Binocular contrast adaptation explained by separate gain controls before and after the site of binocular summation
Bibliographic record
Abstract
Adaptation to contrast elevates contrast detection/discrimination thresholds and reduces apparent contrast. The study of binocular contrast adaptation over the past 50 years has focused on the relative effect of adapting and testing to the same compared to opposite eyes in order to yield a measure of interocular transfer (IOT), a protocol that only involves monocular adapting and test stimuli. However a full characterization of binocular adaptation requires measurements of binocular as well monocular adapting and test stimuli. Using 3.6 cpd grating stimuli we measured threshold versus contrast (TvC) functions for the full gamut of combinations of monocular and binocular adapting and test stimuli. For each combination of adapt/test eye(s), the adapted TvC data followed the classic 'dipper' curve similar to the unadapted data, but displaced obliquely to higher contrasts. Adaptation had effectively re-scaled all contrasts by a common factor Cs that varied with the combination of adapt and test eye(s), an example of contrast gain control. Cs was well described by a simple 2-parameter model that had separate gain controls, sited before and after binocular summation respectively. When these 2 levels of adaptation were inserted into an existing model for contrast discrimination, the extended 2-stage model gave a good account of the TvC functions, their shape invariance with adaptation, and the contrast scaling factor.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".