Neural Correlates of Existential Engagement: A Conceptual and Computational Model of First-Person Reflection versus Analytical Detachment

This paper presents a theoretical and computational framework for exploring the dynamics of first-person reflective cognition versus analytical detachment. We model the interactions of key neural networks—Default Mode Network (DMN), Central Executive Network (CEN), and Salience Network (SN)—as a stochastic dynamical system, from which emergent cognitive modes E(t), representing reflective engagement, and A(t), representing analytical processing, arise. Hybrid states, identified conceptually as x_d approximately equal to x_e, capture moments of co-activation between these modes, suggesting a dynamic interplay between introspection and detachment. Sensitivity and phase-plane analyses reveal regions of heightened responsiveness and potential hybrid cognition, offering insights into how parameter variations influence cognitive stability. Importantly, all results are derived theoretically, based on the analytical structure of the governing equations; no empirical simulations were performed. This framework bridges computational neuroscience and philosophical inquiry, providing a formalized model to interpret the oscillatory tension between subjective reflection and objective analysis, and highlighting the potential for theoretical models to illuminate the architecture of human cognition.