Neuronal Correlates for Preparatory Set Associated with Pro-Saccades and Anti-Saccades in the Primate Frontal Eye Field
Bibliographic record
Abstract
Diversity in behavioral responses to sensory stimuli has been attributed to variations in preparatory set. Variability in oculomotor responses toward identical visual stimuli has been well documented, but the neuronal processes underlying this variability are poorly understood. Here, we report evidence for set-related activity for saccadic eye movements in single neurons in the frontal eye field (FEF) in monkeys trained on a task in which they either had to look toward a visual stimulus (pro-saccade) or away from the stimulus (anti-saccade) depending on a previous instruction. A portion of FEF neurons were identified as neurons projecting directly to the superior colliculus (SC) with antidromic activation techniques. Saccade-related neurons in the FEF had lower prestimulus and stimulus-related activity on anti-saccade trials compared with pro-saccade trials. The level of prestimulus activity correlated with saccadic reaction times, express saccade occurrence, and errors in the anti-saccade task. In addition, saccade-related activity in the FEF was higher for pro-saccades than for anti-saccades. These results demonstrate that the direct descending pathway from the FEF to the SC carries preparatory set-related activity for pro-saccades and anti-saccades. The results also provide insights into the neuronal basis of variations in saccadic reaction times and in the control of the prepotent response to glance to a flashed stimulus.
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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 | 0.000 |
| 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 | 0.000 |
| 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".