Asian=Machine, Black=Animal? The Racial Asymmetry of Dehumanization Hui Bai and Xian Zhao

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Citation: Bai & Zhao. (in press). Asian=Machine, Black=Animal? The Racial Asymmetry of Dehumanization. Journal of Personality and Social Psychology.

Asian=Machine, Black=Animal? The Racial Asymmetry of Dehumanization Hui Bai and Xian Zhao

Author Note Hui Bai is a Stanford Impact Labs post-doctoral researcher at the Polarization and Social Change Lab at Stanford University and the director of the Political Belief Lab, an independent research lab.

Xian Zhao is a Golub Capital Social Impact post-doctoral fellow at Kellogg School of Management, Northwestern University. *Address correspondence to Hui Bai at maxhuibai@gmail.com. The authors declare no conflict of interest. Code, data, and the online supplemental materials that include study materials can be found at https://osf.io/r6kde.

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How different racial minorities experience racism differently remains underexplored in existing research. Here, we show that Asian and Black people are often dehumanized differently. Twelve studies spotlight a racial asymmetry in dehumanization using a wide array of methods (experimental, archival, and computational) and data sources (online samples, word embeddings, and U.S. Bureau of Labor Statistics data): whereas Black people are more often subjected to animalistic dehumanization, Asian people are predominantly subjected to mechanistic dehumanization. We demonstrate this asymmetry from the vantage point of victims (Studies 1a and 1b) and perpetrators (Studies 2a-2d). We further document the prevalence of this asymmetry across diverse domains, from everyday language (Study 3) to perceptions in the realms of romantic relationships (Study 4a), crime rates (Study 4b), and business skills (Study 4c). Finally, we demonstrate the asymmetry’s realworld consequences in labor market segregation (Studies 5 and 6). Our findings shed light on the distinct experiences of racism encountered by different racial groups, and more critically, introduce a framework that unifies and integrates scattered empirical observations on perceptions of Asian people.

Keywords: Race, Asian, Black, dehumanization, prejudice

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Imagine a world where Asian people are viewed as akin to emotionless machines, while Black people are likened to animals. Unsettling as it may be, we argue in this paper that such a world is not a figment of dystopian fiction, but a reflection of our society's painful reality.

Dehumanization, we will show, can operate in racially specific ways, casting long shadows on the lives of different racial groups. This paper delves into this disturbing, yet under-explored. In doing so, we unearth the different dimensions of experiences faced by Asian and Black people that have been long underserved in the existing literature and provide a framework that unifies and integrates scattered empirical observations on societal perceptions of Asian people.

Dehumanization and Its Two Forms Dehumanization occurs when a person is perceived to be less than human, lacking human traits, or derogatorily compared to non-human entities (Haslam & Loughnan, 2014; Kteily & Landry, 2022). Dehumanization can be inconspicuously subtle (e.g., perceive a group to experience fewer “uniquely human” emotions; Cortes et al., 2005) or overtly blatant (e.g., explicitly liken a group to apes; Goff et al., 2008; Kteily & Bruneau, 2017). Marginalized minorities are often dehumanized by advantaged majorities (Bain et al., 2009; Boccato et al, 2008; Goff, et al., 2008; Saminaden et al. 2010; Vaes & Paladino, 2010). The role of dehumanization in injustice is painfully pervasive in society as dehumanized perceptions of marginalized people have enabled discrimination as well as justified violence against them (Bruneau et al., 2020).

Haslam (2006) introduced two key forms of dehumanization and corresponding to them two senses of humanness. Animalistic dehumanization stands in contrast to human uniqueness. It happens when individuals perceive or treat others as animals and assume they lack attributes that are unique to humans, including civility, moral sensibility, cultural refinement, and rationality or the ability to use logic. Mechanical dehumanization, contrasting human nature, occurs when one sees or

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treats others as if they are machines and assumes them to lack attributes that are part of human nature, including emotional responsiveness, interpersonal skills, open-mindedness, and agency1.

However, past studies often do not distinguish the two forms of dehumanization in the intergroup context. For example, in the study of blatant dehumanization (i.e., research related to how people overtly compare someone to non-humans), researchers’ focus is especially lopsided, as dehumanization is often treated and operationalized the same as animalistic dehumanization (Goff et al., 2008; Kteily et al., 20152; also see Haslam and Loughnan, 2014).

The Racial Asymmetry of the Two Forms of Dehumanization We contend that the existing approach overlooks the complexity of how dehumanization is experienced by different racial groups, especially Asian people. Here, we argue that Asian and Black people experience dehumanization in different ways such that while Black people primarily experience animalistic dehumanization, Asian people face mechanical dehumanization more, a contrast that we refer to as a racial asymmetry of dehumanization (or “the asymmetry” hereafter).

Our contention is consistent with existing, yet compartmentalized, findings on perceptions of Asian and Black people. Although Asian people are believed to be industrial (Maykovich, 1972), competent, and efficient (Fiske, et al., 2002), they are also perceived as lacking creativity (Xie & Paik, 2019; Lu, 2024; Lee, unpublished manuscript), agency (in terms of assertiveness; Lu et al., 2022), social, and emotional skills (Mauss et al., 2010; Nam et al., 2018). The stereotypes that Asian people are efficient but lack interpersonal warmth and are capable of thinking but not feeling (Fiske et al., 2002; Waytz & Norton, 2014) resonate with the conceptualization of mechanical dehumanization

1 In a related vein, the act of objectifying human beings, treating them as instruments, overlooking their feelings, and depriving their individuality (such as the digital and pornographic representation of women) was theorized as a type of mechanical dehumanization (see Haslam, 2006).

2 Though in their Study 5, the researchers used some measures of blatant mechanical dehumanization.

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(Haslam, 2006; Bui, 2020). These stereotypes about Asian people are also incongruent with the construct of animalistic dehumanization, as the often-used analogy of apes in the dehumanization literature (Goff et al. 2008; Kteily et al., 2015) embodies the traits of being emotional and aggressive (Gosling, 2001). Sociologically speaking, conformity, harmony, and uniformity are prized in many Asian cultures (e.g., Schwartz, 2006), and Asian people in the labor market and the modern global economy are often employed in roles that require repetitive mechanical labor (e.g., Bui, 2020), potentially undergirding the mechanical dehumanization of Asian people in western society.

Consistent with these ideas, Bain and colleagues (2009) found that White Australians rate Chinese higher on traits associated with mechanical dehumanization (e.g., unemotional) than traits associated with animalistic dehumanization (e.g., rude), and implicitly associate robot-related words with Asian facial images more than White facial images. Similarly, Andrighetto and colleagues (2014) reveal that Italians rate Japanese higher on traits associated with mechanical dehumanization than Haitians and vice versa for traits associated with animalistic dehumanization.

In contrast, Black people are perceived as socially savvy (Czopp & Monteith, 2006), athletic (Devine & Elliot, 1995), and even virulent like animals (Goff et al., 2008), but unintelligent, incompetent (Fiske et al., 2002), incapable of emotional regulation (Wittenbrink et al., 1997) and self-control (Gray et al., 2007), and unable to obtain socioeconomic achievement (Koch et al., 2016).

Due to the historical association between dehumanization and slavery (e.g., Ota Benga exhibited at the Bronx Zoo in 1906; Smith, 2020), Black people were often compared to animals (most frequently, apes in particular), but not machines. Consistent with this observation, Italians perceive Haitians to have more animalistic traits than mechanical traits (Andrighetto, et al., 2014).

Therefore, due to various historical and social factors, Asian and Black people are stereotyped in contemporary society in very distinct ways, and as a result, the way they are

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stigmatized and dehumanized can also be qualitatively different. Our current studies specifically test the hypotheses about the asymmetry.

Theoretical Contribution Our proposed asymmetry contributes to the literature by not only documenting how Asian and Black people each experience stigmatization differently but also provides a framework that explains how the two racial groups experience various stigmatization in society. This contribution is particularly timely given that research on Asian people, a high-status group that has ostensibly achieved much economic success, remains limited and a general theory that can unite the scattered findings about stereotypes of Asian people is still underdeveloped. For example, recent work reveals that Asian people still struggle to obtain leadership positions, hitting “bamboo ceilings” in part because they are seen as less assertive and sociable (Lu et al., 2022; Lu, 2023), and severely underrepresented in creative domains such as performance art because they are seen as uncreative (Ramón et al., 2023). However, these attributes can be directly mapped onto the conceptualization of human nature, or what distinguishes humans from machines (Haslam, 2006). As such, the current theory of asymmetry aptly unifies these seemingly unrelated findings about perceptions of Asian people on different traits by identifying human nature—mechanical dehumanization as a general dimension of attributes along which Asian people are often stigmatized.

