trait judgments

trait judgments

https://doi.org/10.1177/0956797618788882

Psychological Science 2018, Vol. 29(11) 1807 –1823 © The Author(s) 2018 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/0956797618788882 www.psychologicalscience.org/PS

ASSOCIATION FOR PSYCHOLOGICAL SCIENCEResearch Article

Faces are rich in information: They provide clues about gender, race, age, and trait attributes, which are inferred spontaneously and ubiquitously (Engell, Haxby, & Todorov, 2007; Todorov, 2017). Moreover, such infer- ences often guide our social behavior—for instance, we decide whom to trust on the basis of how trustworthy a face looks (Rezlescu, Duchaine, Olivola, & Chater, 2012; Van’t Wout & Sanfey, 2008). Many trait judgments made by participants across generations and cultures show consensus (Cogsdill, Todorov, Spelke, & Banaji, 2014; Lin, Adolphs, & Alvarez, 2017; Rule et al., 2010). But are trait judgments from faces accurate?

Previous research has shown that trait judgments from faces can be associated with important real-world social outcomes, such as dating and mating (Olivola et al., 2014; Valentine, Li, Penke, & Perrett, 2014), earn- ings and fundraising (Genevsky & Knutson, 2015; Hamermesh, 2011; Ravina, 2012), science communica- tion (Gheorghiu, Callan, & Skylark, 2017), sentencing decisions (Berry & Zebrowitz-McArthur, 1988; Blair, Judd, & Chapleau, 2004; Wilson & Rule, 2015; Zebrowitz

& McDonald, 1991), and leader selection (Todorov, Mandisodza, Goren, & Hall, 2005; for reviews, see Antonakis & Eubanks, 2017; Todorov, Olivola, Dotsch, & Mende-Siedlecki, 2015). Yet this prior research on the association between trait judgments from faces and real- world outcomes leaves open two important questions. First, most associations have focused on prosocial out- comes (e.g., correlations between competence judg- ments and election success; Todorov et al., 2005). Second, most associations are plausibly driven not by the behavior of the targets whose face is being judged but by the interests of the perceivers who are making the judgments (e.g., correlations between interesting- looking scientists and the perceiver’s interest in their work). Here, we investigated an antisocial judgment that

788882 PSSXXX10.1177/0956797618788882Lin et al.Facial Inferences and Corruption research-article2018

Corresponding Author: Chujun Lin, California Institute of Technology, Division of Humanities and Social Sciences, HSS 228-77, 1200 E. California Blvd., Pasadena, CA 91125 E-mail: [email protected]

Inferring Whether Officials Are Corruptible From Looking at Their Faces

Chujun Lin, Ralph Adolphs, and R. Michael Alvarez Division of Humanities and Social Sciences, California Institute of Technology

Abstract While inferences of traits from unfamiliar faces prominently reveal stereotypes, some facial inferences also correlate with real-world outcomes. We investigated whether facial inferences are associated with an important real-world outcome closely linked to the face bearer’s behavior: political corruption. In four preregistered studies (N = 325), participants made trait judgments of unfamiliar government officials on the basis of their photos. Relative to peers with clean records, federal and state officials convicted of political corruption (Study 1) and local officials who violated campaign finance laws (Study 2) were perceived as more corruptible, dishonest, selfish, and aggressive but similarly competent, ambitious, and masculine (Study 3). Mediation analyses and experiments in which the photos were digitally manipulated showed that participants’ judgments of how corruptible an official looked were causally influenced by the face width of the stimuli (Study 4). The findings shed new light on the complex causal mechanisms linking facial appearances with social behavior.

Keywords face perception, corruption, social attribution, stereotyping, political psychology, open data, open materials, preregistered

Received 9/25/17; Revision accepted 6/3/18

http://www.psychologicalscience.org/ps
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It is always better to give some background information before you talk about your study. what is the topic?
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The purpose of the study, or what is the issue/problem?
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How did you conduct the experiment? I.e., how you tested you question.
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what are the findings?
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what are conclusions or implications?
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mailto:[email protected]
https://us.sagepub.com/en-us/journals-permissions
http://crossmark.crossref.org/dialog/?doi=10.1177%2F0956797618788882&domain=pdf&date_stamp=2018-09-12
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may offer a clearer insight into associations with the judged person’s own behavior: political corruption.

