Sub‐threshold transcranial magnetic stimulation applied after the go‐signal facilitates reaction time under control but not startle conditions
Bibliographic record
Abstract
The presentation of a startling acoustic stimulus (SAS) in a simple reaction time (RT) task significantly reduces RT due to the involuntary early initiation of a prepared movement; however, the underlying neural mechanism remains unclear. It has been proposed that a SAS triggers a cortically stored motor program by involuntarily increasing initiation-related activation. Sub-threshold transcranial magnetic stimulation (TMS) can be used to investigate cortical processes, as it increases cortical excitability for 6-30 ms and significantly reduces RT. The purpose of the present experiments was to determine whether the application of sub-threshold TMS over motor cortex in close temporal proximity to a SAS would facilitate startle RT in the same manner as control RT, providing evidence for cortical involvement in startle-related RTs. Participants completed a simple RT task requiring targeted wrist extension in response to an auditory go-signal, which was randomly replaced by a SAS on 25% of trials. On a subset of trials, sub-threshold TMS was applied 30 ms following the go-signal in control trials or at -15, 0, +15 or +30 ms with respect to the SAS in startle trials. In all three experiments, sham and real TMS significantly reduced RT in control trials, with real TMS having a larger effect, but there was no effect of either real or sham TMS on startle-related RT. These results suggest that there may be limited cortical involvement in the initiation of movements in response to a SAS. As an alternative, startle may produce the fastest possible RTs, with little room for additional facilitation.
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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 | 0.000 |
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".