Older Driver Performance on a Simulator: Associations between Simulated Tasks and Cognition
Bibliographic record
Abstract
This study examined whether the performance on one driving task was predictive of the performance on other driving tasks, and the study also investigated the relationship between cognition and driving. It was hypothesized that driving performance would be correlated across driving tasks, and that cognitive tests would predict driving performance. Twenty-six participants (5 male, 21 female; mean age = 63.0 years) completed three scenarios on a driving simulator (rural highway course, parking lot course, construction zone course) and three cognitive tests Useful Field of View® (UFOV), Attention Network Test (ANT), Trail Making Test (TMT). Results showed that performance on one driving task was not predictive of performance on other driving tasks, suggesting that the driving tasks involve independent skill sets. The UFOV and TMT predicted performance on the rural highway course, while the ANT predicted performance on the rural highway and parking lot courses. These results suggest that simulators can be used to examine separate driving tasks and that the value of the ANT for driving research should be examined further.
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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 | 0.001 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".