Political corruption has been a major cause of regime change and an important subject of much study in political science and economics (Rose-Ackerman, 2013). The possibility that corruptibility inferences from faces might be associated with real-world measures of cor- ruption is raised by three areas of previous research. First, theories of self-fulfilling prophecy argue that the impressions and expectations a face creates (e.g., how corruptible an official looks) influence how other peo- ple interact with the face bearer (e.g., how likely others would be to bribe the official) and that those recurrent interactions in turn shape the face bearer’s behavior so as to confirm other people’s impressions and expecta- tions (Haselhuhn, Wong, & Ormiston, 2013; Jussim, 1986; Slepian & Ames, 2016). Second, analyses of sen- tencing decisions show that evaluations of guilt and recommendations of punishment are influenced by the defendant’s facial appearance (Berry & Zebrowitz- McArthur, 1988; Blair et al., 2004; Wilson & Rule, 2015; Zebrowitz & McDonald, 1991). These findings suggest that officials who look more corruptible might be more likely to be accused, prosecuted, and convicted. Third, some studies have argued that the face contains a kernel of truth about a person’s nature—such as personality and criminal inclinations (Penton-Voak, Pound, Little, & Perrett, 2006; Valla, Ceci, & Williams, 2011)—even though the diag- nostic validity and the causal mechanisms remain obscure.

Given past research, we hypothesized that elected officials’ corruption records would be associated with traits, such as corruptibility, inferred from their facial appearances. We examined this association in three preregistered studies, where participants made trait inferences on the basis of the photos of unfamiliar government officials. To account for the possibility that this association might depend on the severity of corrup- tion and the level of office, we inspected both serious violations (i.e., cases considered political corruption) and minor violations (i.e., cases meriting a fine) and included officials at different levels of government (fed- eral, state, and local). In a fourth preregistered study, we explored which facial features might be causally mediating the impression of how corruptible an official was, using mediation analyses as well as experimental manipulations of the face stimuli. In this fourth study, we focused on metrics of facial structures—in particu- lar, facial width (relative to facial height) because it has been reported that men with wider faces are judged as less trustworthy (Haselhuhn et al., 2013; Stirrat & Perrett, 2010), more threatening (Geniole, Denson, Dixson, Carré, & McCormick, 2015), and less than fully human (Deska, Lloyd, & Hugenberg, 2018), although it remains unknown whether facial width-to-height ratio associates with actual behavior.

We have reported all measures, all conditions, all data exclusions, and how sample sizes were determined in this article and on the Open Science Framework (https://osf.io/k4mds/). All materials, data, and analysis codes for the present research can be accessed at this link.

Study 1

Our first study focused on federal and state officials and compared those who had clean records with those who were convicted of political corruption.

Method

Participants. This study was preregistered before data collection began (https://osf.io/mge8r/). A sample size of 100 participants was predetermined on the basis of two pilot studies—one carried out in the lab in May 2016 and the other via Amazon’s Mechanical Turk (MTurk) in October 2016. The lab study included 32 participants recruited from the general public of Southern California, and the MTurk study had 18 participants. For the hypoth- esis that elected officials’ corruption records would be associated with face-based inferences of corruptibility, the laboratory pilot study yielded an estimated effect size of 1.06, and the MTurk pilot study yielded an estimated effect size of 1.05, justifying a minimum sample size of 16 participants. Given these results and to ensure sufficient power even with dropout, we recruited 100 MTurk par- ticipants in November 2016. We selected participants who were native English speakers, located in the United States, and 18 years old or older. In addition, they had to have normal or corrected-to-normal vision, an educa- tional attainment of high school or above, a good MTurk participation history (a human-intelligence-task, or HIT, approval rate ≥ 95% and ≥ 1,000 HITs approved), and no prior participation in our pilot studies.

Eighteen individuals were excluded in total, 2 for not being native English speakers, 6 for pressing the same response key for all trials in a block, and 10 for failing to input valid responses for more than 10% of the trials in a block (responses were considered not valid if missing or entered within 100 ms—the minimum time needed for visual exploration of the face; Olivola & Todorov, 2010). After exclusion, there were 82 par- ticipants in our final sample (42 female; age: M = 39 years, SD = 12; 84% White, 10% Black, 5% Asian).

