Deciding the fate of others: the cognitive underpinnings of racially biased juror decision making.

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Deciding the Fate of Others: The Cognitive Underpinnings of Racially Biased Juror Decision Making a

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Heather M. Kleider , Leslie R. Knuycky & Sarah E. Cavrak

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Georgia State University Published online: 13 Jun 2012.

To cite this article: Heather M. Kleider , Leslie R. Knuycky & Sarah E. Cavrak (2012) Deciding the Fate of Others: The Cognitive Underpinnings of Racially Biased Juror Decision Making, The Journal of General Psychology, 139:3, 175-193, DOI: 10.1080/00221309.2012.686462 To link to this article: http://dx.doi.org/10.1080/00221309.2012.686462

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The Journal of General Psychology, 2012, 139(3), 175–193 C 2012 Taylor & Francis Group, LLC Copyright


Deciding the Fate of Others: The Cognitive Underpinnings of Racially Biased Juror Decision Making HEATHER M. KLEIDER LESLIE R. KNUYCKY SARAH E. CAVRAK Georgia State University

ABSTRACT. In criminal law, jurors are supposed to ignore defendant race when considering factual matters of culpability. However, when judging the merits of a criminal case, jurors’ ability (or inability) to avoid bias may affect verdicts. Fact-based decision making expend cognitive resources, while heuristic-based decisions (e.g., using criminal stereotypes) conserve resources. Here, we investigated whether differences in cognitive resources and prejudice attitudes about Blacks influenced trial outcomes. We tested the impact of working memory capacity (WMC), cognitive load, prejudice, and target race (Black, White) on penalties ascribed to fictional criminal defendants in ambiguous-fact cases. Results showed that when “loaded,” prejudiced-low-WMC persons supported guilty verdicts with higher confidence more often for Black than White defendants. Conversely, regardless of WMC or prejudice attitude, participants penalized White defendants more often when not loaded. We suggest that cognitive resources and prejudice attitude influence fact-based decisions. Links to juror judgments and potential trial outcomes are discussed. Keywords: cognitive resources, decision making, race, stereotype

A CENTRAL GUARANTEE of the American criminal justice system is the right to a fair trial, where guilt or innocence is determined from case evidence, regardless of a defendant’s race, religion, or other traits. However, Black defendants are convicted more often and receive more severe punishments than their White counterparts (Baldus, Woodworth, Zuckerman, Weiner, & Broffitt, 1998; Bowers, Steiner, & Sandys, 2001). Moreover, these disparities emerge even among mock jurors judging controlled trial summaries, which lack the stress and complexity of

We thank Samuel Sommers, Christian Meissner and Steve Goldinger for their helpful comments on an earlier version of this article. Address correspondence to Heather M. Kleider, Ph.D., Department of Psychology, Georgia State University, Atlanta, GA 30030, USA; [email protected] (e-mail). 175


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actual trials (e.g., Mitchell, Haw, Pfeifer, & Meissner, 2005). These disparities in trial outcomes seem to partly reflect a ubiquitous stereotype of Black American men as violent and criminal (Allport & Postman, 1947). Potentially, this racial stereotype is active during juror decision making, as the majority of wrongfully convicted men who are later exonerated with DNA evidence are Black (60%; The Innocence Project, 2011). This highlights the need for greater empirical exploration into alternative contributions (e.g., factors within individuals) leading to legal inequities. The complexities of juror decision making generally, and the inconsistencies in the empirical evidence (showing that race influences trial outcomes specifically; see Sommers, 2007 for a review), has created a challenge for researchers attempting to understand what facilitates racially biased trial outcomes. The majority of published studies examining race and legal decisions has focused on situational factors (wherein defendant race biases trial outcomes) but has not addressed the cognitive mechanisms that underlie these decisions. A handful of studies have considered individual differences to account for some of the variability in defendant race effects and find that certain personality traits (e.g., social dominance, Kemmelmeier, 2005; authoritarianism, McGowen & King, 1982) moderate the influence of race on decision making. However, other relevant individual differences that have remained largely unexamined in the literature are juror racial attitudes (i.e., prejudice) and cognitive capacity (i.e., working memory capacity). Racial stereotypes are known to affect quick decision making in speeded response paradigms (Correll, Urland, & Ito, 2006), wherein people do not have the opportunity to contemplate their decision but rather make an automatic/reflective response indicative of heuristic processing (i.e., rule-of-thumb). Other studies show that in a juror-type judgment, heuristics linked to racial stereotypes influence what people remember when reviewing case write-ups. For example, participants judging crime vignettes were more likely to convict the defendant and levy more severe punishments when the crime and the defendant were consistent with racial stereotypes and expectations (e.g., a Black man charged with armed robbery) rather than inconsistent (e.g., a Black man charged with embezzlement cases; Jones & Kaplan, 2003). Moreover, these results also revealed that the quantity of evidence required to convict was lower for consistent crime-defendant cases than inconsistent crime-defendant cases. Heuristic strategies (e.g., using stereotypes) are cognitively efficient and thus are a useful alternative to more controlled resource-dependent strategies when the decision-making situation is complex or requires evaluation of multiple pieces of information (Horowitz, FosterLee, & Brolly, 1996). Not only are heuristic strategies cognitively efficient, they are automatically activated such that cognitive resources are required to decide not to use them when making judgments (Bodenhausen, 2005). Although level of individual prejudice does not influence one’s awareness of cultural racial stereotypes or the automatic activation of a stereotype, it does


