Marble melancholy: using crossmodal correspondences of shapes, materials, and music to predict music-induced emotions.
crossmodal correspondences
machine learning
materials
music-induced emotions
random forests
sensory interactions
shapes
Journal
Frontiers in psychology
ISSN: 1664-1078
Titre abrégé: Front Psychol
Pays: Switzerland
ID NLM: 101550902
Informations de publication
Date de publication:
2023
2023
Historique:
received:
17
02
2023
accepted:
08
08
2023
medline:
18
9
2023
pubmed:
18
9
2023
entrez:
18
9
2023
Statut:
epublish
Résumé
Music is known to elicit strong emotions in listeners, and, if primed appropriately, can give rise to specific and observable crossmodal correspondences. This study aimed to assess two primary objectives: (1) identifying crossmodal correspondences emerging from music-induced emotions, and (2) examining the predictability of music-induced emotions based on the association of music with visual shapes and materials. To achieve this, 176 participants were asked to associate visual shapes and materials with the emotion classes of the Geneva Music-Induced Affect Checklist scale (GEMIAC) elicited by a set of musical excerpts in an online experiment. Our findings reveal that music-induced emotions and their underlying core affect (i.e., valence and arousal) can be accurately predicted by the joint information of musical excerpt and features of visual shapes and materials associated with these music-induced emotions. Interestingly, valence and arousal induced by music have higher predictability than discrete GEMIAC emotions. These results demonstrate the relevance of crossmodal correspondences in studying music-induced emotions. The potential applications of these findings in the fields of sensory interactions design, multisensory experiences and art, as well as digital and sensory marketing are briefly discussed.
Identifiants
pubmed: 37720661
doi: 10.3389/fpsyg.2023.1168258
pmc: PMC10502175
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1168258Informations de copyright
Copyright © 2023 Mesz, Tedesco, Reinoso-Carvalho, Ter Horst, Molina, Gunn and Küssner.
Déclaration de conflit d'intérêts
Author GM was employed by company Bayesian Solutions LLC, Charlotte, NC, United States. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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