Stimuli. Stimuli were photos of 72 real elected officials. All were Caucasian males who have held federal or state legislative offices in the United States. Photos were offi- cial headshots obtained from government websites and personal campaign websites (63%), news articles (23%), and Wikipedia (14%). All photos were converted to gray-scale

https://osf.io/k4mds/
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review the literature that is more relevant to this current question.
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hypothesis
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their approach to test the question
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good practice! You will find all the face images from this link.
https://osf.io/mge8r/
Facial Inferences and Corruption 1809

images on a plain gray background and cropped to a uni- form size. All faces were frontal, smiling, in clear focus, and centered in the middle of the image.

Among the 72 officials, half were convicted of political corruption (corrupt officials), and the other half had clean records (noncorrupt officials). The cor- rupt officials were from two Wikipedia data sets (list of American state and local politicians convicted of crimes: https://en.wikipedia.org/wiki/List_of_American_state_ and_local_politicians_convicted_of_crimes; list of American federal politicians convicted of crimes: https://en.wikipedia.org/wiki/List_of_American_fed eral_politicians_convicted_of_crimes). To reduce sources of variability, we included only officials who were Caucasian, were male, held federal or state legisla- tive offices, and were convicted between 2001 and 2016 of political corruption conducted while in office (brib- ery, money laundering, embezzlement, mail fraud, wire fraud, tax fraud, conflict of interest, misusing funds, misusing office, or falsifying records). In addition, age information for these officials had to be publicly avail- able, as did frontal photographs of acceptable clarity in which the official was smiling. All photographs had been taken while officials were in office. Most photos of the corrupt officials had a known creation date, and we confirmed that the photos were taken before their conviction (72%); for the rest of the photos (28%), the creation date was unknown (analyses were also per- formed when excluding data for these stimuli; the pat- tern of results did not change). The noncorrupt officials were randomly matched from the list of incumbents who had clean records, were holding the same office in the same state, and were of the same gender, the same race, and similar age (±12 years) as the corrupt officials during the period of their misconduct. For instance, if the stimuli contained a Caucasian male cor- rupt official who was a member of the Arizona House

of Representatives during his misconduct at the age of 55, then a noncorrupt official would be randomly selected from our available stimulus set from the list of Arizona House of Representatives incumbents who had a clean record and who was a Caucasian male between the ages of 43 and 67.

Procedures. Participants were not informed of the pur- pose of the study or the sampling of the stimuli. In par- ticular, they were not given any information about the percentage of politicians in our stimulus set who might be corrupt in real life. They were told only that they would view a series of politician photos and that they should judge how corruptible, dishonest, selfish, trust- worthy, and generous these politicians looked to them (experiment instructions are available at https://osf.io/ k4mds/). Participants completed five blocks of experi- ments, with each block corresponding to judging one trait for all faces. The ordering of the faces within each block as well as the ordering of the blocks were randomized.

Each block started with an instruction screen that specified the trait to be judged (e.g., corruptibility). Participants were instructed to make their decisions as quickly and precisely as possible. Six practice trials familiarized participants with the task. Participants viewed photos of officials one at a time in randomized order and made judgments. Each trial began with a fixation cross, followed by the photo (1 s) with a 5-point Likert scale below it. Scales were anchored with bipolar adjectives (Fig. 1). Participants could make a decision as soon as the photo appeared and within 4 s after the photo disappeared. The orientation of the scale was randomized across blocks, and scores were reverse- coded as needed.

After completing all five blocks of ratings, partici- pants were asked whether they had recognized any of the officials and filled out a short survey questionnaire

0.5–1.5 s 1 s Maximum of 4 s 0.5 s

Time

Fig. 1. An example trial in the corruptibility-judgment block. Each trial began with a fixation cross. Then a photo of an official appeared for 1 s. The orientation of the scale was randomly flipped for each block and each participant. Participants made a decision by pressing one of the number keys from “1” to “5” on their keyboard. As soon as a valid key was pressed (or 4 s after the photo disappeared if no valid key was pressed), the trial ended, and there was a blank interstimulus interval.

https://en.wikipedia.org/wiki/List_of_American_state_and_local_politicians_convicted_of_crimes
https://en.wikipedia.org/wiki/List_of_American_state_and_local_politicians_convicted_of_crimes
https://en.wikipedia.org/wiki/List_of_American_federal_politicians_convicted_of_crimes
https://en.wikipedia.org/wiki/List_of_American_federal_politicians_convicted_of_crimes
https://osf.io/k4mds/
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on demographic characteristics, political attitudes, and personality.

