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Crossed-categorization, evaluation, and face recognition
Journal of Experimental Social Psychology 46 (2010) 449–452
Contents lists available at ScienceDirect
Journal of Experimental Social Psychology journal homepage: www.elsevier.com/locate/jesp
FlashReport
Crossed-categorization, evaluation, and face recognition Devin G. Ray *, Nate Way, David L. Hamilton University of California, Santa Barbara, California 93106, United States
a r t i c l e
i n f o
Article history: Received 7 August 2009 Revised 22 November 2009 Available online 6 December 2009 Keywords: Crossed-categorization Multiple categorization Face recognition Cross race effect Own group effect Prejudice
a b s t r a c t We investigated the relationship between evaluative intergroup bias and biased errors in intergroup face recognition after crossed-categorization (the combination of two social categories in defining a target of perception). Although evaluative bias and recognition bias often operate in parallel, we draw on two previously unconnected literatures to predict a divergence between these two processes after crossed-categorization. We tested this hypothesis by assessing participants’ evaluations of and recognition of targets who shared two ingroups with participants, targets who shared only one ingroup with participants, and targets who shared neither ingroup. Consistent with predictions, targets’ shared and unshared group memberships combined additively to affect evaluation, but targets who shared two ingroup memberships were better recognized than all other category combinations. These results document the relationship between evaluative bias and recognition bias after crossed-categorization and indicate that crossedcategorization affects evaluative bias and recognition bias in different ways. Ó 2009 Elsevier Inc. All rights reserved.
Introduction The separation of people into different categories has numerous powerful effects. This article is concerned with two of those effects and their interface. Perhaps the most pervasive such effect is ingroup bias, an evaluative preference for ingroup members over outgroup members (Sherif, 1966; Tajfel & Turner, 1986). A second robust effect of intergroup categorization is recognition bias. Specifically, people recognize the faces of ingroup members better than those of outgroup members (Bernstein, Young, & Hugenberg, 2007; Meissner & Brigham, 2001). Generally, evaluative bias and recognition bias seem to operate independently, but in parallel to one another. Their independence is suggested by the fact that evaluative bias does not appear to cause recognition errors (Meissner & Brigham, 2001). On the other hand, the two phenomena often respond similarly to the same antecedents. For example, minimal groups create both ingroup favoritism and a recognition advantage for ingroup members, and intergroup contact reduces both evaluative bias and recognition bias, although the latter effect is less consistent (Allport, 1954; Bernstein et al., 2007; Meissner & Brigham, 2001; Tajfel & Turner, 1986). In the present research, we examine and compare the effects of applying multiple social categories to a target of perception (crossed-categorization) on evaluative bias and on recognition bias.
* Corresponding author. E-mail address: [email protected] (D.G. Ray). 0022-1031/$ - see front matter Ó 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.jesp.2009.12.001
The effects of crossed-categorization on evaluative bias are well understood. Preference for ingroup members over outgroup members typically combines multiple categories additively so that those who share two ingroups (double ingroup members) are preferred over those who share only one ingroup (partial ingroup members), and those who share only one ingroup are in turn preferred over those who share no ingroups (double outgroup members; Crisp & Hewstone, 2007; Migdal, Hewstone, & Mullen, 1998; Urban & Miller, 1998). This pattern of evaluation is aptly named the additive pattern. In an evaluative process such as this, the implications of the two categories are considered simultaneously. Although partial ingroup members belong to an outgroup on one dimension, shared group membership on a second dimension tempers the negativity of that outgroup membership. Although the additive pattern is the most common outcome of crossed-categorization, under certain conditions other patterns of evaluation have been observed. For example, incidental negative affect creates the social exclusion pattern in which double ingroup members are preferred over partial ingroup members, but in which partial ingroup members and double outgroup members are evaluated equally poorly (Kenworthy, Canales, Weaver, & Miller, 2003). In the social exclusion pattern, there is no evidence that multiple categories receive simultaneous consideration. Whether a person is an outgroup member in only one regard or in more than one regard, they are evaluated equally negatively. The effect of crossed-categorization on recognition bias has not been adequately explored. In work on recognition bias, multiple categories have often been viewed as a nuisance and either dismissed or avoided (Ackerman et al., 2006; Hayward, Rhodes, & Schwaninger, 2008; Platz & Hosch, 1988). There are two recent
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D.G. Ray et al. / Journal of Experimental Social Psychology 46 (2010) 449–452
