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人类听觉系统中线性而非螺旋式自动音高表示的证据。

Evidence for Linear but Not Helical Automatic Representation of Pitch in the Human Auditory System.

机构信息

The Hebrew University of Jerusalem.

出版信息

J Cogn Neurosci. 2019 May;31(5):669-685. doi: 10.1162/jocn_a_01374. Epub 2019 Jan 18.

DOI:10.1162/jocn_a_01374
PMID:30657000
Abstract

The perceptual organization of pitch is frequently described as helical, with a monotonic dimension of pitch height and a circular dimension of pitch chroma, accounting for the repeating structure of the octave. Although the neural representation of pitch height is widely studied, the way in which pitch chroma representation is manifested in neural activity is currently debated. We tested the automaticity of pitch chroma processing using the MMN-an ERP component indexing automatic detection of deviations from auditory regularity. Musicians trained to classify pure or complex tones across four octaves, based on chroma-C versus G (21 participants, Experiment 1) or C versus F# (27, Experiment 2). Next, they were passively exposed to MMN protocols designed to test automatic detection of height and chroma deviations. Finally, in an "attend chroma" block, participants had to detect the chroma deviants in a sequence similar to the passive MMN sequence. The chroma deviant tones were accurately detected in the training and the attend chroma parts both for pure and complex tones, with a slightly better performance for complex tones. However, in the passive blocks, a significant MMN was found only to height deviations and complex tone chroma deviations, but not to pure tone chroma deviations, even for perfect performers in the active tasks. These results indicate that, although height is represented preattentively, chroma is not. Processing the musical dimension of chroma may require higher cognitive processes, such as attention and working memory.

摘要

音高的知觉组织通常被描述为螺旋状,具有音高高度的单调维度和音高色度的圆形维度,这解释了八度音阶的重复结构。尽管音高高度的神经表示已经得到广泛研究,但音高色度表示如何在神经活动中表现出来目前仍存在争议。我们使用 MMN-ERP 成分来测试音高色度处理的自动性,该成分用于索引对听觉规则偏离的自动检测。音乐家们接受了基于色度 C 与 G(21 名参与者,实验 1)或 C 与 F#(27 名参与者,实验 2)的跨四个八度的纯音或复音分类训练。接下来,他们被动地接受了 MMN 协议测试,旨在自动检测音高和色度偏差。最后,在“关注色度”块中,参与者必须在类似于被动 MMN 序列的序列中检测色度偏差。在训练和关注色度部分,纯音和复音的色度偏差音都被准确地检测到,而复音的表现稍好一些。然而,在被动块中,仅发现高度偏差和复音色度偏差会产生显著的 MMN,而不会产生纯音色度偏差,即使对于主动任务中的完美执行者也是如此。这些结果表明,尽管高度是预注意表示的,但色度不是。处理色度的音乐维度可能需要更高的认知过程,例如注意力和工作记忆。

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