�����Ƶ

Racism is embedded in U.S. culture and systems. A foundation built not by accident, but with deliberate determinism, by and upon the enslaved and oppressed to uphold hegemony and hierarchy. Racism was enshrined in the Constitution through a provision limiting African Americans to three-fifths personhood. Years of slavery, lynching, and brutalism were supported by racist legislation, leading to a segregationist and discriminatory society. Despite this scorched foundation, after the U.S. civil rights movement, there was optimism for some that the country was forging a new path with the introduction of normative and legal changes. This optimism was ostensibly supported by national surveys revealing emerging positive sentiment toward Black people.1 Contrasting the racialized beliefs before the 1960s with the changing culture offered signs of hope, as the nation appeared to support the principles of racial integration and equal treatment openly and enthusiastically. Enter the myth of racial progress, whereby White Americans began to falsely believe that the United States had achieved considerable racial equality, when in fact racial disparities were (and are) deeply ingrained in American society.2 This myth was coupled with the growing societal perspective that bias, discrimination, and racism were wrong, and expressing such bias was, in many spheres of society, frowned upon.3

The Past: The Origins of Implicit Bias

Were racial biases actually decreasing? And could scientists find a way to assess the tension between a cultural shift toward favoring equality and the reality of racial bias embedded in systems and apparent in daily life? Intergroup scholars at the time thought that public opinion surveys may not accurately capture people’s true beliefs. Furthermore, although there was progress in legal changes and norms, the racist structures and systems, prevalent stereotypes and prejudices, and human motivation to favor one’s group cast looming shadows on equality. For example, although public opinion polls in the 1970s and 1980s showed the country moving away from explicit racial biases, discrimination could be identified in laboratory experiments, particularly when the participants were unaware that racial bias was the experiment’s focus. Social psychologists Faye Crosby, Stephanie Bromley, and Leonard Saxe summarized these studies and called into question the assumption that verbal reports accurately reflect individuals’ sentiments.4 They concluded that White Americans were more prejudiced than they were willing to admit, theorizing that individuals might not disclose their genuine opinions on surveys for fear of judgment, but would reveal them when they felt safe or were unaware that researchers were investigating racial bias. Researchers at the time believed that even individuals who valued equality would sometimes exhibit discriminatory behavior.5 Consequently, many considered self-reports to be unreliable. This perspective was consistent with a broader trend in social psychology that approached self-reports with skepticism and favored cognitive tasks as a more reliable measure of attitudes.6 At this time, cognitive psychology was exploring how priming a concept for a subject (such as by showing someone a word or picture) before they performed a given task could shape their responses. This general approach, that one can prime a concept that activates related concepts or prepares folks to view others in a related way without needing self-reflection, would significantly shape the development of implicit racial bias measures.

Researchers viewed behavioral implicit measures as a way to understand why individuals who consciously reject prejudice, such as egalitarians, still exhibit biased behavior. Enter Patricia Devine. In 1989, Devine, a social psychologist specializing in prejudice and stereotypes, suggested that discriminatory behavior and self-reports represented authentic psychological processes in conflict.7 One process was automatic antipathy, resulting from repeated exposure to negative cultural information about social groups. The other was a more deliberate reflection of genuine beliefs or values (for example, I want to be or should be egalitarian). This idea fostered the modern perspective that stereotypes and prejudices are learned associations influenced partly from culture.8 Devine’s perspective was popular among researchers: it offered optimism (people might be able to control their bias), intervention possibilities (perhaps we can foster self-control of bias), and historical resonance (this is why self-reports are deviating from widespread systemic biases in wealth, health, education, policing, and employment). However, Devine did not provide a direct measure of spontaneous group associations (that is, implicit bias) but instead attempted to demonstrate them using “unobtrusive” methods: namely implicit behavioral measures.9

Social psychologist Russell H. Fazio and colleagues, and later Anthony G. Greenwald and colleagues, introduced indirect behavioral measures of spontaneous group associations, known as implicit bias, and introduced the term “implicit social cognition” to describe cognitive processes related to social psychological constructs that occur outside of awareness or control.10 The general premise is that people lack self-reflective access to the cause of their behavior and are terrible at introspection, and that these new measures of implicit bias avoided the need for accurate self-reflection.11 Researchers could immediately see the appeal of tapping into biases without needing self-report. Over the next twenty-five years, there was a proliferation of implicit behavioral measures and the application of these measures to real-world domains, such as mental health, consumer decision-making, policing, legal decisions, education, health care, and political behavior.12 The popularity of these measures only gained as time went on. Implicit bias, measured behaviorally, quickly entered the public lexicon and was even mentioned in the 2020 presidential debate between Hillary Clinton and Donald Trump.13

But what is implicit bias, and how is it measured? Intergroup implicit (and explicit) associations are evaluations or beliefs about social groups. One difference between these associations is that individuals report explicit evaluations, whereas implicit associations are measured indirectly.14 Therefore, like other memory/evaluative associations, implicit race-based evaluations are partly acquired through repeated paired associations with a group (such as culturally or environmentally learned association) and can be applied without deliberation. Explicit attitudes are also partly acquired through environmental/cultural learning. Therefore, discriminatory responses can occur without intention, even when counter to deliberative unbiased beliefs. Individuals may thus feel genuine positivity about an out-group (that is, a member from a different racial group than the perceiver) and support equality, but still exhibit implicit bias.15

Over the last twenty-four years, researchers have consistently found that the majority of people in the United States show some degree of negative implicit associations about Black people and positive associations about White people. Our research has even observed these associations with self-identified Black Americans when they interact more with White people.16 Furthermore, researchers have observed greater implicit bias in more segregated counties in the United States, in places with a history of chattel slavery, or among individuals whose parents have greater implicit racial bias.17 Therefore, substantial evidence shows that the systems, culture, and whom we interact with shape implicit racial biases. People are absorbing these associations about groups from their environments whether they want to or not–even negative associations about their own group.

As the implicit bias revolution gained steam in social psychology, researchers wondered whether there were ways to assess the evaluative and cognitive processes underlying implicit bias without a response requirement. At this point, most of the research was conducted by social psychologists, and they were rightfully concerned that individuals would attempt to control their behaviors to avoid appearing biased when forced to respond or were aware that the measure might assess racial bias. Moreover, researchers were concerned that some implicit bias measures were potentially contaminated by task demands (such as forcing people to compare groups or to make a response).18 These two factors partly motivated a new era in implicit bias research in which scholars sought means to assess racial bias uncontained by these factors. Starting in the 1990s and increasing in the early 2000s, a new field took form, social neuroscience, that allowed researchers to investigate implicit racial bias via neural measures without asking people what they think, while also allowing scholars to outline the underlying levers and gears that produce these biases.

The Present: Social Neuroscience of Implicit Bias

Neuroscience methods allow researchers to assess implicit processes impacting how we think, feel, and behave toward marginalized/minoritized individuals in real time without needing self-report or behavioral responses. In these ways, neuroscience is a fantastic tool for peeking inside our minds and unpacking component processes contributing to behavior, allowing scholars to understand how the brain works at the cellular and molecular levels, how different brain regions are connected and interact, and how information is processed and integrated. Fundamentally neuroscience allows us to measure mechanisms (think of mechanism as what is under the hood making the car move; the how of implicit bias). Knowledge about mechanisms can shed light on underlying cognitive, social, emotional, and behavioral processes. Because of this, neuroscience provides valuable insights into the cognitive and affective processes that drive racial bias, and minimizes many of the criticisms of behavioral measures of implicit bias.

The use of neuroscience in social psychology is a relatively recent development that has gained momentum.19 More concretely, neuroimaging has provided several advantages for studying racial bias, including assessing ongoing psychological processes without the intrusive questions and socially desirable responses that can occur with self-report.20 Moreover, neuroimaging offers sensitivity to the engagement of distinct psychological processes that underlie otherwise similar behavior, allowing scholars to determine, for example, whether lapses in cognitive control rather than negative evaluations are more predictive of implicit bias.21 Assessing simultaneously multiple and rapid unfolding processing is extremely difficult, if not impossible, with most implicit behavioral measures. 

