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To the probabilistic-predictive theory of consciousness

Igor F. Mikhailov

Philosophy of Science and Technology December 12, 2025 DOI: 10.21146/2413-9084-2025-30-2-19-32 (opens in new tab)

Summary

AI-generated from the abstract

Consciousness is examined as a mechanism that helps living systems maintain a stable, non-equilibrium state by continuously generating and updating predictions based on sensory input. Organisms operate as probabilistic models that minimize prediction errors and optimize interaction with the environment. The article discusses Bayesian cognitive science, applying principles of Bayesian inference to understand cognitive processes, and shows how predictive processing and free energy minimization contribute to innovative theories of consciousness. It provides a brief overview of existing theories that derive consciousness from hierarchical Bayesian inference, and outlines aspects for a future unified theory, emphasizing an interdisciplinary approach.

Study at a glance

Characteristics Theoretical or philosophical paper Peer reviewed
Key finding Proposes that consciousness functions as a mechanism for maintaining a non-equilibrium stable state through continuous prediction updating based on sensory input, grounded in Bayesian inference and free energy minimization.

Abstract

The article is dedicated to the probabilistic-predictive theory of consciousness, which examines the relationship between cognitive processes and the mechanism of predicting the surrounding environment. It focuses on the concept that living systems operate as probabilistic models, minimizing prediction errors and optimizing their interaction with the surrounding world. A significant part of the article presents consciousness as a mechanism that helps maintain a non-equilibrium stable state, where organisms continuously generate and update predictions based on sensory input. The concept of Bayesian cognitive science is discussed, which applies principles of Bayesian inference to understand cognitive processes. The article demonstrates how the principles of predictive processing and free energy mini­mization contribute to the development of innovative theories of consciousness based on these principles. A brief overview of existing theories is provided, which derive phenomena of consciousness from various aspects of hierarchical Bayesian inference – a for­mal system underlying the understanding of living organisms as non-equilibrium systems that maintain homeostasis through the continuous updating of predictions regarding their environment. Aspects that should be considered in the future unified theory of conscious­ness are outlined, emphasizing the need for an interdisciplinary approach to creating such a theory.

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