Aging Yields a Smaller Number of Fixations and a Reduced Gaze Amplitude When Driving in a Simulator
Bibliographic record
Abstract
With the increasing number of elderly drivers, it is important to better understand if strategies for capturing visual information are affected by age and by the complexity of the driving contexts. Ten young (aged 21-31 years) and older (aged 65-75 years) active drivers drove through a continuous simulated scenario (STISIM, v2.0). The scenario included drinving on open roads (less demanding), stopping at intersections and passing maneuvers (more demanding). Eye movements were recorded with an oculometer (ASL, model 510). Compared to younger drivers, older drivers showed a smaller horizontal amplitude between fixations and a smaller variance in the amplitude of the eye movements. They also showed a smaller number of fixations/sec for the more complex driving maneuvers that were analyzed (passing maneuvers). Overall, this may reveal a "tunnel effect" (or perceptual narrowing) phenomenon when the driving context increases in complexity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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 teacher head, 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".