Cited by 177Schizophrenia research and treatmentVisual perception and processing mechanismsHallucinations in medical conditions

The DOI matches its Crossref record; 91 of the 92 references it cites were found in a registry or an index; 5 of the 104 sources its claims cite were checked for support.

What visual illusions teach us about schizophrenia

Open access
Notredame, Charles-Edouard, Pins, Delphine, Deneve, Sophie, Jardri, Renaud·2014·Frontiers in Integrative Neuroscience

Abstract

Illusion, namely a mismatch between the objective and perceived properties of an object present in the environment, is a common feature of visual perception, both in normal and pathological conditions. This makes illusion a valuable tool with which to explore normal perception and its impairments. Although still debated, the hypothesis of a modified, and typically diminished, susceptibility to illusions in schizophrenia patients is supported by a growing number of studies. The current paper aimed to review how illusions have been used to explore and reveal the core features of visual perception in schizophrenia from a psychophysical, neurophysiological and functional point of view. We propose an integration of these findings into a common hierarchical Bayesian inference framework. The Bayesian formalism considers perception as the optimal combination between sensory evidence and prior knowledge, thereby highlighting the interweaving of perceptions and beliefs. Notably, it offers a holistic and convincing explanation for the perceptual changes observed in schizophrenia that might be ideally tested using illusory paradigms, as well as potential paths to explore neural mechanisms. Implications for psychopathology (in terms of positive symptoms, subjective experience or behavior disruptions) are critically discussed.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This review argues that schizophrenia is often associated with diminished susceptibility to visual illusions, interpreted as an overweighting of sensory evidence relative to prior knowledge within a hierarchical Bayesian framework. It proposes circular inference as a unifying account linking altered visual perception to hallucinations and delusions.

Key findings

  • A growing number of studies support reduced susceptibility to visual illusions in schizophrenia; Dakin et al. (2005) found the contrast-contrast effect was diminished in schizophrenia (n=15) compared with healthy and psychiatric controls (n=20), with patients more accurate in contrast discrimination. Replications and extensions include surround suppression, binocular depth inversion, apparent motion, and motion-induced blindness.
  • Resistance to illusions has been linked to positive symptoms: Keane et al. (2013) found resistance to binocular depth inversion positively correlated with need for structured treatment and positive symptoms; Sanders et al. (2013) found apparent motion illusion strength inversely correlated with delusional belief conviction. However, other studies found no or only weak relationships.
  • Neurophysiological evidence includes reduced visual cortex GABA in schizophrenia (Yoon et al., 2010), with lower GABA correlating with less biased contrast discrimination despite misleading context; Dima et al. (2009, 2010) reported reversed effective connectivity patterns (predominance of feedforward modulation) in patients during the hollow-mask illusion.
  • Bayesian integration via predictive coding and circular inference models explains symptoms through imbalance between prior and sensory evidence; circular inference predicts either overweighting of sensory evidence (lower illusion susceptibility) or prior knowledge (higher illusion susceptibility) depending on impaired upward or downward loops. Visual hallucinations have a point prevalence of 27% in the psychosis spectrum (Waters et al., 2014), and schizophrenia affects approximately 1% of the general population (McGrath et al., 2008).

Methods

  • Narrative review of published visual illusion experiments in schizophrenia and pro-psychotic states, with findings summarized in Table 2; no new empirical data or meta-analysis are reported.
  • Reviewed paradigms include surround suppression, binocular depth inversion, apparent motion, motion-induced blindness, three-flash, contour detection, and spatial frame illusions, in patients with schizophrenia, first-episode psychosis, prodromal states, bipolar/major depression, substance or sleep-deprivation models, and controls.
  • Main analysis is qualitative synthesis and theoretical integration into hierarchical Bayesian inference, including predictive coding and circular inference frameworks.

Limitations

  • Stated: findings are heterogeneous; negative or nuanced results often involve surround-suppression illusions, which may be affected by methodological issues and covariates; lack of statistical power may explain some studies; the conflict between Bayesian hypotheses (over- vs under-weighted prediction errors) is unresolved, and neurophysiological causes remain unclear.
  • Stated: the paper calls for more rigorous methodology, precise classifications, and conceptual definitions.
  • Evident from text: as a narrative review, it does not describe a systematic search strategy, inclusion criteria, or formal quality appraisal; many cited studies have small samples (e.g., n=7–38 in several paradigms), and clinical correlations are inconsistent across studies.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3389/fnint.2014.00063

