When Memory is Better for Out-group Faces: On Negative Emotions and Gender Roles

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1 J Nonverbal Behav (2011) 35:51 61 DOI /s ORIGINAL PAPER When Memory is Better for Out-group Faces: On Negative Emotions and Gender Roles Eva G. Krumhuber Antony S. R. Manstead Published online: 10 October 2010 Ó Springer Science+Business Media, LLC 2010 Abstract Memory for in-group faces tends to be better than memory for out-group faces. Ackerman et al. (Psychological Science 17: , 2006) found that this effect reverses when male faces display anger, supposedly due to their functional value in signaling intergroup threat. We explored the generalizability of this reverse effect. White participants viewed Black and White male or female faces displaying angry, fearful, or neutral expressions. Recognition accuracy for White male faces was better than for Black male faces when faces were neutral, but this reversed when the faces displayed anger or fear. For female targets, Black faces were generally better recognized than White faces, and female faces were better remembered when they displayed anger rather than fear, whereas male faces were better remembered when they displayed fear rather than anger. These findings are difficult to reconcile with a functional account and suggest (a) that the processing of male out-group faces is influenced by negative emotional expressions in general; and (b) that gender role expectations lead to differential remembering of male and female faces as a function of emotional expression. Keywords Out-group Threat Black faces Gender stereotype Recognition memory Out-group homogeneity (OH) refers to the tendency to see members of an out-group as more similar to each other than members of the in-group (Linville et al. 1989; Tajfel 1982). One manifestation of the OH effect is found in memory for faces. People tend to have poorer memory for out-group faces than for in-group faces (see Anthony et al. 1992; Bothwell et al. 1989). In previous research, degree of interracial contact and racial attitudes have been proposed as explanatory factors for OH in memory for faces, but these E. G. Krumhuber (&) Jacobs University Bremen, Research IV, Campus Ring 1, Bremen, Germany e.krumhuber@jacobs-university.de A. S. R. Manstead School of Psychology, Cardiff University, Cardiff, UK

2 52 J Nonverbal Behav (2011) 35:51 61 explanations have not attracted much empirical support (Meissner and Brigham 2001; Slone et al. 2000). Another approach focused on differences in the perceptual processing of in-group and out-group faces. For example, it has been suggested that race-specifying information is encoded at the expense of the individuating information that is needed for successful performance on recognition tasks (Levin 2000). Ackerman et al. (2006) recently proposed a functional account of OH in memory for faces, arguing that perceivers usually remember in-group faces better than out-group faces because it is functional to attend to and remember in-group information. However, if the faces in question display anger this OH effect reverses because it is functional to pay more attention to out-group faces that have potential threat value. Consistent with this argument, Ackerman et al. found that memory for Black male faces was better than for White male faces when faces displayed anger. This was especially the case when processing resources were limited by the use of brief stimulus presentations. The purpose of the present research was to examine the generalizability of this reverse OH effect by including fear faces as well as anger faces. Both anger and fear can be regarded as threat-related emotions (Becker 2009; Mogg and Bradley 1999; Öhman and Mineka 2001), signaling to perceivers that a threat might be present. However, there are reasons for thinking that it might be functional to attend closely to in-group faces displaying fear, and to out-group faces displaying anger. In evolutionary terms, in-group fear faces presumably served to signal possible threat of attack by predators or rivals, whereas out-group anger faces presumably accompanied such attacks. According to a functional account, therefore, perceivers should be inclined to better remember in-group fear faces and out-group anger faces. An alternative possibility is that out-group fear and anger faces prime notions of negative intergroup relations that are characterized by hostility and fear. Specifically, the cultural stereotype of Black people as being aggressive, hostile, or criminal (Devine 1989) may predispose evaluations of Blacks to be negative (Dixon and Linz 2000; Dovidio and Gaertner 2000). This negative evaluative context may render intergroup antagonism salient, thereby facilitating the processing of negative expressions (see Hugenberg 2005). As a consequence, both anger and fear may lead to better memory when displayed by out-group members rather than in-group members. In previous research, gender has been shown to be a social cue that influences the interpretation of emotions (e.g., Hess et al. 2000; Hugenberg and Sczeny 2006; Kirouac and Hess 1999) and moderates the influence of race (e.g., Berdahl and Moore 2006). Interestingly, in most research on the OH effect target gender has not been tested or included in research designs (e.g., Anthony et al. 1992; Bothwell et al. 1989; Corneille et al. 2007; Hugenberg 2005; Meissner and Brigham 2001; Tanaka et al. 2004). Ackerman et al. (2006) stimulus material consisted only of male faces, raising the possibility that their findings might be specific to such faces. Given that the association of Blacks with hostility and threat may be particularly pronounced for male faces (Hugenberg and Bodenhausen 2003; Navarrete et al. 2009), a reverse OH effect might not be found if the target faces were female. Moreover, there are grounds for thinking that the gender of the person displaying the emotion might in itself make a difference. Research on gender stereotypes has shown that anger is considered to be more typical for men, whereas fear is both observed and believed to be more common in women (Brody and Hall 1993; Fischer 1993; Hess et al. 2004). A possible consequence is that angry female faces and fearful male faces are more unexpected and more informative because they constitute counter-stereotypic behavior (Hess et al. 2000). This, in turn, might attract greater attention and thereby lead to better memory performance.

