Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-460
Authors: Jonauskaite, Domicele
Wicker, Jörg
Mohr, Christine
Dael, Nele
Havelka, Jelena
Papadatou-Pastou, Marietta
Zhang, Meng
Oberfeld-Twistel, Daniel
Title: A machine learning approach to quantify the specificity of colour-emotion associations and their cultural differences
Online publication date: 4-Nov-2019
Language: english
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: 150 Psychologie
150 Psychology
Institution: Johannes Gutenberg-Universität Mainz
Department: FB 02 Sozialwiss., Medien u. Sport
Place: 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
Publication type: Zeitschriftenaufsatz
License: CC BY
Information on rights of use: https://creativecommons.org/licenses/by/4.0/
Journal: Royal Society Open Science
6
9
Pages or article number: Art. 190741
Publisher: Royal Soc. Publ.
Publisher place: London
Issue date: 2019
ISSN: 2054-5703
Publisher URL: http://dx.doi.org/10.1098/rsos.190741
Publisher DOI: 10.1098/rsos.190741
Annotation: Oberfeld-Twistel, Daniel veröffentlicht unter: Oberfeld, Daniel
Appears in collections:JGU-Publikationen

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