In 1992, social psychologists John Cacioppo and Gary Berntson introduced the term social neuroscience to describe an interdisciplinary approach to mapping social behavior and cognition by integrating our understanding of psychology with neuroscience (that is, the mind and body). From there, the field rapidly expanded due to the increased availability of noninvasive central nervous system measures. Among the first technique was event-related brain potentials (ERPs), which are derived from electroencephalograms (EEGs) and measure electrical activity of the brain at the scalp.22 ERPs allow scholars to assess electrical activity in real-time as people view others or respond to prompts. ERPs are particularly useful for studying racial bias because they allow researchers to understand when a process is happening in time. Researchers often strip away the context in the lab and simply show people faces varying in social group membership. When they do so, they find that individuals process information about perceived race, gender, age, status, and emotion within two hundred milliseconds of encountering someone.23 That is incredibly fast! This tells us that information about these social categories gets into our minds early and can guide impressions. Most important, it is spontaneous. Even when we ask people to stop, the brain still processes social category information rapidly.24

We can not only understand when things are happening in time with EEG but also view which areas of the brain are processing social group information using functional magnetic resonance imaging (fMRI), which measures changes in blood flow in brain regions while participants perform tasks or view images, helping us map mechanisms, and is another essential tool for neuroscientists.25 Although it does not provide precise timing information like ERPs, it offers excellent spatial resolution by providing information about the specific brain areas associated with mental operations. Over the years, researchers have found a host of regions involved in social group processing, including a few usual neural suspects.26 Specifically, these regions support the identification of faces (fusiform face area [FFA]), the evaluation of others based on their perceived race (orbitofrontal cortex [OFC], amygdala, and ventral striatum [VS]), how we represent the minds of others based on their perceived race or perform theory of mind (dorsomedial prefrontal cortex [DMPFC] and temporoparietal junction [TPJ]), and the regulation of bias (dorsolateral prefrontal cortex [DLPFC], anterior cingulate cortex [ACC], and ventrolateral prefrontal cortex [VLPFC]). Importantly, these areas are very similar to the areas that are involved in the processing and regulation of other emotional and social stimuli more generally, as seen in Figure 1.

Therefore, researchers have found that individuals process perceived race both extremely fast and in the same way they process other emotion-laden stimuli. Most important, it is unintentional. Even when we ask people to stop, the brain still processes this information rapidly. During this time, neuroscientists began to disentangle the processing of another’s perceived race from the production of implicit bias.27 Although perceiving race, whether accurate or not, is necessary to produce implicit associations–that is, one must categorize someone as belonging to a group to bring to mind (or activate) stereotypes and prejudices about the group–it is not sufficient. Just because folks in the United States process race does not mean they will have implicit or explicit biases, or that perceived race is an innate category. Race is a culturally and socially constructed category imbued with evaluative and semantic meaning. Consequently, the brain processes this culturally and socially constructed category similarly to other emotionally charged or salient information in the environment that culture or one’s social network has deemed positive, negative, or important.

For the past twenty-five years, neuroscientists have diligently mapped implicit racial bias’s neural foundations. One key finding is that implicit racial bias appears to be rooted partly in the brain’s evaluative system, which can operate spontaneously. This is not surprising given that individuals learn evaluative associations about groups from the culture and their environment, and this learning is then reflected in patterns of brain activity. One region that is part of the evaluative brain network, the amygdala, has been a common focus of fMRI studies examining how people process perceived race.28 The amygdala is vital for retaining, forming, and expressing negative evaluations, including fear.29 Additionally, the amygdala has a more extensive function in quickly identifying biologically significant stimuli, facilitating rapid attention and memory.30 Specifically, the amygdala responds with greater activation to faces of people from racial groups that are less familiar or positively viewed.31 Differences in amygdala activity to perceived race have sometimes, though certainly not always, correlated with implicit racial bias (and typically not explicit bias).32 Despite some disagreement on its interpretation, the general consensus is that the discovery of amygdala responses to perceived race in the U.S. context suggests that White individuals perceive Black people as highly noticeable (salient) and potentially threatening.

Recent research has uncovered significant variation in how the amygdala responds to perceived race and the degree to which the amygdala is solely or partly producing implicit bias (if even producing it at all).33 For example, when additional information about group membership or traits is available, preferential amygdala activity is frequently absent based on perceived race.34 Therefore, our comprehension of the amygdala’s role in perceived race-based assessment is more intricate and adaptable than previously believed. The current agreement suggests that this region is not the primary source of implicit racial bias. Instead, amygdala sensitivity to perceived race might result from several factors, ranging from culturally learned stereotypes to the social threat of being seen as prejudiced.35 These cultural associations can differ from one person to another based on their formative experiences. In line with this perspective, more interracial interactions during childhood correlates with decreased amygdala responses to familiar (compared to unfamiliar) perceived Black individuals.36

Another key finding is that implicit bias depends on self-regulation. The ability to adjust and control our behavior is a valuable human skill that enables us to act flexibly to achieve goals. Researchers, such as Devine, have suggested that individuals in the United States have conflicts between culturally or environmentally acquired stereotypes and prejudices and personal or societal norms to appear antiracist.37 In other words, inconsistency arises from the desire to respond without racial prejudice and the activation of stereotypical or prejudicial associations. This has led researchers to suggest that implicit bias is partly a self-control failure or an inability to regulate that conflict. Existing research has shed light on the neural mechanisms underlying this self-regulation process, revealing a network of brain regions that are believed to identify the need for control, maintain regulatory goals, and facilitate the selection of actions that align with the desired goals while inhibiting actions that do not (that is, goal-congruent versus goal-incongruent responses).

The anterior cingulate cortex was initially linked to detecting conflicts between prepotent and intentional response tendencies.38 However, in recent years, new models of ACC function suggest that this region is involved in computing the value of engaging in cognitive control based on various factors, including task difficulty, feedback, uncertainty, and reward.39 A U.S. study found that implicit racial bias increases ACC activity when viewing perceived Black individuals (as opposed to White individuals) when the faces are less prototypical (that is, inconsistent with racial stereotypes).40 These studies assume that cognitive conflict arises due to differences in the participants’ implicit biases and their motivations to be and/or appear egalitarian. Sensitivity to race in the ACC and other control-related brain areas is consistently most evident when folks know the study is about race and when participants believe that task responses indicate racial bias.41 Research also finds that a greater internal drive to respond without prejudice may amplify cognitive conflict, even without explicit instructions to control racial bias.42 Therefore, when people are cognizant that racial bias might be assessed, they may engage in more self-control as a strategy to avoid bias.

Another important region involved in self-regulation of racial bias is the dorsolateral prefrontal cortex (DLPFC).43 The DLPFC is responsible for the executive control of sensory and motor operations that align with operational goals.44 Recent research indicates that younger adults exhibit greater DLPFC activity when viewing perceived Black faces compared to perceived White faces than do older adults, who, in this study, had less self-regulation abilities.45 The DLPFC and ACC may work together to regulate implicit racial bias.46 The ACC may detect conflicts between explicit intentions and implicit associations, while the DLPFC may help to regulate the expression of implicit bias.47 However, like the amygdala, there is less evidence that self-regulation, as reflected by DLPFC or ACC activity, is the driver of implicit bias alone.

