FMRI activation related to preparatory set is correlated with saccade latency in human frontal eye fields but not in the supplementary motor area
Bibliographic record
Abstract
Variation in saccade latency in response to identical sensory stimuli has been attributed to variation in preparatory set. Here we report the first evidence for a relationship between saccade latency and set-related activity in the human frontal eye fields (FEF). Event-related fMRI was used to examine the activation time-courses during a preparatory gap period (2 sessions of 144 trials with 5 subjects), during which no visual stimulus was presented and no saccades were made. The subject simply anticipated the appearance of a flashed peripheral target. Each trial began with the presentation of a central fixation cue (3 s), followed by a green (pro-saccade) or red (anti-saccade) central instructional cue (3 s). This was followed by a 0 (no gap) or 2 s (gap) period of darkness, followed by appearance of a flashed peripheral target (100 ms). Saccade direction and latency were recorded during scanning for each subject. 2-S gap trials were sorted according to short (top 25%) vs. long (bottom 25%) saccade latencies. Examination of the time-courses of activation in the FEF for 2-s gap trials showed a greater build-up in activity during the gap period for short as compared to long latency saccades. In contrast, the supplementary motor area (SMA) exhibited preparatory activity that was not different for short- and long-latency saccades. Replicating our previous work, activation in the intraparietal sulcus (LIP+) did not show preparatory build-up during the gap. These data provide evidence that the FEF contributes to the generation of preparatory set and that such signals may underlie the observed behavioral variability. Moreover, the differences in the pattern of activation we observed in FEF, SMA, and LIP+ demonstrate a functional dissociation between these three primary human oculomotor areas.
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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".