Bibliographic record
Abstract
Purpose: We address the issue of whether color vision can support both radial and translational global motion. Previous studies have argued that translational chromatic global motion is significantly impaired, however, these have not addressed the role of both contrast and coherence level in global motion discrimination, and/or have not used stimulus parameters appropriate for chromatic spatio-temporal contrast sensitivity. In addition, the chromatic contribution to radial motion has not been investigated. Method: We analysed translational and radial global motion processing for random dot kinetograms (RDKs) calibrated for the selective activation of the L/M cone-opponent (red-green), S-cone-opponent (blue-yellow) or achromatic systems. Each RDK consisted of 50 Gaussian ‘dots’ (σ = 0.25deg.) presented using a limited lifetime paradigm in a circular window (12° diameter). ‘Dot’ speed was 5.4°/sec., and stimulus duration was 240msec. RDKs were presented in a Gaussian temporal window (σ=0.125sec). We measured direction discrimination thresholds (% coherence) using a method of constant stimuli over a wide range of stimulus contrasts scaled in multiples of detection threshold. Results: We find that, for both chromatic and achromatic RDKs, coherence thresholds for discriminating global motion decrease as contrast increases. At higher contrasts, coherence thresholds for isoluminant red-green and S-cone isolating chromatic RDKs are similar to those for achromatic stimuli for both radial and translational motion, indicating that the chromatic system can perform as well in global motion processing as the luminance system. Conclusion: We conclude that global motion processing is available to both red-green and blue-yellow color vision for both translational and radial motion stimuli.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".