Interocular suppression in amblyopia for global orientation processing
Bibliographic record
Abstract
We developed a dichoptic global orientation coherence paradigm to quantify interocular suppression in amblyopia. This task is biased towards ventral processing and allows comparison with two other techniques-global motion processing, which is more dorsally biased, and binocular phase combination, which most likely reflects striate function. We found a similar pattern for the relationship between coherence threshold and interocular contrast curves (thresholds vs. interocular contrast ratios or TvRs) in our new paradigm compared with those of the previous dichoptic global motion coherence paradigm. The effective contrast ratios at balance point (where the signals from the two eyes have equal weighting) in our new paradigm were larger than those of the dichoptic global motion coherence paradigm but less than those of the binocular phase combination paradigm. The measured effective contrast ratios in the three paradigms were also positively correlated with each other, with the two global coherence paradigms having the highest correlation. We concluded that: (a) The dichoptic global orientation coherence paradigm is effective in quantifying interocular suppression in amblyopia; and (b) Interocular suppression, while sharing a common suppression mechanism at the early stage in the pathway (e.g., striate cortex), may have additional extra-striate contributions that affect both dorsal and ventral streams differentially.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".