Age and guile vs. youthful exuberance: Sensory and attentional challenges as they affect performance in older and younger drivers
Bibliographic record
Abstract
With age there are reductions in sensory, attentional, and motor function that would predict deficits in performance in older drivers. A variety of studies suggest that the magnitude of these effects varies with the attentional demands of the task: age-deficits in performance are especially notable in tasks where there is high attentional load. These studies typically manipulate attentional load by imposing a secondary task that does not go naturally with driving (e.g. mental arithmetic). In this study, an attempt was made to manipulate the demands of the drive by using challenge factors intrinsic to driving. Three manipulations were investigated: a sensory challenge (driving in fog as compared to driving on a clear day); a traffic density challenge (driving in high as compared to low density traffic); and a navigational challenge (having to use memorized directions, signs and landmarks to navigate while driving as compared to simply “following the road”). The effects of these manipulations were investigated alone and in combination in 19 older adults (M age = 70.8 years) and 21 younger adults (M age = 18.2 years). Participants were tested in a high fidelity driving simulator. Hazard RT, collisions, steering performance and navigational errors were measured. Contrary to prediction, when the driving task was made more challenging, the older drivers performed as well or better than the younger adults, with significantly fewer collisions and marginally lower hazard RT. This high level of performance may have arisen because older drivers adjusted their speeds more appropriately in the face of different driving challenges. Speed adjustment indices were calculated for each condition and participant. For the older adults, these speed adjustment indices correlated with measures of selective and divided attention, which suggests that older adults with deficits in attentional processing adjust their driving speeds to compensate.
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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 | 0.002 |
| 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) | 0.001 | 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".