Figure 1.

(A) Schematic summary of the “Bayesian brain” notion that the brain contains an internal model consisting of beliefs about the states of the environment. These give rise to predictions about sensory inputs. The discrepancy between the actual and the predicted sensory inputs (prediction error) serves to update the model. Adapted from Figure 3 in Haker et al. (82), with permission. (B) An illustration of the concept of “beliefs” as probability distributions. Here, we consider Gaussian probability distributions (or, more precisely, densities) that are characterized by an expectation (or mean; represented by the vertical dashed line) and precision (inverse variance; symbolized by the horizontal double arrow). The x-axis (red) indicates the entity that the belief represents (e.g. the temperature of a particular object). The y-axis (violet) represents, simply speaking, the probability that is assigned to each possible instantiation of this entity (in the above example: the probability that object temperature has a particular value).