Laurie A Rudman
email: rudman@rci.rutgers.edu

Richard D Ashmore

Discrimination and the Implicit Association Test

Keywords: discrimination, implicit prejudice, implicit stereotypes, racial stereotypes, social cognition, intergroup relations

 are linked to harmful intergroup behaviors. In Study 1, the IAT predicted self-reported racial discrimination, including verbal slurs, exclusion, and physical harm. In Study 2, the IAT predicted recommended budget cuts for Jewish, Asian, and Black student organizations (i.e. economic discrimination). In each study, evaluative stereotype (but not attitude) IATs predicted behaviors even after controlling for explicit attitudes. In concert, the fi ndings suggest that implicit stereotypes are more predictive of overtly harmful actions than implicit attitudes in the intergroup relations domain.

Empirical observation distinguishes scientifi c psychologists from philosophers, novelists, and priests, who are also purveyors of truths about human nature. Because the quality of our insights depends on the clarity of our tools, methodological advances are the lifeblood of psychology. When a method is introduced, it should rightly be the target of skepticism and debate until its usefulness has been established. This was the expected trajectory for the Implicit Association Test (IAT; [START_REF] Greenwald | Measuring individual differences in implicit cognition: The Implicit Association Test[END_REF], a response latency task that was developed to measure implicit attitudes, but has since proved useful for assessing other constructs, including implicit stereotypes (e.g. [START_REF] Greenwald | Implicit partisanship: Taking sides for no reason[END_REF].

The ingeniously simple concept underlying the IAT is that tasks are performed well when they rely on well-practiced associations between objects and attributes. In the attitude IAT, respondents categorize two classes of objects (e.g. dogs and cats) with both good and bad words (e.g. vacation vs. poison). An automatic preference for dogs is shown to the extent that the pro-dog task (dogs + good/cats + bad) is performed faster and more accurately than the pro-cat task (dogs + bad/cats + good). For stereotype IATs, good and bad words are replaced with specifi c attributes associated with each object (e.g. loyal vs. aloof). If the stereotype congruent task (dogs + loyal/cats + aloof) is performed faster and more accurately than the stereotype incongruent task (dogs + aloof/cats + loyal), an implicit stereotype is shown.

Although still young, the IAT has been tested in over 100 studies-far more so than any other response latency technique. A recent metaanalysis [START_REF] Poehlman | Measuring and using the Implicit Association Test: III. Meta-analysis of predictive validity[END_REF] supported the IAT's temporal stability, internal consistency, and criterion validity (e.g. the IAT predicted voting, Scholastic Aptitude Test [SAT] scores, and consumer choice). Most promisingly, the IAT predicted behaviors better than self-reports did when the domain concerned prejudice and stereotypes. The behaviors included target evaluations, hiring decisions, and pro-social indicators (both verbal and nonverbal), suggesting a wide range of utility for the IAT. Given that self-reported prejudice was less useful when these behaviors were at stake, the IAT appears to be a promising methodological advance.

In addition, implicit associations behave in accord with classic attitude and intergroup theories (for a review, see [START_REF] Uhlmann | Automatic associations: Personal attitudes or cultural knowledge? Unpublished manuscript[END_REF]. For example, they are sensitive to context and conditioning, just as attitudes and prejudice are [START_REF] Blair | The malleability of automatic stereotypes and prejudice[END_REF][START_REF] Fazio | Implicit measures in social cognition research: Their meaning and use[END_REF]. Further, IAT scores have supported cognitive consistency principles [START_REF] Greenwald | Implicit partisanship: Taking sides for no reason[END_REF], the contact hypothesis [START_REF] Rudman | Unlearning' automatic biases: The malleability of implicit stereotypes and prejudice[END_REF], aversive racism theory (Son [START_REF] Hing | Inducing hypocrisy to reduce prejudicial responses among aversive racists[END_REF], social identity theory (Ashburn-Nardo, Voils, & Monteith, 2001), and system justifi cation theory [START_REF] Jost | Non-conscious forms of system justifi cation: Implicit and behavioral preferences for higher status groups[END_REF][START_REF] Rudman | Minority members' implicit attitudes: Ingroup bias as a function of group status[END_REF]. Finally, the relationship between implicit and explicit attitudes can be characterized as heterogeneous [START_REF] Blair | Implicit stereotypes and prejudice[END_REF][START_REF] Fazio | Implicit measures in social cognition research: Their meaning and use[END_REF][START_REF] Nosek | Harvesting implicit group attitudes and beliefs from a demonstration web site[END_REF], but it is moderated by theoretically expected variables, including attitude strength, social desirability, and measurement error [START_REF] Cunningham | Implicit attitude measures: Consistency, stability, and convergent validity[END_REF][START_REF] Karpinski | Attitude importance as a moderator of the relationship between implicit and explicit attitude measures[END_REF][START_REF] Nosek | Moderators of the relationship between implicit and explicit evaluation[END_REF].

