Anodal HD-tDCS of vmPFC facilitates learning of monosemous and polysemous words: A computational modeling study.

Vocabulary learning requires learners to establish stable mappings between symbols and meanings. Reinforcement learning (RL) principles, through feedback-based value computation, provide a compelling framework supporting such mappings, with the ventromedial prefrontal cortex (vmPFC) playing a key role in value-based learning and decision-making. In this study, we combined artificial pseudowords with a probabilistic reversal learning paradigm and applied high-definition transcranial direct current stimulation (HD-tDCS) in the vmPFC to investigate its role in vocabulary acquisition. Computational modeling revealed that anodal HD-tDCS led to more stable integration of feedback and greater choice consistency. Parametric fMRI further showed enhanced value coding in the putamen and postcentral gyrus for monosemous words, along with strengthened frontostriatal connectivity that supported the acquisition of polysemous words. Collectively, these findings demonstrate that anodal HD-tDCS over the vmPFC, along with RL mechanisms, effectively facilitates vocabulary acquisition, advancing our understanding of the neural basis of language learning.