Resource Effects on In-Group Boundary Formation With Regard to Sexual Identity

Abstract

Previous research has shown that resource scarcity decreases inclusiveness of racially ambiguous individuals when categorizing racial in-group members. Given that sexual identity can be visually ambiguous, the present studies sought to test this effect on in-group boundary formation for sexual identity in-groups. In Studies 1 and 2, participants were randomly assigned to view a slideshow representing resource scarcity or abundance (i.e., priming procedure). Next, participants categorized 24 photographs into sexual identity groups. As predicted, participants in the scarcity condition categorized fewer faces as in-group members compared to those in the abundance condition. In Study 3, a no-prime control group revealed that for straight participants, in-group overexclusion was due to a perceived resource scarcity, while for sexual minority participants, this effect was due to perceived resource abundance. Implications are discussed in terms of real-world applications of the findings as well as the methodology utilized in this study.

Keywords

resource scarcity resource abundance in-group boundary formation sexual identity

Recent work on in-group boundary formation (Rodeheffer, Hill, & Lord, 2012) has elucidated the effects of economic recession on the in-group overexclusion hypothesis (Leyens & Yzerbyt, 1992; Yzerbyt, Leyens, & Bellour, 1995), wherein the expansion of group membership is protected from potentially displeasing nonmembers, particularly by people with high identification of group membership (Castano, Yzerbyt, Bourguignon, & Seron, 2002). Namely, individuals with ambiguous social status are not likely to be granted access to a given group if there is a perception of a shortage in available resources. This is in accord with a sociofunctional threat-based approach to prejudice, in which the competition for resources is likely to encourage a predisposition toward in-group protection and shape the response to out-group members (Cottrell & Neuberg, 2005). Rodeheffer, Hill, and Lord (2012) have found that White college students who were primed toward resource scarcity (compared to abundance) were less likely to categorize racially ambiguous faces as White. It is important to note that they operationalized racial ambiguity by using biracial faces in their study created from face-averaging software. They proposed that there may be a relationship between these findings and the rise in unemployment rates in the African American population following the recent economic recession.

What is unknown, however, is whether other ambiguous social contexts outside of race (e.g., sexual identity) will also be influenced by resource scarcity. Information about peoples’ sexual identity is not always salient. Herek and Capitanio (1999) argue that sexual minority status was previously thought of as a concealable stigma. It is important to understand the processes utilized in person perception and how those processes influence decisions to include (vs. exclude) others in the formation of social categories so that we may better understand intergroup relations, especially as they apply to other majority and minority groups. It is clear that the in-group overexclusion effect is observed in members of the majority group (Leyens & Yzerbyt, 1992; Yzerbyt et al., 1995), but what is not clear is whether and how this process plays out in members of a minority group. In other words, is in-group overexclusion context- dependent? Or is it relatively context free, meaning any group can be influenced by the perception of resource availability?

A growing body of literature has investigated the accuracy of perception of gay and lesbian sexual identity through the use of gendered facial cues (Freeman, Johnson, Ambady, & Rule, 2010; Johnson & Ghavami, 2011; Rule, Ambady, Adams, & Macrae, 2008; Rule, Ambady, & Hallett, 2009). These studies have repeatedly found that people have correctly identified sexual identity with above-chance probability (Rule et al., 2008; Rule et al., 2009) and that this accuracy was driven by the utilization of facial features that were counterstereotypic to one’s gender category (i.e., gay men were more accurately identified when they possessed feminine facial features; Freeman et al., 2010; Johnson & Ghavami, 2011). This line of work has recently been expanded to include the categorization of bisexual individuals based on facial features (Ding & Rule, 2012). Although people were not able to accurately identify bisexual individuals, they were able to discern between straight versus bisexual and straight versus gay/lesbian targets through facial information. Importantly, gay/lesbian and bisexual individuals were indistinguishable from one another in these studies (Ding & Rule, 2012). This implies first and foremost that sexual minority status may be perceived by the lay public without direct disclosure of such status from the individual. It also implies that the nuances of sexual identity might be lost, at least from the perspective of straight perceivers, which is helpful to keep in mind when studying the perception of the sexual identity of targets.

