Peer Passenger Influences on Male Adolescent Drivers' Visual Scanning Behavior During Simulated Driving
Bibliographic record
Abstract
PURPOSE: There is a higher likelihood of crashes and fatalities when an adolescent drives with peer passengers, especially for male drivers and male passengers. Simulated driving of male adolescent drivers with male peer passengers was studied to examine passenger influences on distraction and inattention. METHODS: Male adolescents drove in a high-fidelity driving simulator with a male confederate who posed either as a risk-accepting passenger or as a risk-averse passenger. Drivers' eye movements were recorded. The visual scanning behavior of the drivers was compared when driving alone with when driving with a passenger and when driving with a risk-accepting passenger with a risk-averse passenger. RESULTS: The visual scanning of a driver significantly narrowed horizontally and vertically when driving with a peer passenger. There were no significant differences in the times the drivers' eyes were off the forward roadway when driving with a passenger versus when driving alone. Some significant correlations were found between personality characteristics and the outcome measures. CONCLUSIONS: The presence of a male peer passenger was associated with a reduction in the visual scanning range of male adolescent drivers. This reduction could be a result of potential cognitive load imposed on the driver due to the presence of a passenger and the real or perceived normative influences or expectations from the passenger.
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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 | 0.002 |
| 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) | 0.002 | 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".