Universal Patterns in Color-Emotion Associations Are Further Shaped by Linguistic and Geographic Proximity

Domicele Jonauskaite; Ahmad Abu-Akel; Nele Dael; Daniel Oberfeld; Ahmed M. Abdel-Khalek; Abdulrahman S. Al-Rasheed; Jean Philippe Antonietti; Victoria Bogushevskaya; Amer Chamseddine; Eka Chkonia; Violeta Corona; Eduardo Fonseca-Pedrero; Yulia A. Griber; Gina Grimshaw; Aya Ahmed Hasan; Jelena Havelka; Marco Hirnstein; Bodil S.A. Karlsson; Eric Laurent; Marjaana Lindeman; Lynn Marquardt; Philip Mefoh; Marietta Papadatou-Pastou; Alicia Pérez-Albéniz; Niloufar Pouyan; Maya Roinishvili; Lyudmyla Romanyuk; Alejandro Salgado Montejo; Yann Schrag; Aygun Sultanova; Mari Uusküla; Suvi Vainio; Grażyna Wąsowicz; Sunčica Zdravković; Meng Zhang; Christine Mohr

doi:10.1177/0956797620948810

Universal Patterns in Color-Emotion Associations Are Further Shaped by Linguistic and Geographic Proximity

Domicele Jonauskaite, Ahmad Abu-Akel, Nele Dael, Daniel Oberfeld, Ahmed M. Abdel-Khalek, Abdulrahman S. Al-Rasheed, Jean Philippe Antonietti, Victoria Bogushevskaya, Amer Chamseddine, Eka Chkonia, Violeta Corona, Eduardo Fonseca-Pedrero, Yulia A. Griber, Gina Grimshaw, Aya Ahmed Hasan, Jelena Havelka, Marco Hirnstein, Bodil S.A. Karlsson, Eric Laurent, Marjaana LindemanLynn Marquardt, Philip Mefoh, Marietta Papadatou-Pastou, Alicia Pérez-Albéniz, Niloufar Pouyan, Maya Roinishvili, Lyudmyla Romanyuk, Alejandro Salgado Montejo, Yann Schrag, Aygun Sultanova, Mari Uusküla, Suvi Vainio, Grażyna Wąsowicz, Sunčica Zdravković, Meng Zhang, Christine Mohr

Research output: Contribution to journal › Article › peer-review

Abstract

Many of us “see red,” “feel blue,” or “turn green with envy.” Are such color-emotion associations fundamental to our shared cognitive architecture, or are they cultural creations learned through our languages and traditions? To answer these questions, we tested emotional associations of colors in 4,598 participants from 30 nations speaking 22 native languages. Participants associated 20 emotion concepts with 12 color terms. Pattern-similarity analyses revealed universal color-emotion associations (average similarity coefficient r =.88). However, local differences were also apparent. A machine-learning algorithm revealed that nation predicted color-emotion associations above and beyond those observed universally. Similarity was greater when nations were linguistically or geographically close. This study highlights robust universal color-emotion associations, further modulated by linguistic and geographic factors. These results pose further theoretical and empirical questions about the affective properties of color and may inform practice in applied domains, such as well-being and design.

Original language	English
Pages (from-to)	1245-1260
Number of pages	16
Journal	Psychological Science
Volume	31
Issue number	10
DOIs	https://doi.org/10.1177/0956797620948810
State	Published - 1 Oct 2020
Externally published	Yes

Bibliographical note

Funding Information:
For their help with the translation of the International Color-Emotion Survey into their respective languages, we thank Agnieszka Gawda (Polish), Aurika Jonauskienė (Lithuanian), Afrodite Kapsaridi (Greek), Bruno Kemm (Spanish and Portuguese), Richard Klein (French), Riina Martinson (Estonian), Galina Paramei (Russian), Angeliki Theodoridou (Greek), Evelina Thunell (Swedish), Alessandro Tremea (Italian), and Yaffa Yeshurun (Hebrew). For their help in distributing and promoting the survey in their countries, we thank Sanne Brederoo (The Netherlands), Cornelis B. Doorenbos (The Netherlands), Tinatin Gamkrelidze (Georgia), Lise Lesaffre (France), Arzu Memmedova (Azerbaijan), Mariam Okruashvili (Georgia), C. Alejandro Párraga (Spain), Vilde Johanna Solheim Lie (Norway), Halvor Stavland (Norway), Hedda Andrea Struksnæs Sørdal (Norway), and Zumrud Sultanova (Azerbaijan).

Publisher Copyright:
© The Author(s) 2020.

