University of South Australia.
Northwestern University, Evanston, IL.
J Cogn Neurosci. 2024 Sep 1;36(9):1898-1936. doi: 10.1162/jocn_a_02196.
The extent to which the brain predicts upcoming information during language processing remains controversial. To shed light on this debate, the present study reanalyzed Nieuwland and colleagues' (2018) [Nieuwland, M. S., Politzer-Ahles, S., Heyselaar, E., Segaert, K., Darley, E., Kazanina, N., et al. Large-scale replication study reveals a limit on probabilistic prediction in language comprehension. eLife, 7, e33468, 2018] replication of DeLong and colleagues (2015) [DeLong, K. A., Urbach, T. P., & Kutas, M. Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience, 8, 1117-1121, 2005]. Participants (n = 356) viewed sentences containing articles and nouns of varying predictability, while their EEG was recorded. We measured ERPs preceding the critical words (namely, the semantic prediction potential), in conjunction with postword N400 patterns and individual neural metrics. ERP activity was compared with two measures of word predictability: cloze probability and lexical surprisal. In contrast to prior literature, semantic prediction potential amplitudes did not increase as cloze probability increased, suggesting that the component may not reflect prediction during natural language processing. Initial N400 results at the article provided evidence against phonological prediction in language, in line with Nieuwland and colleagues' findings. Strikingly, however, when the surprisal of the prior words in the sentence was included in the analysis, increases in article surprisal were associated with increased N400 amplitudes, consistent with prediction accounts. This relationship between surprisal and N400 amplitude was not observed when the surprisal of the two prior words was low, suggesting that expectation violations at the article may be overlooked under highly predictable conditions. Individual alpha frequency also modulated the relationship between article surprisal and the N400, emphasizing the importance of individual neural factors for prediction. The present study extends upon existing neurocognitive models of language and prediction more generally, by illuminating the flexible and subject-specific nature of predictive processing.
大脑在语言处理过程中预测即将到来的信息的程度仍然存在争议。为了阐明这一争论，本研究重新分析了 Nieuwland 及其同事（2018 年）[Nieuwland, M. S., Politzer-Ahles, S., Heyselaar, E., Segaert, K., Darley, E., Kazanina, N., et al. Large-scale replication study reveals a limit on probabilistic prediction in language comprehension. eLife, 7, e33468, 2018]对 DeLong 及其同事（2015 年）[DeLong, K. A., Urbach, T. P., & Kutas, M. Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience, 8, 1117-1121, 2005]的复制研究。参与者（n = 356）观看了包含不同可预测性的冠词和名词的句子，同时记录了他们的 EEG。我们在关键单词之前测量了 ERPs（即语义预测潜力），并结合了单词后的 N400 模式和个体神经指标。将 ERP 活动与两种词汇可预测性测量方法进行了比较： cloze 概率和词汇意外度。与先前的文献相比，语义预测潜力的振幅并没有随着 cloze 概率的增加而增加，这表明该成分可能不反映自然语言处理过程中的预测。冠词处的初始 N400 结果提供了语言中语音预测的证据，与 Nieuwland 及其同事的发现一致。然而，令人惊讶的是，当句子中前一个单词的意外度被纳入分析时，冠词的意外度增加与 N400 振幅的增加相关，这与预测解释一致。当句子中前两个单词的意外度较低时，这种意外度和 N400 振幅之间的关系并没有观察到，这表明在高度可预测的条件下，冠词的预期违反可能被忽略。个体阿尔法频率也调节了冠词意外度与 N400 之间的关系，强调了个体神经因素对预测的重要性。本研究通过阐明预测处理的灵活和个体特异性本质，扩展了现有的语言和预测的神经认知模型。
