When should you warn the driver about the moose?: The effect of auditory cue timing on hazard localization in naturalistic videos
Bibliographic record
Abstract
Safely responding to a road hazard and avoiding a crash requires knowing when the hazard occurs and where it is in the scene. Cueing attention to the hazard’s location speeds localization in naturalistic road videos, whereas invalid spatial cues slow localization (Wolfe et al., 2021). Since any alerting technology will be imperfect, invalid spatial cues produced in error are particularly dangerous on the road. Temporal cues (telling a driver when a hazard occurs) may be a more useful alternative, but it is unclear what cue-hazard timings are effective. Here, we investigated the effect of auditory cue timing on the speed and accuracy of hazard localization using a set of naturalistic driving videos showing real road hazards. Thirty licensed drivers aged 18-25 watched brief road videos lasting 2-8 seconds. Each video contained an annotated hazard that occurred at an unpredictable time before the end of the video. Observers indicated whether the hazard appeared on the left or right half of the screen. The auditory cue was presented at one of four times relative to hazard onset: -500ms, -250ms, 0ms, and 100ms (after hazard onset). Across all cue timing conditions, mean hazard localization accuracy exceeded 90%, and reaction times were significantly faster when a cue was present compared to a no-cue baseline (t >= 4.5, p <= 0.001 in each case). Hazards were localized faster when cues preceded the hazard, with the fastest reaction times in the -500ms condition (-500ms: M = 77ms, SE = 37ms vs. 0ms: M = 289ms, SE = 25ms; t = 6.46, p < 0.001). These results suggest that non-spatial auditory cues that are heard earlier (up to 500ms before the situation) help to orient attention to road hazards, even in the absence of specific location information in the cue.
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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 | 0.026 |
| 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".