Mind-Wandering and Driving: Comparing Thought Report and Individual Difference Measures
Bibliographic record
Abstract
Mind-wandering is a cognitive state in which attention is diverted from the main task and towards more personal thoughts, which can interfere with performance. This study investigated differences in patterns of mind-wandering and driving performance measured during thought-probe versus post-task selfreport conditions, and further differentiated based on individual differences in working memory—as measured by the Operation Span (OSPAN) and Sustained Attention to Response Task (SART). Participants completed two 30-minute drives. Those in the thought-probe condition were asked whether they were thinking of driving; the proportion of trials where they answered “no” was used as the index of mind-wandering. In the post-task condition participants estimated the percentage of time they had mind-wandered during each drive. Speed, steering variability, headway distance, and hazard response time to a lead vehicle braking were also measured. Results showed that the magnitude of mind-wandering captured in the thought-probe condition was greater than in the post-task condition, though hazard response times were also faster despite greater mindwandering reports. Higher OSPAN scores were associated with greater reports of mind-wandering, but only in the post-task condition. Conversely, in the post-task condition those with low SART scores responded slower to hazards than those with high scores; in the thought-probe condition these groups did not differ. Findings indicate a differential impact of report-type on participant experience, emphasizing the need for more covert measures of mind-wandering—e.g., eyetracking or electroencephalography—that provide accurate estimates of task engagement but don’t interfere with task flow.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".