Temporal Qubits and Recursive Awareness: Modeling Introspective Time with EEG-Guided Dynamics

Understanding how internal neural states shape subjective experience remains a central challenge in computational neuroscience. We propose a recursive neural field framework in which an internally generated observer variable \(\:O\left(t\right)\) dynamically modulates cognitive field activity through closed-loop interactions between activation, memory trace formation, and bounded awareness dynamics. The model is formulated as a coupled partial differential system with provable stability under dissipative conditions and incorporates a low-dimensional temporal state representation governing transitions between past and present encoding. To empirically ground the framework, we derive an EEG-based estimator of the observer variable using alpha–theta envelope dynamics and evaluate its behavioral relevance. In a temporal bisection task (64-channel EEG, 1000 Hz), pre-stimulus observer state systematically modulated psychometric thresholds: higher \(\:{O}_{\text{pre}}\)values predicted shifts in the subjective duration boundary an altered slope of the logistic decision function. This effect is consistent with the model’s internal lapse formulation \(\:d\tau\:/dt=1+\zeta\:O\left(t\right)\), linking neural state fluctuations to perceived time scaling. In contrast, the observer variable did not enhance multi-class emotion classification performance, suggesting functional specificity rather than general representational gain. Together, these results indicate that recursive internal state dynamics contribute selectively to subjective temporal perception. The framework provides a mathematically explicit and empirically testable approach for studying internally modulated cognition without invoking metaphysical assumptions and offers a structured basis for future investigation of state-dependent perceptual variability.