exceptions to this trend, both of which explore moderators of the cross-race effect (CRE), that is, better recognition of same-race faces than of other-race faces. Shriver, Young, Hugenberg, Bernstein, and Lanter (2008) demonstrated that the CRE can be modified by socioeconomic status (SES, Study 1) or by university affiliation (Study 2). Chiroro, Tredoux, Radaelli, and Meissner (2008) demonstrated that the CRE is moderated by nationality. The results of both investigations converged: recognition bias did not combine additively, but instead conformed more closely to a social exclusion pattern. The authors of these respective papers explain their results in very different ways. Shriver et al. suggested that racial distinctions might be primary, such that White individuals distinguish between high SES and low SES for same-race targets, but not for other-race targets. Chiroro et al. attributed their results to differential contact and perceptual expertise. In neither case did the authors interpret their findings in the framework of crossed-categorization, nor were evaluative judgments assessed in these studies. Our analysis draws on Levin’s (1996, 2000) categorizationbased explanation of the Cross-Race Effect (CRE) to suggests a more parsimonious explanation. Levin (2000) proposed that an initial feature-present/feature-absent assessment directs initial categorization of the target, which then determines later processing. When an outgroup feature is detected (feature-present), that face is categorized as an outgroup member and is processed superficially, resulting in relatively poor recognition rates. When an outgroup feature is not detected (feature-absent), that face is processed more deeply, resulting in better recognition. Extending this model and applying it to crossed-categorization, we propose that any indication of outgroup membership might trigger feature-present superficial processing. Rather than taking into account the combined implications of multiple categories, as in the additive pattern of evaluation, face processing might only distinguish between ingroup and outgroup with no shades of grey. This process would produce the social exclusion pattern in face recognition. These considerations suggest a provocative discrepancy in the effects of crossed-categorization on evaluative bias and on recognition bias. Specifically, evaluative bias reflects the combined effects of the two categories on judgments (i.e., the additive pattern), whereas recognition bias reflects only a single ingroup-outgroup distinction, with any cue to outgroup membership resulting in poor recognition (i.e., the social exclusion pattern). However, no previous work has measured evaluation and face recognition after crossed-categorization in the same paradigm. Our experiment did just that. In our research we crossed political party (Democrat or Republican) with position on abortion (pro-choice or pro-life) and measured both evaluations and face recognition for ingroup and outgroup members. We predicted that crossed-categorization would have divergent effects on evaluation and face recognition. We predicted that (a) evaluative measures would conform most closely to an additive pattern, in which double ingroup members are preferred over partial ingroup members, and partial ingroup members are preferred over double outgroup members, whereas (b) face recognition would conform most closely a social exclusion pattern, in which double ingroup members are recognized better than partial ingroup members, but partial ingroup members and double outgroup members are recognized equally poorly.
Methods Participants and design Twenty-nine White male college undergraduates who selfidentified as Democrats and as Pro-Choice participated in a 2 (tar-
get political party: Democrat and Republican) X 2 (target abortion stance: Pro-Choice and Pro-Life) X 2 (measure: face recognition and prejudice) within subjects design in exchange for course credit in an introductory psychology course. Materials and procedure Participants arrived at the lab in small groups, were seated in individual cubicles, and received all experimental instructions and materials by computer. Participants then completed the evaluative measures followed by the face recognition task. For both the evaluative measures and the recognition task, the order in which the target’s two category memberships were described was counterbalanced (e.g., category labels indicated Democrat and Pro-Life or Pro-Life and Democrat). Evaluative measures Participants rated each of the target groups (Pro-choice Democrats, Pro-life Democrats, Pro-choice Republicans, and Pro-life Republicans) on three 7-point semantic differentials anchored at – 3 (bad, negative, unfavorable) and +3 (good, positive, favorable). The semantic differentials were presented one at time on separate screens in a random order. Face recognition Participants were instructed to pay close attention to faces that would appear onscreen and were informed that their memory for those faces would be tested. Next, participants viewed 24 greyscale photos of White male faces in a random order. There were six photos for each of the four possible target category combinations. The target’s category membership was indicated by a label (e.g., Democrat and Pro-Life) that appeared for two seconds before the presentation of each photo. Each photo appeared for three seconds. A blank screen appeared for two seconds between each labelphoto pairing. The category membership of particular photos was counterbalanced. Participants’ face recognition was tested immediately afterwards. Participants saw the same 24 faces to which they had just been exposed intermixed with 24 new faces. For each face, participants indicated whether the face was ‘‘old” or ‘‘new”. For both old and new faces a label indicating the target’s category membership preceded the presentation of each face and stayed onscreen for two seconds. For old faces, the label-face pairing did not change between exposure and recognition. Lastly, participants were thanked and debriefed about the nature of the study. Results We averaged the semantic differentials to form an aggregate measure of evaluation for each target (a’s ranged from .86 to .94). Recognition accuracy for face photos was assessed within a signal detection framework, where d’ is used as an index of recognition accuracy. For each participant, a separate d’ was calculated for each of the four sets of photos that corresponded to the four experimental groups. We corrected for perfect accuracy using the log linear approach (Snodgrass & Corwin, 1988). We had clear and differing predictions for evaluative judgments and for face recognition; we predicted an additive pattern in evaluation and a social exclusion pattern in face recognition. The additive and social exclusion patterns are defined by and distinguished from one another by the comparison of the double ingroup to the partial ingroups and by the comparison of the double outgroup to the partial ingroups. In both the additive pattern and the social exclusion pattern, the difference between the double ingroup and
