Tracking Lexical and Semantic Prediction Error Underlying the N400 Using Artificial Neural Network Models of Sentence Processing.

IF 3.6 Q1 LINGUISTICS Neurobiology of Language Pub Date : 2024-04-01 eCollection Date: 2024-01-01 DOI:10.1162/nol_a_00134
Alessandro Lopopolo, Milena Rabovsky
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Abstract

Recent research has shown that the internal dynamics of an artificial neural network model of sentence comprehension displayed a similar pattern to the amplitude of the N400 in several conditions known to modulate this event-related potential. These results led Rabovsky et al. (2018) to suggest that the N400 might reflect change in an implicit predictive representation of meaning corresponding to semantic prediction error. This explanation stands as an alternative to the hypothesis that the N400 reflects lexical prediction error as estimated by word surprisal (Frank et al., 2015). In the present study, we directly model the amplitude of the N400 elicited during naturalistic sentence processing by using as predictor the update of the distributed representation of sentence meaning generated by a sentence gestalt model (McClelland et al., 1989) trained on a large-scale text corpus. This enables a quantitative prediction of N400 amplitudes based on a cognitively motivated model, as well as quantitative comparison of this model to alternative models of the N400. Specifically, we compare the update measure from the sentence gestalt model to surprisal estimated by a comparable language model trained on next-word prediction. Our results suggest that both sentence gestalt update and surprisal predict aspects of N400 amplitudes. Thus, we argue that N400 amplitudes might reflect two distinct but probably closely related sub-processes that contribute to the processing of a sentence.

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利用句子处理的人工神经网络模型追踪 N400 的词汇和语义预测误差。
最近的研究表明,在已知会调节 N400 这一事件相关电位的几种条件下,句子理解的人工神经网络模型的内部动力学显示出与 N400 振幅相似的模式。这些结果促使 Rabovsky 等人(2018 年)提出,N400 可能反映了与语义预测错误相对应的内隐意义预测表征的变化。这一解释可以替代 N400 反映由词语惊奇估计的词汇预测错误的假设(Frank 等人,2015 年)。在本研究中,我们使用在大规模文本语料库中训练的句子格式塔模型(McClelland et al.这样就可以根据认知模型对 N400 波幅进行定量预测,并将该模型与 N400 的其他模型进行定量比较。具体来说,我们将句子格式塔模型的更新测量值与根据下一个单词预测训练的类似语言模型估计的意外值进行了比较。我们的结果表明,句子格式塔更新和惊奇都能预测 N400 波幅的各个方面。因此,我们认为 N400 波幅可能反映了两个不同但可能密切相关的子过程,它们都有助于句子的处理。
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来源期刊
Neurobiology of Language
Neurobiology of Language Social Sciences-Linguistics and Language
CiteScore
5.90
自引率
6.20%
发文量
32
审稿时长
17 weeks
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