The work seems to suggest that the evaluative brain network and the cognitive-control regulatory brain network both seem to partly contribute to implicit racial bias. Therefore, current research suggests that implicit racial bias is a complex phenomenon involving multiple neural pathways and mechanisms that rely on evaluative and cognitive control systems. While neuroscience can help us understand the underlying processes, it is essential to again underscore that implicit bias is not just a matter of individual brain activity but also a product of cultural and social factors that shape our biases. Research suggests that vital brain systems have been co-opted, in a sense, to process this socially constructed category–race–and help to produce implicit bias because the culture has imbued racial groups with meaning, particularly negative biases toward marginalized or minoritized groups. Just because researchers can identify how the brain processes others based on race does not mean racial bias is innate.

Because culture and the environment have amplified biases toward marginalized or minoritized groups, intervening at the systemic level would likely have the most significant impact. However, neuroscience can inform how changes in our environment or new pieces of information shape implicit bias, providing valuable insights about the flexibility of these processes. One of the most promising avenues for reducing racial bias (both implicit and explicit) that has behavioral and neuroscience support is via interracial contact. Psychologist Jasmin Cloutier and colleagues were some of the first neuroscientists to investigate how contact influences neural mechanisms and reduction in racial bias.48 In their 2011 study, people were first familiarized with perceived Black faces and White faces, then went into the scanner and viewed faces, some of which were new, and some were the same faces they had already seen. What they found for the novel faces (faces they had never seen) was similar to what neuroscientist Elizabeth Phelps and colleagues detected in 2000.49 For self-identified White Americans, the amygdala responses were greater to perceived Black individuals than White individuals. However, the amygdala difference disappeared when respondents were more familiar with the perceived individuals. This was even more pronounced for folks with more childhood interracial contact. In fact, Jasmin Cloutier and colleagues found in their 2014 study that greater interracial childhood contact reduced amygdala responses in adulthood eighteen-plus years later.50 Around the same time, neuroscientist Eva Telzer and colleagues found that increased early deprivation, characterized by a delayed age of adoption, correlated with heightened amygdala differences toward race. These findings highlight the influence of early social intergroup interactions on the functioning of the amygdala in later stages of life.51

Interracial contact also shapes how individuals mentalize about out-group members. The ability to mentalize, also known as “theory of mind,” enables humans to make inferences about the emotions, intentions, goals, and motivations of others, thereby aiding in navigating complex social interactions. One great thing about neuroscience tools is that they allow scientists to measure mentalizing in real time. Research indicates that the dorsomedial prefrontal cortex (DMPFC) and temporoparietal junction (TPJ), among other brain regions, are consistently activated when individuals infer the mental states of others, particularly for in-group members relative to out-group members.52 However, recent research suggests that folks with more interracial contact (for example, quality contact with Black individuals for White participants) engage in similar mentalizing when viewing perceived Black faces and White faces.53 Moreover, mentalizing processes may help perceivers determine whether they observe social injustice during violent interracial interactions. For example, our recent research with self-identified White Americans finds that greater interracial contact increases mentalizing when watching videos showing violent arrests of perceived Black civilians by White officers.54 Together this work points to the importance of mentalizing processes in diminishing racial bias and facilitating the identification of racial injustice. It appears that mentalizing may act on explicit rather than implicit bias, but more research must be done to investigate this possibility.

Early fMRI work focused on specific brain regions, but contemporary neuroscience considers how entire brain networks coordinate when encountering or interacting with others. What is fascinating is that interracial contact not only determines how one region of the brain responds–for example, the amygdala–but our recent research demonstrates that contact shapes how entire brain networks respond to others, particularly those involved in social evaluation and mentalizing.55 Therefore, contact has a powerful impact on how our brain works in concert when encountering others. This research, combined with excellent behavioral work in social psychology, suggests that intergroup contact may work as an intervention in some situations, but it is only sometimes feasible. It can put marginalized and minoritized folks in spaces they might not want to be in, and creating meaningful contact where strangers build relationships is a challenge. So, it is not a perfect solution.

Overall, neuroscience can provide valuable insights into the evaluative and cognitive mechanisms underlying implicit bias and the effects of different interventions and social contexts on these biases. Additionally, the new network-neuroscience approach may be more suited for mapping not only the constellation of factors that give rise to implicit bias but also how they function in concert and how changes in the coordination of these networks may reduce implicit bias. By incorporating insights from neuroscience into implicit bias research, we may better understand how implicit biases operate and identify effective strategies for reducing their impact.

The Future: Skepticism and What Is on the Horizon for Neuroscience Research of Implicit Bias

Although neuroscience and social psychology have provided essential insights into implicit bias’s origins, production, and consequences, the field of implicit bias has faced criticism. For one, researchers need to clarify how crucial implicit bias is in producing everyday discrimination.56 Moreover, implicit bias training can enhance knowledge on the topic but does not consistently reduce implicit bias or impact behavior.57 For example, while many individual studies have shown significant relationships between implicit measures and discriminatory behaviors, the overall impact tends to be small.58 This has led some critics to consider the construct insignificant to our understanding of discrimination or racism.59 Although this possibility is important, it is premature to write off implicit bias entirely. For one, it is still vital to understand every contributing factor to racial bias and racism. Moreover, different implicit measures show different predictive validity, so throwing them all out rather than understanding their strengths and weaknesses could impair our understanding.60 Finally, it appears that implicit bias at the population level is a relatively good predictor of some aspects of systemic biases and racism, and some neuroscientists have started to map how implicit bias at the population level shapes neural responses.61 After all, individuals make up systems and institutions. Individual biases and racialized interactions are ingrained into institutional policies and societal systems, propagating the development and perpetuation of systemic racism.

However, during one-on-one interactions, it appears that having implicit racial bias does not necessarily indicate the presence of a single person’s racial prejudice or the likelihood that someone will discriminate, as going from associations to actions is complex and multifaceted. Instead, the person, situation, and culture influence discriminatory actions. It remains unclear how critical implicit bias is to structural racism over and above needs for power or status, in-group, the group one identifies with or belongs to, favoritism, or explicit bias. Therefore, it may be inappropriate to generalize from a single implicit bias behavioral or neural measure, even if it pertains to a significant conceptual grouping, as it may not reflect a fundamental or widespread change in the level of prejudice in the population or decrease racism. These interpretations must be cautiously approached since social phenomena may continue to be influenced strongly by racism even as implicit bias decreases in the population.62

Neuroscience alone cannot fully explain social group biases. Racial bias is shaped by a complex interplay of cognitive, affective, social, cultural, and environmental factors. Neuroscientists can only partially understand this phenomenon as the current methods often focus on one person’s mental operations. Although it can provide us with rich information about the mechanisms that occur when we process others from different racial groups, produce implicit bias, or take discriminatory actions, the field is relatively new. There is still much to discover! As we delve deeper into social neuroscience, we must be cautious and mindful of the potential pitfalls that can affect the rigor and inference of neuroscience research, especially when dealing with complex social interactions.63 For example, most neuroscience research examining how people perceive race and respond to racial out-group members typically shows pictures of faces that are disembodied and out of context. Although this allows researchers to isolate different aspects of the process, it does not represent the multitude of information and contexts available in real-life encounters. Unfortunately, these factors may be critical drivers or mitigators of bias, but without investigating them, we may have a blind spot. Moreover, most current research fails to examine whether neural processes predict discriminatory behavior. In other words, just because we see an area of the brain involved in processing individuals of different perceived racial groups, it does not mean that part of the brain is necessary for discrimination. Therefore, we do not have a sense of the predictive power of neuroscience for understanding real-world discrimination.64

Using brain imaging techniques to study implicit racial bias has been criticized for potentially reinforcing the idea of inherent or innate racial bias rather than focusing on the social and cultural factors contributing to biased attitudes and behaviors. The public and even other scholars will misconstrue a response in the brain as evidence of the innateness of bias. Social neuroscientists have firmly pushed back against this interpretation, suggesting that culture largely drives these biases, but this misinterpretation still plagues the science.65 Moreover, once neuro measures are involved, the tendency to view the process as innate almost medicalizes the solutions. For example, one 2012 study demonstrated that a drug, a common beta blocker propranolol, reduced implicit bias.66 One can imagine the headlines: Pills to Cure Racism! While potentially providing insights into some biological processes, this study raised troubling public discussions about developing a drug to treat racism and, in effect, biologizing racism.67 Others suggested that focusing on a particular brain region and levying a neurological intervention would cure this social ill. The truth is that brain regions, like our neurophysiology and endocrinology, are intertwined, and each typically has multiple functions. In this way, these statements are wildly inappropriate and highly inaccurate, representing extreme forms of how neuroscience research can be misinterpreted. There is no magic pill. There is no neurological or biochemical solution, and making these claims distracts from the historical and social factors that shape and reinforce racism. Racism is rooted in our structures and systems. How we process information is a byproduct of those systems. Neuroscience measures allow us to assess that byproduct with more nuance than behavioral measures alone. They can guide our understanding of racial bias, but we cannot and should not turn to a biological solution for racism.

