Orientation tuning of binocular summation in color vision assessed with subthreshold summation
Bibliographic record
Abstract
Previous work by our laboratory found that color vision lacks orientation tuning at low spatial frequencies for monocular presentation (Gheiratmand et al JOV, 2013; Gheiratmand & Mullen, Sci Rep, 2014). Our current research aims to investigate whether orientation tuning is gained when low spatial frequency monocular color signals are binocularly summated. We assess color and luminance vision at low (0.375 c/deg) and mid (1.5 c/deg) spatial frequencies using the psychophysical method of subthreshold summation. By using low, near-threshold contrast levels, we are theoretically able to bypass the processes of contrast normalization and access underlying neural detection mechanisms. Grating stimuli are presented at threshold levels both monocularly and dichoptically over a wide range of orientation differences. Orientation bandwidths of binocular mechanisms are computed using a probability summation model in which monocular signals are binocularly combined with a non-linear transducer and spatially combined using Minkowski summation. Preliminary results point to binocular orientation tuning in color and luminance at both low and mid spatial frequencies. Therefore, tuning is acquired at the binocular level for low spatial frequency color vision. Intriguingly, low spatial frequency color vision summation ratios are higher than other conditions over all orientation differences. We speculate that this result may indicate that under binocular conditions, low spatial frequency color vision has access to both tuned binocular and isotropic monocular signals.
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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 | 0.000 |
| 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".