five

The Electrophysiology of Basic Phrase Building

收藏
NIAID Data Ecosystem2026-03-09 收录
下载链接:
https://figshare.com/articles/dataset/The_Electrophysiology_of_Basic_Phrase_Building/3998778
下载链接
链接失效反馈
官方服务:
资源简介:
A defining trait of linguistic competence is the ability to combine elements into increasingly complex structures to denote, and to comprehend, a potentially infinite number of meanings. Recent magnetoencephalography (MEG) work has investigated these processes by comparing the response to nouns in combinatorial (blue car) and non-combinatorial (rnsh car) contexts. In the current study we extended this paradigm using electroencephalography (EEG) to dissociate the role of semantic content from phonological well-formedness (yerl car). We used event-related potential (ERP) recordings in order to better relate the observed neurophysiological correlates of basic combinatorial operations to prior ERP work on comprehension. We found that nouns in combinatorial contexts (blue car) elicited a greater centro-parietal negativity between 180-400ms, independent of the phonological well-formedness of the context word. We discuss the potential relationship between this ‘combinatorial’ effect and classic N400 effects. We also report preliminary evidence for an early anterior negative deflection immediately preceding the critical noun in combinatorial contexts, which we tentatively interpret as an electrophysiological reflex of syntactic structure initialization.

语言能力的核心特征之一,是能够将元素组合为愈发复杂的结构,以表征并理解潜在无限的语义内涵。既往脑磁图(magnetoencephalography,MEG)研究通过对比不同语境下名词的神经响应,探究了此类组合加工过程:一类为组合性语境(如“蓝色汽车”),另一类为非组合性语境(如“rnsh 汽车”)。本研究采用脑电图(electroencephalography,EEG)拓展了这一实验范式,旨在将语义内容与语音合规性(如“yerl 汽车”)的作用分离开来。我们借助事件相关电位(event-related potential,ERP)记录,以期将基础组合加工所对应的神经生理关联,与此前有关语义理解的ERP研究建立更紧密的联系。研究发现,在组合性语境(如“蓝色汽车”)中的名词,会在180~400ms区间引发更强的中央顶叶负波,且该效应不受语境词语音合规性的影响。我们探讨了这一“组合性”效应与经典N400效应间的潜在关联。此外,本研究还报告了一项初步发现:在组合性语境中,关键名词出现前即刻会出现早期前负偏转,我们初步将其解释为句法结构初始化的电生理反射。
创建时间:
2016-10-12
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作