Dehumanization and the Current Theory’s Relations with Other Prevailing Theories One may ask whether the construct of dehumanization is needed in the first place and whether the findings can be fully explained by other existing theories. In terms of dehumanization, though a detailed discussion of the validity of dehumanization as a “real” construct is beyond the scope of the current paper (e.g., see Over, 2020 and Bloom 2022 for related discussion), recent work

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identifies that it is a distinct domain of social perceptions—indeed perceptions of people along the line of dehumanization has its own linguistic features (e.g., more likely to use impersonal pronouns, such as “it” and “other”; Markowitz & Slovic, 2020), psychological hallmarks (e.g., denial of human uniqueness and nature; Haslam, 2006 ), and social implications (e.g., a strong predictor of hostile intergroup behavior; Kteily & Bruneau, 2017) different from other domains of social perceptions (see Kteily & Landry, 2022).

Regarding existing theories, two of the most prevailing models related to perceptions of different racial groups are the stereotype content model, which focuses on perceptions in the dimensions of warmth and competence (Fiske et al., 2019), and the two axes of subordination, which focuses on perceptions in the dimensions of status and foreignness (Zou & Cheryan, 2017).

One may ask whether these other general dimensions of social perceptions are sufficient to explain how Asian and Black people are stigmatized. In short, the current asymmetry provides a qualitatively different lens of social perceptions that view marginalized social groups from very different angles.

Conceptually, the warmth and competence dimensions describe a perceivers’ general impressions of a target based on the target’s (perceived) intention and potential to pose a threat to the in-group, and the two axes of subordination focus on the positionality of various social targets based on their relations to other groups (relative status) and the in-group (cultural similarity to the ingroup, such as using the same language). The social target’s relations to one’s in-group and resource acquisition are foundational elements in both models. However, these frameworks cannot accommodate the findings of stereotypes about Asian people’s lack of agency, creativity, and social-emotional skills as discussed earlier.

The dehumanization view of the social targets takes a different perspective from these models and directly engages the stereotypes about the targets without any references to the

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positionality of the targets with each other or with one’s in-group. Conceptually, many stereotypes relevant to the two types of dehumanization, such as agency and creativity, do not map well onto these existing dimensions. Empirically, the earlier models’ dimensions are also not related to the two types of dehumanization “bijectively.” For example, data suggests that denial of humanity requires a combination of low warmth and competence, not just one or the other (Harris & Fiske, 2006), and warmth and competence do not map onto the two types of dehumanization in the “one-to-one” sense (Kuljian & Hohman, 2022)3.

In summary, though the stereotype content model and two axes of subordination are undoubtedly related to dehumanization perceptions, they take entirely different lenses viewing the social groups from different angles.

Current Studies Twelve studies test the asymmetry hypothesis and its implications in a collection of fifteen individual hypotheses summarized in Tables 1a (H1a-H2d) and 1b (all hypotheses). Studies 1a and 1b focus on the victim perspective by testing two hypotheses: Asian people experience more mechanical than animalistic dehumanization (H1a), and vice versa for Black people (H1b). Studies 2a through 2d test the asymmetry from the perceivers’ perspective in four hypotheses: People mechanically dehumanize Asian people more than animalistically dehumanize them (H2a), and vice

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Looking ahead, in our own data, we also found in additional supplemental analyses of Study 2d that perceptions of the foreignness of the racial groups predict both the animalistic and mechanical dehumanization of them (b=-.15, p<.001 for animalistic dehumanization and b=-.25, p<.001 for mechanical dehumanization), but the perceptions of the status do not (b=-.02, p=.519 for animalistic dehumanization and b=.04, p=.292 for mechanical dehumanization; see the online supplemental materials). These findings provide additional evidence that the dehumanization perspective of racial perceptions is distinct from the existing frameworks.

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versa for Black people (H2b). Furthermore, we also test the hypothesis that people animalistically dehumanize Black people more than Asian people (H2c), and vice versa for mechanical dehumanization (H2d). Table 1a summarizes these hypotheses.

It should be stressed that H2c and H2d are more peripheral to the current asymmetry theory because, theoretically, the asymmetry is about how different racial groups experience dehumanization differently, and H2c and H2d focus on which groups are stigmatized more on each type of dehumanization, questions that are not of core interest to the current research4. Nonetheless, given that Black people often experience some of the most severe forms of racism today in general and research on racism often focuses on racism toward Black people (e.g., Sears & Henry, 2003), these contrasts provide a very conservative benchmark to show that Asian people’s experience of stigmatization is qualitatively different from that of Black people. For this reason, Black people are also specifically selected to contrast Asian people in the current studies.

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For this reason, we did not pre-register or test parallel hypotheses regarding whether Asian participants report more experience of mechanical dehumanization than Black participants or vice versa for Black participants in Studies 1a and 1b. Additionally, we also considered that whereas all other comparisons, including H2c and H2d, occur within participants (i.e., comparing the same participants’ rating of different racial groups on different measures), these types of comparisons occur not only between different participants but also between groups (i.e., comparing Asian participants’ responses against Black participants’ responses). Therefore, these differences could introduce potential confounds. For example, Black participants may report experiencing a higher level of both types of dehumanization, which we observed in the data.

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As mechanical dehumanization is more applicable to Asian people and animalistic dehumanization more applicable to Black people, dehumanization of Asian and Black people on these two types may consequently contribute to the overall evaluation of them differently. As such, we test the implications of the asymmetry with the following hypotheses (not pre-registered): the effect of mechanical dehumanization on the overall evaluation of the minority groups is stronger for perceptions of Asian people than perceptions of Black people (H3a), and vice versa for the effect of animalistic dehumanization (H3b).

We also test the implication of this asymmetry in the real world and in social domains where racial biases often occur. Four studies investigate whether the asymmetry is reflected in how Asian and Black people are each associated with attributes along the dimension of human nature— mechanical dehumanization and human uniqueness—animalistic dehumanization in people’s minds.

Using a computational approach, Study 3 shows that these associations are reflected in the language used about Asian and Black people. Studies 4a-c demonstrate the asymmetry in perceptions of Asian and Black people’s a. traits that are commonly valued in the romantic relationship context (Study 4a), b. actions in various crimes (Study 4b), and c. mastery of various business skills in the workplace (Study 4c). Parallel to H2a and H2b, Studies 3-4 test H4a: Asian people are less likely to be associated with constructs reflecting human nature than constructs reflecting human uniqueness, and H4b: Conversely, Black people are less likely to be associated with human uniqueness constructs than human nature constructs. To demonstrate that Asian and Black people are indeed experiencing dehumanization differently, we also test H4c: the reversal in H4a and H4b across Asian and Black people is significant.

Finally, we extend our theory’s real-world implication by building upon Study 4c in additional studies. We demonstrate that the asymmetry is reflected in the racial segregation in the workforce using employment data from the U.S. Bureau of Labor Statistics (Study 5), and we show

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that employers’ biased selection of Asian and Black employees for different positions is a key contributor to this segregation (Study 6). In these studies, we test the hypotheses that, H5a: Asian people are more likely to be segregated into occupations that involve less human nature (reflecting more mechanic dehumanization), H5b: Black people are more likely to be segregated into occupations that reflect less human uniqueness (reflecting more animalistic dehumanization), H5c: the reversal in H5a and H5b across Asian and Black people is significant, and H6: Asian and Black people are each seen as less suitable for different positions in an asymmetry such that Asian people are perceived to be less suitable for jobs that require human nature attributes whereas Black people are perceived to be less suitable for jobs that require human uniqueness attributes.

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Open Science and Technicalities All measures, manipulations, exclusions, and methods of determining the final sample size in all studies are reported. No additional data collection was continued after analysis in any study. Table 2 describes the samples of Studies 1-2 and 5. The sample size and sources were determined by convenience and funding availability, and the overall aggregated sample size is large compared to most studies commonly found in psychological research. All studies received approval from the institutional review board. All descriptive statistics, correlation tables, and materials, including manipulation and question wordings, are in the online supplemental material. All analytic code, materials, appendices, and data for all studies (other than that of Study 2a due to IRB rules) can be found at https://osf.io/r6kde. Several studies were collected as part of larger collaborative surveys that include contents from various researchers unrelated to the current studies, as detailed in the preregistration forms. Pre-registrations are accessible via: https://osf.io/vtm2z for Study 1a, https://osf.io/w4yx7 for Studies 1b and 2b, https://osf.io/94h3b and https://osf.io/jsb3t for Study 2a, https://osf.io/958wa for Study 2c, https://osf.io/sczvb for Study 2d, https://osf.io/szgjw for Study 5 and https://osf.io/2mznk for Study 6. Hypotheses 1a, 1b, 2a, 2b, 2c, 2d, 5a, 5b, and 6 were pre-registered in at least one study, and the remaining hypotheses (Hypotheses 3a, 3b, 4a, 4b, 4c, and 5c) were not pre-registered.

