The Moving Dynamic Random Dot Stereosize Test: Validity and Repeatability
Bibliographic record
Abstract
PURPOSE: We have developed a new test suitable for measuring stereopsis in young children and individuals with communication difficulties. It consists of a drifting, computer-generated red and green, dynamic random dot, disparate shape. The motion direction is indicated by the subject (subjective response) or by a naive observer judging the eye movements of the subject (objective response). Disparity is maintained at 616 sec arc and the dependent variable is the angular subtense of the target, which varies from 11 degrees to 11 ft. In this study, precision (ie, measuring repeatability and objective-subjective agreement) and validity were assessed. SUBJECTS AND METHODS: Sixteen subjects with normal vision participated in the repeatability study and 10 in the validity studies. A two-up/one-down, 2 alternative forced choice staircase procedure was used to measure objective and subjective threshold on two occasions with a 1-week separation. Sensitivity for detecting anisometropia was estimated with simulated anisometropia (0 to 3 D) and by comparison with the Randot test. Sensitivity for detecting amblyopia or strabismus was tested in 10 subjects. RESULTS: Subjective repeatability was 0.31 log units (2 levels of the test) and objective repeatability was 0.49 log units (3.2 levels of the test). The test was able to detect 2.0 D of simulated anisometropia in 8 of 10 cases (above the 95% confidence interval). None of the amblyopic subjects demonstrated stereopsis. CONCLUSION: This test of dynamic global stereopsis has potential as a clinical or screening tool for anisometropia, amblyopia, and strabismus.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".