Perceptual multistability: a window for a multi-facet understanding of psychiatric disorders

Dana\'e Rolland; Pantelis Leptourgos; Philipp Sterzer; Renaud Jardri; Shervin Safavi

REVIEW 3 major objections 6 minor 209 references

Perceptual multistability: a window for a multi-facet understanding of psychiatric disorders

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper argues that perceptual multistability can serve as a non-invasive, transdiagnostic window into psychiatric disorders, bridging Bayesian inference and reinforcement learning and linking behavior to neural circuits.

desk verdict A useful, honest narrative review that overreaches slightly in its transdiagnostic framing; the binocular-rivalry evidence is solid but the cross-paradigm consistency is not. read the letter →

arxiv 2506.18176 v1 pith:JTIICRL7 submitted 2025-06-22 q-bio.NC

Shervin Safavi , Danaé Rolland , Philipp Sterzer , Renaud Jardri , Pantelis Leptourgos This is my paper · ORCID

classification q-bio.NC

keywords perceptualmultistabilitybinocularrivalryambiguousfiguresstructure-from-motioncomputationalpsychiatryBayesianinferencereinforcementlearningswitchrate

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This review argues that perceptual multistability—the spontaneous alternation between two or more interpretations of an unchanging ambiguous image—is a particularly informative window into psychiatric and neurodevelopmental disorders. The authors organize existing findings into three facets: how percepts are formed, how stable they are, and how often they fail, and show that each facet is altered in clinical populations. They then propose that these facets map onto the two main computational approaches in psychiatry: Bayesian inference, which treats perception as unconscious inference, and reinforcement learning, which treats perceptual switches as internal actions. If the synthesis is right, multistability tasks could become a non-invasive, transdiagnostic assay that ties altered perception to specific computational deficits and to neural circuits that can be studied across species. This is a synthesis paper, so its contribution is a unified framework and a roadmap, not new experimental data.

What carries the argument

The central object is the multistable percept itself: an ambiguous stimulus that supports more than one interpretation, studied through binocular rivalry, ambiguous figures, and structure-from-motion. Its behavioral readouts are switch rate and dominance duration, which the paper treats as trait-like indices of perceptual stability. The computational machinery has three parts: hierarchical Bayesian inference, including predictive coding and circular inference, where message-passing uses a correction factor $a$ to remove redundant information and $a<1$ produces overcounted evidence; reinforcement learning formulated as a partially observable Markov decision process (POMDP) in which a perceptual switch is an internal action chosen to maximize long-run utility; and the authors' integrative proposal to combine them by enriching the POMDP's belief update or by adopting active inference. These components carry the argument because they let clinical differences in percept formation, stability, and failure be restated as differences in prior weighting, precision, information-loop strength, reward sensitivity, and decision urgency.

What would settle it

Measure binocular rivalry and structure-from-motion switch rates in the same individuals across a transdiagnostic sample spanning bipolar disorder, depression, schizophrenia, anxiety, and controls; if cross-paradigm correlations are near zero or the direction of group differences reverses with paradigm, the shared-mechanism premise that carries the transdiagnostic synthesis would be falsified.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a single phenomenon—perceptual multistability—exposes three separable dimensions of perceptual dysfunction that recur across diagnoses: altered integration of priors, emotions, and sensory cues during percept formation; altered perceptual stability, most consistently increased stability in binocular rivalry for bipolar disorder, major depressive disorder, and schizophrenia, with decreased stability in anxiety disorders; and increased 'mixed' percept failures in autism and ADHD. The authors' claim is that these dimensions align with distinct computational accounts, so that the same behavioral task can bridge Bayesian and reinforcement-learning frameworks and link behavior to circuit-level mechanisms such as excitatory-inhibitory balance and dopaminergic modulation. They argue that multistability is therefore a practical tool for translational research, connecting human experiments to animal models and to transdiagnostic computational psychiatry.

Load-bearing premise

The load-bearing premise is that binocular rivalry, ambiguous figures, and structure-from-motion share enough of a common timing mechanism, and that switch rate and dominance duration are stable trait-like measures of it, so that findings from different paradigms and species can be pooled into a single transdiagnostic pattern.