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Studies 1a and 1b Studies 1a and 2b first demonstrate that Asian and Black people experience the two types of dehumanization differently from the perspectives of Asian and Black people.

Method In Study 1a, Asian and Black participants first learned about animalistic dehumanization and mechanical dehumanization by reading a short paragraph about them. They then responded to two questions that ensured their understanding of the constructs (only participants who responded correctly were included per pre-registration). They were then asked how much (1=Not at all; 7=Very often) they believe that discrimination and biases in terms of animalistic dehumanization and mechanical dehumanization are experienced in society by various targets that included “Blacks,” “Asians,” and “Yourself.” The averages of Black participants’ responses to “Blacks” and “Yourself” for the two types of dehumanization experience were used as the measures for Black people’s experience of the two types of dehumanization (αanimalistic=.73; αmechanical=.76). That of Asian participants (i.e., “Asian” and “Yourself”) was averaged similarly (αanimalistic=.86; αmechanical=.83).

In Study 1b, Black and Asian participants were asked: “Based on your personal experience in the United States, how often do you feel you are treated by people like a machine as opposed to a

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human? (1 = Not at all; 10 = very often)” and a parallel question where “machine” is replaced with “animal.” In Study 1a, participants who were not Asian or Black were allowed to enter the survey, though their responses were not our focus. In Study 1b, participants who self-identified as White at the beginning of the survey were branched off to Study 2b (see below) instead of responding to the above questions.

Results All variables were linearly transformed to run from 0 to 1, where 1 represents the highest level of the construct they represent before analyses.

Individual Studies T-tests in both studies revealed that Asian participants experienced more mechanical (Study1a: M=.49, SD=.27; Study 1b: M=.33, SD=.28) than animalistic dehumanization (Study 1a: M=.42, SD=.25; Study 1b: M=.21, SD=.22), mean difference = 0.07, 95% CI [0.03, 0.12], t(112) = 3.11, p = 0.002, d = 0.29 for Study 1a, and mean difference = 0.12, 95% CI [0.07, 0.17], t(71) = 4.68, p < .001, d = 0.55 for Study 1b. In contrast, only in Study 1a Black participants experienced more animalistic dehumanization (M=.73, SD=.27) than mechanical dehumanization (M=.47, SD=.30), mean difference = -0.26, 95% CI [-0.35, -0.16], t(32) = -5.61, p < .001, Cohen's d = -0.98. In Study 1b, Black participants reported experiencing exactly the same level of the two types of dehumanization (M=.49, SD=.33 for both), mean difference<0.01, 95% CI [-0.13, 0.13], t(20) = 0.00, p > .999, d = 0.00. This might be due to the small number of Black participants because of the COVID-19 disruption in class. Overall, these results support H1a and somewhat support H1b.

Combined Data To provide an overall view of the pattern, we analyzed the data combined from the two studies. To compare Asian people’s experience of the two types of dehumanization, we used a variable for dehumanization type (1=animalistic dehumanization, 0=mechanical dehumanization) to

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predict Asian participants’ responses, along with the indicator-coded variable for Study (1=Study 1a, 0=Study 1b) in a linear regression. The results are described in Table 3’s Model 1 and Figure 1’s left half. As expected, Asian participants experienced more mechanical than animalistic dehumanization, b=-.09, p<.001, supporting H1a. A parallel analysis was conducted for Black participants (see Table 2’s Model 2 and Figure 2’s right half). As expected, the pattern reversed for them: Black participants experienced more animalistic than mechanical dehumanization, b=.16, p<.001, supporting H1b. An additional regression, Model 3, adds the interaction between experience type and race (1=Black, 0=Asian), and the interaction was significant b=.25, p<.001, suggesting that the reversal was significant.

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Figure 1. Asian and Black people’s experiences of the two types of dehumanization using the aggregated data from Studies 1a and 2b with raw data points, standard errors, and distribution Perceptions from Other Racial Groups Are these differences in how Asian and Black people experienced dehumanization recognized by people who were not from these racial groups? Though this question is not core to the current research question, based on Study 1a data, responses to the same items for “Black” and “Asian” targets from non-Asian participants revealed that they believed Asian people experienced more mechanical (M=.56, SD=.29) than animalistic dehumanization (M=.39, SD=.26), t(503)=12.20, p<.001, Mdiff 95% CI=[-1.20, -0.87], d=-.54, and non-Black participants believed

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Black people experienced more animalistic (M=.69, SD=.27) than mechanical dehumanization (M=.48, SD=.31), t(584)=14.68, p<.001, Mdiff 95% CI=[0.18, 0.23], d=.61. Therefore, the two types of dehumanization experienced by Asian and Black people differentially were indeed recognized by others, providing indirect evidence that perceivers outside of the victim groups may dehumanize Asian and Black people differently in the hypothesized pattern.

Discussion Overall, these two studies and analyses based on the combined data support H1a and H1b, demonstrating that Black people and Asian people experience the two types of dehumanization differently from the perspective of the victims.

Studies 2a-2d Studies 2a through 2d test the asymmetry from the perceivers’ perspective using different dehumanization measures.

Method All four studies are within-subject experiments where participants repeatedly rated Asian and Black people on the two types of dehumanization. In other words, ratings of the target’s race (Asian vs Black) and rating type (animalistic vs mechanical dehumanization measure) were manipulated within participants in a 2 × 2 design5.

In Study 2a, participants rated how similar each racial group was to “artificial intelligence” and “calculator” (averaged as a mechanical dehumanization measure; α=.88 for Asian target, .89 for Black target) as well as “chimpanzee” and “ape” (averaged as an animalistic dehumanization measure; α=.96 for Asian target, .97 for Black target)6.

5 They also rated other social groups, such as White people, in some studies for exploratory purposes.

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Participants received two versions of wording, in one, the racial groups were referred to as “Asians,” “Blacks” and in the other version, they were “Asian Americans,” “Black Americans” and “White Americans.” The results are similar with both wordings, as the wording do not have any interaction with the effect of race: instead of t tests, four

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In Study 2b, White participants rated Black and Asian people on mechanical and animalistic dehumanization traits derived from Haslam (2006; mechanical traits: 1=cold to 10=interpersonally warm; 1=rigid to 10=cognitively open; 1=passive/fungible to 10=agentic/has a sense of individuality; 1=superficial to 10=has depth; 1=inert to 10=emotionally responsive; α=.91 for Asian target, .90 for Black target. Animalistic traits: 1=lack of culture to 10=civilized; 1=coarse to 10=refined; 1=amoral/lack of self-restraint to 10=morally sensible; 1=irrational/instinctual to 10=rational/logical; 1=childish to 10=mature; α=.92 for Asian target, .94 for Black target).

In Studies 2c and 2d7, participants rated the level of similarity that Black and Asian people (along with other targets, i.e., White people, Muslims, and immigrants) have with machines and animals as opposed to modern humans on pictographic scales from 0 to 100 where 100 is coded as most similar to primitive machines (see Figure 2a) or animals (see Kteily et al., 2005). The animalistic dehumanization pictograph was from Kteily and colleagues (2015). The mechanical dehumanization pictograph was developed by us8. In all studies, participants also rated their feelings toward the racial groups from 0=coldest/least favorable to 100=warmest/most favorable. This variable was reversecoded and used as a measure of participants’ prejudice toward the racial groups. The animalistic pictograph scale was developed with a strong theoretical foundation focusing on racism toward Black people. By contrasting the mechanical pictographic scale as a measure of blatant

regressions predict dehumanization rating using racial group, wording, and their interaction. The interaction term is not significant in all cases, p values ranging from .615 to .996. So the participants receiving the two alternative versions of the wordings were combined in all the analyses reported in the main text. We also included glass and plastic as filler items.

7 This study also tests additional pre-registered hypotheses focusing on how the two types of dehumanization are related to people’s romantic preferences for them. These results provide mixed support for the symmetry hypothesis but relegated to the online supplemental materials considering the paper’s length.

8 We also considered a criticism of this scale may be that some people see machines as more competent than humans in terms of, for example, rationality and logical reasoning. However, we deem that this particular concern unlikely to undermine the validity of the measure. This is because the way the scale is constructed suggests that a mechanically dehumanized target would be more similar to a rudimentary machine (i.e., a calculator), whereas a more humanized target would be more similar to a “competent” machine (similar to a humanoid robot).

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dehumanization against the animalistic pictographic scale, these studies highlight the overlooked complexity of different racial groups’ experiences in the existing theory and measures of racism.

Figure 2a. Pictographic Mechanical Dehumanization Scale

Results All variables were linearly transformed to run from 0 to 1 before analyses like Studies 1a and 1b. All correlation and descriptive statistics can be found in the online supplemental materials.

