dc.contributor.author |
Jonauskaite, D |
en |
dc.contributor.author |
Wicker, Joerg |
en |
dc.contributor.author |
Mohr, C |
en |
dc.contributor.author |
Dael, N |
en |
dc.contributor.author |
Havelka, J |
en |
dc.contributor.author |
Papadatou-Pastou, M |
en |
dc.contributor.author |
Zhang, M |
en |
dc.contributor.author |
Oberfeld, D |
en |
dc.contributor.editor |
Dunn, A |
en |
dc.date.accessioned |
2019-10-21T02:35:47Z |
en |
dc.date.issued |
2019-09-04 |
en |
dc.identifier.citation |
Royal Society Open Science 6(9):190741-190741 Article number 9 04 Sep 2019 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/48586 |
en |
dc.description.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. |
en |
dc.relation.ispartofseries |
Royal Society Open Science |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
en |
dc.title |
A machine learning approach to quantifying the specificity of color-emotion associations and their cultural differences |
en |
dc.type |
Journal Article |
en |
dc.identifier.doi |
10.1098/rsos.190741 |
en |
pubs.issue |
9 |
en |
pubs.begin-page |
190741 |
en |
pubs.volume |
6 |
en |
dc.rights.holder |
Copyright: The authors |
en |
pubs.end-page |
190741 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
Article |
en |
pubs.elements-id |
783291 |
en |
pubs.org-id |
Science |
en |
pubs.org-id |
School of Computer Science |
en |
dc.identifier.eissn |
2054-5703 |
en |
pubs.number |
9 |
en |
pubs.record-created-at-source-date |
2019-10-02 |
en |
pubs.online-publication-date |
2019-09-25 |
en |
pubs.dimensions-id |
31598303 |
en |