3 J Nonverbal Behav (2011) 35: In the present research we tested recognition memory for angry, fearful, and neutral faces displayed by both male and female Black and White targets. For the reasons outlined above, we expected target gender to moderate the role of facial expression in memory for in-group and out-group faces. Specifically, we expected the recognition advantage of angry out-group faces demonstrated in previous research to be restricted to male faces. This should be reflected in a three-way interaction between target race, expression and target gender. Furthermore, we investigated whether the reverse OH effect generalizes across angry and fearful faces. According to the functional account, recognition memory for Black faces should be enhanced for anger (signaling threat), but not for fear expressions (accompanying threat). In contrast, the intergroup antagonism hypothesis predicts that recognition of Black faces should be better than that for White faces when either anger or fear is displayed. Our study is based on the paradigm used by Ackerman et al. and should show which of these theoretically plausible outcomes is actually observed. Method Participants and Design One hundred and four white university students (60 women, 44 men), aged years (M = 20.92) at Cardiff University, UK, participated on a voluntary basis. Target race (Black vs. White) and expression valence (neutral vs. negative) were within-subjects factors, and target emotion (anger or fear) and target gender (male or female) were between-subjects factors. Twenty-six participants were allocated to each of the four groups (n = 26) formed by the between-subjects factors of target gender and target emotion. Stimuli and Procedure Ninety-six front-oriented face stimuli were selected from three databases (Beaupré and Hess 2005; Minear and Park 2004; Tottenham et al. 2009). All face stimuli were edited in Paint Shop Pro 5 to be gray-scale and of similar size (approx. 4.5-in. 9 5-in.). To ensure that effects of target emotion on recognition memory were not due to facial identity, the same male and female faces were used across groups to express neutral vs. anger and neutral vs. fear. Angry and fearful expressions were of similar intensity (M = 4.28 to M = 4.67, 7-point scale, all ps [.05) and were equally recognized as such on Black and White faces (M = 90.8% to M = 93.3%, all ps [.05), as indicated by an independent sample of 10 participants (5 men, 5 women, M = years, SD = 3.83). 1 MediaLab (Empirisoft) software was used for stimulus presentation. Participants were told that they would see brief images of faces and that they should pay close attention while viewing. In each of the four groups, participants first viewed 12 target faces (6 Black and 6 White faces, half of which were neutral and the other half negative) presented in random order. Each face was presented for 1,000 ms. Next, a 6.45-min distractor film clip 1 All stimuli showed the face and hair in order to reflect naturally occurring faces. In order to control for possible confounds of hair style, we tested whether target stimuli and foil stimuli of each emotion and race significantly differed from each other. Results with the independent sample of 10 participants showed no significant differences in expression recognition (McNemar, all ps [.05) or intensity (t-test, all ps [.05) between target stimuli and foil stimuli, suggesting that extraneous factors were not confounded with expressions.