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Discussion Our research is the first to compare evaluation and face recognition in the context of multiple social categories. As predicted, crossed-categorization had divergent effects on evaluative judgments and on face recognition in the same study. Evaluative judgments manifested the additive pattern, in which double ingroup members were liked more than partial ingroup members, who were liked more than double outgroup members. In contrast, recognition bias manifested the social exclusion pattern, in which double ingroup members were recognized better than all other category combinations and in which no distinction was drawn between double outgroup members and partial ingroup members. These divergent effects indicate that crossed-categorization affects information processing differently in evaluation and in face recognition. In making evaluative judgments, participants clearly took into account the implications of both categories, thereby producing the graded effect of the additive pattern. In face recognition,
Table 1 Contrast weights. ii = pro-choice Democrats, io = pro-life Democrats, oi = pro-choice Republicans, oo = pro-life Republicans. Measure Evaluation Target category
Contrast 1 Contrast 2
Recognition Target category
ii
io
oi
oo
+2 0
1 +1
1 +1
0 2
ii 2 0
io
oi
oo
+1 1
+1 1
0 +2
Semantic Differentials
1.50
1.20
Evaluation
1.00 0.57
0.50
0.24
0.00 -0.50 -1.00
-0.48
ii
1.5
io
oi
oo
Face Recognition
1.75
d'
the partial ingroups should be significant. The difference between the partial ingroups and the double outgroup, however, should be significant in the additive pattern, but not in the social exclusion pattern. Our prediction’s clear specification strongly recommends a priori contrasts over more exploratory ANOVA (Abelson, 1995; Rosenthal, Rosnow, & Rubin, 1999; also see Crisp, Walsh, & Hewstone, 2006; Ensari & Miller, 1998; Hewstone, Isam, & Judd, 1993). To test these predictions, we standardized both measures and subjected them to the contrasts presented in Table 1. Because these contrasts test the difference between two differences, they are conceptually and statistically identical to interactions. Contrast 1 tests the interaction between measure type and the comparison of the double ingroup to the partial ingroups. According to our predictions Contrast 1 should be non-significant. Contrast 2 tests the interaction between measure type and the comparison of the partial ingroups to the double outgroup. According to our predictions Contrast 2 should be significant. Unstandardized cell means for both measures are graphed in Fig. 1. As predicted, Contrast 1 was non-significant, t(28) = 1.06, p = .298 and Contrast 2 was significant, t(28) = 3.58, p = .001. Planned follow-up comparisons indicated that the double ingroup was both evaluated, t(28) = 2.92, p = .007, and recognized, t(28) = 2.11, p = .043, better than the partial ingroups, whereas the double outgroup received worse evaluations than the partial ingroups, t(28) = 4.61, p < .001, but was not recognized at significantly different rates than the partial ingroups, t(28) = 0.37, p = .713. These results indicate that, as we predicted, crossed-categorization affected evaluation and face recognition differently. Although both evaluative measures and recognition were sensitive to the distinction between the double ingroup and partial ingroups, evaluation and face recognition diverged with regard to the distinction between double outgroups and partial ingroups. Only evaluative measures appeared sensitive to this distinction. We observed no evidence for similar sensitivity in face recognition errors.
1.49
1.25 1.07
1.14
1.05
1 0.75 ii
io
oi
oo
Fig. 1. Unstandardized cell means. ii = pro-choice Democrats, io = pro-life Democrats, oi = pro-choice Republicans, oo = pro-life Republicans.
an ingroup-outgroup distinction was strictly applied, such that partial ingroup members were still seen as outgroup members. This process generated the social exclusion pattern. Our findings have important implications for the relationship between prejudice and errors in face recognition. Although past research has suggested that evaluative bias does not cause recognition bias, prejudice and recognition errors often operate in parallel in response to the same antecedents (Allport, 1954; Bernstein et al., 2007; Meissner & Brigham, 2001; Sherif, 1966; Tajfel & Turner, 1986). Rather than operating in parallel, we find that evaluation and face recognition diverge in response to crossedcategorization. But what is different about evaluative bias and recognition bias in the context of multiple social categories? One possibility is that the feature-present/feature-absent processing proposed by Levin (2000) is initially governed by relatively automatic processes, whereas evaluative judgments require more deliberative processing. If so, then implicit measures of evaluation might be more predictive of face recognition than explicit evaluations. As implicit measures have not yet been adapted to a crossed-categorization context, this possibility is difficult to assess. Alternatively, extreme time pressure might undermine controlled evaluation and produce a social exclusion pattern in evaluative judgments after crossedcategorization. One might also argue that if participants’ exposure to each face was not limited to 3 seconds, we might have observed a different pattern of recognition. We find this possibility implausible as 3 seconds appears to be sufficient time for controlled processes to override conflicting automatic processes (Greenwald, McGhee, & Schwartz, 1998; Todorov & Uleman, 2002). Another possible explanation for these results is that evaluation and face recognition might be governed by unrelated neural systems. Indeed, evidence from functional neuroimaging studies suggests that lower-order visual systems respond to other-race faces and same-race faces in fundamentally distinct ways, whereas higher order processes govern evaluative responses (Willadsen-Jensen & Ito, 2006; Ito & Cacioppo, 2001; Zebrowitz, 2006). In sum, possible mechanisms are diverse and complicated.
