How Do Cognitive Interventions Impact Driver Aggressiveness in China?—A Driving Simulator Study
Bibliographic record
Abstract
Aggressive driving behavior is one of the main reasons for traffic crashes in China. However, how cognitive interventions affect impulsive driving behavior is still unknown. In this study, a simulated drive was constructed to evaluate the influence of different cognitive interventions. In addition to speeding behavior in limit zones, speed when passing pedestrians, at intersections, and on the whole drive was adopted as a measure to evaluate aggressive driving behaviors. Forty-eight young drivers were recruited with monetary rewards. Compared to the control group, the penalty feedback intervention significantly reduced the mean speed in the 40 km/h zone, the 80 km/h zone, and when passing pedestrians. The combined feedback intervention significantly reduced the distance ratio of speeding in the 40 km/h and 80 km/h zones, as well as the mean speed at the intersection and on the whole drive. However, the interaction effects between the driving task and intervention method were not remarkably observed during aggressive driving behaviors. The findings from this study provide evidence that different cognitive interventions contribute distinctively to improving aggressive driving behaviors. These conclusive results have possible implications for the design of vehicle warning systems and traffic safety 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".