Older Driver Failures of Attention at Intersections: Using Change Blindness Methods to Assess Turn Decision Accuracy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A modified version of the flicker technique to induce change blindness was used to examine the effects of time constraints on decision-making accuracy at intersections on a total of 62 young (18-25 years), middle-aged (26-64 years), young-old (65-73 years), and old-old (74+ years) drivers. Thirty-six intersection photographs were manipulated so that one object (i.e., pedestrian, vehicle, sign, or traffic control device) in the scene would change when the images were alternated for either 5 or 8 s using the modified flicker method. Young and middle-aged drivers made significantly more correct decisions than did young-old and old-old drivers. Logistic regression analysis of the data indicated that age and/or time were significant predictors of decision performance in 14 of the 36 intersections. Actual or potential applications of this research include driving assessment and crash investigation.
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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 | 0.001 |
| 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 it