Neuromuscular recruitment related to stimulus presentation and task instruction during the anti-saccade task
Bibliographic record
Abstract
The contextual control of movement requires the transformation of sensory information into appropriate actions, guided by task-appropriate rules. Previous conceptualizations of the sensorimotor transformations underlying anti-saccades (look away from a stimulus) have suggested that stimulus location is first registered and subsequently transformed into its mirror location before being relayed to the motor periphery. Here, by recording neck muscle activity in monkeys performing anti-saccades, we demonstrate that stimulus presentation induces a transient recruitment of the neck muscle synergy used to turn the head in the wrong direction, even though subjects subsequently looked away from the stimulus correctly. Such stimulus-driven aspects of recruitment developed essentially at reflexive latencies (∼60-70 ms after stimulus presentation), and persisted at modest eccentricities regardless of head-restraint. Prior to stimulus presentation, neck muscle activity also reflected whether the animals were preparing for an anti-saccade or a pro-saccade (look toward a stimulus). Neck muscle activity prior to erroneous anti-saccades also resembled that observed prior to pro-saccades. These results emphasize a parallel nature to the sensorimotor transformations underlying the anti-saccade task, suggesting that the top-down and bottom-up processes engaged in this task influence the motor periphery. The bottom-up aspects of neck muscle recruitment also fit within the context of recent results from the limb-movement literature, showing that stimulus-driven activation of muscle synergies may be a generalizing strategy in inertial-laden systems.
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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) | 0.000 | 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 | 0.001 |
| 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".