Examining the contributions of radial and lamellar optic flow gain to quiet stance
Bibliographic record
Abstract
The visual system plays an integral role in maintaining quiet stance. When visual feedback is amplified by increasing the gain of optic flow, individuals develop a tighter control of upright stance. The pattern of optic flow can also vary depending on the eccentricity of gaze, where looking to the side or down can increase the proportion of lamellar, compared to radial optic flow. Further, previous work has shown differences between visual motion perception when exposed to varying types of optic flow. It currently remains unknown how the type of optic flow contributes to postural control while under the influence of modified gain. Therefore, this study aimed to better understand how the gain of radial and lamellar optic flow, manipulated by changing head orientation, contributes to balance control during quiet stance among healthy adults. Participants were recruited to stand quietly with feet together on a foam pad placed over a force plate while wearing a virtual reality head-mounted display Three head orientations (forward, 45° left, 45° down) were used to expose participants to primarily radial (forward) or lamellar (side or down) optic flow. For each head orientation, participants completed 3 trials, where the gain of optic flow was amplified to either 1x, 4x, or 16x normal optic flow. Overall, an increase in optic flow gain decreased amplitude and increased frequencies of balance measures. Some mediolateral amplitude measures of balance were also greatest when looking to the side; however, the effect of optic flow gain on center of pressure and head displacement were similar across head orientations.
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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) | 0.002 | 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".