未经音乐训练的儿童音乐性的心理物理学相关性:儿童音乐睡眠者的证据

Sridhar Sampath, Devi Neelamegarajan
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引用次数: 0

摘要

音乐学习会引起神经系统的重大变化,提高听觉、语言和视觉空间技能、智商和语言感知能力。研究表明,成人和儿童的大脑结构和功能会因音乐训练而发生可塑性变化。虽然音乐能力与正规训练有关,但值得注意的是,在成人中也存在无需正规训练即可提高语言感知能力的 "音乐睡眠者"。本研究填补了了解儿童此类现象的空白,探索了擅长音乐的儿童的心理物理能力,旨在对康复模式产生影响。一项实验前研究涉及 164 名未接受过音乐训练的发育正常儿童(平均年龄:9.93 岁)。使用 DMDX 软件中的蒙特利尔音乐能力评估测试(MBEMA)缩略版对音乐能力进行评估,包括旋律、节奏和记忆等子测试。MATLAB 中的心理物理测试评估了时间、频率、强度、音色和双耳分辨率。根据 MBEMA 测试得分的中位数(中位数:42),84 名儿童的音乐能力较差,80 名儿童的音乐能力较好。对 MBEMA 分数和心理物理测试进行了描述性统计。由于数据不符合正态分布,因此采用了非参数推断统计。Mann-Whitney U 检验显示,在所有评估项目中,音乐能力良好者均有显著差异:间隙推理 (p = 0.01)、500 赫兹和 4 千赫兹音高辨别 (p = 0.00)、500 赫兹和 4 千赫兹音强辨别 (p = 0.00) 和 (p = 0.01)、轮廓分析 (p=0.01)、耳间电平差 (p = 0.023) 和耳间时间差 (p=0.038)。斯皮尔曼相关性显示,在 500 赫兹(r = - 0.538)、4 千赫(- 0.416)、4 千赫(r = - 0.367)和 500 赫兹(r = - 0.311)的音高辨别、轮廓分析(r = - 0.313)、耳间电平差(r = - 0.276)和间隙扣减(r = - 0.235)方面,斯皮尔曼相关性最高。除耳间时差外,其他指标均有意义(p < 0.01)。在心理物理任务中,擅长音乐的儿童超过了能力较弱的儿童。这与过去的研究相呼应,强调了听觉辨别力对未经训练的儿童音乐技能的影响。这些结果有可能支持儿童音乐睡眠者的概念。主要的预测因素包括音高辨别力,而耳间时差的相关性很小。利用这些评估可以预测听觉障碍儿童的音乐训练结果,但还需要进行更多可靠的统计调查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Psychophysical correlates of musicality in musically untrained children: evidence for musical sleepers in children
Music learning induces significant neural changes, enhancing auditory, verbal, and visuospatial skills, IQ, and speech perception. Research studies reveal structural and functional brain plasticity due to music training in adults and children. While musical abilities are linked to formal training, the existence of “musical sleepers” with heightened speech perception without formal training is noteworthy in adults. This study addresses the gap in understanding such phenomena in children, exploring psychophysical abilities in musically adept children, and aiming to impact rehabilitation models. A pre-experimental study involved 164 typically developing children (mean age: 9.93) without musical training. Musical abilities were evaluated using the abbreviated version of Montreal Battery for Evaluation of Musical Abilities (MBEMA) test in DMDX software, covering melody, rhythm, and memory subtests. Psychophysical tests in MATLAB assessed temporal, frequency, intensity, timbre, and binaural resolution. Using a median split based on MBEMA test scores (median: 42), 84 children exhibited poor musical abilities, while 80 showed good ones. Descriptive statistics for MBEMA scores and psychophysical tests were conducted. As data did not follow normal distribution, non-parametric inferential statistics were employed. Mann–Whitney U tests revealed significant differences favouring good musical abilities in all assessments: gap deduction (p = 0.01), pitch discrimination at 500 Hz and 4 kHz (p = 0.00), intensity discrimination at 500 Hz (p = 0.00) and 4 kHz (p = 0.01), profile analysis (p = 0.01), interaural level difference (p = 0.023), and interaural time difference (p = 0.038). Spearman correlation showed highest correlation with pitch discrimination at 500 Hz (r = − 0.538), 4 kHz (− 0.416), intensity discrimination at 4 kHz (r = − 0.367), and 500 Hz (r = − 0.311), profile analysis (r = − 0.313), interaural level difference (r = − 0.276), and gap deduction (r = − 0.235). All were significant (p < 0.01), except for the interaural time difference. Children excelling in music surpassed those with weaker abilities in psychophysical tasks. This echoes past research, underscoring auditory discrimination’s influence on musical skills in untrained children. These results potentially support the concept of musical sleepers in children. Key predictors include pitch discrimination, while interaural time difference exhibited minimal correlation. Utilising such assessments may predict musical training outcomes for children with auditory disorders, but additional robust statistical investigation is necessary.
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