Unravelling individual rhythmic abilities using machine learning.

ABSTRACT: Humans can easily extract the rhythm of a complex sound, like music, and move to its regular beat, like in dance. These abilities are modulated by musical training and vary significantly in untrained individuals. The causes of this variability are multidimensional and typically hard to grasp in single tasks. To date we lack a comprehensive model capturing the rhythmic fingerprints of both musicians and non-musicians. Here we harnessed machine learning to extract a parsimonious model of rhythmic abilities, based on behavioral testing (with perceptual and motor tasks) of individuals with and without formal musical training (n = 79). We demonstrate that variability in rhythmic abilities and their link with formal and informal music experience can be successfully captured by profiles including

SUBMITTER: Dalla Bella S 

PROVIDER: S-EPMC10784578 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature