Sensitivity to color and luminance transformations in real versus phase-scrambled natural scenes
Bibliographic record
Abstract
Aim Traditionally, thresholds for discriminating colour and luminance differences have been measured using stimuli such as disks, gratings or gabors, and accounted for in terms of the responses of relatively low-level mechanisms in the visual pathway. On this basis we would not expect the higher-order structure of, for example, natural scenes to be a factor determining colour and luminance discrimination thresholds. We therefore decided to compare discrimination thresholds between natural scenes and phase-scrambled versions of the same scenes. Method The stimuli were fifty calibrated color photographs of everyday scenes and fifty phase-scrambled images. The chromaticity and saturation of every pixel was represented as a vector in a modified version of the MacLeod-Boynton color space, and could be translated, rotated, compressed, or randomly repositioned within that color space. Thresholds for detection of each type of transformation were measured using a two-alternate forced choice method. Results Thresholds for all types of transformations in color space were significantly lower in natural scenes compared to phase-scrambled images. Thresholds for detecting random changes in color, in the form of either Gaussian or fractal noise, were considerably lower in natural compared to phase-scrambled images. Conclusion The structure of natural scenes plays a significant role in our ability to discriminate colour and luminance differences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".