Modulation of stepping balance reactions through the alignment of vestibular input with body perturbation axis
Bibliographic record
Abstract
Balance reactions depend on sensing imbalances to direct recovery responses, though the role of vestibular feedback in scaling these responses remains uncertain. While vestibular input can alter anticipatory postural adjustments before a compensatory step (McMorran, Bent, and Zettel 2024), this study aimed to clarify whether this reflects vestibular-based scaling of step-recovery or if vestibular input acts as a stability reference in feedforward control of step execution. To investigate vestibular input's influence on step scaling, galvanic vestibular stimulation (GVS) was aligned to amplify or diminish the sense of perturbed postural motion in forward step recovery, through inducing the sensation of forward (FGVS) or backward (BGVS) postural motion. Effects of altered vestibular input was analyzed in terms of postural and step motion and restabilization. GVS modulated forward step responses asymmetrically, with BGVS exhibiting greater postural motion in advance of the step as indicated through forward stability, while FGVS evoked larger and faster forward steps relative to the body. Upon landing the step, these differences culminated as skewed stability according to GVS direction, with a smaller stability margin in BGVS compared to a larger one in FGVS. This stability shift continued post-recovery, with a forward-BGVS and backward-FGVS shift when re-establishing equilibrium. These results demonstrate step-recovery scaling according to the GVS direction, indicating vestibular-based modulation of compensatory stepping reactions.
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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 | 0.001 | 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".