Functional specialization and distributed processing across marmoset lateral prefrontal subregions
Bibliographic record
Abstract
A prominent aspect of primate lateral prefrontal cortex organization is its division into several cytoarchitecturally distinct subregions. Neurophysiological investigations in macaques have provided evidence for the functional specialization of these subregions, but an understanding of the relative representational topography of sensory, social, and cognitive processes within them remains elusive. One explanatory factor is that evidence for functional specialization has been compiled largely from a patchwork of findings across studies, in many animals, and with considerable variation in stimulus sets and tasks. Here, we addressed this by leveraging the common marmoset (Callithrix jacchus) to carry out large-scale neurophysiological mapping of the lateral prefrontal cortex using high-density microelectrode arrays, and a diverse suite of test stimuli including faces, marmoset calls, and spatial working memory task. Task-modulated units and units responsive to visual and auditory stimuli were distributed throughout the lateral prefrontal cortex, while those with saccade-related activity or face-selective responses were restricted to 8aV, 8aD, 10, 46 V, and 47. Neurons with contralateral visual receptive fields were limited to areas 8aV and 8aD. These data reveal a mixed pattern of functional specialization in the lateral prefrontal cortex, in which responses to some stimuli and tasks are distributed broadly across lateral prefrontal cortex subregions, while others are more limited in their representation.
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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 | 0.001 |
| 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".