Fig. 2.

(A) Schematic model of altered hierarchical inference in the visual system. Sensory input represents processing in early visual cortex. Low-level “sensory” beliefs are encoded at the next higher hierarchical level, eg, mid- or high-level visual areas, and high-level “conceptual” beliefs at the highest cortical levels, eg, PFC. Arrows represent top-down signaling of prior beliefs and bottom-up signaling of prediction errors (PEs), with arrow thickness representing their respective precisions. The putative decrease in precision of low-level beliefs may lead to increased weighting of the sensory input, thus enhancing PEs, potentially compensated by increased precision of conceptual high-level beliefs.⁵⁷ Brain image courtesy of Flickr/IsaacMao. (B) Schematic representation of the Hierarchical Gaussian Filter (adapted from Mathys et al¹³). Levels $x_1^{(k)}$$x_2^{(k)}$$x_3^{(k)}$ represent hidden environmental states at time k. They depend on their immediately preceding values $x_2^{(k-1)}$$x_3^{(k-1)}$ and on the parameters κ (coupling of levels 2 and 3), ω (step size at level 2), and ϑ (learning speed about environmental volatility). The probability at each level is determined by the variables and parameters at the level above. The levels relate to each other by determining the step size of a random walk. Note that the levels of this model are not equivalent to those in (A). However, reduced learning speed about environmental volatility might contribute to a stronger top-down influence of high-level beliefs.