Cognitive demand and sensory modality influence the impact of a cognitive task on postural control
Bibliographic record
Abstract
To address the current variability in the posture-cognition data, the aim of the present experiment was to evaluate the modulating effects of cognitive demand and sensory modality on postural control in young adults. Seventeen healthy young adults (23.71 ± 1.99 years; 9 F, 8 M) were instructed to stand feet together on a force platform while concurrently performing cognitive tasks of varying difficulty (easy, moderate, and difficult) and sensory modality (auditory and visual). The auditory tasks consisted of silently counting the total occurrence of one or two letters in a sequence of individual letters or three-letter words and completing a string of words broken down into individual letters. The visual tasks consisted of silently counting the total occurrence of one or two numbers in a 3-digit and 5-digit number sequence. Increasing cognitive demand resulted in a significant reduction in area of 95% confidence ellipse and medial-lateral (ML) sway variability. Presenting the cognitive tasks visually resulted in a greater reduction in ML sway variability compared to auditorily presenting the tasks. Contrary to previous literature (Pellecchia, 2003; Prado et al., 2007), the present findings suggest that visually presented cognitive task of higher demand can facilitate greater postural stability. The observed improvement may be a result of a shift in attention and the establishment of a visual anchor.
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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".