Dynamic and Opposing Adjustment of Movement Cancellation and Generation in an Oculomotor Countermanding Task
Bibliographic record
Abstract
Adaptive adjustments of strategies help optimize behavior in a dynamic and uncertain world. Previous studies in the countermanding (or stop-signal) paradigm have detailed how reaction times (RTs) change with trial sequence, demonstrating adaptive control of movement generation. Comparatively little is known about the adaptive control of movement cancellation in the countermanding task, mainly because movement cancellation implies the absence of an outcome and estimates of movement cancellation require hundreds of trials. Here, we exploit a within-trial proxy of movement cancellation based on recordings of neck muscle activity while human subjects attempted to cancel large eye-head gaze shifts. On a subset of successfully cancelled trials where gaze remains stable, small head-only movements to the target are actively braked by a pulse of antagonist neck muscle activity. The timing of such antagonist muscle recruitment relative to the stop signal, termed the "antagonist latency," tended to decrease or increase after trials with or without a stop-signal, respectively. Over multiple time scales, fluctuations in the antagonist latency tended to be the mirror opposite of those occurring contemporaneously with RTs. These results provide new insights into the adaptive control of movement cancellation at an unprecedented resolution, suggesting it can be as prone to dynamic adjustment as movement generation. Adaptive control in the countermanding task appears to be governed by a dynamic balance between movement cancellation and generation: shifting the balance in favor of movement cancellation slows movement generation, whereas shifting the balance in favor of movement generation slows movement cancellation.
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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 | 0.004 |
| 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 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".