Dichoptic masking in color and luminance vision
Bibliographic record
Abstract
We have investigated the selectivity of contrast gain control for red-green color and luminance contrast thresholds using the method of cross orientation masking (XOM). Previously, for monocular and binocular stimuli, we have found that luminance contrast does not mask chromatic thresholds, suggesting selective, independent mechanisms of gain control for color and luminance pathways (Mullen et al., 12(9): 107, 2012). Here we explore dichoptic masking, and find very different results. Methods: First, we compare dichoptic XOM for three conditions: (1) chromatic test and mask (red-green isoluminant); (2) luminance test and mask; and (3) chromatic test and luminance mask (cross condition). Detection threshold vs contrast (TvC) masking functions were measured for horizontal Gabor targets overlaid with vertical Gabor masks for a range of spatiotemporal conditions (0.375, 0.75 & 1.5 cpd; at 2 & 8 Hz), with the test and mask presented dichoptically using a stereoscope. Second, we compare the timing for dichoptic and monocular XOM for chromatic and luminance stimuli by measuring the build-up of masking as a function of the duration of the target and mask. Results: Significant dichoptic masking is present with the same magnitude in all three conditions. In all conditions, dichoptic XOM is somewhat greater at low temporal frequencies (2Hz) than high (8Hz), and is independent of spatial frequency. Dichoptic masking builds up more slowly than monocular masking with no difference between chromatic and luminance contrast. Conclusion: The mechanism for dichoptic suppression is unselective, responding equally to both color and luminance contrast and their combination, with a similar time course for each. It is likely that there is a common color-luminance pathway for the dichoptic masking process, in comparison to the independent and selective pathways found for monocular and binocular conditions. Meeting abstract presented at VSS 2014
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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) | 0.001 | 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".