首页> 美国卫生研究院文献>other >EEG decoding of spoken words in bilingual listeners: from words to language invariant semantic-conceptual representations
【2h】

EEG decoding of spoken words in bilingual listeners: from words to language invariant semantic-conceptual representations

机译:双语听众中口语的EEG解码:从单词到语言不变的语义概念表示

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

Spoken word recognition and production require fast transformations between acoustic, phonological, and conceptual neural representations. Bilinguals perform these transformations in native and non-native languages, deriving unified semantic concepts from equivalent, but acoustically different words. Here we exploit this capacity of bilinguals to investigate input invariant semantic representations in the brain. We acquired EEG data while Dutch subjects, highly proficient in English listened to four monosyllabic and acoustically distinct animal words in both languages (e.g., “paard”–“horse”). Multivariate pattern analysis (MVPA) was applied to identify EEG response patterns that discriminate between individual words within one language (within-language discrimination) and generalize meaning across two languages (across-language generalization). Furthermore, employing two EEG feature selection approaches, we assessed the contribution of temporal and oscillatory EEG features to our classification results. MVPA revealed that within-language discrimination was possible in a broad time-window (~50–620 ms) after word onset probably reflecting acoustic-phonetic and semantic-conceptual differences between the words. Most interestingly, significant across-language generalization was possible around 550–600 ms, suggesting the activation of common semantic-conceptual representations from the Dutch and English nouns. Both types of classification, showed a strong contribution of oscillations below 12 Hz, indicating the importance of low frequency oscillations in the neural representation of individual words and concepts. This study demonstrates the feasibility of MVPA to decode individual spoken words from EEG responses and to assess the spectro-temporal dynamics of their language invariant semantic-conceptual representations. We discuss how this method and results could be relevant to track the neural mechanisms underlying conceptual encoding in comprehension and production.
机译:口语单词的识别和产生要求在声学,语音和概念性神经表示之间进行快速转换。双语者使用本地和非本地语言执行这些转换,从等同但听觉上不同的词派生出统一的语义概念。在这里,我们利用双语者的这种能力来调查大脑中输入不变的语义表示。我们获得了EEG数据,而精通英语的荷兰人则用两种语言(例如“ paard”-“ horse”)听了四个单音节和听觉上不同的动物单词。应用多元模式分析(MVPA)来识别EEG响应模式,该模式可区分一种语言中的单个单词(在语言范围内),并在两种语言之间进行广义化(跨语言概括)。此外,采用两种EEG特征选择方法,我们评估了时间和振荡EEG特征对我们分类结果的贡献。 MVPA显示,单词发作后的很宽的时间范围内(〜50-620 ms),语言内的辨别是可能的,这可能反映了单词之间的语音,语音和语义概念上的差异。最有趣的是,大约550-600毫秒左右的跨语言概括是可能的,这表明激活了荷兰语和英语名词中常见的语义概念表示。两种类型的分类都显示出12 Hz以下振荡的强烈贡献,表明低频振荡在单个词和概念的神经表示中的重要性。这项研究证明了MVPA可以解码来自EEG响应的单个口语单词,并评估其语言不变语义概念表示的时空动态。我们讨论了这种方法和结果如何与追踪理解和生产中概念编码背后的神经机制相关。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号