The effects of practice in a useful field of view task on driving performance
Bibliographic record
Abstract
The Useful Field of View (UFOV) measures the extent of the visual field from which information is extracted in a single glance. The UFOV is influenced by dividing attention, especially in older subjects (e.g., Sekuler et al., 2000), and the effects of attention predict performance in complex tasks like driving (e.g., Myers et al., 2000). Practice in a UFOV task reduces the effects of divided attention in younger and older subjects (Richards et al., 2006), and also has been shown to improve driving performance in older adults (e.g., Roenker et al., 2003). To the best of our knowledge though, no one has examined if UFOV training affects driving performance similarly across the life span. Therefore, we tested younger adults on a desktop driving simulator before and after nine UFOV training sessions. The UFOV task comprised a central identification task and a peripheral localization task performed under focused- and divided-attention conditions. The driving simulator task consisted of short routes in which we measured overall performance as well as reaction time to central detection and peripheral localization tasks. Results from five younger subjects show that, in the UFOV task, performance on the peripheral task under divided attention conditions improved linearly until it was statistically similar to peripheral task performance under focused attention conditions. This result is similar to that found in Richards et al. (2006). In the driving simulator task, however, we did not find an effect of UFOV practice on either the central or peripheral task. We are currently testing older adults to see if UFOV training offers differing benefits across the lifespan, and examining the effect of driving task difficulty on transfer of learning.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".