Limb Stiffness Is Modulated With Spatial Accuracy Requirements During Movement in the Absence of Destabilizing Forces
Bibliographic record
Abstract
The motor system can use a number of mechanisms to increase movement accuracy and compensate for perturbing external forces, interaction torques, and neuromuscular noise. Empirical studies have shown that stiffness modulation is one adaptive mechanism used to control arm movements in the presence of destabilizing external force loads. Other work has shown that arm muscle activity is increased at movement end for reaching movements to small visual targets and that changes in stiffness at movement end are oriented to match changes in visual accuracy requirements such as target shape. In this study, we assess whether limb stiffness is modulated to match spatial accuracy requirements during movement, conveyed using visual stimuli, in the absence of external force loads. Limb stiffness was estimated in the middle of reaching movements to visual targets located at the end of a narrow (8 mm) or wide (8 cm) visual track. When greater movement accuracy was required, we observed modest but reliable increases in limb stiffness in a direction perpendicular to the track. These findings support the notion that the motor system uses stiffness control to augment movement accuracy during movement and does so in the absence of external unstable force loads, in response to changing accuracy requirements conveyed using visual cues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".