RACE AND DEHUMANIZATION Individual Studies To test H2a through H2d, corresponding t-tests were used to compare participants’ two types of dehumanization of Asian and Black people only using White participants’ responses, per pre-registrations. As summarized in Table 4, all four hypotheses were supported in all studies with one exception—the comparison of the two types of dehumanization of Black people in Study 2c.

Therefore, the results overall support the asymmetry.

Table 4. Testing the four comparisons of the asymmetry using Studies 2a-2d data by study.

Combined data Like in Studies 1a and 1b, the four studies’ data were combined in additional analyses to provide an overall view of the pattern. We conducted four linear regressions using the combined

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data and included participants of all racial backgrounds (given that the patterns generalize to nonWhite participants as well, as will be shown below). The results are summarized in Table 5 and Figure 2b.

Model 4 compares the animalistic and mechanical dehumanization of Asian people by using the dehumanization type (1=mechanical; 0=animalistic) and study indicators to predict the dehumanization ratings. Supporting H2a, participants dehumanized Asian people more mechanically than animalistically, b=.08, p<.001. Model 5 with the parallel specification for dehumanization of Black people supports H2b—participants dehumanized Black people more animalistically than mechanically, b=-.03, p<.001.

Model 6 compares the animalistic dehumanization of Black and Asian people by using the type of target of dehumanization (1=Black; 0=Asian) to predict participants’ dehumanization rating, along with indicators for the data of studies. This model supports H2c that Black people were dehumanized more animalistically than Asian people, b=.05, p<.001. Finally, Model 7 is parallel to it except that it is about mechanical dehumanization, and it supports H2d, i.e., Asian people were dehumanized more mechanically than Black people, b= -.07, p<.001.

An additional regression adding the interaction between type and race revealed that the interaction was significant b=-.11, p<.001, suggesting that the aforementioned reversal was significant. Graphically, it means that the difference between the two mean differences described in the right two bars and the left two bars of Figure 2 is significant. Mathematically, it is tantamount to the idea that the difference between the two mean differences described in the first and third bars and the second and fourth bars of Figure 2 is significant.

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Figure 2b. Comparing the two types of dehumanization of Asian and Black people using the aggregated data from Studies 2a-2d with raw data points, standard errors, and distribution

To test H3a and H3b about the differential effect of the two types of dehumanization on the overall evaluation of the two racial groups, four linear regression models were conducted in steps.

These results are summarized in Table 6. In the first step, Model 8 uses mechanical dehumanization,

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animalistic dehumanization, the racial group of the targets (1=Black, 0=Asian), and indicator-coded study numbers to predict people’s negative attitudes toward the racial groups (measured with reverse-coded feeling thermometers). Both dehumanization terms were significant, suggesting that they each have a unique contribution to people’s overall evaluation.

In the second step, Model 9, an additional term, Mechanical dehumanization × Target, was added, and it was significant, b=-.06, p=.012. This suggests that the effect of the mechanical dehumanization of Asian people on overall feelings toward Asian people was stronger than the effect of mechanical dehumanization of Black people on overall feelings toward people, supporting H3a. In the third regression, Model 10, the interaction Animalistic dehumanization × Target was added instead, and this term was significant (b=.08, p<.001), revealing that the opposite was true for animalistic dehumanization, supporting H3b. Finally, in the last model, Model 11, both interactions were included, and they both remain significant in the expected direction (b=-.14 and b=.16 respectively, ps<.001 for both), revealing the robustness of the effects.

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Auxiliary Findings about Minorities’ Dehumanization of Asian and Black People Though minorities’ dehumanized perceptions of Asian and Black people were not our primary goal, we also found that the pattern described in H2a-H3b mostly generalized to non-White participants.

When Model 4 was re-estimated using non-White participants from the aggregated data (Asian and Black participants included), participants still dehumanized Asian people more mechanically than animalistically, b=.12, p<.001. This difference was more pronounced among non-

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White participants: an additional interaction term for Target × White participant (1=White; 0=nonWhite) was added along with the main effect term for White participants, and the interaction term was significant (b=-.05, p=.006). However, in re-estimated Model 5, non-White participants dehumanized Black people similarly between the two types of dehumanization (b=.01, p=.610).

In models parallel to Models 6 and 7, minority participants still animalistically dehumanized Black people more than Asian people (b=.03, p=.020), but mechanically dehumanized Asian people more than Black people (b=-.08, p<.001). In fact, these two comparisons for White participants and non-White participants do not differ significantly. When an additional interaction term for Target × White participant (1=White; 0=non-White) was added along with the main effect term for White participants, the interaction terms were not significant in either case (b=.01, p=.268 and b=.01, p=.546 in each model respectively). Therefore, the patterns described in H2a, H2c, and H2d, though not H2b, were also found among minority participants’ perceptions of Asian and Black people.

Minorities’ Dehumanization of Asian and Black People and Evaluation of Them When Model 11 was re-estimated using non-White participants, the two interaction terms were very similar to that of Model 11, b=-.15, p=.002 for Mechanical dehumanization × Target, and b=.15, p=.004 for Animalistic dehumanization × Target. In fact, these two interaction terms for White participants and non-White participants did not differ significantly—in another model parallel to Model 11, three-way interaction terms for Mechanical dehumanization × Target × White participant (1=White; 0=non-White) and Animalistic dehumanization × Target × White participant were added (along with other corresponding lower-order two-way interaction terms), and neither term was significant, b=.03, p=.589 and b<.01, p=.949, respectively. Therefore, the extent to which White participants and minority participants use the two types of dehumanization to guide their evaluation of Asian and Black people did not significantly differ.

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Are the Dehumanization Measures Measuring Prejudice?

One may ask whether the dehumanization measures we developed in Studies 2a, 2c, and 2d are truly about dehumanization or prejudiced perceptions (as opposed to neutral or positive perceptions). They likely are for two reasons. First, these studies demonstrate robust associations between the dehumanization measures and the negative overall evaluation of Asian and Black people (i.e., the feeling thermometers9), so these measures are likely to capture negative perceptions.

Second, in additional analyses, we found that the dehumanization measures we developed were correlated with other variables that were either racial prejudice or variables often related to racial prejudice. In Study 2a, the dehumanization measures were correlated with preferences for social hierarchy (i.e., social dominance orientation; Pratto et al., 1994; rs range between .22 and .33 for both types of dehumanization of both Asian and Black people, ps<.001 for all), preferences for socially distancing from racial out-groups (Wolsko et al., 2006; rs range between .22 and .34, ps<.001 for all), and ideological conservatism (rs range between .15 and .23, ps<.001 for all). In Study 2c and 2d, they were correlated with ideological conservatism (Sidanius et al., 1996; rs range between .09 and .21, ps<.050 for all). In all cases, these variables correlated with the animalistic dehumanization of Black people more than the mechanical dehumanization of Black people. In contrast, these variables correlated with the mechanical dehumanization of Asian people more than the animalistic dehumanization of Asian people (again, all correlations and question wordings for these measures can be found in the online supplemental materials). These patterns further support the racial asymmetry of the two forms of dehumanization.

9 Though to be clear, the dehumanization measures are also not entirely substitutable to the prejudice measure.

Reviewing Models 8 through 11, the dehumanization measures explain prejudice at about 10% variances, suggesting the distinctiveness of dehumanization.

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Discussion The aggregated data from Studies 2a-2d support all four aspects of the asymmetry hypothesis from the perceiver's perspective. Additionally, it demonstrates that the two types of dehumanization differentially contribute to people’s overall impression of Asian and Black people, further demonstrating the idea that the two types of dehumanization are differentially applicable to evaluations of Asian and Black people.

Studies 3-5 The next set of studies investigated the asymmetry by identifying what types of constructs, including psychological characteristics, crime types, and occupations, are more likely to be related to or possessed by Asian and Black people—both in perceivers’ perceptions as well as in reality.

Specifically, these studies test whether Asian people are more likely to be associated with constructs that reflect mechanical dehumanization than animalistic dehumanization, and vice versa for Black people.

Study 3 demonstrates that Asian and Black people are each associated with words reflecting different types of dehumanization differently in the real world. Studies 4a-4c reveal that these associations indeed reflect people’s perceptions of their possession of attributes in various contexts.

Study 5 identifies that the asymmetry is reflected in the real-world labor market. These studies are similar in the way that they use various constructs’ scoring of human uniqueness (or animalistic dehumanization) and human nature (or mechanical dehumanization) to predict the extent to which these constructs are associated with Asian and Black people. Departing from Studies 1-2’s approach, Studies 3-5’s unit of analysis is the constructs (e.g., characteristics and occupations) as opposed to individual participants. An internal meta-analysis aggregating these studies’ results is reported after Study 5 with the aggregated results visualized in Figure 3.

