Language is composed of small building blocks, which combine to form larger meaningful structures. To understand language, we must process, track, and concatenate these building blocks into larger linguistic units as speech unfolds over time. An influential idea is that phase-locking of neural oscillations across different levels of linguistic structure provides a mechanism for this process. Building on this framework, the goal of the current study was to determine whether neural phase-locking occurs more robustly to novel linguistic items that are successfully learned and encoded into memory, compared to items that are not learned. Participants listened to a continuous speech stream composed of repeating nonsense words while their EEG was recorded and then performed a recognition test on the component words. Neural phase-locking to individual words during the learning period strongly predicted the strength of subsequent word knowledge, suggesting that neural phase-locking indexes the subjective perception of specific linguistic items during real-time language learning. These findings support neural oscillatory models of language, demonstrating that words that are successfully perceived as functional units are tracked by oscillatory activity at the matching word rate. In contrast, words that are not learned are processed merely as a sequence of unrelated syllables and thus not tracked by corresponding word-rate oscillations.

语言由小的构建块组成，这些构建块结合起来形成更大的有意义的结构。为了理解语言，我们必须随着语音的发展而处理、跟踪这些构建块并将其连接成更大的语言单元。一个有影响力的想法是，跨不同语言结构层次的神经振荡的锁相为这一过程提供了一种机制。在此框架的基础上，当前研究的目标是确定与未学习的项目相比，成功学习并编码到记忆中的新语言项目的神经锁相是否更稳健地发生。参与者聆听由重复的无意义单词组成的连续语音流，同时记录他们的脑电图，然后对组成单词进行识别测试。学习期间对单个单词的神经锁相强烈预测了后续单词知识的强度，这表明神经锁相索引了实时语言学习过程中特定语言项目的主观感知。这些发现支持语言的神经振荡模型，证明被成功感知为功能单元的单词是通过匹配词率的振荡活动来跟踪的。相反，未学习的单词仅被处理为一系列不相关的音节，因此不会被相应的字率振荡所跟踪。