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One potential criticism of this research is that the targets of evaluation were social categories whereas the targets of recognition judgments were individual category members. If evaluating individual category members leads to a preference for double ingroup members over all other category combinations, then this difference between the evaluation and recognition tasks could account for the results observed. Fortunately, using social categories as targets and using individual category members as targets have comparable effects on evaluation after crossed-categorization; both targets of judgment result in the additive pattern in the absence of moderators, although more personalized targets elicit less evaluative bias overall (Crisp & Hewstone 1999; Urban & Miller, 1998). This evidence indicates that the difference in target between our evaluative and recognition tasks cannot account for the present findings. Our choice of combining two correlated category memberships, political party and position on abortion, raises questions about the effects of expectancy violation on encoding. Pro-choice Republicans and Pro-life Democrats are less frequent than Pro-choice Democrats and Pro-life Republicans in the general population. However, these categories are by no means perfectly related. Prochoice Republicans and Pro-life Democrats, although in the minority, are currently serving in prominent elected offices (e.g. Republican governor of California, Arnold Schwarzenegger; Mississippi Democratic congressional representative Travis Childers and other Blue Dog Democrats) and are frequently mentioned in political news coverage. Importantly, differences between expectancy-violating and expectancy-confirming category combinations cannot account for our findings. Another limitation of this research, as well as most other research on face recognition, is that ecological validity might be undermined by studying inanimate photos rather than recognition of animate faces (see Bruce, 2009; Lander & Bruce, 2003). Both prejudice and recognition errors influence day-to-day intergroup interaction and can also play key roles in critical situations like eyewitness identification and jury decision-making (Meissner & Brigham, 2001). To truly understand intergroup relations, it is vital that we understand how both facial recognition and prejudice are affected by inevitable complexities, such as crossed-categorization, that arise in intergroup interactions. References Abelson, R. P. (1995). Statistics as principled argument. Hillsdale, NJ: Lawrence Erlbaum. Ackerman, J. M., Shapiro, J. R., Neuberg, S. L., Kenrick, D. T., Vaughn Becker, D., Griskevicius, V., et al. (2006). The all look the same to me (unless they’re angry): From out-group homogeneity to out-group heterogeneity. Psychological Science, 17, 836–840. Allport, G. W. (1954). The nature of prejudice. Garden City, NY: Doubleday. Bernstein, M. J., Young, S. G., & Hugenberg, K. (2007). The cross category effect: Mere social categorization is sufficient to elicit an own-group bias in face recognition. Psychological Science, 18, 706–712. Bruce, V. (2009). Remembering faces. In J. R. Brockmole (Ed.), The visual world of memory: Current issues in memory series (pp. 66–88). New York: Psychology Press.
Chiroro, P. M., Tredoux, C. G., Radaelli, S., & Meissner, C. A. (2008). Recognizing faces across continents: The effect of within-race variations on the own-race bias in face recognition. Psychonomic Bulletin & Review, 15, 1089–1092. Crisp, R. J., & Hewstone, M. (1999). Differential evaluation of crossed category groups: Patterns, processes, and reducing intergroup bias. Group Processes & Intergroup Relations, 2, 307–333. Crisp, R. J., & Hewstone, M. (2007). Multiple social categorization. In M. P. Zanna (Ed.), Advances in experimental social psychology (pp. 163–254). San Diego: Elsevier Academic Press. Crisp, R. J., Walsh, J., & Hewstone, M. (2006). Crossed-categorization in common ingroup contexts. Personality and Social Psychology Bulletin, 32, 1204–1218. Ensari, N., & Miller, N. (1998). Effect of affective reactions by the outgroup to preferences for crossed-categorization discussion partners. Journal of Personality and Social Psychology, 75, 1503–1527. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74, 1464–1480. Hayward, W. G., Rhodes, G., & Schwaninger, A. (2008). An own-race advantage for components as well as configurations in face recognition. Cognition, 106, 1017–1027. Hewstone, M., Isam, M. R., & Judd, C. M. (1993). Models of crossed-categorization and intergroup relations. Journal of Personality and Social Psychology, 64, 779–793. Ito, T. A., & Cacioppo, J. T. (2001). Affect and attitudes: A social neuroscience approach. In J. P. Forgas (Ed.), Handbook of affect and cognition (pp. 50–74). Mahwah, NJ: Lawrence Erlbaum Associates Publishers. Kenworthy, J. B., Canales, C. J., Weaver, K. D., & Miller, N. (2003). Negative incidental affect and mood congruency in crossed-categorization. Journal of Experimental Social Psychology, 39, 195–219. Lander, K., & Bruce, V. (2003). The role of motion in learning new faces. Visual Cognition, 10, 897–912. Levin, D. T. (1996). Classifying faces by race. The structure of face categories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 