Decoding Individual differences and musical preference via music-induced movement
Agrawal, Y., Carlson, E., Toiviainen, P., & Alluri, V. (2022). Decoding Individual differences and musical preference via music-induced movement. Scientific Reports, 12(1), Article 2672. https://doi.org/10.1038/s41598-022-06466-3
Julkaistu sarjassa
Scientific ReportsPäivämäärä
2022Tekijänoikeudet
© The Author(s) 2022
Movement is a universal response to music, with dance often taking place in social settings. Although previous work has suggested that socially relevant information, such as personality and gender, are encoded in dance movement, the generalizability of previous work is limited. The current study aims to decode dancers’ gender, personality traits, and music preference from music-induced movements. We propose a method that predicts such individual difference from free dance movements, and demonstrate the robustness of the proposed method by using two data sets collected using different musical stimuli. In addition, we introduce a novel measure to explore the relative importance of different joints in predicting individual differences. Results demonstrated near perfect classification of gender, and notably high prediction of personality and music preferences. Furthermore, learned models demonstrated generalizability across datasets highlighting the importance of certain joints in intrinsic movement patterns specific to individual differences. Results further support theories of embodied music cognition and the role of bodily movement in musical experiences by demonstrating the influence of gender, personality, and music preferences on embodied responses to heard music.
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Julkaisija
Nature Publishing GroupISSN Hae Julkaisufoorumista
2045-2322Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/104352079
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Rahoittaja(t)
Suomen AkatemiaRahoitusohjelmat(t)
Huippuyksikkörahoitus, SA; Akatemiahanke, SALisätietoja rahoituksesta
This work was supported by funding from the Academy of Finland, project numbers 332331 and 346210.Lisenssi
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