Cites

91 of 91 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Figure 1 Main classical illusions. In the Ebbinghaus (A), Ponzo (B) and Müller-Lyer (C) illusions, same-sized patterns are misevaluated because of the context. In the Poggendorff illusion (D), the context disrupts the impression of continuity. Herman's grid (E) generates illusory gray points at each intersection of the white lines. The Boring wife/mother-in-law (F), the Necker Cube (G) and Rubin's Maltese Cross (H) are ambiguous figures that result in different interpretations.
Figure 1 Main classical illusions. In the Ebbinghaus (A), Ponzo (B) and Müller-Lyer (C) illusions, same-sized patterns are misevaluated because of the context. In the Poggendorff illusion (D), the context disrupts the impression of continuity. Herman's grid (E) generates illusory gray points at each intersection of the white lines. The Boring wife/mother-in-law (F), the Necker Cube (G) and Rubin's Maltese Cross (H) are ambiguous figures that result in different interpretations.CC BY · PubMed Central
Figure 2 An example of a context suppression effect: the Ebbinghaus illusion. Depending on whether the peripheral circles are large or small, the central targets appear smaller or larger, respectively. When comparing two targets, the actual difference (here, left one smaller compared with right one) (A) appears reduced with the misleading context (B) or increased with the facilitating context (C).
Figure 2 An example of a context suppression effect: the Ebbinghaus illusion. Depending on whether the peripheral circles are large or small, the central targets appear smaller or larger, respectively. When comparing two targets, the actual difference (here, left one smaller compared with right one) (A) appears reduced with the misleading context (B) or increased with the facilitating context (C).CC BY · PubMed Central
Figure 3 Necker Cube. (A) The ambiguity results in the subjective impression of two interpretations switching: the phenomenon of bistability. (B) The shadow introduces a cue that is supposed to bias perception toward one interpretation. The fact that bistability persists despite the presence of the cue ensures this cube responds to the definition of a visual illusion, i.e., a dissociation between perception and the physical characteristics of its support.
Figure 3 Necker Cube. (A) The ambiguity results in the subjective impression of two interpretations switching: the phenomenon of bistability. (B) The shadow introduces a cue that is supposed to bias perception toward one interpretation. The fact that bistability persists despite the presence of the cue ensures this cube responds to the definition of a visual illusion, i.e., a dissociation between perception and the physical characteristics of its support.CC BY · PubMed Central
Figure 4 Hierarchical inference with Gaussian variables. In this toy example (left part), the inference that corresponds to the hidden cause (x) could be understood as the probability that the green color that I am observing (sensory evidence, represented by the magenta arrows) is due to the presence of a leaf, given my knowledge of the existence of a tree (prior expectation, represented by the violet arrows). Blue and yellow lines fit for the feedback and feedforward connections that enable the inferential process. Green and black circles fit for the controlling inhibitory system. The right part of the figure represents the probability distribution of each variable and the resulting posterior probability (in red).
Figure 4 Hierarchical inference with Gaussian variables. In this toy example (left part), the inference that corresponds to the hidden cause (x) could be understood as the probability that the green color that I am observing (sensory evidence, represented by the magenta arrows) is due to the presence of a leaf, given my knowledge of the existence of a tree (prior expectation, represented by the violet arrows). Blue and yellow lines fit for the feedback and feedforward connections that enable the inferential process. Green and black circles fit for the controlling inhibitory system. The right part of the figure represents the probability distribution of each variable and the resulting posterior probability (in red).CC BY · PubMed Central
Figure 5 Circular inference and relationship with predictive coding. If both descending and climbing loops are impaired (A), both sensory evidence and prior knowledge are reverberated and over counted (multiplication of the pink and violet arrows). In the Predictive coding framework, this results in an overconfident (narrowing of the posterior distribution) but not biased (unchanged K) inferred belief. In contrast, when the impairment only affects climbing loops (B), sensory evidence, but not prior knowledge, is reverberated. The prediction error is emphasized (K is too large), and the inferred belief is biased toward sensory evidence. If, in contrast, the inhibitory disequilibrium disfavors the descending loops (C), only the prior knowledge is over counted because of its reverberation. The prediction error is then minimized (K is too small), and the resulting posterior is biased toward expectations. Note that case (B,C), the inferred belief is associated with an excessive degree of confidence.
Figure 5 Circular inference and relationship with predictive coding. If both descending and climbing loops are impaired (A), both sensory evidence and prior knowledge are reverberated and over counted (multiplication of the pink and violet arrows). In the Predictive coding framework, this results in an overconfident (narrowing of the posterior distribution) but not biased (unchanged K) inferred belief. In contrast, when the impairment only affects climbing loops (B), sensory evidence, but not prior knowledge, is reverberated. The prediction error is emphasized (K is too large), and the inferred belief is biased toward sensory evidence. If, in contrast, the inhibitory disequilibrium disfavors the descending loops (C), only the prior knowledge is over counted because of its reverberation. The prediction error is then minimized (K is too small), and the resulting posterior is biased toward expectations. Note that case (B,C), the inferred belief is associated with an excessive degree of confidence.CC BY · PubMed Central

Verification

Claims the paper makes

71 Own interpretation · 61 Not checked yet · 8 No basis given · 2 Partly supported · 1 Supported · 1 Supported so far · 1 Own method · accuracy not yet measured

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Show all 81 claims

  • Probably not a substantial claim. 31
  • It may state two separate claims as one. 39
64 of 64
  • The authors state this as an assumption.

