Cone weights for the cone opponent detection mechanisms in human peripheral vision
Bibliographic record
Abstract
Aims: To determine whether there are any changes between foveal and peripheral vision in the L, M and S-cone weights for the two cone opponent chromatic mechanisms (red-green and blue-yellow). Methods: We measured detection threshold contours in three different planes in 3-d cone contrast space, chosen to best reveal the cone weights to the two chromatic mechanisms: the L/M plane, the (L+M)/S plane, and the isoluminant plane (L−xM)/S. Thresholds were measured for foveal and peripheral stimuli (15 or 20 degs in the nasal field). Stimuli were spatio-temporal Gaussian “blobs” on a gray background (49.7 cd/m2) with a spatial sigma fixed in the horizontal meridian (0.5 deg), and variable in the vertical meridian (0.5–1.8 deg, scaled by the cortical magnification factor). To suppress the increased contribution of the luminance mechanism relative to the chromatic mechanisms in peripheral vision, achromatic masking noise (2-d, dynamic) was added to the test stimulus when measuring in the L/M and L+M/S planes in the periphery. Detection thresholds were measured using a 2AFC staircase procedure in three normal subjects. Results: Detection thresholds were fitted by ellipses in each plane at each eccentricity. Comparisons of the orientations of the ellipses between fovea and periphery show no consistent changes with eccentricity across subjects. Conclusions: Cone weights for the foveal mechanisms support those of previous studies (Sankeralli and Mullen, JOSA A, 1996). Our results indicate that cone weights for the red-green and blue-yellow chromatic mechanisms are invariant up to 20 deg in the nasal visual field.
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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 | 0.002 |
| 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".