[The predictive mind: An introduction to Bayesian Brain Theory]

Abstract

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.

Keywords: Bayesian brain; Bayesianism; Bayésianisme; Belief; Belief updating; Cerveau bayésien; Codage prédictif; Computational neuroscience; Computational psychiatry; Croyance; Interoception; Mise à jour des croyances; Neurosciences computationnelles; Predictive coding; Predictive processing; Traitement prédictif.