Cross-orientation masking in the red-green isoluminant and luminance systems
Bibliographic record
Abstract
Purpose: Cross-orientation masking (XOM) is defined psychophysically as the phenomenon whereby detection of a test grating is masked by the presence of a superimposed stimulus at an orthogonal orientation. A previous study found that XOM for achromatic stimuli is strongest at mid-high temporal and low spatial frequencies, the putative M cell range (Meese and Holmes, Proc. R. Soc. B., 274, p127, 2007). Here we investigate whether color vision can support XOM by using red-green isoluminant stimuli, and achromatic stimuli for comparison with the luminance system. Methods: Horizontal Gabor stimuli (Gaussian contrast envelope, σ=2 degrees) were modulated at two spatial (0.375 & 0.75cpd) and two temporal frequencies (2 & 4Hz). An orthogonal vertical Gabor patch with the same spatio-temporal configuration was superimposed (i.e. a plaid). 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). Results: We find three new results for color vision: 1. robust XOM for color vision for the spatio-temporal frequencies tested over a wide range of mask contrasts; 2. greater cross-orientation facilitation at low mask contrasts for chromatic than for achromatic stimuli, and 3. significantly greater masking for the chromatic than the achromatic stimuli when mask contrast is high. Conclusions: Such robust and distinct chromatic masking effects indicate that M cells do not exclusively support cross orientation masking in this spatio-temporal range and suggest differential constraints on chromatic compared to achromatic cross-orientation suppression along the cortical or subcortical streams.
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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) | 0.001 | 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".