Evidence map›Paper›PMID 37720661›Full record

ArticleFrontiers in psychology2023

Marble melancholy: using crossmodal correspondences of shapes, materials, and music to predict music-induced emotions.

Bruno Mesz, Sebastián Tedesco, Felipe Reinoso-Carvalho, Enrique Ter Horst, German Molina, Laura H Gunn, Mats B Küssner

Abstract read
In one paragraph

Article in Frontiers in psychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Bruno MeszInstituto de Investigación en Arte y Cultura, Universidad Nacional de Tres de Febrero, Sáenz Peña, Argentina.
Sebastián TedescoInstituto de Investigación en Arte y Cultura, Universidad Nacional de Tres de Febrero, Sáenz Peña, Argentina.
Felipe Reinoso-CarvalhoUniversidad de los Andes School of Management, Bogotá, Colombia.
Enrique Ter HorstUniversidad de los Andes School of Management, Bogotá, Colombia.
German MolinaBayesian Solutions LLC, Charlotte, NC, United States.
Laura H GunnDepartment of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC, United States.
Mats B KüssnerDepartment of Musicology and Media Studies, Humboldt-Universität zu Berlin, Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: 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. Methods: 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. Results: 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. Discussion: 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.

Indexed as

crossmodal correspondencesmachine learningmaterialsmusic-induced emotionsrandom forestssensory interactionsshapes

Identifiers

PMID37720661
PMCPMC10502175

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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.