Why is stereoacuity poor in amblyopia? Evidence from a disparity noise-masking paradigm
Bibliographic record
Abstract
People with amblyopia generally have a reduced ability to use stereopsis to make depth judgements. Our understanding of this reduced ability is limited by the insensitivity of standard clinical stereo tests. These tests fail to detect residual stereo function in some amblyopic subjects. The current study employs a test designed for individuals with poor stereoacuity. The subject identifies the location of a 3D target in a random-dot display. The target is presented in stereoscopic depth using 3D shutter-glasses. We applied the equivalent noise method to determine the role of equivalent internal noise (signal to noise ratio of disparity signals) and processing efficiency (how efficiently the system processes noisy input) in amblyopic stereopsis. We tested 30 amblyopic (7 strabismic, amblyopic eye visual acuity above 20/200) and 17 control (visual acuity above 20/20) adults. Our test detected stereoacuity in 50% of amblyopic participants. Amblyopic stereoacuity thresholds (m =118 arsec) were significantly higher than those from controls (m = 57 arcsec) (t = 2.8, p < 0.05). From a linear amplifier model fit, we found higher mean equivalent internal noise in amblyopic subjects (239 arsec) compared to controls (134 arcsec) (t = 3.45, p < 0.05). The two groups did not significantly differ in the processing efficiency for the task. A multiple linear regression was performed to determine the contribution of the two factors (equivalent internal noise and efficiency) to the individual differences in amblyopic stereoacuity. The two factors accounted for 66% of the variance in stereoacuity, with differences in equivalent internal noise as the strongest predictor. This study introduced a more sensitive assessment of residual stereopsis in amblyopia and evaluated how two factors contribute to individual performance. Overall, we find that reduced amblyopic stereoability is explained by poorer input quality to the stereoscopic disparity processing mechanism.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".