Since faces have been used in the past to predict sexual identity (Ding & Rule, 2012; Freeman et al., 2010; Johnson & Ghavami, 2011; Rule et al., 2008; Rule et al., 2009), they were used in this study to test the role of resource scarcity on in-group boundary formation with regard to sexual identity. In Studies 1 and 2, participants were primed with either resource scarcity or resource abundance through a photo slideshow. It was predicted that priming resource scarcity would result in fewer faces being categorized as straight (in Study 1, a sample of straight participants) or as sexual minority (in Study 2, a sample of sexual minorities) compared to priming resource abundance (i.e., in-group overexclusion hypothesis). A secondary hypothesis was that there would be no categorization distinctions between the dichotomous and the trichotomous response options (see methods subsequently; cf. Ding & Rule, 2012). In Study 3, this effect was probed more closely with the addition of a control condition and the inclusion of lesbian, gay, and bisexual (LGB) and straight participants in attempts to identify the underlying causal mechanism.

Study 1

Method

Participants

Based on a power analysis (medium effect size: Cohen’s d = .25, power = .80, α = .05), 128 Americans were recruited from Amazon’s Mechanical Turk (MTurk; Buhrmester, Kwang, & Gosling, 2011) and completed the study online using Qualtrics software in exchange for US$0.40. Participants identifying themselves as anything other than heterosexual or straight were removed, leaving a sample of 119 (53% female), and power was not significantly affected by attrition. Participants were between the ages of 18 and 68 years old (M = 34.80, SD = 13.14) and the majority (73%) were Caucasian, followed by Asian American (11%) and African American (7%).

Design, Procedure, and Materials

In a 2 (resource: scarcity, abundance) × 2 (categorization: dichotomous, trichotomous) between-subject design, participants were randomly assigned to one of four conditions. Participants were told they were involved in two ostensibly unrelated studies on visual recognition (i.e., “Do we remember a news story better if we see it in pictures versus read it in words?”) and visual perception (i.e., “Can we categorize pictures more quickly than we categorize words?”). Participants were first shown a 1-min slideshow (i.e., supraliminal prime) depicting a “news story” (see Rodeheffer et al., 2012, Study 1; see also Hill, Rodeheffer, Griskevicius, Durante, & White, 2012, Study 3). Participants were asked to recall as many pictures as possible and to guess what the “news story” was about, to reduce possible suspicion of the priming procedure. Participants then viewed photos of 24 different faces (12 females and 12 males; randomized in presentation) and were asked to categorize each face as either lesbian/gay or straight (dichotomous condition) or as lesbian/gay or bisexual or straight (trichotomous condition). Finally, participants completed demographic information (age, gender, ethnicity, and sexual orientation). Upon completion of the study, participants were debriefed as to the true nature of the study and the use of deception and were given the opportunity to withdraw their data from analyses. They were thanked for their time and compensated. Please see Online Supporting Materials for details about the slideshows and the faces (all of which are available from the corresponding author).

Results

The number of faces participants categorized as straight (i.e., in-group) was the primary dependent variable. Gender, age, and ethnicity of participants were not predictive of the number of faces categorized as straight (see Online Supporting Materials for detailed statistics). A 2 (resource: scarcity, abundance) × 2 (categorization: dichotomous, trichotomous) between-subject analysis of variance (ANOVA) showed no Resource × Categorization interaction on the number of faces categorized as straight (p = .993). As predicted, participants in the scarcity condition categorized fewer faces as straight (M = 14.20, SD = 4.52) compared to participants in the abundance condition (M = 16.75, SD = 4.36), F(1, 109) = 10.75, p = .001, Cohen’s d = .63. There was no main effect of categorization condition on the number of faces categorized as straight (p = .066) in that participants given a dichotomous response option (i.e., lesbian/gay or straight) categorized just as many faces as straight as those given a trichotomous response option (i.e., lesbian/gay or bisexual or straight).

Discussion

Resource availability affected in-group inclusiveness with regard to sexual identity. Specifically, when primed with resource scarcity (compared to abundance), straight participants categorized fewer faces as straight (i.e., in-group overexclusion). This effect did not vary by gender or by the number of categories available for categorization, implying that the effect of resource scarcity on sexual orientation in-group boundary formation is not affected by gender or by the refinement of sexual orientation categories. One limitation of this study and of previous work is the use of a sample that holds higher social and political power (i.e., heterosexuals in this case; Whites in Rodeheffer et al., 2012). It may be the case that the in-group overexclusion hypothesis is a mechanism displayed exclusively by members of a powerful majority group to maintain their elevated social status by limiting the transmission of this status for ambiguously characterized individuals. Therefore, the goal of Study 2 was to test the generalizability of this effect in a nonstraight (i.e., LGB) sample.

