Effector-specific spatial codes in dorsolateral prefrontal cortex during a head-unrestrained reach task
Bibliographic record
Abstract
Dorsolateral prefrontal cortex (DLPFC) is associated with executive control and response selection, but the extent to which it is involved in effector-specific transformations is unclear. We addressed this question by recording single neurons from dorsolateral prefrontal cortex (DLPFC) while two trained monkeys performed a head-unrestrained reaching paradigm that allowed freely coordinated motion of gaze, head reaching in depth. Animals touched one of three central LEDs at waist level while maintaining gaze on a central fixation dot and were rewarded if they touched a target appearing at one of 15 locations in a 40° x 20° (visual angle) array. Preliminary analysis of 271 neurons in both monkeys showed an assortment of target/stimulus, gaze, pre-reach and reach-timed responses in DLPFC. We first tested for gaze, head, and hand gain fields during the different neuronal responses and found that 38% of the responses were gain modulated by initial hand position. A small fraction of neurons showed gain fields for initial eye position (4%), and for both initial eye and hand position (6%). After removing the gain field effects, we fitted the residual data against various spatial models and found that the visual response best encoded the target relative to space (Ts), whereas responses at gaze and hand onset showed a tendency towards coding hand displacement (dA). In addition, some (20%) neurons showed a preference for coding final head position or displacement. A more complete analysis will describe the complete coding and distribution of gaze, head, and reach signals in this region.
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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".