No impact of luminance noise on chromatic motion perception
Bibliographic record
Abstract
The purpose of the present experiments was to investigate the mechanism underlying the perception of chromatic motion. In Experiment 1, we measured contrast thresholds in a direction discrimination task at TFs ranging from 1 to 16 Hz. Results show a bandpass sensitivity function for luminance motion, and lowpass function for chromatic motion, with greater sensitivity for chromatic motion at TFs below 4 Hz, roughly equal sensitivities at 4 Hz, and greater sensitivity to luminance motion at TFs above 4 Hz. In Experiment 2, a direction discrimination task was used to measure contrast thresholds for luminance and chromatic motion as a function of noise contrast in two conditions: an intra-attribute condition (luminance signal and noise, chromatic signal and noise) and an inter-attribute condition (luminance signal with chromatic noise, chromatic signal with luminance noise). Analysis of threshold versus noise contrast curves in the intra-attribute condition shows different calculation efficiencies and levels of internal equivalent noise for luminance and chromatic motion direction discrimination. Inter-attribute noise failed to produce an increase in contrast thresholds at any TF. This shows a double dissociation between colour and luminance motion processing. Taken together, the results of Experiments 1 and 2 indicate that chromatic motion and luminance motion are processed by distinct mechanisms and are consistent with the notion that chromatic motion is processed by a tracking mechanism. Further experiments will investigate the mechanism underlying chromatic motion processing at higher TFs.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".