Stereocoherence thresholds as a measure of global stereopsis
Bibliographic record
Abstract
Purpose: The purpose of this study was to develop a robust and reliable clinical test of stereopsis that is complementary to the conventional disparity threshold tests. \n Methods: \nRandom dot stereograms containing disparity-defined gratings were displayed on a ViewPixx® monitor using LCD shutter glasses. Participants discriminated grating orientation. Form coherence was degraded by assigning random disparities to a variable proportion of dots. The threshold proportion of signal dots required for form discrimination is called the stereocoherence threshold (stereoCT). We explored the various stimulus parameters that can affect stereoCT. StereoCT were also measured for a variety of simulated abnormal binocular vision conditions and in patients with amblyopia. \nResults: StereoCT was lowest (most sensitive) for a stimulus with a spatial frequency of 1cpd ,a 5.5 arc min dot size, 183 dots/deg2 dot density and a disparity amplitude of 108 arc sec. StereoCT showed higher sensitivity and reduced variability relative to stereothresholds obtained on conventional disparity thresholds under various simulated abnormal vision conditions (interocular luminance and contrast differences, unilateral blur, and unilateral Bangerter filters). In patients with amblyopia, stereoCT improved with contrast reduction in the fellow eye relative to the amblyopic eye. \n Conclusions: StereoCT testing targets the second stage of stereoscopic processing ‘global stereopsis where the local matches of stereoscopic images between two eyes are unified into a global perception of depth. Therefore, stereoCT may provide a useful measure of higher-level stereoscopic vision that is complementary to current tests, which rely on disparity thresholds.
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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 | 0.002 |
| 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) | 0.003 | 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".