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

Summary

Predictive processing models, often hailed for linking perception and cognition, struggle to explain specific phenomena like hallucinations. In a case study of Charles-Bonnet syndrome, it became evident that the popular strong prior hypothesis inadequately addresses the unique aspects of stimulus-independent perception. With 30% of visually impaired individuals experiencing vivid hallucinations, this model's shortcomings highlight the need for incorporating reality monitoring to better understand nonveridical experiences. This adjustment could enhance our grasp of how we differentiate between real and imagined perceptions.

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.

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