In addition, despite the inherent dynamism of social interactions and processes, there is a lack of neuroscience work examining dynamic intergroup interactions. New techniques are now changing this. To increase the generalizability of brain research, scholars have adopted approaches such as hyperscanning, mobile EEG, fNIRs (functional near-infrared spectroscopy), and portable physiological tools, which enable us to extend our inquiry to real dynamic interactions and reach communities that were previously difficult to include due to financial or geographical constraints. These portable methodologies also remove cost barriers associated with fMRI and expand the sample and researcher demographics who can participate in social neuroscience. Ultimately, this will improve our understanding of neural correlates of racial bias because we can assess these biases during real interactions and with samples of individuals other than undergraduates at universities rich enough to afford an fMRI scanner.

While neuroscience research on implicit bias has provided essential insights that even behavioral research alone could not provide (for example, the role of mentalizing in intergroup bias), it is just a starting place for much-needed research. Current racial bias research may not generalize across samples, stimuli, cultures, or historical points. This is vital because race is a cultural construct, with the meaning changing across history and cultures. Most of the current research focuses on White folks in the United States viewing perceived Black faces and White faces. Additionally, the people included in the studies (the sample) and whom they view (the stimuli) are typically young and self-identify as cisgender men or women. Moreover, researchers often do not even ask about political ideology or sexual orientation. These oversights impair our understanding as certain groups are more or less likely to attend to and discriminate against others based on perceived race. Therefore, we know little about how intersectional identities shape how people process race, representing a more naturalistic understanding of intergroup dynamics.

Social psychologists and social neuroscientists have primarily examined bias with people who espouse equality. However, plenty of folks explicitly hate others based on their social group of belonging. This is a critical missing piece in our understanding of racial bias as these individuals express hate and an intention to act upon it. They might like intergroup discrimination and violence and perceive it as just. Understanding the drivers of explicit bias with neuroscience and behavioral research methods (not simply implicit bias) could allow researchers to characterize who is vulnerable to espousing hate or joining hate groups, what processes underlie explicit bias, and how we may intervene when individuals are entrenched in hate.

By examining the human brain, both neuroscience and the study of implicit bias can provide insight into why we treat others with cruelty or kindness and exhibit empathy or apathy. While social neuroscience has yet to contribute significantly to our understanding of overall social injustice, the discipline is poised to push this frontier further. However, achieving social justice requires understanding the complex issues, including historical and structural factors, that affect equity and inclusion and mitigate racial bias, and this understanding must be integrated into our scholarship. Although neuroscience can uncover our biases and prevent us from denying the inclinations of our minds, it does not justify maintaining or acting on those biases. By mapping how our brains function, we can acknowledge and start to understand racial biases. This awareness may assist us in defeating these biases in everyday interactions and collaborating toward a more fair and equitable society. To do so, we must consider structures, individuals, and groups in our research and be inclusive in our scientific endeavors. Finally, addressing implicit bias alone is insufficient to create a genuinely united society in the twenty-first century. The most effective means of changing bias is likely through altering the overall social structures and conditions that underpin and reinforce racism. A united national leadership and culture must speak out against racial bias, discrimination, poverty, failing health care and schools, and other insidious factors contributing to injustice. The neuroscience of implicit bias must be understood as a situated approach, whereby we recognize the significance of environmental and cultural factors in shaping the cognitive and evaluative mechanisms that give rise to racial bias.

