Orientation tuning in color vision measured in the absence of contrast gain control
Bibliographic record
Abstract
It is thought that measuring response tuning using sinewave masking confounds the effects of the within channel response with the more broadly tuned cross-channel gain controls activated by the use of high contrast mask stimuli (Cass et al., JOV 9(12), 2009; Meese & Holmes, JOV 10(12), 2010). Here we use a method of subthreshold summation to measure the orientation tuning of color vision at very low contrasts, without the influence of contrast gain controls. Methods: Stimuli were oriented sine‐wave gratings (sf = 1.5 cpd) in a circular patch (10 degs) with rasied cosine edges. Gratings were presented alone or as overlaid pairs to form a plaid. The orientation difference between the two gratings was varied from 0 to 90 degs. For each plaid angle, psychometric functions were obtained for the detection of each grating and the "plaid", and a summation ratio was determined. Orientation tuning was measured for isoluminant red-green and achromatic stimuli presented monocularly. Results: Summation ratios as a function of orientation difference were fitted with Gaussian functions with an offset parameter (~1.3 dB) to take into account probability summation. At this spatial frequency, we find orientation-tuned responses for color vision that are similar to those obtained for achromatic vision with a bandwidth (HWHH) of 12.0 ± 3.8 degs for color and 9.0 ± 2.4 degs for achromatic stimuli (average of three subjects). Conclusion: Subthreshold summation reveals orientation tuning in color vision at mid spatial frequencies, whereas masking reveals very broad or isotropic effects (Medina & Mullen, JOV 9(3), 2009). This difference is likely due to the influence of contrast gain control in the masking paradigm. Orientation tuning for color contrast that matches that for achromatic contrast supports a primary role for color vision in shape perception. Meeting abstract presented at VSS 2012
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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 | 0.001 | 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 | 0.001 |
| 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".