Study 2

Method

Participants

Sexual minority (i.e., LGB) American adults (n = 128) were recruited from MTurk and completed the study online using Qualtrics software in exchange for US$0.40. Eleven participants did not finish the entire study and were removed from data analysis. Participants identifying themselves as straight (n = 15) were removed as were five participants who chose to withdraw their data, leaving a sample of 97 (53% female), and power was reduced by this attrition (.52). Participants were between the ages of 19 and 51 years (M = 29.10, SD = 7.85), and the majority (75%) were Caucasian, followed by Latino American (7%), African American (6%), and Asian American (6%). Participants self-identified as lesbian (18%), gay (31%), and bisexual (50%).

Design, Procedure, and Materials

The design, procedure, and materials were identical to that in Study 1.

Results

The number of faces participants categorized as sexual minority (i.e., lesbian, gay, and/or bisexual; in-group) was the primary dependent variable. Gender, age, and ethnicity of participants were not predictive of the number of faces categorized as sexual minority (see Online Supporting Materials for detailed statistics). A 2 (resource: scarcity, abundance) × 2 (categorization: dichotomous, trichotomous) between-subject ANOVA showed no Resource × Categorization interaction on the number of faces categorized as sexual minority (p = .594). As predicted, participants in the scarcity condition categorized fewer faces as sexual minority (M = 10.05, SD = 4.29) compared to participants in the abundance condition (M = 11.81, SD = 3.93), F(1, 92) = 4.11, p = .046, Cohen’s d = .41. There was no main effect of categorization condition on the number of faces categorized as sexual minority (p = .097).

Discussion

Once again, resource availability affected in-group inclusiveness. Specifically, when primed with resource scarcity (compared to abundance), sexual minority participants categorized fewer faces as sexual minority (i.e., in-group overexclusion). This effect did not vary by gender or by the number of categories available for categorization. One limitation to this study was the definition of in-group—it was broadly defined as sexual minorities which included lesbians, gay men, and bisexual women and men. However, when bisexual participants were categorizing faces in the dichotomous categorization conditions (lesbian/gay or straight), they were not able to use their specific in-group as an option (e.g., although gay participants could categorize male faces as potentially gay, bisexuals did not have that option). Furthermore, participants were not asked to think of their own identity before the categorization task. Since categorization condition did not affect in-group ratings for straight or sexual minority participants in Studies 1 and 2, it was eliminated as a factor in Study 3 (i.e., all participants rated faces with the trichotomous options: lesbian/gay or bisexual or straight).

Another possible limitation is the large number of bisexual participants (relative to the number of lesbians and gay men; although it is in line with population estimates of bisexuals; Gates, 2011). Although some of their relationships might appear heterosexual in nature (i.e., opposite-sex partner), bisexuals are sexual minorities when compared to the sexual majority (straights; Herek, 2002). Research also shows that bisexuals are viewed as a minority even within the sexual minority community (Herek, Norton, Allen, & Sims, 2010). This relates to the definition of in-group as noted earlier in that how one defines his or her in-group limits who will be included in that in-group. In the case of sexual identity, it is not quite clear whether (for example) a bisexual woman will view herself in terms of the broadest level (sexual minority), some intermediate level (bisexual), or a very narrow level (bisexual woman). This definition may dictate who is granted in-group status (all sexual minorities, all bisexuals, or all bisexual women, respectively), and if resources are directly influencing this boundary formation, it is important to know where to look for these boundaries in the first place.

A final limitation was the lack of a control group. Results from Studies 1 and 2 show a difference between the scarcity and abundance conditions without an anchor to show the “direction” of that effect. Prior research (Rodeheffer et al., 2012) suggests that changes in the perception of resource scarcity were driving this effect, so a no-prime control condition was added to Study 3. The goals of Study 3 were twofold, that is, (1) to explore the effects of resource scarcity on in-group boundary formation using a control group as a comparison to resource scarcity and abundance and (2) to explore the various definitions of in-group for lesbian, gay, bisexual, and straight participants. It was still predicted that priming resource scarcity would result in fewer faces being categorized as in-group compared to priming resource abundance (i.e., in-group overexclusion hypothesis); however, it was predicted that the mechanism would differ for straight and LGB participants. Resource loss is more salient than resource gain (Hobfall, 2001); however, since resource loss is less likely for those with resources (i.e., straight people are the privileged group in society), a loss of resources would be markedly perceptible. Since resource gain is less likely for those without resources (i.e., sexual minorities are the nonprivileged group in society), a gain of resources would be markedly perceptible (Hobfall, 2001).

