Visual Warning Signals Optimized for Human Perception: What the Eye Sees Fastest
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it