AB071. Psychophysical investigation of dichoptic blur suppression in human vision
Bibliographic record
Abstract
Background: In situations where one eye gives a more blurred input to visual processing than the other, the input from the sharper eye tends to dominate the percept. This phenomenon has clinical relevance for monovision treatment, where the two eyes are corrected separately for different distances. We performed a psychophysical investigation of subjects’ ability to identify which of a set of images was blurred in one eye. Methods: We tested 17 subjects with normal or corrected-to-normal vision. On each trial, subjects viewed an array of four pictures using a monitor with shutter goggles. In the first experiment, three of the pictures were sharp in both eyes (distractors). The fourth picture was sharp in one eye and blurred by a low-pass filter in the other. Subjects identified that odd-one-out target over many trials with different degrees of blur. In the second experiment the target picture was given the same treatment, but the three non-target pictures were made monocular (sharp in one eye, mean grey in the other). Results: The results from the first experiment with binocular distractors followed our expectations, with subjects showing better performance at detecting more severe blurs. In the second experiment with monocular distractors, we found large individual differences between our observers. Some performed the same as they did in the first condition, others now found the task impossible, and a few performed worse with severe blurs than they did with slight blurs. Conclusions: Previous studies have reported individual differences in blur suppression, however this study reveals that these differences may depend on the precise details of the judgements being made.
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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 | 0.000 | 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) | 0.004 | 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".