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Relationships Between Support for the Death Penalty and Cognitive Processing: A Comparison of Students and Community Members Monica Miller, Steve M. Wood and Julianna C. Chomos Criminal Justice and Behavior published online 20 November 2013 DOI: 10.1177/0093854813509369 The online version of this article can be found at: http://cjb.sagepub.com/content/early/2013/11/19/0093854813509369

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CJBXXX10.1177/0093854813509369Criminal Justice and BehaviorMiller et al. / Jurors’ Cognitive Processing

Relationships Between Support for the Death Penalty and Cognitive Processing A Comparison of Students and Community Members Monica Miller Steve M. Wood Julianna C. Chomos University of Nevada, Reno

Cognitive Experiential Self-Theory (CEST) posits that individuals process information rationally (measured by Need for Cognition [NFC]) or experientially (measured by Faith in Intuition [FI]). This study investigated whether information processing traits (NFC and FI) and states (CEST logic problems) are related to general death penalty attitude and sentencing verdict—and whether these relationships differed for students versus community members. FI and NFC were related to sentencing verdicts. An increase in FI was related to a higher likelihood of a death sentence; an increase in NFC was related to a higher likelihood of a life sentence. CEST logic problems were related to sentencing verdicts and general attitudes. However, these relationships were moderated: For community members, but not students, a decrease in rational processing was related to a higher likelihood of a death sentence and support for the death penalty. Results have implications for psychology and the legal system. Keywords: death penalty, information processing, Faith in Intuition, Need for Cognition, jury decision-making

J

urors are an integral part of the American legal system. As such, researchers have long studied jury decision-making topics (for review, see Devine, Clayton, Dunford, Seying, & Pryce, 2001) such as differences between judicial and jury decisions or factors that influence a juror’s decision. Recently, researchers have focused not only on the actual decision that is made, but also on the process by which jurors make that decision (e.g., Butler & Moran, 2007b; M. K. Miller, 2006; see generally Groscup & Tallon, 2009). In addition, researchers have occasionally studied how information processing affects jurors’ verdicts (e.g., Groscup & Tallon, 2009, for review). Individuals differ in their general information processing style and can process information differently from moment to moment. Cognitive Experiential Self-Theory (CEST) posits that individuals can process information in either a rational (e.g., logical) or an experiential

Authors’ note: Preliminary findings were presented at the 2010 conference of the American PsychologyLaw Society. Correspondence concerning this article should be addressed to Monica Miller, University of Nevada, Reno, UNR mailstop 214, Reno, NV 89557; e-mail: [email protected]. CRIMINAL JUSTICE AND BEHAVIOR, Vol. XX, No. X, Month, 2013, 1–19. DOI: 10.1177/0093854813509369 © 2013 International Association for Correctional and Forensic Psychology

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2 Criminal Justice and Behavior

(e.g., emotional) manner (Epstein, Pacini, Denes-Raj, & Heier, 1996). Rational processing is measured by the Need for Cognition (NFC) scale and experiential processing is measured by the Faith in Intuition (FI) scale. These relatively enduring tendencies to process information experientially or rationally can be thought of as processing “traits.” People can also differ in how they process information at any given moment, which could be thought of as processing “states.” Specifically, at any time, individuals can process information rationally or experientially. This study investigates the relationship between jurors’ cognitive processing and their support for the death penalty. It will also investigate whether this relationship is similar for community members and university students, as there has been much interest in recent years as to whether students are good proxies for community members and jurors (e.g., Reichert, Miller, Bornstein, & Shelton, 2011; Wiener, Krauss, & Lieberman, 2011). In general, the legal system encourages jurors to rely on evidence, instructions, and law (see discussion below); such decisions could be characterized as systematic, logical decision making represented by NFC and rational processing. Moreover, the system discourages jurors from relying on gut feelings, instincts, or emotions; such decision making could be characterized as intuitive, emotional decision making represented by FI, and experiential processing. Thus, the “ideal” juror is, arguably, one who is high in NFC, low in FI, and who is currently thinking rationally rather than experientially. On the other hand, it could be argued that it is acceptable for jurors to make emotional decisions, especially in decisions in which there is no “correct” answer. Indeed, some aspects of legal decisions are based in emotions; for example, some death penalty sentencing schemes ask jurors to determine whether a crime was heinous and thus more deserving of death. In general, however, experiential thinkers make errors in probability judgments (for a review, see Epstein & Pacini, 1999) and rational jurors make more objective decisions, are less likely to be influenced by the defendant’s attractiveness (Lieberman, 2002), and are better able to integrate contrasting case evidence (Krauss, Lieberman, & Olson, 2004). Krauss et al. (2004) sum up the differences in processing by saying “experiential processing also produces a strong tendency for individuals to use capricious and limited information to generate broad and holistic impressions of targets” (Krauss et al., 2004, p. 807; italics added). The Supreme Court has explicitly scorned capricious decision making in death penalty trials (Furman v. Georgia, 1972). Arguably, if jurors are making capricious decisions (e.g., based on attractiveness, emotions, or stereotypes), this puts them at odds with Furman v. Georgia (1972), discussed in the next section. Thus, there might be some room for emotional decision making in death penalty decisions. Rational jurors might not be better jurors unless they make mistakes in evaluating evidence. However, in general, it could be argued that capricious decisions made using only limited information and influenced by irrelevant factors (e.g., defendant attractiveness, the jurors’ gut instincts) and emotions are less desirable than rational decisions based on logic and more complete information. It is thus interesting to uncover whether jurors’ general death penalty attitudes and sentencing verdicts are related to how they process information— and whether these relationships hold for students and community members. Death Penalty Process and Legal Decisions

To understand the legal issues surrounding jurors’ information processing, it is necessary to understand a few relevant court decisions. In Furman v. Georgia (1972), the Supreme Downloaded from cjb.sagepub.com at UNIV OF NEVADA RENO on February 11, 2014

