Happy Thanksgiving, everyone! Here’s a little more (okay, a lot more) to digest along with your turkey (or tofurkey) this year…
Given that one of the major goals of Tiltfactor’s current research is to design games aimed at reducing implicit bias held toward (or by) women in science, technology, engineering, and math (STEM), I thought it would be worthwhile to take a step back and discuss what psychologists have discovered about implicit bias – and how games might be an especially powerful means of reducing or combating it.
By definition, implicit bias is an unconscious negative evaluation or association that gets incorporated into one’s mental representation of a particular concept (be it a person, group, place, event, object, idea, value, etc.). What’s particularly insidious about implicit bias is that it does not depend on (and often occurs in spite of) an explicit endorsement or intention to have a negative attitude toward a particular target. For example, when it comes to implicit racial bias, many people who genuinely and legitimately consider themselves to be non-prejudiced and committed to egalitarianism at a conscious level could, at the same time, hold a negative association toward individuals of another race at an implicit level. Such unconscious biases could originate from any number of sources, one of the most likely culprits being repeated exposure to stereotypical depictions or prejudicial attitudes toward individuals of another race in one’s personal life (e.g., growing up constantly hearing racist views or jokes expressed by others at home or school) or in the broader culture (e.g., seeing caricatured representations of a group’s members in films or TV shows).
In regard to women in STEM, implicit bias could take the form of an unconscious association between “math” and “bad” (or “science” and “hard”) held by females or, alternatively, an association between “female” and a stereotypical trait such as “illogical.” Such associations have been shown to be automatically elicited by exposure to a target and, as a result, often affect individuals’ judgments and behaviors outside of their awareness (e.g., Bargh & Chartrand, 1999). For example, implicit bias regarding the proficiency of girls and women in STEM subjects could lead parents and teachers to form gender-stereotypical expectations, which they may transmit subtly and unwittingly to girls (e.g., in the level of attention or encouragement they provide to girls versus boys who express an interest in STEM), even if they do not explicitly endorse these stereotypes (e.g., Jacobs & Eccles, 1992). At the same time, even subtle reminders of beliefs about their gender can trigger stereotype threat in females, by which the mere activation of a stereotyped identity can elicit performance-debilitating anxiety and preoccupation about confirming a stereotype about their group (e.g., Logel et al., 2009). In other words, implicit bias has a deleterious effect on attitudes held both toward and by members of a group for which a negative stereotype is known or has been propagated.
The most widely used tool to measure implicit bias is the Implicit Association Test (or IAT, for short), developed by psychologist Anthony Greenwald and his colleagues (Greenwald et al., 1998). In a nutshell, the IAT requires respondents to simultaneously and rapidly categorize two types of stimuli (e.g., images of African-American/Caucasian faces and good/bad adjectives in a race IAT) and measures how long it takes participants to respond to presented stimuli (e.g., individual faces or adjectives) when particular category labels share a response key on the computer. For example, if a respondent takes less time to categorize faces and adjectives on trials when the category labels “African American” and “Bad” share one response key (and the category labels “Caucasian” and “Good” share the other) compared to trials when “African American” and “Good” share one response key (and “Caucasian” and “Bad” share the other), this pattern might suggest a stronger automatic association between “African American” and “bad” than between “African American” and “good” in the respondent’s mental representation. Similarly, an IAT measuring gender bias might have respondents simultaneously categorize male/female names and words or images related either to the humanities or STEM subjects (e.g., a math book or an English book). You can try out a lot of different IATs (and experience first-hand what your own implicit associations might be) here: https://implicit.harvard.edu/implicit/demo/
Attacking implicit bias requires reversing or retraining the unconscious negative or stereotypical associations individuals hold – that is, replacing or overriding existing automatic associations with new associations. For example, prior research has shown that exposing individuals to numerous counter-stereotypical role models (e.g., having girls read about successful female scientists and engineers) or giving them practice in negating a stereotype (e.g., having them press a button labeled “No” every time a picture of a group member paired with a stereotypical trait – such as an image of a female paired with the word “weak” – appeared on a screen) can reduce the automatic activation of the stereotype (Stout et al. 2011; Forbes & Schmader, 2010; Kawakami et al., 2000). The success of these interventions rests on the assumption that they can change existing mental representations and, through repetition, eventually “automatize” a new counter-stereotypical association. Thus, reversing automatic gender stereotypes might entail training girls to reject the stereotype that math and science are too difficult for them to pursue and replace this association with the opposite proposition that STEM subjects are fun, challenging, and within their grasp. Despite the success of such strategies, most investigations to date have assessed only the immediate or short-term effect of a one-time experience with such mental training – and few, if any, long-term intervention programs have been proposed or validated. Given that targeting implicit bias means overcoming associations that have likely been implanted and reinforced by frequent reminders of stereotypes and discrimination in people’s everyday experiences, it’s putting it mildly to state that coming up with such a program is likely to be a massive undertaking.