Study 3

Method

Participants

American adults (N = 292) were recruited from MTurk (power analyses suggested recruiting 288 participants for a medium effect size of .25, power = .80, α = .05) and completed the study online using Qualtrics software in exchange for US$0.50. Participants identifying themselves as transgender (n = 4), not wanting to report their gender (n = 4), and pansexual (n = 2) were removed from analyses as were six participants who chose to withdraw their data, leaving a sample of 276 (54% female), and power was not significantly affected by attrition. Participants were between the ages of 18 and 69 years (M = 30.84, SD = 10.60), and the majority (88%) were Caucasian, followed by African American (8%), Asian American (7%), Latino American (6%), and bi- and multiracial (6%). Participants self-identified as lesbian (24%), gay (20%), bisexual (29%), and straight (27%). Quota sampling was used to ensure equal representation across all sexual identity groups.

Design, Procedure, and Materials

In a between-subject design, participants were randomly assigned to one of the following three resource conditions: scarcity, abundance, and control. The control condition did not see a slideshow; otherwise, the procedure and materials were identical to that in Studies 1 and 2. Since categorization condition did not affect in-group ratings for straight or sexual minority participants in Studies 1 and 2, all participants rated faces with the trichotomous options: lesbian/gay or bisexual or straight.

Results

To explore the various definitions of sexual identity (hence, in-group), results are presented in three different ways, namely, sexual majority/minority, heterosexual/bisexual/homosexual, and straight/lesbian/gay/bisexual.

Sexual Majority Versus Minority

To mirror Studies 1 and 2, sexual identity was dichotomized into sexual majority and minority for this set of analyses. The number of faces participants categorized as their in-group was the primary dependent variable. For straight participants, in-group was the number of faces rated as straight. For LGB participants, in-group was the number of faces rated as lesbian, gay, or bisexual. Gender, age, and ethnicity of participants were not predictive of the number of faces categorized as in-group members (see Online Supporting Materials for detailed statistics).

A 3 (resource: scarcity, abundance, control) × 2 (sexual identity: majority, minority) between-subject ANOVA showed a marginal Resource × Sexual identity interaction, F(2, 257) = 2.63, p = .074, Cohen’s d = .29 (see Figure 1). For all participants, scarcity elicited fewer faces categorized as in-group compared to abundance (although this simple effect was not significant); however, the control condition is what differed between sexual identity groups. Simple effects tests showed that for those in the control condition, straight participants categorized more faces as in-group (M = 14.88, SD = 4.26) than LGB participants categorized as in-group (M = 12.10, SD = 4.06), t(94) = 2.97, p = .004, Cohen’s d = .67. Consistent with predictions, for straight participants, it appears that changes in perceptions of resource scarcity (relative to the control) are driving the trend, t(45) = 1.75, p = .087, Cohen’s d = .50 (as was predicted by Rodeheffer et al., 2012), but for LGB participants, it appears that changes in perceptions of resource abundance (relative to the control) are driving the trend, t(129) = −1.87, p = .064, Cohen’s d = .33 (see Figure 1).

Figure 1.

Mean number of faces categorized as in-group (defined as sexual majority vs. minority) as a function of priming condition and sexual identity.

Heterosexuals Versus Bisexuals Versus Homosexuals

Sexual minorities might not think of themselves in terms of one collective group. Rather, sexual identity might be conceptualized in terms of a “same-sex” collective identity (i.e., homosexual, including lesbians and gay men), which would be different from a “both sex” identity (i.e., bisexual). To explore this possibility, the number of faces participants categorized as their in-group was the primary dependent variable but this time in terms of three sexual identity groups. For lesbians and gay men, the number of faces rated as either lesbian or gay was considered the in-group. For bisexual and straight participants, the number of faces rated as bisexual and straight (respectively) was considered the in-group. Ethnicity of participants was not predictive of the number of faces categorized as in-group members (p = .146). However, as age increased, the number of faces categorized as in-group also increased (r = .34, p < .001). Also, men categorized more faces as in-group (M = 9.59, SD = 5.14) compared to women (M = 8.25, SD = 4.15), t(259) = 2.33, p = .021, Cohen’s d = .29. Age was therefore included as a covariate (it did not, however, qualify any of the effects) while gender was included as a factor in the model.

