Isotropic orientation tuning for masking in human color vision
Bibliographic record
Abstract
Purpose: Cross-orientation masking (XOM) is defined psychophysically as the phenomenon whereby detection of a test grating is masked by a superimposed stimulus at an orthogonal orientation and is thought to be part of a system of gain control that modulates detection and visibility of the test stimulus. Here we investigate the chromatic gain control mechanisms, particularly their orientation tuning. Methods: Horizontal Gabor stimuli (spatial envelope, σ=2 degrees) were presented in nine combinations of three spatial (0.375, 0.75, 1.5cpd) and three temporal frequencies (2, 4, 8Hz). The mask had the same spatio-temporal frequency and chromaticity as the test but was superimposed with a range of orientations (15-90degs) relative to the test. Binocular contrast detection thresholds were determined using a temporal 2AFC staircase method over a wide range of mask contrasts, scaled in multiples of detection threshold. We used isoluminant red-green or achromatic stimuli. Results: We find that chromatic XOM (mask at 90 degs) is significantly greater than luminance XOM at equivalent mask contrasts. Chromatic XOM is invariant across all spatiotemporal conditions, unlike luminance XOM that is greatest in the high temporal, low spatial frequency range. We also find that chromatic masking is invariant across orientation difference between test and mask, and remains isotropic for both high and low mask contrasts. This differs from luminance masking, which shows orientation tuning, as previously reported. Conclusions: The results indicate distinct physiological origins for chromatic and luminance cross-channel masking. We argue for a predominantly cortical site for chromatic XOM masking, whereas previous studies have proposed subcortical M-cell influences for luminance XOM.
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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 | 0.000 |
| 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".