Miller et al. / Jurors’ Cognitive Processing 3

Court ruled that the imposition of the death penalty was unconstitutional because it was given in an arbitrary and capricious way.1 In concurring with the findings of the Court, Justice Stewart stated that defendants who receive the death penalty are among a capriciously selected random handful upon whom the sentence of death has in fact been imposed. . .the Eighth and Fourteenth Amendments cannot tolerate the infliction of a sentence of death under legal systems that permit this unique penalty to be so wantonly and so freakishly imposed. (Furman v. Georgia, 1972, pp. 309-310)

Several years later, the Court in Gregg v. Georgia (1976) approved of a system that requires jurors to weigh aggravators (information that may increase the degree of culpability and punishment) and mitigators (information that may decrease the degree of culpability and punishment).2 This weighing of aggravators and mitigators was designed to help jurors come to more rational, guided, and constitutionally sound verdicts (Garner, 1999). The Gregg ruling emphasized the Court’s concern over whether jurors make logical decisions rather than ones based on extraneous factors or biases (see also Gardner v. Florida, 1977). Because of this concern that jurors make rational rather than capricious decisions, it is important that this study determine any differences in information processing related to jurors’ sentencing verdicts and general death penalty attitudes. Cest and Information Processing

CEST posits that there are two main ways of processing information: rational and experiential (Epstein, 1973). Rational processing is measured by the Need for Cognition (NFC) scale, which measures whether a participant enjoys and engages in intellectual activities (Cacioppo, Petty, & Kao, 1984). Individuals high in NFC tend to be analytic and logical, and tend to systematically process complex concepts (Epstein, Lipson, Holstein, & Huh, 1992). Furthermore, high NFC people tend to have higher math and verbal abilities and general academic performance (see Sladek, Bond, & Phillips, 2010, for a review of how NFC relates to many individual characteristics). Jurors high in NFC also remember more information and are more likely to correct for personal biases (Martin, 1986; Strack, 1992). NFC scores were positively related to being influenced by judges’ instructions, evidence, and logic and reason; NFC scores were negatively related to being influenced by instincts and feelings about the defendant (M. K. Miller, 2006). These studies suggest that participants high in NFC are more “ideal” jurors than those low in NFC—assuming that jurors should make decisions based on instructions, evidence, and logic but not on emotions, instincts, and feelings. Experiential processing is measured by the Faith in Intuition (FI) scale, which assesses how much an individual typically relies on feelings and immediate impressions (Epstein et al., 1996). Experiential processing is assumed to be automatic, affective, and influenced by heuristics (Epstein et al., 1992). People high in experiential thinking are more emotionally expressive, trust more in relationships, and are less likely to use categorical thinking (Pacini & Epstein, 1999; see Sladek et al., 2010, for a review of how FI relates to many individual characteristics). More relevant to the current study, jurors who were high in FI were more likely to be influenced by their feelings about the case, their instincts about the case, and their feelings about the defendant (M. K. Miller, 2006). Assuming that emotion-based judgments are undesirable, these studies support the notion that jurors high in FI are not “ideal” jurors. Downloaded from cjb.sagepub.com at UNIV OF NEVADA RENO on February 11, 2014


In addition to processing traits (NFC and FI), this study measures processing states. A state is the way a person is making a decision at a specific time. Responses to logic problems indicate whether the person is currently processing rationally (e.g., logically). For instance, one logic problem provides the participant with two stories with identical negative outcomes (i.e., a person’s car was hit in a parking lot). The stories only differ in whether the person’s behavior was constrained (i.e., he took the last parking spot) or whether he had a choice (i.e., he had many parking spots to choose from). A respondent who is processing rationally will recognize that neither person in the stories could foresee the future and thus will rate the two persons as equally foolish. However, a respondent who is not processing rationally will indicate that the person in the story who had a choice in parking spots was more foolish, whereas the person who took the only parking spot was less foolish (presumably, because he did not have a choice). One study that manipulated processing state found that mock jurors manipulated to thinking rationally weighed actuarial evidence more heavily, while those manipulated to think experientially weighed clinical evidence more heavily (Krauss et al., 2004). Compared with actuarial evidence, clinical evidence is typically less accurate, can be based on limited information, can be affected by biases (e.g., stereotypes), and undervalues mathematical probabilities. Thus, jurors in a rational state preferred more scientifically valid evidence. The finding that jurors in a less rational state prefer less-accurate evidence has been found outside the death penalty context as well (e.g., sexually violent predator evaluations; Lieberman, Krauss, Kyger, & Lehoux, 2007). Studies that manipulate processing states support the notion that processing can be a state as well as a trait. They also support the notion that people who are currently thinking rationally are making decisions by using stronger and more valid evidence than participants thinking experientially; thus, rational thinking jurors could be more ideal jurors. Relationship Between Cest Processing Measures and Death Penalty Support

Information processing states (measured by CEST logic problems) and traits (FI and NFC) might be related to general death penalty attitudes and sentencing verdicts. Research is mixed or scarce; however, there is enough evidence to warrant examination. General death penalty attitudes might be related to cognitive processing. The more an individual supports the death penalty in general, the more likely he is to endorse aggravators and the less likely he is to endorse mitigators (Butler & Moran, 2007a). Thus, a person’s attitudes might affect how he or she weighs (and processes) information and comes to a verdict. More broadly, CEST processing is related to attitudes about punishment in general. NFC is negatively related to the belief that criminals deserve payback and FI is positively associated with the belief that murderers deserve death (M. K. Miller, 2006). Less rational thinking on the CEST logic problem was associated with less agreement that criminals should be forgiven and shown mercy and greater agreement that criminals should receive payback and murderers should be put to death (M. K. Miller, 2006). There are no studies that we know of that specifically address the relationship between NFC, FI, or CEST processing and general death penalty attitudes. Sentencing verdicts might be related to cognitive processing. One study found that individuals low in NFC are more likely to give a guilty verdict and death sentence than those Downloaded from cjb.sagepub.com at UNIV OF NEVADA RENO on February 11, 2014


