Abstract
Background and Hypothesis: Hallucinations are characterized by disturbances in perceptual decision-making about environmental stimuli. When integrating across multiple stimuli to form a perceptual decision, typical observers engage in "robust averaging"by down-weighting extreme perceptual evidence, akin to a statistician excluding outlying data. Furthermore, observers adapt to contexts with more unreliable evidence by increasing this down-weighting strategy. Here, we test the hypothesis that hallucination-prone individuals (n=38 high vs n=91 low) would show a decrease in this robust averaging and diminished sensitivity to changes in evidence variance. Study Design: We used a multielement perceptual averaging task to elicit dichotomous judgments about the "average color"(red/blue) of an array of stimuli in trials with varied strength (mean) and reliability (variance) of decision-relevant perceptual evidence. We fitted computational models to task behavior, with a focus on a log-posterior-ratio (LPR) model which integrates evidence as a function of the log odds of each perceptual option and produces a robust averaging effect. Study Results: Hallucination-prone individuals demonstrated less robust averaging, seeming to weigh inlying and outlying extreme or untrustworthy evidence more equally. Furthermore, the model that integrated evidence as a function of the LPR of the two perceptual options and produced robust averaging showed poorer fit for the group prone to hallucinations. Finally, the weighting strategy in hallucination-prone individuals remained insensitive to evidence variance. Conclusions: Our findings provide empirical support for theoretical proposals regarding evidence integration aberrations in psychosis and alterations in the perceptual systems that track statistical regularities in environmental stimuli.
| Original language | English |
|---|---|
| Pages (from-to) | 59-68 |
| Number of pages | 10 |
| Journal | Schizophrenia Bulletin |
| Volume | 50 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2024 |
Keywords
- adaptive gain
- computational modeling
- liberal acceptance
- perceptual averaging
- psychosis
- schizotypy
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