Measuring the Useful Field of View During Simulated Driving With Gaze-Contingent Displays
Bibliographic record
Abstract
OBJECTIVE: We aimed to develop and test a new dynamic measure of transient changes to the useful field of view (UFOV), utilizing a gaze-contingent paradigm for use in realistic simulated environments. BACKGROUND: The UFOV, the area from which an observer can extract visual information during a single fixation, has been correlated with driving performance and crash risk. However, some existing measures of the UFOV cannot be used dynamically in realistic simulators, and other UFOV measures involve constant stimuli at fixed locations. We propose a gaze-contingent UFOV measure (the GC-UFOV) that solves the above problems. METHODS: Twenty-five participants completed four simulated drives while they concurrently performed an occasional gaze-contingent Gabor orientation discrimination task. Gabors appeared randomly at one of three retinal eccentricities (5°, 10°, or 15°). Cognitive workload was manipulated both with a concurrent auditory working memory task and with driving task difficulty (via presence/absence of lateral wind). RESULTS: Cognitive workload had a detrimental effect on Gabor discrimination accuracy at all three retinal eccentricities. Interestingly, this accuracy cost was equivalent across eccentricities, consistent with previous findings of "general interference" rather than "tunnel vision." CONCLUSION: The results showed that the GC-UFOV method was able to measure transient changes in UFOV due to cognitive load in a realistic simulated environment. APPLICATION: The GC-UFOV paradigm developed and tested in this study is a novel and effective tool for studying transient changes in the UFOV due to cognitive load in the context of complex real-world tasks such as simulated driving.
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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 | 0.003 |
| 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) | 0.001 | 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".