4 54 J Nonverbal Behav (2011) 35:51 61 about deserts was shown. 2 Then participants were shown the same 12 target faces intermixed with 12 new foil faces (6 Black and 6 White faces, half of which were neutral and the other half negative). The number of neutral and negative faces was the same across target and foil stimuli, equating base rates of previously seen and unseen faces. Order of presentation was random. For each of the 24 faces, participants indicated whether they had seen the face earlier ( Did you see this face earlier in this study? ) on a 6-point scale anchored by definitely did not see (1) and definitely did see (6). Data Analysis Following Ackerman et al. (2006), these ratings were transformed into binary judgments by combining ratings between 1 and 3 into a No judgment and ratings between 4 and 6 into a Yes judgment. Recognition was assessed by using signal detection measures of memory sensitivity (A 0 ) and response bias (B 00 D) (see Donaldson 1992; Stanislaw and Todorov 1999). Sensitivity reflects the accuracy with which participants were able to distinguish between old (previously presented) faces and new ones. This measure varies from 0 to 1, with higher values corresponding to better performance (0.5 being chance). Response bias reflects the general tendency to respond did see versus did not see to a stimulus. It ranges from?1 (conservative, i.e., there was a bias to say did not see to stimuli) to -1 (liberal, i.e., there was a bias to say did see to stimuli). 3 Results Sensitivity Preliminary analyses showed that sex of participant produced no significant main effect, F(1, 96) = 0.27, p =.60, g p 2 =.00, or interactions, all Fs \ 2, ps [.05. Results were therefore collapsed across male and female participants. A mixed-model ANOVA with the within-subjects factors target race (Black vs. White) and expression valence (neutral vs. negative) and the between-subjects factors target emotion (anger or fear) and target gender (male or female) was used to investigate differences in face recognition scores. As predicted, there was a significant three-way interaction between target race, expression valence and target gender, F(1, 100) = 8.55, p \.01, g p 2 =.08, suggesting that the OH effect varied as a function of target gender. 4 2 Distractor tasks are commonly used in face recognition research. For the present experiment we would not expect substantial differences in recognition memory between immediate and delayed recall because both require long-term memory. Moreover, there is evidence documenting own-race bias in memory experiments with relatively short (e.g., 2 min) retention intervals (O Toole et al. 1994) and with retention intervals extending to days (Slone et al. 2000). 3 Our scoring system is consistent with that of Donaldson (1992), in which positive numbers represent a conservative bias, and negative numbers represent a liberal bias. For reasons of readability, Ackerman and colleagues reversed this scoring procedure. Interpretation of the response bias remains the same. 4 The interaction of target race by expression valence was also significant when analyzing mean scores on the 6-point scale (1-6), F(1, 100) = 8.27, p \.01, and when using a binary approach with only 1 and 2 ( No ) and 5 and 6 ( Yes ) as response items, F(1, 84) = 9.03, p \.01. For both kinds of analyses, there was a tendency to recognize White neutral faces (M = 4.37, A 0 =.835) better than Black neutral faces (M = 4.27, A 0 =.822), and Black negative faces (M = 4.96, A 0 =.858) better than White negative faces (M = 4.57, A 0 =.755).

5 J Nonverbal Behav (2011) 35: Notably, the interaction between target race, expression valence and target emotion was not significant, F(1, 100) = 3.06, p =.08, g p 2 =.03; and nor was the interaction qualified by a four-way interaction of target race, expression valence, target emotion, and target gender, as would be predicted by the functional account, F(1, 100) = 2.38, p =.13, g p 2 =.02. Nonetheless, in order to rule out the functional hypothesis, both the neutral versus angry and the neutral versus fearful conditions should separately show the neutral over negative advantage for White faces and a reversal of this pattern for Black faces. To provide a stringent test of the functional hypothesis and the intergroup antagonism hypothesis, we therefore analyzed the neutral versus angry and neutral versus fearful conditions separately. As such, four separate 2 (target race: Black vs. White) 9 2 (expression valence: neutral vs. negative) ANOVAs were conducted, two at each level of target emotion and target gender. The relevant means and standard errors are reported in Table 1. Table 1 Recognition memory for target faces as a function of target gender, target race and target expression Target face Hit rate False alarm rate A 0 B 00 D Male White Neutral.668 (.05).230 (.04).790 (.03).188 (.11) Angry.636 (.05).461 (.06).604 (.05) (.14) Black Neutral.640 (.05).459 (.06).628 (.05) (.14) Angry.775 (.03).332 (.04).806 (.02) (.11) White Neutral.670 (.06).154 (.03).826 (.03).266 (.11) Fearful.702 (.04).365 (.06).712 (.05) (.13) Black Neutral.728 (.04).308 (.05).779 (.04).023 (.13) Fearful.789 (.03).333 (.04).807 (.03) (.12) Female White Neutral.688 (.05).237 (.04).789 (.04).187 (.13) Angry.793 (.04).360 (.05).777 (.04) (.12) Black Neutral.682 (.05).247 (.04).797 (.03).061 (.14) Angry.829 (.04).318 (.04).828 (.03) (.12) White Neutral.580 (.05).317 (.06).671 (.05).115 (.14) Fearful.664 (.05).452 (.05).652 (.04) (.13) Black Neutral.609 (.05).218 (.04).763 (.04).279 (.12) Fearful.814 (.04).365 (.05).791 (.03) (.10) A 0 recognition sensitivity, B 00 D response bias Standard errors are in parentheses

