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Whether you see red, feel blue, or go green with envy may depend on what country you call home, a new study suggests. And when given data on how a person associated colors with emotions, researchers could correctly predict where they were from 80% of the time.
The scientists surveyed 711 people from China, Germany, Greece, and the United Kingdom. Volunteers read the word for 12 colors, such as “green” and “turquoise.” They then indicated which of 20 emotions the colors brought to mind, and how strongly the color was tied to the feeling.
Across the board, the colors that inspired the most emotion were red, black, and pink, whereas brown and purple had weaker associations. Black was associated with sadness across all countries, for example, and red with positive emotions like love and pleasure, along with negative feelings such as anger and hate, the researchers report today in Royal Society Open Science.
Still, there were some cultural differences (see graphic, below). For instance, people in Germany associated brown more strongly with disgust than any other country, and people from Greece were the only group to associate purple primarily with sadness. White was rated as more negative in China (people there traditionally wear white to funerals), and yellow was positive in all countries except Greece.
What your feelings about color say about you
Volunteers from four countries in Europe and Asia indicated which emotions they associated with the colors below. The bars represent the number of people who indicated that the emotion was strongly associated with the specified color. Given these emotional profiles, researchers used a machine-learning algorithm to guess which colors were being assessed and which countries respondents were from.
A. Cuadra/Science
By using a type of artificial intelligence known as machine learning, which uses data to “train” a program to make guesses or correlations that wouldn’t be immediately obvious to humans, the researchers could predict which country the person was from. The team says the study shows how such technology can be used to make sense of emotion research—a field where data are often complex and nuanced, and rarely black and white.