However, implicit researchers can be criticized -along with prejudice researchers in generalfor rarely assessing overtly hostile behaviors [START_REF] Mackie | Intergroup relations: Insights from a theoretically integrative approach[END_REF]. This oversight affords an opening for implicit social cognition critics. In particular, the IAT's predictive utility in the prejudice domain has been questioned by [START_REF] Arkes | Attributions of implicit prejudice, or 'would Jesse Jackson "fail" the Implicit Association Test?[END_REF]-along with behavioral data for other implicit measures (e.g. [START_REF] Dovidio | Implicit and explicit prejudice and interracial interactions[END_REF][START_REF] Fazio | Variability in automatic activation as an unobtrusive measure of racial attitudes. A bona fi de pipeline[END_REF]-on the basis that some behaviors might refl ect emotions other than antipathy (e.g. nonverbal reactions, such as gaze or speech disfl uencies, might indicate guilt or anxiety). However, in Poehlman et al.'s (2004) meta-analysis of IAT fi ndings, less than a third of the behaviors assessed were nonverbal (or otherwise ambiguous). Yet, as already noted, the IAT was a better predictor of these behaviors, compared with self-reports. Moreover, the role of anxiety in prejudicial responding has long been recognized (e.g. [START_REF] Islam | Dimensions of contact as predictors of intergroup anxiety, perceived out-group variability, and out-group attitude: An integrative model[END_REF][START_REF] Stephan | Intergroup anxiety[END_REF]. That is, people may feel anxious in the presence of outgroup members, but this does not mean they are egalitarians. In fact, prejudice is typically defi ned as a negative orientation that can be expressed as moving against (animus) or moving away from outgroup members (which can also refl ect guilt and anxiety; [START_REF] Ashmore | The problem of intergroup prejudice[END_REF].

Nonetheless, although automatic biases have been linked to negative judgments of Blacks (e.g. [START_REF] Jackson | Automatically activated racial attitudes[END_REF][START_REF] Lambert | On the predictive validity of implicit attitude measures: The moderating effect of perceived group variability[END_REF][START_REF] Rudman | Implicit and explicit consequences of exposure to violent and misogynous rap music[END_REF], and female job applicants [START_REF] Rudman | Prescriptive gender stereotypes and backlash toward agentic women[END_REF], unambiguously harmful behaviors are seldom investigated, whether explicit or implicit bias is assessed. The present research sought to begin to fi ll this gap in the implicit literature. To do so, we focused on the IAT because it has borne the brunt of researchers' criticisms. In Study 1, we assessed participants' reports of their harmful actions toward Blacks in the past. Behaviors consisted of both active harm (e.g. verbal insults and physical violence) and passive harm (e.g. avoidance or exclusion). In Study 2, we measured people's willingness to cut the budget for student organizations designed to support Jews, Asians, and Blacks. If attitude and stereotype IAT scores predict these behaviors, results would lend confi dence to the IAT's ability to tap implicit prejudice, as opposed to 'mere associations' (Arkes & Tetlock, 2004, p. 268).

Study 1

Our primary aim was to examine the relationship between IAT-assessed biases and non-Black participants' self-reported harmful actions toward Blacks. To encourage candid responding, we asked participants whether they had been the victim of each behavior prior to their report of being a perpetrator. A secondary aim was to compare the attitude and stereotype IATs as predictors of harmful discrimination. To do so, we used evaluative stereotypes because they are conceptually akin to prejudice (see also Rudman et al., 2001;[START_REF] Wittenbrink | Evidence for racial prejudice at the implicit level and its relationship with questionnaire measures[END_REF]. Because the stereotype IAT consists of both evaluative and cognitive associations, they might capture the implicit prejudice construct more completely [START_REF] Breckler | Empirical validation of affect, behavior, and cognition as distinct components of attitude[END_REF]. Finally, we included self-report measures as a means of testing whether implicit associations can predict discrimination above and beyond explicit attitudes.

Method

Participants Sixty-four volunteers (21 male, 43 female) participated to partially fulfi ll an introductory psychology course requirement (M age = 20). Of these, 52 were White (81%), 6 were Asian American (9%), and 6 were Latino (9%). Data from six participants showing high error rates (> 25%) on the computer tasks were eliminated, as were data from 13 African American participants.

IAT measures

The attitude IAT and stereotype IAT each used 7 White male names (e.g. John, Andrew, Peter, Brad) and 7 Black male names (e.g. Lamar, Malik, Rashan, Leroy) as the target concepts. Table 1 shows the remaining stimuli. The attitude IAT used the pleasant and unpleasant words shown in columns 1-2. The stereotype IAT used negative attributes associated with Blacks (e.g. lazy, hostile) and positive attributes associated with Whites (e.g. ambitious, calm) shown in columns 3-4.

The IATs were adopted from and administered exactly as in past research (Rudman et al., 2001). The order in which participants performed the critical blocks was counterbalanced across subjects, as was the order in which participants performed the IATs (these procedural variables did not infl uence results). The IAT effect was computed so that high scores refl ect greater tendency to associate Blacks with negative versus positive attributes, compared to when these associations were reversed. Scoring for the IAT followed recent recommendations [START_REF] Greenwald | Understanding and using the Implicit Association Test: I. An improved scoring algorithm[END_REF]. Specifi cally, we used the D statistic because it has been shown to be less infl uenced by procedural variables (e.g. counterbalancing).

Explicit measures

Explicit attitudes Participants completed a feeling thermometer and the Modern Racism Scale (MRS; [START_REF] Mcconahay | Modern racism, ambivalence, and the Modern Racism Scale[END_REF]. The feeling thermometer asked participants to indicate, separately for African American men and White American men, the extent to which they felt positively toward each group (0 = extremely cold, or unfavorable ; 99 = extremely warm, or favorable). The difference between these measures was computed such that high scores represented more positive attitudes toward White than Black men (to mirror the IAT). The MRS consists of seven Note: IAT stimuli were adopted from past research (Rudman et al., 2001).

items (e.g. 'Blacks are getting too demanding in their push for equal rights'), scored on a scale ranging from 1 (strongly agree) to 5 (strongly agree). MRS scores were averaged (α = .82) so that high scores refl ect more anti-Black attitudes.

