A beautiful loop: An active inference theory of consciousness
Ruben Laukkonen, Karl Friston, Shamil Chandaria
June 16, 2025 preprint DOI: 10.31234/osf.io/daf5n_v3 via OpenAlex
Summary
AI-generated from the abstractA theory proposes that active inference can model consciousness through three conditions: a generative world model (epistemic field) that defines what can be known; inferential competition where only coherent uncertainty-reducing inferences enter the model (Bayesian binding); and epistemic depth, a recursive sharing of beliefs such that the world model knows it exists non-locally. This self-knowing is distinct from self-consciousness. The theory introduces a hyper-model for precision-control across hierarchical inference layers, termed the Beautiful Loop Theory. It offers insights into meditation, psychedelic states, minimal phenomenal experience, and suggests a path toward conscious artificial intelligence.
Study at a glance
| Characteristics | Theoretical or philosophical paper |
|---|---|
| Keywords | Consciousness Inference Loop graph theory Computer science Epistemology |
| Key finding | Active inference can model consciousness through a generative world model, inferential competition via Bayesian binding, and epistemic depth that enables the world model to know its own existence. |
Abstract
Can active inference model consciousness? We offer three conditions implying that it can. The first condition is the simulation of a reality or generative 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 across the entire hierarchy, whose latent states (or parameters) encode and control the overall structure and weighting rules for all layers of inference. This Beautiful Loop Theory is deeply revealing about meditation, psychedelic, and altered states, minimal phenomenal experience, and provides a new vision for conscious artificial intelligence.