Explanatory power by vagueness. Challenges to the strong prior hypothesis on hallucinations exemplified by the Charles-Bonnet-Syndrome.

Predictive processing models, often proposed as a unified theory of perception, action, and cognition, fall short when applied to specific phenomena like hallucinations. Using Charles-Bonnet Syndrome as a case study, the authors argue that the current predictive processing account—specifically the strong prior hypothesis—fails to capture essential characteristics of stimulus-independent perception. This omission has critical phenomenological implications. To address the explanatory gap, the authors propose incorporating reality monitoring into the strong prior hypothesis, enabling it to account for nonveridical perceptual experiences beyond just veridical percepts.

Study at a glance

Abstract

Predictive processing models are often ascribed a certain generality in conceptually unifying the relationships between perception, action, and cognition or the potential to posit a 'grand unified theory' of the mind. The limitations of this unification can be seen when these models are applied to specific cognitive phenomena or phenomenal consciousness. Our article discusses these shortcomings for predictive processing models of hallucinations by the example of the Charles-Bonnet-Syndrome. This case study shows that the current predictive processing account omits essential characteristics of stimulus-independent perception in general, which has critical phenomenological implications. We argue that the most popular predictive processing model of hallucinatory conditions - the strong prior hypothesis - fails to fully account for the characteristics of nonveridical perceptual experiences associated with Charles-Bonnet-Syndrome. To fill this explanatory gap, we propose that the strong prior hypothesis needs to include reality monitoring to apply to more than just veridical percepts.