Endnotes

• 1Lawrence Bobo, “Racial Attitudes and Relations at the Close of the Twentieth Century,” in America Becoming: Racial Trends and Their Consequences, ed. Neil J. Smelser, William Julius Wilson, and Faith Mitchell (Washington, D.C.: National Academies Press, 2001), 264–301.
• 2Jennifer A. Richeson, “” The Atlantic, September 2020; and Ivuoma N. Onyeador, Natalie M. Daumeyer, Julian M. Rucker, et al., “Disrupting Beliefs in Racial Progress: Reminders of Persistent Racism Alter Perceptions of Past, but Not Current, Racial Economic Equality,” Personality and Social Psychology Bulletin 47 (5) (2021): 753–765.
• 3John F. Dovidio and Samuel L. Gaertner, “Aversive Racism,” Advances in Experimental Social Psychology 36 (2004): 1–52.
• 4Faye Crosby, Stephanie Bromley, and Leonard Saxe, “Recent Unobtrusive Studies of Black and White Discrimination and Prejudice: A Literature Review,” Psychological Bulletin 87 (3) (1980): 546–563.
• 5Ibid.
• 6Robert S. Wyer and Donal E. Carlston, Social Cognition, Inference, and Attribution (London: Psychology Press, 1979).
• 7Crosby, Bromley, and Saxe, “Recent Unobtrusive Studies of Black and White Discrimination and Prejudice.”
• 8Benedek Kurdi, Alison E. Seitchik, Jordan R. Axt, et al., “Relationship between the Implicit Association Test and Intergroup Behavior: A Meta-���Բ����������,” American Psychologist 74 (5) (2019): 569; and Tessa E. S. Charlesworth, Aylin Caliskan, and Mahzarin R. Banaji, “Historical Representations of Social Groups across 200 Years of Word Embeddings from Google Books,” Proceedings of the National �����Ƶ of Sciences 119 (28) (2022): e2121798119.
• 9Thomas K. Srull and Robert S. Wyer Jr., “The Role of Category Accessibility in the Interpretation of Information about Persons: Some Determinants and Implications,” Journal of Personality and Social Psychology 37 (10) (1979): 1660–1672.
• 10Russell H. Fazio, Joni R. Jackson, Bridget C. Dunton, and Carol J. Williams, “Variability in Automatic Activation as an Unobtrusive Measure of Racial Attitudes: A Bona Fide Pipeline?” Journal of Personality and Social Psychology 69 (6) (1995): 1013–1027; Anthony G. Greenwald, Debbie E. McGhee, and Jordan L. K. Schwartz, “Measuring Individual Differences in Implicit Cognition: The Implicit Association Test,” Journal of Personality and Social Psychology 74 (6) (1998): 1464–1480; and Anthony G. Greenwald and Mahzarin R. Banaji, “Implicit Social Cognition: Attitudes, Self-Esteem, and Stereotypes,” Psychological Review 102 (1) (1995): 4–27.
• 11Richard E. Nisbett and Timothy D. Wilson, “Telling More Than We Can Know: Verbal Reports on Mental Processes,” Psychological Review 84 (3) (1977): 231–259.
• 12Matthew K. Nock, Jennifer M. Park, Christine T. Finn, et al., “Measuring the Suicidal Mind: Implicit Cognition Predicts Suicidal Behavior,” Psychological Science 21 (4) (2010): 511–517; Bethany A. Teachman, Elise M. Clerkin, William A. Cunningham, et al., “Implicit Cognition and Psychopathology: Looking Back and Looking Forward,” Annual Review of Clinical Psychology 15 (2019): 123–148; Dominika Maison, Anthony G. Greenwald, and Ralph H. Bruin, “Predictive Validity of the Implicit Association Test in Studies of Brands, Consumer Attitudes, and Behavior,” Journal of Consumer Psychology 14 (4) (2004): 405–415; Andrew Perkins and Mark Forehand, “Implicit Social Cognition and Indirect Measures in Consumer Behavior,” in Handbook of Implicit Social Cognition: Measurement, Theory, and Applications, ed. Bertram Gawronski and B. Keith Payne (New York: The Guilford Press, 2010), 535–547; Calvin K. Lai and Jaclyn A. Lisnek, “The Impact of Implicit-Bias-Oriented Diversity Training on Police Officers’ Beliefs, Motivations, and Actions,” Psychological Science 34 (4) (2023): 424–434; Joshua Correll, Sean M. Hudson, Steffanie Guillermo, and Debbie S. Ma, “The Police Officer’s Dilemma: A Decade of Research on Racial Bias in the Decision to Shoot,” Social and Personality Psychology Compass 8 (5) (2014): 201–213; Kristin A. Lane, Jerry Kang, and Mahzarin R. Banaji, “Implicit Social Cognition and Law,” Annual Review of Law and Social Science 3 (1) (2007): 427–451; Justin D. Levinson, Robert J. Smith, and Koichi Hioki, “Race and Retribution: An Empirical Study of Implicit Bias and Punishment in America,” UC Davis Law Review 53 (2) (2019): 839; Jerry Kang and Kristin Lane, “Seeing through Colorblindness: Implicit Bias and the Law,” UCLA Law Review 58 (2) (2010): 465; Jennifer Neitzel, “Research to Practice: Understanding the Role of Implicit Bias in Early Childhood Disciplinary Practices,” Journal of Early Childhood Teacher Education 39 (3) (2018): 232–242; Colin A. Zestcott, Irene V. Blair, and Jeff Stone, “Examining the Presence, Consequences, and Reduction of Implicit Bias in Health Care: A Narrative Review,” Group Processes & Intergroup Relations 19 (4) (2016): 528–542; Luciano Arcuri, Luigi Castelli, Silvia Galdi, et al., “Predicting the Vote: Implicit Attitudes as Predictors of the Future Behavior of Decided and Undecided Voters,” Political Psychology 29 (3) (2008): 369–387; Anthony G. Greenwald, et al., “Implicit Race Attitudes Predicted Vote in the 2008 U.S. Presidential Election,” Analyses of Social Issues and Public Policy 9 (1) (2009): 241–253; Bertram Gawronski, Jan De Houwer, and Jeffrey W. Sherman, “Twenty-Five Years of Research Using Implicit Measures,” Social Cognition 38 (2020): S1–S25; and Bertram Gawronski and Adam Hahn, “Implicit Measures: Procedures, Use, and Interpretation,” in Measurement in Social Psychology, ed. Hart Blanton, Jessica M. LaCroix, and Gregory D. Webster (London: Routledge, 2018), 29–55.
• 13“” The Washington Post, September 26, 2016.
• 14Adam Hahn, Charles M. Judd, Holen K. Hirsch, and Irene V. Blair, “Awareness of Implicit Attitudes,” Journal of Experimental Psychology: General 143 (3) (2014): 1369–1392.
• 15Wilhelm Hofmann, Bertram Gawronski, Tobias Gschwendtner, et al., “A Meta-Analysis on the Correlation between the Implicit Association Test and Explicit Self-Report Measures,” Personality and Social Psychology Bulletin 31 (10) (2005): 1369–1385.
• 16Brian A. Nosek, Anthony G. Greenwald, and Mahzarin R. Banaji, “The Implicit Association Test at Age 7: A Methodological and Conceptual Review,” in Social Psychology and the Unconscious: The Automaticity of Higher Mental Processes, ed. John A. Bargh (Abingdon-on-Thames: Routledge/Psychology Press, 2007), 265–292; Anthony G. Greenwald, T. Andrew Poehlman, Eric Luis Uhlmann, and Mahzarin R. Banaji, “Understanding and Using the Implicit Association Test: III. Meta-Analysis of Predictive Validity,” Journal of Personality and Social Psychology 97 (1) (2009): 17–41; and Jennifer T. Kubota, Jaelyn Peiso, Kori Marcum, and Jasmin Cloutier, “Intergroup Contact throughout the Life-span Modulates Implicit Racial Biases across Perceivers’ Racial Group,” PLOS ONE 12 (7) (2017): e0180440.
• 17James R. Rae, Anna-Kaisa Newheiser, and Kristina R. Olson, “Exposure to Racial Out-Groups and Implicit Race Bias in the United States,” Social Psychological and Personality Science 6 (5) (2015): 535–543; B. Keith Payne, Heidi A. Vuletich, and Jazmin L. Brown-Iannuzzi, “Historical Roots of Implicit Bias in Slavery,” Proceedings of the National �����Ƶ of Sciences 116 (24) (2019): 11693–11698; and Sylvia P. Perry, Deborah Wu, Jamie Abaied, et al., “” PsyArXiv, last edited November 27, 2023.