high in NFC (Butler & Moran, 2007b); however, in two other studies, NFC was not related to mock jurors’ death penalty sentencing verdicts (M. K. Miller, 2006). In one of the two studies, those low in FI were more in favor of a life sentence than those high in FI (M. K. Miller, 2006). NFC is also related to participants’ ability to weigh aggravators and mitigators (in one of the two studies; M. K. Miller, 2006). In a “control” condition (without a manipulated argument thought to affect weighing of aggravators and mitigators), those high in NFC were able to weigh aggravators and mitigators better than those who were low in NFC. However, when participants heard an emotional appeal (the treatment condition), NFC did not affect their ability to weigh aggravators and mitigators. Thus, high NFC individuals are better decision makers than those low in NFC, unless they are exposed to emotion. This study offers some evidence that NFC—and emotion—are related to jurors’ verdicts (as measured by weighing aggravators and mitigators). Students Versus Community Members

Within jury decision-making research, there is debate about whether students are appropriate samples to represent a population of jurors (e.g., Wiener et al., 2011). Some research has indicated that university students and community members make different legal decisions, use different decision-making processes, and demonstrate different cognitive biases. Fox, Wingrove, and Pfeifer (2011) found that jury panelists and students differed: Panelists awarded more punitive damages and endorsed a recovery scheme that was more punitive toward the defendant. Panelists and students also assessed information differently (e.g., panelists are less likely to appropriately compartmentalize information). Furthermore, the attitudes and biases that affect legal decisions are different for students and community members; for instance, the Attitude Toward Homicide Adjudication Scale was predictive of support for the prosecution for students but not community members; the Juror Bias Scale was a predictor of support for the prosecution for community members but not students (Keller & Wiener, 2011). Even when researchers try to discourage people from relying on their social cognitive biases, they find differences among groups. McCabe and Krauss (2011) found that a “bias correction intervention” worked for students but not community members. Furthermore, community members showed “testimony bias” (i.e., favoring clinical over actuarial testimony), whereas students did not. In sum, community members and students often differ in not only their verdicts—but also in the decision-making processes by which that verdict is reached. In addition, community members and students might differ in their information processing states and traits (i.e., NFC, FI, CEST processing). McCabe and Krauss (2011) found that a community sample had higher rational scores (i.e., NFC) and higher experiential (i.e., FI) scores than students—but the two groups were examined in two different studies so no direct statistical comparison was made. In contrast, McCabe, Krauss, and Lieberman (2010) found that students scored higher on the NFC measure than community members. Furthermore, whether NFC and FI are related to legal decision making might depend on the identity of the decision maker. McCabe and Krauss (2011) determined that students’ scores on the experiential scale (FI) were highly predictive of verdicts for students but not for community members. Similarly, McCabe et al. (2010) found that students’ verdicts were related to FI and NFC scores, but community members’ verdicts were not. Such studies suggest that community samples are not affected by their processing traits as much as college Downloaded from cjb.sagepub.com at UNIV OF NEVADA RENO on February 11, 2014


students. A comparison between the McCabe et al. (2010) study and a similar study using a more ecologically valid sample of people called for jury duty (Krauss, McCabe, & Lieberman, 2012) further supports the notion that sample does matter and thus should be studied further; the current study aims to do this. Studies such as these indicate that there are some critical differences between community members and students. The groups sometimes differ not only in their actual decisions (e.g., verdicts), but also in how they reach those decisions. Thus, it is important to not only determine whether information processing is related to death penalty support, but it is also important to see whether these relationships are the same for students and community members. Overview of Study

While many researchers have studied jury decisions (e.g., Butler & Moran, 2007a; M. K. Miller, 2006), few have investigated how these decisions are made (e.g., using rational or experiential processing). This study investigates how information processing states and traits are related to two measures of support for the death penalty (i.e., death penalty attitudes and sentencing verdicts)—and whether results hold for students and community members. The support measures are not entirely independent of one another. For instance, participants with more positive death penalty attitudes were more likely to give a death sentence as compared with participants with less positive attitudes (Butler & Moran, 2007a). Thus, these two measures (attitudes and sentencing verdicts) are intertwined and can be seen as two different measures of support for the death penalty. Even so, they represent two different ways to express one’s support: The first is a general attitude and the second is a behavior (sentencing verdict). University students and community members read a trial summary and completed a survey. The following hypotheses are the focus of this research: Hypothesis 1: NFC is related to support for the death penalty. The lower the NFC score, the more supportive participants are of the death penalty (i.e., supportive general attitudes toward the death penalty and choosing a death sentence). Hypothesis 2: FI is related to support for the death penalty. The higher the FI score, the more supportive participants are of the death penalty. Hypothesis 3: CEST logic problems are related to support for the death penalty. The higher the CEST logic problem score (i.e., less rational), the more supportive participants are of the death penalty. Research Question 1: Do the relationships described in Hypotheses 1, 2, and 3 hold for students and community members?

