Slow dynamics of human balance control
Bibliographic record
Abstract
When standing on a tilting surface, humans' sway behavior at frequencies below 0.1 Hz indicates the contribution of a slow feedback component. We suggest this may reflect a self-calibration mechanism of the balance control system, constantly referencing orientation estimates based on kinematic sensory cues to a reference based on force cues. However, attempts to identify this mechanism have been limited by insufficient experimental trial durations and small sample sizes. This study aimed to assess the properties of the mechanism that reduces body sway at very low frequencies in upright standing. Anteroposterior body sway responses to short- and long-duration surface tilts were measured and interpreted using balance control models. Four feedback control model variants, with different mechanisms to account for the slow dynamics, were fit to experimental data. Furthermore, we tested how estimates of the slow component are affected by stimulus period duration. We hypothesized that the model variants containing force cues would provide the best fit to experimental sway responses, particularly in response to long-duration surface tilts. Our results confirm this hypothesis and suggest that humans use integrated force afferents from the feet and legs in a slow, positive feedback mechanism during standing to remain upright. Despite stimulus period durations of ~ 180 s, some properties of this mechanism were difficult to estimate. The positive torque feedback mechanism aligns with the notion of self-calibration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".