• 18Bertram Gawronski, Alison Ledgerwood, and Paul W. Eastwick, “Implicit Bias ≠ Bias on Implicit Measures,” Psychological Inquiry 33 (3) (2022): 139–155.
• 19Tiffany A. Ito and Jennifer T. Kubota, “Bioelectrical Echoes from a Career at the Cutting Edge: John Cacioppo’s Legacy and the Use of ERPs in Social Psychology,” Social Neuroscience 16 (1) (2021): 83–91; and Tiffany A. Ito and Jennifer T. Kubota, “The Social Neuroscience of Social Cognition,” in Handbook of Social Cognition (Oxford: Oxford University Press, forthcoming).
• 20David M. Amodio, “Can Neuroscience Advance Social Psychological Theory? Social Neuroscience for the Behavioral Social Psychologist,” Social Cognition 28 (6) (2010): 695–716; Elliot T. Berkman, William A. Cunningham, and Matthew D. Lieberman, “Research Methods in Social and Affective Neuroscience,” in Handbook of Research Methods in Social and Personality Psychology, ed. Harry T. Reis and Charles M. Judd (New York: Cambridge University Press, 2014), 123–158; John T. Cacioppo, Gary Berntson, John F. Sheridan, et al., “Multilevel Integrative Analyses of Human Behavior: Social Neuroscience and the Complementing Nature of Social and Biological Approaches,” Psychological Bulletin 126 (6) (2000): 829–843; Tiffany A. Ito and John T. Cacioppo, “Attitudes as Mental and Neural States of Readiness: Using Physiological Measures to Study Implicit Attitudes,” in Implicit Measures of Attitudes, ed. Bernd Wittenbrink and Norbert Schwarz (New York: The Guilford Press, 2007), 125–158; Nira Liberman, Jens Foerster, and E. Tory Higgins, “Completed vs. Interrupted Priming: Reduced Accessibility from Post-Fulfillment Inhibition,” Journal of Experimental Social Psychology 43 (2) (2007): 258–264; and Damian A. Stanley and Ralph Adolphs, “Toward a Neural Basis for Social Behavior,” Neuron 80 (3) (2013): 816–826.
• 21Amodio, “Can Neuroscience Advance Social Psychological Theory?”; Berkman, Cunningham, and Lieberman, “Research Methods in Social and Affective Neuroscience”; and John T. Cacioppo, “Social Neuroscience: Understanding the Pieces Fosters Understanding the Whole and Vice Versa,” American Psychologist 57 (11) (2002): 819–831.
• 22John T. Cacioppo and Gary G. Berntson, “Social Psychological Contributions to the Decade of the Brain: Doctrine of Multilevel ���Բ����������,” American Psychologist 47 (8) (1992): 1019–1028; John T. Cacioppo and Curt A. Sandman, “Physiological Differentiation of Sensory and Cognitive Tasks as a Function of Warning, Processing Demands, and Reported Unpleasantness,” Biological Psychology 6 (3) (1978): 181–192; John T. Cacioppo, “If Attitudes Affect How Stimuli Are Processed, Should They Not Affect the Event-Related Brain Potential?” Psychological Science 4 (2) (1993): 108–112; and Monica Fabiani, Gabriele Gratton, and Michael G. H. Coles, “Event-Related Brain Potentials: Methods, Theory,” in Handbook of Psychophysiology, 2nd edition, ed. John T. Cacioppo, Louis G. Tassinary, and Gary G. Berntson (Cambridge: Cambridge University Press, 2000), 53–84.
• 23Jennifer T. Kubota and Tiffany A. Ito, “You Were Always on My Mind: How Event-Related Potentials Inform Impression Formation Research,” in Handbook of Prejudice, Stereotyping and Discrimination, ed. T. D. Nelson (New York: Psychology Press, 2009), 279–299; and Bradley D. Mattan, Kevin Y. Wei, Jasmin Cloutier, and Jennifer T. Kubota, “The Social Neuroscience of Race- and Status-Based Prejudice,” Current Opinion in Psychology 24 (2018): 27–34.
• 24Jennifer T. Kubota and Tiffany Ito, “Rapid Race Perception Despite Individuation and Accuracy Goals,” Social Neuroscience 12 (4) (2017): 468–478.
• 25James V. Haxby, Elizabeth A. Hoffman, and M. Ida Gobbini, “The Distributed Human Neural System for Face Perception,” Trends in Cognitive Sciences 4 (6) (2000): 223–233; Nancy Kanwisher, Josh McDermott, and Marvin M. Chun, “The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception,” Journal of Neuroscience 17 (11) (1997): 4302–4311.
• 26Mattan, Wei, Cloutier, and Kubota, “The Social Neuroscience of Race- and Status-Based Prejudice”; Jennifer T. Kubota, Mahzarin R. Banaji, and Elizabeth A. Phelps, “The Neuroscience of Race,” Nature Neuroscience 15 (7) (2012): 940–948; Keith B. Senholzi and Jennifer T. Kubota, “The Neural Mechanisms of Prejudice Intervention,” in Neuroimaging Personality, Social Cognition, and Character, ed. John Absher and Jasmin Cloutier (Amsterdam: Elsevier, 2016), 337–354; Jennifer T. Kubota and Elizabeth A. Phelps, “Insights from Functional Magnetic Resonance Imaging Research on Race,” in Handbook of Prejudice, Stereotyping, and Discrimination, ed. Todd D. Nelson (Abingdon-on-Thames: Routledge/Psychology Press, 2016), 299–312; and Jennifer T. Kubota and Elizabeth A. Phelps, “Exploring the Brain Dynamics of Racial Stereotyping and Prejudice,” in Social Cognitive Neuroscience, Cognitive Neuroscience, Clinical Brain Mapping (Amsterdam: Elsevier, 2015), 241–246.
• 27Damian Stanley, Elizabeth A. Phelps, and Mahzarin R. Banaji, “The Neural Basis of Implicit Attitudes,” Current Directions in Psychological Science 17 (2) (2008): 164–170.
• 28Allen J. Hart, Paul J. Whalen, Lisa M. Shin, et al., “Differential Response in the Human Amygdala to Racial Outgroup Versus Ingroup Face Stimuli,” NeuroReport 11 (11) (2000): 2351–2354; Matthew D. Lieberman, Ahmad Hariri, Johanna M. Jarcho, et al., “An fMRI Investigation of Race-Related Amygdala Activity in African-American and Caucasian-American Individuals,” Nature Neuroscience 8 (6) (2005): 720–722; Jaclyn Ronquillo, Thomas F. Denson, Brian Lickel, et al., “The Effects of Skin Tone On Race-Related Amygdala Activity: An fMRI ���Ա�����پ������پ��Dz�,” Social Cognitive and Affective Neuroscience 2 (1) (2007): 39–44; Jennifer A. Richeson, Abigail A. Baird, Heather L. Gordon, et al., “An fMRI Examination of the Impact of Interracial Contact on Executive Function,” Nature Neuroscience 6 (12) (2003): 1323–1328; and Austen L. Krill and Steven M. Platek, “In-Group and Out-Group Membership Mediates Anterior Cingulate Activation to Social Exclusion,” Frontiers in Evolutionary Neuroscience 1 (1) (2009).
• 29Elizabeth A. Phelps and Joseph E. LeDoux, “Contributions of the Amygdala to Emotion Processing: From Animal Models to Human Behavior,” Neuron 48 (2) (2005): 175–187.
• 30Luiz Pessoa and Ralph Adolphs, “Emotion Processing and the Amygdala: From a ‘Low Road’ to ‘Many Roads’ of Evaluating Biological Significance,” Nature Reviews Neuroscience 11 (11) (2010): 1–10; and William A. Cunningham and Tobias Brosch, “Motivational Salience Amygdala Tuning from Traits, Needs, Values, and Goals,” Current Directions in Psychological Science 21 (1) (2012): 54–59.