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influence judgments (e.g., Devine, 1989; Eberhardt, Davies, Purdie-Vaughns, & Johnson, 2006). For example, high- compared to low-prejudice persons associate more negative traits with Black people and rate Black target faces with less favorable sentiments (Lambert et al., 2003). Monitoring self-responses to avoid bias is (cognitive) resource-demanding for high prejudice persons and can impair performance on future cognitive tasks (Richeson & Shelton, 2003). Thus, it follows that, to evaluate trial evidence in a non-biased manner, a juror must be both internally motivated to avoid prejudice attitudes and have sufficient cognitive processing resources to avoid using stereotypes when circumstances emphasize their utility. Theories of social judgment generally suggest that people engage in “modern racism” (Gaertner & Dovidio, 1986), whereby White people are motivated to avoid the appearance of racial bias and will avoid the overt expression of such biases instead opting for a race-neutral judgment if possible. This is fueled by social pressures and norms regarding political correctness, which became pervasive in the 1990s and discouraged prejudice toward Blacks (Plant & Devine, 1998; Blanchard, Lilly, & Vaughn, 1991), motivating White people to avoid the appearance of racial bias. Therefore, if people have the processing resources available to avoid making racially biased decisions, then they will attempt to present themselves as unbiased. However, models of decision making and mental control, such at the Ironic Process Theory (Wegner, 1994), suggest that intention to avoid such decisions may actually have the ironic effect of facilitating such decisions when cognitive resources are unavailable (i.e., when people are performing a dual task or holding a cognitive load). The theory holds that, when attempting to influence mental states, two processing systems are required. One is an operating process that promotes one’s intended mental state (in this example, attempting to appear unbiased) by searching for mental contents consistent with the intended state. The other is a monitoring process that tests whether the operating process is needed by searching for mental contents that are inconsistent with the intended mental state (here, the system would monitor for activated racial stereotype content). The two systems work in harmony; however, the operating process requires more cognitive capacity than the monitoring process. Thus, in conditions that reduce cognitive resources, the monitoring process may supersede the operating process and produce sensitivity to mental contents that are opposite the intended state. Cognitive resource availability can be limited by either individual differences in working memory capacity (WMC), an internal limitation, or the dual presentation of challenging mental tasks (i.e., “load”), an external limitation on capacity (Kane & Engle, 2000). The cognitive efficiency of heuristic processing (e.g., using stereotypes) makes their use a viable strategy when processing resources are taxed or overextended. Moreover, biased decision making is more likely to occur when cognitive processing ability is limited, as one’s personal beliefs are elevated/salient, and thus given more consideration in such situations (Sherman & Bessenoff, 1999) compared to situations where cognitive resources are plentiful.


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Therefore, it seems likely that reliance upon the Black-man-criminal stereotype would be especially likely when WMC is low or processing resources are engaged in other tasks. WMC varies across individuals and is indexed by the extent to which one can attend to a goal task and ignore other stimuli that compete for attention (Rosen & Engle, 1998). WMC has been shown to reliably impact deliberative decision making across many tasks (e.g., simultaneously making word and auditory tone judgments, Unsworth & Engle, 2007). Controlled decision making can be reduced by either internal capacity limitations (i.e., low WMC) or by external demands on one’s cognitive resources (e.g., a cognitive load). Persons with high WMC demonstrate superior cognitive control over automatic cognitions (e.g., increased impulse control, decreased application of default heuristic) and can suppress intrusive thought processes on a regular basis (Rosen & Engle, 1998, von Hippel & Gonsalkorale, 2005). By comparison, low WMC persons are less adept at controlling automatic cognitions because such tasks require greater mental processing than they can harness. When cognitive control is taxed via external cognitive load (e.g., performing simultaneous tasks, Kane & Engle, 2000; emotional arousal, Kleider & Parrott, 2009; time limitations, Payne, Lambert, & Jacoby, 2002), everyone relies on automatic cognitive processes to some extent (Beilock & Carr, 2005). In these situations, high WMC persons respond similarly to the low WMC persons (who consistently perform worse than high capacity persons when not loaded). Theoretically, this is due to high WMC person’s inability to utilize the control they previously exhibited because the taxing stimulus has inhibited cognitive capacity. High cognitive load, therefore, leaves high WMC persons to rely on automatic processes (e.g., heuristics) as found with their low WMC counterparts (Rosen & Engle, 1998). One’s ability to stay focused on a single goal and ignore other information that competes for attention may also influence outcomes in juror-type judgments, but it is thus far not an identified component of biased trial outcomes. To render unbiased judgments of Black defendants in a juror-type task requires sufficient cognitive resources to suppress or ignore obvious stereotypes of the criminal-Black man. Moreover, suppression of negative stereotypes during a judgment task should be most challenging for those people whose personal attitudes endorse the stereotype. Therefore, the focus of the current paper was two-fold. First, our main goal was to test whether differences in cognitive resources lead to biased juror-type decisions. Specifically, we tested whether a relatively low-cognitive capacity (i.e., WMC), a high processing load, or a combination of the two was associated with biased trial outcomes as indexed by guilty verdicts and associated confidence, rendered to Black compared to White defendants. Second, we tested whether prejudiced racial attitudes were a factor in biased juror-type decisions and whether expression of this attitude occurred more often when cognitive resources were limited. Thus, we tested whether high compared to low prejudice, combined with limited cognitive


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resources (low WMC, high cognitive load, or a combination of the two), resulted in biased verdict outcomes. In the present study, we presented participants with case facts depicting varying degrees of evidence strength (weak, ambiguous, strong). As the strength of bona-fide evidence dominates juror verdicts (Visher, 1987), we anticipated that the decision-making process would be relatively easy for the strong and weak cases (for a discussion of case strength and plea bargaining, see McAllister & Bregman, 1986). Alternatively, the ambiguous cases were expected to be more difficult as the decision to convict or exonerate the accused was not obvious, thus, providing the opportunity for a juror to evaluate the evidence through a biased lens. Thus, for theoretical (i.e., ambiguity of evidence increasing opportunity for bias) and applied (i.e., ambiguity of evidence increasing chances of jury trial) reasons, the ambiguous evidence cases were the main focus of this study. Our working hypothesis was that, in the ambiguous evidence cases, either low WMC and/or high cognitive load coupled with high prejudice, would lead to more guilty verdicts of Black than White defendants. Method Participants One hundred ninety eight Georgia State University undergraduate students participated for course credit. Participants self-identified their race (54 White, 57 Black, and 87 other race) and gender (141 female, 57 male). Materials Automated Operation Span Task (Unsworth, Heitz, Schrock, & Engle, 2005) The automated operation span task (OPSPAN) is a computer-administered test that assesses WMC. First, an equation (e.g., “9/3 + 5 = 8”) is presented, and participants are prompted for a true-false response. Next, a to-be-remembered letter is displayed for 800ms. The equation/letter sequence is repeated until 2–7 letters have been presented. When prompted, participants are required to recall all letters, in presentation order, via mouse-click. On-screen feedback displays the total number of correctly recalled letters for each trial. There are a total of 75 equation/letter trials that vary in set size. The OPSPAN score is the number of correctly recalled letter sequences across all trials. Higher scores reflect higher WMC. Case/Photograph Profiles Fictitious, single-paragraph court cases were presented with a 4×5 mug shot of the “defendant.” Pretest data verified (on a 1–7 Likert-type scale) that all defendants were of equivalent attractiveness (M = 4.35), criminality (M = 4.37), and