Results

Reliability of face-based trait inferences. Following our preregistered plan, we excluded from further analy- sis any responses faster than 100 ms and data for officials who were recognized. Among the 82 participants, 7 rec- ognized one official (in total, four officials were recog- nized). The percentages of participants who used the full scale to rate the faces ranged from 59% to 68% across the five traits, and over 90% of the participants used scores on both sides of the midpoint to rate the faces (see Fig. S1 in the Supplemental Material available online).

First, we checked whether participants gave consis- tent judgments for a face across different traits. We expected consistent ratings for a face on traits with the same valence to be positively correlated and ratings on traits with opposite valences to be negatively corre- lated. Although this was not planned in our preregistra- tion, we computed repeated measures correlations (the R function rmcorr) to determine the common within- individuals correlations for ratings between each pair of traits, to handle the issue of nonindependence in repeated measures. Results (see Table S1 in the Supple- mental Material for coefficients and 95% confidence intervals, or CIs) showed that at an individual level, judgments of a face for traits with the same valence were positively correlated (repeated measures rs rang- ing from .24 to .31, ps < .001), and judgments of a face for traits with opposite valences were negatively cor- related (repeated measures rs ranging from –.30 to –.21, ps < .001). Following our preregistered plan, we also analyzed the consistency of these ratings at an aggre- gate level. Ratings for each face were first averaged over participants, and (tie-corrected) Spearman correlation coefficients were calculated for each pair of traits with those averaged ratings. Aggregate-level judgments for a face were highly consistent across traits because they averaged out the measurement noise inherent in the individual-level correlations (|r| ≥ .75; see Table S2 in the Supplemental Material).

Next, intraclass correlation coefficients (ICCs) were computed for each trait separately to test whether infer- ences of a trait showed consensus across participants— ICCs were computed according to type ICC(2, k) on the basis of complete cases. A high ICC indicates that the total variance in ratings is mainly explained by rating variance across images instead of across participants. In line with prior literature (see the introduction), our results showed high consensus among participants for inferences of corruptibility, ICC = .81, F(48, 3888) = 6.4, 95% CI = [.73, .88]; dishonesty, ICC = .82, F(45, 3645) =

6.7, 95% CI = [.74, .89]; selfishness, ICC = .86, F(42, 3402) = 8.1, 95% CI = [.80, .91]; trustworthiness, ICC = .82, F(43, 3483) = 6.7, 95% CI = [.74, .89]; and generos- ity, ICC = .82, F(43, 3483) = 6.6, 95% CI = [.74, .89].

Association between corruption records and face- based trait inferences: preregistered analyses. Our primary interest in the current study was the extent to which trait inferences from a face were associated with actual corruption records. First, we followed the analysis methods planned in our preregistration and tested for these associations on the basis of inference judgments aggregated across participants and individually within subjects. For inferences of a negative trait, we deemed an official to be categorized accurately if he was convicted of corruption and received a high rating (> 3) or, con- versely, if he had a clean record and received a low rating (≤ 3); for inferences of a positive trait, we deemed an official to be categorized accurately if he was convicted of corruption and received a low rating (< 3) or, con- versely, if he had a clean record and received a high rat- ing (≥ 3).

One-sample, one-tailed proportion tests against chance (50%) were performed on the aggregated-level accuracies across officials. One-sample one-tailed t tests against chance (50%) were performed on the individual-level accuracies across participants (we also calculated individual-level accuracies by categorizing midpoint 3 in the opposite way; see Table S3 in the Supplemental Material). Results (summarized in Table 1) showed that both aggregate-level and individual- level inferences of traits were associated with actual corruption records of the facial identities at a level better than chance (see Fig. S2 in the Supplemental Material for full distributions of individual-level accuracies).

Association between corruption records and face- based trait inferences: extensions to preregistered analyses. Beyond our planned preregistered analyses, we conducted three additional robustness checks on the asso- ciation between trait inferences from faces and corruption records. First, we confirmed that the above-chance accu- racy we observed was not driven just by a small subset of faces: For each trait, we ranked the officials by the num- ber of participants who categorized them accurately; we then calculated the average individual-level accuracy for subsets of stimuli in which the officials were progres- sively excluded one by one from the official who was accurately categorized by most participants to the official who was accurately categorized by fewest participants. For all five traits, average individual-level accuracies decreased smoothly as the highest ranked officials were removed and stayed above chance even after the 12th

Facial Inferences and Corruption 1811

highest ranked official was excluded from the stimulus set (see Table S4 in the Supplemental Material).

