Short‐duration stimulation of the supplementary eye fields perturbs anti‐saccade performance while potentiating contralateral head orienting
Bibliographic record
Abstract
Many forms of brain stimulation utilize the notion of state dependency, whereby greater influences are observed when a given area is more engaged at the time of stimulation. Here, by delivering intracortical microstimulation (ICMS) to the supplementary eye fields (SEF) of monkeys performing interleaved pro- and anti-saccades, we show a surprising diversity of state-dependent effects of ICMS-SEF. Short-duration ICMS-SEF passed around cue presentation selectively disrupted anti-saccades by increasing reaction times and error rates bilaterally, and also recruited neck muscles, favoring contralateral head turning to a greater degree on anti-saccade trials. These results are consistent with the functional relevance of the SEF for anti-saccades. The multiplicity of stimulation-evoked effects, with ICMS-SEF simultaneously disrupting anti-saccade performance and facilitating contralateral head orienting, probably reflects both the diversity of cortical and subcortical targets of SEF projections, and the response of this oculomotor network to stimulation. We speculate that the bilateral disruption of anti-saccades arises via feedback loops that may include the thalamus, whereas neck muscle recruitment arises via feedforward polysynaptic pathways to the motor periphery. Consideration of both sets of results reveals a more complete picture of the highly complex and multiphasic response to ICMS-SEF that can play out differently in different effector systems.
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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".