Time Course of Dichoptic Masking in Normals and Suppression in Amblyopes
Bibliographic record
Abstract
PURPOSE: To better understand the relationship between dichoptic masking in normal vision and suppression in amblyopia we address three questions: First, what is the time course of dichoptic masking in normals and amblyopes? Second, is interocular suppression low-pass or band-pass in its spatial dependence? And third, in the above two regards, is dichoptic masking in normals different from amblyopic suppression? METHODS: We measured the dependence of dichoptic masking in normal controls and amblyopes on the temporal duration of presentation under three conditions; monocular (the nontested eye-i.e., dominant eye of normals or nonamblyopic eye of amblyopes, being patched), dichoptic-luminance (the nontested eye seeing a mean luminance-i.e., a DC component) and dichoptic-contrast (the nontested eye seeing high-contrast visual noise). The subject had to detect a letter in the other eye, the contrast of which was varied. RESULTS: We found that threshold elevation relative to the patched condition occurred in both normals and amblyopes when the nontested eye saw either 1/f or band-pass filtered noise, but not just mean luminance (i.e., there was no masking from the DC component that corresponds to a channel responsive to a spatial frequency of 0 cyc/deg); longer presentation of the target (corresponding to lower temporal frequencies) produced greater threshold elevation. CONCLUSIONS: Dichoptic masking exhibits similar properties in both subject groups, being low-pass temporally and band-pass spatially, so that masking was greatest at the longest presentation durations and was not greatly affected by mean luminance in the nontested eye.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".