114 Prejudice and Discrimination

Social Cognition: Planting the Seeds of Prejudice

How  we think about our social world influences how we behave in it. Think about the number of faces you see on a typical trip to get groceries. Your visual system “sees” everyone whose image passes across your retina, but you only perceive some of these people. Most are ignored, but a few stand out: perhaps an unattended child captures your attention, or maybe you see someone you know well. Categorization heuristics  are necessary mental shortcuts that help us navigate the world around us. Categorization itself isn’t evil; it helps us quickly recognize a child in danger or a close friend without having to examine every available piece of information. These are heuristics (covered in Section 72), cognitive shortcuts we use to cut down the amount of information but can also plant the seeds of prejudice. This is best demonstrated with an example.

The representativeness heuristic is a useful categorization trick where we estimate the likelihood of one event based on how typical it is of another event. Imagine we have a large room with 100 people, 20 women and 80 men, and we choose one at random, how likely it’s a woman? Most people correctly answer 20%. What about the chance of getting a man? Again, most people would correctly answer 80%. But let’s make this a little more interesting.​ Let’s pull someone at random again, but this time there’s a personality description provided. This person wears make-up, volunteers at an elementary school, and regularly practices yoga. Now what are the odds of them being a woman? It does seem more like a woman. Did you want to change your estimate this time? If so, you’ve fallen victim to the representativeness heuristic, because the answer is still 20% despite the personality details. In this case we’d call it the representativeness bias, because it resulted in an error (Kahneman & Tversky, 1973).

The representativeness bias is strongly influenced by stereotypes, pre-existing expectations about an individual based upon their group membership. Women are statistically more likely to wear make-up, volunteer at a school, and practice yoga; for most people, this comes to mind without explicitly thinking about it. Stereotypes are heuristics used to make quick decisions, but they’re prone to error. If you were asked to picture a chair, you’d probably imagine a piece of familiar furniture with a flat surface to sit on, a back rest, and four legs supporting it on the floor. But have a look at Figure SP.21. Are these all considered chairs?

Figure SP.21 What makes a chair? Only one of these chairs fits the prototypical description, but all three are recognizable examples you can sit in. Categorization saves us time but is error-prone.

Group Identity and Self-esteem: Fertilizing Prejudice

Decades of research reveals of our vulnerability to categorization errors, and the dangerous consequences it can produce in how we view fellow humans. With our instant access to information in the digital age, everyone can know about this, so why do prejudice and discrimination still exist? As we’ve learned, it takes little conscious effort to create in-groups and out-groups, we prefer people in our in-groups, and this can easily lead to in-group bias. But what if our in-group favouritism hurts people in out-groups?

In 1968, Jane Elliott, an elementary school teacher, showed how the arbitrary definition of out-groups can affect how people are treated in a simple, yet powerful, demonstration with her 3rd grade students. She divided the class into eye colour groups and told them that brown-eyed kids were superior. The video below (link to learning) describes how this minimal group category rapidly changed their childrens’ behaviour.

Like in Tajfel’s study (1970), Elliott’s group divisions were arbitrary and unrelated to the stereotypes attached to them. Yet students quickly embraced their assigned groups, accepted unfounded claims of in-group superiority, and let these labels shape their self-views, emotions, and treatment of one another (Peters, 1987).

When we treat stereotypes as self-evident truths, we may treat other people based on our expectation that they will fit that stereotype. Others may then react to how we treat them in a way that confirms our initial expectation, creating a self-fulfilling prophecy. For example, let’s say you assume your Intro Psych professor is rude. When you meet them at office hours, you might act coldly, like barely saying “hi” or rolling your eyes when they ask you a follow-up question. Confused, your professor may respond in an annoyed tone, and you walk away thinking, “I knew it, they’re rude!” This creates a cycle that’s difficult to get out of.

Alternatively, when we’re aware of negative stereotypes about our own group, we may fear acting in a way that confirms them, and that anxiety can then impair our performance, called stereotype threat.  Imagine that your family holds an annual pie-eating contest and that you get to participate for the first time. In the weeks leading up to it, several relatives remind you jokingly that “the newbies never win.” On contest day, you remember their comments and immediately get more nervous; your stomach churns, you feel a little nauseous, and you end up being able to eat only a quarter of a pie, losing to your undefeated cousin.

