Achromatic and chromatic perceived contrast are reduced in the visual periphery
Bibliographic record
Abstract
The loss of contrast sensitivity with eccentricity is well documented, and is steeper for higher spatial frequencies, and for L/M cone-opponent stimuli compared to achromatic or S-cone-opponent. Here, we ask how perceived contrast depends on eccentricity when stimuli are presented at suprathreshold contrasts, and test two opposing predictions. Contrast constancy predicts no loss in perceived contrast across the visual field regardless of changes in detection threshold - appearance depends only on physical contrast. Conversely, perceived contrast may be scaled in the same way as detection threshold, reflecting the proportional increase in stimulus contrast above threshold. We measured perceived contrast for L/M cone-opponent, S-cone opponent, and Ach stimuli up to 18 degrees of eccentricity using a 2AFC contrast matching method between fovea and periphery. We tested a range of reference contrasts from low (close to detection threshold) to high suprathreshold contrasts and we relate suprathreshold perceived contrast to measured detection thresholds. We find evidence for a hybrid model in which apparent contrast is reduced with eccentricity for stimuli in the low and mid contrast range, with contrast constancy only attained at the highest contrasts. When equated for similar sensitivity losses, we find no difference between chromatic and Ach contrast responses.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".