Examining the relationship between individual differences in visual sensitivity and distribution of covert attention around the visual field
Bibliographic record
Abstract
Visual performance varies across the visual field and differs between individuals, potentially contributing to errors in everyday tasks. These variations may stem from anatomical factors, such as photoreceptor density, as well as attentional factors, including the allocation of covert attention. This study investigated the relationship between individual differences in low-level spatial resolution and the distribution of covert attention across the visual field. In addition, we examined participants’ awareness of these performance differences across the visual field. Participants performed two distinct tasks: a line bisection task to evaluate low-level spatial resolution, and a visual search task to examine how covert attention is distributed across the visual field, while maintaining fixation at the center of the display. Stimuli were presented at 4º eccentricity across 4 cardinal and 4 diagonal locations. The bisection task measured spatial resolution by having participants judge whether the stem of a T-shape was offset to the left or right across ten levels (-0.12-0.12º). The second task assessed allocation of covert attention with a search task in which participants reported the orientation of a rotated T among Ls, placed in a random location around fixation. All stimuli were presented at the same 4º eccentricity. To assess whether participants were aware of their performance differences around the visual field, participants provided confidence ratings for their responses in both tasks. Results revealed no significant relationship between spatial resolution in the T-bisection task and accuracy in the search task, suggesting that these measures reflect distinct aspects of visual processing. This was further supported by confidence ratings, which tracked accuracy in the search task, but did not correlate with accuracy in the bisection task. Together, these findings point to distinct mechanisms underlying performance asymmetries around the visual field.
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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 | 0.006 |
| 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".