Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-460
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dc.contributor.authorJonauskaite, Domicele-
dc.contributor.authorWicker, Jörg-
dc.contributor.authorMohr, Christine-
dc.contributor.authorDael, Nele-
dc.contributor.authorHavelka, Jelena-
dc.contributor.authorPapadatou-Pastou, Marietta-
dc.contributor.authorZhang, Meng-
dc.contributor.authorOberfeld-Twistel, Daniel-
dc.date.accessioned2019-11-04T10:18:08Z-
dc.date.available2019-11-04T11:18:08Z-
dc.date.issued2019-
dc.identifier.urihttps://openscience.ub.uni-mainz.de/handle/20.500.12030/462-
dc.description.abstractThe 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.en_GB
dc.description.sponsorshipDFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin-
dc.language.isoeng-
dc.rightsCC BYde_DE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subject.ddc150 Psychologiede_DE
dc.subject.ddc150 Psychologyen_GB
dc.titleA machine learning approach to quantify the specificity of colour-emotion associations and their cultural differencesen_GB
dc.typeZeitschriftenaufsatzde_DE
dc.identifier.urnurn:nbn:de:hebis:77-publ-593896-
dc.identifier.doihttp://doi.org/10.25358/openscience-460-
jgu.type.dinitypearticle-
jgu.type.versionPublished versionen_GB
jgu.type.resourceText-
jgu.organisation.departmentFB 02 Sozialwiss., Medien u. Sport-
jgu.organisation.number7910-
jgu.organisation.nameJohannes Gutenberg-Universität Mainz-
jgu.rights.accessrightsopenAccess-
jgu.journal.titleRoyal Society Open Science-
jgu.journal.volume6-
jgu.journal.issue9-
jgu.pages.alternativeArt. 190741-
jgu.publisher.year2019-
jgu.publisher.nameRoyal Soc. Publ.-
jgu.publisher.placeLondon-
jgu.publisher.urihttp://dx.doi.org/10.1098/rsos.190741-
jgu.publisher.issn2054-5703-
jgu.notes.publicOberfeld-Twistel, Daniel veröffentlicht unter: Oberfeld, Danielde_DE
jgu.organisation.placeMainz-
jgu.subject.ddccode150-
opus.date.accessioned2019-11-04T10:18:08Z-
opus.date.modified2019-12-06T10:46:08Z-
opus.date.available2019-11-04T11:18:08-
opus.subject.dfgcode00-000-
opus.organisation.stringFB 02: Sozialwissenschaften, Medien und Sport: Psychologisches Institutde_DE
opus.identifier.opusid59389-
opus.institute.number0204-
opus.metadataonlyfalse-
opus.type.contenttypeKeinede_DE
opus.type.contenttypeNoneen_GB
opus.affiliatedOberfeld-Twistel, Daniel-
jgu.publisher.doi10.1098/rsos.190741
jgu.organisation.rorhttps://ror.org/023b0x485
Appears in collections:JGU-Publikationen

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