Task-dependent recruitment across ankle extensor muscles and between mechanical demands is driven by the metabolic cost of muscle contraction
Bibliographic record
Abstract
The nervous system is faced with numerous strategies for recruiting a large number of motor units within and among muscle synergists to produce and control body movement. This is challenging, considering multiple combinations of motor unit recruitment may result in the same movement. Yet vertebrates are capable of performing a wide range of movement tasks with different mechanical demands. In this study, we used an experimental human cycling paradigm and musculoskeletal simulations to test the theory that a strategy of prioritizing the minimization of the metabolic cost of muscle contraction, which improves mechanical efficiency, governs the recruitment of motor units within a muscle and the coordination among synergist muscles within the limb. Our results support our hypothesis, for which measured muscle activity and model-predicted muscle forces in soleus-the slower but stronger ankle plantarflexor-is favoured over the weaker but faster medial gastrocnemius (MG) to produce plantarflexor force to meet increased load demands. However, for faster-contracting speeds induced by faster-pedalling cadence, the faster MG is favoured. Similar recruitment patterns were observed for the slow and fast fibres within each muscle. By contrast, a commonly used modelling strategy that minimizes muscle excitations failed to predict force sharing and known physiological recruitment strategies, such as orderly motor unit recruitment. Our findings illustrate that this common strategy for recruiting motor units within muscles and coordination between muscles can explain the control of the plantarflexor muscles across a range of mechanical demands.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".