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Media type:
E-Article
Title:
Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech
Contributor:
Orpella, Joan;
Assaneo, M. Florencia;
Ripollés, Pablo;
Noejovich, Laura;
López-Barroso, Diana;
Diego-Balaguer, Ruth de;
Poeppel, David
imprint:
Public Library of Science (PLoS), 2022
Published in:PLOS Biology
Language:
English
DOI:
10.1371/journal.pbio.3001712
ISSN:
1545-7885
Origination:
Footnote:
Description:
<jats:p>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.</jats:p>