They don’t all look the same – could better facial discrimination lead to less racial discrimination?

View Images

It’s been a big week. With a simple words, Barack Obama became the first black President of a country whose history has been so haunted by the spectre of racial prejudice. His election and inauguration are undoubtedly proud moments but they must not breed complacency. Things may be changing outwardly, but problems remain.

View Images

For a start, it goes without saying that many people, even the most liberal and left-wing among us, still harbour unconscious prejudices against members of other races. These “implicit biases” may be hidden, but their effects are often not. For example, a study published last year showed that unconscious biases can hold greater sway over a person’s voting decisions than their conscious, rational preferences.

Their influence becomes apparent even when we simply look at people of other races. It’s a well-known fact that people generally find it more difficult  to distinguish between the faces of people from other ethnic groups than those of their own. This so-called “other-race effect” is the phenomenon behind claims that “they all look the same”. But looks are in the eye of the beholder and the other-race effect can be negated through experience with members of different races. For example, African infants who are adopted by white families develop a bias in distinguishing between faces that matches those of white children.

Sophie Lebrecht from Brown University sensed a link between poorer facial discrimination and greater racial discrimination. Her idea is simple: if someone finds it hard to tell the difference between people of a certain race, they will be more likely to characterise that entire group with broad stereotypes. When the lines between individuals blur, generalities start seeping in and implicit biases have a stronger influence. But if that’s the case, there may be a way around it – indeed, Lebrecht found that by training people to better discriminate between faces of other races, she could help to reduce their biased attitudes towards those races.

Lebrecht recruited 20 white volunteers and found that they showed typical other-race effects. She asked them to memorise 24 Chinese and Black faces and later, say which they recognised from a larger set of 48.  Lebrecht also tested the volunteers for signs of implicit biases using a test called the “affective lexical priming score” or ALPS. Like similar tests, this one relies on the fact that relationships that certain mental tasks are more difficult if they challenge a person’s hidden prejudices.

View Images

The volunteers saw a series of white, black or oriental faces, each paired with a word. The word could be positive (such as “love”), negative (“hate”), neutral (“tree”) or nonsense (“malk”) and the volunteer’s job iwas to classify it accordingly. If the volunteers bore any hidden prejudices against, say, black people, their response times would be quicker if black faces were paired with negative words, and slower if they were paired with positive words.

After these initial tasks, the volunteers were split into two groups. One was asked to categorise various faces as either Chinese or Black; all they had to do was to consider the faces on a very general level. The other volunteers were faced with a more difficult training exercise, where they learned to press a different key for each of eight Black faces (and the same key for each of eight Chinese faces). They had to learn to recognise the headshots on an individual basis.

Finally, Lebrecht put all the volunteers through the recognition test and the ALPS a second time. As expected, Lebrecht found that those who were trained became better at telling the difference between faces of other races. Their scores in the recognition test improved, while the volunteers who just had to categorise the faces were no better the second time round.

Better yet, the trained group showed fewer implicit biases against black faces. Before the training, they took significantly longer to respond when Black faces were paired with positive words than when they were paired with negative ones. Afterwards, these delayed responses were, on average, nowhere to be seen. Lebrecht even found that volunteers who showed the greatest progress in discriminating between ethnically diverse faces also showed the greatest declines in their implicit biases. For comparison, the volunteers who merely categorised Black and Asian faces still bore the same hidden prejudices that they showed in the first test.

It’s not just experience with other races that made a difference – after all, both groups of volunteers saw the same sets of faces and only one developed smaller implicit biases. To Lebrecht, it was the motivation to consider the faces as individuals rather than members of a group that did the trick. She isn’t claiming that this training is a complete or foolproof solution to the problem of unconscious racial bias, merely that it could go some way towards reducing it. Perhaps it will find uses among immigration workers, teachers, policemen or other professions who regularly come across people from diverse ethnic backgrounds.  

However, it’s worth noting that the study had a very small sample size, a weakness that Lebrecht acknowledges. That doesn’t invalidate the study, but it does leave its conclusions on shakier territory. Nonetheless, it’s an interesting piece of work with fascinating social ramifications; it surely deserves to be replicated with bigger numbers.

Reference: Sophie Lebrecht, Lara J. Pierce, Michael J. Tarr, James W. Tanaka (2009). Perceptual Other-Race Training Reduces Implicit Racial Bias PLoS ONE, 4 (1) DOI: 10.1371/journal.pone.0004215

More on race and stereotypes: