Stopping and Restarting an Unfolding Action at Various Times
Bibliographic record
Abstract
The ability to inhibit an unfolding action is usually investigated using a stop signal (or gostop) task. The data from the stop-signal task are often described using a horse-race model whose key assumption is that each process (i.e., go, stop) exhibits stochastic independence. Using three variations of a coincident-timing task (i.e., go, gostop, and gostopgo) we extend previous considerations of stochastic independence by analysing the go latencies for prior effects of stopping. On random trials in the gostopgo task the signal sweep was paused for various times at various distances before the target. Significant increases in latency errors were reported on those trials on which the signal was paused (p <.005). Further analyses of the pause trials revealed significant effects for both the stopping interval (p <.001) and the pause interval (p <.05). Tukey post hoc analyses demonstrated increased latency errors as a linear function of the stopping interval, as expected, and decreased latency errors as a nonlinear function of the pause interval. These latter results indicate that the latencies of the go process, as reflected in the latency errors, may not exhibit stochastic independence under certain conditions. Various control mechanisms were considered in an attempt to explain these data.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".