Preparatory Set Associated With Pro-Saccades and Anti-Saccades in Humans Investigated With Event-Related fMRI
Bibliographic record
Abstract
Previous studies have shown that the BOLD functional MRI (fMRI) signal is increased in several cortical areas when subjects perform anti-saccades compared with pro-saccades. It remains unknown, however, whether this increase is due to an increased cortical motor signal for anti-saccades or due to differences in preparatory set between pro- and anti-saccade trials. To address this question, we measured event-related fMRI in a paradigm that allowed us to separate instruction-related brain activity from saccade-related brain activity. In this paradigm, the instruction to either generate a pro-saccade or an anti-saccade was conveyed by a switch in the color of the central fixation stimulus and preceded the presentation of a peripheral stimulus by either 6, 10, or 14 s. Cortical areas were functionally mapped using the general linear model comparing standard pro- and anti-saccade blocks with fixation blocks. When the trials were aligned on the onset of the instruction stimulus, bilateral frontal eye fields and right hemisphere dorsolateral prefrontal cortex showed an increased signal during the instruction period on anti-saccade trials as compared with pro-saccade trials. When the trials were aligned on the movement stimulus and the instruction period activity was subtracted, there were no differences between pro- and anti-saccades. This finding suggests that the increased cortical activation found in previous blocked designs originates predominately from differences in preparatory set and not from differences in the motor signal between pro- and anti-saccades.
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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) | 0.001 | 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".