Ocular Dominance Plasticity: Measurement Reliability and Variability
Bibliographic record
Abstract
A bstract In the last decade, studies have shown that short-term monocular deprivation strengthens the deprived eye’s contribution to binocular vision. However, the magnitude of the change in eye dominance after monocular deprivation (i.e., the patching effect) has been found to be different between for different methods and within the same method. There are three possible explanations for the discrepancy. First, the mechanisms underlying the patching effect that are probed by different measurement tasks might exist at different neural sites. Second, test-retest variability in the measurement might have led to inconsistencies, even within the same method. Third, the patching effect itself in the same subject might fluctuate across separate days or experimental sessions. To explore these possibilities, we assessed the test-retest reliability of the three most commonly used tasks (binocular rivalry, binocular combination, and dichoptic masking) and the repeatability of the shift in eye dominance after short-term monocular deprivation for each of the task. Two variations for binocular phase combination were used, at one and many contrasts of the stimuli. Also, two variations of the dichoptic masking task was tested, in which the orientation of the mask grating was either horizontal or vertical. This makes five different measurement methods in all. We hope to resolve some of the inconsistencies reported in the literature concerning this form of visual plasticity. In this study, we also aim to recommend a measurement method that will allow us to better understand its physiological basis and the underpinning of visual disorders.
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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 | 0.052 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".