Sub-threshold transcranial magnetic stimulation does not facilitate reaction time in startle conditions, regardless of movement complexity
Bibliographic record
Abstract
Application of sub-threshold transcranial magnetic stimulation (TMS) over motor cortex has been shown to facilitate reaction time (RT), likely by increasing motor cortical activation. Yet, a similar effect was not observed for movements triggered early by a startling acoustic stimulus (SAS), supporting the hypothesis that a SAS acts by triggering a sub- cortically stored motor program without cortical involvement. An alternative explanation is that the simple movement used in previous experiments was already maximally speeded by the startle and no further RT facilitation was possible. The present experiment tested this by employing a more complex movement, which has previously resulted in increased startle RT. It was expected that this would provide a greater available range for sub-threshold TMS to facilitate RT during startle trials. In two experiments, participants performed a simple RT task requiring three keypresses with varying timing complexity following an auditory go-signal or a SAS. On a subset of trials, subthreshold TMS or sham TMS was applied over the motor cortex 30ms following the go-signal or 15ms following the SAS. Results showed that increased timing complexity increased RT in control trials, but in contrast to previous reports had no effect on startle RTs. Additionally, both real and sham TMS facilitated RT in control trials, whereas there was no effect of either stimulation condition on startle RT. As increased complexity did not increase RT for startle trials it cannot be concluded whether there is limited cortical involvement in SAS-triggered movements, or if an RT floor was reached.Acknowledgments: Supported by NSERC and the Ontario Ministry of Research and Innovation and Science
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".