Auditory Research Group Zurich (ARGZ), Division Neuropsychology, Institute of Psychology, University of Zurich, Binzmühlestrasse 14/25, Zurich 8050, Switzerland; Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute, L'Hospitalet de Llobregat, Barcelona 08097, Spain.
Auditory Research Group Zurich (ARGZ), Division Neuropsychology, Institute of Psychology, University of Zurich, Binzmühlestrasse 14/25, Zurich 8050, Switzerland; Department of Internal Medicine, University Hospital, University of Zurich, Zurich 8091, Switzerland; University Research Priority Program, "Dynamics of Healthy Aging", University of Zurich, Zurich 8050, Switzerland.
Neuroimage. 2021 Jul 15;235:118051. doi: 10.1016/j.neuroimage.2021.118051. Epub 2021 Apr 10.
Neural oscillations constitute an intrinsic property of functional brain organization that facilitates the tracking of linguistic units at multiple time scales through brain-to-stimulus alignment. This ubiquitous neural principle has been shown to facilitate speech segmentation and word learning based on statistical regularities. However, there is no common agreement yet on whether speech segmentation is mediated by a transition of neural synchronization from syllable to word rate, or whether the two time scales are concurrently tracked. Furthermore, it is currently unknown whether syllable transition probability contributes to speech segmentation when lexical stress cues can be directly used to extract word forms. Using Inter-Trial Coherence (ITC) analyses in combinations with Event-Related Potentials (ERPs), we showed that speech segmentation based on both statistical regularities and lexical stress cues was accompanied by concurrent neural synchronization to syllables and words. In particular, ITC at the word rate was generally higher in structured compared to random sequences, and this effect was particularly pronounced in the flat condition. Furthermore, ITC at the syllable rate dynamically increased across the blocks of the flat condition, whereas a similar modulation was not observed in the stressed condition. Notably, in the flat condition ITC at both time scales correlated with each other, and changes in neural synchronization were accompanied by a rapid reconfiguration of the P200 and N400 components with a close relationship between ITC and ERPs. These results highlight distinct computational principles governing neural synchronization to pertinent linguistic units while segmenting speech under different listening conditions.
神经振荡构成了功能大脑组织的固有特性，通过大脑与刺激的对齐，促进了在多个时间尺度上跟踪语言单位。这个普遍存在的神经原则已被证明可以促进基于统计规律的语音分割和单词学习。然而，目前还没有达成共识，即语音分割是否是通过神经同步从音节到单词率的转变来介导的，或者这两个时间尺度是否是同时跟踪的。此外，目前还不清楚在可以直接使用词汇重音线索来提取单词形式的情况下，音节转换概率是否有助于语音分割。使用跨试一致性（ITC）分析结合事件相关电位（ERP），我们表明，基于统计规律和词汇重音线索的语音分割伴随着对音节和单词的同时神经同步。特别是，与随机序列相比，在结构序列中，单词率的 ITC 通常更高，而在平坦条件下，这种效应更为明显。此外，在平坦条件下，音节率的 ITC 在整个块中动态增加，而在强调条件下则没有观察到类似的调制。值得注意的是，在平坦条件下，两个时间尺度的 ITC 相互关联，神经同步的变化伴随着 P200 和 N400 成分的快速重新配置，ITC 与 ERP 之间存在密切关系。这些结果突出了在不同听力条件下分割语音时，支配神经同步到相关语言单位的不同计算原则。
