Stereo-anomaly is found more frequently in tasks that require discrimination between depths
Bibliographic record
Abstract
A bstract Within the population of humans with otherwise normal vision, there exists some proportion whose ability to perceive depth from binocular disparity is poor or absent. The prevalence of this “stereoanomaly” has been investigated in previous studies, some finding the proportion to be as small as 2%, others finding it to be as great as 30%. In this study, we set out to investigate the possible reason for the wide range of results found in these studies. We used a digital stereoacuity measurement tool that could measure performance in tasks requiring either the detection of disparity or the discrimination of the sign of disparity. The stimulus design was otherwise similar between the two tasks. In a cohort of 228 participants, we found that 98% were able to consistently perform the detection task. In contrast, only 69% consistently performed the discrimination task. The 31% of participants who had difficulty with the discrimination task could further be divided into 17% who were consistently unable to perform the task (seeming to behave at chance), and 14% who showed some ability to perform the task. We propose that the greater prevalence of stereo-anomaly is revealed when tasks require the judgement of the direction of disparity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".