Bibliographic record
Abstract
OBJECTIVE: The principal objective of the present work was to examine the effects of mind state (mind-wandering vs. on-task) on driving performance in a high-fidelity driving simulator. BACKGROUND: Mind-wandering is thought to interfere with goal-directed thought. It is likely, then, that when driving, mind-wandering might lead to impairments in critical aspects of driving performance. In two experiments, we assess the extent to which mind-wandering interferes with responsiveness to sudden events, mean velocity, and headway distance. METHOD: Using a car-following procedure in a high-fidelity driving simulator, participants were probed at random times to indicate whether they were on-task at that moment or mind-wandering. The dependent measures were analyzed based on the participant's response to the probe. RESULTS: Compared to when on-task, when mind-wandering participants showed longer response times to sudden events, drove at a higher velocity, and maintained a shorter headway distance. CONCLUSION: Collectively, these findings indicate that mind-wandering affects a broad range of driving responses and may therefore lead to higher crash risk. APPLICATION: The results suggest that situations that are likely associated with mind-wandering (e.g., route familiarity) can impair driving performance.
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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 | 0.001 | 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".