Neural and computational correlates of strategic aborting and long-run policy optimization in the dorsolateral prefrontal cortex

Real-world choices often require balancing short- and long-term goals. We reasoned that seemingly suboptimal single-trial decisions may reflect strategic planning over longer timescales. We demonstrate that male macaques freely navigating in virtual reality strategically aborted offers, forgoing immediate rewards to maximize session-long returns. This behavior was highly individual-specific, suggesting that macaques account for their own long-run performance. Reinforcement-learning models suggest that this strategy is supported by modular actor-critic networks in which a policy module optimizes long-term value while also incorporating state-action values for rapid policy adjustment. These models predict that policy changes for matched offers should emerge at offer presentation, even when aborts occur much later. Consistent with this prediction, units and population dynamics in dorsolateral prefrontal cortex (dlPFC), but not parietal area 7a or dorsomedial superior temporal area (MSTd), encoded upcoming reward-optimizing aborts at offer onset. These findings cast dlPFC as a specialized policy module within closed-loop behaviors.