Startle reveals independent preparation and initiation of triphasic EMG burst components in targeted ballistic movements
Bibliographic record
Abstract
Muscles involved in rapid, targeted movements about a single joint often display a triphasic [agonist (AG1)-antagonist (ANT)-agonist (AG2)] electromyographic (EMG) pattern. Early work using movement perturbations suggested that for short movements, the entire EMG pattern was prepared and initiated in advance (Wadman WJ, Dernier van der Gon JJ, Geuze RH, Mol CR. J Hum Mov Stud 5: 3-17, 1979), whereas more recent transcranial magnetic stimulation evidence indicates that the ANT may be programmed separately (MacKinnon CD, Rothwell JC. J Physiol 528: 633-645, 2000) with execution of the bursts occurring serially (Irlbacher K, Voss M, Meyer BU, Rothwell JC. J Physiol 574: 917-928, 2006). The purpose of the current study was to investigate the generation of triphasic EMG bursts for movements of different amplitudes. In experiment 1, participants performed rapid elbow extension movements to 20° and 60° targets, and on some trials, a startling acoustic stimulus (SAS), which is thought to trigger prepared motor commands at short latency, was delivered at the onset of AG1. For short movements, this perturbation elicited ANT and AG2 early, suggesting the agonist and antagonist bursts may have been programmed independently. In contrast, the same manipulation did not disrupt EMG timing parameters for the long movements, raising the possibility that ANT and AG2 were not fully programmed in advance of movement onset. In experiment 2, an SAS was delivered later in the movement, which produced early onset of both ANT and AG2. We propose that the triphasic pattern is executed serially but believe the trigger signal for initiating the ANT burst occurs not in relation to the AG1 burst, but rather in close temporal proximity to the expected onset of ANT.
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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) | 0.002 | 0.001 |
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".