Method Participants

Participants (552 students and 174 community members) were recruited through a university subject pool and through community groups with a variety of interests (e.g., religion, gym membership, volunteering with disadvantaged children, and music). Some were compensated with a small monetary payment or course credit, while others volunteered. Downloaded from cjb.sagepub.com at UNIV OF NEVADA RENO on February 11, 2014


Participant age varied from 18 to 86 (M = 26.6; Median = 20.0); 64% were female and 88% were White. Most were Protestant (49%), followed by Catholic (24%), and some had no particular religious affiliation (14%). Procedure and Trial Stimuli

Participants completed the study in person, in a group setting (e.g., a laboratory or a room in a community center) using printed materials. They first completed the attitudes toward the death penalty measure and then read a 1,900-word trial summary of the sentencing phase of a death penalty trial. They were told that the defendant had already been convicted, so their job was to decide the sentence. The summary was based on the actual murder trial of Daniels v. North Carolina (1995), described in-depth below. Next, participants read an abbreviated version of the North Carolina sentencing instructions (which included instructions on how to weigh aggravators and mitigators) and gave a sentencing verdict. Finally, they completed a short survey measuring demographics and information processing traits and states. Measures

Independent Variables

NFC, FI (processing traits), and CEST logic problem scores (processing states) were used as independent measures. NFC and FI. Processing traits were measured using Epstein’s REI scale (Epstein et al., 1996), which includes two subscales (NFC and FI), each scored on a 5-point Likert-type scale from completely false to completely true. NFC and FI are two unipolar dimensions that can establish their own patterns of relationships with other variables (i.e., if a person is high in NFC and opposes the death penalty, it does not mean that a person high in FI will support the death penalty), but might also simultaneously work together because behavior is a function of FI and NFC (Epstein et al., 1996). The NFC subscale (Cronbach’s α = .78 in our sample) had five questions (e.g., I prefer complex to simple problems); the FI subscale (Cronbach’s α = .83 in our sample) had five questions (e.g., I trust my initial feelings about people). Items were averaged for each scale. CEST logic problems. Participants’ processing states were measured by two logic problems taken from previous studies measuring rational processing (Epstein et al., 1992). The first measure is the parking lot problem described above (D. T. Miller, Turnbull, & McFarland, 1990). The second measure, the stock-trading problem, consists of a hypothetical scenario involving two stockholders, Paul and George (Kahneman & Tversky, 1982). Paul owns stock in company A and switches his stock to company B. George owns shares in company B and decides against switching to company A. In both instances, the stock in company A skyrockets. The only difference between the scenarios is that Paul committed an act of commission (an individual performs a behavior) and George committed an act of omission (an individual does not perform a behavior). Rational participants recognize that neither actor could foresee the future and thus rate the two actors as equally foolish. Nonrational participants rate the actor who commits an act of commission as more foolish. This results because, typically, individuals think it is more foolish to act than not act. Note that Downloaded from cjb.sagepub.com at UNIV OF NEVADA RENO on February 11, 2014


the problems measure rational processing only—They do not measure experiential processing and thus the term nonrational will be used. For both CEST logic problems, participants indicated which of the two men was more foolish using a Likert-type scale ranging from (e.g., in the stock trading problem) 1 = Paul is much more foolish to 9 = George is much more foolish, with a 5 indicating Paul and George are equally foolish. Scores closer to 5 indicate rational processing, while scores closer to 1 and 9 indicate less rational processing. Scores of 5 (equally foolish) were rescored as 0, scores of 4 and 6 were rescored as a 1, scores of 3 and 7 were rescored as 2, and so on. For analysis purposes, the stock trading and parking problem scores were combined to create an overall CEST logic problem measure. The two measures were significantly correlated (p < .001; r = .29). Scores on the combined measure ranged from 0 (rational processing) to 8 (nonrational processing). Control Variables

Prior research indicates that participants’ religious affiliation (e.g., M. K. Miller & Hayward, 2008; O’Neil, Patry, & Penrod, 2004), gender (O’Neil et al., 2004), and race (e.g., Young, 1992) are related to views concerning the death penalty. Therefore, these variables are included as covariates in the analyses. Participants checked boxes indicating their responses. Dependent Variables

Three support measures were included as DVs. General death penalty attitude measure. This measure was adapted from the Attitude toward the Death Penalty Questionnaire used in past research (e.g., Wiener et al., 2004). Participants indicated which of four statements best described their attitude toward the death penalty: (a) If the defendant was found guilty of murder, I would always vote to sentence the defendant to death even if the facts in the case did not show that the defendant deserved a death sentence (the “strongly supportive” group); (b) I am in favor of the death penalty, but I would not necessarily vote for it in every case where the law allowed it. I would consider the facts of the particular case that pertain to the death penalty and then decide whether to sentence the defendant to death (the “somewhat supportive” group); (c) Although I have doubts about the death penalty, I would be able to find the defendant guilty and to vote for a death sentence where the law allowed it, if the facts of the case showed that the defendant was guilty and should be given a death sentence (the “somewhat unsupportive” group); or (d) I have such strong doubts about the death penalty that I would be unable to find the defendant guilty and vote for a death sentence where the law allowed it, even if the facts of the case showed that the defendant was guilty and deserved a death sentence (the “strongly unsupportive” group). Sentencing verdict. Participants checked a box to indicate their sentencing verdicts: death

penalty or life in prison without the possibility of parole. Sentencing verdict was dummy coded as 1 = death and 0 = life. Community member/student status. Participants indicated whether they were a student or community member by checking a box. Community member/student status was dummy coded as 1 = student and 0 = community member. Downloaded from cjb.sagepub.com at UNIV OF NEVADA RENO on February 11, 2014


Results

The final sample was 519 (72.28% of entire sample) participants who chose life (398 students; 121 community members) and 199 (27.72%) who chose death (149 students; 50 community members) sentences.3 The four death penalty attitude groups (strongly supportive, somewhat supportive, somewhat unsupportive, and strongly unsupportive) were collapsed into two groups (supportive and unsupportive). This was done because of the small number of students (n = 22) and community members (n = 3) indicating strong support for the death penalty. The final sample was 363 (50.35% of entire sample) “unsupportive” participants (286 student; 77 community members) and 358 (49.65%) “supportive” participants (264 students; 94 community members).4 See Table 1 for a complete demographic breakdown of students and community members. Differences Between Students and Community Members on Processing Variables

