Eye movement strategies: A comparison between individuals with normal vision and simulated scotomas
Bibliographic record
Abstract
Purpose: Central visual field deficits (i.e., macular degeneration, MD) can impair individuals' visual performance. Individuals might use alternative eye movement (EM) strategies to compensate the deficiency, e.g. by making use of the unaffected periphery. Furthermore, visual tasks for those individuals would be effortful, that is, they would need to increase EM frequencies and durations to make accurate responses. Method: We presented computer-generated (MATLAB) images either with or without simulated scotomas to normally sighted individuals. Scotoma properties were varied to correspond to different stages of MD. We recorded EM (EyeLink II eye tracker) when observers were making target discrimination tasks. Data were collected for fixations, X and Y positions of EM, and behavioral responses such as accuracy and reaction time (RT) in a 3D texture discrimination task. Results: Fixation durations and variability were bigger for simulated MD. Number of fixations was also higher for MD. Mean X and Y positions were similar for control and MD, but they were more variable for MD. Behaviorally, RTs were longer and more variable for MD and more errors occurred. Conclusion: Central visual field loss impaired visual performance. Visual tasks could still be performed, but EM parameters and temporal and spatial EM patterns needed to be adjusted. This suggests that specific visual aids and training programs could be designed by incorporating residual visual functions (i.e., peripheral visual field) during 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".