What Is the Role of Sustained Visual Attention in the Maintenance of Postural Control in Young Adults?
Bibliographic record
Abstract
Abstract: Dual tasks requiring sustained visual attention and upright stance are common, yet their impact on standing balance is not well understood. We investigated the role of visual attention in the maintenance of postural control, using the multiple-object tracking (MOT) task. Healthy young adults ( n = 12) performed the MOT task at three object movement speeds while seated or standing. MOT performance was assessed using tracking capacity ( k). Metrics calculated to assess mediolateral (ML) and anterior–posterior (AP) postural control included: maximum difference between CoM and CoP position (CoM–CoP Max), root mean square distance for center of pressure and center of mass position (CoP and CoM RMS distance), and correlation between CoM and CoP time series signals (CoM/CoP correlation). As predicted, k decreased significantly as object movement speed increased for both standing and seated conditions. Object movement speed also significantly affected AP CoM–CoP Max in seated conditions ( p = .021) and AP CoM/CoP correlation for standing conditions ( p = .002). The results demonstrate utility of the MOT task in understanding the role of visual attention in postural control, even though healthy young adults were able to compensate for the addition of a sustained visual attention task, with minimal deficits to postural control.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".