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stereotypicality1 (M = 4.34) regardless of race. In an effort to activate expectations of criminality associated with Black men, all crimes were Black race-consistent and included armed robbery, grand theft auto, drive-by shooting, and carjacking (see Jones and Kaplan, 2003). A total of twelve court cases were generated, including three versions of each of the four crime types, which varied by strength of case evidence (weak, ambiguous, strong). Key evidence was modified according to case strength. For example, in a strong case, a defendant might be identified by a witness and provide a false alibi; in an ambiguous case, the defendant might be identified by a witness but provide an alibi that is later confirmed, in a weak case the defendant might not be identified by any of the witnesses and provide an alibi that is later confirmed. These determinations were validated in a pretest: Ten na¨ıve raters were presented with the cases, without accompanying photographs. Strong cases resulted in 100% guilty verdicts, weak evidence cases in 0% guilty verdicts, and ambiguous cases in 50% guilty verdicts. Case evidence was varied such that participants would feel compelled by the comparative contrast in case strength to consider the evidence before rendering a decision. Social Distance Scale (Bogardus, 1925; Parillo & Donoghue, 2005) The Social Distance Scale (SDS) is an established self-report measure of prejudice against African Americans wherein participants are presented with the phrase ‘I would be willing to have a White American person as my:’ followed by a sequence of 14 nouns (e.g., next door neighbor, romantic date, governor, wife or husband). Participants rate the degree to which they agree with each statement using a 9-point Likert-type scale. Next, participants are presented with the phrase “I would be willing to have a Black American person as my . . . ” followed by the same sequence of 14 nouns. Design and Procedure Participants signed informed consent documents and then completed the computer-based OPSPAN task, which they were told assessed their ability to perform multiple tasks simultaneously. Next, they read 12 randomly presented case/photograph profiles, including one of each case strength level (i.e., weak, ambiguous, strong) for each of the four crime types. Following procedures used in Goldinger, Kleider, Azuma, and Beike (2003), on load trials (6 case/photograph profiles), participants first viewed case information, followed by six non-words presented simultaneously for 30 seconds that they were required to hold in memory (load manipulation). Then participants rendered a case verdict (guilty, not guilty), followed by a rating of confidence in their judgment. Finally, participants attempted to recall all previously seen non-words for that trial. In no-load trials (six case/photograph profiles), case information was presented, and verdict as well as confidence ratings were provided, completing the trial. Cases were counterbalanced across load condition and defendant race, and defendant photographs


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within a racial category were also counterbalanced across case. Each trial (defendant race × evidence strength) was randomly presented, and both case presentation and decision time were unlimited. Immediately following the presentation of the court cases, the SDS was administered to assess participants’ individual levels of prejudicial attitudes. Once completed, participants were debriefed and dismissed. The experiment was a 2 × 2 × 2 × 2 [Cognitive Capacity: low WMC, high WMC × Prejudice: low, high × Load Condition: load, no load × Defendant Race: Black, White] mixed-model design, with Cognitive Capacity and Prejudice as between-subjects variables, and Load Condition and Defendant Race as withinsubjects variables. Cognitive Capacity and Prejudice were not manipulated factors. Instead they were subject variables and people were allocated to a high or low groups as a function of their WMC and prejudice scores (see below). It is important to note that these scores were not statistically related to one another, r(196) = .06, p = .42. The dependent variables included verdict decision (guilty, not guilty) and confidence in rendered verdict (Likert-type scale: 1–7, not at all confident – highly confident). These variables were then converted to a combined Verdict/Confidence score for each case type (see below). Verdict Decisions Following the procedures of Jones and Kaplan (2003), we created conglomerated verdict/confidence (VC-race) scores for each participant across each of the 12 cases. This combined score allowed for comparisons of the magnitude of each case decision, as well as the frequency of each “guilty” or “not guilty” response, at the same time. The intersection of these two factors provided a more complete picture of individual variability in juror-type judgments, relative to polarized outcomes, which necessarily force the agreement with a choice but preclude the acknowledgement of uncertainty with that choice. Case responses were coded (−1 = not guilty, +1 = guilty) and multiplied by the confidence rating for that particular case decision, resulting in 12 VC-race scores (per subject) along a 14-point verdict continuum (see Table 1). We next subtracted VC-race scores for White defendants from VC-race scores for Black defendants for each case-type (ambiguous/load, ambiguous/no load, etc.). The new combined score is now called VC score. Positive verdict/confidence (VC) scores reflect bias against the Black defendants, whereas negative VC scores reflect bias against the White defendants. The farther the value is away from zero, the greater the discrepancy. These values were used as the primary dependent variable in the data analyses. WMC (OPSPAN Scores) A tertiary split was used to separate participants into WMC categories (Goldinger, et al., 2003). Approximately one-third of the original 198 participants were considered to have low WMC (n = 67; range: 3–33), one-third (n = 67; WMC scores ranging from 34–49) were considered to have average WMC, and the remaining one-third were considered to have high WMC (n = 64; range: 50–75).

