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High-powered computer sees red

In almost all human cultures, colours are associated with different emotions such as hate, love, anger and sadness.

Now for the first time, scientists have used machine learning to analyse how we associate particular emotions with particular colours and say the results mean the application of this type of artificial intelligence is likely to become much more common.

The research team included five psychology researchers and one computer scientist, Dr Joerg Wicker from the University of Auckland. Researchers collected raw data from an online survey of 711 people from China, Germany, Greece and the United Kingdom. Participants were given a list of 12 colour names and asked which emotion they associated with which colour. They had 20 emotions to choose from including love, hate, sadness, guilt and disgust.

The study used machine learning – the ability of a computer to ‘learn’ from data rather than having to be programmed - to analyse the survey results.

Dr Wicker says the computer was able to detect ‘hidden’ patterns in the data and a higher number of patterns than more orthodox methods.

After being ‘trained’ to produce a particular model based on the data, the computer was able to determine the country someone was from by the emotions they associated with a particular colour.

It was also able to predict which colour participants meant when they listed the emotions they associated with it: if a participant said I associate this colour with love and anger, the computer knew they meant red.

Machine learning did however produce more accurate results from the data if it was from people who came from a single country: for example results were more accurate if all the survey participants were from China.

It also found it easier to classify some colours when a colour association was consistent across participants from different countries: for example red is commonly associated with love in many cultures.

Other findings included the association between the colour brown and disgust which was stronger in Germany than other countries, including in China where it was almost non-existent. Participants from Greece were the only group that strongly associated purple with sadness while white was frequently associated with negative emotions in China compared with the other countries.
Dr Wicker says that, as a computer scientist, the study was both challenging and rewarding.

“Machine learning and data mining is my field of interest and I feel really strongly that this type of data analysis should be applied in other disciplines such as psychology or any research on emotion.

“This work contributes directly to fundamental understandings in psychology research and wouldn’t have been possible without applying machine learning.”

Associate Professor Paul Corballis from the University of Auckland’s School of Psychology, who was not involved in the current study, says the novel machine learning approach taken by the researchers revealed patterns from a complicated dataset that would be more difficult to detect using traditional methods.

“The way we associate colour with emotion addresses a very old question in psychology: is our response to colour innate, that is, hard-wired, or is it determined by culture and therefore learned?” he says.

“I think a really interesting aspect of this latest study is that it goes some way to resolving this question by suggesting response to colour is both innate but is also modified by culture.”

The study is published in Royal Society Open Science and the research project, which is ongoing, was financially supported by the Swiss National Science Foundation and is an ongoing effort of over a hundred researchers.


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