A Comparative Investigation of the Predictive Validity of Four Indirect Measures of Bias and Prejudice

Although measures of implicit associations are influential in the prejudice literature, comparative tests of the predictive power of these measures are lacking. A large-scale (N > 100,000) analysis of four commonly used measures-the Implicit Association Test (IAT), Single-Category IAT (SC-IAT), evaluative priming task (EPT), and sorting paired features task (SPF)-across 10 intergroup domains and 250 outcomes found clear evidence for the superiority of the SC-IAT in predictive and incremental predictive validity. Follow-up analyses suggested that the SC-IAT benefited from an exclusive focus on associations toward stigmatized group members, as associations toward non-stigmatized group members diluted the predictive strength of relative measures like the IAT, SPF, and EPT. These results highlight how conclusions about predictive validity can vary drastically depending on the measure selected and reveal novel insights about the value of different measures when focusing on predictive than convergent validity.

How Can Debiasing Research Aid Efforts to Reduce Discrimination?

PUBLIC ABSTRACT

Scientists studying intergroup biases are often concerned with lessening discrimination (unequal treatment of one social group versus another), but many interventions for reducing such biased behavior have weak or limited evidence. In this review article, we argue one productive avenue for reducing discrimination comes from adapting interventions in a separate field-judgment and decision-making-that has historically studied "debiasing": the ways people can lessen the unwanted influence of irrelevant information on decision-making. While debiasing research shares several commonalities with research on reducing intergroup discrimination, many debiasing interventions have relied on methods that differ from those deployed in the intergroup bias literature. We review several instances where debiasing principles have been successfully applied toward reducing intergroup biases in behavior and introduce other debiasing techniques that may be well-suited for future efforts in lessening discrimination.

Racial Prejudice Affects Representations of Facial Trustworthiness

What makes faces seem trustworthy? We investigated how racial prejudice predicts the extent to which perceivers employ racially prototypical cues to infer trustworthiness from faces. We constructed participant-level computational models of trustworthiness and White-to-Black prototypicality from U.S. college students' judgments of White (Study 1, N = 206) and Black-White morphed (Study 3, N = 386) synthetic faces. Although the average relationships between models differed across stimuli, both studies revealed that as participants' anti-Black prejudice increased and/or intergroup contact decreased, so too did participants' tendency to conflate White prototypical features with trustworthiness and Black prototypical features with untrustworthiness. Study 2 (N = 324) and Study 4 (N = 397) corroborated that untrustworthy faces constructed from participants with pro-White preferences appeared more Black prototypical to naive U.S. adults, relative to untrustworthy faces modeled from other participants. This work highlights the important role of racial biases in shaping impressions of facial trustworthiness.

How White American Children Develop Racial Biases in Emotion Reasoning

For decades, affective scientists have examined how adults and children reason about others' emotions. Yet, our knowledge is limited regarding how emotion reasoning is impacted by race-that is, how individuals reason about emotions displayed by people of other racial groups. In this review, we examine the developmental origins of racial biases in emotion reasoning, focusing on how White Americans reason about emotions displayed by Black faces/people. We highlight how racial biases in emotion reasoning, which emerge as early as infancy, likely contribute to miscommunications, inaccurate social perceptions, and negative interracial interactions across the lifespan. We conclude by discussing promising interventions to reduce these biases as well as future research directions, highlighting how affective scientists can decenter Whiteness in their research designs. Together, this review highlights how emotion reasoning is a potentially affective component of racial bias among White Americans.

The good and the bad: Are some attribute words better than others in the Implicit Association Test?

The Implicit Association Test (IAT) is one of the most popular measures in psychological research. A lack of standardization across IATs has resulted in significant variability among stimuli used by researchers, including the positive and negative words used in evaluative IATs. Does the variability in attribute words in evaluative IATs produce unwanted variability in measurement quality across studies? The present work investigated the effect of evaluative stimuli across three studies using 13 IATs and over 60,000 participants. The 64 positive and negative words that we tested provided similar measurement quality. Further, measurement was satisfactory even in IATs that used only category labels as stimuli. These results suggest that common sense is probably a sufficient method for selection of evaluative stimuli in the IAT. For reasonable measurement quality, we recommend that researchers using evaluative IATs in English select words randomly from the set we tested in the present research.

Were Americans' Political Attitudes Linked to Objective Threats From COVID-19? An Examination of Data From Project Implicit During Initial Months of the Pandemic

The COVID-19 pandemic has created objectively threatening situations in everyday life (e.g., unemployment, risk of infection), and researchers have begun to ask whether threats from the pandemic are linked to people's political attitudes. However, scholars currently lack a systematic answer to this question. Here, we examined whether objective COVID-19 threats (cases, deaths, and government restrictions) occurring over the initial months of the pandemic (February-June 2020) were associated with seven different assessments of political attitudes among Project Implicit users in the United States (N = 34,581). We did not consistently observe meaningful associations between COVID-19 threats and political attitudes. The lack of consistent meaningful associations emerged regardless of the level of analysis (country, state, and county) or participant's self-identified ideology. Collectively, these findings failed to find evidence that political attitudes were tied to COVID-19 threats in a meaningful way during the initial months of the pandemic.

Can the Implicit Association Test Measure Automatic Judgment? The Validation Continues

In this commentary, we welcome Schimmack’s reanalysis of Bar-Anan and Vianello’s multitrait multimethod (MTMM) data set, and we highlight some limitations of both the original and the secondary analyses. We note that when testing the fit of a confirmatory model to a data set, theoretical justifications for the choices of the measures to include in the model and how to construct the model improve the informational value of the results. We show that making different, theory-driven specification choices leads to different results and conclusions than those reported by Schimmack (this issue, p. 396). Therefore, Schimmack’s reanalyses of our data are insufficient to cast doubt on the Implicit Association Test (IAT) as a measure of automatic judgment. We note other reasons why the validation of the IAT is still incomplete but conclude that, currently, the IAT is the best available candidate for measuring automatic judgment at the person level.