Bibliographic record
Abstract
Mind-wandering occurs when individuals experience task-unrelatedthoughts, which can interfere with their performance. The goal of this study was to investigate mind-wandering while driving, as predicted both by time on task, and by individual differences in executive working memory, as measured by the Sustained Attention to Response Task (SART). Participants completed a total of three drives during their hour in the driving simulator. During these drives, participants were periodically asked whether they were thinking of driving; the proportion of trials where they reported they were not thinking of driving was used as an index of mind-wandering. As a secondary index, at the end of each drive, participants also rated how difficult they felt it was to focus during the drive. Driving speed, steering variability, and self-report driving performance were also recorded. As predicted, self-reports indicated that drivers had increased difficulty focusing their attention with time on task, particularly in the last two drives; however, the increase in off-task thoughts per drive did not reach significance. Similarly, although driving speed increased as a function of time-on-task, and SART scores predicted driving speed, the interaction between SART scores and time-on-task did not have the predicted effect on steering variability. Overall, the best predictors of mind-wandering were fatigue and number of hours of sleep the previous night. Lastly, those who reported more mind-wandering also reported more instances of emotional rumination (e.g., worries, feeling guilty).
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".