Functional architecture of visual responses in supplementary eye field
Bibliographic record
Abstract
Previously, we have described the laminar organization of neurons in the supplementary eye field that signal error, reward gain and loss, conflict, event timing, and goal maintenance. Here we describe the laminar organization of visually responsive neurons that were active during performance of a saccade stop-signal task. Around one-third of isolated neurons exhibited enhanced or suppressed responses to a visual target for a potential saccade, where the majority exhibited enhanced activity. Three-fourths of these neurons had broad spikes. Laminar current-source density aligned on visual target presentation revealed the earliest sink in layers 3 followed by a prolonged strong sink more superficially coupled with a weaker prolonged sink in layer 5 and a transient sink in layer 6. Visually responsive neurons were observed in all layers but were less common in L5/6. Response latencies were comparable to those reported previously, which are significantly later than those measured in occipital and temporal visual areas but overlapping those measured in cingulate cortex. Task-related visual response latency varied across cortical layers. Response latency was significantly earlier for neurons with narrow spikes. Neurons with task-related visual responses discharged until after saccade production. The visual responses were rarely sensitive to amount of expected reward. Around three-fifths of visually responsive neurons were most sensitive to the visual target appearing in one hemifield. Unexpectedly, many neurons in layer 2 had ipsilateral receptive fields. These findings complete the first catalogue of laminar organization of functional signals in a frontal lobe area.
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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 | 0.001 | 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".