Second, although participants were not informed of the purpose of the study or the percentage of corrupt politicians in our stimulus set (they were told only that these people were politicians), their beliefs (implicit or explicit) about the base rates of corrupt politicians in the real world or the percentage of corrupt politicians in our experiment might bias the ratings they gave. We corrected for such possibly idiosyncratic biases among our participants by calculating individual-level accura- cies using an alternative method. Ratings for each par- ticipant were centered on that participant’s mean across all of his or her ratings on a trait (see Fig. S3 in the Supplemental Material for the full distributions of mean ratings).

For this analysis, inferences of a negative trait were deemed accurate if the official had been convicted of corruption and received a rating from a participant that was higher than the participant’s mean rating or, con- versely, if the official had a clean record and received a rating from a participant that was lower than the participant’s mean rating; inferences of a positive trait were deemed accurate if the official was convicted of corruption and received a rating from a participant that was lower than the participant’s mean rating or, con- versely, if the official had a clean record and received a rating from a participant that was higher than the participant’s mean rating. One-sample, one-tailed t tests against chance (50%) were performed on individual- mean-centered accuracies across participants. Corrobo- rating the results reported previously, individual-level trait inferences correlated with officials’ corruption records at a level better than chance, and the effect sizes were large—corruptibility inferences: M = 55.57%, SD = 7.75%, lower bound of 95% CI = 54.14%, t(81) = 6.50, p < .001, d = 0.72; dishonesty inferences: M = 55.12%, SD = 6.43%, lower bound of 95% CI = 53.94%,

t(81) = 7.22, p < .001, d = 0.80; selfishness inferences: M = 54.95%, SD = 7.87%, lower bound of 95% CI = 53.50%, t(81) = 5.69, p < .001, d = 0.63; trustworthiness inferences: M = 55.59%, SD = 6.53%, lower bound of 95% CI = 54.39%, t(81) = 7.75, p < .001, d = 0.86; and gener- osity inferences: M = 55.31%, SD = 6.95%, lower bound of 95% CI = 54.03%, t(81) = 6.92, p < .001, d = 0.76.

Third, to address the concern that dichotomizing rat- ings into accurate and inaccurate might lead to loss of measurement sensitivity and to handle the nonindepen- dence in ratings due to repeated measures designs, we performed general linear mixed-model (GLMM) analyses for inferences of each trait, respectively. Officials’ cor- ruption records (1 = conviction, 0 = clean) were regressed on individual-level ratings in logistic models, and par- ticipants were treated as random factors (N = 5,757; N was determined by the number of participants multi- plied by the number of faces, excluding omitted obser- vations; observations from a participant for a face would be omitted if ratings were not available for all five traits). In addition, photo characteristics (the official’s age and smile intensity; the presence of glasses, a beard, a mus- tache, and a bald head; image clarity; and image sources) were included as control variables in all models. All continuous variables were standardized.

We observed significant effects of trait ratings: Officials who were rated as looking more corruptible, b = 0.23, SE = 0.03, 95% CI = [0.17, 0.29], z = 7.66, p < .001; dis- honest, b = 0.17, SE = 0.03, 95% CI = [0.11, 0.23], z = 5.75, p < .001; and selfish, b = 0.20, SE = 0.03, 95% CI = [0.14, 0.26], z = 6.77, p < .001, were more likely to have been convicted of corruption, whereas officials who were rated as looking more trustworthy, b = −0.19, SE = 0.03, 95% CI = [−0.25, −0.13], z = −6.41, p < .001, and generous, b = −0.20, SE = 0.03, 95% CI = [−0.26, −0.14], z = −6.59, p < .001, were less likely to have been convicted of corruption (for complete lists of coeffi- cients, see Table S5 in the Supplemental Material).

Table 1. Results for Correctly Categorized Officials Based on Aggregate-Level Trait Inferences and Individual-Level Trait Inferences From Study 1

Trait

Aggregate-level accuracy Average individual-level accuracya

Percentage of correctly categorized

officials (N = 72)

Lower bound of 95% CI χ2(1) p

Mean accuracy (N = 82) SD

Lower bound of 95% CI t(81)

Cohen’s d

Corruptibility 69.44% 59.22% 10.13 < .001 55.73% 6.95% 54.46% 7.47 0.82 Dishonesty 70.83% 60.67% 11.68 < .001 54.82% 6.41% 53.64% 6.81 0.75 Selfishness 66.67% 56.36% 7.35 .003 55.10% 6.76% 53.86% 6.83 0.75 Trustworthiness 68.06% 57.79% 8.68 .002 55.03% 6.41% 53.85% 7.10 0.78 Generosity 63.89% 53.53% 5.01 .013 54.97% 5.99% 53.87% 7.51 0.83

Note: CI = confidence interval. aAll ps for this variable are less than .001.

