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

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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Joan Orpella, Ernest Mas-Herrero, Pablo Ripollés, Josep Marco-Pallarés, Ruth de Diego-Balaguer
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/1e584ef9e8b64d81b5f495f70608350b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:1e584ef9e8b64d81b5f495f70608350b
record_format	dspace
spelling	oai:doaj.org-article:1e584ef9e8b64d81b5f495f70608350b2021-12-02T19:54:37ZLanguage statistical learning responds to reinforcement learning principles rooted in the striatum.1544-91731545-788510.1371/journal.pbio.3001119https://doaj.org/article/1e584ef9e8b64d81b5f495f70608350b2021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pbio.3001119https://doaj.org/toc/1544-9173https://doaj.org/toc/1545-7885Statistical 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.Joan OrpellaErnest Mas-HerreroPablo RipollésJosep Marco-PallarésRuth de Diego-BalaguerPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Biology, Vol 19, Iss 9, p e3001119 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Joan Orpella Ernest Mas-Herrero Pablo Ripollés Josep Marco-Pallarés Ruth de Diego-Balaguer Language statistical learning responds to reinforcement learning principles rooted in the striatum.
description	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.
format	article
author	Joan Orpella Ernest Mas-Herrero Pablo Ripollés Josep Marco-Pallarés Ruth de Diego-Balaguer
author_facet	Joan Orpella Ernest Mas-Herrero Pablo Ripollés Josep Marco-Pallarés Ruth de Diego-Balaguer
author_sort	Joan Orpella
title	Language statistical learning responds to reinforcement learning principles rooted in the striatum.
title_short	Language statistical learning responds to reinforcement learning principles rooted in the striatum.
title_full	Language statistical learning responds to reinforcement learning principles rooted in the striatum.
title_fullStr	Language statistical learning responds to reinforcement learning principles rooted in the striatum.
title_full_unstemmed	Language statistical learning responds to reinforcement learning principles rooted in the striatum.
title_sort	language statistical learning responds to reinforcement learning principles rooted in the striatum.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/1e584ef9e8b64d81b5f495f70608350b
work_keys_str_mv	AT joanorpella languagestatisticallearningrespondstoreinforcementlearningprinciplesrootedinthestriatum AT ernestmasherrero languagestatisticallearningrespondstoreinforcementlearningprinciplesrootedinthestriatum AT pabloripolles languagestatisticallearningrespondstoreinforcementlearningprinciplesrootedinthestriatum AT josepmarcopallares languagestatisticallearningrespondstoreinforcementlearningprinciplesrootedinthestriatum AT ruthdediegobalaguer languagestatisticallearningrespondstoreinforcementlearningprinciplesrootedinthestriatum
_version_	1718375887321169920

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

Ejemplares similares