Frames of reference for eye–head gaze shifts evoked during frontal eye field stimulation
Bibliographic record
Abstract
The frontal eye field (FEF), in the prefrontal cortex, participates in the transformation of visual signals into saccade motor commands and in eye-head gaze control. The FEF is thought to show eye-fixed visual codes in head-restrained monkeys, but it is not known how it transforms these inputs into spatial codes for head-unrestrained gaze commands. Here, we tested if the FEF influences desired gaze commands within a simple eye-fixed frame, like the superior colliculus (SC), or in more complex egocentric frames like the supplementary eye fields (SEFs). We electrically stimulated 95 FEF sites in two head-unrestrained monkeys to evoke 3D eye-head gaze shifts and then mathematically rotated these trajectories into various reference frames. In theory, each stimulation site should specify a specific spatial goal when the evoked gaze shifts are plotted in the appropriate frame. We found that these motor output frames varied site by site, mainly within the eye-to-head frame continuum. Thus, consistent with the intermediate placement of the FEF within the high-level circuits for gaze control, its stimulation-evoked output showed an intermediate trend between the multiple reference frame codes observed in SEF-evoked gaze shifts and the simpler eye-fixed reference frame observed in SC-evoked movements. These results suggest that, although the SC, FEF and SEF carry eye-fixed information at the level of their unit response fields, this information is transformed differently in their output projections to the eye and head controllers.
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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".