Response triggering by an acoustic stimulus increases with stimulus intensity and is best predicted by startle reflex activation
Bibliographic record
Abstract
In a simple reaction time task, the presentation of a startling acoustic stimulus has been shown to trigger the prepared response at short latency, known as the StartReact effect. However, it is unclear under what conditions it can be assumed that the loud stimulus results in response triggering. The purpose of the present study was to examine how auditory stimulus intensity and preparation level affect the probability of involuntary response triggering and the incidence of activation in the startle reflex indicator of sternocleidomastoid (SCM). In two reaction time experiments, participants were presented with an irrelevant auditory stimulus of varying intensities at various time points prior to the visual go-signal. Responses were independently categorized as responding to either the auditory or visual stimulus and those with or without SCM activation (i.e., SCM+/-). Both the incidence of response triggering and proportion of SCM+ trials increased with stimulus intensity and presentation closer to the go-signal. Data also showed that participants reacted to the auditory stimulus at a much higher rate on trials where the auditory stimulus elicited SCM activity versus those that did not, and a logistic regression analysis confirmed that SCM activation is a reliable predictor of response triggering for all conditions.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".