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<p>Learning involves reducing the uncertainty of incoming information—does it reflect meaningful change (volatility) or random noise? Normative accounts of learning capture the interconnectedness of this uncertainty: learning increases when changes are perceived as meaningful (volatility) and reduces when changes are seen as noise. Misestimating uncertainty—especially volatility—may contribute to psychotic symptoms, yet studies often overlook the interdependence of noise. We developed a block-design task that manipulated both noise and volatility using inputs from ground-truth distributions, with incentivised trial-wise estimates. Across three general population samples (online <i>N</i>s = 580/147; in-person <i>N</i> = 19), participants showed normative learning overall. However, psychometric schizotypy and delusional ideation were linked to non-normative patterns. Paranoia was associated with poorer performance and reduced insight. All traits showed inflexible adaptation to changing uncertainty. Computational modelling suggested that non-normative learning may reflect difficulties inferring noise. This could lead one to misinterpret randomness as meaningful. Capturing joint uncertainty estimation offers insights into psychosis and supports clinically relevant computational phenotyping.</p>

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The joint estimation of uncertainty and its relationship with psychotic-like traits and psychometric schizotypy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月31日

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  • Toni Gibbs-Dean,
  • Teresa Katthagen,
  • Ruixin Hu,
  • Margaret L. Westwater,
  • Thomas Spencer,
  • Kelly M. J. Diederen

摘要

Learning involves reducing the uncertainty of incoming information—does it reflect meaningful change (volatility) or random noise? Normative accounts of learning capture the interconnectedness of this uncertainty: learning increases when changes are perceived as meaningful (volatility) and reduces when changes are seen as noise. Misestimating uncertainty—especially volatility—may contribute to psychotic symptoms, yet studies often overlook the interdependence of noise. We developed a block-design task that manipulated both noise and volatility using inputs from ground-truth distributions, with incentivised trial-wise estimates. Across three general population samples (online Ns = 580/147; in-person N = 19), participants showed normative learning overall. However, psychometric schizotypy and delusional ideation were linked to non-normative patterns. Paranoia was associated with poorer performance and reduced insight. All traits showed inflexible adaptation to changing uncertainty. Computational modelling suggested that non-normative learning may reflect difficulties inferring noise. This could lead one to misinterpret randomness as meaningful. Capturing joint uncertainty estimation offers insights into psychosis and supports clinically relevant computational phenotyping.