Language statistical learning responds to reinforcement learning principles rooted in the striatum

Statistical learning (SL) is the ability to extract regularities from environment. In domain of language, this fundamental in words and structural rules. lack reliable online measures, statistical word rule have been primarily investigated using offline (post-familiarization) tests, which gives limited insights into dynamics SL its neural basis. Here, we capitalize on a novel task that tracks simple syntactic structures combined with computational modeling show responds reinforcement principles rooted striatal function. Specifically, demonstrate—on 2 different cohorts—that temporal difference model, relies prediction errors, accounts for participants’ behavior. We then trial-by-trial development predictions through strongly correlates activity both ventral dorsal striatum. Our results thus provide detailed mechanistic account language-related an explanation oft-cited implication striatum tasks. This work, therefore, bridges long-standing gap between language phenomena.