[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119777-en":3,"doc-seo-119777-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119777,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Unravelling individual rhythmic abilities using machine learning","Humans can extract rhythm from complex sounds such as music and align movement to a regular beat, but rhythmic abilities vary substantially across untrained individuals and are modulated by musical experience. Existing explanations remain limited because variability is multidimensional and not captured by single behavioral tasks, and no comprehensive model describes rhythmic “fingerprints” for both musicians and non-musicians. This study uses machine learning to build a parsimonious model from perceptual and motor testing in participants with and without formal training (n = 79). Results show that rhythmic variability and its links to formal and informal music experience can be represented by profiles using a minimal set of behavioral measures, supporting applications in healthy and clinical populations and enabling guidelines for personalizing rhythm-based interventions.","bioRxiv preprint doi: [https://doi.org/10.1101/2023.03.25.533209](https://doi.org/10.1101/2023.03.25.533209); this version posted March 26, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is  \nmade available under aCC-BY-NC-ND 4.0 International license.  \nUNRAVELLING INDIVIDUAL RHYTHMIC ABILITIES 1  \nUnravelling individual rhythmic abilities using machine learning  \nSimone Dalla Bella*1,2,3,4, Stefan Janaqi5 , Charles-Etienne Benoit 6, Nicolas Farrugia7 , Valentin Bégel8 , Laura Verga9, 10 , Eleanor E. Harding 11, & Sonja A. Kotz*10, 12  \n1. International Laboratory for Brain, Music, and Sound Research (BRAMS), Montreal, Canada  \n2. Department of Psychology, University of Montreal, Montreal, Canada  \n3. Centre for Research on Brain, Language and Music (CRBLM), Montreal, Canada  \n4. University of Economics and Human Sciences in Warsaw, Warsaw, Poland  \n5. EuroMov Digital Health in Motion, University of Montpellier IMT Mines Ales, France  \n6. Univ Lyon, University Claude Bernard Lyon 1, Inter-University Laboratory of Human Movement Biology, EA 7424, 69 622, Villeurbanne, France  \n7. IMT Atlantique, Brest, France  \n8. Université Paris Cité, Paris, France  \n9. Comparative Bioacoustics group, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands  \n10. Dept. of Neuropsychology & Psychopharmacology, Maastricht University, Maastricht, The Netherlands  \n11. Dept. of Otorhinolaryngology/Head and Neck Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands  \n12. Dept. of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany  \nRunning head: Unravelling individual rhythmic abilities  \n* Corresponding authors  \nSimone Dalla Bella  \nInternational Laboratory for Brain, Music and Sound Research (BRAMS)  \nDept. of Psychology, University of Montreal  \nPavillon Marie-Victorin  \nCP 6128 Succursale Centre-Ville  \nMontréal, QC, H3C 3J7  \nCanada  \ne-mail: [simone.dalla.bella@umontreal.ca](simone.dalla.bella@umontreal.ca)  \n[phone: 514 343-6111 \\#44069](phone: 514 343-6111 #44069)  \nwebsite: [https://dallabellaLAB.ca/](https://dallabellaLAB.ca/)  ; [http://www.brams.org/](http://www.brams.org/)  \n[Sonja A. Kotz](Sonja A. Kotz)  \nFaculty of Psychology and Neuroscience  \nDept. of Neuropsychology & Psychopharmacology  \nP.O. 616  \n6200 MD Maastricht  \nThe Netherlands  \nTel: +31 43 3881653  \nEmail: [sonja.kotz@maastrichtuniversity.nl](sonja.kotz@maastrichtuniversity.nl)  \nbioRxiv preprint doi: [https://doi.org/10.1101/2023.03.25.533209](https://doi.org/10.1101/2023.03.25.533209); this version posted March 26, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is  \nmade available under aCC-BY-NC-ND 4.0 International license.  \nUNRAVELLING INDIVIDUAL RHYTHMIC ABILITIES 2  \nAbstract  \nHumans can easily extract the rhythm of a complex sound, like music, and move to its regular beat, for example 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 with 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 the behavioral testing (with perceptual and motor tasks) of individuals with and without formal musical training (n = 79) . We demonstrate that the variability of 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 profiles can shed light on individual variability in healthy and c","cbCaiuSvGWjRmi70","https://ap.wps.com/l/cbCaiuSvGWjRmi70","pdf",1966849,1,49,"English","en",105,"# Abstract\n# Introduction\n## Perception-action coupling through music\n## Neural systems supporting rhythmic beat extraction","[{\"question\":\"How does musical training influence individual rhythmic abilities?\",\"answer\":\"Rhythmic abilities are modulated by musical training and vary significantly between trained and untrained individuals.\"},{\"question\":\"Why is a multidimensional approach needed to explain variability in rhythmic skills?\",\"answer\":\"The causes of variability are multidimensional and typically cannot be grasped with single tasks, motivating a more comprehensive model.\"},{\"question\":\"What does the machine learning model capture in the study?\",\"answer\":\"It extracts a parsimonious profile of rhythmic abilities from perceptual and motor behavioral tests, showing how variability relates to formal and informal music experience.\"}]","Unravelling individual rhythmic abilities using machine learning | PDF",1785726262,123,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"unravelling-individual-rhythmic-abilities-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/unravelling-individual-rhythmic-abilities-using-machine-learning/119777/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does musical training influence individual rhythmic abilities?","Question",{"text":75,"@type":76},"Rhythmic abilities are modulated by musical training and vary significantly between trained and untrained individuals.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is a multidimensional approach needed to explain variability in rhythmic skills?",{"text":80,"@type":76},"The causes of variability are multidimensional and typically cannot be grasped with single tasks, motivating a more comprehensive model.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the machine learning model capture in the study?",{"text":84,"@type":76},"It extracts a parsimonious profile of rhythmic abilities from perceptual and motor behavioral tests, showing how variability relates to formal and informal music experience.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]