The impact of aging on postural reactivity generated by simulated ophthalmic lenses distortions
Bibliographic record
Abstract
Purpose: The purpose of this study was to determine whether aging could have a significant impact on postural control in presence of dynamic optical distortions. Methods: We used a full immersive virtual environment to simulate dynamic distortions normally produced by ophthalmic lens corrections for myopes and hyperopes. Two young and senior groups were tested and asked to stand still with feet together and arms crossed. Their task was to track a red ball with their eyes that was moving on the horizontal axis without moving the head. While tracking the ball, a dynamic distortion model was applied to the background room represented in a form of a grid. Body sway amplitude was calculated from the electromagnetic trackers positioned on the body. Results: The data show that young subjects had a clear postural reactivity as a function of both negative and positive distortions. The body sway increased as a function of amplitude of the distortion demonstrating that it was the distortion itself that was generating postural reactivity. For the older observers, impact of distortion on postural reactivity was clearly lower than for young. Further, sway of older group was significantly lower regardless of distortion amplitude. Conclusions: This is the first clear evidence that simulated ophthalmic lenses distortions have significant effects on postural control. Target pursuit tasks such as the one used here are often performed in naturalistic contexts. The present results have implications for understanding the different tolerances often expressed by older and younger observers to ophthalmic lens distortions. This would imply that new lens wearers of the older age group should have higher tolerances than new wearers from the younger age group. This aging effect could be due to the reduced resources of older observers for processing simultaneous sources of information (ball tracking vs. perceptual motion of background grid).
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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 | 0.001 |
| 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".