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Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech

People of all ages display the ability to detect and learn from patterns in seemingly random stimuli. Referred to as statistical learning (SL), this process is particularly critical when learning a spoken language, helping in the identification of discrete words within a spoken phrase. Here, by cons...

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Autores principales: Orpella, Joan, Assaneo, M. Florencia, Ripollés, Pablo, Noejovich, Laura, López-Barroso, Diana, de Diego-Balaguer, Ruth, Poeppel, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9292101/
https://www.ncbi.nlm.nih.gov/pubmed/35793349
http://dx.doi.org/10.1371/journal.pbio.3001712
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author Orpella, Joan
Assaneo, M. Florencia
Ripollés, Pablo
Noejovich, Laura
López-Barroso, Diana
de Diego-Balaguer, Ruth
Poeppel, David
author_facet Orpella, Joan
Assaneo, M. Florencia
Ripollés, Pablo
Noejovich, Laura
López-Barroso, Diana
de Diego-Balaguer, Ruth
Poeppel, David
author_sort Orpella, Joan
collection PubMed
description People of all ages display the ability to detect and learn from patterns in seemingly random stimuli. Referred to as statistical learning (SL), this process is particularly critical when learning a spoken language, helping in the identification of discrete words within a spoken phrase. Here, by considering individual differences in speech auditory–motor synchronization, we demonstrate that recruitment of a specific neural network supports behavioral differences in SL from speech. While independent component analysis (ICA) of fMRI data revealed that a network of auditory and superior pre/motor regions is universally activated in the process of learning, a frontoparietal network is additionally and selectively engaged by only some individuals (high auditory–motor synchronizers). Importantly, activation of this frontoparietal network is related to a boost in learning performance, and interference with this network via articulatory suppression (AS; i.e., producing irrelevant speech during learning) normalizes performance across the entire sample. Our work provides novel insights on SL from speech and reconciles previous contrasting findings. These findings also highlight a more general need to factor in fundamental individual differences for a precise characterization of cognitive phenomena.
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spelling pubmed-92921012022-07-19 Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech Orpella, Joan Assaneo, M. Florencia Ripollés, Pablo Noejovich, Laura López-Barroso, Diana de Diego-Balaguer, Ruth Poeppel, David PLoS Biol Research Article People of all ages display the ability to detect and learn from patterns in seemingly random stimuli. Referred to as statistical learning (SL), this process is particularly critical when learning a spoken language, helping in the identification of discrete words within a spoken phrase. Here, by considering individual differences in speech auditory–motor synchronization, we demonstrate that recruitment of a specific neural network supports behavioral differences in SL from speech. While independent component analysis (ICA) of fMRI data revealed that a network of auditory and superior pre/motor regions is universally activated in the process of learning, a frontoparietal network is additionally and selectively engaged by only some individuals (high auditory–motor synchronizers). Importantly, activation of this frontoparietal network is related to a boost in learning performance, and interference with this network via articulatory suppression (AS; i.e., producing irrelevant speech during learning) normalizes performance across the entire sample. Our work provides novel insights on SL from speech and reconciles previous contrasting findings. These findings also highlight a more general need to factor in fundamental individual differences for a precise characterization of cognitive phenomena. Public Library of Science 2022-07-06 /pmc/articles/PMC9292101/ /pubmed/35793349 http://dx.doi.org/10.1371/journal.pbio.3001712 Text en © 2022 Orpella et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Orpella, Joan
Assaneo, M. Florencia
Ripollés, Pablo
Noejovich, Laura
López-Barroso, Diana
de Diego-Balaguer, Ruth
Poeppel, David
Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech
title Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech
title_full Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech
title_fullStr Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech
title_full_unstemmed Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech
title_short Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech
title_sort differential activation of a frontoparietal network explains population-level differences in statistical learning from speech
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9292101/
https://www.ncbi.nlm.nih.gov/pubmed/35793349
http://dx.doi.org/10.1371/journal.pbio.3001712
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