Bibliographic record
Abstract
Previously, it has been shown that dichoptic color-contrast masking can be dramatically reduced by the introduction of task-irrelevant binocular features. It is unclear, however, whether or not the task-irrelevant features need to be matched in the two eyes in order to reduce dichoptic masking. We measured dichoptic masking between target and mask luminance decrement patches and between target and mask isoluminant violet patches. The stimuli were surrounded by a task-irrelevant feature that consisted of a ring of various widths: either a luminance decrement, an isoluminant violet, or an isoluminant red. When the ring was presented to just the target eye-that is, the eye opposite to that of the mask-dichoptic masking was reduced just as much as when the ring was binocular-that is, presented to both eyes. A model that incorporated the combined influence of interocular inhibition from all stimulus components-that is, mask, target, and rings-was found to give a good account of the pattern of dichoptic masking across the full range of conditions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".