Driving performance is better predicted by unintentional rather than intentional mind-wandering in high density traffic
Bibliographic record
Abstract
Mind-wandering occurs when attention is diverted from a primary task by off-task thoughts. When the primary task is driving, mind-wandering can interfere with driving performance. Some theories consider mind-wandering to be primarily an intentional response to low demand conditions (cognitive underload) while others consider it the result of unintentional attentional lapses. If mind-wandering is primarily intentional, it might be expected to be more prevalent in low demand drives than high. In contrast, if mind-wandering is the result of an unintentional lapse, it might be as likely to occur in low and high demand conditions, and thus more closely related to driver performance than intentional mind-wandering because it occurs more on high demand drives. We used a driving simulator to assess drivers ( N = 34), manipulating the amount of traffic to vary the cognitive demands of the drive. We also measured mind-wandering using periodic thought-probes to assess whether or not the drivers were engaged in off-task thought (mind-wandering), and if they were, whether they were doing so intentionally or unintentionally. Overall, there was significantly more mind-wandering on low than high traffic drives, and in particular, more intentional mind-wandering (a marginal effect: p = 0.055). However, reports of intentional mind-wandering were relatively rare. In the low traffic drive, there was 4 times more unintentional than intentional mind-wandering; in the high traffic drive there was 6 times more. Furthermore, unintentional mind-wandering predicted driving performance best, accounting for 14–18 % of the variance in speed variability, steering, and hazard response in the high traffic drive.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".