Reinforcement-learning signals support dynamic adaptive control during language switching

Language switching exemplifies a real-world model of adaptive control, as bilinguals select languages in response to continuously changing contextual demands. Although the Adaptive Control Hypothesis (ACH) highlights context-dependent language control in bilinguals, how these strategies are learned remains unclear. Drawing on reinforcement learning (RL) theory, we examined whether reward prediction error (RPE) drives adjustments in voluntary language switching. Chinese-English bilinguals performed a voluntary picture-naming task with probabilistic (i.e., high, medium, and low) reward feedback that generated RPE depending on the switch decisions. Computational modeling revealed that RPE dynamically updated an abstract, generalizable switch policy. Representational similarity analysis (RSA) suggested the learned value of the control policy was represented in the MTL, whereas RPE representations emerged in the dorsolateral prefrontal cortex (dlPFC). Furthermore, connectome-based predictive modeling (CPM) revealed a multi-stage network process. A distributed, cross-network pattern initially supported early exploratory learning, which then transitioned into a centralized, hub-centric network for policy exploitation. Finally, the network specialized into a localized circuit for policy automation, coupled with a globally distributed network for monitoring unexpected errors. Together, these findings establish adaptive language control as a value-based RL process. This provides a neurocomputational framework for strategy learning, extending RL principles from simple stimulus-response to high-level cognitive control. Language control is not driven by static costs or preferences, but by the dynamic learning of an abstract, generalizable action policy.