6 56 J Nonverbal Behav (2011) 35:51 61 Male Targets When the target emotion was anger, there was a significant interaction between target race and expression valence, F(1, 24) = 20.19, p \.001, g p 2 =.46. Neutral White faces were better recognized than neutral Black faces, F(1, 24) = 11.23, p \.01, g p 2 =.32. This reflects the fact that the false alarm rate was higher for neutral Black faces than for neutral White faces, F(1, 24) = 13.13, p \.01, g p 2 =.35, showing that participants had difficulty distinguishing between old and new neutral Black faces. By contrast, angry Black faces were better recognized than angry White faces, F(1, 24) = 14.07, p \.01, g p 2 =.37. There was a tendency for there to be fewer false alarms in response to emotional Black faces than to emotional White faces, F(1, 24) = 3.74, p =.06, g p 2 =.13. Overall, this pattern of results is consistent with the one reported by Ackerman et al. (2006). With fear as the target emotion, the analyses indicated a very similar pattern of results. The interaction between target race and expression valence was significant, F(1, 24) = 4.89, p \.05, g p 2 =.17. There was a tendency, albeit non-significant, for neutral White faces to be better recognized than neutral Black faces, F(1, 24) = 1.02, p =.32, g p 2 =.04. In particular, the false alarm rate was higher for neutral Black faces than for neutral White faces, F(1, 24) = 4.97, p \.05, g p 2 =.17, indicating that participants had difficulty distinguishing between old and new neutral Black faces. In contrast, fearful Black faces were better recognized than fearful White faces, F(1, 24) = 4.66, p \.05, g p 2 =.16. There was a non-significant tendency for there to be fewer false alarms in response to fearful Black faces than to fearful White faces, F(1, 24) = 0.35, p =.56, g p 2 =.01. Overall, this pattern shows that the reverse OH effect was observed with both angry and fearful faces. Female Targets When the target emotion was anger, the interaction between target race and expression valence was not significant, F(1, 24) = 0.37, p =.55, g p 2 =.01; nor were the main effects of race, F(1, 24) = 0.79, p =.38, g p 2 =.03, and expression valence, F(1, 24) = 0.09, p =.76, g p 2 =.00. There were no significant differences in recognition accuracy between Black and White faces, for either neutral, F(1, 24) = 0.02, p =.88, g p 2 =.00, or angry expressions, F(1, 24) = 1.43, p =.24, g p 2 =.06. Overall, the false alarm rate was lower for neutral faces than for angry faces, F(1, 24) = 4.45, p \.05, g p 2 =.16, showing that participants had less difficulty differentiating the old from the new neutral faces. When the target emotion was fear, a significant main effect of target race emerged, F(1, 24) = 13.76, p \.01, g p 2 =.36. Neutral Black faces were better recognized than neutral White faces, F(1, 24) = 4.61, p \.05, g p 2 =.16. Furthermore, recognition of fearful Black faces was significantly better than recognition of fearful White faces, F(1, 24) = 8.28, p \.01, g p 2 =.26. The false alarm rate was significantly lower for neutral faces than for fearful faces, F(1, 24) = 8.26, p \.01, g p 2 =.26, and for Black faces than for White faces, F(1, 24) = 5.15, p \.05, g p 2 =.19, suggesting that participants were better at differentiating among neutral and Black faces than fearful and White faces. Interaction Target Gender by Target Emotion Consistent with previous literature, there was a significant interaction between target gender and target emotion, F(1, 100) = 10.24, p \.01, g p 2 =.09 (see Fig. 1). For male targets, recognition was better when fearful rather than angry expressions were shown,

7 J Nonverbal Behav (2011) 35: Fig. 1 Recognition sensitivity as a function of target gender (male, female) and target emotion (anger, fear). Error bars indicate standard errors t(50) =-2.18, p \.05. In particular, the false alarm rate tended to be higher for angry than for fearful faces, t(50) = 1.90, p =.06. The reverse was true for female targets, who were better recognized when showing angry rather than fearful expressions, t(50) = 2.35, p \.05. There was a tendency for there to be fewer false alarms in response to female angry than female fearful faces, although this did not reach significance, t(50) =-1.18, p =.24. Response Bias A mixed-model ANOVA with target race (Black vs. White) and expression valence (neutral vs. negative) as within-participants factors and target emotion (anger or fear), target gender (male or female), and