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Figure 3. Regression predicting constructs’ association with Asian and Black people using the construct’s animalistic and mechanical dehumanization scoring aggregating results from Studies 3-5

Study 3 Using natural language processing, Study 3 demonstrates the asymmetry in our everyday use of language. To test the asymmetry’s prediction that Asian people experience more mechanical dehumanization, whereas Black people experience more animalistic dehumanization, this study demonstrates the following: Asian people are less likely to be associated with words about attributes reflective of human nature (or what distinguish humans from machines), and conversely, Black people are less likely to be associated with words reflective of human uniqueness (or what distinguish humans from animals) through word embeddings.

Method First, an extensive list of 1150 attributes about people’s psychological and behavioral characteristics was compiled by reviewing and referencing various sources, including dictionaries,

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personality tests, and literature on individual differences (e.g., Chandler, 2018). These attributes include positive, neutral, as well as negative attributes. The cosine similarity (a measure of words’ association with each other) between each of the attributes and the phrases "asian_people" and "black_people"10 was computed as a measure of the attributes’ association with each racial group.

The computation used ConceptNet Numberbatch (Speer et al., 2017) embeddings, a pre-trained set of word embeddings that encapsulates the relationships between words based on their cooccurrence in a large collection of text sources. These sources include databases like ConceptNet (crowd-sourced and expert-created resources), as well as embeddings from Word2Vec and GloVe, which were trained on extensive text data from a variety of resources (e.g., Wikipedia, Google News, online games, and movie subtitles). Therefore, any association between the attributes and the phrases “asian_people” and “black_people” would reflect the real-world associations between them.

The cosine similarity score ranges from -1 (a completely negative association) to 1 (a completely positive association) where 0 indicates no association.

The 1150 attributes were then coded by GPT-4, an algorithm capable of human-like information processing (Open AI, 2023; Bai et al., 2023), on the degree to which they reflect human uniqueness and human nature. Human uniqueness was measured with the question “How well do the following attributes describe animals?” This question was reverse-coded because all attributes were human attributes and the ones rated low on this question would be better at capturing what distinguishes humans from animals. Similarly, human nature was measured with “How well do the following attributes describe machines?” in reverse coding (rating from 0=0%; 100=100%; rescaled to range from 0 to 1.00 so that it is in a more similar scale to the cosine similarity scores). GPT-4 coded the attributes three times separately, and their average scores for the two questions were used 10 Alternative embeddings such as “asian” and “black” were considered but ultimately ruled out due to polysemy (i.e., black can refer to the color), and the choices of “asian_people” and “black_people” are the least confounded phrases we could think of for the intended purposes.

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as measures for the attributes’ reflection of human nature (reliability is α=.88) and human uniqueness (α=.94).

For some examples, “elaborate” is an attribute that scores high on human uniqueness (0.80) and moderately on human nature (0.27), so it is a high human uniqueness attribute that can distinguish humans from animals well but not distinguish humans from machines well. It is one of the words least associated with Black people, ranking at the 1076th word most associated with Black people out of the list of 1150. “Blissful” is an attribute that scores high on human nature (0.97) and moderately on human uniqueness (0.43), so it is a high human nature attribute that can distinguish humans from machines well but not distinguish humans from animals well. It is the word least associated with Asian people among all 1150 words.

Results The attributes’ human uniqueness and human nature indices were positively correlated, r=.33, p<.001, though not at a very high level. Two regressions predicted Asian and Black people’s associations with the attributes using the attributes’ humanness rating, the humanness rating type (1=human nature, 0=human uniqueness), and their interaction (Models 12 and 13 in Table 7).

Consistent with the expectation, the interaction is negative in the model for perceptions of Asian people (b=-.08, p<.001; supporting H4a), but positive in the model for Black people (though only marginally; b=.02, p=.056; somewhat supportive of H4b). In another regression (Model 14), a threeway interaction term between humanness rating, humanness rating type, and race was added, and the term’s significance (b=.10, p<.001; supporting H4c) reveals that there is a reversal of the abovementioned pattern across Asian and Black people.

Discussion Therefore, Asian people are less likely to be associated with “human nature” traits that distinguish humans from machines, and Black people are less likely to be associated with “human

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uniqueness” traits that distinguish humans from animals. These results provide evidence for the asymmetry hypothesis and reveal that this asymmetry is embedded in the language used in people’s everyday life.

Study 4a: Interpersonal Traits Extending Study 3, this study more directly tests whether Asian and Black people are each dehumanized differently in terms of whether they are seen as less likely to possess traits that reflect different degrees of human nature and human uniqueness. To demonstrate the practical implication that Asian and Black people may face different types of biases in the dating market through people’s expectations of them, the study tests traits that are commonly valued in romantic relationships. Like Study 3, Study 4a demonstrates that Asian people are seen as less likely to have human nature traits, and Black people are seen as less likely to have human uniqueness traits.

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Method We recruited 4111 participants from Prolific in 2023 (50% female; average age= 42.2; 35 White, 1 Asian, 2 Black, 2 Mixed, 1 racial background missing; 5 self-identified as bisexual, 3 homosexual, and remaining heterosexual) to rate a hypothetical Asian and a hypothetical Black individual whom they were to have a blind date with on 100 traits that are commonly valued in romantic relationships in two questions: “Now, picture yourself going on a blind date with an Asian individual. How much would you anticipate that this person possesses the following traits?” and “[…] a Black […]” 0=not at all; 100=a great deal. These 100 traits were then rated by GPT-3.5, an algorithm, on the degree to which they reflect human uniqueness and human nature to form two indices: “To what extent can animals have the following trait? Please rate from 1=not at all to 5=a great deal. Please respond with 1, 2, 3, 4, or 5 only.” and “[…] machines […].” The responses were reverse-coded as in Study 3. Unlike Study 3, GPT-3.5’s (Open AI, 2023) rating was retrieved as a probability distribution directly for each response through its API, which was then used to compute the mean response to each question similar to the “population mean” (μ) that the model gives to each question. Therefore, multiple ratings in this study were not needed like in Study 3. Though a number of the traits overlap with Study 3, many are unique to the romantic relationship context, such as “supportive of personal growth” and “honest communication about needs and boundaries”. The “affectionate in public” trait was what the Asian target was rated the lowest on (38.98) and it reflects the highest level of human nature (5) but a moderate level of human uniqueness (2.60). “Multilingual” was what the Black target was rated the lowest on (31.8), and it reflects the highest level of human uniqueness (5) but a low level of human nature (1.07).

11 Though the number of raters is small, it should be stressed that it is the mean ratings across all of the participants on the traits that were used as the input for analyses.

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Results The traits’ human uniqueness and human nature indices were positively correlated r=.22 p=.027. Two regressions predicted Asian and Black people’s possession of the traits using the traits’ humanness rating, the humanness rating type (1=human nature, 0=human uniqueness), and their interaction (Models 15 and 16 in Table 8). Consistent with the expectation, the interaction was negative in the model for perceptions of Asian people (b=-.27, p<.001, supporting H4a), but positive in the model for Black people (b=1.61, p=.011, supporting H4b). In another regression (Model 17), a three-way interaction term between humanness rating, humanness rating type, and race was added, and the term’s significance (b=4.33, p<.001, supporting H4c) revealed that there was a reversal of the above-mentioned pattern across Asian and Black people.

Discussion Therefore, Asian people were seen as less likely to possess “human nature” traits that distinguish humans from machines, and Black people were seen as less likely to possess “human uniqueness” traits that distinguish humans from animals. These results provide additional evidence for the asymmetry hypothesis in the context of romantic relationships, suggesting potential biases Asian and Black people may each face in the dating market among people who value different traits in their romantic relationships. These biases could ultimately diminish their opportunities in the dating market and perpetuate the existing racial inequality by limiting the prospect of interracial marriages.

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Study 4b: Crime Rate Perceptions This study tests whether Asian and Black people are seen as more likely to commit crimes that reflect the deficiency of humanity along the two dimensions of humanness. One’s actions in different crimes can be seen as reflecting deficiencies in human nature or human uniqueness (e.g., see Bastian et al., 2013). For example, crimes involving cold, calculated planning might be seen as more indicative of a lack of human nature (mechanical dehumanization) because they involve a lack of empathy and emotional connection. On the other hand, crimes driven by impulsive and aggressive behavior could be associated with animalistic dehumanization, as they display a lack of self-control and rational thinking. Understanding these associations can help shed light on how people perceive and judge others based on their actions in the criminal justice context.

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Method We recruited 51 participants to rate 86 crimes12 on their perception of what percentage of each crime was committed by Asian and Black people: “In your perception, what percentage of the following crime on average are committed by Asian/Black people?” 0=0%, 100=100%. These 86 crimes were then rated by GPT-4 on the degree to which they reflect human uniqueness and human nature as two indices five times separately in two questions: “Please evaluate the following crime considering the extent to which committing it reflects a diminished sense of humanity, with the perpetrator appearing more machine-like. Please rate from 0=not at all to 100=a great deal, and “[…] animal-like in nature […]”. Their average scores for the two questions were used as measures for the crimes’ reflection of mechanical dehumanization (α=.98) and animalistic dehumanization (α=.98).