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TABLE 1. VC-race scores, M(SE), to A) Ambiguous, B) Strong, and C) Weak Evidence Cases as a Function of Cognitive Ability, Prejudice, Load Condition, and Defendant Race A) Ambiguous Cases


Load

Low Prejudice Black Defendant White Defendant High Prejudice Black Defendant White Defendant

No Load

Low WMC

High WMC

Low WMC

High WMC

.52 (.97) 1.56 (1.04)

.79 (1.12) .84 (1.19)

−.44 (.77) 3.88 (.87)

−.84 (1.17) 3.63 (.99)

3.70 (1.02) .52 (1.09)

2.77 (1.04) −.57 (1.06) 3.50 (1.11) 2.04 (.90) B) Strong Cases

1.32 (1.09) 1.50 (.92)

Load


No Load

Low WMC

High WMC

Low WMC

High WMC

1.44 (.89) 3.44 (.91)

1.79 (1.02) 1.00 (1.04)

.80 (.84) 5.24 (.60)

3.47 (.97) 5.84 (.69)

4.00 (.93) 1.87 (.94)

2.64 (.95) 3.74 (.88) 4.23 (.97) 5.17 (.63) C) Weak Cases

3.18 (.90) 4.27 (.64)

Load


No Load

Low WMC

High WMC

Low WMC

High WMC

−5.84 (.52) −3.44 (.80)

−5.37 (.59) −5.16 (.91)

−5.60 (.61) −3.76 (.75)

−5.42 (.70) −3.68 (.86)

−5.48 (.54) −4.39 (.83)

−5.59 (.55) −3.59 (.85)

−5.30 (.63) −3.70 (.78)

−4.73 (.65) −3.86 (.80)

Negative values reflect “not guilty” verdicts and positive values reflect “guilty” verdicts. Values in italics are standard deviations.

Prejudice Groups Prejudice scores were calculated by summing participant responses to each of 14 statements (i.e., 1–9) separately for the two question-types (White American person, Black American person) on the SDS. The response total of the questions


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pertaining to Black American persons was subtracted from the response total of the questions pertaining to White American persons. This calculation was performed separately for each participant. Prejudice groups were created via tertiary split. Participants designated as “low prejudice” were those with a negative SDS score (n = 63, range: −78 to −3), indicating a bias in favor of Black persons. Participants designated as “medium prejudice” were those with scores in the middle of the full SDS distribution (n = 72, range: −2 to 2), indicating participants with a neutral attitude towards Black persons. Participants designated as “high prejudice” were those with positive SDS scores (n = 63, range: 3 to 76), indicating a bias against Black persons.

Results Manipulation Check A repeated measures ANOVA was conducted with case strength (weak, ambiguous, strong) as the independent variable and average VC scores (collapsed across defendant race and load condition) as the dependant variable to ensure that the variability among the evidence was consistent with pilot testing. As expected, the main effect of case strength was significant, F(2,390) = 696.75, p < .01, η2p = .78, as participants rendered highly negative VC scores to weak cases (M = −4.66), highly positive VC scores to strong cases (M = 3.61), and slightly positive VC scores (M = .98) to the ambiguous evidence cases. All simple comparisons were significant (ps < .01).

Experimental Cells In the analyses that follow, we were primarily interested in examining the comparison between participants at the extremes of both WMC and prejudice distributions. Participants with medium WMC or medium prejudice were not used, and a modified sample of 89 participants was analyzed (race: 24 White, 30 Black, and 35 other race; gender: 69 female, 20 male). The distribution of WMC × Prejudice groups is as follows: low WMC/low prejudice, n = 25; low WMC/high prejudice, n = 23; high WMC/low prejudice, n = 19; high WMC/high prejudice, n = 22.

Main Analyses Analyses were conducted for each of the three case strengths separately. As the ambiguous evidence cases were the focus of our predictions, these cases were analyzed first. All analyses moving forward use the VC score.


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Ambiguous Cases We hypothesized that participants would find the Black versus White defendant guiltier when cognitive resources were limited due to (1) low cognitive capacity (WMC), (2) load trials, (3) or a combination of these resource limiting factors. Furthermore, we expected that this effect would be moderated by prejudice, such that participants highly prejudiced against Black persons would only show evidence of these beliefs when cognitive resources were low. We address each of these predictions in turn with a mixed ANOVA, in which Cognitive Capacity and Prejudice were between-participant variables and Load Condition was a within-participant factor. First, we find a main effect of load condition, F(1,85) = 12.94, p < .01, η2p = .13, where participants are harsher on the Black defendant when loaded (M = .34) than not loaded (M = −2.90). Second, there is a main effect of prejudice, F(1,85) = 5.45, p = .02, η2p = .06, where low prejudice participants (M = −2.47) are harsher on the White defendant than high prejudice participants(M = −.09). Finally, we see the predicted interaction of cognitive capacity, prejudice and load condition, F(1,85) = 4.31, p = .04, η2p = .05, see Figure 1. As there were no significant two-way interactions, we begin decomposing the three-way interaction by evaluating the simple effects comparisons of each variable. The effect of the load condition was only significant for participants with low WMC and high prejudice and for those with high WMC and low prejudice. Low WMC/high prejudice

FIGURE 1. VC score to ambiguous cases as a function of Cognitive Capacity, Prejudice, and Load Condition. Error bars represent standard error.


