Voluntary Control of Human Jaw Stiffness
Bibliographic record
Abstract
Recent studies of human arm movement have suggested that the control of stiffness may be important both for maintaining stability and for achieving differences in movement accuracy. In the present study, we have examined the voluntary control of postural stiffness in 3D in the human jaw. The goal is to address the possible role of stiffness control in both stabilizing the jaw and in achieving the differential precision requirements of speech sounds. We previously showed that patterns of kinematic variability in speech are systematically related to the stiffness of the jaw. If the nervous system uses stiffness control as a means to regulate kinematic variation in speech, it should also be possible to show that subjects can voluntarily modify jaw stiffness. Using a robotic device, a series of force pulses was applied to the jaw to elicit changes in stiffness to resist displacement. Three orthogonal directions and three magnitudes of forces were tested. In all conditions, subjects increased the magnitude of jaw stiffness to resist the effects of the applied forces. Apart from the horizontal direction, greater increases in stiffness were observed when larger forces were applied. Moreover, subjects differentially increased jaw stiffness along a vertical axis to counteract disturbances in this direction. The observed changes in the magnitude of stiffness in different directions suggest an ability to control the pattern of stiffness of the jaw. The results are interpreted as evidence that jaw stiffness can be adjusted voluntarily, and thus may play a role in stabilizing the jaw and in controlling movement variation in the orofacial system.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".