Impact of macular scotoma and tubular vision on oculomotor behavior and performance in visuospatial comparison tasks
Bibliographic record
Abstract
Our aim in this study was to understand how we perform visuospatial comparison tasks by analyzing ocular behavior and to examine how restrictions in macular or peripheral vision disturb ocular behavior and task performance. Two groups of 18 healthy participants with normal or corrected visual acuity performed visuospatial comparison tasks (computerized version of the elementary visuospatial perception [EVSP] test) (Pisella et al., 2013) with a gaze-contingent mask simulating either tubular vision (first group) or macular scotoma (second group). After these simulations of pathological conditions, all participants also performed the EVSP test in full view, enabling direct comparison of their oculomotor behavior and performance. In terms of oculomotor behavior, compared with the full view condition, alternation saccades between the two objects to compare were less numerous in the absence of peripheral vision, whereas the number of within-object exploration saccades decreased in the absence of macular vision. The absence of peripheral vision did not affect accuracy except for midline judgments, but the absence of central vision impaired accuracy across all visuospatial subtests. Besides confirming the crucial role of the macula for visuospatial comparison tasks, these experiments provided important insights into how sensory disorder modifies oculomotor behavior with or without consequences on performance accuracy.
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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 | 0.000 | 0.002 |
| 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".