However, there are several reasons why games strategically targeting implicit bias could, in essence, serve the function of a long-term intervention tool. For one, if a game has a reasonable level of replay potential, any impact it has as a benefit of its design or mechanic would be significantly magnified over time – and given that practice and repetition are key to targeting implicit bias, high replay value is essential. In addition, if players find a game especially diverting and become fully immersed in the world of its narrative or characters, then it’s likely that any pre-existing mental associations they came into the game play situation with (including components of their own self-concept, such as self-doubt, or any implicit biases or stereotypes they hold) are likely to be temporarily de-activated and replaced by the representations, rules, and roles created by the game. Some of my own research has shown that reducing the level of activation of individuals’ self-concept before they read a fictional narrative renders them more likely to simulate the experiences and identity of a protagonist in a fictional narrative – and more likely to exhibit changes to their self-concept after they emerge from the narrative world. Similarly, an especially absorbing game should, in essence, put players in a state of mind that makes them receptive to any new associations or information the game offers. At the same time, game designers have the advantage of relying on metaphorical representations of real-life people, events, and situations, as well as the option of hiding or embedding a mental process (such as the elicitation of automatic responses and repeated practice with techniques to control or override them) in the mechanic of the game, both of which could be especially effective strategies when dealing with such a hot-button issue as implicit bias.
Bargh, J. A., & Chartrand, T. L. (1999). The unbearable automaticity of being. American Psychologist, 54, 462-479.
Forbes, C. E., & Schmader, T. (2010). Retraining Attitudes and Stereotypes to Affect Motivation and Cognitive Capacity Under Stereotype Threat. Journal of Personality and Social Psychology, 999, 740-754.
Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74, 1464-1480.
Jacobs, J. E., & Eccles, J. S. (1992). The impact of mothers’ gender-role stereotypic beliefs on mothers’ and children’s ability perceptions. Journal of Personality and Social Psychology, 63, 932-944.
Kawakami, K., Dovidio, J. F., Moll, J., Hermsen, S., & Russin, A. (2000). Just say no (to stereotyping): Effects of training in the negation of stereotypic associations on stereotype activation. Journal of Personality and Social Psychology, 78, 871-888.
Logel, C., Iserman, E. C., Davies, P. G., Quinn, D. M., & Spencer, S. J. (2009). The perils of double consciousness: The role of thought suppression in stereotype threat. Journal of Experimental Social Psychology, 45, 299-312.
Shih, M., Pittinsky, T. L., & Ambady, N. (1999). Stereotype susceptibility: Identity salience and shifts in quantitative performance. Psychological Science, 10, 80-83.
Stout, J. G., Dasgupta, N., Hunsinger, M., & McManus, M. A. (2011). STEMing the tide: Using ingroup experts to inoculate women’s self-concept in science, technology, engineering, and mathematics (STEM). Journal of Personality and Social Psychology, 100, 255-270.