Mental Effort and Expressive Interaction in Expert and Student String Quartet Performance

Music & Science(2023)

引用 0|浏览0
暂无评分
摘要
Resilience to changes in performance environments is a hallmark of expertise in music performance. Research has shown that skilled non-expert ensemble musicians maintain synchronization when visual interaction between them is disrupted, but that the quality of their expressive body motion changes. Our study extended these findings by testing how an expert string quartet responds to playing conditions that disrupt visual contact. We tested for potential effects on the musicians’ expressive head motion, sound quality, and mental effort. The Danish String Quartet (DSQ), a world-class classical ensemble, performed an excerpt from a Haydn piece five times without an audience, then once for an audience of about 20 people. During the performances without audience, their seating configuration was manipulated to disrupt their audiovisual interaction. Audio, head motion, eye-tracking, and pupillometry data were collected. The DSQ's data were compared to data from a student quartet who completed the same experiment. Our results show that the DSQ maintained the quality of their musical sound and interactive body motion across disruptive and non-disruptive conditions, but mental effort (indexed by pupil size) was greater in non-disruptive conditions. In contrast, the student quartet moved less overall than the DSQ, moved less when they could not see each other, and did not show differences in pupil size across conditions. The quartets spent a similar percentage of performance time watching their co-performers. These findings suggest that the quality of audio and visual components of the DSQ's performance do not require visual interaction to maintain; however, these musicians do interact visually when given the opportunity. This visual interaction stimulates greater mental effort, perhaps reflecting increased social engagement.
更多
查看译文
关键词
student string quartet performance,mental effort,expressive interaction
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要