Toward task mapping of primate prefrontal cortex
Bibliographic record
Abstract
The lateral prefrontal cortex (LPFC) flexibly supports diverse cognitive functions, including working memory, decision making, and inhibitory control. Unlike neurons in sensory cortices, LPFC neurons exhibit adaptive coding, dynamically changing their tuning to encode task-relevant information across domains. These context-dependent responses challenge traditional approaches to functional mapping. In this review, we propose adapting stimulus mapping techniques for 'task mapping' of LPFC. We highlight key challenges in this endeavour, arising from the structural and functional properties of LPFC, including large spatiotemporal receptive fields, dynamic tuning, and integrative connectivity. To address these challenges, we introduce topographic similarity analysis (TSA), an approach inspired by representational similarity analysis (RSA). We discuss the potential of TSA for detecting reorganization of functional topographies with learning and changing task demands, with empirical work applying TSA to macaque cell recordings underway. This work motivates further exploration of topographies across a richer task space. By extending TSA to high-field fMRI in humans, future research may uncover cognitive dimensions underlying LPFC function, fostering a deeper understanding of flexible cognition.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".