Stopping and Restarting an Unfolding Action at Various Times
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 it