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

Joan Orpella, Ernest Mas-Herrero, Pablo Ripollés, Josep Marco-Pallarés, Ruth de Diego-Balaguer

Research output: Contribution to journalArticlepeer-review


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

Original languageEnglish (US)
Article numbere3001119
JournalPLoS biology
Issue number9
StatePublished - Sep 2021

ASJC Scopus subject areas

  • General Neuroscience
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences


Dive into the research topics of 'Language statistical learning responds to reinforcement learning principles rooted in the striatum'. Together they form a unique fingerprint.

Cite this