Functional levels of consciousness for computational modeling
This research proposes a computational framework for minimal functional consciousness based on recursive predictive simulation, viability regulation, and dynamic self-modeling. Instead of treating consciousness as a discrete module or static representation, the model conceptualizes consciousness as an emergent regulatory process arising from the continuous interaction between monitoring, validation, simulation, and action. The proposed Prediction Simulation Model (PSM) integrates biologically inspired functional mappings associated with interoception, error detection, emotional evaluation, executive prediction, episodic reconstruction, and motor modulation. Through recursive feedback loops, the system continuously compares its internal state, predicted futures, and environmental conditions to preserve adaptive stability under uncertainty.
Within this framework, conscious awareness emerges from the coherent integration of self-referential monitoring and future-oriented simulation across closed feedback cycles. The model introduces the concept of dynamic viability attractors, where agency and awareness correspond to the maintenance of coherent trajectories in a multidimensional state space. By combining principles from cognitive architectures, predictive processing, neuroscience, and dynamical systems theory, this research provides a unified perspective in which consciousness is understood as a self-regulating emergent phenomenon linked to adaptive behavior, systemic continuity, and recursive predictive control in artificial agents.