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participants were significantly harsher on the black defendant when loaded than when not loaded, (Mload = 3.17, Mnoload = −2.61, t(22) = 3.33, p < .01), which is consistent with our predictions. High WMC/low prejudice were significantly harsher on the White defendant when not loaded (Mload = −.05, Mnoload = −4.47), t(18) = 2.92, p = .01. This effect was not predicted but may arguably suggest that a low level of prejudice is reflective of a bias in favor of Black persons and/or against White persons. Neither participants with low WMC and low prejudice (Mload = −1.04, Mnoload = −4.32), t(24) = 1.75, p = .09, nor those with high WMC and high prejudice participants (Mload = −.73, Mnoload = −.18), t(21) = −.29, p = .78, differed in their VC scores as a function of load condition. The effect of the prejudice was only significant for low WMC participants when loaded, and for high WMC participants when not loaded. Low WMC participants under load were significantly harsher on the Black defendant if they were high (versus low) prejudice, t(46) = −2.11, p = .04), which is consistent with our predictions. High WMC under conditions of no load were were harsher on the White defendant if they were low (versus high) prejudice, t(39) = −2.43, p = .02. This effect was also not predicted but may again be reflective of a bias against White persons. Neither participants with low WMC under conditions of no load, t(46) = −.87, p = .39, nor those with high WMC under conditions of load, t(39) = −.37, p = .72, differed in their VC scores as a function of prejudice. The effect of the cognitive capacity (WMC) was only significant for high prejudice participants when loaded, t(43) = 2.53, p = .02, such that high prejudice/low WMC participants under load were significantly harsher on the Black defendant than high prejudice/high WMC participants. This is consistent with our predictions. No other comparisons were significant: low prejudice/load, t(42) = −.43, p = .67; low prejudice/no load, t(42) = .08, p = .94; high prejudice/noload, t(43) = −1.23, p = .21. Our first prediction was not supported, as the main effect of cognitive capacity was not significant, suggesting that WMC alone does not impact biased decision making. In support of our second prediction, there was a main effect of load condition, such that participants assigned higher VC scores to Black defendants under conditions of load than no load. A main effect of prejudice also emerged, with high-prejudiced persons assigning lenient VC scores to White defendants and low-prejudiced persons assigning harsher VC scores to White defendants. This finding is not surprising, because (as stated previously) a low level of prejudice is reflective of a bias in favor of Black persons and/or against White persons. Finally, in support of our third prediction, the significant three-way interaction demonstrates that when cognitive resources are insufficient, through a combination of low WMC and external load, prejudiced participants cannot suppress their biases and instead stereotypical responses are given. Strong Cases For the strong evidence cases, our independent variables were not predicted to influence judgments, as the evidence alone should warrant a guilty verdict for all


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defendants with high confidence, regardless of cognitive ability, load condition, or prejudice. First, a main effect of load condition emerged, F(1,85) = 7.64, p = .01, η2p = .08, as participants assigned higher VC scores to the White defendant when not loaded (M = −2.33) than when loaded (M = −.17). Surprisingly, a significant two-way interaction between cognitive capacity and prejudice emerged, F(1,85) = 6.03, p = .02, η2p = .07. Simple effect comparisons reveal that this interaction is driven by the low WMC participants, t(46) = −2.94, p = .01, such that low WMC/high prejudice participants (M = .35) are significantly harsher on the Black defendant than low WMC/low prejudice participants (M = −3.22). None of the other comparisons were significant: high WMC, t(39) = .49, p = .63; low prejudice, t(42) = −1.90, p = .06; high prejudice, t(43) = 1.55, p = .13. Weak Cases Similar to the strong cases, manipulated factors were not expected to influence outcomes in weak evidence cases. The evidence alone should warrant a not guilty verdict for all defendants with high confidence regardless of cognitive capacity, load condition, or prejudice. As expected, results revealed no significant main effects or interactions. General Discussion Many studies have investigated whether race influences juror judgments (see Sommers, 2007, for a review); however, this study is among the first to examine the cognitive underpinnings that may facilitate biased trial outcomes both in mock trials and in actual jury trials. The issue addressed in this study is not whether people are aware of the Black-man-criminal stereotype, as its ubiquitous nature has been previously demonstrated (Eberhardt, Goff, Purdie, & Davies, 2004). Rather, we were interested in the cognitive factors that influence whether or not this stereotype is included in the deliberative decision making required of a mock-juror. As previously discussed, ambiguous evidence cases are likely to be the most similar to actual evidence cases presented in a jury trial, and thus were the primary focus here. We found that prejudicial attitudes coupled with low WMC and cognitive load resulted in biased judgments as indexed by high conviction/confidence ratings for Black compared to White defendants. This suggests that for prejudiced people, ignoring expectations of criminality during the decision process is cognitively resource demanding. Moreover, even in a mock-juror-type scenario devoid of time limitations or the other pressures that accompany an actual trial, as presented in this study, cognitive resource limitations and negative attitudes about Black men influenced the verdict decisions. We expected that low compared to high levels of cognitive capacity (WMC) would increase the likelihood of a racially biased verdict decision. The heuristic nature of stereotyping requires fewer cognitive resources than controlled decision making. It follows that low WMC or cognitive load would increase reliance on stereotypes. Moreover, we expected that high prejudiced persons, who


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intrinsically held negative attitudes about Black persons, would have the most difficulty avoiding stereotypes when cognitive resources were occupied. However, we only expected these factors to matter when case evidence alone was insufficient to determine a verdict. Data supported this expectation. Notably, these findings were revealed in ambiguous evidence cases while stereotypical outcomes were not influenced by the manipulated factors for strong and weak evidence cases. This further reinforces the assumption that prejudicial attitudes require suppression for a fair trial outcome and that cognitive capacity is a factor when such suppression is required. Consistent with the Ironic Process Model (Wegner, 1994), our data suggest that regardless of prejudicial attitudes, people can monitor and avoid using racial stereotypes in decision making when cognitive resources are sufficient. Thus, the operating process that supports the intention to make socially appropriate decisions is in force when cognitive resources are available leading to higher VC scores for the White rather than Black defendants. However, the automatic monitoring process overtakes the resource-dependent operating process when WMC is low and cognitive load is high leading to an increase in Black defendant VC scores. This outcome demonstrates an inability to suppress the unintentional use of the criminal stereotype during the decision process. It is interesting that among the high-prejudiced persons in the cognitive load condition all assigned similar VC scores to all defendants, with the exception of the low WMC persons who were especially lenient on the White defendants. We suggest that this reflects a substantial restriction in cognitive resource availability (low WMC + cognitive load). With limited WMC, participants who self-reportedly maintain racial bias, rely on the irrelevant factor of defendant race in order to inform their verdict/confidence judgments. Although defendant race logically should have no bearing on outcome decisions - the information is easily accessed and effortful to avoid. On the contrary, participants with an “excess” of cognitive capacity (i.e., high WMC) were able to disregard their biases as irrelevant factors with regard to rendering juror decisions and instead assigned consistent and fair VC scores regardless of defendant race. This finding replicates previous findings in the WMC literature wherein high, compared to low, WMC persons are able to make logical and consistent decisions while avoiding the biasing influence of immaterial details (see Goldinger, et al., 2003; Moore, Clark, & Kane, 2008). Anecdotally, people may assume that prejudicial attitudes alone should facilitate biased verdict decisions, as people who hold such attitudes would convey bias given the opportunity. However, high levels of prejudice alone did not lead to biased verdict decisions in the current study. The high prejudice people were not harsher in their judgments of one race defendant over the other. This suggests that even people who are prejudiced against Black persons are aware that expressing such attitudes is not acceptable and thus make efforts to avoid behaviors that could be interpreted as racist (e.g., Gaertner & Dovidio, 1986; Sommers & Ellsworth, 2000). This was evident in that for ambiguous cases, White defendants were found


