Predictability influences stopping and response control.
Bibliographic record
Abstract
Using a continuous tracking task, the authors examined whether stopping is resistant to expectancies as well as whether it is a representative measure of response control. Participants controlled the speed of a moving marker by continuously adjusting their response force. Participants stopped their ongoing tracking in response to auditory signals on 25%, 50%, 75%, or 100% of trials. Stopping was contrasted with accelerating, in which participants accelerated the marker in response to the signals. In Experiment 1, on each trial participants either stopped or accelerated, allowing a trade-off between the two. In Experiments 2 and 3, participants only stopped or only accelerated, thus decreasing the likelihood of a trade-off. When a trade-off was possible, stopping was resistant to expectancies. However, with little or no trade-off, expectancies influenced stopping and accelerating similarly. These findings contrast with the established view that stopping is insensitive to expectancies. In addition, when trade-offs are prevented, these results confirm that stopping is representative of other response adjustment measures.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".