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Neural dynamics in ventrolateral prefrontal cortex underlie learning from feedback

Runhao LuiD, Mikiko KadohisaiD, Makoto Kusunoki AiD, Daniel J. MitchelliD, Alexandra WoolgariD, Mark J. BuckleyiD, John DuncaniD

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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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