• Hugo Bottemanne.
• Institut du Cerveau - Paris Brain Institute, Institut National de la Santé et de la Recherche Médicale (INSERM) U1127, Centre National de la Recherche Scientifique (CNRS) Unité Mixte de Recherche, 7225 Paris, 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; 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. Electronic address: hugo.bottemanne@aphp.fr.
• Neuroscience. 2024 Dec 4.

AbstractBayesian brain theory, a computational framework derived from the principles of Predictive Processing (PP), proposes a mechanistic formulation of belief generation and updating. 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 the computational neuroscience of belief and show how this dynamic of prediction and updating offers a comprehensive account of the phenomenology of belief in psychiatry.Copyright © 2024. Published by Elsevier Inc.

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