sex of participant (men or women) as betweenparticipants factors was conducted on the response-bias measure. Analyses showed that sex of participant produced no significant main effect, F(1, 96) = 0.04, p =.83, g p 2 =.00. There was a significant 5-way interaction between target gender, target race, expression valence, target emotion, and sex of participant, F(1, 96) = 11.54, p =.001, g p 2 =.11, but no systematic pattern of results occurred across race and emotion. The sex of participant variable was therefore not considered further. Previous research has documented a tendency to respond more liberally to out-group faces (i.e., responding did see to stimuli) than in-group faces (see Slone et al. 2000). In the present analyses, a significant main effect of target race was consistent with this, F(1, 96) = 4.07, p \.05, g p 2 = Decomposing this effect as a function of target gender and target emotion showed that it was close to significance only for male targets when the target emotion was anger, F(1, 24) = 3.11, p =.09, g p 2 =.11. Participants exhibited a liberal bias when judging Black faces, and a conservative bias for when judging White faces. In addition, a significant main effect of expression valence reflected a more liberal bias for the recognition of negative compared to neutral faces, F(1, 96) = 35.56, p \.001, g p 2 =.27. This effect was significant for male targets (anger: F(1, 24) = 3.03, p =.09, g p 2 =.12; fear: F(1, 24) = 13.83, p \.01, g p 2 =.37) and female targets (anger: F(1, 24) = 9.84, p \.01, g p 2 =.29; fear: F(1, 24) = 13.06, p \.01, g p 2 =.35. The interaction between target race and expression valence was not significant, F(1, 96) = 0.13, p =.72, g p 2 = Similar results were obtained when collapsing across male and female participants: target race, F(1, 100) = 3.59, p =.06, g p 2 =.03; expression valence, F(1, 100) = 39.05, p \.001, g p 2 =.28; target race by expression valence, F(1, 100) = 0.47, p =.49, g p 2 = 00.

8 58 J Nonverbal Behav (2011) 35:51 61 Discussion The current study was designed to examine the generalizability of the reverse OH effect, by including angry and fearful expressions, as well as male and female faces. Given that both anger and fear are negatively valenced expressions, we tested whether similar effects of these emotions would be found for the recognition of out-group faces, and also examined whether the effects would apply to female as well as male faces. According to the functional hypothesis, Black targets should be better remembered when displaying anger (signaling threat) but not when displaying fear (accompanying threat), as compared to neutral expressions. For angry versus neutral male faces, our results replicate those of Ackerman et al. (2006). Whereas recognition memory was better for White male faces than for Black male faces when the faces were neutral, this OH effect was reversed when the faces displayed anger. However, the same pattern was evident for male faces displaying fear. Here, too, there was superior recognition of Black faces. Consequently, the reverse OH effect is not limited to angry faces. Given that both neutral versus angry and neutral versus fearful conditions yielded a reversal of the recognition advantage for Black targets, the results are difficult to reconcile with the functional hypothesis proposed by Ackerman et al. The reversal of the OH effect appears to be less to do with in-group or individual survival (for here it could reasonably be argued that greater attention should be paid to in-group fear faces) and more to do with intergroup antagonism more generally. A Black face that expresses anger or fear provides a link between the ethnicity of the face and the negative emotions, which makes intergroup antagonism more salient. Such negative evaluative context created by Black targets may in turn facilitate the processing of negative expressions. Similar findings have been reported by Hugenberg (2005), who found a response latency advantage for negative expressions in Black faces. In his study participants were faster in correctly categorizing emotions when Black targets displayed anger or sadness. He explained this in terms of the evaluative negativity of Black faces, which goes beyond the stereotype of Black people as being angry or aggressive. With respect to memory sensitivity, Corneille et al. (2007) failed to show a recognition advantage for angry Black faces. On the contrary, they obtained higher recognition rates for faces displaying evaluatively incongruent expressions (happy Black faces and angry White faces). However, as noted by Corneille et al., this contrasting finding may be attributable to the use of different learning paradigms. Whereas Corneille et al. relied on an incidental learning paradigm in which participants had to detect as quickly and accurately the location of a probe on the computer screen after face-exposure (thereby minimizing the impact of motivational processes in face encoding), both Ackerman et al. study and the present one were based on an intentional learning paradigm (allowing full attention