Occlusion Paradigm as a Tool to Assess Visual Distraction from In-Vehicle Telematics
Bibliographic record
Abstract
Driver distraction associated with the use of on-board ITS technologies has become an issue of considerable public concern. The ease with which a task can be partitioned, referred to as task chunkability, can likely be a tool to assess the distraction potential of a secondary in-vehicle task to a driver. The present study explored the role of task chunking on distraction. Twenty-four participants, between the ages of 21 and 34, completed two separate experimental sessions. In one session they performed three in-vehicle tasks (a radio-tuning task and two simulated ITS device visual search tasks) under occlusion and while unoccluded. A task chunkability index, a ratio of the mean total shutter open time to the mean unoccluded total task time, was computed for each task. In another session, participants completed the same in-vehicle tasks while driving in a simulator at an approximate speed of 80 km/h, without occlusion. Measures of driving performance (standard deviation of lane position, the number and duration of lane exceedances, and the time to line crossing) under dual task conditions were related to corresponding chunkability indices to determine the association between task chunkability and driving performance. Results indicated that tasks differed significantly in terms of chunkability, however no differences were observed between tasks for the driving performance measures collected. A modified NASA TLX rating scale was also used to assess subjective workload for each task when performed alone and while driving. Significant differences were found between tasks in terms of mental demand, effort, frustration, and safety for both task assessment conditions. Results and implications for future research 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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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".