2026-06-14T03:31:38Zhttps://repisalud.isciii.es/rest/oai/request
oai:repisalud.isciii.es:20.500.12105/187032024-09-21T22:05:57Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16927col_20.500.12105_16962
Repisalud author Orpella, Joan author Assaneo, M Florencia author Ripollés, Pablo author Noejovich, Laura author López-Barroso, Diana author Diego-Balaguer, Ruth de author Poeppel, David 2024-02-27T15:09:03Z 2024-02-27T15:09:03Z 2022-07-06 10.1371/journal.pbio.3001712 1545-7885 PLoS biology http://hdl.handle.net/10668/20411 35793349 http://hdl.handle.net/20.500.12105/18703 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. eng Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech. research article