How much visual field loss can you tolerate on the road? Impact of central and peripheral scotomas on road hazard localization
Bibliographic record
Abstract
Our ability to perceive the gist of a scene in a glance is well-established for static (Greene & Oliva, 2009) and dynamic scenes (Wolfe et al. 2019). Strong views of the Useful Field or Functional Visual Field argue that dynamic scene tasks require central vision whereas peripheral vision is less useful. However, little empirical evidence speaks to this claim. Here, we examined the extent to which observers could tolerate foveal and peripheral visual field loss in a road hazard localization task. Eight licensed drivers viewed 2s excerpts of 270 dashcam videos from Road Hazard Stimuli (Song et al. 2024; videos subtended 39 x 22 DVA). 66% of videos contained hazards, defined as situations which require immediate driver response to avoid a collision. Hazards occurred in the left or right of frame in equal proportion. The remaining 33% of videos contained no hazards. Participants indicated whether a hazard was on the left, on the right or absent. Using a gaze-contingent display, we simulated central and peripheral scotomas in two separate blocks. Central scotomas were simulated by removing a circular window at the gaze location, leaving the rest of the video visible. Peripheral scotomas only displayed the video inside a circular window at the gaze location. To determine the threshold scotoma size at 80% localization performance, the diameter of the circular window was controlled with a trialwise 3-up-1-down staircase. In the central scotoma condition, participants tolerated a 6.80 DVA central scotoma. In the peripheral scotoma condition, participants required an 8.34 DVA visible window. Our results suggest that detecting immediate hazards in road scenes is highly resistant to visual field loss. Moreover, central and peripheral vision both support this task, suggesting a more complex account of peripheral vision use in dynamic scene perception.
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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 | 0.004 |
| 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) | 0.003 | 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".