Influence of central and peripheral visual field on the postural control when viewing an optic flow stimulus
Bibliographic record
Abstract
Purpose: During upright stance condition, vision is known to have an important role in maintaining posture. Central vision is essential for details and motion perception while peripheral vision is primarily specialized for motion perception. However, little is known about their respective roles for postural control in optical flow conditions. In the present study, different visual field areas were stimulated to examine the roles of central and peripheral vision in the control of posture. Methods: Body sway amplitude (BSA) and instability index (II; velocity RMS) were recorded in a group of 19 healthy young adults maintaining upright stance while immersed in a full-immersive virtual environment. The visual stimulation was a 3D tunnel, either static or moving sinusoidaly in the anterior-posterior direction. There were nine visual field conditions: four central conditions (4, 7, 15 and 30°); four peripheral conditions (central occlusions of 4, 7, 15 and 30°); and a full visual field condition. The virtual tunnel respected all the aspects of a real physical tunnel (i.e. stereoscopy and size increase with proximity). Results: Results showed no significant effect of visual field on postural reactivity for the static condition. By contrast, dynamic visual flow reveals a significant increase of postural reactivity when stimulating peripheral visual field (wider than 7°) compared to central. There was no significant difference between the peripheral and full visual field conditions. Conclusions: Under static conditions, central and peripheral vision appear to have equal importance for the control of stance when using a stimulus that equally stimulates these areas. In the presence of an optic flow, peripheral vision has a crucial role in the control of stance, since it is responsible for a compensatory sway, whereas central vision may have an accessory role related to spatial orientation.
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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) | 0.002 | 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".