to the face stimuli). However, participants were not told in any of these studies that they would later be tested on their memory for the faces. As well as examining different mechanisms underlying the reverse OH effect, the present study points to the role of target gender in the recognition of own-race and otherrace faces. In most previous work on OH female faces have been excluded from study designs (e.g., Anthony et al. 1992; Bothwell et al. 1989; Corneille et al. 2007; Hugenberg 2005; Meissner and Brigham 2001), making it unclear whether the results apply to female as well as male faces. Men have historically been the primary agents of intergroup aggression in humans (Daly and Wilson 1988; Kelly 2005). In recent research by Navarrete et al. (2009) resistance to extinction of a conditioned fear response toward Black out-group targets was also found to occur only when the exemplars were male. The present study

9 J Nonverbal Behav (2011) 35: demonstrates that evidence of the moderating role of facial expressions in memory for in-group and out-group faces was limited to male faces. Whereas male out-group members were better remembered when they expressed negative emotions, female out-group faces were generally better remembered than female in-group faces, regardless of emotional expression. Given this unexpected finding, further research should aim to identify the processes (e.g., approach motivation) underlying this superior recognition of female outgroup faces. The pattern of results suggests that female faces and male faces are processed differently and that effects found by Ackerman et al. and previous OH literature may not generalize to female targets. Interestingly, we found a recognition advantage for facial expressions that are incongruent with gender stereotypes. Whereas males were better recognized when they showed fearful faces, females were better recognized when displaying angry faces. This suggests that gender role expectations regarding emotion (Brody and Hall 1993; Fischer 1993; Hess et al. 2004) lead to differential memory for male and female faces. To the extent that fear expressions are seen as inconsistent with men s gender role, and anger expressions are seen as inconsistent with women s gender role, such expressions may be more conspicuous, atypical and may thereby be more memorable. In this research we only focused on the impact on recognition memory of neutral and negative emotional expressions in White and Black faces. An important question is whether the reverse OH effect generalizes to positive expressions. If better memory of angry or fearful male Black faces is due to outgroup negativity that facilitates the recognition of negative expressions, positive emotions should not lead to a reversal of the OH effect in out-group faces. Indeed, there is recent evidence that happy expressions eliminate attentional biases in the processing of Black male faces (Richeson and Trawalter 2008). In a follow-up study with 14 participants (7 men, 7 women, M = years) using the same procedure as described above, but with happy versus neutral expressions shown by White and Black male targets, we did not replicate the interaction of target race and expression, F(1, 12) = 0.39, p =.54, g p 2 =.03. It therefore seems reasonable to attribute the current results to intergroup antagonism rather than to the mere influence of emotion in the recognition of out-group faces. Future research could systematically examine how the OH effect is moderated by different positive and negative emotions, including social emotions (e.g., pride, embarrassment, contempt). Furthermore, out-group faces other than ones of African American origin could be used to examine whether the processes underlying superior recognition of negative out-group faces are due to intergroup dynamics or to the ethnicity of the face. For example, White perceivers evaluations of Asians as typically cooperative, self-controlled, and eager to please (Chang and Demyan 2007) suggest that there is a weaker association between negative emotions and Asian males. As a result both the OH effect in neutral faces and the reverse OH effect in negative faces might be less evident than they are in the case of Black male faces. An additional avenue for future research could be the investigation of the link between implicit prejudice, social categorization, and perception of emotional expressions. Considerable evidence suggests that with higher levels of implicit prejudice there is an increasing tendency to perceive anger in Blacks (Hugenberg and Bodenhausen 2003) and to categorize angry racially ambiguous faces as Black (Hugenberg and Bodenhausen 2004; Hutchings and Haddock 2008). In conclusion, the present research provided a competitive test of the functional hypothesis and the intergroup antagonism hypothesis and investigated whether similar effects are found for male and female targets. The results clearly showed that the reverse OH effect in memory for faces extends to fearful as well as angry expressions, thereby

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