, Global Workspace Theory, and Predictive Processing, offering a structural alternative grounded in internal coherence rather than representational or statistical models. The paper further explores implications for cognitive architecture, suggesting that both human and artificial systems can be modeled as reflective differentiators rather than symbol processors or statistical predictors.