A machine learning approach to quantify the specificity of colour-emotion associations and their cultural differences.
Geneva Emotion Wheel (GEW)
colour
cultural specificity
emotion
machine learning
multivariate pattern classification
Journal
Royal Society open science
ISSN: 2054-5703
Titre abrégé: R Soc Open Sci
Pays: England
ID NLM: 101647528
Informations de publication
Date de publication:
Sep 2019
Sep 2019
Historique:
received:
19
04
2019
accepted:
22
08
2019
entrez:
11
10
2019
pubmed:
11
10
2019
medline:
11
10
2019
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 31598303
doi: 10.1098/rsos.190741
pii: rsos190741
pmc: PMC6774957
doi:
Banques de données
figshare
['10.6084/m9.figshare.c.4660808']
Types de publication
Journal Article
Langues
eng
Pagination
190741Informations de copyright
© 2019 The Authors.
Déclaration de conflit d'intérêts
The authors declare that they have no competing interests.
Références
J Pers Soc Psychol. 1987 Oct;53(4):712-7
pubmed: 3681648
Q J Exp Psychol (Hove). 2016;69(8):1619-30
pubmed: 26339950
Atten Percept Psychophys. 2011 May;73(4):971-95
pubmed: 21264748
Acta Psychol (Amst). 2018 May;186:47-53
pubmed: 29698847
Cogn Emot. 2012;26(8):1445-58
pubmed: 22671854
Nat Neurosci. 2005 May;8(5):686-91
pubmed: 15852013
Hum Factors. 2011 Jun;53(3):284-98
pubmed: 21830513
Science. 2009 Feb 27;323(5918):1226-9
pubmed: 19197022
Front Psychol. 2019 Feb 26;10:206
pubmed: 30863330
Ergonomics. 2014;57(4):503-10
pubmed: 24588355
Behav Res Methods. 2016 Jun;48(2):686-728
pubmed: 25987304
Psychol Bull. 1994 Jan;115(1):102-41
pubmed: 8202574
J Exp Psychol Gen. 1994 Dec;123(4):394-409
pubmed: 7996122
Q J Exp Psychol (Hove). 2010 Oct;63(10):1999-2011
pubmed: 20401809
Behav Res Methods. 2017 Apr;49(2):443-456
pubmed: 26936461
PLoS One. 2016 Mar 29;11(3):e0152194
pubmed: 27022909
Proc Natl Acad Sci U S A. 2009 Nov 24;106(47):19785-90
pubmed: 19901327
Psychon Bull Rev. 2014 Jun;21(3):771-6
pubmed: 24222366
Psychol Bull. 1986 Jan;99(1):100-17
pubmed: 3704032
Emotion. 2009 Dec;9(6):898-902
pubmed: 20001133
Psychol Res. 2018 Sep;82(5):896-914
pubmed: 28612080
Psychol Bull. 2002 Mar;128(2):203-35
pubmed: 11931516
Front Psychol. 2017 Mar 06;8:317
pubmed: 28321202
Emotion. 2013 Jun;13(3):380-4
pubmed: 23647454
Int J Psychophysiol. 2007 Nov;66(2):141-53
pubmed: 17544534
Neuroscientist. 2009 Jun;15(3):274-90
pubmed: 19436076
Psychol Bull. 2011 Sep;137(5):834-55
pubmed: 21766999
Ergonomics. 1972 Nov;15(6):645-54
pubmed: 4652863
Biol Lett. 2015 May;11(5):20150166
pubmed: 25972401
J Exp Psychol Gen. 2007 Feb;136(1):154-68
pubmed: 17324089
Neural Netw. 2015 Jan;61:85-117
pubmed: 25462637
Proc Natl Acad Sci U S A. 2005 Jun 7;102(23):8386-91
pubmed: 15923257