Peripheral Motion Contrast Thresholds as a Predictor of Older Drivers' Performance During Simulated Driving
Bibliographic record
Abstract
Older drivers have an increased rate of automobile crashes per kilometer driven, likely due in part to age-related declines in motion perception. Our research group has developed the "Peripheral Motion Contrast Threshold test" (PMCT) test and, more recently, the "Rapid PMCT" test as measures of peripheral motion sensitivity. Results from these tests have been shown to correlate strongly with indicators of driving performance in older individuals. In the current study, we investigated how effective the PMCT and RPMCT tests are at predicting driving safety on a driving simulator. We tested 26 younger drivers and 26 older drivers in the STISIM high fidelity driving simulator. Performance was measured in terms of number of collisions and minimum distance of approach to hazards. The PMCT presents participants with Gabors (0.4 cpd, 13.75°/s drift) at one of four locations 15° from fixation, and uses method of limits to measure contrast threshold. The RPMCT presents the same stimuli positioned 15° to either side of fixation, and uses a 2AFC variation on the ascending Bekesy Method. Results from the older drivers show a positive correlation between PMCT & RPMCT and collision rate.. There was also a significant negative correlation between PMCT & RPMCT measures and minimum distance of approach to hazards. Our research demonstrates the ability of both measures to effectively predict the occurrence of hazardous driving under simulated conditions in older subjects. Meeting abstract presented at VSS 2013
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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) | 0.001 | 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".