1. Language statistical learning responds to reinforcement learning principles rooted in the striatum
- Author
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Joan Orpella, Ernest Mas-Herrero, Pablo Ripollés, Josep Marco-Pallarés, and Ruth de Diego-Balaguer
- Subjects
Male ,Social Sciences ,Task (project management) ,Diagnostic Radiology ,Learning and Memory ,Functional Magnetic Resonance Imaging ,Medicine and Health Sciences ,Reinforcement learning ,Psychology ,Biology (General) ,Function (engineering) ,Cervell ,media_common ,Language ,Cognitive science ,Brain Mapping ,Grammar ,Psycholinguistics ,General Neuroscience ,Radiology and Imaging ,Brain ,Language acquisition ,Magnetic Resonance Imaging ,Psicolingüística ,Dynamics (music) ,Pattern Recognition, Physiological ,Female ,Anatomy ,General Agricultural and Biological Sciences ,Temporal difference learning ,Reinforcement, Psychology ,Word (computer architecture) ,Research Article ,Imaging Techniques ,QH301-705.5 ,media_common.quotation_subject ,Cognitive Neuroscience ,Neuroimaging ,Biology ,Research and Analysis Methods ,Language Development ,General Biochemistry, Genetics and Molecular Biology ,Domain (software engineering) ,Young Adult ,Magnetic resonance imaging ,Diagnostic Medicine ,Imatges per ressonància magnètica ,Aprenentatge automàtic ,Machine learning ,Reaction Time ,Humans ,Learning ,Syntax ,Language Acquisition ,Behavior ,General Immunology and Microbiology ,Cognitive Psychology ,Biology and Life Sciences ,Linguistics ,Corpus Striatum ,Neostriatum ,Cognitive Science ,Probability Learning ,Neuroscience - 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., Statistical learning 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. This study uses a combination of computational modelling and functional MRI to reveal a fundamental link between online language statistical learning and reinforcement learning at the algorithmic and implementational levels.
- Published
- 2021