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guilty more often under conditions of no load, regardless of self-reported prejudice beliefs. This suggests that cognitive resources are required to avoid reliance upon a strongly held attitude. Of applied importance, this finding suggests that prejudicial juror attitudes in a courtroom may emerge in situations where cognitive resource demands are high and influence how evidence is evaluated. Although racially biased judgments were indicated primarily with low WMC/high prejudiced participants under conditions of load, we did entertain the idea that the ubiquitous nature of the Black-man-criminal stereotype (regardless of individually held attitudes about race) could be sufficient to facilitate verdict inequities. However, biased outcomes primarily emerged in VC scores rendered by prejudiced subjects. Knowledge of the criminal stereotype alone was not sufficient for biased deliberative decisions as required in a jury trial. These findings further hone the understanding of how prejudicial attitudes, apart from the knowledge of criminal stereotypes, influences outcomes - an issue not directly tested in previous studies. The application of negative racial stereotypes during the decision making process is automatic and resource-conserving (Devine, 1989), and is revealed when quick decision making is required (e.g., shoot/don’t-shoot tasks; Correll, et al., 2006). Recent work suggests that similar mechanisms are also engaged in deliberative decision making as required of a juror. For example, cognitive load increases reliance on racial stereotypes, such that negative stereotyped persons (e.g., drug addicts) are more likely to be convicted of crimes than positive stereotyped persons (e.g., bank employees) in juror-judgment tasks (van Knippenberg, Dijksterhuis, & Vermeulen, 1999). However, unlike quick decisions, people have control over how case evidence is evaluated in a trial and may be motivated to avoid appearing prejudiced. Therefore, the driving question is whether people can maintain an objective stance or whether cognitive restrictions in capacity are sufficient to derail balanced decision making. We expected that one or a combination of restrictions on cognitive resources would facilitate expression of prejudicial attitudes. We found that external load or a combination low WMC, load and high prejudice were indicative of biased juror-type decision making in ambiguous evidence cases that required evaluation. Thus, even people with highly negative attitudes about Black men can avoid application of this prejudice regardless of internal or external restrictions on processing resources. This is a positive outcome, as highly prejudiced people may endorse a fair decision if sufficient cognitive resources are available during evaluation. As noted earlier, in the no load condition, people judged the White defendants much more harshly than the Black defendants. We suggest that this finding was driven by the participants identified as being low in prejudice. Lowprejudiced participants were labeled as such because they reported a bias in favor of Black persons. However, in doing so, they necessarily reported a bias against White persons. Thus, it would not be surprising for persons who are biased against White persons to render harsher VC scores to White than Black defendants. This


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was supported by the main effect of prejudice wherein low prejudice persons assigned negative VC scores reflecting a bias against White defendants. Alternatively, harsh evaluation and potentially unfair trial outcomes were endorsed by low WMC/high prejudice persons when cognitively loaded. Thus, the extent to which a juror can maintain an objective viewpoint when making evaluative judgments is a function of their attitudes about race and criminality, their intrinsic cognitive capacity (WMC), and the cognitive load created in a courtroom situation. It stands to reason that low WMC persons in a stressful trial are more likely than their high WMC counterparts to convict a Black man of a crime based on circumstantial evidence. Apart from juror-type decisions, this finding lends further support to previous work noting the cognitive boost facilitated by high WMC in logical decision making. Not only is WMC thought to be an intrinsic measure of cognitive capacity and highly correlated with intelligence (see Unsworth & Engle, 2005) it is predictive of logical decision making in other areas such as moral dilemmas (Moore, et al., 2008) which has obvious links to juror judgments in determining whether or not a person should be incarcerated. In addition, WMC is thought to be flexible to an extent but is arguably a nonchangeable indicator of cognitive capacity (Unsworth & Engle, 2007). Although, not unlike the cognitive requirements that are encountered in everyday living, it is unsettling that low WMC persons (coupled with a dual task) are the most vulnerable to making biased verdict decisions when they hold prejudicial attitudes. Moreover, it is especially disconcerting that a subset of the community jury pool may be most likely to render biased decisions in an actual trial, and doing so may be unavoidable. Overall, this study is an initial investigation into establishing what cognitive factors may influence not only racially biased jury-type judgments in a laboratory setting but actual trial outcomes as well. Highly prejudiced people are not necessarily those jurors that mete out harsh judgments to Black men. Rather, a constellation of factors that serve to reduce cognitive resources albeit controlled processing coupled with prejudicial attitudes facilitate biased trial outcomes. Theoretically, these findings coincide with well-established theories on dual-processing of controlled and automatic thoughts (e.g., Jacoby, 1991). That is, if sufficient processing capacity is not available to make a controlled decision, people will make heuristic-based decisions. Here, we demonstrated that even highly prejudiced people could avoid a reflexive biased response unless sufficient cognitive resources were unavailable. From an applied perspective, the findings here suggest that for highly prejudiced/low WMC jurors, a balanced evaluation of trial evidence may not be probable if the trial context creates a cognitive load (e.g., highly emotional testimony or complicated evidence). Potentially, low WMC/high prejudiced jurors are partially responsible for the false incarceration of Black prisoners who are later exonerated with DNA evidence. Although the findings of this study inform both cognitive science and the legal system more generally, there are possible limitations with regard to ecological


