Relationships of prediction and alignment between musicians' movement and rhythmical and tonal contexts of music

Music performance requires high levels of motor control to express musical intentions. In this study, we analysed motion and audio data of 20 expert saxophone players performing four musical fragments varying in the degree of technical difficulty. Using a computational model of the auditory periphery, we extracted emergent acoustical properties of sound to inference critical cognitive patterns of music processing and relate them to motion data. Results showed that knee flexion is causally linked to real-time tone expectations as measured by our auditory model and correlated to rhythmical density, a likelihood measure on rhythmic events that relies on the roughness properties of the sound. These findings underline the robustness of body movement in musical performance, providing valuable insights for the understanding of musical expression and development of motor learning cues.