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Constrained Structure of Ancient Chinese Poetry Facilitates Speech Content Grouping

机译:中国古代诗歌的受限结构有助于语音内容分组

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摘要

Ancient Chinese poetry is constituted by structured language that deviates from ordinary language usage [1, 2]; its poetic genres impose unique combinatory constraints on linguistic elements [c]. How does the constrained poetic structure facilitate speech segmentation when common linguistic [ ] and statistical cues [5, 9] are unreliable to listeners in poems? We generated artificial Jueju, which arguably has the most constrained structure in ancient Chinese poetry, and presented each poem twice as an isochronous sequence of syllables to native Mandarin speakers while conducting magnetoencephalography (MEG) recording. We found that listeners deployed their prior knowledge of Jueju to build the line structure and to establish the conceptual flow of Jueju. Un precedentedly, we found a phase precession phenomenon indicating predictive processes of speech segmentation-the neural phase advanced faster after listeners acquired knowledge of incoming speech. The statistical co-occurrence of monosyllabic words in Jueju negatively correlated with speech segmentation, which provides an alternative perspective on how statistical cues facilitate speech segmentation. Our findings suggest that constrained poetic structures serve as a temporal map for listeners to group speech contents and to predict incoming speech signals. Listeners can parse speech streams by using not only grammatical and statistical cues but also their prior knowledge of the form of language.
机译:古代中国诗歌由结构化语言偏离普通语言使用[1,2];其诗歌类型对语言元素进行了独特的组合限制[C]。当常见的语言[]和统计提示[5,9]与诗歌中的听众不可靠,如何促进语音分割?我们生成了人造尤建国,可以在中国古代诗歌中具有最受限制的结构,并在进行磁性脑图(MEG)记录时向当地普通话扬声器呈现每首诗两倍。我们发现听众部署了他们的先前了解jueju以建立线路结构并建立杜居的概念流动。先前,我们发现了一个阶段预测现象,表明语音分割的预测过程 - 在听众获得了对传入语音的知识后更快地提出了神经阶段的先进。尤建族统计共同发生与语音分割的统计学与语音分割相关,这提供了统计提示如何促进语音分割的替代方面。我们的研究结果表明,受约束的诗歌结构用作听众的颞映射,用于分组语音内容并预测进入的语音信号。听众可以通过不仅使用语法和统计提示来解析语音流,也可以证明他们的语言形式的先验知识。

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  • 来源
    《Current Biology: CB》 |2020年第7期|共14页
  • 作者单位

    Max Planck Inst Empir Aesthet Dept Neurosci D-60322 Frankfurt Germany;

    Google Inc 111 8th Ave New York NY 10010 USA;

    New York Univ Shanghai Div Arts &

    Sci Shanghai 200122 Peoples R China;

    Max Planck Inst Empir Aesthet Dept Neurosci D-60322 Frankfurt Germany;

    NYU Shanghai NYU ECNU Inst Brain &

    Cognit Sci Shanghai 200062 Peoples R China;

    New York Univ Shanghai Div Arts &

    Sci Shanghai 200122 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物科学;
  • 关键词

  • 入库时间 2022-08-20 16:25:07

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