Autoren:	Jonauskaite, Domicele Wicker, Jörg Mohr, Christine Dael, Nele Havelka, Jelena Papadatou-Pastou, Marietta Zhang, Meng Oberfeld-Twistel, Daniel
Titel:	A machine learning approach to quantify the specificity of colour-emotion associations and their cultural differences
Online-Publikationsdatum:	4-Nov-2019
Erscheinungsdatum:	2019
Sprache des Dokuments:	Englisch
Zusammenfassung/Abstract:	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.
DDC-Sachgruppe:	150 Psychologie 150 Psychology
Veröffentlichende Institution:	Johannes Gutenberg-Universität Mainz
Organisationseinheit:	FB 02 Sozialwiss., Medien u. Sport
Veröffentlichungsort:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-460
URN:	urn:nbn:de:hebis:77-publ-593896
Version:	Published version
Publikationstyp:	Zeitschriftenaufsatz
Nutzungsrechte:	CC BY
Informationen zu den Nutzungsrechten:	https://creativecommons.org/licenses/by/4.0/
Zeitschrift:	Royal Society Open Science 6 9
Seitenzahl oder Artikelnummer:	Art. 190741
Verlag:	Royal Soc. Publ.
Verlagsort:	London
Erscheinungsdatum:	2019
ISSN:	2054-5703
URL der Originalveröffentlichung:	http://dx.doi.org/10.1098/rsos.190741
DOI der Originalveröffentlichung:	10.1098/rsos.190741
Anmerkung:	Oberfeld-Twistel, Daniel veröffentlicht unter: Oberfeld, Daniel
Enthalten in den Sammlungen:	JGU-Publikationen