Simulated low vision with young and old adults: How do they see?
Bibliographic record
Abstract
Purpose: The fovea plays a crucial role in human vision, but various conditions (e.g., Age-related Macular Degeneration) can cause an irreversible loss of central visual functioning. How well can people utilize peripheral visual function to perform visual tasks when their central visual field is impaired? Does age make a difference in how people adjust to a loss of central vision? How do compensatory eye movement patterns affect visual performance? To what extent can we dissociate attention from foveal vision? Method: We used a gaze-contingent technique to generate a simulated central scotoma to obscure observers' central visual field while they were performing a variety of computer-generated (MATLAB) visual tasks. Young and old observers with normal vision participated. Tasks included shape-from-texture identification, and discriminations of orientation, motion direction and velocity. Eye movements were recorded with an eye tracker. We measured task performance and many eye movement parameters with scotomas of different sizes. Results: We found rapid perceptual learning in both young and old groups. Observer's fixation position shifted as a function of scotoma size (only along X-axis). Visual performance in terms of dwell time, fixation duration, number of saccades, and saccade amplitude was significantly different between young and old observers. Old observers also had greater difficulties to initiate a first response when we used the largest scotoma (8 deg). Conclusion: Without a functioning central visual field, observers were still able to use the periphery to perform a variety of visual tasks that are usually performed with the intact fovea. Although it was more effortful to complete tasks, especially for old observers, young and old observers were both able to respond accurately to identification and discrimination tasks. This implies that the peripheral visual field can be trained relatively quickly to perform various qualitatively different tasks.
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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".