%0 Journal Article
%A Orpella, Joan
%A Assaneo, M Florencia
%A Ripollés, Pablo
%A Noejovich, Laura
%A López-Barroso, Diana
%A Diego-Balaguer, Ruth de
%A Poeppel, David
%T Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech.
%D 2022
%U http://hdl.handle.net/10668/20411
%X 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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