1364–1382. Levin, D. T. (2000). Race as a visual feature: Using visual search and perceptual discrimination tasks to understand face categories and the cross-race recognition deficit. Journal of Experimental Social Psychology, General, 129, 559–574. Meissner, C. A., & Brigham, J. C. (2001). Thirty years of investigating the own-race bias in memory for faces: A meta-analytic review. Psychology, Public Policy, and Law, 7, 3–35. Migdal, M. J., Hewstone, M., & Mullen, B. (1998). The effects of crossedcategorization in intergroup evaluation: A meta-analysis. British Journal of Social Psychology, 37, 303–324. Platz, S. J., & Hosch, H. M. (1988). Cross-racial/ethnic eyewitness identification: A field study. Journal of Applied Social Psychology, 18, 972–984. Rosenthal, R., Rosnow, R., & Rubin, D. (1999). Contrasts and effect sizes in behavioral research. A correlational approach. New York: Cambridge University Press. Sherif, M. (1966). Groups in harmony and tension. New York: Harper. Shriver, E. R., Young, S. G., Hugenberg, K., Bernstein, M. J., & Lanter, J. R. (2008). Class, race and the face: Social context modulates the cross-race effect in face recognition. Personality and Social Psychology Bulletin, 34, 260–274. Snodgrass, J. G., & Corwin, J. (1988). Pragmatics of measuring recognition memory: Applications to dementia and amnesia. Journal of Experimental Psychology: General, 117, 34–50. Tajfel, H., & Turner, J. C. (1986). The social identity theory of intergroup behavior. In W. G. Austin & S. Worchel (Eds.), Psychology of intergroup relations (pp. 7–24). Chicago: Nelson Hall. Todorov, A., & Uleman, J. S. (2002). Spontaneous trait inferences are bound to actors’ faces: Evidence from a false recognition paradigm. Journal of Personality and Social Psychology, 83, 1051–1065. Urban, L. M., & Miller, N. (1998). A theoretical analysis of crossed-categorization effects: A meta-analysis. Journal of Personality and Social Psychology, 74, 894–908. Willadsen-Jensen, E. C., & Ito, T. A. (2006). Ambiguity and the timecourse of racial perception. Social Cognition, 24, 580–606. Zebrowitz, L. A. (2006). Finally, faces find favor. Social Cognition, 24, 657–701.
Contents lists available at ScienceDirect
Journal of Experimental Social Psychology journal homepage: www.elsevier.com/locate/jesp
FlashReport
Crossed-categorization, evaluation, and face recognition Devin G. Ray *, Nate Way, David L. Hamilton University of California, Santa Barbara, California 93106, United States
a r t i c l e
i n f o
Article history: Received 7 August 2009 Revised 22 November 2009 Available online 6 December 2009 Keywords: Crossed-categorization Multiple categorization Face recognition Cross race effect Own group effect Prejudice
a b s t r a c t We investigated the relationship between evaluative intergroup bias and biased errors in intergroup face recognition after crossed-categorization (the combination of two social categories in defining a target of perception). Although evaluative bias and recognition bias often operate in parallel, we draw on two previously unconnected literatures to predict a divergence between these two processes after crossed-categorization. We tested this hypothesis by assessing participants’ evaluations of and recognition of targets who shared two ingroups with participants, targets who shared only one ingroup with participants, and targets who shared neither ingroup. Consistent with predictions, targets’ shared and unshared group memberships combined additively to affect evaluation, but targets who shared two ingroup memberships were better recognized than all other category combinations. These results document the relationship between evaluative bias and recognition bias after crossed-categorization and indicate that crossedcategorization affects evaluative bias and recognition bias in different ways. Ó 2009 Elsevier Inc. All rights reserved.
Introduction The separation of people into different categories has numerous powerful effects. This article is concerned with two of those effects and their interface. Perhaps the most pervasive such effect is ingroup bias, an evaluative preference for ingroup members over outgroup members (Sherif, 1966; Tajfel & Turner, 1986). A second robust effect of intergroup categorization is recognition bias. Specifically, people recognize the faces of ingroup members better than those of outgroup members (Bernstein, Young, & Hugenberg, 2007; Meissner & Brigham, 2001). Generally, evaluative bias and recognition bias seem to operate independently, but in parallel to one another. Their independence is suggested by the fact that evaluative bias does not appear to cause recognition errors (Meissner & Brigham, 2001). On the other hand, the two phenomena often respond similarly to the same antecedents. For example, minimal groups create both ingroup favoritism and a recognition advantage for ingroup members, and intergroup contact reduces both evaluative bias and recognition bias, although the latter effect is less consistent (Allport, 1954; Bernstein et al., 2007; Meissner & Brigham, 2001; Tajfel & Turner, 1986). In the present research, we examine and compare the effects of applying multiple social categories to a target of perception (crossed-categorization) on evaluative bias and on recognition bias.
