Cortical color-luminance contrast interactions revealed by dichoptic masking
Bibliographic record
Abstract
A range of psychophysical experiments have revealed the existence of separate luminance and color mechanisms in the human visual system. Analysis of the visual scene, however, requires the integration of color and luminance contrasts. We used suprathreshold masking to investigate interactions between color and luminance mechanisms with a focus on the orientation tuning properties. To assess interactions specific to the cortical stage of processing, dichoptic and monoptic presentations were compared. Methods: Test stimuli were red-green isoluminant horizontal Gabors (0.75cpd, 2Hz) and masks were chromatic or luminance Gabors of the same spatial and temporal frequency as the test. Test detection was measured as a function of mask contrast with a vertical overlay mask (cross orientation masking), or as a function of mask orientation with a high contrast mask (orientation tuning). Results: With a red-green test and mask we found that dichoptic masking was stronger than monoptic and was orientationally tuned, while no tuning was found for monoptic presentations, supporting our previous results (Gheiratmand, et al., J Vis 9(14): 84, 2009). With a red-green test and luminance mask, very significant differences between dichoptic and monoptic presentations were found. Dichoptic presentations showed strong masking effects, whereas monoptic presetations showed facilitation. These color-luminance interactions showed no orientation tuning. Conclusion: There are non-oriented interactions between color and luminance mechanisms at high contrasts, however, these only occur in dichoptic presentations indicating a cortical origin. The differences between dichoptic and monoptic masking in color-luminance and color-color interactions suggest that distinct neural mechanisms are involved in monoptic and dichoptic conditions.
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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".