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

Bayesian Brain Theory, rooted in Predictive Processing, provides a mathematical and mechanistic account of how the mind works. It posits that the brain encodes probabilistic beliefs to generate predictions about sensory input, then updates those beliefs using prediction errors. This theory integrates concepts from philosophy of mind with neuroscience data to explain perception, decision-making, learning, action, emotion, and even psychiatric symptoms. The paper introduces the fundamentals of this computational approach, showing how it translates complex cognitive processes into a unified framework.

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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.