Editorial extensions

If this is right

Switch rate and dominance duration could serve as transdiagnostic trait markers, detectable in first-degree relatives and along the psychosis spectrum before full diagnosis.
Because multistability is observed across species, the same behavioral readouts can be used in animal models, where causal tools can test whether altered stability arises from specific circuits or neuromodulators.
Reward manipulations during rivalry should modulate switch rates if switches are internal actions, giving clinical studies a handle on motivational and dopaminergic contributions to perception.
Bayesian and reinforcement-learning accounts can be unified, so that phenomena one account explains poorly—such as increased stability in depression or schizophrenia—can be reinterpreted as interactions between belief updating and value-based action selection.
Mixed percepts offer a separate axis tied to excitatory-inhibitory balance, potentially distinguishing neurodevelopmental conditions from mood and psychotic disorders.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Beyond the paper's claims, a direct test of its shared-mechanism premise would be to compute cross-paradigm correlations of switch rates within the same clinical subjects; the paper itself notes the rivalry–structure-from-motion correlation is limited, so low correlations would force the transdiagnostic story to separate into paradigm-specific stories.
Beyond the paper's claims, the reinforcement-learning account would predict that altering average reward rate—through monetary incentives, motivational state, or dopaminergic medication—should shift switch rates in clinical groups in the direction of opportunity-cost theory, a prediction testable in existing rivalry paradigms.
Beyond the paper's claims, if perceptual switches are internal actions, multistability could serve as a stripped-down model for other internally generated cognitive sequences such as mental simulation in planning, connecting this framework to cognitive-map research in psychiatry.
Beyond the paper's claims, a matrix design crossing the three facets (formation, stability, failure) with three paradigms could dissociate psychiatric symptom dimensions better than any single task, since the facets may map onto distinct computational parameters.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Dear colleague,

What you should know: this is a narrative review and research roadmap, not a primary report. There is no new data, no new equations, no model fits. Its value is in the synthesis and in the explicit acknowledgment of contradictions in the clinical literature. The authors are careful—they flag 'but see' references, color-code Table 1 by evidence level, and admit that SCZ findings go in opposite directions across paradigms. That is more honest than most reviews in this area.

What is actually new is modest: the proposal to bridge Bayesian and reinforcement-learning accounts of multistability by replacing the flat POMDP belief update with hierarchical inference, plus the active-inference reinterpretation of reward-modulated rivalry. These are sketched in a few paragraphs, with no worked examples. As a research agenda it is plausible; as a contribution it is thin.

The main soft spot is the transdiagnostic claim. The paper says there is compelling evidence for increased perceptual stability in binocular rivalry across psychiatric conditions, and takes this to support a transdiagnostic perspective. But the paper itself cites evidence that binocular rivalry and structure-from-motion switch rates correlate only weakly (Box 1, ref 33), and that in SCZ, binocular rivalry shows slower switching while other paradigms show reduced stability or null results. The authors do not hide this—they put the contradictions in italics in Table 1 and in the text. Yet the abstract and Figure 1d still present multistability as a general window on psychiatric disorders. That framing rests on a binocular-rivalry-specific effect. If these paradigms are not measuring the same latent computation, the transdiagnostic story is largely a rivalry story. This is a limitation in the internal synthesis, not an external attack; the cited literature itself contains the disconfirming pattern. Given the paper's embrace of a transdiagnostic window, this tension should have been addressed head-on, ideally with a quantitative check of whether the effects actually generalize across paradigms.

The theoretical scaffolding leans heavily on the authors' own prior work—circular inference, the bistable circular-inference model, and the RL account of multistability. That is not a flaw in a review, but it means the proposed Bayesian-RL bridge is built from one school's models and remains a verbal agenda.

Bottom line: this is a fair, well-written review that will help people entering multistability or computational psychiatry, and clinicians wanting a compact map of the evidence. It is not a quantitative synthesis, and the transdiagnostic language overstates cross-paradigm consistency. It deserves a serious referee and likely publication after revision, with more careful framing of the generality claim. I would not cite it as a source for transdiagnostic conclusions without checking the primary papers, but I would point newcomers to it as a starting point.

Referee Report

3 major / 6 minor

Summary. This perspective/review paper argues that perceptual multistability offers a transdiagnostic window into psychiatric and neurodevelopmental disorders. It organizes clinical findings into three facets—percept formation, percept stability, and percept failures—and then reviews two computational psychiatry frameworks, Bayesian inference (sampling, predictive coding, circular inference) and reinforcement learning (perceptual switches as internal actions in a POMDP). The central claim is that multistability can bridge these frameworks, link human and animal studies, and connect behavior to neural circuits, making it a promising non-invasive translational tool. The paper includes boxes with formal descriptions of predictive coding, circular inference, and POMDPs, a summary table of evidence, and an 'Outstanding questions' list.

