Measuring the Useful Field of View During Simulated Driving With Gaze-Contingent Displays
Why this work is in the frame
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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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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it