Spatiotemporal Coding in the Macaque Supplementary Eye Fields: Landmark Influence in the Target-to-Gaze Transformation
Bibliographic record
Abstract
Eye-centered (egocentric) and landmark-centered (allocentric) visual signals influence spatial cognition, navigation, and goal-directed action, but the neural mechanisms that integrate these signals for motor control are poorly understood. A likely candidate for egocentric/allocentric integration in the gaze control system is the supplementary eye fields (SEF), a mediofrontal structure with high-level “executive” functions, spatially tuned visual/motor response fields, and reciprocal projections with the frontal eye fields (FEF). To test this hypothesis, we trained two head-unrestrained monkeys ( Macaca mulatta ) to saccade toward a remembered visual target in the presence of a visual landmark that shifted during the delay, causing gaze end points to shift partially in the same direction. A total of 256 SEF neurons were recorded, including 68 with spatially tuned response fields. Model fits to the latter established that, like the FEF and superior colliculus (SC), spatially tuned SEF responses primarily showed an egocentric (eye-centered) target-to-gaze position transformation. However, the landmark shift influenced this default egocentric transformation: during the delay, motor neurons (with no visual response) showed a transient but unintegrated shift (i.e., not correlated with the target-to-gaze transformation), whereas during the saccade-related burst visuomotor (VM) neurons showed an integrated shift (i.e., correlated with the target-to-gaze transformation). This differed from our simultaneous FEF recordings (Bharmauria et al., 2020), which showed a transient shift in VM neurons, followed by an integrated response in all motor responses. Based on these findings and past literature, we propose that prefrontal cortex incorporates landmark-centered information into a distributed, eye-centered target-to-gaze transformation through a reciprocal prefrontal circuit.
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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) | 0.000 | 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".