Startle reveals decreased response preparatory activation during a stop-signal task
Bibliographic record
Abstract
In a stop-signal task participants are instructed to initiate a movement in response to a go signal, but to inhibit this movement if an infrequent stop signal is presented after the go. Reaction time (RT) in a stop-signal task is typically longer compared with that in a simple RT task, which may be attributed to a reduced readiness to initiate the response caused by the possibility of having to inhibit the response. The purpose of this experiment was to probe the preparatory activation level of the motor response during a stop-signal task using a startling acoustic stimulus (SAS), which has been shown to involuntarily trigger sufficiently prepared responses at a short latency. Participants completed two separate tasks: a simple RT task, followed by a stop-signal RT task. During both tasks, an SAS (120 dB) was pseudorandomly presented concurrently with the go signal. As expected, RT during the simple RT task was significantly shorter than during the stop-signal task. A significant reduction in RT was noted when an SAS was presented during the simple RT task; however, during the stop-signal task, an SAS resulted in either a significant speeding or a moderate delay in RT. Additionally, the subset of SAS trial responses with the shortest RT latencies produced during the stop-signal task were also delayed compared with the short-latency SAS trial responses observed during the simple RT task. Despite evidence that a response was prepared in advance of the go signal during a stop-signal task, it appears that the amount of preparatory activation was reduced compared with that achieved during a simple RT task.
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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 | 0.001 |
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".