Department of Psychology, New York University, New York, New York, United States of America.
Institute of Neurobiology, National Autonomous University of Mexico, Juriquilla, Querétaro, Mexico.
PLoS Biol. 2022 Jul 6;20(7):e3001712. doi: 10.1371/journal.pbio.3001712. eCollection 2022 Jul.
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.
各年龄段的人都表现出从看似随机的刺激中检测和学习模式的能力。这种过程被称为统计学习（SL），在学习口语时尤为关键，有助于识别口语短语中的离散单词。在这里，通过考虑言语听觉运动同步的个体差异，我们证明了特定神经网络的募集支持言语 SL 中的行为差异。虽然 fMRI 数据的独立成分分析（ICA）显示，在学习过程中听觉和上运动前/皮质区域的网络普遍被激活，但只有一些个体（高听觉运动同步者）还会额外且选择性地参与一个额顶网络。重要的是，这个额顶网络的激活与学习表现的提升有关，并且通过发音抑制（AS；即在学习过程中产生不相关的言语）干扰该网络可以使整个样本的表现正常化。我们的工作为言语 SL 提供了新的见解，并调和了先前相互矛盾的发现。这些发现还强调了一个更普遍的需求，即需要考虑基本的个体差异，以精确描述认知现象。