A 3 (resource: scarcity, abundance, control) × 3 (sexual identity: heterosexual, bisexual, homosexual) × 2 (gender: male, female) between-subject ANOVA showed a significant Resource × Sexual identity × Gender interaction, F(4, 237) = 2.52, p = .042, Cohen’s d = .41 (see Figure 2). Simple effects tests showed that across all three resource conditions, there was a main effect of sexual identity; bisexual participants categorized the fewest number of faces as in-group, straight participants categorized the most number of faces as in-group, and LG participants were in between. Women followed this trend but men did not. For men in the abundance and control conditions, LG and bisexual participants categorized a similar number of faces as in-group, which was significantly fewer than straight participants. For men in the scarcity condition, there was no main effect of sexual identity on the number of faces categorized as in-group (see Online Supporting Materials for detailed statistics).

Figure 2.

Mean number of faces categorized as in-group (defined as heterosexual vs. bisexual vs. homosexual) as a function of priming condition, sexual identity, and gender.

Lesbian Versus Gay Versus Bisexual Versus Straight

Sexual minorities might think of themselves in four distinct groups. To explore this possibility, the number of faces participants categorized as their in-group was the primary dependent variable but this time in terms of four sexual identity groups. For lesbians, the number of female faces rated as lesbian was considered the in-group. For gay men, the number of male faces rated as gay was considered the in-group. For straight women and men, the number of female and male faces (respectively) rated as straight was considered the in-group. For bisexual women and men, the number of female and male faces (respectively) rated as bisexual was considered the in-group. Gender and ethnicity of participants were not predictive of the number of faces categorized as in-group members (see Online Supporting Materials for detailed statistics). Age, however, was positively related (r = .27, p < .001) and was therefore included in the model as a covariate (however, it did not qualify any of the effects).

A 3 (resource: scarcity, abundance, control) × 4 (sexual identity: L, G, B, S) between-subject ANOVA showed only a sexual identity main effect, F(3, 245) = 40.93, p < .001, Cohen’s d = 1.40. Bisexual participants categorized the fewest number of faces as in-group (M = 5.39, SD = 2.39), straight participants categorized the most (M = 13.70, SD = 4.92), and lesbian (M = 8.98, SD = 3.52) and gay participants (M = 7.81, SD = 4.85) were in the middle (significantly different from bisexuals and straights, ps < .05 but not significantly different from one another, p = .398).

Discussion

Resource availability affected in-group inclusiveness, but the picture was more nuanced with the inclusion of a control group and with variability in the definition of in-group for sexual minorities. When defining sexual identity in terms of majority/minority, the in-group overexclusion effect was present for all participants although only in trend. However, the locus of the effect in relation to the control group differed. As predicted, for straight participants, changes in perceptions of resource scarcity (relative to the control) were salient whereas for LGB participants, changes in perceptions of resource abundance (relative to the control) were salient. When making more nuanced distinctions of sexual identity, the effect gets more complex (bringing gender into the picture) and then disappears.

One limitation to Study 3 was the use of quota sampling, but it was difficult to ensure equal representation across the four distinct sexual identity groups in any other way. Although this was not portrayed as a study about sexual identity and group formation, recruitment based on sexual identity might have been salient enough to elicit a demand effect, given the short duration of the study (approximately 5 min total). On a positive note, it was possible to recruit large numbers of LGB participants on MTurk; however, it is not clear whether this sample is representative of the LGB population. It can be argued that LGB participants responding to an MTurk work request for LGB participants are visible, out members who identify with the larger community. On the flip side, it can also be argued that completing an online study in private might attract members who would not otherwise sign up for a study of this nature in public. This is a testable issue for future research on the use of Internet recruitment of LGB samples.

General Discussion

Distinctions can be more easily made about whom to include in an in-group when the individuating characteristics are visible and unambiguous. However, some characteristics may be more ambiguous than others (e.g., biracial faces) and the larger social context might also be influencing this process (see Rodeheffer et al., 2012). In Studies 1 and 2, resource availability affected in-group inclusiveness with regard to sexual identity. Specifically, when primed with resource scarcity (compared to resource abundance), straight and LGB participants categorized fewer faces as straight and LGB, respectively (i.e., in-group overexclusion). To date, this is the first test of the in-group overexclusion hypothesis (Leyens & Yzerbyt, 1992; Yzerbyt et al., 1995) with respect to sexual identity groups. It is also noteworthy that the effect of resource scarcity on in-group overexclusion was present for both sexual majorities and sexual minorities, suggesting generalizability to this effect.