Discriminatory behaviors To assess harmful behaviors, we used a slightly shorter version of a past measure [START_REF] Contrada | Measures of ethnicity-related stress: Psychometric properties, ethnic group differences, and associations with well-being[END_REF]. Participants were asked to report how often, over the course of their lifetime, they had engaged in specifi c actions on a scale ranging from 1 (never) to 7 (very often). The verbal index averaged two items pertaining to making ethnically offensive comments and jokes, either in the presence of targets or behind their backs (r(62) = .56, p < .001) (M = 3.65, SD = 1.30). The defensive index averaged two items pertaining to avoiding or excluding others from social gatherings and organizations because of their ethnicity (r(62) = .69, p < .001) (M = 3.72, SD = 1.35). The offensive index averaged three items pertaining to nonverbal hostility (e.g. giving 'the fi nger'), and physically hurting targets or their property (or threatening to do so) because of their ethnicity, (α = .89; M = 2.65, SD = .80). For each item, participants fi rst indicated the extent to which they had been the target of ethnic discrimination (e.g. 'How often have you been the target of offensive comments because of your ethnicity?'). The purpose of these items was to encourage reporting discrimination toward others (i.e. to justify participants' own behavior). As expected, these items covaried with reports of verbal, defensive, and offensive behaviors (all rs > .31, ps < .05).

Because they served no other purpose, they are not further discussed. 1

Procedure Volunteers participated individually in separate cubicles. Measures were administered using a computer program that presented items randomly, within each measure. To increase participants' confi dence in their anonymity, they generated their own identifi cation number. They fi rst completed the attitude and stereotype IATs, in counterbalanced order. They then completed the explicit attitude and behavior measures, in counterbalanced order. Consistent with past research, the order of administrating the IAT and self-reports did not affect scores on either implicit or explicit measures (e.g. [START_REF] Greenwald | Understanding and using the Implicit Association Test: I. An improved scoring algorithm[END_REF].

Results and discussion

Preliminary analyses To assess internal consistency, we correlated practice trials with critical trials within each IAT. The coeffi cient for the attitude IAT was reliable (r(62) = .69, p < .001), as it was for the stereotype IAT (r(62) = .71, p < .001). Note: IAT results are displayed using the D statistic [START_REF] Greenwald | Understanding and using the Implicit Association Test: I. An improved scoring algorithm[END_REF]. High scores on the IATs and the thermometer index refl ect more positive evaluation of Whites compared with Blacks.

As seen in Table 2, the attitude and stereotype IATs covaried (see also Rudman et al., 2001). The two explicit measures were also related.The attitude IAT was reliably related to the MRS and to the thermometer. The stereotype IAT was positively but unreliably related to the explicit measures.

Predicting discrimination Study 1's main objective was to test whether the IAT predicts unambiguously harmful behaviors. As shown in Table 3, the attitude IAT covaried with verbal discrimination (e.g. ethnic slurs and jokes), whereas the stereotype IAT was related to each behavioral index (verbal, defensive, and offensive). Finally, the MRS reliably covaried with all three behavioral indexes, whereas the thermometer index reliably covaried with defensive and offensive, but not verbal, discrimination.

The behavioral measures were robustly related (α = .80), and were therefore combined. A hierarchical regression analysis was then conducted to examine whether the IAT predicts unique variance in discrimination, after accounting for explicit measures. Table 4 shows the results. The discrimination index was reliably predicted by the stereotype (but not the attitude) IAT, even after accounting for the MRS and the thermometer index, which also contributed unique variance.

In sum, Study 1's focal results were the linkages shown between implicit associations and participants' history of anti-Black discrimination. The behaviors predicted by the stereotype IAT ranged from active harm (e.g. verbal slurs and personal and property violations) to more passive harm (e.g. exclusion and avoidance), whereas the attitude IAT predicted offensive comments and jokes (using bivariate analyses). Although the attitude IAT did not contribute unique variance to the discrimination index after accounting for explicit measures, this may be due to the stronger correlations between it and the direct measures, compared with the stereotype IAT. Alternatively, the stereotype IAT, because it combines beliefs with evaluation, may be a superior measure of implicit bias. For exploratory purposes, we conducted a hierarchical regression on the behavioral index in which the attitude IAT was entered fi rst, followed by the stereotype IAT. In Step 1, the attitude IAT was a reliable predictor (β = .27, p < .05), but it was reduced to nonsignifi cance in Step 2 (β = .17, p = .16), suggesting that the stereotype IAT is a more effective predictor of harmful actions (β = .28, p < .05).

Study 2

In Study 2, we extended our analysis to include economic discrimination against Jews, Asians, and Blacks. Data concerning each group were collected over three phases, during a time period of approximately three months. Each investigation examined predictive utility for IAT-assessed evaluative stereotypes (e.g. negative attributes associated with Jews and positive Note: Correlations were computed using the D statistic [START_REF] Greenwald | Understanding and using the Implicit Association Test: I. An improved scoring algorithm[END_REF]. Correlations using log transformed latencies were similar. [START_REF] Greenwald | Understanding and using the Implicit Association Test: I. An improved scoring algorithm[END_REF].

attributes associated with Christians) vis-a-vis budget cuts for minority student organizations.

As in Study 1, the MRS and feeling thermometers were included for comparison purposes. The Asian-White and the Black-White phases also included an attitude IAT.