* Corresponding author. E-mail address: [email protected] (D.G. Ray). 0022-1031/$ - see front matter Ó 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.jesp.2009.12.001
The effects of crossed-categorization on evaluative bias are well understood. Preference for ingroup members over outgroup members typically combines multiple categories additively so that those who share two ingroups (double ingroup members) are preferred over those who share only one ingroup (partial ingroup members), and those who share only one ingroup are in turn preferred over those who share no ingroups (double outgroup members; Crisp & Hewstone, 2007; Migdal, Hewstone, & Mullen, 1998; Urban & Miller, 1998). This pattern of evaluation is aptly named the additive pattern. In an evaluative process such as this, the implications of the two categories are considered simultaneously. Although partial ingroup members belong to an outgroup on one dimension, shared group membership on a second dimension tempers the negativity of that outgroup membership. Although the additive pattern is the most common outcome of crossed-categorization, under certain conditions other patterns of evaluation have been observed. For example, incidental negative affect creates the social exclusion pattern in which double ingroup members are preferred over partial ingroup members, but in which partial ingroup members and double outgroup members are evaluated equally poorly (Kenworthy, Canales, Weaver, & Miller, 2003). In the social exclusion pattern, there is no evidence that multiple categories receive simultaneous consideration. Whether a person is an outgroup member in only one regard or in more than one regard, they are evaluated equally negatively. The effect of crossed-categorization on recognition bias has not been adequately explored. In work on recognition bias, multiple categories have often been viewed as a nuisance and either dismissed or avoided (Ackerman et al., 2006; Hayward, Rhodes, & Schwaninger, 2008; Platz & Hosch, 1988). There are two recent
450
D.G. Ray et al. / Journal of Experimental Social Psychology 46 (2010) 449–452
exceptions to this trend, both of which explore moderators of the cross-race effect (CRE), that is, better recognition of same-race faces than of other-race faces. Shriver, Young, Hugenberg, Bernstein, and Lanter (2008) demonstrated that the CRE can be modified by socioeconomic status (SES, Study 1) or by university affiliation (Study 2). Chiroro, Tredoux, Radaelli, and Meissner (2008) demonstrated that the CRE is moderated by nationality. The results of both investigations converged: recognition bias did not combine additively, but instead conformed more closely to a social exclusion pattern. The authors of these respective papers explain their results in very different ways. Shriver et al. suggested that racial distinctions might be primary, such that White individuals distinguish between high SES and low SES for same-race targets, but not for other-race targets. Chiroro et al. attributed their results to differential contact and perceptual expertise. In neither case did the authors interpret their findings in the framework of crossed-categorization, nor were evaluative judgments assessed in these studies. Our analysis draws on Levin’s (1996, 2000) categorizationbased explanation of the Cross-Race Effect (CRE) to suggests a more parsimonious explanation. Levin (2000) proposed that an initial feature-present/feature-absent assessment directs initial categorization of the target, which then determines later processing. When an outgroup feature is detected (feature-present), that face is categorized as an outgroup member and is processed superficially, resulting in relatively poor recognition rates. When an outgroup feature is not detected (feature-absent), that face is processed more deeply, resulting in better recognition. Extending this model and applying it to crossed-categorization, we propose that any indication of outgroup membership might trigger feature-present superficial processing. Rather than taking into account the combined implications of multiple categories, as in the additive pattern of evaluation, face processing might only distinguish between ingroup and outgroup with no shades of grey. This process would produce the social exclusion pattern in face recognition. These considerations suggest a provocative discrepancy in the effects of crossed-categorization on evaluative bias and on recognition bias. Specifically, evaluative bias reflects the combined effects of the two categories on judgments (i.e., the additive pattern), whereas recognition bias reflects only a single ingroup-outgroup distinction, with any cue to outgroup membership resulting in poor recognition (i.e., the social exclusion pattern). However, no previous work has measured evaluation and face recognition after crossed-categorization in the same paradigm. Our experiment did just that. In our research we crossed political party (Democrat or Republican) with position on abortion (pro-choice or pro-life) and measured both evaluations and face recognition for ingroup and outgroup members. We predicted that crossed-categorization would have divergent effects on evaluation and face recognition. We predicted that (a) evaluative measures would conform most closely to an additive pattern, in which double ingroup members are preferred over partial ingroup members, and partial ingroup members are preferred over double outgroup members, whereas (b) face recognition would conform most closely a social exclusion pattern, in which double ingroup members are recognized better than partial ingroup members, but partial ingroup members and double outgroup members are recognized equally poorly.