Professors and pies aside, self-fulfilling prophecies and stereotype threat can have serious consequences. Rosenthal and Jacobson’s (1968) classic study showed that teachers’ expectations alone can shape students’ academic performance. After randomly designating five students per class as “academic spurters” based on a fictitious test, researchers found that these students made the greatest intellectual gains, especially in early grades. Teachers also described “spurters” more positively, curious, well-adjusted, and autonomous, while rating “non-spurters” less favourably even when they improved. This phenomenon is known as the Rosenthal or Pygmalion effect.

The Rosenthal effect has been widely replicated. Recent studies show that teachers’ negative expectations predict lower student achievement and influence how students view their own academic abilities (Bergold & Steinmayr, 2023; Gentrup et al., 2020). Jane Elliott’s brown eyes/blue eyes demonstration illustrates stereotype threat: a blue-eyed student who previously read at a sixth-grade level began making reading and spelling errors after being told blue-eyed children were less intelligent than brown-eyed children.

What if these types of stereotypes were associated not with eye, but skin colour? Or gender? Or socioeconomic status? A study by Word et al. (1974) showed that white employers treated Black interviewees less warmly by sitting farther away and giving them less time. When these behaviours were replicated with white participants, those treated like the Black interviewees performed worse, demonstrating a self‑fulfilling prophecy. Research on stereotype threat shows similar effects: women perform worse on math tests when reminded of gender stereotypes (Brown & Josephs, 1999; Spencer et al., 1999), and Black students underperform on cognitive tests when primed with racial stereotypes (Steele & Aronson, 1995). Among first responders seeking promotion, Black employees reported more stereotype threat, which predicted higher anxiety, lower self‑efficacy, and poorer test performance (Chung et al., 2010).

Although prejudiced attitudes and stereotypes happen inside our heads, the studies in this section show how easily they can influence our behaviour in the social world, sometimes unknowingly. While we don’t always mean to discriminate against certain groups, it’s easier for our brain to remember stereotype-consistent information (Bodenhausen, 1988) . In contrast, disconfirming stereotypes takes mental effort. Because our brain always strives for efficiency, we rely on categories and stereotypes to guide behaviour, sometimes at the cost of harming out-group members or even ourselves.

Measuring Prejudice and Discrimination

Are you prejudiced? Yes or no? You might not want to answer this question for a variety of reasons, which makes studying prejudicial attitudes challenging for researchers. Prejudice is generally measured in two main ways, using explicit or implicit measures of attitudes. With explicit measures, you simply ask about feelings, attitudes, and opinions related to specific groups. Implicit measures instead focus on automatic behaviours towards members of these groups.

Typically, explicit attitude measures are self-report scales or surveys. One of the simplest explicit measures is the Feelings Thermometers (Figure SP.23), where people rate their warmth or coldness toward a particular group with visual analogue scales displayed as warm or cool (Nelson, 2008). Negative stereotyping scales (Lee et al., 2024) or Likert scales like the Symbolic Racism Scale (Henry & Sears, 2002; Kinder & Sears, 1981) are also commonly used. Explicit measures can be further subdivided into direct or indirect measures. Direct measures clearly ask respondents to rate their attitudes about a specified group, while indirect measures ask respondents to rate their attitudes toward issues related to a specific group (see Figure SP.23).

Figure SP.23 Direct (left) and indirect (right) explicit measures of prejudice. Note: This figure is adapted from vecteezy.com (top left), the Lee and Colleagues’ (2024) Negative Stereotyping Scale (bottom left), and the Symbolic Racism Scale (Henry & Sears, 2002; Kinder & Sears, 1981; right). It is included in accordance with the Vecteezy Free License for web use policy and fair dealing principles.

The Implicit Association Test (IAT)  is a widely used measure of implicit attitudes. In this computerized task, people sort words into categories on the left or right side of a screen as quickly as possible, for example, identifying “flower” as “good” or “bugs” with “bad.” Reaction times across different blocks reveal unconscious biases, based on the idea that responses are faster when paired concepts are strongly associated. Because the task limits time for reflection, it can expose preferences people may not report explicitly. You can try the IAT yourself through Harvard Project Implicit (link to learning below).