Relationships Between Older Drivers’ Cognitive Abilities as Assessed on the MoCA and Glance Patterns During Visual-Manual Radio Tuning While Driving
Bibliographic record
Abstract
Objective: Research has established that long off-road glances increase crash risk, and other work has shown increased off-road glance behavior in older drivers. This study investigated the relationship between older drivers' (M = 66.3, range 61-69 years) cognitive abilities and the duration of off-road glances while engaged in secondary visual-manual activities. Method: Twenty-two drivers completed the Montreal Cognitive Assessment (MoCA) prior to driving an instrumented vehicle and completing a set of radio-tuning tasks. Glance behavior was recorded and manually coded into 7 glance regions (toward the forward roadway, instrument cluster, center stack, rearview mirror, left, right, and other). Results: On average, older drivers with higher MoCA scores used shorter glances and glanced away from the forward roadway for less total time when manually tuning the radio. Discussion: These findings suggest that lower MoCA scores may represent a driving force behind the "age" differences reported in earlier studies of off-road glance behavior. Questions are raised concerning the identification of MoCA scores that might be used as inclusion cut-points in driving research and in identifying individuals needing further evaluation related to suitability for continuance of driving.
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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".