A machine learning approach to quantify the specificity of colour–emotion associations and their cultural differences

Détails

Ressource 1Télécharger: Jonauskaite_etal_2019_RSOS.pdf (1423.56 [Ko])
Etat: Public
Version: de l'auteur
Licence: CC BY 4.0
ID Serval
serval:BIB_C8254806DB29
Type
Article: article d'un périodique ou d'un magazine.
Sous-type
Compte-rendu: analyse d'une oeuvre publiée.
Collection
Publications
Titre
A machine learning approach to quantify the specificity of colour–emotion associations and their cultural differences
Périodique
Royal Society Open Science
Auteur(s)
Jonauskaite Domicele, Wicker Jörg, Mohr Christine, Dael Nele, Havelka Jelena, Papadatou-Pastou Marietta, Zhang Meng, Oberfeld Daniel
ISSN
2054-5703
2054-5703
Statut éditorial
Publié
Date de publication
27/09/2019
Peer-reviewed
Oui
Volume
6
Numéro
9
Pages
190741
Langue
anglais
Résumé
The link between colour and emotion and its possible similarity across cultures are questions that have not been fully resolved. Online, 711 participants from China, Germany, Greece and the UK associated 12 colour terms with 20 discrete emotion terms in their native languages. We propose a machine learning approach to quantify (a) the consistency and specificity of colour–emotion associations and (b) the degree to which they are country-specific, on the basis of the accuracy of a statistical classifier in (a) decoding the colour term evaluated on a given trial from the 20 ratings of colour–emotion associations and (b) predicting the country of origin from the 240 individual colour–emotion associations, respectively. The classifier accuracies were significantly above chance level, demonstrating that emotion associations are to some extent colour-specific and that colour–emotion associations are to some extent country-specific. A second measure of country-specificity, the in-group advantage of the colour-decoding accuracy, was detectable but relatively small (6.1%), indicating that colour– emotion associations are both universal and culture-specific. Our results show that machine learning is a promising tool when analysing complex datasets from emotion research.
Open Access
Oui
Financement(s)
Fonds national suisse / P0LAP1_175055
Création de la notice
25/09/2019 8:58
Dernière modification de la notice
26/09/2019 6:08
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