Chalehchaleh Amirhossein, Winchester Martin M, Di Liberto Giovanni M
J Neural Eng. 2025 Jan 13;22(1). doi: 10.1088/1741-2552/ada30a.
. Speech comprehension involves detecting words and interpreting their meaning according to the preceding semantic context. This process is thought to be underpinned by a predictive neural system that uses that context to anticipate upcoming words. However, previous studies relied on evaluation metrics designed for continuous univariate sound features, overlooking the discrete and sparse nature of word-level features. This mismatch has limited effect sizes and hampered progress in understanding lexical prediction mechanisms in ecologically-valid experiments.. We investigate these limitations by analyzing both simulated and actual electroencephalography (EEG) signals recorded during a speech comprehension task. We then introduce two novel assessment metrics tailored to capture the neural encoding of lexical surprise, improving upon traditional evaluation approaches.. The proposed metrics demonstrated effect-sizes over 140% larger than those achieved with the conventional temporal response function (TRF) evaluation. These improvements were consistent across both simulated and real EEG datasets.. Our findings substantially advance methods for evaluating lexical prediction in neural data, enabling more precise measurements and deeper insights into how the brain builds predictive representations during speech comprehension. These contributions open new avenues for research into predictive coding mechanisms in naturalistic language processing.
言语理解涉及检测单词并根据前文的语义语境解释其含义。这一过程被认为是由一个预测神经系统支撑的，该系统利用语境来预测即将出现的单词。然而，以往的研究依赖于为连续单变量声音特征设计的评估指标，而忽略了单词级特征的离散性和稀疏性。这种不匹配限制了效应大小，并阻碍了在生态有效实验中理解词汇预测机制方面的进展。我们通过分析在言语理解任务中记录到的模拟和实际脑电图（EEG）信号来研究这些局限性。然后，我们引入了两种新颖的评估指标，专门用于捕捉词汇意外性的神经编码，并改进了传统的评估方法。所提出的指标显示出的效应大小比传统时间响应函数（TRF）评估所达到的效应大小大140%以上。这些改进在模拟和真实EEG数据集上都是一致的。我们的研究结果极大地推进了评估神经数据中词汇预测的方法，能够进行更精确的测量，并更深入地洞察大脑在言语理解过程中如何构建预测表征。这些贡献为自然语言处理中的预测编码机制研究开辟了新的途径。