Results The crimes’ mechanical dehumanization and animalistic dehumanization were negatively correlated, r=-.87, p<.001. Two general linear regressions13 predicted the perception of Asian and Black people’s crime activities using the crimes’ dehumanization rating, dehumanization rating type (1=mechanical dehumanization, 0=animalistic dehumanization), and their interaction (Models 18 and 19 in Table 9). Consistent with the expectation, the interaction was positive in the model for perceptions of Asian people (b=.004, p=.003, supporting H4a), but negative in the model for Black people (b=-.018, p<.001, supporting H4b). Another regression adding a three-way interaction like in

12 We also considered contrasting the perception of the crime rate against the actual crime rate for various crimes by race after we conducted this study. However, the most relevant data on the actual crime rate we could find was from the Federal Bureau of Investigation’s tabulation (see ucr.fbi.gov/crime-in-the-u.s/2019/crime-in-the-u.s.-2019/tables/table43), which does not break down the crimes as granularly as we did in the current research, rendering it difficult to compare.

13As Asian and Black people have a low base rate of population share, the dependent variables’ distributions were very skewed, and the log link function was used in the model (see the supplemental figure for this study in the online supplemental materials for the distributions).

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Study 3a testing the reversal of the pattern between the models for Asian and Black people was significant (b=-.023, p<.001, supporting H4c; Model 20).

Discussion Therefore, Asian people were seen as more likely to commit “mechanical dehumanization” crimes, and Black people were seen as more likely to commit “animalistic dehumanization”. These results provide additional evidence for the asymmetry hypothesis in the context of crime perceptions, suggesting potential biases that Asian and Black people may differentially experience in the criminal justice context. For instance, law enforcement and the judiciary may be biased, albeit unconsciously, towards expecting and identifying different types of crimes with Asian and Black people. This could, as a result, contribute to biases in prosecution and sentencing practices, and form a vicious cycle of stereotype confirmation and reinforcement.

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Study 4c: Business Skills Extending Studies 4a-b, this study tests whether Asian and Black people are seen as deficient in business skills along the two dimensions of humanness that are commonly valued in workplace settings.

Method We recruited 102 individuals who have both hiring experience as well as managerial experience to rate 50 professional skills that are commonly needed in business settings. Among the full sample, 49 participants rated the skills on how much they each reflect human uniqueness and human nature. Namely, after reading an explanation of what human nature and human uniqueness were (see the online supplemental materials), they responded to the question: “In your opinion, how much do you believe the following skills reflect human uniqueness, which distinguishes humans from animals?”, 0=Not at all reflect any human nature; 100=Reflect the highest level of human nature (what makes humans different from machines) and a similar question “[…] human nature, which distinguishes humans from machines?” […].

The remaining 53 participants rated their perceptions of average Asian and Black employees’ level of mastery of the skills in two questions: “In your opinion, how skilled do you believe Asian employees on average are at each of the following skills on a scale of 0 to 100? There are no right or wrong answers” and a similar question “[…] Black employees […]”, 0=Not skilled at all; 100=Extremely skilled. The average ratings of each skill were computed as indices for the skills’ human uniqueness and human nature, and indices for how Asian and Black employees are perceived in their mastery of the skills.

Results The skills’ human uniqueness and human nature indices were not correlated r=-.11 p=.430, so the skills’ reflection of humanness along one dimension was unrelated to that of the other. Two

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regressions predicted the perceptions of skill mastery of Asian and Black people using the skills’ humanness rating, the humanness rating type (1=human nature, 0=human uniqueness), and their interaction (Models 21 and 22 in Table 10). Consistent with the expectation, the interaction was negative in the model for perceptions of Asian employees (b=-.27, p=.046, supporting H4a), but positive for Black employees (b=.48, p<.001, supporting H4b). Another model that includes a threeway interaction like in Studies 3a-3b revealed a similar significance (b=.75, p<.001, supporting H4c; Model 23).

Discussion Therefore, whereas Asian employees were seen as less capable of business skills that reflect human nature (what sets humans apart from machines), Black employees were seen as less capable of skills that reflect human uniqueness (or what sets humans apart from animals). Together, these findings reveal that the asymmetry is reflected in disparate expectations of professional aptitude among Asian and Black employees, and these could, in turn, perpetuate racial disparities in career advancement and occupational segregation along the two dimensions of humanity.

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Study 4c suggests the possibility that the biased perceptions of Asian and Black people’s mastery of different skills could eventually contribute to occupational segregation. Study 5 directly investigates this possibility by demonstrating that in the real-world labor market, Asian and Black people are indeed racially segregated in occupations that vary along the two dehumanization dimensions.

Method We obtained the 2021 data from the U.S. Bureau of Labor Statistics on the percentage of Asian and Black individuals in various occupations. A research assistant who was unaware of the hypothesis rated how self-explanatory the occupation titles were, and based on this criterion alone, we selected 120 occupations for the current study. We retained occupations with self-explanatory titles so that coders could meaningfully code them. In total, about 55,288,000 individuals in the U.S., 3,433,000 of whom are Asian and 6,791,000 Black, have these 120 occupations. Twenty-two coders unaware of the hypothesis then independently rated each of the 120 occupations on how “mechanical” and “animalistic” the occupations were based on their perceived working experiences and the skillsets required for them (see the online supplemental materials for a sample of coders’ worksheet). These were averaged into two indices for how mechanically dehumanized (α=.87) and how animalistically dehumanized (α=.85) the occupations were from 0 to 100.

The three least mechanically dehumanized occupations were musicians, artists, and interior designers (Means<20), and the three most mechanically dehumanized were drywall installers, dishwashers, and printing press operators (Means>70). The three least animalistically dehumanized occupations were dentists, medical scientists, and surgeons (Means<12), and the three most animalistically dehumanized were janitors, freight laborers, and refuse collectors (Means>58). To verify that the ratings of dehumanization did not incidentally reflect something desirable about the

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occupations, we reviewed these ratings with coders’ ratings of the occupations on other domains. As expected, occupations of a greater level of animalistic and mechanical dehumanization negatively correlate with the rating of both the status (animalistic dehumanization: r=-.87, p<.001; mechanical dehumanization: r=-.44, p<.001) and desirability (animalistic dehumanization: r=-.86, p<.001; mechanical dehumanization: r=-.50, p<.001), suggesting that higher dehumanization ratings of the occupations indeed reflected negative attributes of the occupations as intended.

Results The occupations’ mechanical and animalistic dehumanization ratings were correlated positively but not to a very high degree (r=.53, p<.001). Figure 4 shows the percentage of Asian and Black workers in the occupations. The existence of racial segregation in the labor market is visually observable from this figure, as the occupations that Asian people tend to have generally do not overlap with that of Black people.

Two general linear regressions14 predicted the percentage of Asian and Black employees in the occupations using the occupations’ dehumanization rating, the humanness rating type (1=mechanical dehumanization, 0=animalistic dehumanization), and their interaction (Models 24 and 25). Supporting H5a and H5b, the interaction was positive in the model for the share of Asian people (b=.02, p<.001), but negative in the model for Black people (b=-.01, p=.010). Another regression testing the interaction of the reversal of the pattern between the models for Asian and Black people was significant, supporting H5c (b=-.02, p<.001; Model 26).

14 Like in Study 4b, Asian and Black people have a low base rate of population share, the distribution of the dependent variables were very skewed, and the log link function was used in the model (see the supplemental figure for this study in the online supplemental materials for the distributions).

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Figure 4. Scatterplot for the Association between the Percentages of Asian and Black Workers in the Occupations.

Note. The occupation data points’ size indicates the number of workers in the occupation. Their color and shade indicate how mechanically dehumanized (light yellow) relative to how animalistically dehumanized (dark green) the occupations were rated. Consistent with the regression results, this figure suggests that relatively mechanically dehumanized occupations (marked with greater intensity of light yellow) tend to have a greater share of Asian workers. The relatively animalistically dehumanized occupations (marked with greater intensity of dark green) tend to have more Black workers.

Discussion Therefore, this study shows that Asian and Black people were segregated into different occupations that vary along the two types of dehumanization in the real-world labor market, empirically confirming the practical implication of the racial asymmetry hypothesis identified in Study 4c. Generally, these results also further underscore the stark reality that the opportunities and experiences of Asian and Black people in the real world are each limited in different ways based on their racial background.

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Studies 3-5 Meta-Analysis We conducted an internal meta-analysis to provide an overall picture of the results from Studies 3-5.