Because some investigators have argued that the IAT is a measure of environmental associations [START_REF] Karpinski | Attitudes and the Implicit Association Test[END_REF][START_REF] Olson | Reducing the infl uence of extra-personal associations on the Implicit Association Test: Personalizing the IAT[END_REF], we also included a direct measure of cultural favoritism. If IAT scores are related to perceptions that cultural stereotypes are more positive for majority than minority groups, support for this reasoning will be shown. However, Nosek and Hansen (in press), using over 50 attitude objects (including groups based on ethnicity, religion, sexual orientation, and gender) and thousands of Web site respondents, consistently found a negligible link between cultural favoritism and the IAT. Therefore, we expected to fi nd a similar pattern.

Method

Participants All participants volunteered in exchange for partial fulfi llment of their Introductory Psychology course research requirement. Only data from group members represented in the IAT were used in the analyses. In the Jewish-Christian phase, there were 89 volunteers (64 Christians, 25 Jews). Of these, 37 were men and 52 were women. In the Asian-White phase, there were 89 volunteers (59 Whites, 30 Asians). Of these, 38 were men and 51 were women. In the Black-White phase, there were 126 volunteers (89 Whites, 37 Blacks). Of these, 34 were men and 92 were women.

IAT measures

Jewish-Christian phase The stereotype IAT used six negative Jewish attributes (e.g. cheap, controlling, dominating) and six positive Christian attributes (e.g. generous, charitable, friendly). Following past research [START_REF] Rudman | Measuring the automatic components of prejudice: Flexibility and generality of the Implicit Association Test[END_REF], target concepts consisted of six Jewish surnames (e.g. Shapiro, Cohen, Katz) and six Christian surnames (e.g. Miller, Taylor, Johnson). The attitude IAT was not administered in this phase.

Asian-White phase Each IAT used six Asian surnames (e.g. Chang, Kwan, Yamashita) and six White names (e.g. Miller, Taylor, Johnson) as the target concepts. The attitude IAT was otherwise identical to Experiment 1's. The stereotype IAT used six negative Asian attributes (e.g. reserved, stiff, inhibited) and six positive White attributes (e.g. warm, friendly, outgoing).

Black-White phase

The attitude and stereotype IATs were adopted from Experiment 1.

Explicit measures

Explicit attitudes The thermometer index was identical to Experiment 1's, with participants indicating their feelings toward the appropriate groups in each phase. A difference score was computed such that high scores indicated more positive evaluation of Christians compared with Jews, Whites compared with Asians, or Whites compared with Blacks. When necessary, the MRS was modifi ed by replacing Blacks with either Jews or Asians as the target group (all αs > .84). The measure was scored such that high scores refl ected more symbolic prejudice.

Cultural knowledge

Participants were asked to rate how positive the cultural stereotypes of each group represented in the IATs were on scales ranging from 1 (not at all) to 10 (extremely). A difference score was formed so that high scores reflected judging stereotypes about majority groups as more positive than stereotypes about majority groups for each phase (e.g. Christians higher than Jews in the Jewish-Christian phase).

Economic discrimination Following past research [START_REF] Haddock | Assessing the structure of prejudicial attitudes: The case of attitudes toward homosexuals[END_REF][START_REF] Zanna | The naive epistemology of a working social psychologist (or the working epistemology of a naive social psychologist): The value of taking 'temporary givens' seriously[END_REF], participants completed a budget measure that was presented as a survey conducted on behalf of the Psychology Department (i.e. separate from the main study), and was prefaced by the following statement:

We have been asked to administer this short survey as part of all of our research protocols this year, as a means of gathering student opinion. The student government has been forced to cut funding to student organizations by 20%. We ask that you help out by recommending which organizations listed below should have their funds decreased. Current funding for each organization is listed in column 1. Place your recommended funding in column 2. Keep in mind that your suggestions should result in an approximately 20% decrease in funding. Please place this survey in the box when you are through. The results of this survey will be presented to the student government.

Eight student organizations were then listed, including the focal groups (Chabad Jewish Student Organization, Japanese Cultural Association, and Blacks United to Save Themselves) and fi ve fi llers (e.g. the PIRG organization, the drama club, and the marching band). Current funding for the focal group in each phase was listed as US$11, 500. The difference between this and participants' recommended funding for that group was computed so that high scores indicated greater budget cuts (i.e. economic discrimination).

Procedure Upon entering the lab, participants were escorted to a separate room and asked to complete the budget recommendation measure before participating in the 'main study'. Participants placed their completed survey (subtly coded with their identifi cation number) in a box marked 'Psychology Department Survey' to enhance the cover story. Participants were then led to a private cubicle where they performed the attitude and stereotype IATs in counterbalanced order (except in the Jewish-Christian phase, when only stereotypes were assessed), as well as the explicit measures (in the order described above). The implicit and explicit measures were administered in counterbalanced order. The IATs were administered exactly as in Experiment 1 (e.g. with task order counterbalanced). The effects of these procedural variables were nonsignifi cant in each phase. Upon completion of the measures, participants returned to the main room for a process debriefi ng. No subject expressed suspicion that the budget measure was part of the protocol.

Results and discussion

Preliminary analyses Internal consistency analyses of the IATs revealed reliable coeffi cients for the Jewish-Christian phase (r(87) = .61, p < .01); the Asian-White phase (rs(87) = .59 and .60 for the attitude and stereotype IATs, respectively, ps < .01); and the Black-White phase (rs(124) = .57 and .63) for the attitude and stereotype IATS, respectively, ps < .01. Table 5 shows descriptive statistics for each phase of Study 2's data collection, as a function of group membership (Christians compared with Jews, Whites compared with Asians, and Whites compared with Blacks).

