Differentiating between non-oriented and orientation-tuned responses to color contrast using subthreshold summation
Bibliographic record
Abstract
In receptoral and early post-receptoral color vision, behavioral and physiological data have been well reconciled, however at the cortical stage these links frequently remain obscure. For example, a wealth of psychophysical studies has demonstrated that color vision has orientation-tuned mechanisms and there is little direct evidence for non-oriented mechanisms. Yet multiple neurophysiological studies have revealed a distinct subgroup of highly color sensitive, isotropic neurons in V1. To measure orientation tuning in color vision and differentiate between non-oriented and orientation-tuned responses to color contrast, we have adapted the classic method of subthreshold summation. The method uses a linking model to tie subthreshold summation data to the underlying detector bandwidths. This method also has the advantage of using very low contrast stimuli, ensuring the color pathway is well isolated from the modulatory effects of cross-orientation masking that contaminate orientation tuning measurements obtained at higher contrasts. At mid spatial frequencies, our results show evidence for orientation-tuned detectors with similar bandwidths for chromatic and achromatic contrast. At low spatial frequencies, however, orientation tuning in color vision becomes extremely broad, and is compatible with detection by non-oriented color mechanisms. These isotropic chromatic mechanisms only appear under monocular conditions. Isotropic detectors, which could be called “blob” detectors, are well equipped for the representation of surface color, whereas orientation-tuned responses are best equipped for edge and contour detection. Such links remain only speculative, however. We are also using the subthreshold summation method to determine the orientation tuning of binocular summation, discussed in a related presentation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".