Figure 2. Model schematic for Hierarchical Gaussian Filter (HGF) model.

a. The HGF is a generative model of sensory observations, combining states of the world (X1-3) and sensory evidence (U) into percepts and behavioral responses (in combination with a response model) in a specific experimental context. In this case, the HGF is modeling responses during the Auditory Conditioned Hallucinations task (Powers et al., 2017). Crucially, this model can be inverted based upon data (i.e., actual participant responses and sensory evidence strength) to provide estimates of beliefs about states of the world (μ1-3) are generated from a combination of incoming sensory information and prior beliefs (X1) about those states, which can be differentially weighted (nu) in terms of their contribution to updating these parameters at some time later (t). These prior beliefs are themselves modified by beliefs about those beliefs (x2), and beliefs about those beliefs (x3), each of which is also affected by an evolution rate (w1 and w2, respectively), which can be thought of as the overall volatility of beliefs across time. b-c. Shown here are possible “maps” of hallucination space, or possible combinations of model parameters (from the model detailed in figure 2) that are predicted to lead to more or less hallucinations. Part a shows a hypothetical model space based on simulations generated to be consistent with trends observed in previous work: namely that as beta and nu increase, the chance of hallucinations increases as well. Part b shows a similar plot generated directly from data from a single study, that shows the power of such a mapping approach. What we see in this figure are local pockets of high and low hallucinogenic potential that do not map as neatly onto linear trends of individual model parameters, but rather reflect hallucinogenic “hotspots” that may or may not map onto individuals who are experiencing hallucinations in the moment.