Kate Stone, Bruno Nicenboim, Shravan Vasishth, Frank Rösler
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However, constraint-related PNP findings are inconsistent. We sought to conceptually replicate Federmeier et al. (2007) and Kuperberg et al. (2020), who observed that the PNP, but not the N400 or the P600, was affected by constraint at unexpected but plausible words. Using a pre-registered design and statistical approach maximising power, we demonstrated a dissociated effect of predictability and constraint: strong evidence for predictability but not constraint in the N400 window, and strong evidence for constraint but not predictability in the later window. However, the constraint effect was consistent with a P600 and not a PNP, suggesting increased conflict between a strong representation and unexpected input rather than greater update of the representation. We conclude that either a simple strong/weak constraint design is not always sufficient to elicit the PNP, or that previous PNP constraint findings could be an artifact of smaller sample size.</p>","PeriodicalId":34845,"journal":{"name":"Neurobiology of Language","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10205153/pdf/","citationCount":"4","resultStr":"{\"title\":\"Understanding the Effects of Constraint and Predictability in ERP.\",\"authors\":\"Kate Stone, Bruno Nicenboim, Shravan Vasishth, Frank Rösler\",\"doi\":\"10.1162/nol_a_00094\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Intuitively, strongly constraining contexts should lead to stronger probabilistic representations of sentences in memory. 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Using a pre-registered design and statistical approach maximising power, we demonstrated a dissociated effect of predictability and constraint: strong evidence for predictability but not constraint in the N400 window, and strong evidence for constraint but not predictability in the later window. However, the constraint effect was consistent with a P600 and not a PNP, suggesting increased conflict between a strong representation and unexpected input rather than greater update of the representation. 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引用次数: 4
摘要
直观地说,强约束上下文应该导致句子在记忆中的更强的概率表示。因此,与预期单词相比,遇到意外单词可能会在这些表征中引发代价更高的变化。然而,通常用于研究概率处理的心理语言学测量,如N400事件相关电位(ERP)组件,对单词可预测性敏感,但对上下文约束不敏感。一些研究表明,约束相关的加工成本可以通过N400后的ERP阳性来测量,称为前N400后阳性(PNP)。PNP反映了句子表征的更新,与反映冲突检测和再分析的后验P600不同。然而,约束相关的PNP发现是不一致的。我们试图从概念上复制Federmeier et al.(2007)和Kuperberg et al.(2020),他们观察到PNP,而不是N400或P600,会受到意外但合理的单词约束的影响。使用预先注册的设计和统计方法最大化功率,我们证明了可预测性和约束的分离效应:在N400窗口中有很强的可预测性而不是约束的证据,在后面的窗口中有很强的约束而不是可预测性的证据。然而,约束效应与P600一致,而与PNP不一致,这表明强表征与意外输入之间的冲突增加,而不是表征的更大更新。我们得出的结论是,简单的强/弱约束设计并不总是足以引发PNP,或者以前的PNP约束结果可能是较小样本量的工件。
Understanding the Effects of Constraint and Predictability in ERP.
Intuitively, strongly constraining contexts should lead to stronger probabilistic representations of sentences in memory. Encountering unexpected words could therefore be expected to trigger costlier shifts in these representations than expected words. However, psycholinguistic measures commonly used to study probabilistic processing, such as the N400 event-related potential (ERP) component, are sensitive to word predictability but not to contextual constraint. Some research suggests that constraint-related processing cost may be measurable via an ERP positivity following the N400, known as the anterior post-N400 positivity (PNP). The PNP is argued to reflect update of a sentence representation and to be distinct from the posterior P600, which reflects conflict detection and reanalysis. However, constraint-related PNP findings are inconsistent. We sought to conceptually replicate Federmeier et al. (2007) and Kuperberg et al. (2020), who observed that the PNP, but not the N400 or the P600, was affected by constraint at unexpected but plausible words. Using a pre-registered design and statistical approach maximising power, we demonstrated a dissociated effect of predictability and constraint: strong evidence for predictability but not constraint in the N400 window, and strong evidence for constraint but not predictability in the later window. However, the constraint effect was consistent with a P600 and not a PNP, suggesting increased conflict between a strong representation and unexpected input rather than greater update of the representation. We conclude that either a simple strong/weak constraint design is not always sufficient to elicit the PNP, or that previous PNP constraint findings could be an artifact of smaller sample size.