Significance. If the central claim holds, multistability would be a valuable transdiagnostic assay that connects altered perception to specific computational deficits and to circuit-level mechanisms across species. The paper's strengths are its broad coverage of paradigms, species, and modalities; its explicit acknowledgment of contradictory findings; its self-contained mathematical boxes; and its concrete translational suggestions (animal models, pharmacology, genetics, online data collection). The synthesis is programmatic rather than demonstrative, but the clinical and computational integration it proposes is timely and could guide future meta-analyses and model-based studies. The main risk is that the transdiagnostic conclusion rests on pooling paradigms whose shared variance is weak, as the manuscript itself notes.

major comments (3)

[Percept stability; Box 1; Table 1] The transdiagnostic 'increased perceptual stability' claim is load-bearing for the Abstract and Figure 1, but it is built on pooling binocular rivalry (BR), ambiguous figures, and structure-from-motion (SFM) as one construct. The manuscript itself provides the evidence against this pooling: Box 1 states that BR and SFM switch rates show only limited within-individual correlation [33], and the Percept stability section reports opposite directions in schizophrenia across paradigms (slower BR [72,79–81], decreased stability for the Rubin vase [55], no difference for the Schröder staircase [54], reduced stability for intermittent SFM [78,82,83], and faster continuous-SFM switching [84]). Table 1 codes BR as green and relegates the contradictions to italics, so the 'compelling evidence' conclusion is effectively supported by the paradigm with the least established shared core. A meta-analysis with paradigm as a moderator, or within-subject cross-paradigm correlations in clinical groups, is needed before the transdiagnostic stability conclusion can stand.
[Table 1, color-code legend] The Table 1 legend defines red as 'low level of evidence: contradictory results across studies,' yet the SCZ, BD, and MDD rows are green while containing italicized contradictory references (notably [68] under SCZ and MDD). By the legend's own definition, contradictory results should downgrade the row to red, or the legend should explain why a contradiction does not meet that threshold. As published, the evidence grading is internally inconsistent and overstates support for the transdiagnostic claim.
[Toward a unified synthesis] The proposed integration of Bayesian inference and reinforcement learning is only sketched as two broad options—replacing a POMDP's belief update with hierarchical inference, or adopting active inference—without a concrete model, parameter mapping, or falsifiable prediction linking reward, priors, and dominance durations. Since the Abstract's bridging claim is a central contribution, at least one explicit computational example (e.g., a hierarchical-belief POMDP or an active-inference formulation) with a stated predicted dissociation would substantiate the synthesis rather than leaving it as an agenda.

minor comments (6)

[Toward a unified synthetis (heading)] The section heading 'Toward a unified synthetis' contains a typo; it should read 'synthesis'.
[Concluding remarks] The phrase 'two distincts domains' should be 'two distinct domains.'
[Box 1] Spelling is inconsistent: Box 1 uses 'Schroeder stairs' while the Glossary and text use 'Schröder staircase'; please standardize.
[Percept stability] Reference [68] is cited as a 'but see' for both MDD and SCZ, but the reference is a bipolar-disorder study; the intended contradictory evidence for those diagnostic groups should be re-checked and replaced with appropriate citations if needed.
[Reference list] Many entries lack volume, page, or year information (e.g., [2], [6], [7], [18], [23], [30], [36], [49], [51], [67], [68]), which hinders verification of the claims in Table 1 and the text; the bibliography should be completed.
[Box 3] The message-passing equations do not define the summation set or the relationship between M_ji and M_ij; adding one sentence to clarify indices would make the model self-contained.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation chain; self-citations are descriptive scaffolding, not forced inputs.

