Effects of Attentional Focus and Age on Suprapostural Task Performance and Postural Control
Bibliographic record
Abstract
PURPOSE: Suprapostural task performance (manual tracking) and postural control (sway and frequency) were examined as a function of attentional focus, age, and tracking difficulty. Given the performance benefits often found under external focus conditions, it was hypothesized that external focus instructions would promote superior tracking and reduced postural sway for both age groups, most notably as a function of tracking difficulty. METHOD: Postural sway, frequency of postural adjustments, and tracking accuracy under two levels of task difficulty were assessed for younger (M(age) = 20.98 years) and older (M(age) = 70.80 years) participants while they manually tracked a pursuit-rotor target. Participants received instructions to focus on either their actions (internal focus) or the effect of their actions (external focus). RESULTS: Analyses revealed a beneficial effect of an external focus on suprapostural performance on the less-difficult (0.5 Hz) tracking task, and this performance was associated with a modest improvement in medial-lateral postural sway. CONCLUSION: The findings offer limited support for external focus-of-attention benefits under a mildly challenging tracking task. While older adults tend to adopt a conservative postural control strategy regardless of tracking task difficulty, external focus instructions on a suprapostural task promoted a modest, beneficial shift in 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 | 0.004 |
| 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) | 0.003 | 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".