Figure 3.

A general scheme of “Bayesian brain” theories of cognition. Here, the overall goal is to minimize prediction errors. Prediction errors represent, simply speaking, the difference between actual sensory input (or sensation, green arrow) and a prediction about the input which originates from a prior belief (red arrow). Minimization of the prediction error can either be achieved by updating the brain’s model (perceptual inference, e.g., according to predictive coding; middle part of figure) or by choosing actions such that beliefs are fulfilled, and the predicted sensory inputs occur (active inference; lower part of figure). In addition to inference and action, hierarchical Bayesian models also allow for forecasting future states and offer an opportunity to integrate metacognition as a top-level that monitors levels of prediction errors (upper part of figure). Reprinted from Petzschner et al. (25), with permission from Elsevier.