Equating the "visibility" of luminance- and contrast-modulations
Bibliographic record
Abstract
Purpose. Form discrimination thresholds are known to change with stimulus contrast. Therefore, care must be taken when comparing discrimination thresholds for the same property (e.g., orientation) conveyed via different media (e.g., luminance, chromatic or contrast modulations). In recognition of this issue, it is common practice to set stimuli to equal multiples of their respective detection thresholds when the discrimination task is performed. Our purpose is to demonstrate that the multiple of threshold technique embodies a flawed theory of perceptual scaling. We compared orientation discrimination for gratings defined by luminance and contrast modulations. Method. (i) Detection thresholds were established for luminance and contrast modulations (referred to generally as contrasts). (ii) Contrast discrimination thresholds (JNDs) were determined for the first JND above detection threshold and many suprathreshold contrasts. (iii) A function was fit to the detection threshold and increment threshold data, in order to specify one JND above any given contrast. (iv) Orientation discrimination thresholds were determined for the first 10 JNDs above detection threshold for both luminance and contrast modulations. (v) Results were obtained for two experienced psychophysical observers. Results. Luminance and contrast modulations that were equal numbers JNDs above their respective detection thresholds were not generally at equal multiples of detection threshold, indicating that the multiple of threshold technique embodies a flawed theory of perceptual scaling. Orientations discrimination thresholds decreased as stimulus contrast increased but the conclusions drawn about the differences between luminance and contrast modulations depended on the scale used (i.e., JNDs vs multiples of threshold). Conclusions. Setting stimuli to equal multiples of detection threshold does not ensure that they have the same “visibility”, “salience”, “perceptual impact”, or perceptual contrast.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".