full rationale

This is a narrative synthesis and position paper rather than a derivation with fitted parameters or quantitative predictions. The clinical observations (altered perceptual stability, prior biases, mixed percepts) are taken from external empirical literature, and none of the equations in Boxes 2-4 are fitted to those observations and then reported back as predictions. Box 3 reproduces the Jardri-Denève message-passing equations, but the paper presents them as an existing computational account of circular inference, not as a parameter fitted to the multistability data. The reinforcement-learning framing of perceptual switches as internal actions is cited to the authors' own prior work (Safavi & Dayan, refs [5,148]), but it is offered as one interpretive lens alongside Bayesian and active-inference alternatives, and the paper explicitly lists at least two distinct integration routes. The claim that increased perceptual stability is transdiagnostic is an empirical summary that the paper itself qualifies with conflicting results across paradigms (Rubin vase, Schröder staircase, intermittent vs continuous SFM, and Killebrew et al.'s faster switching in psychosis), with Table 1 marking contradictory references in italics. That inconsistency is a correctness risk about paradigm comparability, not a circularity. No prediction is fitted to a subset of data and then reported as confirmation, no uniqueness theorem authored by the present group is invoked to force a choice, and no result is equivalent by construction to its inputs. The self-citations are frequent and provide much of the theoretical vocabulary, but they are published models with independent experimental support (e.g., ref [126]) and are not used as the sole admissible framework. A score of 1 reflects the substantial self-citation footprint without any load-bearing circular reduction.

Assumptions & free parameters 0 free parameters · 6 assumptions · 0 invented entities

The paper introduces no fitted parameters. Its central claims rest on domain assumptions imported from the computational psychiatry literature: Bayesian perception, POMDP-based internal action, cross-paradigm comparability, transdiagnostic validity, and the dopamine-as-opportunity-cost hypothesis. The most fragile of these is cross-paradigm comparability because the authors themselves report mixed correlations. No new entities are invented; 'internal actions' is a glossary term inherited from prior work.

assumptions (6)

domain assumption Perception is a form of Bayesian inference over hidden causes in a generative model
Invoked throughout 'Bayesian accounts of perceptual multistability' and formalized in Box 2 (Eq. 1). This is the foundational assumption of the predictive-coding framing the review adopts.
domain assumption Perceptual switches can be modeled as internal actions chosen to maximize cumulative utility in a POMDP
Introduced in 'Reinforcement learning accounts of perceptual multistability' and Box 4. This is the central modeling premise of the RL branch and is not derived in this paper.
domain assumption Binocular rivalry, ambiguous figures, and structure-from-motion share partially common mechanisms reflected in similar temporal dynamics
Box 1 asserts gamma-like dominance durations and similar blank-period effects across paradigms, and the 'Percept stability' section pools across paradigms to build the transdiagnostic pattern. The paper itself notes limited cross-paradigm correlations, so this premise is load-bearing but imperfectly tested.
domain assumption Psychiatric and neurodevelopmental conditions are better understood transdiagnostically, by shared computational dimensions, than by diagnostic categories alone
Adopted in the introduction and concluding remarks ('transdiagnostic fashion'), and used to interpret increased binocular rivalry stability across bipolar, depression, and schizophrenia as a shared phenotype.
standard math Bayes theorem, belief propagation, and gradient descent are valid mathematical tools for the described inference algorithms
Used in Boxes 2 and 3 (Eqs. 1-4); these are standard mathematical constructs, not ad hoc to this paper.
domain assumption Tonic dopamine encodes average reward rate and thereby controls the opportunity cost of time
Used in the RL section to link dopamine to faster perceptual switching, citing Niv et al. 2007. This external empirical theory is assumed as input.

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Cite this review

Pith. "Pith review of Perceptual multistability: a window for a multi-facet understanding of psychiatric disorders." pith.science (2026). https://pith.science/paper/JTIICRL7

@misc{pith2026250618176,
  author       = {Pith},
  title        = {Pith review of: Perceptual multistability: a window for a multi-facet understanding of psychiatric disorders},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JTIICRL7}},
  note         = {Machine review of arXiv:2506.18176}
}

read the original abstract

Perceptual multistability, observed across species and sensory modalities, offers valuable insights into numerous cognitive functions and dysfunctions. For instance, differences in temporal dynamics and information integration during percept formation often distinguish clinical from non-clinical populations. Computational psychiatry can elucidate these variations, through two primary approaches: (i) Bayesian modeling, which treats perception as an unconscious inference, and (ii) an active, information-seeking perspective (e.g., reinforcement learning) framing perceptual switches as internal actions. Our synthesis aims to leverage multistability to bridge these computational psychiatry subfields, linking human and animal studies as well as connecting behavior to underlying neural mechanisms. Perceptual multistability emerges as a promising non-invasive tool for clinical applications, facilitating translational research and enhancing our mechanistic understanding of cognitive processes and their impairments.

Figures

Figures reproduced from arXiv: 2506.18176 by the authors.

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