A Within Trial Measure of the Stop Signal Reaction Time in a Head-Unrestrained Oculomotor Countermanding Task
Bibliographic record
Abstract
The countermanding (or stop-signal) task, which requires the cancellation of an impending response on the infrequent presentation of a stop signal, enables study of the contextual control of movement generation and suppression. Here we present a novel and empirical measure of the time needed to cancel an impending gaze shift by recording neck muscle activity during a head-unrestrained oculomotor countermanding paradigm. On a subset of stop signal trials, subjects generated small head movements toward a target even though gaze remained stable due to a compensatory vestibular-ocular reflex. On such trials, we observed a burst of antagonist neck muscle activity during the small head-only error. Such antagonist neck muscle activity served as an active braking pulse as its magnitude scaled with the kinematics of the head-only error. This activity was selective for trials in which the head was arrested in mid-flight and did not appear on trials without a stop signal, on noncancelled stop signal trials when the gaze shift was completed, or on stop signal trials without head motion. Importantly, the timing of this antagonist activity related best to the onset of the stop signal (lagging it by ∼180 ms), and strongly correlated with behavioral estimates of the time needed to cancel a movement (the stop signal reaction time). These results are consistent with the notion that such selective antagonist neck muscle activity arises as a peripheral expression of the oculomotor stop process that successfully cancelled the gaze shift. Studying movement cancellation within nested systems like the head-unrestrained gaze shifting system offers a unique opportunity for investigating underlying neural mechanisms as the overall goal (i.e., to cancel a gaze shift) can be achieved despite motion of other components; on such individual trials, the oculomotor stop process is expressed as an active braking pulse.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".