Differential Effects of Startle on Reaction Time for Finger and Arm Movements
Bibliographic record
Abstract
Recent studies using a reaction time (RT) task have reported that a preprogrammed response could be triggered directly by a startling acoustic stimulus (115-124 dB) presented along with the usual "go" signal. It has been suggested that details of the upcoming response could be stored subcortically and are accessible by the startle volley, directly eliciting the correct movement. However, certain muscles (e.g., intrinsic hand) are heavily dependent on cortico-motoneuronal connections and thus would not be directly subject to the subcortical startle volley in a similar way to muscles whose innervations include extensive reticular connections. In this study, 14 participants performed 75 trials in each of two tasks within a RT paradigm: an arm extension task and an index finger abduction task. In 12 trials within each task, the regular go stimulus (82 dB) was replaced with a 115-dB startling stimulus. Results showed that, in the arm task, the presence of a startle reaction led to significantly shorter latency arm movements compared with the effect of the increased stimulus intensity alone. In contrast, for the finger task, no additional decrease in RT caused by startle was observed. Taken together, these results suggest that only movements that involve muscles more strongly innervated by subcortical pathways are susceptible to response advancement by startle.
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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) | 0.001 | 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 | 0.000 |
| 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".