A Comparison Between Two Ocular Dominance Tests:
Bibliographic record
Abstract
Purpose: Ocular dominance can be measured by a variety of tests, which may not yield the same results. This study compared the repeatability and agreement for two ocular dominance tests, a newer letter dominance test and a well-established binocular rivalry test.Methods: Thirty-nine adults (28 females and 11 males) with normal vision completed three sessions involving letter dominance and the binocular rivalry tests. An additional seven participants completed only one session. Within-test repeatability was assessed through intraclass correlation and standard deviation. Between-tests agreement was assessed through a Bland-Altman test, intraclass correlation, and ocular dominance directions.Results: Within-test analysis indicated that the letter dominance test had better repeatability than the grating rivalry test (intraclass correlation coefficient: letter dominance 0.829, rivalry 0.790; standard deviation: letter dominance 0.015 [median], rivalry 0.023 [median], P = .015). Between-test analysis indicated that the two tests had moderate to good agreement (intraclass correlation coefficient 0.712) and identified the same eye as dominant for most participants, although not all (39 consistent across tests, seven inconsistent when a strict measure of equidominance was adopted).Conclusion: These analyses indicate that the letter dominance test is a more repeatable measure of ocular dominance than the grating rivalry test, and that ocular dominance magnitude metrics do vary across tests.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".