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validity. This is a lab-based study, wherein case information is presented on a computer without the real pressures experienced by an actual juror. Although the design of this study allowed for control of extraneous factors and isolation of those factors that contribute to juror bias, mock-juror-type studies are limited in their direct application to real trial outcomes. An alternative option, utilized by Eberhardt and colleagues (2004), would be to review actual legal records to establish predictive patterns in type of crime, strength of evidence used, race of defendant, etc. However, given that our research question was largely focused on juror variables (i.e., cognitive capacity, prejudice beliefs)—this approach would have been insufficient to address our question. A second alternative could have involved participants playing the role of actual mock jurors in a staged mock trial, wherein actors would play the roles of attorney, defendant, judge, etc. Yet, this too seemed insufficient to address our question, as more than 12 separate mock trials would have been needed (to reflect each of the cases presented) and further would have required that case strength and defendant race become between-subjects variables. The former seemed a reasonable concern given the resources available to us, and the latter seemed reasonable as this would have decreased our statistical power. Another possible limitation of this study is that we did not evaluate the extent to which participants were “loaded” in the load condition. Although variations in amount of load may have rendered different results, assessing quantity of load was difficult to qualify and may have been unnecessary. Participants were presented with a list on nonsense words to remember, and then after they rendered verdict and confidence decisions, they attempted to recall as many nonsense words as possible from that trial. Given that the words were both unfamiliar and fake, it was difficult to find sufficient criteria to decipher if any missed and/or forgotten words were due to individual capacity limitations or simply the inability to recall the spelling and sequencing of a series of unfamiliar words with no previous exposure to them. Moreover, the load method we employed has been validated by previous studies (e.g., Goldinger, et al., 2003), thus we did not feel that quantifying amount of load was necessary at this stage of experimentation. Future studies could focus on testing the types and level of cognitive loads (i.e., highly emotional testimony) present in a courtroom that may predict biased outcomes. However, to apply these findings to a courtroom setting, individual differences in cognitive processing resources should also be tested with regard to jury deliberation. In addition, due to our cell groupings by WMC and prejudice level we were not able to adequately evaluate the influence of participant race on juror-type judgments. Future studies may address this issue specifically. In conclusion, this study identifies cognitive processing resources as a potential source of biased jury judgments that may underpin findings in previous studies of racial bias. More importantly, these findings are a step towards identifying cognitive factors that may contribute to the lopsided distribution of conviction rates in actual jury trials.

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NOTE 1. Stereotypicality refers to the subjective association of specific facial features, (e.g., broad nose, thick lips) with a particular racial group (Eberhardt, Davies, Purdie-Vaughns, & Johnson, 2006).


AUTHOR NOTES Heather M. Kleider is an Associate professor at Georgia State University and is the chair of the Cognitive Sciences Area. She received her doctorate from Arizona State University in 2001. Leslie R. Knuycky is a senior graduate student in the cognitive sciences area at Georgia State University. Sarah E. Cavrak is a senior graduate student at Georgia State University in the Cognitive Sciences Area.

REFERENCES Allport, G. W., & Postman, L. J. (1947). The psychology of rumor. Oxford, UK: Holt. Baldus, D. C., Woodworth, G., Zuckerman, D., Weiner, N.A., & Broffitt, B. (1998). Racial discrimination and the death penalty in the post-Furman era: An empirical and legal overview, with recent findings from Philadelphia. Cornell Law Review, 83, 1638–1770. Beilock, S. L., & Carr, T. H. (2005). When high-powered people fail: Working memory and “choking under pressure” in math. Psychological Science, 16(2), 101–105. doi:10.1111/j.0956-7976.2005.00789.x Blanchard, F. A., Lilly, T., & Vaughn, L. A. (1991). Reducing the expression of racial prejudice. Psychological Science, 2, 101–105. Bodenhausen, G. V. (2005). The role of stereotypes in decision-making processes. Medical Decision Making, 25(1), 112–118. doi:10.1177/0272989x04273800 Bogardus, E. S. (1925). Measuring social distance. Journal of Applied Sociology, 9, 299–309. Bowers, W. J., Steiner, B. D., & Sandys, M. (2001). Death sentencing in Black and White: An empirical analysis of the role of jurors’ race and jury racial composition. University of Pennsylvania Journal of Constitutional Law, 3, 171–274. Correll, J., Urland, G. R., & Ito, T. A. (2006). Event-related potentials and the decision to shoot: The role of threat perception and cognitive control. Journal of Experimental Social Psychology, 42(1), 120–128.doi:10.1016/j.jesp.2005.02.006 Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of Personality and Social Psychology, 56(1), 5–18.doi:10.1037/00223514.56.1.5 Eberhardt, J. L., Davies, P. G., & Purdie-Vaughns, V. J., & Johnson, S. L. (2006). Looking deathworthy: Perceived stereotypicality of black defendants predicts capital-sentencing outcomes. Psychological Science, 17, 383–386. Eberhardt, J. L., Goff, P., Purdie, V. J., & Davies, P. G. (2004). Seeing Black: Race, crime, and visual processing. Journal of Personality and Social Psychology, 87(6), 876–893.doi:10.1037/0022-3514.87.6.876 Gaertner, S. L., & Dovidio, J. F. (1986). The aversive form of racism. In J. F. Dovidio, & S. L. Gaertner (Eds.), Prejudice, discrimination, and racism (pp. 61–89). Orlando, FL: Academic Press. Goldinger, S. D., Kleider, H. M., Azuma, T., & Beike, D. R. (2003). Blaming the victim under memory load. Psychological Science, 14, 81–85.


