Neck Muscles in the Rhesus Monkey. II. Electromyographic Patterns of Activation Underlying Postures and Movements
Bibliographic record
Abstract
Electromyographic (EMG) activity was recorded in < or = 12 neck muscles in four alert monkeys whose heads were unrestrained to describe the spatial and temporal patterns of neck muscle activation accompanying a large range of head postures and movements. Some head postures and movements were elicited by training animals to generate gaze shifts to visual targets. Other spontaneous head movements were made during orienting, tracking, feeding, expressive, and head-shaking behaviors. These latter movements exhibited a wider range of kinematic patterns. Stable postures and small head movements of only a few degrees were associated with activation of a small number of muscles in a reproducible synergy. Additional muscles were recruited for more eccentric postures and larger movements. For head movements during trained gaze shifts, movement amplitude, velocity, and acceleration were correlated linearly and agonist muscles were recruited without antagonist muscles. Complex sequences of reciprocal bursts in agonist and antagonist muscles were observed during very brisk movements. Turning movements of similar amplitudes that began from different initial head positions were associated with systematic variations in the activities of different muscles and in the relative timings of these activities. Unique recruitment synergies were observed during feeding and head-shaking behaviors. Our results emphasize that the recruitment of a given muscle was generally ordered and consistent but that strategies for coordination among various neck muscles were often complex and appeared to depend on the specifics of musculoskeletal architecture, posture, and movement kinematics that differ substantially among species.
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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 | 0.001 |
| 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) | 0.001 | 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".