Go-activation endures following the presentation of a stop-signal: Evidence from startle
Bibliographic record
Abstract
Logan & Cowen (1984) proposed that in a stop-signal task (SST) independent go- and stop-processes "race" such that if the go-process wins, an overt response is produced, while if the stop-process wins, the response is withheld. Based on this model, one could predict that if a process is provided with additional activation, it would be more likely to win the race. A startling acoustic stimulus (SAS) has been shown to provide added activation, resulting in early release of a response. In the present study a SAS was employed to manipulate response outcome by adding activation to either the go- (prior to the stop-signal) or stop-process (after the stop-signal). Participants produced an isometric wrist extension in response to a visual go-stimulus (green), however, if a subsequent stop-signal appeared (stimulus turned red) they were to inhibit the response. Participants completed 100-trials in a SST, including 25 stop-signal trials presented at a fixed delay corresponding to a probability of responding of 0.4 (determined from a baseline block). On stop-signal trials a SAS was presented either with the go-signal, with the stop-signal, stop-signal+100, stop-signal+150, or stop-signal+200ms. Results showed that presenting a SAS during stop-trials led to an increase in probability of responding regardless of whether the SAS was presented before or after the stop-signal. The increase in probability of responding suggests that activation related to the go-response increases rapidly following the go-signal and remains high after the presentation of the stop-signal (when the stop-process is inhibiting the response). This suggests the two processes interact rather than remain independent.Acknowledgments: Supported by the Natural Sciences and Engineering Council of Canada & the Ontario Graduate Scholarship Program
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".