Trends and perspectives in music cognition research and technology
Trends and perspectives in music cognition research and technology
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音乐认知研究和技术的趋势和观点
DOI：
10.1080/09540090902734549
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发表时间：
2010
影响因子：
5.3
通讯作者：
Purwins H
中科院分区：
文献类型：
--
作者：
Purwins H
这本关于音乐、大脑和认知的特刊旨在阐明当前和未来音乐研究和技术中的一些关键问题。认知音乐学由塞弗特（1993）和莱曼（1994）设想，由不同的学科组成，如大脑研究和人工智能，努力对音乐现象进行更科学的理解。在格里菲斯和托德（1994）编辑的《连接科学中的音乐和创造力》特刊出版15年后，这期杂志再次展示了该领域的视野是如何继续扩大的。近年来，计算神经科学吸引了巨大的愿望，例如硅视网膜（Chow et al. 2004）和雄心勃勃的蓝脑计划，旨在通过用新皮质柱模型取代计算机的微电路来彻底改变计算机（Markram 2006）。听觉神经科学的研究活动，特别是应用于音乐的研究活动，正在赶上视觉研究的科学进步。Shamma（2001）提出，视觉和听觉域发生相同的神经处理。其他研究人员提出了特定于听觉领域的生物启发模型;例如，Smith和Lewicki（2006）将音乐信号分解为伽马酮功能，类似于猫的基底膜的脉冲响应。脑机接口（Blankertz et al. 2004）和脑成像方法（如脑电图）的快速发展进一步鼓励了音乐研究。大脑成像赠款直接而不是通过心理实验和受试者的口头反馈来获得与音乐相关的大脑过程。在听觉神经科学方面，已经进行了大量的实验工作，特别是探索音乐能力的先天组成部分。在音乐的发育研究中，脑磁图已被用于研究胎儿的音乐感知（Eswaran et al. 2002）。新生儿的不匹配负波显示了婴儿如何区分音高、音色和节奏（Stefanics et al. 2007）。Koelsch和Zerner（2005）对音乐中的脑电图研究进行了总结，提出了一个受生理学启发的模型，该模型由多个模块组成，例如，用于完形形成和结构构建，其中模型的特殊功能是反馈连接，使结构重新分析和修复成为可能。我们可以假设顺序处理（与音乐句法和语法有关）、布罗卡区（Maess，Koelsch，Gunter和Friederici 2001）和时间信息处理、右颞听觉皮层和上级颞回（Peretz和Zatorre 2005）之间存在功能和生理上的分离。顺序处理可以被视为统计学习（Saffran，约翰逊，Aslin和纽波特1999），在这种情况下，学习N阶转换序列。莱布尼茨（Leibniz）在1712年提出了统计学习的概念：“音乐是一个灵魂无意识地进行计算的隐藏的数学努力。另一方面，时间信息与运动规划和运动学密切相关。时间方面的关系
This special issue on Music, Brain, & Cognition aims to shed light on some of the key issues in current and future music research and technology. Cognitive musicology was envisaged by Seifert (1993) and Leman (1994) to be composed from diverse disciplines such as brain research and artificial intelligence striving for a more scientific understanding of the phenomenon of music. One and a half decades following the special issue on Music and Creativity in Connection Science, edited by Griffith and Todd (1994), this issue, again, demonstrates how the horizons in the field have continued to expand. In recent years, computational neuroscience has attracted great aspirations, exemplified by the silicon retina (Chow et al. 2004) and the ambitious Blue Brain Project that aims at revolutionising computers by replacing their microcircuits by models of neocortical columns (Markram 2006). Research activity in auditory neuroscience, applied to music in particular, is catching up with the scientific advances in vision research. Shamma (2001) proposed that the same neural processing takes place for the visual as well as for the auditory domain. Other researchers suggested biologically inspired models specific to the auditory domain; eg, Smith and Lewicki (2006) decomposed musical signals into gammatone functions that resemble the impulse response of the basilar membrane measured in cats. The fast advancement of the brain computer interface (Blankertz et al. 2004) and brain-imaging methodology such as the electroencephalogram has further encouraged music research. Brain imaging grants access to music-related brain processes directly rather than circuitously via psychological experiments and verbal feedback by the subjects. A lot of experimental work in auditory neuroscience has been performed, in particular exploring the innate components of music abilities. In developmental studies of music, magnetoencephalograms have been used to study fetal music perception (Eswaran et al. 2002). Mismatch negativity in newborns has shown how babies discriminate pitch, timbre, and rhythm (Stefanics et al. 2007). A summary of electroencephalogram research in music leads Koelsch and Siebel (2005) to a physiologically inspired model composed of modules, eg, for gestalt formation and structure building where the special features of the model are the feedback connections enabling structural reanalysis and repair. We may assume a functional and physiological separation between sequential processing (related to musical syntax and grammar), Broca’s area (Maess, Koelsch, Gunter, and Friederici 2001), and the processing of timing information, right temporal auditory cortex and superior temporal gyrus (Peretz and Zatorre 2005). Sequential processing can be seen as statistical learning (Saffran, Johnson, Aslin, and Newport 1999), in this case, learning Nth order transition sequences. The idea of statistical learning has been anticipated by Leibniz (1712):‘Music is the hidden mathematical endeavour of a soul unconscious it is calculating’. On the other hand, timing information is closely related to movement planning and kinematics. The relation between timing aspects of
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DOI：
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发表时间：
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DOI：
--
发表时间：
2008
期刊：
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新生儿的听觉时间分组。
影响因子：
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作者：
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通讯作者：
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