Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan.
Centre for Neuroscience in Education, University of Cambridge, Cambridge, UK.
Sci Rep. 2024 Sep 27;14(1):22297. doi: 10.1038/s41598-024-73689-x.
Human improvisational acts contain an innate individuality, derived from one's experiences based on epochal and cultural backgrounds. Musical improvisation, much like spontaneous speech, reveals intricate facets of the improviser's state of mind and emotional character. However, the specific musical components that reveal such individuality remain largely unexplored. Within the framework of human statistical learning and predictive processing, this study examined the temporal dynamics of uncertainty and surprise (prediction error) in a piece of musical improvisation. This cognitive process reconciles the raw auditory cues, such as melody and rhythm, with the musical predictive models shaped by its prior experiences. This study employed the Hierarchical Bayesian Statistical Learning (HBSL) model to analyze a corpus of 456 Jazz improvisations, spanning 1905 to 2009, from 78 distinct Jazz musicians. The results indicated distinctive temporal patterns of surprise and uncertainty, especially in pitch and pitch-rhythm sequences, revealing era-specific features from the early 20th to the 21st centuries. Conversely, rhythm sequences exhibited a consistent degree of uncertainty across eras. Further, the acoustic properties remain unchanged across different periods. These findings highlight the importance of how temporal dynamics of surprise and uncertainty in improvisational music change over periods, profoundly influencing the distinctive methodologies artists adopt for improvisation in each era. Further, it is suggested that the development of improvisational music can be attributed to the adaptive statistical learning mechanisms. This study explores the period-specific characteristics in the temporal dynamics of improvisational music, emphasizing how artists adapt their methods to resonate with the cultural and emotional contexts of their times. Such shifts in improvisational ways offer a window into understanding how artists intuitively respond and adapt their craft to resonate with the cultural zeitgeist and the emotional landscapes of their respective times.
人类的即兴表演包含一种内在的个性,这种个性源自于个人基于时代和文化背景的经历。音乐即兴创作与自发演讲类似,揭示了即兴创作者的思维状态和情感特征的复杂方面。然而,揭示这种个性的具体音乐元素在很大程度上仍未得到探索。在人类统计学习和预测处理的框架内,本研究考察了音乐即兴创作中不确定性和惊讶(预测误差)的时间动态。这个认知过程将原始的听觉线索(如旋律和节奏)与由其先前经验塑造的音乐预测模型进行协调。本研究采用分层贝叶斯统计学习(HBSL)模型,分析了一个由 78 位不同爵士音乐家在 1905 年至 2009 年间创作的 456 首爵士即兴曲的语料库。结果表明,惊讶和不确定性具有独特的时间模式,尤其是在音高和音高-节奏序列中,揭示了从 20 世纪早期到 21 世纪的时代特征。相反,节奏序列在不同时代表现出一致的不确定性程度。此外,声学特征在不同时期保持不变。这些发现强调了在即兴音乐中,惊讶和不确定性的时间动态如何随时间变化的重要性,这对不同时代艺术家采用的即兴创作独特方法产生了深远影响。此外,有人认为即兴音乐的发展可以归因于适应性统计学习机制。本研究探讨了即兴音乐中时间动态的特定时期特征,强调了艺术家如何调整其方法以与所处时代的文化和情感背景产生共鸣。即兴方式的这种转变提供了一个窗口,可以了解艺术家如何直观地响应并调整其技艺,以与当时的文化思潮和情感景观产生共鸣。