Methods Participants and design Twenty-nine White male college undergraduates who selfidentified as Democrats and as Pro-Choice participated in a 2 (tar-
get political party: Democrat and Republican) X 2 (target abortion stance: Pro-Choice and Pro-Life) X 2 (measure: face recognition and prejudice) within subjects design in exchange for course credit in an introductory psychology course. Materials and procedure Participants arrived at the lab in small groups, were seated in individual cubicles, and received all experimental instructions and materials by computer. Participants then completed the evaluative measures followed by the face recognition task. For both the evaluative measures and the recognition task, the order in which the target’s two category memberships were described was counterbalanced (e.g., category labels indicated Democrat and Pro-Life or Pro-Life and Democrat). Evaluative measures Participants rated each of the target groups (Pro-choice Democrats, Pro-life Democrats, Pro-choice Republicans, and Pro-life Republicans) on three 7-point semantic differentials anchored at – 3 (bad, negative, unfavorable) and +3 (good, positive, favorable). The semantic differentials were presented one at time on separate screens in a random order. Face recognition Participants were instructed to pay close attention to faces that would appear onscreen and were informed that their memory for those faces would be tested. Next, participants viewed 24 greyscale photos of White male faces in a random order. There were six photos for each of the four possible target category combinations. The target’s category membership was indicated by a label (e.g., Democrat and Pro-Life) that appeared for two seconds before the presentation of each photo. Each photo appeared for three seconds. A blank screen appeared for two seconds between each labelphoto pairing. The category membership of particular photos was counterbalanced. Participants’ face recognition was tested immediately afterwards. Participants saw the same 24 faces to which they had just been exposed intermixed with 24 new faces. For each face, participants indicated whether the face was ‘‘old” or ‘‘new”. For both old and new faces a label indicating the target’s category membership preceded the presentation of each face and stayed onscreen for two seconds. For old faces, the label-face pairing did not change between exposure and recognition. Lastly, participants were thanked and debriefed about the nature of the study. Results We averaged the semantic differentials to form an aggregate measure of evaluation for each target (a’s ranged from .86 to .94). Recognition accuracy for face photos was assessed within a signal detection framework, where d’ is used as an index of recognition accuracy. For each participant, a separate d’ was calculated for each of the four sets of photos that corresponded to the four experimental groups. We corrected for perfect accuracy using the log linear approach (Snodgrass & Corwin, 1988). We had clear and differing predictions for evaluative judgments and for face recognition; we predicted an additive pattern in evaluation and a social exclusion pattern in face recognition. The additive and social exclusion patterns are defined by and distinguished from one another by the comparison of the double ingroup to the partial ingroups and by the comparison of the double outgroup to the partial ingroups. In both the additive pattern and the social exclusion pattern, the difference between the double ingroup and
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Discussion Our research is the first to compare evaluation and face recognition in the context of multiple social categories. As predicted, crossed-categorization had divergent effects on evaluative judgments and on face recognition in the same study. Evaluative judgments manifested the additive pattern, in which double ingroup members were liked more than partial ingroup members, who were liked more than double outgroup members. In contrast, recognition bias manifested the social exclusion pattern, in which double ingroup members were recognized better than all other category combinations and in which no distinction was drawn between double outgroup members and partial ingroup members. These divergent effects indicate that crossed-categorization affects information processing differently in evaluation and in face recognition. In making evaluative judgments, participants clearly took into account the implications of both categories, thereby producing the graded effect of the additive pattern. In face recognition,
Table 1 Contrast weights. ii = pro-choice Democrats, io = pro-life Democrats, oi = pro-choice Republicans, oo = pro-life Republicans. Measure Evaluation Target category
Contrast 1 Contrast 2
Recognition Target category
ii
io
oi
oo
+2 0
1 +1
1 +1
0 2
ii 2 0
io
oi
oo
+1 1
+1 1
0 +2
Semantic Differentials
1.50
1.20
Evaluation
1.00 0.57
0.50
0.24
0.00 -0.50 -1.00
-0.48
ii
1.5
io
oi
oo
Face Recognition
1.75
d'
the partial ingroups should be significant. The difference between the partial ingroups and the double outgroup, however, should be significant in the additive pattern, but not in the social exclusion pattern. Our prediction’s clear specification strongly recommends a priori contrasts over more exploratory ANOVA (Abelson, 1995; Rosenthal, Rosnow, & Rubin, 1999; also see Crisp, Walsh, & Hewstone, 2006; Ensari & Miller, 1998; Hewstone, Isam, & Judd, 1993). To test these predictions, we standardized both measures and subjected them to the contrasts presented in Table 1. Because these contrasts test the difference between two differences, they are conceptually and statistically identical to interactions. Contrast 1 tests the interaction between measure type and the comparison of the double ingroup to the partial ingroups. According to our predictions Contrast 1 should be non-significant. Contrast 2 tests the interaction between measure type and the comparison of the partial ingroups to the double outgroup. According to our predictions Contrast 2 should be significant. Unstandardized cell means for both measures are graphed in Fig. 1. As predicted, Contrast 1 was non-significant, t(28) = 1.06, p = .298 and Contrast 2 was significant, t(28) = 3.58, p = .001. Planned follow-up comparisons indicated that the double ingroup was both evaluated, t(28) = 2.92, p = .007, and recognized, t(28) = 2.11, p = .043, better than the partial ingroups, whereas the double outgroup received worse evaluations than the partial ingroups, t(28) = 4.61, p < .001, but was not recognized at significantly different rates than the partial ingroups, t(28) = 0.37, p = .713. These results indicate that, as we predicted, crossed-categorization affected evaluation and face recognition differently. Although both evaluative measures and recognition were sensitive to the distinction between the double ingroup and partial ingroups, evaluation and face recognition diverged with regard to the distinction between double outgroups and partial ingroups. Only evaluative measures appeared sensitive to this distinction. We observed no evidence for similar sensitivity in face recognition errors.
