A common inhibition mechanism underlies both anti and countermanded saccades
Bibliographic record
Abstract
We rely on our ability to inhibit prepotent reactions to our surroundings and execute appropriate behaviors. Both antisaccade and countermanding tasks have been used extensively to test theories about inhibitory control within the primate saccade system. Although it is believed that each task may tap similar neural mechanisms of inhibition, to date there have been no direct comparisons. Here, we compared performance on antisaccade and countermanding saccade tasks in the same group of subjects to test the hypothesis that a common mechanism underlies inhibiting saccades in both tasks. We predicted that subjects with strong inhibition in one task should show high inhibition in the other task. In both tasks, the predominant response was to make a saccade toward a salient visual cue. In the antisaccade task, subjects were instructed to make a saccade away from the cue, which required that one first inhibit a saccade to the cue. In the countermanding task, subjects were instructed to cancel a planned saccade when an auditory stop signal was emitted at various delays following the appearance of the visual cue, i.e., stop signal delays (SSD). Subjects' eye movements were monitored to measure their ability to suppress unwanted saccades, saccade initiation times, and a latent variable called the stop signal reaction time (SSRT). The SSRT is thought to index the time one needs to inhibit a saccade. In the countermanding task, stop-success rate declined as SSD increased. In the antisaccade task, the probability of successfully generating an antisaccade increased with increasing initiation times. Moreover, estimates of SSRT from the countermanding task correlated with one's ability to successfully generate antisaccades. Our results provide strong evidence that a common mechanism underlies saccade suppression in both tasks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".