Breaking and restoring ocular balance: Temporal interactions in binocular rivalry and stereopsis
Bibliographic record
Abstract
Binocular integration and interocular suppression are fundamental processes underlying binocular vision, giving rise to stereopsis and binocular rivalry, respectively. To investigate how the visual system dynamically coordinates these processes to form a unified percept, we conducted four psychophysical experiments examining the temporal interactions between binocular rivalry and stereopsis. In Experiment 1a, binocular rivalry, especially with high-contrast stimuli, impaired subsequent stereopsis, significantly elevating average stereo detection thresholds from 60.5 to 111.8 arcsec. Experiment 1b revealed no effect on contrast detection, confirming that the suppression was specific to stereopsis rather than due to general attentional distraction. Experiment 2a revealed that preceding stereopsis rebalanced subsequent rivalry dynamics by reducing ocular dominance asymmetry and increasing mixed percepts, without affecting alternation rate. Experiment 2b further demonstrated that anti-correlated stereograms, which do not elicit stable stereopsis, exerted no effect on subsequent rivalry dynamics. These findings underscore a dynamic interplay between binocular integration and suppression in resolving perceptual ambiguity and achieving unified visual perception. Crucially, our results reinforce that stereopsis is not merely a passive consequence of binocular integration, but actively contributes to rebalancing ocular dominance, thus offering insights for interventions aimed at restoring binocular function.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".