To explore the possible mechanism underlying this effect, a no-prime control group was added in Study 3, and participants from across the sexual identity spectrum were sampled. In-group overexclusion was still present across the board when comparing resource scarcity to resource abundance (even if only in trends). When comparing sexual majorities to minorities, the control group highlighted the relative deprivation of resources for straight participants (usually present for a group with power), while it highlighted the relative provision of resources for LGB participants (usually absent for a group without power), which is consistent with some resource-based theories (Hobfall, 2001) and the current predictions. Although these effects were only marginal, they should not be discounted, as they fit within the larger theoretical context (Leyens & Yzerbyt, 1992; Rodeheffer et al., 2012; Yzerbyt et al., 1995).

Sexual identity is multifaceted and when you define identity more specifically (from two to three to four groups), the effect of resources on in-group formation gets more complex and then disappears. If in-group boundary formation is not affected by resources for unambiguous or homogeneous groups (e.g., lesbians) but it is for more ambiguous or heterogeneous groups (sexual minorities), this is an area worth exploring in future research. It was not surprising that straights categorized more faces as in-group compared to lesbians, gay men, and bisexuals, given that there are fewer LGB people in the population (Gates, 2011). What was surprising though is that LGB participants categorized more faces as in-group than population estimates suggest. Keeping in mind that the sexual identities of the faces in this study were not known, these estimates could very well be true. However, this base-rate neglect has been shown in previous studies of sexual identity research (Lyons, Lynch, Brewer, & Bruno, 2013), and Plöderl (2014) warned of applying estimates found in the lab to the real world. When participants are given multiple categories on which to make judgments of multiple stimuli, it is reasonable to assume that they will try to find stimuli for the various categories thereby overestimating LGB faces.

This still does not however explain away the resources’ effects. What is it specifically about priming resource scarcity that causes in-group overexclusion in terms of sexual identity groups? Rodeheffer and colleagues (2012) did not offer a causal mechanism, but one possible explanation is that priming resource scarcity narrows the mind-set, whereas priming resource abundance broadens it. Loersch and Payne (2011) suggested that priming inductions do not have a direct effect on behaviors and judgments but rather alter the accessibility of primed content. When primes are not salient, they are more likely to be misattributed. In this study, the primes were supraliminal (arguably, salient) but were auspiciously unrelated to the judgment task where the afforded question dealt with the sexual identity of given faces. Loersch and Payne (2011) argue that there is flexibility in the use of that primed information (i.e., scarcity and abundance were markedly different with respect to the no-prime control), which is consistent with this study. If resource scarcity produced this general change in that it narrowed the mind-set, the participant would overexclude members from their in-group. This should be tested in future research on other groups: in the case of race, testing it in non-White samples or in arbitrary groups created in the lab.

Future research should also examine resource effects on the in-group overexclusion hypothesis using signal detection analyses (Johnson & Ghavami, 2011; Stanislaw & Todorov, 1999). By using stimuli with known sexual identities (i.e., faces of both sexual majorities and minorities), one could calculate direct measures of sensitivity (d′; Johnson & Ghavami, 2011) for both sexual majorities and sexual minorities, which could identify the source of accuracy in perceptions (is accuracy higher for sexual majorities?). One could also calculate the threshold (β) for rendering a certain categorization. Specifically, does resource abundance change the criterion (c) making it more likely to categorize a face as one’s own sexual identity?

These findings have implications for intergroup relations. There is a plethora of research on attitudes and behaviors toward out-group members (e.g., Cottrell & Neuberg, 2005) and on straights’ attitudes toward nonstraights (e.g., Herek, 2002; Herek & Capitanio, 1999). The current research takes a step back by highlighting who is included in an in-group. The priming of resource scarcity limits these in-groups and if related to a narrower mind-set might also affect reactions to those excluded out-group members. These methods, too, have implications for the generalizability of the findings. Scarcity of resources was primed in a very general sense with a series of visual images presented as a “news story.” The cover story (i.e., recall for visual images as opposed to written information) is consistent with how we get information about resource availability and economics (e.g., a few photos accompanied with headlines in print, television, and online sources). Nevertheless, this prime produced a strong in-group overexclusion effect. Future research should explore other primes (both supraliminal and subliminal) to replicate this effect.

Footnotes

Acknowledgments

The authors wish to thank Thierry Devos and Melody Sadler for feedback on an earlier version of this article. Thanks also to Rob Holland and our anonymous reviewers.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by the University Grants Program of San Diego State University (grant #G00009853) awarded to the first author.

Supplemental Material

The online supplemental material is available at .

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