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Association between corruption records and face- based trait inferences: further exploration of poten- tial mechanisms. Finally, we performed two additional analyses that were also beyond our preregistration. We performed GLMM analyses on two subsets of data to test two photo-selection–related mechanisms underlying the face–corruption-record association we found. To test the hypothesis that potential negative biases in the convicted officials’ photos that were from sources beyond the con- trol of the officials might be driving the association, we conducted GLMM analyses on a subset of data that included only officials whose photos were self-selected—that is, those from government websites and personal campaign websites (n = 45; 20 were convicted of corruption; in this subset, only 1 official had a beard, and only 2 officials were bald, and therefore these two predictors were removed from the model).

The associations between trait inferences and records remained significant—corruptibility inferences: b = 0.24, SE = 0.04, 95% CI = [0.17, 0.32], z = 6.81, p < .001; dishonesty inferences: b = 0.19, SE = 0.04, 95% CI = [0.12, 0.26], z = 5.21, p < .001; selfishness inferences: b = 0.18, SE = 0.04, 95% CI = [0.11, 0.25], z = 5.07, p < .001; trustworthiness inferences: b = −0.20, SE = 0.04, 95% CI = [–0.27, –0.13], z = −5.63, p < .001; and gener- osity inferences: b = −0.17, SE = 0.04, 95% CI = [–0.24, –0.10], z = −4.66, p < .001.

To test the hypothesis that potential negative biases in the convicted officials’ photos that were taken after conviction might be driving the face–corruption-record association, we conducted GLMM analyses on a subset of data that included only officials whose photo dates were known (and were prior to the date of conviction, for convicted officials; n = 62; 26 were convicted of corruption). The associations between trait inferences and records became weaker but remained significant— corruptibility inferences: b = 0.17, SE = 0.03, 95% CI = [0.10, 0.23], z = 4.93, p < .001; dishonesty inferences: b = 0.11, SE = 0.03, 95% CI = [0.04, 0.18], z = 3.29, p = .001; selfishness inferences: b = 0.16, SE = 0.03, 95% CI = [0.09, 0.22], z = 4.64, p < .001; trustworthiness inferences: b = −0.14, SE = 0.03, 95% CI = [–0.20, –0.07], z = −4.06, p < .001; and generosity inferences: b = −0.19, SE = 0.03, 95% CI = [–0.26, –0.13], z = −5.74, p < .001. This indicates that while potential biases in photo selec- tion can explain some of the relationship between trait ratings and officials’ records, they cannot entirely account for our main findings.

Two additional analyses were preregistered but are not presented in this article; the codes to conduct those analyses can be found at https://osf.io/k4mds/. In our preregistration, we proposed an alternative approach to analyze individual-level ratings (logistic regression with adjusting standard errors for clustering). These

analyses are not presented here because the GLMM analyses reported previously are more appropriate for handling repeated measures. We had also planned analyses of correlations between individual-level accu- racies and response times, but these were intended to answer a question that is beyond the scope of the cur- rent article.

Study 2

Study 1 showed that compared with peers with clean records, federal and state officials who were convicted of political corruption were perceived as more corrupt- ible, dishonest, and selfish and less trustworthy and generous. To assess the generalizability of these find- ings, we next tested whether they would also hold for officials from lower levels of governments and for the comparison between officials with clean records and officials who violated campaign finance laws.

Method

Participants. This study was preregistered before data collection began (https://osf.io/tgzpz/). A pilot study with 24 MTurk workers conducted in February 2017 yielded an estimated effect size of 1.39, justifying a mini- mum sample size of 10 participants. To ensure sufficient power and to have a sample size comparable with that of Study 1, we predetermined the sample size to be 100 participants. The same inclusion and exclusion criteria as in Study 1 were applied (including exclusion of partici- pants from Study 1). We excluded 22 individuals, 3 for not being native English speakers, 2 for pressing the sa

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