Using electrooculography for glance analysis during simulated driving
Bibliographic record
Abstract
This article, from a special issue on driving simulator applications in research and clinical practice, reports on a study that examined the feasibility of using electrooculography (EOG) to monitor eye movements during simulated driving. The authors created three versions of a driving scenario that differed only in terms of how navigation instructions were provided. Two versions included visual navigation instructions, such as one would get from a global positioning device. In one version, the visual instructions appeared in the lower right corner of the middle screen (GPS group); and in the other, the instructions appeared in the centre of the middle screen, such that drivers did not have to move their eyes from the road to view the instructions. The third version included auditory navigation instructions only. The study measured glance presence, and calculated glance latency and length during the three seconds following the onset of 12 randomly selected visual instructions. During this time, participants in the GPS group looked away from the road to the right significantly more often than those in the other two groups. As a result, these participants spent significantly more total time looking away from the road during the drive when compared to the other two groups. Groups did not differ significantly on any of the individual driving mistake categories, or on the total number of driving mistakes. The authors conclude that electrooculography is a feasible and affordable way to measure eye movement during simulated driving. Though electrooculography does not provide the same amount or quality of data as head-mounted eye trackers and multiple camera systems, it does yield sufficient data to address questions such as the ones posed in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".