A beautiful loop: An active inference theory of consciousness
Can active inference model consciousness? We offer three conditions implying that it can. The first condition is the simulation of a world or reality model, which determines what can be known or acted upon. The second is inferential competition to enter the world model. Only the inferences that coherently reduce long-term uncertainty win, determining the threshold for consciousness and what we call Bayesian binding. The third is epistemic depth, which is the reflexive sharing of the world model throughout the system. Due to this recursive loop in a hierarchical system (such as a brain), the world model contains the knowledge that it exists. This is different from self- consciousness, because the entire world model non-locally knows itself and continuously evidences this knowing (i.e., field-evidencing). This Epistemic Depth Theory is deeply revealing about meditation and psychedelic states, minimal phenomenal experience, and provides a new vision for conscious artificial intelligence.