Method The data from these studies were aggregated, and a variable for the various constructs’ association with a racial group was the outcome variable. The outcome variable was the words’ association with (S3), romantic traits’ rating for (S4a), perceived crime rate from (S4b), perceived skills of (S4c), and occupations’ percentage of (S5) Asian and Black people. The predictor was the extent to which the constructs reflect dehumanization. So it is the extent to which the attributes (S3), romantic traits (S4a), crimes (S4b), skills (S4c), and occupations (S5) reflect animalistic and mechanical dehumanization from the studies15. Both outcome and predictor were standardized within the study before aggregation so that they ranged on a comparable scale16. All models weigh

15 In Study 3 and 4c, it is from the reverse-coded human uniqueness and human nature score.

16 For reference, the predictors’ value ranges from 0 to 1 in Study 5, 1 to 5 in Study 4a, and 0 to 100 in other studies. The outcome ranges from -1 to 1 in Study 3, but 0 to 100 in other studies.

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the data from the five studies equally, given that a very unequal number of attributes were tested in different studies.

Results In two linear regression models (one for Asian people and one for Black people separately), the outcome variable was predicted by the predictor (the constructs’ reflection of dehumanization), the predictor’s dehumanization type (animalistic vs mechanical), their interaction, and indicators for the studies (Models 27 and 28 in Table 12). Like in the individual studies, the interaction terms were both significant: the constructs reflecting mechanical dehumanization were more related to Asian people than those related to animalistic dehumanization (b=.31, p<.001). Conversely, constructs reflecting animalistic dehumanization are more related to Black people than constructs reflecting mechanical dehumanization (b=.51, p<.001). An additional model (Model 29) combining the data from Asian and Black people reveals that the interactions were indeed significantly different across Asian and Black people (b=-.62, p<.001).

Figure 3 describes these overall patterns, as mentioned earlier. As it reveals, the more a concept is reflective of mechanical dehumanization, the more it is associated with Asian people and vice versa for animalistic dehumanization and Black people. Additionally, Figure 3 also suggests that Asian people are possibly less likely to experience animalistic dehumanization and Black people are less likely to experience mechanical dehumanization. Although detailed discussions of these patterns are beyond the scope of the current studies, these results testify to the claim that different racial groups’ dehumanization experiences can be qualitatively different from each other, and the existing focus on animalistic dehumanization can overlook Asian people’s experiences.

Together, this set of studies supports the asymmetry hypothesis using different methods across different domains of perceptions. Asian and Black people were differentially associated with attributes along the human nature (or mechanical dehumanization) and human uniqueness (or

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animalistic dehumanization) dimensions, supporting hypotheses from H4a through H4c. In short, the meta-analysis supports the idea that whereas Asian people are more likely to experience mechanical dehumanization, Black people are more likely to experience animalistic dehumanization.

Study 6 As Studies 4c and 5 show, Asian and Black workers were perceived to be deficient in skills varying along the two senses of humanity, and their occupations were segregated along these two lines. But is it really a result of employers’ biased decision-making? Complementing these findings, Study 6 experimentally demonstrates that Asian and Black people can face different types of biases

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in employment selection such that an Asian worker and a Black worker are less likely to be selected for a position that requires “human nature” traits and “human uniqueness” attributes respectively.

Method We recruited 484 White participants who have had hiring experiences in the past17. In a revised employment selection paradigm from Galinsky et al. (2013), participants reviewed a hypothetical candidate’s profile and decided which one of two positions, Position A and Position B, the candidate would be given (see the online supplemental materials). Position A is a high “human uniqueness” position, and its description says that it requires traits that are reflective of human uniqueness (e.g., civility and moral sensibility) and it needs to avoid traits that resemble animals (irrationality and childlikeness) based on Haslam (2006). In contrast, Position B is a high “human nature position.” Its description says that it requires human nature traits that differentiate humans from machines (e.g., emotional responsiveness and interpersonal warmth) and it needs to avoid traits that resemble machines (e.g., passivity and superficiality), also based on Haslam (2006)18. To be clear, like Galinsky et al. (2013), this paradigm investigates the “bias” Asian and Black people face along with the two forms of dehumanization, not “discrimination” in terms of being devalued.

Participants decided which position to give to the candidate and rated how suitable the candidate was for the two positions (from Not suitable at all=1 to Extremely suitable=7). The job candidate’s race (Black, White, or Asian) and gender (male or female) were manipulated in the profile with the applicant’s name, and the applicant’s race was checked in a box visible to participants. The “White” condition was included only for “baseline” reference like in Galinsky et al.

17 That is, participants who responded “Yes” to the question “Do you have any experience in making hiring decisions (i.e. have you been responsible for hiring job candidates)?” to a screener.

18 In fact, the description of the positions was directly modified from Figure 1 of the Haslam paper (2006). In the figure, “Human uniqueness” was replaced with “Top traits required for Position A,” and “Animalistic dehumanization” was replaced with “Top traits to be avoided for Position A,” and likewise for “Human Nature” and “Mechanical dehumanization” in Position B.

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(2013), as our focus is primarily on Asian and Black targets (see the pre-registration). Our expectation was that the pattern in the White condition would appear to be between that of the Asian and Black conditions. The applicant’s gender is a theoretical random factor in this study, as we did not have a priori theory regarding its role. The manipulations about the candidate’s demographic background were identical to those of Galinsky et al. (2013). To ensure that participants noticed the applicant’s race, they were asked to identify the applicant’s race after answering two other filler questions about the applicant but before they decided which position to give to the applicant.

Analyses using these questions are more exploratory, as we discussed in the pre-registration.

Results Perceived suitability. To compare whether Asian and Black candidates were perceived to be differentially suitable for the two positions (hence White targets were excluded from the analysis), participants’ perceived suitability of the candidates for the positions was predicted by the candidates’ race (Black vs Asian), the position type (Position A vs Position B), and their interaction in linear regression, controlling for the candidate’s sex. Table 13 summarizes the results and Figure 5 describes these patterns. The race × position interaction was significant, b=-.42, p=.034.

Selection decision. To test the hypothesis using the decision variable, we conducted a 2 (Race of Candidate: Asian vs Black) × 2 (Sex of Candidate: male vs female) × 2 (Position type: Position A vs Position B) log-linear analysis following Galinsky et al. (2013). There were 75.3% of participants nominated the Asian targets and 61.5% nominated the Black targets for Position A (for reference, 69.0% of participants nominated the White targets, a pattern consistent with the expectation) which required traits that differentiate humans from animals, and the differences between the selection

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rates for Asian and Black targets were significantly different (p=.025), consistent with the hypothesis.19 See Figure 6.

Discussion The results of both analyses supported H6: Asian and Black people were each seen as less suitable for different positions in an asymmetry such that Asian people were perceived to be less suitable for jobs that required human nature attributes whereas Black people were perceived to be less suitable for jobs that required human uniqueness attributes. In other words, participants’ perceived suitability for different positions was indeed determined by the candidates’ race and how the positions were described in a manner consistent with the current theory. Complementing Studies 4c and 5, these results demonstrated that Asian and Black people faced biases in employment selection along the two forms of dehumanization.

19

The differences between the selection rate for Black and White targets were also significantly different, p=.048, though the differences between the selection rate for Asian and White targets were not significantly different, p=.856.

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Figure 5. Perceived suitability by position and race in Study 5 with raw data points, standard errors, and distribution

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Figure 6. The proportion of participants’ choice for Position A (as opposed to Position B) by the racial background of the hypothetical applicant with standard errors

General Discussion The tendency to dehumanize racial minority groups has captured researchers’ attention in past studies, but the popular operational focus on animalistic dehumanization has belied the complexity of how different racial groups are dehumanized differently. This lacuna in the literature prompted assumptions that Asian people, as “model minorities” (Chou & Feagin, 2015), are relatively immune to dehumanization (as discussed in detail in the introduction). We proposed and found evidence for a racial asymmetry of dehumanization through fifteen hypotheses such that Asian people are primarily mechanically dehumanized and Black people are primarily animalistically dehumanized (again, see Table 1b for the hypotheses, which we shall not review more in detail for brevity).

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Our studies interrogate the proposed asymmetry from the perspective of victims (Studies 1a and 1b) as well as perceivers (Studies 2a-2d), demonstrating the asymmetry’s existence in the lab (Studies 1-2, 4a-4c, and 6) as well as in the real world (Studies 3 and 5). The studies feature a variety of sources of data—including online samples, large word embeddings, and national occupation data—and methodologies, including experiments, archival analysis, and computational methods20.