A preliminary analysis examined whether students and community members differed on their NFC, FI, and CEST logic problems scores. Community members (M = 20.13, SD = 3.84) were higher than students (M = 19.33, SD = 3.42) on their NFC scores, p = .01. Community members (M = 18.67, SD = 3.90) were also higher than students (M = 17.32, SD = 3.95) on their FI scores, p < .001. However, students (M = 2.33, SD = 2.14) were higher on their CEST logic problems scores than community members (M = 1.63, SD = 2.14; p < .001). These findings suggest that each of the processing variables should be examined separately for students and community members in subsequent analyses. This includes the creation of interaction variables for student status and each processing variable in the multivariate predictive models. Relationship Between Processing Variables

Pearson’s correlations (one set for community members and one set for students) examined the relationship between the NFC, FI, and CEST logic problem scores. For community members, no comparisons were significant, ps > .10. For students, NFC and CEST logic problems were negatively correlated, r(518) = −.17, p < .001, as expected. All other comparisons were not significant, ps > .10. In general, most of the processing measures were not related to one another. This lack of relationship justifies examining each variable individually and reduces concerns regarding multicollinearity that arise from including all three variables in one model. Relationship Between Death Penalty Support Variables

The relationship between the two support variables (general death penalty attitudes and sentencing verdict) was examined. Separate analyses were done for student and community member samples. For students, death sentence verdicts were positively correlated with supportive general death penalty attitudes, ϕ(545) = .28, p < .001. For community members, death sentence verdicts were also positively correlated with supportive general death penalty attitudes, ϕ(168) = .37, p < .001. Overall, the variables are related, even though one measures general attitudes and one measures behavior (chosen verdict). Both variables are treated separately in the analysis below because, despite the relationships between these support measures, they might relate to processing differently. For instance, Downloaded from cjb.sagepub.com at UNIV OF NEVADA RENO on February 11, 2014

10 Criminal Justice and Behavior Table 1: Demographic Breakdown (in Percent) for Students and Community Members.

Age Under 21 21-30 31-40 41-50 51-60 61-70 71-80 81-90 Sex Male Female Race African American Asian American Caucasian Hispanic Native American Other Religion Agnostic Atheist Buddhist Catholic Hindu Jewish Muslim Protestant Other No faith

Students (n = 552)

Community Members (n = 174)

368 (66.9%) 167 (30.4%) 9 (1.6%) 3 (0.6%) 2 (0.3%) 1 (0.2%) 0 (0.0%) 0 (0.0%)

1 (0.6%) 31 (18.7%) 41 (24.7%) 28 (16.9%) 28 (16.9%) 20 (12.0%) 13 (7.8%) 4 (2.4%)

218 (39.5%) 334 (60.5%)

43 (25.3%) 127 (74.7%)

14 (2.5%) 24 (4.3%) 477 (86.4%) 11 (2.0%) 6 (1.1%) 20 (3.6%)

1 (0.6%) 3 (1.7%) 161 (93.6%) 2 (1.2%) 3 (1.7%) 2 (1.2%)

21 (3.8%) 12 (2.2%) 12 (2.2%) 149 (27.1%) 1 (0.2%) 1 (0.2%) 4 (0.7%) 248 (45.1%) 32 (5.8%) 70 (12.7%)

6 (3.5%) 0 (0.0%) 1 (0.6%) 23 (13.3%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 104 (60.1%) 9 (5.2%) 30 (17.3%)

Note. Percentages listed are valid percentages and may differ due to missing data.

NFC is related to verdicts (e.g., Butler & Moran, 2007b) but the relationship between NFC and general attitudes has not been explored. Thus, it is important to investigate both variables separately. Cognitive Processing, Attitudes, Verdicts, and Student Status

Binary logistic regression analyses were conducted to determine the effect of the predictor variables (e.g., NFC, CEST) on the outcome variables (general death penalty attitude and sentencing verdict). One analysis was conducted for each dependent variable. Each analysis included NFC score (centered on the mean), FI score (centered on the mean), CEST logic problems score (centered on the mean), student status, NFC score × student status interaction, FI score × student status interaction, and CEST logic problems score × student status interaction as independent variables. Participant sex (dummy coded with female as the reference category), participant race (dummy coded with Caucasian as the reference category), and participant religion (dummy coded with Protestant as the


Miller et al. / Jurors’ Cognitive Processing 11 Table 2: Logistic Regression Models Across Death Penalty Support Measures. Model 1 (General Death Penalty Attitudes) Predictors Student NFC FI CEST NFC × Student FI × Student CEST × Student Male Other race Native American Hispanic Asian African American No faith Agnostic Atheist Other religion Muslim Buddhist Catholic Constant

Model 2 (Sentencing Verdict)

Wald

OR

95% CI

Wald

OR

95% CI

7.27** 0.16 0.82 17.51*** 2.45 0.02 11.16** 14.15*** 0.29 0.08 0.02 0.53 7.97** 0.05 1.50 0.31 7.18** 0.01 0.00 5.44* 6.43

0.51 0.98 1.04 1.62 0.92 0.99 0.66 1.95 0.76 1.25 0.92 0.71 0.05 0.95 0.59 1.43 0.35 0.89 1.01 0.61 1.84

[0.32, 0.83] [0.89, 1.08] [0.95, 1.15] [1.29, 2.03] [0.82, 1.02] [0.90, 1.10] [0.52, 0.84] [1.38, 2.77] [0.29, 2.03] [0.26, 6.00] [0.26, 3.29] [0.28, 1.80] [0.01, 0.40] [0.58, 1.55] [0.25, 1.38] [0.40, 5.05] [0.16, 0.76] [0.11, 6.90] [0.24, 4.29] [0.40, 0.92] —

