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音乐训练与口语处理过程中更好的分句有关。

Music training is associated with better clause segmentation during spoken language processing.

机构信息

Department of Psychology, Renmin University of China, Beijing, China.

College of Humanities, Shanghai Normal University, Shanghai, China.

出版信息

Psychon Bull Rev. 2022 Aug;29(4):1472-1479. doi: 10.3758/s13423-022-02076-2. Epub 2022 Mar 22.

DOI:10.3758/s13423-022-02076-2
PMID:35318581
Abstract

Musical expertise is known to affect speech perception at units below clause/sentence. This study investigated whether the musician's advantage extends to a higher and more central level of speech processing (i.e., clause segmentation). Two groups of participants (musician vs. nonmusician) were presented with sentences that contain an internal clause boundary. The acoustic correlates of the boundary were manipulated in six conditions: all-cue, pause-only, final-lengthening-only, pitch-reset-only, pause-and-final-lengthening-in-combination, and no-cue conditions. Participants were asked to judge whether the sentence they heard had an internal boundary. Results showed that the musicians detected more boundaries than the nonmusicians in the all-cue and the pause-only conditions, but fewer boundaries in the no-cue condition. Further analyses of cue weight showed that both musicians and nonmusicians placed more importance on pause than the other two cues, but this weighting bias was more pronounced for the musicians. These results suggest that music training is associated with increased perceptual acuity not only to the acoustic markings of speech boundaries but also to the weighting of the cues. Our findings extend the role of musical expertise to sentence-level speech processing.

摘要

音乐专业知识被认为会影响到低于句子的语言感知单元。本研究探讨了音乐家的优势是否延伸到了更高、更核心的语言处理水平(即分句切分)。两组参与者(音乐家与非音乐家)分别听取包含内部分句边界的句子。边界的声学特征在六种条件下进行了操控:全线索、仅停顿、仅结尾拉长、仅音高重置、停顿和结尾拉长相结合、无线索条件。参与者被要求判断他们听到的句子是否存在内部边界。结果表明,在全线索和仅停顿条件下,音乐家比非音乐家能检测到更多的边界,但在无线索条件下则检测到更少的边界。对线索权重的进一步分析表明,音乐家和非音乐家都比其他两种线索更重视停顿,但音乐家的这种权重偏差更为明显。这些结果表明,音乐训练不仅与语言边界的声学标记的感知敏锐度有关,而且与线索的权重有关。我们的发现将音乐专业知识的作用扩展到了句子级别的语言处理。

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