A machine learning approach to quantifying the specificity of color-emotion associations and their cultural differences

Domicele Jonauskaite, Jörg Wicker, Chrisine Mohr, Nele Dael, Jelena Havelka, Marietta Papadatou-Pastou, Meng Zhang, Daniel Oberfeld : A machine learning approach to quantifying the specificity of color-emotion associations and their cultural differences. In: Royal Society Open Science, Forthcoming.

Abstract

The link between color 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 color terms with 20 discrete emotion terms in their native languages. We propose a machine learning approach to quantify (a) the consistency and specificity of color-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 color term evaluated on a given trial from the 20 ratings of color-emotion association and (b) predicting the country of origin from the 240 individual color-emotion associations, respectively. The classifier accuracies were significantly above chance level, demonstrating that emotion associations are to some extent color-specific and that color-emotion associations are to some extent country-specific. A second measure of country-specificity, the in-group advantage of the color-decoding accuracy, was detectable but relatively small (6.1%), indicating that color-emotion associations are both universal and culture-specific. Our results show that machine learning is a promising tool when analyzing complex datasets from emotion research.

    BibTeX (Download)

    @article{Jonauskaite2019,
    title = {A machine learning approach to quantifying the specificity of color-emotion associations and their cultural differences},
    author = {Domicele Jonauskaite and Jörg Wicker and Chrisine Mohr and Nele Dael and Jelena Havelka and Marietta Papadatou-Pastou and Meng Zhang and Daniel Oberfeld },
    editor = {Andrew Dunn},
    year  = {2019},
    date = {2019-08-26},
    journal = {Royal Society Open Science},
    abstract = {The link between color 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 color terms with 20 discrete emotion terms in their native languages. We propose a machine learning approach to quantify (a) the consistency and specificity of color-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 color term evaluated on a given trial from the 20 ratings of color-emotion association and (b) predicting the country of origin from the 240 individual color-emotion associations, respectively. The classifier accuracies were significantly above chance level, demonstrating that emotion associations are to some extent color-specific and that color-emotion associations are to some extent country-specific. A second measure of country-specificity, the in-group advantage of the color-decoding accuracy, was detectable but relatively small (6.1%), indicating that color-emotion associations are both universal and culture-specific. Our results show that machine learning is a promising tool when analyzing complex datasets from emotion research.},
    keywords = {emotion, machine learning, psychology},
    pubstate = {forthcoming},
    tppubtype = {article}
    }