A Minimal Physiological Model of Perceptual Suppression and Breakthrough in Visual Rivalry
Bibliographic record
Abstract
Abstract Visual rivalry paradigms provide a powerful tool for probing the mechanisms of visual awareness and perceptual suppression. While the dynamics and determinants of perceptual switches in visual rivalry have been extensively studied and modeled, recent advances in experimental design - particularly those that quantify the depth and variability of perceptual suppression - have outpaced the development of computational models. Here we extend an existing dynamical model of binocular rivalry to encompass two novel experimental paradigms: a threshold detection variant of binocular rivalry, and tracking continuous flash suppression. Together, these tasks provide complementary measures of the dynamics and magnitude of perceptual suppression. Through numerical simulation we demonstrate that a single mechanism, competitive (hysteretic) inhibition between slowly adapting monocular populations, is sufficient to account for the suppression depth findings across both paradigms. This unified model offers a foundation for the development of a quantitative theory of perceptual suppression in visual rivalry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".