Dou Jin, Anderson Andrew J, White Aaron S, Norman-Haignere Samuel V, Lalor Edmund C
Department of Biomedical Engineering, University of Rochester, Rochester, New York, United States of America.
Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America.
PLoS Comput Biol. 2025 Apr 28;21(4):e1013006. doi: 10.1371/journal.pcbi.1013006. eCollection 2025 Apr.
In recent years, it has become clear that EEG indexes the comprehension of natural, narrative speech. One particularly compelling demonstration of this fact can be seen by regressing EEG responses to speech against measures of how individual words in that speech linguistically relate to their preceding context. This approach produces a so-called temporal response function that displays a centro-parietal negativity reminiscent of the classic N400 component of the event-related potential. One shortcoming of previous implementations of this approach is that they have typically assumed a linear, time-invariant relationship between the linguistic speech features and the EEG responses. In other words, the analysis typically assumes that the response has the same shape and timing for every word - and only varies (linearly) in terms of its amplitude. In the present work, we relax this assumption under the hypothesis that responses to individual words may be processed more rapidly when they are predictable. Specifically, we introduce a framework wherein the standard linear temporal response function can be modulated in terms of its amplitude, latency, and temporal scale based on the predictability of the current and prior words. We use the proposed approach to model EEG recorded from a set of participants who listened to an audiobook narrated by a single talker, and a separate set of participants who attended to one of two concurrently presented audiobooks. We show that expected words are processed faster - evoking lower amplitude N400-like responses with earlier peaks - and that this effect is driven both by the word's own predictability and the predictability of the immediately preceding word. Additional analysis suggests that this finding is not simply explained based on how quickly words can be disambiguated from their phonetic neighbors. As such, our study demonstrates that the timing and amplitude of brain responses to words in natural speech depend on their predictability. By accounting for these effects, our framework also improves the accuracy with which neural responses to natural speech can be modeled.
近年来，脑电图（EEG）能够反映对自然叙述性言语的理解这一点已变得清晰。通过将EEG对言语的反应与该言语中各个单词在语言上与其前文语境的关联度进行回归分析，可以看到这一事实的一个特别有说服力的例证。这种方法会产生一个所谓的时间响应函数，该函数显示出中央顶叶负波，类似于事件相关电位的经典N400成分。此方法先前实现方式的一个缺点是，它们通常假定语言言语特征与EEG反应之间存在线性、时不变关系。换句话说，分析通常假定对每个单词的反应具有相同的形状和时间，并且仅在幅度方面（线性地）变化。在本研究中，我们在这样的假设下放宽了这一假定，即当单词可预测时，对其的反应可能会被更快地处理。具体而言，我们引入了一个框架，其中标准线性时间响应函数可以根据当前和前文单词的可预测性在幅度、潜伏期和时间尺度方面进行调制。我们使用所提出的方法对一组听由单一讲述者朗读的有声读物的参与者以及另一组收听同时呈现的两本有声读物之一的参与者记录的EEG进行建模。我们表明，预期单词的处理速度更快——引发幅度更低、峰值更早的类似N400的反应——并且这种效应是由单词自身的可预测性以及紧邻前文单词的可预测性共同驱动的。进一步的分析表明，这一发现不能简单地基于单词与语音邻词的区分速度来解释。因此，我们的研究表明，大脑对自然言语中单词的反应的时间和幅度取决于其可预测性。通过考虑这些效应，我们的框架还提高了对自然言语神经反应建模的准确性。