As seen in Table 5, the IATs showed the expected pattern of known groups validity, accompanied by reasonably large group difference effect sizes (all ds > .73), as did the explicit attitude measures (all ds > .74). As in past research, Jews and Asians showed reliable ingroup bias on both sets of measures (Rudman et al., 2002). Blacks demonstrated the typical pattern of showing weak implicit, but robust explicit, ingroup bias (e.g. [START_REF] Nosek | Harvesting implicit group attitudes and beliefs from a demonstration web site[END_REF]. Not surprisingly, majority groups (Christians and Whites) showed greater economic discrimination, compared with minority groups (Jews, Asians, and Blacks; all ds > .62). Finally, the results of the cultural stereotype index (the perceived tendency for society to view majority group members more positively than minority group members) revealed that minority group members tended to report a greater discrepancy than did majority group members. However, this difference was reliable only in the Asian-White phase (t(87) = 2.86, p < .01). In sum, there was general agreement among majority and minority group members concerning how the culture viewed their groups, but little agreement concerning how the groups should be evaluated, either implicitly or explicitly.

Table 6 shows the relationships among Study 2's variables, for each phase. As expected, the IAT was unrelated to cultural stereotype index in each phase (Nosek & Hansen, in press). By contrast, the IATs and thermometer indexes were positively correlated (rs ranged from .27 to .53). The MRS covaried with the Jewish-Christian stereotype IAT and with attitude IAT scores in the Black-White phase (echoing Study 1's results). Finally, the MRS tended to be negatively linked to cultural stereotypes. That is, people who thought the Notes: IAT results are displayed using the D statistic [START_REF] Greenwald | Understanding and using the Implicit Association Test: I. An improved scoring algorithm[END_REF]. For each measure, high scores refl ect greater bias against minority groups ( Jews, Asians, or Blacks) or greater perceived bias in society (cultural stereotypes). The effect size (Cohen's d ) represents group differences. Conventional small, medium, and large effect sizes are .20, .50, and .80, respectively [START_REF] Cohen | Statistical power for the behavioral sciences[END_REF]. Note: IAT correlations were computed using the D statistic [START_REF] Greenwald | Understanding and using the Implicit Association Test: I. An improved scoring algorithm[END_REF]. Correlations using log transformed latencies were similar.

culture favored majority groups with more positive stereotypes than minority groups tended to show less symbolic prejudice (especially toward Jews and Blacks). This suggests that minority groups' justice-seeking behaviors are supported when society is perceived as biased.

Predicting discrimination Our primary aim was to examine the relationship between IAT measures and economic discrimination. Table 6 shows that the stereotype IAT was reliably linked to the budget index in each phase. That is, people who associated minority group members with negative attributes and majority group members with positive attributes were also likely to recommend budget cuts for the target minority group's student organization. The attitude IAT performed similarly in the Asian-White and Black-White phases (it was not administered in the Jewish-Christian phase). Thus, both the stereotype and attitude IAT predicted economic discrimination. The thermometer index was reliably related to the budget measure in the Jewish-Christian and Asian-White phases, but not in the Black-White phase. By contrast, the MRS did not covary with the budget index, irrespective of the attitude object (all rs < .17, ns).

Finally, the cultural stereotype index was not a predictor of economic discrimination (all rs < .12, ns).

A hierarchical regression analysis was conducted for each phase to examine whether the IAT predicts recommended budget cuts after accounting for the thermometer index (the MRS and cultural stereotypes were not included because they showed weak predictive utility). Moreover, we controlled for group identity (coded as 0 = majority, 1 = minority) to provide a more conservative test (cf. [START_REF] Karpinski | Attitude importance as a moderator of the relationship between implicit and explicit attitude measures[END_REF]. Table 7 shows the results. For the Jewish-Christian phase, discrimination was reliably predicted by group identity, the thermometer index, and the stereotype IAT. For the Asian-White phase, discrimination was marginally [START_REF] Greenwald | Understanding and using the Implicit Association Test: I. An improved scoring algorithm[END_REF]. Group identity was dummy coded (0 = majority, 1 = minority).

predicted by the thermometer index (p = .07), and reliably predicted by the stereotype IAT. For the Black-White phase, only the stereotype IAT contributed uniquely to discrimination. Thus, the stereotype IAT remained a predictor even after controlling for explicit attitudes and group identity in each phase. By contrast, the attitude IAT did not account for unique variance, and the thermometer index was only signifi cant in the Jewish-Christian phase.

As in Study 1, we hierarchically regressed the behavioral index on the attitude IAT, followed by the stereotype IAT. For the Asian-White phase, the attitude IAT was a reliable predictor in Step 1 (β = .25, p < .05), but it was reduced to nonsignifi cance in Step 2 (β = .16, p = .11) after the stereotype IAT was accounted for (β = .30, p < .01). For the Black-White phase, the attitude IAT was a reliable predictor in Step 1 (β = .23, p < .05), but in Step 2 it was dramatically reduced (β = .05, ns); by contrast, the stereotype IAT was signifi cant (β = .24, p < .05).

