Neural dynamics in ventrolateral prefrontal cortex underlie learning from feedback
Bibliographic record
Abstract
Abstract Learning often depends on feedback, yet how positive and negative outcomes reorganize target representations to support later memory retrieval remains poorly understood. Accumulating evidence suggests that the ventrolateral prefrontal cortex (vlPFC) acts as a central hub linking learning and retrieval, raising the possibility that it plays a critical role in this process. Here we analysed spiking activity and local field potentials (LFPs) recorded from vlPFC while monkeys performed a multi-cycle object-learning task. During the initial learning cycle, correct and incorrect feedback elicited distinct vlPFC neural responses in both spiking and LFPs. In particular, positive feedback produced elevated theta power and enhanced phase-amplitude coupling (PAC) between theta phase and high-frequency amplitude, associated with sustained suppression of neural spiking. Incorrect feedback induced stronger beta power. Despite comparable levels of object information under both feedback conditions, decoders trained and tested within the same feedback state outperformed those tested across states, revealing feedback-dependent coding formats. State-space and cross-period generalisation analyses further showed that object representations following positive feedback were geometrically closer to and shared a common coding format with those reinstated during later retrieval, indicating that feedback reshapes neural geometry toward retrieval-compatible states. Moreover, these geometric and generalisation effects were selectively expressed on electrodes showing stronger PAC or beta power, suggesting that oscillatory coordination may regulate how feedback signals are transformed into stable target codes. Together, our results reveal how vlPFC serves as a critical bridge between learning and memory retrieval, with feedback-driven dynamics reorganizing population geometry through rhythmic coordination and bringing successful outcome states closer to future retrieval representations.
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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 | 0.001 |
| 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) | 0.001 | 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".