Bottemanne H, Longuet Y, Gauld C
Paris Brain Institute - Institut du Cerveau (ICM), Sorbonne university/CNRS/Inserm, Paris, France; Sorbonne University, Department of Philosophy, SND research unit, UMR 8011, CNRS, Paris, France; Department of psychiatry, Pitié-Salpêtrière hospital, DMU Neuroscience, Sorbonne university, Assistance publique-Hôpitaux de Paris (AP-HP), Paris, France.
Department of psychiatry, Claude Bernard Lyon 1 university, 69000 Lyon, France.
Encephale. 2022 Aug;48(4):436-444. doi: 10.1016/j.encep.2021.09.011. Epub 2022 Jan 7.
The question of how the mind works is at the heart of cognitive science. It aims to understand and explain the complex processes underlying perception, decision-making and learning, three fundamental areas of cognition. Bayesian Brain Theory, a computational approach derived from the principles of Predictive Processing (PP), offers a mechanistic and mathematical formulation of these cognitive processes. This theory assumes that the brain encodes beliefs (probabilistic states) to generate predictions about sensory input, then uses prediction errors to update its beliefs. In this paper, we present an introduction to the fundamentals of Bayesian Brain Theory. We show how this innovative theory hybridizes concepts inherited from the philosophy of mind and experimental data from neuroscience, and how it translates complex cognitive processes such as perception, action, emotion, or belief, or even the psychiatric symptomatology.
心智如何运作的问题是认知科学的核心。它旨在理解和解释感知、决策和学习这三个认知基本领域背后的复杂过程。贝叶斯大脑理论是一种源自预测处理（PP）原则的计算方法，它为这些认知过程提供了一种机械论和数学的表述。该理论假设大脑对信念（概率状态）进行编码，以生成关于感官输入的预测，然后利用预测误差来更新其信念。在本文中，我们对贝叶斯大脑理论的基本原理进行介绍。我们展示了这一创新理论如何将源自心智哲学的概念与神经科学的实验数据相结合，以及它如何转化诸如感知、行动、情感或信念等复杂的认知过程，甚至是精神症状学。
