Institute for Learning & Brain Sciences (I-LABS), University of Washington, Seattle, WA, United States.
Department of Speech and Hearing Sciences, University of Washington, United States.
Neuropsychologia. 2017 Nov;106:289-297. doi: 10.1016/j.neuropsychologia.2017.10.007. Epub 2017 Oct 4.
Musical sounds, along with speech, are the most prominent sounds in our daily lives. They are highly dynamic, yet well structured in the temporal domain in a hierarchical manner. The temporal structures enhance the predictability of musical sounds. Western music provides an excellent example: while time intervals between musical notes are highly variable, underlying beats can be realized. The beat-level temporal structure provides a sense of regular pulses. Beats can be further organized into units, giving the percept of alternating strong and weak beats (i.e. metrical structure or meter). Examining neural processing at the meter level offers a unique opportunity to understand how the human brain extracts temporal patterns, predicts future stimuli and optimizes neural resources for processing. The present study addresses two important questions regarding meter processing, using the mismatch negativity (MMN) obtained with electroencephalography (EEG): 1) how tempo (fast vs. slow) and type of metrical structure (duple: two beats per unit vs. triple: three beats per unit) affect the neural processing of metrical structure in non-musically trained individuals, and 2) how early music training modulates the neural processing of metrical structure. Metrical structures were established by patterns of consecutive strong and weak tones (Standard) with occasional violations that disrupted and reset the structure (Deviant). Twenty non-musicians listened passively to these tones while their neural activities were recorded. MMN indexed the neural sensitivity to the meter violations. Results suggested that MMNs were larger for fast tempo and for triple meter conditions. Further, 20 musically trained individuals were tested using the same methods and the results were compared to the non-musicians. While tempo and meter type similarly influenced MMNs in both groups, musicians overall exhibited significantly reduced MMNs, compared to their non-musician counterparts. Further analyses indicated that the reduction was driven by responses to sounds that defined the structure (Standard), not by responses to Deviants. We argue that musicians maintain a more accurate and efficient mental model for metrical structures, which incorporates occasional disruptions using significantly fewer neural resources.
音乐声和语音一样,是我们日常生活中最突出的声音。它们在时间域中具有高度的动态性,但又以分层的方式具有良好的结构。时间结构增强了音乐声音的可预测性。西方音乐提供了一个很好的例子:虽然音符之间的时间间隔变化很大,但可以实现潜在的节拍。节拍级别的时间结构提供了一种有规律的脉冲感。节拍可以进一步组织成单位,产生强弱节拍交替的感觉(即度量结构或拍子)。在节拍水平上研究神经处理提供了一个独特的机会,可以了解大脑如何提取时间模式、预测未来刺激并优化神经资源以进行处理。本研究使用脑电图(EEG)获得的失匹配负波(MMN),针对节拍处理提出了两个重要问题:1)速度(快与慢)和度量结构类型(二拍:每单位两个节拍与三拍:每单位三个节拍)如何影响非音乐训练个体的度量结构的神经处理,以及 2)早期音乐训练如何调节度量结构的神经处理。度量结构由连续强弱音的模式建立(标准),偶尔会出现破坏和重置结构的违规(偏差)。20 名非音乐家在听到这些音调时被动聆听,同时记录他们的神经活动。MMN 索引了对节拍违规的神经敏感性。结果表明,快速节奏和三拍条件下的 MMN 更大。此外,使用相同的方法对 20 名受过音乐训练的个体进行了测试,并将结果与非音乐家进行了比较。虽然两组的 MMN 都受到速度和度量类型的影响,但与非音乐家相比,音乐家总体上的 MMN 明显减少。进一步的分析表明,这种减少是由对定义结构的声音(标准)的反应驱动的,而不是由对偏差的反应驱动的。我们认为,音乐家保持了更准确和高效的度量结构心理模型,该模型使用明显更少的神经资源来包含偶尔的中断。