학술논문

Universal Patterns in Color-Emotion Associations Are Further Shaped by Linguistic and Geographic Proximity
Document Type
Source
Psychological Science. 31(10):1245-1260
Subject
affect
color perception
cross-cultural
cultural relativity
open data
open materials
pattern analysis
universality
adult
algorithm
article
color vision
demography
female
human
human experiment
language
machine learning
major clinical study
male
speech
theoretical study
wellbeing
Language
English
ISSN
0956-7976
1467-9280
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. © The Author(s) 2020.