Keywords

affect
color perception
cross-cultural
cultural relativity
open data
open materials
pattern analysis
universality

ASJC Scopus subject areas

Psychology (all)

Access to Document

10.1177/0956797620948810

Cite this

Jonauskaite, D., Abu-Akel, A., Dael, N., Oberfeld, D., Abdel-Khalek, A. M., Al-Rasheed, A. S., Antonietti, J. P., Bogushevskaya, V., Chamseddine, A., Chkonia, E., Corona, V., Fonseca-Pedrero, E., Griber, Y. A., Grimshaw, G., Hasan, A. A., Havelka, J., Hirnstein, M., Karlsson, B. S. A., Laurent, E., ... Mohr, C. (2020). Universal Patterns in Color-Emotion Associations Are Further Shaped by Linguistic and Geographic Proximity. Psychological Science, 31(10), 1245-1260. https://doi.org/10.1177/0956797620948810

@article{d8bda3cde8e64ce8b1c522c49b4d8b86,

title = "Universal Patterns in Color-Emotion Associations Are Further Shaped by Linguistic and Geographic Proximity",

abstract = "Many of us “see red,” “feel blue,” or “turn green with envy.” Are such color-emotion associations fundamental to our shared cognitive architecture, or are they cultural creations learned through our languages and traditions? To answer these questions, we tested emotional associations of colors in 4,598 participants from 30 nations speaking 22 native languages. Participants associated 20 emotion concepts with 12 color terms. Pattern-similarity analyses revealed universal color-emotion associations (average similarity coefficient r =.88). However, local differences were also apparent. A machine-learning algorithm revealed that nation predicted color-emotion associations above and beyond those observed universally. Similarity was greater when nations were linguistically or geographically close. This study highlights robust universal color-emotion associations, further modulated by linguistic and geographic factors. These results pose further theoretical and empirical questions about the affective properties of color and may inform practice in applied domains, such as well-being and design.",

keywords = "affect, color perception, cross-cultural, cultural relativity, open data, open materials, pattern analysis, universality",

author = "Domicele Jonauskaite and Ahmad Abu-Akel and Nele Dael and Daniel Oberfeld and Abdel-Khalek, {Ahmed M.} and Al-Rasheed, {Abdulrahman S.} and Antonietti, {Jean Philippe} and Victoria Bogushevskaya and Amer Chamseddine and Eka Chkonia and Violeta Corona and Eduardo Fonseca-Pedrero and Griber, {Yulia A.} and Gina Grimshaw and Hasan, {Aya Ahmed} and Jelena Havelka and Marco Hirnstein and Karlsson, {Bodil S.A.} and Eric Laurent and Marjaana Lindeman and Lynn Marquardt and Philip Mefoh and Marietta Papadatou-Pastou and Alicia P{\'e}rez-Alb{\'e}niz and Niloufar Pouyan and Maya Roinishvili and Lyudmyla Romanyuk and {Salgado Montejo}, Alejandro and Yann Schrag and Aygun Sultanova and Mari Uusk{\"u}la and Suvi Vainio and Gra{\.z}yna W{\c a}sowicz and Sun{\v c}ica Zdravkovi{\'c} and Meng Zhang and Christine Mohr",

note = "Funding Information: For their help with the translation of the International Color-Emotion Survey into their respective languages, we thank Agnieszka Gawda (Polish), Aurika Jonauskienė (Lithuanian), Afrodite Kapsaridi (Greek), Bruno Kemm (Spanish and Portuguese), Richard Klein (French), Riina Martinson (Estonian), Galina Paramei (Russian), Angeliki Theodoridou (Greek), Evelina Thunell (Swedish), Alessandro Tremea (Italian), and Yaffa Yeshurun (Hebrew). For their help in distributing and promoting the survey in their countries, we thank Sanne Brederoo (The Netherlands), Cornelis B. Doorenbos (The Netherlands), Tinatin Gamkrelidze (Georgia), Lise Lesaffre (France), Arzu Memmedova (Azerbaijan), Mariam Okruashvili (Georgia), C. Alejandro P{\'a}rraga (Spain), Vilde Johanna Solheim Lie (Norway), Halvor Stavland (Norway), Hedda Andrea Struksn{\ae}s S{\o}rdal (Norway), and Zumrud Sultanova (Azerbaijan). Publisher Copyright: {\textcopyright} The Author(s) 2020.",

year = "2020",

month = oct,

day = "1",

doi = "10.1177/0956797620948810",

language = "English",

volume = "31",

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Jonauskaite, D, Abu-Akel, A, Dael, N, Oberfeld, D, Abdel-Khalek, AM, Al-Rasheed, AS, Antonietti, JP, Bogushevskaya, V, Chamseddine, A, Chkonia, E, Corona, V, Fonseca-Pedrero, E, Griber, YA, Grimshaw, G, Hasan, AA, Havelka, J, Hirnstein, M, Karlsson, BSA, Laurent, E, Lindeman, M, Marquardt, L, Mefoh, P, Papadatou-Pastou, M, Pérez-Albéniz, A, Pouyan, N, Roinishvili, M, Romanyuk, L, Salgado Montejo, A, Schrag, Y, Sultanova, A, Uusküla, M, Vainio, S, Wąsowicz, G, Zdravković, S, Zhang, M & Mohr, C 2020, 'Universal Patterns in Color-Emotion Associations Are Further Shaped by Linguistic and Geographic Proximity', Psychological Science, vol. 31, no. 10, pp. 1245-1260. https://doi.org/10.1177/0956797620948810

TY - JOUR

T1 - Universal Patterns in Color-Emotion Associations Are Further Shaped by Linguistic and Geographic Proximity

AU - Jonauskaite, Domicele

AU - Abu-Akel, Ahmad

AU - Dael, Nele

AU - Oberfeld, Daniel

AU - Abdel-Khalek, Ahmed M.

