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A beautiful loop: An active inference theory of consciousness.

Ruben Laukkonen, Karl Friston, Shamil Chandaria

Neuroscience and biobehavioral reviews September 1, 2025 DOI: 10.1016/j.neubiorev.2025.106296 via PubMed

Summary

AI-generated from the abstract

A theoretical paper proposes that active inference can model consciousness through three conditions: a world model (epistemic field) defining what can be known, inferential competition (Bayesian binding) selecting only coherent inferences that reduce long-term uncertainty, and epistemic depth—a recursive sharing of beliefs throughout a hierarchical system like the brain. This loop allows the world model to know itself non-locally and continuously evidence that knowing, distinct from self-consciousness. The authors formally propose a hyper-model for precision-control whose latent states encode global weighting rules, enacting epistemic agency and flexibility reminiscent of general intelligence. The theory also addresses altered states, meditation, and the full spectrum of conscious experience.

Study at a glance

Characteristics Theoretical or philosophical paper Peer reviewed
Topics Dreaming Meditation
Keywords Active inference Artificial intelligence Awareness Bayesian inference Computational modelling
Citations 17
Key finding Active inference can model consciousness via three conditions: a world model, inferential competition (Bayesian binding), and epistemic depth, with a hyper-model for precision-control that enacts epistemic agency.

Abstract

Can active inference model consciousness? We offer three conditions implying that it can. The first condition is the simulation of a world model, which determines what can be known or acted upon; namely an epistemic field. The second is inferential competition to enter the world model. Only the inferences that coherently reduce long-term uncertainty win, evincing a selection for consciousness that we call Bayesian binding. The third is epistemic depth, which is the recurrent sharing of the Bayesian beliefs 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 distinct from self-consciousness, because the world model knows itself non-locally and continuously evidences this knowing (i.e., field-evidencing). Formally, we propose a hyper-model for precision-control, whose latent states (or parameters) encode and control the overall structure and weighting rules for all layers of inference. These globally integrated preferences for precision enact the epistemic agency and flexibility reminiscent of general intelligence. This Beautiful Loop Theory is also deeply revealing about altered states, meditation, and the full spectrum of conscious experience.

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