In sum, Study 2 showed that implicit biases predicted economic discrimination toward Jews, Asians, and Blacks, and that the stereotype IAT was either an equal or superior predictor, compared with explicit attitudes. When conditions afforded a comparison of the attitude and stereotype IATs, the latter was more effective vis-a-vis contributing unique variance. In concert with Study 1, the pattern suggests that evaluative stereotypes refl ect implicit biases better than evaluative associations alone. Finally, in each phase, the cultural stereotype index was unrelated to either attitude or stereotype IATs, as in past research (Nosek & Hansen, in press). It was also unrelated to economic discrimination.

General discussion

Across two studies, the stereotype IAT predicted harmful actions toward outgroup members, even after accounting for explicit prejudice measures. In Study 1, behaviors included non-Black participants' reported history of verbal, defensive, and offensive racial discrimination. In Study 2, stereotype IATs predicted budget reductions for Jewish, Asian, and Black student organizations. The behaviors assessed ranged in severity from racial jokes to budget cuts to blatant aggression, but each is overtly discriminatory and therefore of consequence. Moreover, we included behaviors that can be characterized as unambiguously hostile (e.g. giving the fi nger and physically harming an individual or their property). Although we cannot rule out the possibility that these behaviors refl ected other emotions (such as fear or guilt), they undoubtedly also stem from antipathy.

Taken together, the results support conceptualizing the IAT as a measure of individual differences in automatic biases. Study 2 directly tested whether cultural favoritism infl uences the IAT, but found no evidence to support the hypothesis (see also Nosek & Hansen, in press). Majority and minority group members recognized the latter's lower status, but this did not infl uence their implicit biases. Thus, our results are not in line with the strong form of the environmental associations hypothesis, in which it is argued that IAT scores are attributable primarily to cultural, rather than personal, attitudes [START_REF] Arkes | Attributions of implicit prejudice, or 'would Jesse Jackson "fail" the Implicit Association Test?[END_REF][START_REF] Olson | Reducing the infl uence of extra-personal associations on the Implicit Association Test: Personalizing the IAT[END_REF][START_REF] Karpinski | Attitudes and the Implicit Association Test[END_REF]cf. Karpinski et al., 2005 for a more moderate version). This is not to imply that cultural milieu has no infl uence on implicit biases, but rather to stress that there is no clear boundary between self and society-and this may be particularly true at the automatic level [START_REF] Banaji | Implicit attitudes can be measured[END_REF][START_REF] Devine | Stereotypes and prejudice: Their automatic and controlled components[END_REF]. Indeed, there are theoretical reasons to suspect that culture can condition people's attitudes, with or without their consent (e.g. [START_REF] Banaji | Implicit attitudes can be measured[END_REF][START_REF] Devine | Stereotypes and prejudice: Their automatic and controlled components[END_REF][START_REF] Gaertner | The aversive form of racism[END_REF][START_REF] Greenwald | Implicit social cognition: Attitudes, self-esteem, and stereotypes[END_REF]. Moreover, cultural biases may be internalized for many reasons, including self-esteem, system justification, and social adjustment functions. Thus, the relationship between self and society is likely to be interdependent, even for individuals who resist being prejudiced-a fact that leads to the necessity of becoming aware of automatic biases in order to combat them.

The normativeness of implicit bias

The observation that IAT scores predict a range of discriminatory actions suggests that they are person-centered and somewhat refl ective of antipathy (see also [START_REF] Poehlman | Measuring and using the Implicit Association Test: III. Meta-analysis of predictive validity[END_REF]. In the same breath, we do not believe that IAT scores indicate explicit bigotry. Implicit biases are simply automatic (over-learned) evaluations; while they may refl ect hostility, they also stem from many other infl uences [START_REF] Rudman | Sources of implicit attitudes[END_REF], including a natural proclivity for partisanship [START_REF] Greenwald | Implicit partisanship: Taking sides for no reason[END_REF]. In this respect, we are reminded of [START_REF] Allport | The nature of prejudice[END_REF] defense of the normality of social categorization and prejudgment, which softened the moral sting of prejudice without removing responsibility for it [START_REF] Fiske | Intent and ordinary bias: Unintended thought and social motivation create casual prejudice[END_REF].

Indeed, a recurrent insight from response latency measures is that people tend to automatically react with preference for similar others (as they do for themselves; [START_REF] Greenwald | Using the Implicit Association Test to measure selfesteem and self-concept[END_REF]. Although this bias is condoned for many preferences (e.g. for our own children), it raises the specter of bigotry when applied to groups who do not share our genetic makeup or cultural background. Perhaps this is why some authors have argued (prematurely, in our view) that cultural biases are primarily responsible for IAT scores. But if we can view automatic biases as refl ective of the normal human condition, we will be less likely to stiff-arm the messenger and hopefully, more open to becoming aware of them in order to better combat their consequences.

Limitations and future directions

In Study 1, asking participants to report past hostile behaviors was likely to evoke social desirability concerns. To counter this, we allowed participants to fi rst report the extent to which they had suffered each behavior [START_REF] Contrada | Measures of ethnicity-related stress: Psychometric properties, ethnic group differences, and associations with well-being[END_REF]. The goal was to afford justifi cation for respondents' own actions and encourage honesty. Future research should compare results with and without justifi cation items to examine their effect. If they are advantageous, the procedure may provide a template for assessing overt hostility-an important research agenda [START_REF] Mackie | Intergroup relations: Insights from a theoretically integrative approach[END_REF].

