Head kinematic variability is minimal near preferred cadence and independent of the vestibular control of locomotion
Bibliographic record
Abstract
The unstable nature of our bipedal posture requires continuous feedback to maintain internal estimates of self-motion and generate appropriate balance-correcting responses. This feedback control process involves the integration of multisensory information, including vestibular cues of head motion. Minimizing head motion variability may optimize the information transmitted by vestibular signals that are important for balance control and consequently drive vestibular contributions to locomotion which decrease as we move faster. In this study, participants walked outdoors at 40% to 140% of their preferred step cadence while we characterized head kinematic variability and vestibular-evoked balance responses to electrical vestibular stimulation, a common method to generate virtual signals of head movement. Head kinematic variability was lowest near participants' preferred step cadences (90-126 steps/min) and gait speeds (1.1-1.7 m/s) while vestibular-evoked responses decreased exponentially as step cadence and gait speed increased. Hence, the minima of head kinematic variability were close to preferred step cadences, near previously established minima for the metabolic cost of locomotion. The relationship between head kinematic variability and vestibular-evoked balance responses, however, was inconsistent across all step cadences, suggesting that head kinematic variability did not drive vestibular-evoked balance response magnitude. The observed reduction in the variability of head motion signals at the preferred locomotor cadence/speed may serve to improve our self-motion estimates and reduce information processing requirements to ensure effective navigation, thereby potentially contributing to the well-established minimum in metabolic cost near preferred cadence.
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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".