ArticlePLoS biology2021
Language statistical learning responds to reinforcement learning principles rooted in the striatum.
Article in PLoS biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The specific nature of the statistical learning deficit in developmental dyslexia: Impaired temporal and spared spatial learning abilities.Psychonomic bulletin & review · 2026Article
- Individual differences in brain systems supporting declarative and procedural memory during grammatical error processing in children.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- Limited but specific engagement of the mature language network during linguistic statistical learning.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Computational and neural mechanisms of statistical pain learning.Nature communications · 2022Article
- Article
- Brain-correlates of processing local dependencies within a statistical learning paradigm.Scientific reports · 2022Article
- Neural correlates of sequence learning in children with developmental dyslexia.Human brain mapping · 2022Article
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Authors and funding
5 authors.
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Abstract
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.
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