Peripheral Motion Contrast Sensitivity and Older Drivers' Detection Failure Accident Risk
Bibliographic record
Abstract
Eighteen older drivers (66-88) and their passengers both reported on the drivers’ performance using detection deficit questionnaires that elicited responses related to attention and to speed and accuracy of object motion perception. The measure of detection deficit was an equally weighted combination of standardized responses from the 17-item driver questionnaire and the 11-item passenger questionnaire. Peripheral stationary and drifting contrast sensitivity was determined for 0.4 cycles per degree sine wave gratings at fifteen degrees eccentricity. The temporal two-alternative forced choice staircase procedure consisted of randomly interleaved left and right visual field grating presentations. The correlation between log10 motion contrast sensitivity and detection deficit was -.63 (p < .01), between age and detection deficit was .56 (p < .05), and between age and log10 motion contrast sensitivity was -.54 (p < .05). The partial correlation between log10 motion sensitivity and detection deficit, independent of age, was -.47 (p = .054). We concluded that some age-related driving performance deficits are associated with reduced sensitivity to motion in the visual periphery. Peripheral motion contrast sensitivity was discussed in relation to “useful field of view” (UFOV®) measures of visual function, and offered as a primary deficit of high risk drivers with mild Alzheimer's disease.
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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 | 0.000 | 0.003 |
| 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".