• 31Hart, Whalen, Shin, et al., “Differential Response in the Human Amygdala to Racial Outgroup Versus Ingroup Face Stimuli”; Ronquillo, Denson, and Lickel, “The Effects of Skin Tone on Race-Related Amygdala Activity”; Richeson, Baird, Gordon, et al., “An fMRI Examination of the Impact of Interracial Contact on Executive Function”; Krill and Platek, “In-Group and Out-Group Membership Mediates Anterior Cingulate Activation to Social Exclusion”; William A. Cunningham and Tobias Brosch, “Motivational Salience Amygdala Tuning from Traits, Needs, Values, and Goals,” Current Directions in Psychological Science 21 (1) (2012): 54–59; Jasmin Cloutier, William M. Kelley, and Todd F. Heatherton, “The Influence of Perceptual and Knowledge-Based Familiarity on the Neural Substrates of Face Perception,” Social Neuroscience 6 (1) (2011): 63–75; Tianyi Li, Carlos Cardenas-Iniguez, Joshua Correll, and Jasmin Cloutier, et al., “The Impact of Motivation on Race-Based Impression Formation,” NeuroImage 124 (2016): 1–7; Jasmin Cloutier, Tianyi Li, and Joshua Correll, “The Impact of Childhood Experience on Amygdala Response to Perceptually Familiar Black and White Faces,” Journal of Cognitive Neuroscience 26 (9) (2014): 1992–2004; Richa Gautam, Jasmin Cloutier, and Jennifer Kubota, “Social Neuroscience of Intergroup Decision-Making,” in Handbook on the Psychology of Morality, ed. Naomi Ellemers, Stefano Pagliaro, and Félice van Nunspeet (London: Routledge, 2023); Jasmin Cloutier, Tianyi Li, Bratislav Miši, et al., “Brain Network Activity During Face Perception: The Impact of Perceptual Familiarity and Individual Differences in Childhood Experience,” Cerebral Cortex 27 (9) (2016): 1–13; William A. Cunningham, Marcia K. Johnson, Carol L. Raye, et al., “Separable Neural Components in the Processing of Black and White Faces,” Psychological Science 15 (12) (2004): 806–813; Chad E. Forbes, Christine L. Cox, Toni Schmader, and Lee Ryan, “Negative Stereotype Activation Alters Interaction between Neural Correlates of Arousal, Inhibition and Cognitive Control,” Social Cognitive and Affective Neuroscience 7 (7) (2012): 771–781; Damian A. Stanley, Peter Sokol-Hessner, Dominic S. Fareri, et al., “Race and Reputation: Perceived Racial Group Trustworthiness Influences the Neural Correlates of Trust Decisions,” Philosophical Transactions of the Royal Society of London B: Biological Sciences 367 (1589) (2012): 744–753; and Mary E. Wheeler and Susan T. Fiske, “Controlling Racial Prejudice: Social-Cognitive Goals Affect Amygdala and Stereotype Activation,” Psychological Science 16 (1) (2005): 56–63.
• 32Elizabeth A. Phelps, Kevin J. O’Connor, William A. Cunningham, et al., “Performance on Indirect Measures of Race Evaluation Predicts Amygdala Activation,” Journal of Cognitive Neuroscience 12 (5) (2000): 729–738; and Keise Izuma, Ryuta Aoki, Kazuhisa Shibata, and Kiyoshi Nakahara, “Neural Signals in Amygdala Predict Implicit Prejudice toward an Ethnic Outgroup,” Neuroimage 189 (2019): 341–352.
• 33Mattan, Wei, Cloutier, and Kubota, “The Social Neuroscience of Race- and Status-Based Prejudice”; Kubota, Banaji, and Phelps, “The Neuroscience of Race”; David M. Amodio, “The Neuroscience of Prejudice and Stereotyping,” Nature Reviews Neuroscience 15 (10) (2014): 670–682; and David M. Amodio and Mina Cikara, “The Social Neuroscience of Prejudice,” Annual Review of Psychology 72 (2021): 439–469.
• 34Mattan, Wei, Cloutier, and Kubota, “The Social Neuroscience of Race- and Status-Based Prejudice”; Li et al., “The Impact of Motivation on Race-Based Impression Formation”; and Jay J. Van Bavel, Dominic J. Packer, and William A. Cunningham, “The Neural Substrates of In-Group Bias: A Functional Magnetic Resonance Imaging ���Ա�����پ������پ��Dz�,” Psychological Science 19 (11) (2008): 1131–1139.
• 35Mattan, Wei, Cloutier, and Kubota, “The Social Neuroscience of Race- and Status-Based Prejudice”; Amodio, “The Neuroscience of Prejudice and Stereotyping”; Adam M. Chekroud, Jim A. C. Everett, Holly Bridge, and Miles Hewstone, “A Review of Neuroimaging Studies of Race-Related Prejudice: Does Amygdala Response Reflect Threat?” Frontiers in Human Neuroscience 8 (2014): 179; Tanaz Molapour, Armita Golkar, Carlos David Navarrete, et al., “Neural Correlates of Biased Social Fear Learning and Interaction in an Intergroup Context,” NeuroImage 121 (2015): 171–183; Bradley D. Mattan, Jennifer T. Kubota, Tianyi Li, et al., “Motivation Modulates Brain Networks in Response to Faces Varying in Race and Status: A Multivariate Approach,” eNeuro 5 (4) (2018); and Bradley D. Mattan, Jennifer T. Kubota, Tzipporah P. Dang, and Jasmin Cloutier, “External Motivation to Avoid Prejudice Alters Neural Responses to Targets Varying in Race and Status,” Social Cognitive and Affective Neuroscience 13 (1) (2017): 22–31.
• 36Cloutier, Li, and Correll, “The Impact of Childhood Experience on Amygdala Response to Perceptually Familiar Black and White Faces.”
• 37Ito and Kubota, “The Social Neuroscience of Social Cognition”; Kubota and Ito, “You Were Always on My Mind”; and David M. Amodio, Jennifer T. Kubota, Eddie Harmon-Jones, and Patricia G. Devine, “Alternative Mechanisms for Regulating Racial Responses According to Internal vs. External Cues,” Social Cognitive and Affective Neuroscience 1 (1) (2006): 26–36.
• 38Matthew M. Botvinick, Todd S. Braver, Deanna M. Barch, et al., “Conflict Monitoring and Cognitive Control,” Psychological Review 108 (3) (2001): 624–652.
• 39Amitai Shenhav, Jonathan D. Cohen, and Matthew M. Botvinick, “Dorsal Anterior Cingulate Cortex and the Value of Control,” Nature Neuroscience 19 (10) (2016): 1286–1291.
• 40Brittany S. Cassidy, Gregory T. Sprout, Jonathan B. Freeman, and Anne C. Krendl, “Looking the Part (to Me): Effects of Racial Prototypicality on Race Perception Vary by Prejudice,” Social Cognitive and Affective Neuroscience 12 (4) (2017): 685–694.
• 41Richeson, Baird, Gordon, et al., “An fMRI Examination of the Impact of Interracial Contact on Executive Function”; Cunningham, Johnson, Raye, et al., “Separable Neural Components in the Processing of Black and White Faces”; Jennifer S. Beer, Mirre Stallen, Michael V. Lombardo, et al., “The Quadruple Process Model Approach to Examining the Neural Underpinnings of Prejudice,” NeuroImage 43 (4) (2008): 775–783; Joseph E. Dunsmoor, Jennifer T. Kubota, Jian Li, et al., “Racial Stereotypes Impair Flexibility of Emotional Learning,” Social Cognitive and Affective Neuroscience 11 (9) (2016): 1363–1373; and Melike M. Fourie, Kevin G. F. Thomas, David M. Amodio, et al., “Neural Correlates of Experienced Moral Emotion: An fMRI Investigation of Emotion in Response to Prejudice Feedback,” Social Neuroscience 9 (2) (2014): 203–218.
• 42Amodio, Kubota, Harmon-Jones, and Devine, “Alternative Mechanisms for Regulating Racial Responses According to Internal vs. External Cues”; David M. Amodio, James Y. Shah, Jonathan Sigelman, et al., “Implicit Regulatory Focus Associated with Asymmetrical Frontal Cortical Activity,” Journal of Experimental Social Psychology 40 (2) (2004): 225–232; and David M. Amodio, Patricia G. Devine, and Eddie Harmon-Jones, “Individual Differences in the Regulation of Intergroup Bias: The Role of Conflict Monitoring and Neural Signals for Control,” Journal of Personality and Social Psychology 94 (1) (2008): 60–74.
• 43Richeson, Baird, Gordon, et al., “An fMRI Examination of the Impact of Interracial Contact on Executive Function”; and Brittany S. Cassidy and Anne C. Krendl, “Dynamic Neural Mechanisms Underlie Race Disparities in Social Cognition,” NeuroImage 132 (2016): 238–246.
• 44Kartik K. Sreenivasan, Clayton E. Curtis, and Mark D’Esposito, “Revisiting the Role of Persistent Neural Activity during Working Memory,” Trends in Cognitive Sciences 18 (2) (2014): 82–89.
• 45Brittany S. Cassidy, Eunice J. Lee, and Anne C. Krendl, “Age and Executive Ability Impact the Neural Correlates of Race Perception,” Social, Cognitive, and Affective Neuroscience 11 (11) (2016): 1752–1761.