192


Horowitz, I. A., ForsterLee, L., & Brolly, I. (1996). Effects of trial complexity on decision making. Journal of Applied Psychology, 81(6), 757–768.doi:10.1037/00219010.81.6.757 The Innocence Project. Facts on post-conviction DNA exonerations. Retrieved April 23, 2011, from http://www.innocenceproject.org. Jacoby, L. L. (1991). A process dissociation framework: Separating automatic from intentional uses of memory. Journal of Memory and Language, 30(5), 513–541.doi:10.1016/0749-596X(91)90025-F Jones, C. S., & Kaplan, M. F. (2003). The effects of racially stereotypical crimes on juror decision making and information-processing strategies. Basic and Applied Social Psychology, 25(1), 1–13. Kane, M. J., & Engle, R. W. (2000). Working-memory capacity, proactive interference, and divided attention: Limits on long-term memory retrieval. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(2), 336–358.doi:10.1037/02787393.26.2.336 Kemmelmeier, M. (2005). The effects of race and social dominance orientation in simulated juror decision making. Journal of Applied Social Psychology, 35(5), 1030–1045.doi:10.1111/j.1559-1816.2005.tb02158.x Kleider, H. M., & Parrott, D. J. (2009). Aggressive shooting behavior: How working memory and threat influence shoot decisions. Journal of Research in Personality, 43(3), 494–497.doi:10.1016/j.jrp.2008.12.007 Lambert, A. J., Payne, B., Jacoby, L. L., Shaffer, L. M., Chasteen, A. L., & Khan, S. R. (2003). Stereotypes as dominant responses: On the “social facilitation” of prejudice in anticipated public contexts. Journal of Personality and Social Psychology, 84(2), 277–295.doi:10.1037/0022-3514.84.2.277 McAllister, H. A., & Bregman, N. J. (1986). Plea bargaining by prosecutors and defense attorneys: A decision theory approach. Journal of Applied Psychology, 71(4), 686–690.doi:10.1037/0021-9010.71.4.686 McGowen, R., & King, G. D. (1982). Effects of authoritarian, anti-authoritarian, and egalitarian legal attitudes on mock juror and jury decisions. Psychological Reports, 51, 1067–1074. Mitchell, T. L., Haw, R. M., Pfeifer, J. E., & Meissner, C. A. (2005). Racial bias in mock juror decision making: A meta-analytic review of defendant treatment. Law and Human Behavior, 29(6), 621–637.doi:10.1007/s10979-005-8122-9 Moore, A. B., Clark, B. A., & Kane, M. J. (2008). Who shalt not kill? Individual differences in working memory capacity, executive control, and moral judgment. Psychological Science, 19, 549–557. Parrillo, V. N., & Donoghue, C. (2005). Updating the Bogardus social distance studies: A new national survey. Social Science Journal, 42(2), 257–271. Payne, B., Lambert, A. J., & Jacoby, L. L. (2002). Best laid plans: Effects of goals on accessibility bias and cognitive control in race-based misperceptions of weapons. Journal of Experimental Social Psychology, 38(4), 384–396.doi:10.1016/S0022-1031(02)000069 Plant, E., & Devine, P. G. (1998). Internal and external motivation to respond without prejudice. Journal of Personality and Social Psychology, 75(3), 811–832.doi:10.1037/00223514.75.3.811 Richeson, J. A., & Shelton, J. (2003). When prejudice does not pay: Effects of interracial contact on executive function. Psychological Science, 14(3), 287–290.doi:10.1111/14679280.03437 Rosen, V. M., & Engle, R. W. (1998). Working memory capacity and suppression. Journal of Memory and Language, 39(3), 418–436.doi:10.1006/jmla.1998.2590


Kleider et al.

193

Sherman, J. W., & Bessenoff, G. R. (1999). Stereotypes as source-monitoring cues: On the interaction between episodic and semantic memory. Psychological Science, 10(2), 106–110.doi:10.1111/1467-9280.00116 Sommers, S. R. (2007). Race and the decision making of juries. Legal and Criminological Psychology, 12(2), 171–187.doi:10.1348/135532507x189687 Sommers, S. R., & Ellsworth, P. C. (2000). Race in the courtroom: Perceptions of guilt and dispositional attributions. Personality and Social Psychology Bulletin, 26(11), 13671379. 1379.doi:10.1177/0146167200263005 Unsworth, N., & Engle, R. W. (2005). Working memory capacity and fluid abilities: Examining the correlation between operation span and raven. Intelligence, 33(1), 67–81.doi:10.1016/j.intell.2004.08.003 Unsworth, N., & Engle, R. W. (2007). On the division of short-term and working memory: An examination of simple and complex span and their relation to higher order abilities. Psychological Bulletin, 133(6), 1038–1066.doi:10.1037/0033-2909.133.6.1038 Unsworth, N., Heitz, R. P., Schrock, J. C., & Engle, R. W. (2005). An automated version of the operation span task. Behavior Research Methods, 37(3), 498–505. van Knippenberg, A., Dijksterhuis, A., & Vermeulen, D. (1999). Judgment and memory of a criminal act: The effects of stereotypes and cognitive load. European Journal of Social Psychology, 29(2–3), 191–201. Visher, C. A. (1987). Juror decision making: The importance of evidence. Law and Human Behavior, 11(1), 1–17.doi:10.1007/BF01044835 von Hippel, W., & Gonsalkorale, K. (2005). “That is bloody revolting!” Inhibitory control of thoughts better left unsaid. Psychological Science, 16(7), 497–500.doi:10.1111/j.09567976.2005.01563.x Wegner, D. M. (1994). Ironic processes of mental control. Psychological Review, 101(1), 34–52.doi:10.1037/0033-295X.101.1.34

Original manuscript received November 30, 2011 Final version accepted April 16, 2012

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