Effects of Inaccurate Gaze Behavior on Young Drivers’ Hazard Anticipation
Bibliographic record
Abstract
A previous study (Yamani et al., 2018) demonstrated that the administration of expert eye movement videos following hazard anticipation training can improve the proportion of latent hazards anticipated by young drivers compared to control conditions. The current driving simulator study sought to examine whether the improvements observed in the previous study were merely due to drivers’ exposure to videos of the simulated driving scenarios with expert eye movement overlays immediately prior to evaluation, or whether modeling the accuracy of eye movement behavior can lead participants to internalize hazard anticipation skills more effectively. In a between-subject design, 36 drivers (18-21 years) were assigned to one of three experimental conditions – training only, training plus expert eye movements or training plus novice eye movements. All participants navigated four unique driving scenarios, each with their eye movements tracked and recorded. Analyses of the eye movement data showed that young drivers who saw the expert eye movement (accurate) videos immediately following training anticipated a substantially greater proportion of latent hazards compared to the young drivers that saw novice eye movement (inaccurate) videos following training. The data provide some evidence that drivers were able to successfully map and incorporate correct hazard anticipation glance behavior into their mental models. The findings present some implications for the design and evaluation of eye movement-based training interventions.
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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 | 0.011 |
| 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 | 0.001 |
| 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".