AU - Al-Rasheed, Abdulrahman S.

AU - Antonietti, Jean Philippe

AU - Bogushevskaya, Victoria

AU - Chamseddine, Amer

AU - Chkonia, Eka

AU - Corona, Violeta

AU - Fonseca-Pedrero, Eduardo

AU - Griber, Yulia A.

AU - Grimshaw, Gina

AU - Hasan, Aya Ahmed

AU - Havelka, Jelena

AU - Hirnstein, Marco

AU - Karlsson, Bodil S.A.

AU - Laurent, Eric

AU - Lindeman, Marjaana

AU - Marquardt, Lynn

AU - Mefoh, Philip

AU - Papadatou-Pastou, Marietta

AU - Pérez-Albéniz, Alicia

AU - Pouyan, Niloufar

AU - Roinishvili, Maya

AU - Romanyuk, Lyudmyla

AU - Salgado Montejo, Alejandro

AU - Schrag, Yann

AU - Sultanova, Aygun

AU - Uusküla, Mari

AU - Vainio, Suvi

AU - Wąsowicz, Grażyna

AU - Zdravković, Sunčica

AU - Zhang, Meng

AU - Mohr, Christine

N1 - Funding Information: For their help with the translation of the International Color-Emotion Survey into their respective languages, we thank Agnieszka Gawda (Polish), Aurika Jonauskienė (Lithuanian), Afrodite Kapsaridi (Greek), Bruno Kemm (Spanish and Portuguese), Richard Klein (French), Riina Martinson (Estonian), Galina Paramei (Russian), Angeliki Theodoridou (Greek), Evelina Thunell (Swedish), Alessandro Tremea (Italian), and Yaffa Yeshurun (Hebrew). For their help in distributing and promoting the survey in their countries, we thank Sanne Brederoo (The Netherlands), Cornelis B. Doorenbos (The Netherlands), Tinatin Gamkrelidze (Georgia), Lise Lesaffre (France), Arzu Memmedova (Azerbaijan), Mariam Okruashvili (Georgia), C. Alejandro Párraga (Spain), Vilde Johanna Solheim Lie (Norway), Halvor Stavland (Norway), Hedda Andrea Struksnæs Sørdal (Norway), and Zumrud Sultanova (Azerbaijan). Publisher Copyright: © The Author(s) 2020.

PY - 2020/10/1

Y1 - 2020/10/1

N2 - Many of us “see red,” “feel blue,” or “turn green with envy.” Are such color-emotion associations fundamental to our shared cognitive architecture, or are they cultural creations learned through our languages and traditions? To answer these questions, we tested emotional associations of colors in 4,598 participants from 30 nations speaking 22 native languages. Participants associated 20 emotion concepts with 12 color terms. Pattern-similarity analyses revealed universal color-emotion associations (average similarity coefficient r =.88). However, local differences were also apparent. A machine-learning algorithm revealed that nation predicted color-emotion associations above and beyond those observed universally. Similarity was greater when nations were linguistically or geographically close. This study highlights robust universal color-emotion associations, further modulated by linguistic and geographic factors. These results pose further theoretical and empirical questions about the affective properties of color and may inform practice in applied domains, such as well-being and design.

AB - Many of us “see red,” “feel blue,” or “turn green with envy.” Are such color-emotion associations fundamental to our shared cognitive architecture, or are they cultural creations learned through our languages and traditions? To answer these questions, we tested emotional associations of colors in 4,598 participants from 30 nations speaking 22 native languages. Participants associated 20 emotion concepts with 12 color terms. Pattern-similarity analyses revealed universal color-emotion associations (average similarity coefficient r =.88). However, local differences were also apparent. A machine-learning algorithm revealed that nation predicted color-emotion associations above and beyond those observed universally. Similarity was greater when nations were linguistically or geographically close. This study highlights robust universal color-emotion associations, further modulated by linguistic and geographic factors. These results pose further theoretical and empirical questions about the affective properties of color and may inform practice in applied domains, such as well-being and design.

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KW - color perception

KW - cross-cultural

KW - cultural relativity

KW - open data

KW - open materials

KW - pattern analysis

KW - universality

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DO - 10.1177/0956797620948810

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Universal Patterns in Color-Emotion Associations Are Further Shaped by Linguistic and Geographic Proximity

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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