Hallucinations and Strong Priors

Abstract

Hallucinations, perceptions in the absence of objectively identifiable stimuli, illustrate the constructive nature of perception. Here, we highlight the role of prior beliefs as a critical elicitor of hallucinations. Recent empirical work from independent laboratories shows strong, overly precise priors can engender hallucinations in healthy subjects and that individuals who hallucinate in the real world are more susceptible to these laboratory phenomena. We consider these observations in light of work demonstrating apparently weak, or imprecise, priors in psychosis. Appreciating the interactions within and between hierarchies of inference can reconcile this apparent disconnect. Data from neural networks, human behavior, and neuroimaging support this contention. This work underlines the continuum from normal to aberrant perception, encouraging a more empathic approach to clinical hallucinations.

Keywords: auditory verbal hallucinations; hallucinations; predictive coding; prior beliefs; psychosis.

Figure 1.. An Inferential Hierarchy.

Here we sketch the hierarchical message passing thought to underlie predictive coding and expand on the details of between- and within-layer computations. Sensory input is conveyed via ascending prediction errors in superficial pyramidal cells (red arrows, posited to be mediated by fast AMPA glutamate receptor signaling). Posterior expectations are encoded by the activity of deep pyramidal cells. These cells then provide descending priors (black arrows, mediated by slower and more diffuse NMDA and GABA signaling) that inform prediction errors at the lower level – instantiating the computations that remove the expected input, leaving prediction errors to be assimilated or accommodated, depending on their precision (depicted by the balance, hypothesized to be implemented via slower neuromodulators like dopamine, acetylcholine and serotonin, depending on the particular inferential hierarchy). Lateral interactions (horizontal arrows) mediated within-layer predictions about the precision of priors (black) and prediction errors (red).

Figure 2.. How Hierarchies Might Hallucinate

Problems lower in a hierarchy may generate simple hallucinations. Problems that pervade higher levels will engender more complex experiences. Consistent with the empirical data from humans viewing images generated from deep-networks perturbed at low and high levels [135]and DBM models of perception where in hallucinations only arise under the influence of the highest-level generative model [106]. Figure adapted from [135], licensed under a Creative Commons 4.0 International License (http://creativecommons.org/licenses/by/4.0/.)