In two studies, the stereotype IAT predicted a range of discriminatory behaviors, in support of its construct validity. This was true when we controlled for explicit attitudes in both studies, and for group identity in Study 2. The relatively weak performance of the attitude IAT (after accounting for the stereotype IAT, as well as explicit measures) suggests that evaluative beliefs may capture the implicit prejudice construct better than simple good-bad associations. This advantage may stem from the fact that negative outgroup stereotypes (and positive ingroup stereotypes) afford more justification for discrimination than associating ingroup and outgroup members with pleasant and unpleasant words. However, considerably more research that affords a comparison between implicit attitudes and stereotypes is needed before we can have confi dence in the stereotype IAT's superior predictive utility.

In addition, it would be interesting to uncover moderators of reactions to IAT scores. To date, there are indications that people who are motivated not to appear racist, or who are anxious about their scores, tend to react defensively [START_REF] Frantz | A threat in the computer: The race Implicit Association Test as a stereotype threat experience[END_REF]Monteith, Voils, & Ashburn-Nardo, 2001). But some people respond to their IAT scores with greater equanimity. For example, the fi rst author demonstrates her automatic biases in the classroom, to create an atmosphere of trust. Placed in this context, implicit orientations become more normative and less threateningnot because society is to be blamed for them, but because growing up in a culture where some people are valued more than others is likely to permeate our private orientations, no matter how discomfi ting the fact [START_REF] Banaji | Implicit attitudes can be measured[END_REF][START_REF] Devine | Stereotypes and prejudice: Their automatic and controlled components[END_REF]. For this reason, the IAT is a powerful educational tool, as it opens people up to discussions about social justice that might otherwise be dismissed as antiquated [START_REF] Bombardieri | Summers displays new understanding of women's careers[END_REF]. Even people who argue that biases can be rational (e.g. [START_REF] Arkes | Attributions of implicit prejudice, or 'would Jesse Jackson "fail" the Implicit Association Test?[END_REF]) can appreciate the disconcerting fact that Blacks as a group are automatically associated with negative attributes for many Whites.

Future research should also continue to compare the predictive utility of implicit and explicit attitudes. Although it has been argued that implicit biases best predict spontaneous behaviors [START_REF] Fazio | Implicit measures in social cognition research: Their meaning and use[END_REF], self-reports (a controlled behavior) often covary with them. In fact, validity for response latency measures has often relied on controlled judgments (e.g. [START_REF] Livingston | The role of perceived negativity in the moderation of African Americans' implicit and explicit racial attitudes[END_REF][START_REF] Rudman | Implicit and explicit consequences of exposure to violent and misogynous rap music[END_REF]. Although some implicit measures appear to be better at predicting automatic versus controlled behavior [START_REF] Dovidio | Implicit and explicit prejudice and interracial interactions[END_REF], in other cases the behaviors have been a mix of automatic and controlled actions [START_REF] Fazio | Variability in automatic activation as an unobtrusive measure of racial attitudes. A bona fi de pipeline[END_REF][START_REF] Mcconnell | Relations among the Implicit Association Test, explicit attitudes, and discriminatory behavior[END_REF]. The present research used behavioral measures that were likely more controlled than automatic (reporting past discrimination in Study 1 and recommending budget cuts in Study 2). Thus, there is no clean, process-driven divide by which to defi ne the predictive utility of implicit and explicit responses.

Finally, the link between explicit attitudes and behavior is moderated by numerous factors; it cannot be otherwise for implicit attitudes [START_REF] Fazio | Implicit measures in social cognition research: Their meaning and use[END_REF][START_REF] Karpinski | Attitude importance as a moderator of the relationship between implicit and explicit attitude measures[END_REF][START_REF] Nosek | Moderators of the relationship between implicit and explicit evaluation[END_REF]. Moreover, behavior directed toward individuals may be dramatically different from group-related actions; our brains react differently when we individuate [START_REF] Wheeler | Controlling racial prejudice: Social cognitive goals affect amygdala and stereotype activation[END_REF], and so do we [START_REF] Brewer | A dual-process model of impression formation[END_REF][START_REF] Fiske | A continuum of impression formation, from category-based to individuating processes: Infl uences of information and motivation on attention and interpretation[END_REF]. Thus, we caution against assuming that IAT scores refl ect how people will generally behave toward outgroup members.

Notes

1. Readers might be interested in the relationships among being victimized and the implicit and explicit measures. Participants who reported being excluded by outgroup members tended to show high scores on the stereotype IAT (r(62) = .32, p < .01), and marginally, on the MRS and the thermometer (both rs(62) = .22, ps < .08). Targets of physical (or threatened) aggression, and those who received the fi nger, also scored higher on the stereotype IAT (both rs(62) = .24, ps = .05). No other relationships approached signifi cance (all rs(62) < .15, ns). 2. The data for each trial block included mean response latency (in ms) and error rates.

Response latencies greater than 3000 ms and less than 300 ms were recoded as 3000 and 300 ms, respectively; the fi rst two trials of each block were dropped because of their typically lengthened latencies. Analyses of participants' accuracy revealed low error rates on critical trials (an average of 5%); error trials were included in IAT effect scores. Following [START_REF] Greenwald | Understanding and using the Implicit Association Test: I. An improved scoring algorithm[END_REF], we computed the D statistic for use in correlational analyses. Results using log transformed IAT scores were similar. 3. By convention, small, medium, and large effect sizes correspond to .20, .50, and .80, respectively [START_REF] Cohen | Statistical power for the behavioral sciences[END_REF].

Table 1 .

