Abstract
Psychotic phenomena manifest in healthy and clinical populations as complex patterns of aberrant perceptions (hallucinations) and tenacious, irrational beliefs ( delusions). According to predictive processing accounts, hallucinations and delusions arise from atypicalities in the integration of prior knowledge with incoming sensory information. However, the computational details of these atypicalities and their specific phenomenological manifestations are not well characterized. We tested the hypothesis that hallucination-proneness arises from increased reliance on overly general application of prior knowledge in perceptual inference, generating percepts that readily capture the gist of the environment but inaccurately render its details. We separately probed the use of prior knowledge to perceive the gist vs the details of ambiguous images in a healthy population with varying degrees of hallucination- and delusion-proneness. We found that the use of prior knowledge varied with psychotic phenomena and their composition in terms of aberrant percepts vs aberrant beliefs. Consistent with previous findings, hallucination-proneness conferred an advantage using prior knowledge to perceive image gist but, contrary to predictions, did not confer disadvantage perceiving image details. Predominant hallucination-proneness actually conferred advantages perceiving both image gist and details, consistent with reliance on highly detailed perceptual knowledge. Delusion-proneness, and especially predominance of delusion-proneness over hallucination-proneness, conferred disadvantage perceiving image details but not image gist, though evidence of specific impairment of detail perception was preliminary. We suggest this is consistent with reliance on abstract, belief-like knowledge. We posit that phenomenological variability in psychotic experiences may be driven by variability in the type of knowledge observers rely upon to resolve perceptual ambiguity.
Highlights
Hallucinations and delusions can be modeled within a predictive processing framework, in which perceptions and beliefs represent the brain’s best inference about the causes of its sensory inputs.[1,2,3] This framework posits that sensation is inherently ambiguous; the brain must compare sensory measurements to predictions, akin to “perceptual hypotheses,”[4,5] drawn from preexisting knowledge and infer the most likely cause of those sensations
We tested the hypothesis that psychosis-proneness in a nonclinical sample would entail a computational shift in perceptual inference toward generating percepts based on poorer fit between sensory inputs and predictions derived from prior knowledge
Psychosis-proneness should predict better extraction of coarse perceptual gist in situations that heavily rely on predictions, but an impairment in extracting fine perceptual details
Summary
Hallucinations and delusions can be modeled within a predictive processing framework, in which perceptions and beliefs represent the brain’s best inference about the causes of its sensory inputs.[1,2,3] This framework posits that sensation is inherently ambiguous; the brain must compare sensory measurements to predictions, akin to “perceptual hypotheses,”[4,5] drawn from preexisting knowledge and infer the most likely cause of those sensations. The relative influences of sensory evidence and prior knowledge in this integration are determined by their reliabilities[6]: when sensory information is unreliable, predictions should be weighted more strongly, and vice versa. The reliabilities of sensory information and prior knowledge shape learning. Disagreement between predictions and sensory inputs generates “prediction errors” that could reflect meaningful changes in environmental states necessitating new learning, ie, changing one’s predictions by updating internal models.[7,8] Importantly, learning should be scaled to the reliability of information sources, with large changes in internal models taking place only when prediction errors are reliable.[9,10,11]
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