The ERP response to the amount of information conveyed by words in sentences
The ERP response to the amount of information conveyed by words in sentences
复制标题
DOI：
10.1016/j.bandl.2014.10.006
复制
发表时间：
2015-01-01
影响因子：
2.5
通讯作者：
Vigliocco, Gabriella
中科院分区：
文献类型：
--
作者：
Frank, Stefan L.;Otten, Leun J.;Vigliocco, Gabriella
句子中单词的阅读时间取决于单词所传达的信息量，这可以通过概率语言模型来估算。我们研究事件相关电位（ERPs）是否也能通过信息量度来预测。三种语言模型对一组英语句子样本中的每个单词估算了四种不同的信息量度。从阅读相同句子的参与者的脑电图信号中提取了六种不同的事件相关电位偏差。信息量度和事件相关电位之间的比较显示，N400波幅和单词意外性之间存在可靠的相关性。不使用句法结构的语言模型比短语结构语法更适合数据，短语结构语法无法解释N400波幅的独特差异。这些发现表明，不同的信息量度量化了认知上不同的过程，并且读者在对接下来的单词产生预期时没有利用句子的层次结构。（C）2014作者。由爱思唯尔公司出版。
Reading times on words in a sentence depend on the amount of information the words convey, which can be estimated by probabilistic language models. We investigate whether event-related potentials (ERPs), too, are predicted by information measures. Three types of language models estimated four different information measures on each word of a sample of English sentences. Six different ERP deflections were extracted from the EEG signal of participants reading the same sentences. A comparison between the information measures and ERPs revealed a reliable correlation between N400 amplitude and word surprisal. Language models that make no use of syntactic structure fitted the data better than did a phrase-structure grammar, which did not account for unique variance in N400 amplitude. These findings suggest that different information measures quantify cognitively different processes and that readers do not make use of a sentence's hierarchical structure for generating expectations about the upcoming word. (C) 2014 The Authors. Published by Elsevier Inc.