Orientation selectivity in luminance and color vision assessed using 2-d bandpass filtered spatial noise
Bibliographic record
Abstract
Purpose. We evaluate the orientation selectivity of red-green and blue-yellow chromatic mechanisms using an external noise paradigm that allows the assessment of the internal orientation noise, the relative sampling efficiency, and the orientation bandwidth of the underlying orientation-tuned mechanisms. Methods. The task required the measurement of orientation acuity (detection of orientation change) in a temporal 2AFC staircase method. Stimuli were patches of orientation noise defined in the Fourier domain multiplied by a Gaussian envelope in the space-time domain (sigma_x = 1 deg, sigma_t = 500 ms). Orientation acuity (sigma_o) was measured as a function of peak frequency, spatial bandwidth, and stimulus bandwidth in orientation (sigma_e). Internal orientation noise (sigma_i), relative sampling efficiency (N), and orientation bandwidth sigma_e(knee) of the underlying mechanism were derived by fitting the data with a noise model: sigma_o = sqrt(sigma_i^2+sigma_e^2/N) and sigma_e(knee) = sqrt(N).sigma_i Stimuli were cardinal, isolating each of the three postreceptoral mechanisms, and matched in multiples of detection threshold. Results. We find that orientation bandwidth and internal orientation noise are significantly greater in the chromatic than the achromatic systems. Preliminary results indicate that red-green orientation selectivity depends on the spatial properties of the stimulus (peak frequency and spatial bandwidth). Conclusions. We conclude that color vision (red-green and blue-yellow) has a moderate deficiency in orientation selectivity. This may account for the small differences we have found between color and luminance vision on contour integration and shape discrimination tasks (Mullen et al, Vis. Res. 40, 2000; Mullen & Beaudot, Vis. Res., 2002).
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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".