Effects of distraction on threat-related changes in balance control
Bibliographic record
Abstract
Postural threat elicits changes in standing balance and broad shifts in attention focus, including directing more attention to balance. Distracting attention from balance using cognitive tasks has been shown to promote more automatic balance control in healthy individuals in non-threatening conditions, and to normalize balance in patients with persistent postural-perceptual dizziness. This study investigated whether distracting attention from balance modified threat-induced changes in standing balance control in healthy young adults. Participants (n=21) stood without (No Threat) and with (Threat) the possibility of receiving a temporally unpredictable anterior-posterior (AP) support surface translation. In the Threat condition, a perturbation was delivered after a pseudo-random delay (5-60s) following the start of each trial; only standing data from 60s trials were analyzed. In both threat conditions, participants completed no task (Control), or mentally counted how often a pre-selected letter (LS), or number (NS) occurred in a sequence. Electrodermal responses (arousal), AP centre of pressure (COP) mean position, root mean square (RMS), mean power frequency (MPF), and low (0-0.05 Hz) and high (0.5-5 Hz) frequency COP, were calculated. Participants significantly increased arousal, leaned further forward, and increased MPF when threatened, independent of task. High frequency COP increased when threatened, however, this increase was significantly smaller for the LS task. Threat-independent reductions in RMS and low frequency COP were observed for the LS compared to Control task. Distracting attention with a specific cognitive task modified threat-induced high frequency changes in standing balance. The generalizability of this effect should be explored for different threat and dual-task scenarios.Acknowledgments: Natural Sciences and Engineering Research Council of Canada (NSERC)
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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".