Signal timing and hemispheric localization in the human saccadic system: Preparatory processes and the sensorimotor transformation for antisaccades
Bibliographic record
Abstract
Objectives: We used event-related functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) to observe signal timing and hemispheric localization in the human saccadic system for preparatory processes and the sensorimotor transformation. Methods: We carried out fMRI on 21 and MEG on 17 healthy subjects, using the same saccadic paradigm. Subjects performed blocks of randomly mixed prosaccade and antisaccade trials. A cue for prosaccades or antisaccades was presented for 300 ms and was then replaced by the fixation ring. After 1700 ms the fixation ring disappeared and a target appeared around one of the two peripheral dots for 1 s. After that, the fixation point reappeared and lasted for 1 s. Results: In fMRI, beginning shortly after the cue, hemodynamic activity in the frontal eye field (FEF) and supplementary eye field (SEF) reached a peak at 4 s post-cue. Activity in superior temporal gyrus, inferior frontal gyrus, and posterior cingulate gyrus decreased after the cue and showed a negative peak at 4 s post-cue. Activity in the parietal eye field (PEF) showed a later peak at 4 s post-target (i.e. 6 s post-cue). Activity in the FEF, SEF, and PEF was greater during antisaccades than prosaccades. In MEG, the left FEF showed increased neural activity after the cue but 605–755 ms prior to the target, irrespective of the saccadic direction. During antisaccades, activity was observed in the PEF contralateral to the target around 110ms after the target, and in the PEF ipsilateral to the target about 15ms later, around 135 ms post-target. Conclusion: FEF and SEF activation occur prior to PEF activity during saccades, and may thus reflect not only execution but preparation of saccades. A shift in activity from the contralateral to ipsilateral hemisphere consistent with the vector inversion (sensorimotor transformation) required for antisaccades was observed in PEF rather than FEF after target presentation.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".