Visual Warning Signals Optimized for Human Perception: What the Eye Sees Fastest
Bibliographic record
Abstract
This study aimed to answer the question of how to design a visual warning signal that is most easily seen and produces the quickest reaction time. This is a classic problem of bionic optimization—if one knows the properties of the receiver one can most easily find a suitable solution. Because the peak of the spatio-temporal contrast sensitivity function of the human visual system occurs at non-zero spatial and temporal frequencies, it is likely that movement enhances the detectability of threshold visual signals. Earlier studies employing extended drifting sinewave gratings bear out this prediction. We have studied the ability of human observers to detect threshold visual signals for both moving and stationary stimuli. We used discrete, localized signals such as might be employed in aerospace or automotive warning signal displays. Moving stimuli show a superior detectability to non-moving stimuli of the same integrated energy. Moving stimuli at threshold detectability are seen faster than non-moving threshold stimuli. Under some conditions the speed advantage is over 0.25 seconds. Similar advantages have also been shown to occur for suprathreshold signals.
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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 | 0.005 |
| 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 | 0.000 |
| 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".