Simulator Study of Involuntary Driver Distraction Under Different Perceptual Loads
Bibliographic record
Abstract
Involuntary distraction, which occurs when driver attention is diverted unintentionally by irrelevant stimuli or events, is often overlooked in experimental studies. The present work explores how involuntary distraction affects individual drivers and whether varying perceptual load in the driving environment modulates involuntary distraction engagement. In a simulator experiment, variability in glance behavior toward irrelevant stimuli was observed among participants, and higher self-reported everyday distractibility scores using the Cognitive Failures Questionnaire were found to be associated with longer glances, but not the number of glances, toward the irrelevant stimuli. These relationships suggest that the Cognitive Failures Questionnaire scale may correlate better with the ability to disengage from a distraction than with the ability to suppress automatic attentional capture. The study also found delayed accelerator release times to lead vehicle braking events in the presence of irrelevant stimuli. The perceptual responses associated with the accelerator release times show that the delay occurred after participants glanced at the brake light, possibly indicating slower processing of information under distraction. Contrary to expectation, perceptual load, manipulated by the simulated road’s visual complexity, did not affect involuntary distraction engagement but directly affected driving performance. Overall, findings reveal potential safety concerns for involuntary driver distraction, but further work is necessary to understand how individuals with different attentional limitations are affected by this distraction type.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".