Jonauskaite, D, Wicker, J, Mohr, C et al. (5 more authors) (2019) A machine learning approach to quantifying the specificity of colour–emotion associations and their cultural differences. Royal Society Open Science, 6 (9). ISSN 2054-5703
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
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ , which permits unrestricted use, provided the original author and source are credited. |
Keywords: | Geneva emotion wheel; multivariate pattern classification; cultural specifity; emotion; colour; machine learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Psychology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Sep 2019 11:18 |
Last Modified: | 25 Jun 2023 21:59 |
Status: | Published |
Publisher: | The Royal Society |
Identification Number: | 10.1098/rsos.190741 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:150807 |