Premovement suppression of MEP amplitude is greater for simple and go/no-go reaction time tasks compared to choice reaction time
Bibliographic record
Abstract
The amplitude of motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation (TMS) has been shown to decrease in the brief interval (100-300ms) prior to response initiation in reaction time (RT) tasks. The underlying cause of this pre-movement MEP suppression is currently unclear. While some researchers suggest it is indicative of preparation-related inhibition processes preventing the premature release of planned action (i.e., a false start; Greenhouse et al. 2015), others have suggested it represents corticospinal suppression required to initiate a specific motor plan (Ibáñez et al. 2020). To differentiate between these proposed explanations, the present study explored whether the decrease in MEP amplitude leading up to a go-signal is affected by the preparation level of a motor response. Participants completed blocks of simple RT (SRT), choice RT (CRT), and go/no-go (GNG) tasks using a fixed 500ms foreperiod, while TMS was applied at various times between the warning signal and go-signal. It was hypothesized that if MEP suppression relates to preparation level, the greatest suppression would be observed prior to the SRT task since this paradigm allows for the largest amount of advance preparation (Donders, 1969). Results showed MEP amplitudes decreased for all tasks as the go-signal approached; however, both the SRT and GNG had smaller MEP amplitudes 50ms prior to, and coincident with the go-signal compared to the CRT (the task presumably allowing for the least amount of advance preparation). These results suggest the premovement suppression of MEP amplitude may be at least partially attributable to level of response preparation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.004 | 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".