    Perception is closely interwoven with other high-level functions such as belief genesis or reasoning.

    “In line with the assumption that perception is closely interwoven with other high-level functions (such as belief genesis or reasoning; Fletcher and Frith, 2008)”
  • The authors state this as an assumption.

    The Bayesian formalism considers perception as the optimal combination between sensory evidence and prior knowledge.

    “The Bayesian formalism considers perception as the optimal combination between sensory evidence and prior knowledge, thereby highlighting the interweaving of perceptions and beliefs.”
  • The authors state this as an assumption.

    Sensory inputs are inherently ambiguous.

    “Bayesian models set uncertainty and belief as the core features of perception by considering sensory inputs as inherently ambiguous.”
  • The authors state this as an assumption.

    In the brain's hierarchy, top-down and bottom-up beliefs should be propagated only once in each direction, and this requires equally strong inhibitory loops to cancel excitatory feedforward/feedback loops.

    “To properly compute the probability of perceptual variables, the prior and likelihood must be multiplied only once. In the brain's hierarchy, top-down and bottom-up beliefs should be propagated only once in each direction (see figure above). This can be achieved if equally strong inhibitory loops exist to cancel excitatory feedforward/feedback loops (green and black units).”
  • The paper relies on this without support.

    Long-range connections in the brain are overwhelmingly excitatory.

    “Since long-range connections in the brain are overwhelmingly excitatory, these two types of messages would be reverberated endlessly through feed-forward/feedback excitatory loops if they were not controlled, and kept in check, by the presence of equivalently strong inhibitory loops.”
  • The authors state this as an assumption.

    In Predictive Coding, the prior and the likelihood have a Gaussian distribution.

    “Predictive coding applies the Bayes rule while assuming that the prior and the likelihood have a Gaussian distribution.”
  • The authors state this as an assumption.

    During the vast majority of daily life situations, patients resolve the moderate ambiguity of environmental stimuli through strong and unambiguous percepts but with weakened links and coherence between perceptual elements.

    “Although the hypothesis requires further investigation, we can assume that during the vast majority of daily life situations, patients resolve the moderate ambiguity of the environmental stimuli through strong and unambiguous percepts but with weakened links and coherence between perceptual elements (links typically implicitly driven by prior knowledge).”
  • The paper relies on this without support.

    The necessity to adapt to highly ambiguous situations is not permanent.

    “Importantly, because the necessity to adapt to highly ambiguous situations is not permanent, this may also account for the intermittent nature of hallucinations (Jardri et al., 2013).”
  • The paper relies on this without support.

    When ambiguity is particularly strong, visual illusions represent an extreme perceptual task.

    “When the ambiguity is particularly strong, VIs represent an extreme perceptual task.”
9 of 9

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Citations

Cites

Research works printed but not linked: 3

  1. Gregory, 1973, The confounded eye, 49
  2. Müller-Lyer, 1889, Optische urteilstäuschungen, Arch. Für Anat. Physiol. Physiol. Abt, 2, 263
  3. Ponzo, 1910, Intorno ad alcune illusioni nel campo delle sensazioni tattili, sull'illusione di Aristotele e fenomeni analoghi, Arch. Für Gesamte Psychol, 16, 307

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Metadata

Authors
Notredame, C.-E., Pins, D., Deneve, S., & Jardri, R.
Affiliations
  • Université Lille Nord de France (FR)
  • Inserm (FR)
Venue
Frontiers in Integrative Neuroscience
Published
2014-08-12
Volume
8
Pages
63-63
ISSN
1662-5145 (ISSN-L)
OpenAlex
W2049841753
Cited by
177
References
92
Citations by year
2026: 15 · 2025: 8 · 2024: 17 · 2023: 17 · 2022: 16 · 2021: 10 · 2020: 17 · 2019: 11 · 2018: 17 · 2017: 19 · 2016: 13 · 2015: 14 · 2014: 3
FWCI
2.26
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Integrative Neuroscience (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Hallucinations in medical conditions
Keywords
optical illusions, schizophrenia, visual perception, Bayesian inference, perceptual priors, perceptual belief, positive symptoms, subjective experience, neural mechanisms of perception

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