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<p>Predictive processing has revolutionized cognitive neuroscience, offering a comprehensive computational framework for understanding normative behavior and psychiatric illness. This narrative Review evaluates the role of predictive processing in understanding psychosis, revisiting the seminal work of Sterzer and colleagues. It consolidates recent experimental evidence on the alteration of priors and sensory likelihoods across different stages of psychosis in an attempt to reconcile top-down (that is, overly precise priors/noisy sensations) and bottom-up (that is, noisy priors/overly precise sensations) accounts. It evaluates predictive processing alterations across the continuum of psychosis, from non-clinical psychotic experiences to high-risk and first-episode psychosis to schizophrenia, exploring the explanatory potential of predictive processing as a transdiagnostic framework. We discuss the translational potential of predictive processing, including its use as a biomarker and in therapeutic interventions. We emphasize the need for standardized paradigms and longitudinal studies to advance predictive processing theories in clinical practice. By offering a unified theoretical perspective, this Review aims to inspire further research into the neuro-computational mechanisms underlying psychosis and enhance our understanding of psychiatric disorders.</p>

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Predictive processing accounts of psychosis: bottom-up or top-down disruptions

  • 期刊论文

  • 发表日期：

    2026年1月2日

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  • Isabella Goodwin,
  • Kelly M. J. Diederen,
  • Emily J. Hird,
  • Veith Weilnhammer,
  • Marta I. Garrido,
  • Franziska Knolle

摘要

Predictive processing has revolutionized cognitive neuroscience, offering a comprehensive computational framework for understanding normative behavior and psychiatric illness. This narrative Review evaluates the role of predictive processing in understanding psychosis, revisiting the seminal work of Sterzer and colleagues. It consolidates recent experimental evidence on the alteration of priors and sensory likelihoods across different stages of psychosis in an attempt to reconcile top-down (that is, overly precise priors/noisy sensations) and bottom-up (that is, noisy priors/overly precise sensations) accounts. It evaluates predictive processing alterations across the continuum of psychosis, from non-clinical psychotic experiences to high-risk and first-episode psychosis to schizophrenia, exploring the explanatory potential of predictive processing as a transdiagnostic framework. We discuss the translational potential of predictive processing, including its use as a biomarker and in therapeutic interventions. We emphasize the need for standardized paradigms and longitudinal studies to advance predictive processing theories in clinical practice. By offering a unified theoretical perspective, this Review aims to inspire further research into the neuro-computational mechanisms underlying psychosis and enhance our understanding of psychiatric disorders.