0.42 4.10* 5.40* 7.21** 0.65 2.40 3.91* 8.80** 0.38 0.13 2.46 0.54 0.68 0.82 0.00 0.41 4.74* 0.01 0.01 1.29 16.05

0.86 0.90 1.14 1.27 1.05 0.91 0.82 1.75 0.69 0.73 2.67 1.44 0.52 0.78 0.98 0.60 0.35 0.87 1.09 0.77 0.40

[0.53, 1.37] [0.81, 1.00] [1.02, 1.26] [1.07, 1.51] [0.93, 1.19] [0.81, 1.03] [0.67, 1.00] [1.21, 2.53] [0.21, 2.24] [0.13, 4.06] [0.78, 9.11] [0.54, 3.83] [0.11, 2.47] [0.45, 1.34] [0.39, 2.48] [0.12, 2.91] [0.14, 0.90] [0.08, 9.25] [0.24, 5.00] [0.49, 1.21] —

NFC = Need for Cognition; FI = Faith in Intuition; CEST = Cognitive Experiential Self-Theory. *p < .05. **p < .01. ***p < .001.

reference category) were also included in each analysis as statistical controls. There was only one participant who identified as Hindu and one who identified as Jewish. These two religion variables were excluded from the models due to concerns regarding numerical problems (i.e., standard errors above 2).5 See Table 2 for regression coefficients for both models. General Death Penalty Attitude

The overall model indicated a good fit. The independent variables resulted in a statistically significant improvement over the constant-only model, χ2(20, N = 664) = 84.79, p < .001.The independent variables also accounted for 16% of the Nagelkerke pseudo variance. Student status was related to general death penalty attitude, odds ratio (OR) = 0.51, p = .007. The odds of indicating supportive death penalty attitudes were 49% lower for students than community members. CEST logic problem scores were also related to general death penalty support, OR = 1.62, p < .001. A one-point increase in CEST logic problems score was related to an increase in the likelihood of supportive general attitudes toward the death penalty by a factor of 1.62.



Figure 1: CEST Logic Problems × Student Status Interaction on General Death Penalty Attitudes

The student status and CEST logic problems’ main effects were qualified by a significant CEST logic problems score × student status interaction, OR = 0.66, p = .001. As depicted in Figure 1, a simple slopes test indicated that CEST logic problems score was related to general death penalty attitudes for community members, t(643) = 4.56, p < .001, but not students, t(643) = 1.53, p = .13. The findings from Model 1 support Hypothesis 3 (CEST logic problems are related to support for the death penalty) with regard to general death penalty attitudes and answers Research Question 1 (Do the relationships described in Hypotheses 1, 2, and 3 hold for students and community members?) in the negative. Several of the control variables were also related to general death penalty attitudes. Male participants had an increased likelihood of supportive death penalty attitudes, OR = 1.95, p < .001. African Americans, OR = 0.05, p = .005, Catholics, OR = 0.61, p = .02, and individuals who practice an “other” religion, OR = 0.35, p = .007, had a decreased likelihood of supportive death penalty attitudes. Sentencing Verdicts

The overall model indicated a good fit over the constant-only model, χ2 (20, N = 660) = 40.08, p = .005 and explained 9% of the Nagelkerke pseudo variance. The NFC score was related to sentencing verdicts, OR = 0.90, p = .04. For a one-point increase in the NFC score, the odds of a death sentence decreased by a factor of 0.90. The FI score was also related to sentencing verdicts, OR = 1.14, p = .02. For a one-point increase in the FI score, the likelihood of a death verdict increased by a factor of 1.14. CEST logic problems score was related to sentencing verdicts, OR = 1.27, p = .007. A one-point increase in the CEST logic problems score was related to an increase in the likelihood of a death verdict by a factor of 1.27. The relationship between CEST logic problems and sentencing verdict was qualified by a significant CEST × Student interaction, OR = 0.82, p = .048. A simple slopes test Downloaded from cjb.sagepub.com at UNIV OF NEVADA RENO on February 11, 2014


Figure 2: CEST Logic Problems × Student Status Interaction on Sentencing Verdict

indicated that CEST logic problems score was related to sentencing verdict for community members, t(639) = 2.76, p = .006, but not students, t(639) = 0.82, p = .41. See Figure 2 for a graphical representation of the interaction. The findings from Model 3 support Hypotheses 1, 2, and 3 with regard to sentencing verdicts. In addition, Research Question 1 is answered in the negative. Two control variables were also significant predictors of sentencing verdict. Males had an increased likelihood of giving a death verdict, OR = 1.75, p = .003. Participants who identified as practicing an “other” religion had a decreased likelihood of a death verdict, OR = 0.35, p = .03. Figure 3 shows the receiver operating characteristic (ROC) curves and area under the curve (AUC) of the two predictive models that were examined. AUCs were .69 (95% CI [.65, .73], p < .001) for general death penalty attitudes and .64 (95% CI [.59, .69], p < .001) for sentencing verdict. Discussion

This research investigated how jurors’ information processing (NFC, FI, CEST processing) is related to support for the death penalty, as measured by two interrelated, yet different, variables: general attitudes toward the death penalty and whether the juror chooses a death or life in prison sentencing verdict. Furthermore, the study investigated whether these relationships differ based on the mock juror’s status as a student or community member. Relationships Between Death Penalty Support and Information Processing

Several conclusions can be made related to information processing. First, the NFC score was related to sentencing verdicts but not general death penalty attitudes. Specifically, Downloaded from cjb.sagepub.com at UNIV OF NEVADA RENO on February 11, 2014


Figure 3: Receiver Operating Characteristic Graph, Area Under the Curve (AUC), and 95% Confidence Interval (95% CI) of Multivariate Models Predicting Death Penalty Support Note. The broken line represents the receiver operating characteristics curve expected by chance alone.

