Where was the moose? The time course of dynamic road scene perception
Bibliographic record
Abstract
Driving requires us to represent visual information about our environment under time pressure, but how long do we need to examine the road ahead of us to detect, localize and evade road hazards? To answer these questions, participants performed a series of three tasks (n=24, licensed drivers) using dash camera video of real road scenes across two spatial scale conditions (immersive [78º x 44º] vs laptop-scale [26º x 14.7º]). The detection task asked participants to report whether they detected a road hazard (an event requiring an immediate response), and the evasion task asked them to choose whether they would steer left or right to evade it. Responses from each task were used to determine viewing duration thresholds, which were longer for the evasion task compared to the detection task (370 vs 220 ms, p = .006), but not significantly impacted by scale condition (p = .10). In the third task, a localization task, participants clicked where they believed the hazard to be in the video after it was presented (video duration: 33 ms – 600 ms). Measuring localization error (distance from the annotated center of the hazard) across duration revealed above-chance localization performance with very brief video durations (67 ms). However, localization error continued to drop with longer viewing durations, beginning to reach an asymptotic level at durations similar to thresholds from the detection task (233 ms). Localization performance was also unaffected by stimulus scale. Together, these results suggest that drivers have an adequate but imperfect sense of hazard location at viewing durations which are only sufficient for detection, but that planning evasive action requires a more precise spatial representation of the hazard, suggesting that they refine their representation of the dynamic scene to better inform action.
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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 | 0.000 | 0.003 |
| 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) | 0.002 | 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".