A machine learning approach to quantify the specificity of colour-emotion associations and their cultural differences
- Publikationstyp:
- Zeitschriftenaufsatz
- Metadaten:
-
- Autoren
- Domicele Jonauskaite
- Joerg Wicker
- Christine Mohr
- Nele Dael
- Jelena Havelka
- Marietta Papadatou-Pastou
- Meng Zhang
- Daniel Oberfeld
- Autoren-URL
- https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000488745800034&DestLinkType=FullRecord&DestApp=WOS_CPL
- DOI
- 10.1098/rsos.190741
- Externe Identifier
- Clarivate Analytics Document Solution ID: JB7MM
- PubMed Identifier: 31598303
- ISSN
- 2054-5703
- Ausgabe der Veröffentlichung
- 9
- Zeitschrift
- ROYAL SOCIETY OPEN SCIENCE
- Schlüsselwörter
- colour
- emotion
- multivariate pattern classification
- machine learning
- cultural specificity
- Geneva Emotion Wheel (GEW)
- Artikelnummer
- ARTN 190741
- Datum der Veröffentlichung
- 2019
- Status
- Published
- Titel
- A machine learning approach to quantify the specificity of colour-emotion associations and their cultural differences
- Sub types
- Article
- Ausgabe der Zeitschrift
- 6
Datenquelle: Web of Science (Lite)
- Andere Metadatenquellen:
-
- Abstract
- <jats:p>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.</jats:p>
- Autoren
- Domicele Jonauskaite
- Jörg Wicker
- Christine Mohr
- Nele Dael
- Jelena Havelka
- Marietta Papadatou-Pastou
- Meng Zhang
- Daniel Oberfeld
- DOI
- 10.1098/rsos.190741
- eISSN
- 2054-5703
- Ausgabe der Veröffentlichung
- 9
- Zeitschrift
- Royal Society Open Science
- Sprache
- en
- Online publication date
- 2019
- Paginierung
- 190741 - 190741
- Datum der Veröffentlichung
- 2019
- Status
- Published
- Herausgeber
- The Royal Society
- Herausgeber URL
- http://dx.doi.org/10.1098/rsos.190741
- Datum der Datenerfassung
- 2021
- Titel
- A machine learning approach to quantify the specificity of colour–emotion associations and their cultural differences
- Ausgabe der Zeitschrift
- 6
Datenquelle: Crossref
- 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.
- Addresses
- Institute of Psychology, University of Lausanne, Lausanne, Switzerland.
- Autoren
- Domicele Jonauskaite
- Jörg Wicker
- Christine Mohr
- Nele Dael
- Jelena Havelka
- Marietta Papadatou-Pastou
- Meng Zhang
- Daniel Oberfeld
- DOI
- 10.1098/rsos.190741
- eISSN
- 2054-5703
- Externe Identifier
- PubMed Identifier: 31598303
- PubMed Central ID: PMC6774957
- Funding acknowledgements
- Institute of Psychology, Universite de Lausanne:
- Swiss National Science Foundation: P0LAP1_175055
- AkzoNobel:
- Open access
- true
- ISSN
- 2054-5703
- Ausgabe der Veröffentlichung
- 9
- Zeitschrift
- Royal Society open science
- Sprache
- eng
- Medium
- Electronic-eCollection
- Online publication date
- 2019
- Open access status
- Open Access
- Paginierung
- 190741
- Datum der Veröffentlichung
- 2019
- Status
- Published
- Publisher licence
- CC BY
- Datum der Datenerfassung
- 2019
- Titel
- A machine learning approach to quantify the specificity of colour-emotion associations and their cultural differences.
- Sub types
- research-article
- Journal Article
- Ausgabe der Zeitschrift
- 6
Files
https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.190741 https://europepmc.org/articles/PMC6774957?pdf=render
Datenquelle: Europe PubMed Central
- 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.
- Date of acceptance
- 2019
- Autoren
- Domicele Jonauskaite
- Jörg Wicker
- Christine Mohr
- Nele Dael
- Jelena Havelka
- Marietta Papadatou-Pastou
- Meng Zhang
- Daniel Oberfeld
- Autoren-URL
- https://www.ncbi.nlm.nih.gov/pubmed/31598303
- DOI
- 10.1098/rsos.190741
- Externe Identifier
- PubMed Central ID: PMC6774957
- ISSN
- 2054-5703
- Ausgabe der Veröffentlichung
- 9
- Zeitschrift
- R Soc Open Sci
- Schlüsselwörter
- Geneva Emotion Wheel (GEW)
- colour
- cultural specificity
- emotion
- machine learning
- multivariate pattern classification
- Sprache
- eng
- Country
- England
- Paginierung
- 190741
- PII
- rsos190741
- Datum der Veröffentlichung
- 2019
- Status
- Published online
- Titel
- A machine learning approach to quantify the specificity of colour-emotion associations and their cultural differences.
- Sub types
- Journal Article
- Ausgabe der Zeitschrift
- 6
Datenquelle: PubMed
- Author's licence
- CC-BY
- Autoren
- Domicele Jonauskaite
- Jörg Wicker
- Christine Mohr
- Nele Dael
- Jelena Havelka
- Marietta Papadatou-Pastou
- Meng Zhang
- Daniel Oberfeld-Twistel
- Hosting institution
- Universitätsbibliothek Mainz
- Sammlungen
- JGU-Publikationen
- Resource version
- Published version
- URN
- urn:nbn:de:hebis:77-publ-593896
- DOI
- 10.1098/rsos.190741
- Funding acknowledgements
- DFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin
- File(s) embargoed
- false
- Open access
- true
- ISSN
- 2054-5703
- Ausgabe der Veröffentlichung
- 9
- Zeitschrift
- Royal Society Open Science
- Schlüsselwörter
- 150 Psychologie
- 150 Psychology
- Sprache
- eng
- Notes
- Oberfeld-Twistel, Daniel veröffentlicht unter: Oberfeld, Daniel
- Open access status
- Open Access
- Paginierung
- Art. 190741
- Datum der Veröffentlichung
- 2019
- Public URL
- https://openscience.ub.uni-mainz.de/handle/20.500.12030/462
- Herausgeber
- Royal Soc. Publ.
- Herausgeber URL
- http://dx.doi.org/10.1098/rsos.190741
- Datum der Datenerfassung
- 2019
- Datum, an dem der Datensatz öffentlich gemacht wurde
- 2019
- Zugang
- Public
- Titel
- A machine learning approach to quantify the specificity of colour-emotion associations and their cultural differences
- Ausgabe der Zeitschrift
- 6
Files
59389.pdf
Datenquelle: OPENSCIENCE.UB
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