increases in NFC were related to decreases in the likelihood of a death sentence. Although previous studies provided mixed findings for the relationship between NFC and sentencing verdict (Butler & Moran, 2007b), no previous research has examined relationships between general death penalty attitudes and NFC. Second, FI scores were related to verdict, but not general death penalty attitudes. Results are inconsistent with the previous literature finding no relationship between FI and sentencing verdict (M. K. Miller, 2006). More research on the relationships between FI and jury decisions is clearly needed. Third, individuals with supportive attitudes toward the death penalty had less rational scores on the CEST logic problems and those who gave a death penalty sentencing verdict were less rational than those giving a life sentence (although both these effects only occurred for community members, not students). This generally comports with previous findings that those who score more rationally are less punitive (M. K. Miller, 2006), although no analysis conducted here had been specifically examined previously. In sum, current findings suggest that those who support the death penalty are generally more experiential and less rational in their information processing states and traits. In addition to these findings, there were also a few significant results related to status as students or community members. Differences Between Community and Student Samples

As discussed earlier, there is much debate in the literature about potential differences between community and student samples (e.g., Wiener et al., 2011). Some researchers contend that the differences between these groups are either nonexistent or minimal (e.g., Downloaded from cjb.sagepub.com at UNIV OF NEVADA RENO on February 11, 2014


Bornstein, 1999). Conversely, some researchers have found significant differences between these two groups (e.g., Fox et al., 2011; Keller & Wiener, 2011), indicating that community samples are needed to allow for generalizability of findings beyond the student population (Wiener et al., 2011). The current study’s results only partially support the latter argument. Specifically, the two groups did not differ on sentencing verdicts, but community members had more supportive attitudes toward the death penalty than students. This supports previous findings that students tend to be more lenient in other contexts (e.g., sexual violence hearings; McCabe et al., 2010). Furthermore, the current study revealed that the relationships between processing traits (NFC and FI) and the dependent variables were the same for community members and students. Other studies (e.g., McCabe & Krauss, 2011; McCabe et al., 2010) found that community samples are not affected by processing traits, but students are. Those studies were not death penalty studies, which might partially explain why the current study found no differences between students and community members but those studies did. Differences between this and past studies might be further accounted for by differences in the community samples. Other studies (e.g., McCabe & Krauss, 2011) used individuals who had been called for jury duty but were not selected; the current study used community members who were part of a community group or attended a particular gym, etc. Relationships between dependent variables and processing states (i.e., CEST processing measures), however, depended on the participant’s status as student or community member. Specifically, CEST processing was related to general attitudes and sentencing verdict—but only for community members. This suggests that community members are influenced by how they are processing information at the time—but students are not. While no studies directly tested these notions before, McCabe and Krauss (2011) found that a bias correction intervention worked for students but not community members. Perhaps this study further demonstrates that students are better able to correct for their emotions and biases compared with community members. Implications for Psychology

The current findings confirm and further what is known about information processing in general. More specifically, the CEST state (i.e., CEST problem) and trait (i.e., NFC and FI) measures did not reliably relate to each other. This might indicate that they are different, but not necessarily opposite, constructs. For instance, it might be possible for a person to have high need for cognition and high faith in intuition. These results confirm that these are separate constructs, as suggested by Epstein and colleagues (1996). A second implication comes from the finding that NFC was related to the CEST logic problem (but for students only), indicating that “rational” traits are related to “rational” states. One’s “rationality” might not vary much from moment to moment; if one generally is rational on the NFC, one is likely to think rationally at any given moment. In contrast, FI was not related to the CEST logic problems, suggesting that one’s “experientiality” is more malleable. Just because one generally thinks experientially, this does not necessarily mean one will think less rationally at any given moment. This suggests that CEST logic problems are a measure of rationality rather than a measure of experientiality, as discussed above. The finding that NFC relates to CEST logic problem scores only for students is interesting and further suggests that students and community members differ in processing. As discussed above, the differences between students and community members is an area ripe for investigation. Downloaded from cjb.sagepub.com at UNIV OF NEVADA RENO on February 11, 2014

16 Criminal Justice and Behavior Implications for The Legal System

Various legal cases and philosophies have indicated that “ideal” jurors make decisions based on the evidence and jury instructions rather than emotions (e.g., Gregg v. Georgia, 1976). In contrast, the current study finds that, as compared with those who chose a life sentence, individuals who choose a death sentence are lower in NFC, higher on FI, and less rational on the CEST logic problem (the latter was found for community members only). Thus, the verdicts themselves seem affected by processing style in a manner that courts likely would not approve. This might suggest that some legal reform should be considered; for instance, changes in the jury selection process or in the instructions given to jurors might be needed. A moratorium on the death penalty could also be considered until more research can investigate whether death penalty jurors are making decisions in these undesirable ways—and whether there are ways to counter this. The second implication concerns jury selection. Jurors who chose a death sentence verdict were lower in NFC and higher in FI. Lawyers for the defense should thus choose individuals high in NFC and low in FI; prosecutors should choose jurors with the opposite traits. If judges allow lawyers to ask jurors questions from these scales, doing so should help lawyers choose favorable jurors. Even if a judge does not allow lawyers to ask questions from the scales, it is possible to use proxy variables for processing, such as education, hobbies, occupation, or mental state. For instance, a defense attorney might want to remove a potential juror who seems highly emotional, as this person might be high in FI and thus more likely to give a death sentence. In contrast, the prosecutor might exclude a highly educated juror (see Cacioppo, Petty, Feinstein, & Jarvis, 1996 for review) or someone who has a high knowledge of current events (Cacioppo & Petty, 1984), as he might be high in NFC and thus less likely to choose a death sentence. As noted in the literature review above, there are many differences between high and low NFC people (and between high and low FI people; see Sladek et al., 2010 and Cacioppo et al., 1996, for reviews), including the finding that those more knowledgeable in current events are higher in NFC (Cacioppo & Petty, 1984). Future research should examine practical ways in which researchers and attorneys can determine cognitive processing styles during voir dire. Along similar lines, the findings suggest that community members and students differ in their decision making. Community members, but not students, tend to base their general death penalty attitudes and sentencing verdict on their cognitive processing states (i.e., CEST logic problems). Arguably, actual jurors serving on capital trials are more similar to the community sample than a student sample, resulting in jurors who base their decisions on their current processing states. Therefore, the legal system should consider increasing efforts to obtain more student jurors (and not allow students to be excused from jury duty as often). The result would be more jurors who do not base their decisions on processing states. Limitations

