A Comparison of Frontoparietal fMRI Activation During Anti-Saccades and Anti-Pointing
Bibliographic record
Abstract
An anti-saccade, which is a saccade directed toward a mirror-symmetrical position in the opposite visual field relative to the visual stimulus, involves at least three separate operations: covert orienting, response suppression, and coordinate transformation. The distinction between pro- and anti-saccades can also be applied to pointing. We used fMRI to compare patterns of brain activation during pro- and anti-movements, to determine whether or not additional areas become active during the production of anti-movements. In parietal cortex, an inferior network was active during both saccades and pointing that included three foci along the intraparietal sulcus: 1) a posterior superior parietal area (pSPR), more active during the anti-tasks; 2) a middle inferior parietal area (mIPR), active only during the anti-tasks; and 3) an anterior inferior parietal area (aIPR), equally active for pro- and anti-movement. A superior parietal network was active during pointing but not saccades and included the following: 1) a medial region, active during anti- but not pro-pointing (mSPR); 2) an anterior and medial region, more active during pro-pointing (aSPR); and 3) an anterior and lateral region, equally active for pro- and anti-pointing (lSPR). In frontal cortex, areas selectively active during anti-movement were adjacent and anterior to areas that were active during both the anti- and pro-tasks, i.e., were anterior to the frontal eye field and the supplementary motor area. All saccade areas were also active during pointing. In contrast, foci in the dorsal premotor area, the anterior superior frontal region, and anterior cingulate were active during pointing but not saccades. In summary, pointing with central gaze activates a frontoparietal network that includes the saccade network. The operations required for the production of anti-movements recruited additional frontoparietal 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".