The Effect of Driving Experience on Change Blindness at Intersections: Decision Accuracy and Eye Movement Results
Bibliographic record
Abstract
To understand the differences between inexperienced and experienced driver visual behavior for hazard detection at intersections, twelve less experienced drivers aged 18 to 19 and twelve experienced drivers aged 35 to 48 were shown 36 complex intersection images using a modified flicker method. Twenty-four of these intersections contained a changing object that was a pedestrian, vehicle or a traffic control device. The remaining 12 intersections did not contain a changing object. Visual search was measured using a head mounted eye movement system and areas of interest were specified for each image to determine the foci of visual search. The time to view the flickering images affected turn decision accuracy. The pattern of results showed that less experienced drivers tended to fixate on other vehicles within the intersections, whereas experienced drivers fixated on lights and signs. The implications of the results on hazard perception are discussed.
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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 | 0.001 | 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) | 0.000 | 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".