• 46Michael W. L. Chee, Natarajan Sriram, Chun Siong Soon, and Kok Ming Lee, “Dorsolateral Prefrontal Cortex and the Implicit Association of Concepts and Attributes,” NeuroReport 11 (1) (2000): 135–140.
• 47Mattan, Wei, Cloutier, and Kubota, “The Social Neuroscience of Race- and Status-Based Prejudice”; Kubota, Banaji, and Phelps, “The Neuroscience of Race”; Amodio, “The Neuroscience of Prejudice and Stereotyping”; and Amodio and Cikara, “The Social Neuroscience of Prejudice.”
• 48Cloutier, Kelley, and Heatherton, “The Influence of Perceptual and Knowledge-Based Familiarity on the Neural Substrates of Face Perception.”
• 49Elizabeth A. Phelps, Kevin J. O’Connor, William A. Cunningham, et al., “Performance on Indirect Measures of Race Evaluation Predicts Amygdala Activation,” Journal of Cognitive Neuroscience 12 (5) (2000): 729–738.
• 50Cloutier, Li, and Correll, “The Impact of Childhood Experience on Amygdala Response to Perceptually Familiar Black and White Faces.”
• 51Eva H. Telzer, Kathryn L. Humphreys, Mor Shapiro, and Nim Tottenham, “Amygdala Sensitivity to Race Is Not Present in Childhood but Emerges over Adolescence,” Journal of Cognitive Neuroscience 25 (2) (2012): 234–244.
• 52Reginald B. Adams, Nicholas O. Rule, Robert G. Franklin Jr., et al., “Cross-Cultural Reading the Mind in the Eyes: An fMRI ���Ա�����پ������پ��Dz�,” Journal of Cognitive Neuroscience 22 (1) (2010): 97–108; David M. Amodio and Chris D. Frith, “Meeting of Minds: The Medial Frontal Cortex and Social Cognition,” Nature Reviews Neuroscience 7 (4) (2006): 268–277; Jason P. Mitchell, C. Neil Macrae, and Mahzarin R. Banaji, “Dissociable Medial Prefrontal Contributions to Judgments of Similar and Dissimilar Others,” Neuron 50 (4) (2006): 655–663; Rebecca Saxe and Lindsey J. Powell, “It’s the Thought That Counts: Specific Brain Regions for One Component of Theory of Mind,” Psychological Science 17 (8) (2006): 692–699; Frank Van Overwalle, “Social Cognition and the Brain: A Meta-���Բ����������,” Human Brain Mapping 30 (3) (2009): 829–858; and Rebecca Saxe, “Why and How to Study Theory of Mind with fMRI,” Brain Research 1079 (1) (2006): 20.
• 53Grace Handley, Jennifer T. Kubota, and Jasmin Cloutier, “Reading the Mind in the Eyes of Black and White People: Interracial Contact and Perceived Race Affects Brain Activity When Inferring Mental States,” NeuroImage 269 (2023): 119910; and Grace Handley, Jennifer Kubota, and Jasmin Cloutier, “Interracial Contact Differentially Shapes Brain Networks Involved in Social and Non-Social Judgments from Faces: A Combination of Univariate and Multivariate Approaches,” Social Cognitive and Affective Neuroscience 17 (2) (2022): 218–230.
• 54Tzipporah P. Dang, Bradley D. Mattan, Denise M. Barth, et al., “Perceiving Social Injustice during Arrests of Black and White Civilians by White Police Officers: An fMRI ���Ա�����پ������پ��Dz�,” NeuroImage 255 (2022): 119153; and Jennifer T. Kubota, Tzipporah P. Dang, Bradley D. Mattan, et al., “Social Justice Neuroscience, a Valuable and Complex Endeavor: Authors’ Reply to Commentaries on ‘Perceiving Social Injustice during Arrests of Black and White Civilians by White Police Officers: An fMRI ���Ա�����پ������پ��Dz�,’�ĝ NeuroImage 255 (2022): 119155.
• 55Handley, Kubota, and Cloutier, “Interracial Contact Differentially Shapes Brain Networks Involved in Social and Non-Social Judgments from Faces.”
• 56Bertram Gawronski, “Six Lessons for a Cogent Science of Implicit Bias and Its Criticism,” Perspectives on Psychological Science 14 (4) (2021): 574–595.
• 57Calvin K. Lai, Maddalena Marini, Steven A. Lehr, et al., “Reducing Implicit Racial Preferences: I. A Comparative Investigation of 17 Interventions,” Journal of Experimental Psychology: General 143 (4) (2014): 1765–1785; Patrick S. Forscher, Calvin K. Lai, Jordan R. Axt, et al., “A Meta-Analysis of Changes in Implicit Bias” (unpublished manuscript, 2016); Evelyn R. Carter, Ivuoma N. Onyeador, and Neil A. Lewis Jr., “Developing & Delivering Effective Anti-Bias Training: Challenges & Recommendations,” Behavioral Science & Policy 6 (1) (2020): 57–70; and Ivuoma N. Onyeador, Sa-kiera T. J. Hudson, and Neil A. Lewis, Jr., “Moving beyond Implicit Bias Training: Policy Insights for Increasing Organizational Diversity,” Policy Insights from the Behavioral and Brain Sciences 8 (1) (2021): 19–26.
• 58Malte Friese, Wilhelm Hofmann, and Manfred Schmitt, “When and Why Do Implicit Measures Predict Behaviour? Empirical Evidence for the Moderating Role of Opportunity, Motivation, and Process Reliance,” European Review of Social Psychology 19 (1) (2008): 285–338; Marco Perugini, Juliette Richetin, and Cristina Zogmaister, “Prediction of Behavior,” in Handbook of Implicit Social Cognition, ed. Gawronski and Payne; Kurdi, Seitchik, Axt, et al., “Relationship between the Implicit Association Test and Intergroup Behavior”; Greenwald, Poehlman, Uhlmann, and Banaji, “Understanding and Using the Implicit Association Test”; C. Daryl Cameron, Jazmin L. Brown-Iannuzzi, and B. Keith Payne, “Sequential Priming Measures of Implicit Social Cognition: A Meta-Analysis of Associations with Behavior and Explicit Attitudes,” Personality and Social Psychology Review 16 (4) (2012): 330–350; and Frederick L. Oswald, Gregory Mitchell, Hart Blanton, et al., “Using the IAT to Predict Ethnic and Racial Discrimination: Small Effect Sizes of Unknown Societal Significance,” Journal of Personality and Social Psychology 108 (4) (2015): 562–571.
• 59Hart Blanton and James Jaccard, “You Can’t Assess the Forest If You Can’t Assess the Trees: Psychometric Challenges to Measuring Implicit Bias in Crowds,” Psychological Inquiry 28 (4) (2017): 249–257; and Gregory Mitchell, “An Implicit Bias Primer,” Virginia Journal of Social Policy & the Law 25 (1) (2018): 27–58.
• 60Brian A. Nosek, Carlee Beth Hawkins, and Rebecca S. Frazier, “Implicit Social Cognition: From Measures to Mechanisms,” Trends in Cognitive Sciences 15 (4) (2011): 152–159.
• 61Mahzarin R. Banaji, Susan T. Fiske, and Douglas S. Massey, “Systemic Racism: Individuals and Interactions, Institutions and Society,” Cognitive Research: Principles and Implications 6 (2021): 1–21; and Mark L. Hatzenbuehler et al., “Community-Level Explicit Racial Prejudice Potentiates Whites’ Neural Responses to Black Faces: A Spatial Meta-���Բ����������,” Social Neuroscience 17 (6) (2022): 1–12.
• 62Bobo, “Racial Attitudes and Relations at the Close of the Twentieth Century”; Banaji, Fiske, and Massey, “Systemic Racism: Individuals and Interactions, Institutions and Society”;  and Eduardo Bonilla-Silva and Amanda Lewis, The “New Racism”: Toward an Analysis of the U.S. Racial Structure, 1960s–1990s (Ann Arbor: Center for Research on Social Organization at the University of Michigan, 1996).
• 63Kubota, Dang, Mattan, et al., “Social Justice Neuroscience, a Valuable and Complex Endeavor.”
• 64Richa Gautam, Jasmin Cloutier, and Jennifer Kubota, “Social Neuroscience of Intergroup Decision-Making,” in Handbook on the Psychology of Morality, ed. Naomi Ellemers, Stefano Pagliaro, and Félice van Nunspeet (London: Routledge, 2023).
• 65Kubota, Banaji, and Phelps, “The Neuroscience of Race.”
• 66Sylvia Terbeck, Guy Kahane, Sarah McTavish, et al., “Propranolol Reduces Implicit Negative Racial Bias,” Psychopharmacology 222 (3) (2012): 419–424.
• 67Raamy Majeed, “On Biologizing Racism,” The British Journal for the Philosophy of Science (forthcoming).