Mechanical perturbations can elicit triggered reactions in the absence of a startle response
Bibliographic record
Abstract
Mechanical perturbations delivered to the upper limbs elicit reflexive responses in stretched muscle at short- (M1:25-50 ms) and long- (M2:50-100 ms) latencies. When presented in a simple reaction time (RT) task, the perturbation can also elicit a preprogrammed voluntary response at a latency (premotor RT values ~70 ms) that overlaps the M2 response. This early elicitation of the voluntary response by a perturbation has been called a triggered reaction (Houk 1978). Recent work has proposed that unexpected mechanical perturbations may also elicit a reflexive startle response and therefore the StartReact effect underlies initiation of triggered reactions (Ravichandran et al. 2013). The present study investigated whether triggered reactions can also be elicited at short-latency in the absence of a startle response. Twelve participants performed ballistic wrist extension movements following an expected wrist extension perturbation imperative signal. The perturbation elicited stretch responses (M1/M2) in wrist flexors and the preprogrammed voluntary response in wrist extensors. To make comparisons with the StartReact effect, a startling auditory stimulus (SAS) was also presented on random trials. While the SAS consistently elicited a startle response in orbicularis oculi and sternocleidomastoid on 68.2% of trials, the perturbation did not reliably elicit a startle response. Despite this, two-thirds of perturbation-only trials had premotor RTs of less than 100 ms and the earliest responses began at ~70 ms. These findings suggest that an overt startle response is not required for the early elicitation of a triggered reaction via a mechanical perturbation.Acknowledgments: NSERC
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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) | 0.001 | 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) | 0.002 | 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".