Decreasing driver speeding on a simulated drive with feedback and reinforcement
Bibliographic record
Abstract
Background Research suggests that driver feedback combined with reinforcement can increase safe driving (ie, reduce speeding and tailgating). Although promising, it is currently unknown whether both feedback and reinforcement are required to increase safe driving or whether similar results could be achieved with just one of these components. Aims/Objectives/Purpose We investigated the amount of speed reduction that could be achieved on a simulated drive with just one intervention component (ie, feedback alone or reinforcement alone) compared with feedback and reinforcement combined. Methods Twenty-eight men (7 per group) aged 18–29 completed a 30-min simulated drive using a 2×2 design (feedback or not; reinforcement or not). Real-time feedback consisted of a dashboard device informing participants of their current speed relative to the speed limit using lights. Reinforcement consisted of drivers earning points for driving at or below the speed limit; points were later exchanged for a gift card, with its value determined by the number of points earned. Results/Outcome Compared with control participants, drivers who received feedback combined with reinforcement spent less time driving above the speed limit, had a slower mean speed, and had a smaller SD of speed (all p values<0.05). Drivers exposed to reinforcement alone showed speed reductions similar to drivers who received both feedback and reinforcement. Drivers exposed to feedback alone drove at speeds similar to control participants. Significance/Contribution to the Field Reinforcement alone was necessary and sufficient to achieve a reduction in drivers' speed. This information could be used to inform policy-makers and car manufacturers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".