Attentional Demands Associated With Postural Control Depend on Task Difficulty and Visual Condition
Bibliographic record
Abstract
The authors aimed to assess the effect of vision on variations in attentional resources allocated to postural control during tasks of various levels of difficulty. Test-retest reliability of postural and cognitive parameters was also evaluated. Twenty adults stood quietly on a force platform during 30-s trials (primary task). Twelve conditions involving combinations of three bases of support, two vision conditions, and the presence or absence of a simple reaction time task (secondary task) were tested. Baseline trials of the reaction time task were also performed with the participants seated. Reaction time and postural parameters demonstrated good to excellent test-retest reliability in most conditions. Postural control was altered by the reduction of the base of support and by the absence of vision. Maintaining an upright stance increased reaction time compared with a seated position, indicating that quiet standing tasks required some attention even in young adults. Changes in postural steadiness were correlated with changes in reaction time, showing a significant relationship between the difficulty of the postural task and the attentional resources allocated to postural control. However, reaction time increased with the reduction of the base of support only without vision. This dual task paradigm showed that vision can compensate for the increase in attentional demands during the most difficult postural 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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".