1.49
1.25 1.07
1.14
1.05
1 0.75 ii
io
oi
oo
Fig. 1. Unstandardized cell means. ii = pro-choice Democrats, io = pro-life Democrats, oi = pro-choice Republicans, oo = pro-life Republicans.
an ingroup-outgroup distinction was strictly applied, such that partial ingroup members were still seen as outgroup members. This process generated the social exclusion pattern. Our findings have important implications for the relationship between prejudice and errors in face recognition. Although past research has suggested that evaluative bias does not cause recognition bias, prejudice and recognition errors often operate in parallel in response to the same antecedents (Allport, 1954; Bernstein et al., 2007; Meissner & Brigham, 2001; Sherif, 1966; Tajfel & Turner, 1986). Rather than operating in parallel, we find that evaluation and face recognition diverge in response to crossedcategorization. But what is different about evaluative bias and recognition bias in the context of multiple social categories? One possibility is that the feature-present/feature-absent processing proposed by Levin (2000) is initially governed by relatively automatic processes, whereas evaluative judgments require more deliberative processing. If so, then implicit measures of evaluation might be more predictive of face recognition than explicit evaluations. As implicit measures have not yet been adapted to a crossed-categorization context, this possibility is difficult to assess. Alternatively, extreme time pressure might undermine controlled evaluation and produce a social exclusion pattern in evaluative judgments after crossedcategorization. One might also argue that if participants’ exposure to each face was not limited to 3 seconds, we might have observed a different pattern of recognition. We find this possibility implausible as 3 seconds appears to be sufficient time for controlled processes to override conflicting automatic processes (Greenwald, McGhee, & Schwartz, 1998; Todorov & Uleman, 2002). Another possible explanation for these results is that evaluation and face recognition might be governed by unrelated neural systems. Indeed, evidence from functional neuroimaging studies suggests that lower-order visual systems respond to other-race faces and same-race faces in fundamentally distinct ways, whereas higher order processes govern evaluative responses (Willadsen-Jensen & Ito, 2006; Ito & Cacioppo, 2001; Zebrowitz, 2006). In sum, possible mechanisms are diverse and complicated.
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One potential criticism of this research is that the targets of evaluation were social categories whereas the targets of recognition judgments were individual category members. If evaluating individual category members leads to a preference for double ingroup members over all other category combinations, then this difference between the evaluation and recognition tasks could account for the results observed. Fortunately, using social categories as targets and using individual category members as targets have comparable effects on evaluation after crossed-categorization; both targets of judgment result in the additive pattern in the absence of moderators, although more personalized targets elicit less evaluative bias overall (Crisp & Hewstone 1999; Urban & Miller, 1998). This evidence indicates that the difference in target between our evaluative and recognition tasks cannot account for the present findings. Our choice of combining two correlated category memberships, political party and position on abortion, raises questions about the effects of expectancy violation on encoding. Pro-choice Republicans and Pro-life Democrats are less frequent than Pro-choice Democrats and Pro-life Republicans in the general population. However, these categories are by no means perfectly related. Prochoice Republicans and Pro-life Democrats, although in the minority, are currently serving in prominent elected offices (e.g. Republican governor of California, Arnold Schwarzenegger; Mississippi Democratic congressional representative Travis Childers and other Blue Dog Democrats) and are frequently mentioned in political news coverage. Importantly, differences between expectancy-violating and expectancy-confirming category combinations cannot account for our findings. Another limitation of this research, as well as most other research on face recognition, is that ecological validity might be undermined by studying inanimate photos rather than recognition of animate faces (see Bruce, 2009; Lander & Bruce, 2003). Both prejudice and recognition errors influence day-to-day intergroup interaction and can also play key roles in critical situations like eyewitness identification and jury decision-making (Meissner & Brigham, 2001). To truly understand intergroup relations, it is vital that we understand how both facial recognition and prejudice are affected by inevitable complexities, such as crossed-categorization, that arise in intergroup interactions. References Abelson, R. P. (1995). Statistics as principled argument. Hillsdale, NJ: Lawrence Erlbaum. Ackerman, J. M., Shapiro, J. R., Neuberg, S. L., Kenrick, D. T., Vaughn Becker, D., Griskevicius, V., et al. (2006). The all look the same to me (unless they’re angry): From out-group homogeneity to out-group heterogeneity. Psychological Science, 17, 836–840. Allport, G. W. (1954). The nature of prejudice. Garden City, NY: Doubleday. Bernstein, M. J., Young, S. G., & Hugenberg, K. (2007). The cross category effect: Mere social categorization is sufficient to elicit an own-group bias in face recognition. Psychological Science, 18, 706–712. Bruce, V. (2009). Remembering faces. In J. R. Brockmole (Ed.), The visual world of memory: Current issues in memory series (pp. 66–88). New York: Psychology Press.
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