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，它们组合起来形成更大的有意义的结构。为了理解语言，我们必须处理、跟踪这些构建块，并将这些构建块连接成更大的语言单元，因为语音会随着时间的推移而展开。一个有影响的想法是，跨语言结构不同层次的神经振荡的锁相为这个过程提供了一种机制。基于此框架，当前研究的目标是确定与未学习的项目相比，神经锁相对于成功学习并编码到记忆中的新语言项目是否更稳健。参与者在记录他们的脑电图的同时收听由重复的无意义词组成的连续语音流，然后对组成词进行识别测试。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，这表明神经锁相反映了实时语言学习过程中对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可以通过匹配单词率的振荡活动进行跟踪。相比之下，未学习的单词仅作为一系列不相关的音节进行处理，因此不会被相应的词频波动跟踪。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可以通过匹配单词率的振荡活动进行跟踪。相比之下，未学习的单词仅作为一系列不相关的音节进行处理，因此不会被相应的词频波动跟踪。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可以通过匹配单词率的振荡活动进行跟踪。相比之下，未学习的单词仅作为一系列不相关的音节进行处理，因此不会被相应的词频波动跟踪。