 1 Stimuli for implicit prejudice and stereotype measures (Study 1)

	Attitude IAT	Stereotype IAT
	Pleasant	Unpleasant Negative	Positive
	words	words	traits	traits
	sunshine	fi lth	lazy	ambitious
	smile	death	shiftless	industrious
	angel	devil	unemployed successful
	luck	slime	hostile	calm
	rainbow	cancer	dangerous	trustworthy
	paradise	hell	threaten	ethical
	fortune	poison	violent	lawful

Table 2

 2

(fourth row) displays mean latencies for the implicit attitude and stereotyping measures. 2 On average, participants favored Whites over Blacks on both IATs, resulting in large effect sizes for both measures (attitude d = .75, stereotype d = .76). 3 By contrast, negligible prejudice was reported on the MRS and the thermometer index (i.e. on average, Whites were not evaluated more favorably than were Blacks; d = .09).

Table 2 .

 2 Summary statistics for implicit and explicit measures (Study 1)

	Thermometer

*p < .05; **p < .01.

Table 3 .

 3 Relations among intergroup orientations and discrimination (Study 1)

	Behavior Attitude Stereotype
	measure	IAT	IAT	MRS Thermometer
	Verbal	.41**	.34** .37**	.17
	Defensive .15	.30*	.51*	.44**
	Offensive .12	.25*	.53**	.31*
	*p < .05; **p < .01.

Table 4 .

 4 Predicting harmful discrimination from explicit and implicit measures (Study 1)

	Hierarchical
	regression
	model	Step	β	t	R 2	F ∆
	MRS	1	.49	4.51** .35 16.71**
	Thermometer
	index	1	.22	2.00*
	MRS	2	.42	3.86**
	Thermometer
	index	2	.31	2.85**
	Attitude IAT	2	.07	.65
	Stereotype IAT 2	.33	2.98** .44 4.53*

*p < .05; **p < .01. Notes: Standardized regression coeffi cients are shown. IAT effects used in these analyses were based on the D statistic

Table 5 .

 5 Summary statistics for implicit and explicit measures (Study 2)

	IAT	Attitude	Stereotype		Therm	Budget	Cultural
	measure	IAT	IAT	MRS	index	index	stereotypes
	Jewish-Christian
	Christians (n = 64)	-	.37	2.45	11.25	$2480	2.35
	Jews (n = 25)	-	-.35	1.62	-12.91	$1200	3.04
	Pooled SD	-	.44	.60	16.42	$1890	2.59
	Group difference d	-	1.64	1.38	1.47	.68	-.27
	Asian-White
	Whites (n = 59)	.42	.32	2.31	7.97	$1895	2.24
	Asians (n = 30)	-.47	-.28	1.20	-14.17	$632	4.07
	Pooled SD	.63	.47	.64	17.58	$1981	2.94
	Group difference d	1.41	1.28	1.74	1.25	.63	-.62
	Black-White
	Whites (n = 89)	.39	.28	1.70	3.18	$1669	5.44
	Blacks (n = 37)	.04	-.17	1.37	-16.35	$517	6.03
	Pooled SD	.47	.41	.44	19.61	$1711	2.38
	Group difference d	.74	1.09	.75	1.02	.67	-.25

Table 6 .

 6 Correlations among implicit and explicit measures (Study 2)

		IAT measures		Explicit measures
						Cultural
	Measure	Attitude	Stereotype	Thermometer	MRS	stereotypes
	Jewish-Christian
	Thermometer index	-	.53**
	Modifi ed MRS	-	.39**	.53**
	Cultural stereotypes	-	.11	-.05	-.31**
	Budget index	-	.38*	.47**	.11	.11
	Asian-White
	Stereotype IAT	.28**
	Thermometer index	.43**	.28**
	Modifi ed MRS	.15	.05	.33**
	Cultural stereotypes	.02	-.07	-.08	-.12
	Budget index	.25*	.30**	.28**	.16	.10
	Black-White
	Stereotype IAT	.47**
	Thermometer index	.42**	.27**
	MRS	.42**	.14	.30**
	Cultural stereotypes	.02	.01	-.13	-.19*
	Budget index	.23*	.18*	.08	.05	-.03

*p < .05; **p < .01.

Table 7 .

 7 Predicting economic discrimination from explicit and implicit measures (Study 2)

	Hierarchical regression model	Step	β	t	R 2	F∆
	Jewish-Christian
	Group identity	1	-.23	2.40*	.34	11.53**
	Thermometer index	1	.48	4.44**
	Group identity	2	-.27	2.95**
	Thermometer index	2	.33	2.58*
	Stereotype IAT	2	.39	3.03**	.41	9.21**
	Asian-White
	Group identity	1	-.14	1.17	.10	4.32*
	Thermometer index	1	.21	1.73
	Group identity	2	-.01	.10
	Thermometer index	2	.22	1.83
	Attitude IAT	2	.15	1.38
	Stereotype IAT	2	.27	2.41*	.17	3.61*
	Black-White
	Group identity	1	-.24	2.38*	.09	5.93**
	Thermometer index	1	.10	1.00
	Group identity	2	-.17	1.55
	Thermometer index	2	.08	.82
	Attitude IAT	2	.01	.10
	Stereotype IAT	2	.24	2.51*	.14	3.33*

*p < .05; **p < .01. Notes: Standardized regression coeffi cients are shown. IAT effects used in these analyses were based on the D statistic

Acknowledgments

This research was partially supported by Grants BCS-0109997 and BCS-0417335 from the National Science Foundation. Preparation of this article was partially supported by Grant BCS-0417335 from the National Science Foundation to the fi rst author.