The effects of taboo-related distraction on driving performance
Bibliographic record
Abstract
Driver distraction is an important risk factor in motor vehicle crashes. Roadside billboards containing negative and positive emotional content have been shown to have differential effects on driving, however, little is known about the impact of taboo information. Taboo information more reliably evokes emotional arousal than other emotional information and can lead to greater attentional capture due to its inherent ‘shock value.’ We examined the potential for driver distraction from four different types of information presented on roadside billboards: highly arousing taboo words, moderately arousing positive and negative words, and non-arousing neutral words. Results showed that taboo words were associated with better lane control and memory recall compared to the other word types. Our findings suggest that taboo words captured the most attention, but also led to a more careful driving style. One possible explanation is that drivers may be narrowing their attention to the driving task when highly aroused.
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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 | 0.001 | 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) | 0.000 | 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".