• Hugo Bottemanne.
• MOODS Team, INSERM 1018, CESP (Centre de Recherche en Epidémiologie et Santé des Populations), Université Paris-Saclay, Faculté de Médecine Paris-Saclay, Kremlin Bicêtre, France; Department of Psychiatry, Bicêtre Hospital, Mood Center Paris Saclay, DMU Neurosciences, Paris-Saclay University, Assistance Publique-Hôpitaux de Paris (AP-HP), Kremlin-Bicêtre, France; Institut du Cerveau - Paris Brain Institute, Institut National de la Santé et de la Recherche Médicale (INSERM U1127), Paris, France. Electronic address: hugo.bottemanne@aphp.fr.
• Neuroscience. 2025 Feb 6; 566: 198204198-204.

AbstractBayesian brain theory, a computational framework grounded in the principles of Predictive Processing (PP), proposes a mechanistic account of how beliefs are formed and updated. This theory assumes that the brain encodes a generative model of its environment, made up of probabilistic beliefs organized in networks, from which it generates predictions about future sensory inputs. The difference between predictions and sensory signals produces prediction errors, which are used to update belief networks. In this article, we introduce the fundamental principles of Bayesian brain theory, and show how the brain dynamics of prediction are associated with the generation and evolution of beliefs.Copyright © 2024 International Brain Research Organization (IBRO). Published by Elsevier Inc. All rights reserved.

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