We tested the symmetry through perceivers’ general perceptions (Studies 2a-2d) as well as specific evaluations in consequential domains (Studies 4a-4c and 6) of Asian and Black people. The implications of the asymmetry span from the dating market and criminal justice system to the workplace and labor market. We acknowledge that each study has its own shortcomings in different ways, which are inevitably impractical to enumerate exhaustively across all twelve studies. However, throughout the studies, we tested the theory of asymmetry repeatedly using as many different methods as we feasibly could, and the limitation of any one study usually can be addressed by at least one other. For example, though the animalistic and mechanical dehumanization measures in Studies 2c and 2d primarily focus on comparisons with great apes and computerized machines, the corresponding measures in all other studies are reflective of the constructs’ relatedness to animals and machines generally. Overall, the evidence for the claim that Asian and Black people face dehumanization differently is preponderant and expansive, and the patterns we capture are likely robust.

Though our studies focus on White participants’ perceptions, we also found that most of the hypothesized patterns generalized to non-White participants. These results reveal that Asian and Black people’s different experiences of dehumanization are not something uniquely perpetuated

20 Note that the average correlation between mechanistic and animalistic dehumanization across Studies 2a-5 range widely from -.87 to .85 with an average of .34. This is likely due to variations on the wording of the questions, the nature of the measures, and the samples that were used in each study.

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from the dominant group to the minority group. Instead, it flows “in the air” as shared sociocultural knowledge and notorious practices (Adams, 2008; Steele, 1997). Our findings suggest that different racial minority groups are differentially selected and segregated along occupations that vary along the two senses of humanness. This racial division of labor may further confirm and reinforce racial dehumanization (Eagly & Wood, 2011), creating a vicious circle of racial segregation in the workforce.

Contributions The current studies introduce a theory of racial asymmetry that clarifies how different racial groups are stigmatized differently in society as well as the application of dehumanization theory on perceptions of scattered Asian people. In the prevailing approach to understanding racial minorities’ experience of dehumanization, researchers primarily focus on the animalistic sense of dehumanization (see above; also see Smith 2023 for a critique on the existing social psychological model of dehumanization in capturing the construct generally). This has led to findings suggesting that Asian people do not face very much blatant dehumanization (see the introduction). Despite the positive light of the “model minority” myth that Asian people are often cast under, our theory of asymmetry uncovers mechanical dehumanization as an overlooked domain of stigmatization that Asian people are vulnerable to hidden in plain sight. Due to the unique combination of cultural stereotypes they face, different racial groups, while humanized in one form, are dehumanized in another.

More generally, our theory of asymmetry contributes to the literature by providing a more general framework that can explain how Black people and Asian people experience stigma. Critically, though research on Asian people’s experiences has revealed many seemingly unrelated domains where they are perceived as inadequate, a general framework that can unite these findings remains

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missing. As discussed earlier, recent work reveals that Asian people often struggle to obtain leadership positions—hitting a “bamboo ceiling” because they are seen as lacking essential skills for leadership including assertiveness and interpersonal skills (Lu et al., 2022; Lu, 2023). In creative domains such as performance art, Asian people remain severely underrepresented (Ramón et al., 2023)—indeed, it was not until 2023, more than five years after we initially set out to study the asymmetry phenomenon, that the first Asian woman finally won an Academy Award (Lai, 2023).

The current findings unify these disparate findings about perceptions of Asian people by revealing human nature—mechanical dehumanization as a general dimension of attributes along which Asian people are often stigmatized.

The current research also contributes to the literature with its practical implications for interventions about prejudice reduction. To combat these widely-held prejudices, researchers who are interested in designing prejudice-reduction intervention programs should consider that different racial groups, in particular, Black and Asian people, are primarily facing different types of dehumanization. For instance, interventions that increase intergroup contact (Pettigrew, 1998) and construct a common ingroup identity (Gaertner & Dovidio, 2014) are useful in reducing prejudice against the out-group. Current studies suggest that interventions that increase the commonality in emotion and empathy during contact (Sesko & Biernat, 2010) may be especially useful for reducing prejudice against Asian people or other social groups that primarily face mechanical dehumanization.

In contrast, interventions that focus on the mechanism of increasing the commonality in cultural achievement and moral sensibility may be especially useful for reducing prejudice against Black people or other social groups that face animalistic dehumanization.

Our work also makes a key methodological contribution. As part of our effort in operationalization, we also developed a mechanical dehumanization scale (Studies 2c and 2d) that we

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validated by demonstrating its ability to capture meaningful variances and correlate with other outcome variables in the expected ways. This measure complements the Keitly et al. (2015) animalistic dehumanization scale—which asks participants where participants believe a social group is at the evolutionary stage—in the operationalization of the two forms of dehumanization for future researchers.

Limitations The current paper, by topic, focuses on the American context. As such, a key limitation of the current research is its exclusive focus on the United States in the contemporary era, and it remains unclear how well they generalize to other national, cultural, or historical contexts. The way a stigmatized group (whether it is a racial, religious, or any sociocultural group) is diminished in a culture likely reflects the unique sociological context, the features that the group might have, the type of threats they may be perceived to impose on the individuals that engage in the dehumanization, as well as the vocabulary and analogies available to them (e.g., Esses, 2021; Haslam, 2006; Stephan et al., 2016). For example, contrasting the patterns highlighted in the current research, Chinese mainlanders, a relatively low-status group, were compared to locusts by Hongkongers when Hong Kong faced an influx of mainland Chinese migrants and tourists (e.g., Sala, 2017), suggesting that even within a racial group, the way different groups are dehumanized could be different.

Therefore, within the United States, it may also be possible that the changing power relationship between various groups (e.g., see Craig & Richeson, 2013; Bai & Federico., 2021) and major historical events (e.g., Markowitz et al. 2021; Landry et al., 2022) can disrupt the patterns we observed in the current studies. Future research could specifically address these possibilities.

Nevertheless, despite these contextual limitations, the current paper’s contention that Asian and Black people (at least in the U.S.) often face different types of dehumanization remains applicable.

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Understanding exactly what types of sociological contexts create what types of dehumanization, and investigating how they do so from both the societal level and individual levels are some interesting future directions of research. For example, researchers may investigate how historical events, such as whether early Chinese migrant workers' role in railroad construction is related to mechanical dehumanization in context.

Future Directions One future direction is to further test the current theory's applicability to various groups within the Asian population. As recent research suggests, in many people’s minds, the prototype of Asian people is often East Asian people in particular (Goh et al., 2023; Lee & Ramakrishnan, 2020; though see Goh & McCue, 2021 that this pattern is more pronounced in the U.S. and attenuated in the U.K.), and the distinctions between South Asian and East Asian population has been largely overlooked until recent (e.g., Lu et al., 2020). The asymmetry may attenuate if South Asian targets are considered in studies given that they are perceived as not as prototypically Asian as East Asian people are perceived to be. Beyond South and East Asian populations, Southeast Asian populations may require additional attention from future research—observing from existing cultural references or slurs that may be used on them (e.g., “jungle Asian”; Tomas, 2020), it is very possible that Southeast Asian people suffer from both a high level of mechanical dehumanization as well as a high level of animalistic dehumanization. Future research should consider the diversity within the Asian population to gain a more complete picture of the racial prejudice Asian people experience.

Another future direction is to consider the dehumanization of racial minorities and its interface with gender or test the current theory through an intersectional lens (Lei et al., 2023). For example, women often experience sexual objectification in various ways (Fredrickson & Roberts, 1997). How are mechanical and animalistic dehumanization related to objectification? Though

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Haslam (2006) originally theorized women’s experiences of objectification to be more related to mechanical dehumanization than animalistic dehumanization, is it possible that Asian women are nonetheless objectified in ways that are more mechanical while Black women are objectified in ways that are more animalistic? Additionally, recent studies suggest that race itself is gendered such that Black people are seen as more masculine and Asian people more feminine (Galinsky et al., 2013; Hall et al., 2015). How might the “gender” of each race, and related to this, the gender of perceivers, play a role? Future research should consider further exploring these questions. For example, future research could consider addressing this question by replicating Study 4a with a larger sample size with sufficient women and men to explore how gender might interact with race in the way racial minorities are dehumanized.

Investigating the asymmetry in the implicit domain of attitude is another direction of future research. Past research suggests that fine-grained distinctions between valence and stereotypes on explicit attitudes do not always manifest in the implicit domain of attitudes (Kurdi et al., 2019). It could be worthwhile to investigate, for example, whether implicit mechanical dehumanization of Asian people and implicit animalistic dehumanization of Black people are more powerful predictors of attitudes toward each of the racial groups.

Finally, past studies revealed that minorities who perceive that they are dehumanized tend to be more supportive of violent collective action and less willing to assist counterterrorism efforts (Kteily, & Bruneau, 2017). In light of the current findings, it is possible that the two forms of dehumanization are differentially associated with these political tendencies for Black and Asian people, and future studies should consider empirically answering this question.

Conclusion

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In conclusion, we show that animalistic dehumanization and mechanical dehumanization are experienced by Black and Asian people differently in an asymmetry: whereas Black people are more often subjected to animalistic dehumanization, Asian people are predominantly subjected to mechanistic dehumanization.

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