Impairing the useful field of view in natural scenes: Tunnel vision versus general interference
Bibliographic record
Abstract
A fundamental issue in visual attention is the relationship between the useful field of view (UFOV), the region of visual space where information is encoded within a single fixation, and eccentricity. A common assumption is that impairing attentional resources reduces the size of the UFOV (i.e., tunnel vision). However, most research has not accounted for eccentricity-dependent changes in spatial resolution, potentially conflating fixed visual properties with flexible changes in visual attention. Williams (1988, 1989) argued that foveal loads are necessary to reduce the size of the UFOV, producing tunnel vision. Without a foveal load, it is argued that the attentional decrement is constant across the visual field (i.e., general interference). However, other research asserts that auditory working memory (WM) loads produce tunnel vision. To date, foveal versus auditory WM loads have not been compared to determine if they differentially change the size of the UFOV. In two experiments, we tested the effects of a foveal (rotated L vs. T discrimination) task and an auditory WM (N-back) task on an extrafoveal (Gabor) discrimination task. Gabor patches were scaled for size and processing time to produce equal performance across the visual field under single-task conditions, thus removing the confound of eccentricity-dependent differences in visual sensitivity. The results showed that although both foveal and auditory loads reduced Gabor orientation sensitivity, only the foveal load interacted with retinal eccentricity to produce tunnel vision, clearly demonstrating task-specific changes to the form of the UFOV. This has theoretical implications for understanding the UFOV.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".