There are some limitations to the current study. First, participants knew this was a fictitious trial that had no real world consequences; the trial was a brief written summary, rather than a real life trial that would be much more in-depth. Thus, participants might not have taken these tasks as seriously as real jurors might, and they might act differently if presented with a real trial. Some researchers (Bornstein, 1999; Bornstein & McCabe, 2005) have Downloaded from cjb.sagepub.com at UNIV OF NEVADA RENO on February 11, 2014


indicated that verisimilitude and consequentiality concerns are not typically major issues. Even so, future studies should use real (or at least more realistic) trials with real consequences. Next, participants did not deliberate; they only gave individual verdicts. Individual verdicts are sometimes different from group verdicts or postdeliberation individual verdicts (see Devine et al., 2001; M. K. Miller, Maskaly, Green, & Peoples, 2011). It is possible that deliberation attenuates biases and differences between students and community members. Specifically, verdicts might be related to cognitive processing, but only at the individual level. Bothwell, Pigott, Foley, and McFatter (2006) did such a comparison and concluded that subtle biases are “washed out” by complicated deliberation processes. M. K. Miller et al. (2011) suggest that jurors become less biased when forced to publically argue their position in deliberation. Thus, it is important to examine whether the main effects and interactions found here (e.g., that only community members’ CEST processing scores were related to attitudes and verdicts) endure after deliberation. Perhaps community members will rely less on their current processing state if they are asked to deliberate. On a related note, high and low NFC jurors deliberate differently (i.e., high NFC participants were more active and engaged; Shestowsky & Horowitz, 2004); thus, deliberations should be an important part of future studies. Finally, the mere measurement of rational processing (i.e., the CEST logic problem) might have unintentionally increased participants’ level of rational processing. Other studies have induced rational processing by asking participants to do math problems (Lieberman et al., 2007). It is difficult to measure a state variable without also manipulating the variable; this is the difficulty of measuring a state that can also be manipulated. That is, how does one measure rational processing while not encouraging rational processing? Future studies might be able to create measures to do so. But, for the current purposes, even if the rational processing state was inflated, there was not a ceiling effect. Indeed, the average CEST logic problem score was 2.17, with an SD of 2.16 (on a scale of 0 to 8) and only 35.3% (N = 237) of participants scored a 0 (perfect rational processing). Thus, even if completing the CEST problems did increase rationale processing, it did not make everyone think highly rationally. While this is a limitation, it is important to note that the CEST logic problems were the final items in the survey; thus, even if completing the problem affected processing, NFC or FI scores (or the death penalty variables) would not have been affected. Conclusion

This study found that information processing traits (i.e., being low NFC and high FI) and states (i.e., less rational CEST logic problems score) were related to giving a death penalty sentencing verdict. These results for NFC and FI are consistent for students and community members, but community members tend to rely more on their current processing state (measured by CEST logic problems) than students. Information process states, but not traits, were related to supportive general death penalty attitudes. However, this relationship held for community members only. Courts have been concerned that jurors make decisions in logical and nonemotional ways rather than capricious ways (e.g., Gardner v. Florida, 1977; Gregg v. Georgia, 1976). Unfortunately, those who choose a death penalty sentencing verdict have the information processing traits and states similar to those the courts are concerned about (i.e., high FI, low NFC, and experiential CEST scores). Future studies can further investigate these relationships, and psychologists can bring the results to the attention of the courts and Downloaded from cjb.sagepub.com at UNIV OF NEVADA RENO on February 11, 2014


policymakers. Ultimately, such studies can protect the integrity of the legal system by providing information about juries and how they make their decisions. Notes 1. In all these cases, there is much more to the case and ruling than discussed here; this summary is limited to only what is necessary to understand this study. 2. The court also approves of other schemes for sentencing. 3. Eight participants did not answer this question. 4. Five participants did not answer this question. 5. Initial analyses were conducted with these two variables included. The results did not differ between analyses (i.e., predictor variables that were significant in the original model were still significant in the reduced model).

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Monica Miller, JD, PhD, is an associate professor at the University of Nevada, Reno, with a split appointment between the criminal justice department and the Interdisciplinary PhD Program in Social Psychology. She is the director of the Criminal Justice Department’s graduate program. Her interests include the role of religion in the legal system; how the law regulates sexual behavior, pregnancy, and family issues; jury decision-making; and community sentiment and public policy. Steve M. Wood, MA, received his bachelor’s and master’s degrees in psychology from Central Michigan University. He is currently a doctoral candidate in the Interdisciplinary PhD Program in Social Psychology at the University of Nevada, Reno. His research interests are in the areas of attorney performance, jury decision-making, sexual assault/aggression, interpersonal violence, media influence, and child welfare. Julianna C. Chomos, MA, is a doctoral student in the Interdisciplinary PhD Program in Social Psychology at the University of Nevada, Reno. She received her BA in psychology from the University of Pittsburgh in 2007 and her MA in social psychology from the University of Nevada, Reno, in 2011. Her research interests are in the areas of jury decision-making, attributions of blame/responsibility, civil wrongdoing, and eyewitness testimony.
