ArticleScientific reports2024
Unravelling individual rhythmic abilities using machine learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Can you beat the music? Validation of a gamified rhythmic training in children with ADHD.Behavior research methods · 2025Trial
- Cortico-striatal network architecture shapes rhythmic biases in music.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- Distinct Rhythmic Competencies Identified via Internet-Based Assessment.Annals of the New York Academy of Sciences · 2026Article
- The sound of longevity: music and technology for healthy ageing.Aging clinical and experimental research · 2026Review
- Linking spontaneous speech synchronization and cognitive abilities: evidence from a syllable-timed language.Psychological research · 2026Article
- Testing sensorimotor timing across age and music experience in a real-world environment.Scientific reports · 2026Article
- From Finger Taps to Footsteps: Gait as a Model for Investigating and Training Rhythmic Abilities.Annals of the New York Academy of Sciences · 2026Review
- The impact of rhythm perception on chest compression rate during CPR: Insights from a pilot simulation study.SAGE open medicine · 2025Article
- From Sound to Movement: Mapping the Neural Mechanisms of Auditory-Motor Entrainment and Synchronization.Brain sciences · 2024Review
- The Ramp protocol: Uncovering individual differences in walking to an auditory beat using TeensyStep.Scientific reports · 2024Article
- Mobile version of the Battery for the Assessment of Auditory Sensorimotor and Timing Abilities (BAASTA): Implementation and adult norms.Behavior research methods · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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 a minimal set of behavioral measures. These findings highlight that machine learning techniques can be employed successfully to distill profiles of rhythmic abilities, and ultimately shed light on individual variability and its relationship with both formal musical training and informal musical experiences.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.