Orpella, JoanAssaneo, M. FlorenciaRipollés, PabloNoejovich, LauraLópez-Barroso, DianaDiego Balaguer, Ruth dePoeppel, D.2023-02-162023-02-162022-07-061544-9173https://hdl.handle.net/2445/193742People 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.application/pdfengcc-by (c) Orpella, Joan et al., 2022https://creativecommons.org/licenses/by/4.0/ParlaAprenentatgeDiferències individualsXarxes neuronals (Neurobiologia)SpeechLearningIndividual differencesNeural networks (Neurobiology)info:eu-repo/semantics/article7299292023-02-16info:eu-repo/semantics/openAccess