Influence of visual cognitive tasks of varying difficulty on quiet standing
Bibliographic record
Abstract
Empirical evidence indicates that the control of postural sway and cognitive functioning are not independent systems. The present experiment employed a dual-task paradigm to investigate the role of visual cognitive tasks of varying cognitive load on postural control. Eleven participants (aged: 19.45 ± 0.93) stood on a force platform as still as possible while performing a series of five visual cognitive tasks of varying difficulty. For the RT tasks, participants faced a panel at eye level (3 m away) that contained 3 tri-colour (blue, green and red) LEDs and verbally responded “top” once the correct LED was illuminated. For the SRT task, participants responded once the center LED was illuminated red. For the CRT task, participants responded when the left LED was illuminated blue or when the right LED was illuminated green. Additional lights were presented to increase task difficulty. The continuous choice counting task (CCC) was similar to the CRT, however, participants silently counted the number of times blue was illuminated on the left and green was illuminated on the right. For the last two tasks, a 19” computer monitor was used. For the math task, participants silently performed a series of math operations. For the double number task, participants silently counted the number of times two randomized digits were presented in a 3-digit sequence. Results revealed that the math and double number tasks produced the smallest area compared to SRT, CRT and CCC (ps <0.01). The math and double number tasks significantly reduced sway variability in AP and ML compared to SRT, CRT and CCC (ps < 0.01) (ps < 0.05). No significant difference was found between the math and double number tasks. This suggests that by increasing the cognitive load, overt attention is more effectively shifted away from the postural task thereby enhancing postural performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".