Visual Hallucinations Are Characterized by Impaired Sensory Evidence Accumulation: Insights From Hierarchical Drift Diffusion Modeling in Parkinson's Disease

Abstract

Background: Models of hallucinations emphasize imbalance between sensory input and top-down influences over perception, as false perceptual inference can arise when top-down predictions are afforded too much precision (certainty) relative to sensory evidence. Visual hallucinations in Parkinson's disease (PD) are associated with lower-level visual and attentional impairments, accompanied by overactivity in higher-order association brain networks. PD therefore provides an attractive framework to explore contributions of bottom-up versus top-down disturbances in hallucinations.

Methods: We characterized sensory processing during perceptual decision making in patients with PD with (n = 20) and without (n = 25) visual hallucinations and control subjects (n = 12), by fitting a hierarchical drift diffusion model to an attentional task. The hierarchical drift diffusion model uses Bayesian estimates to decompose task performance into parameters reflecting drift rates of evidence accumulation, decision thresholds, and nondecision time.

Results: We observed slower drift rates in patients with hallucinations, which were less sensitive to changes in task demand. In contrast, wider decision boundaries and shorter nondecision times relative to control subjects were found in patients with PD regardless of hallucinator status. Inefficient and less flexible sensory evidence accumulation emerges as a unique feature of PD hallucinators.

Conclusions: We integrate these results with evidence accumulation and predictive coding models of hallucinations, suggesting that in PD sensory evidence is less informative and may therefore be down-weighted, resulting in overreliance on top-down influences. Considering impaired drift rates as an approximation of reduced sensory precision, our findings provide a novel computational framework to specify impairments in sensory processing that contribute to development of visual hallucinations.

Keywords: Bayesian; Bottom up; Hierarchical drift diffusion model; Parkinson’s disease; Perception; Precision; Top down; Visual hallucinations.