Second-Order Beliefs Lower the Performance of Attractive People on Intelligence Tests

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1 Second-Order Beliefs Lower the Performance of Attractive People on Intelligence Tests Youjung Jun a, Keith Wilcox a, Sandra Matz b a Department of Marketing, Columbia Business School, Columbia University, 3022 Broadway, New York, NY b Department of Management, Columbia Business School, Columbia University, 3022 Broadway, New York, NY Keywords: stereotype, second-order belief, attractiveness, intelligence

2 Abstract It has been argued that attractive people should be more intelligent because attractiveness signals good genes. However, cultural stereotypes of attractive people depicted in the mass media propagate the notion that they are less intelligent. The present research suggests that these stereotypical representations of this social category have shaped attractive people s beliefs about how their intelligence is judged based on their appearance, which has a self-fulfilling effect on their intelligence test performance. We demonstrate that the more closely people identity with the social category of attractive people the worse they perform on intelligence tests because they conform to a second-order belief that others expect them to be less intelligent. This effect also emerges when tests are merely framed as intelligence tests, but measure another construct. Reducing people s focus on this belief diminishes its negative impact on test performance. An analysis of field data suggests that the self-fulfilling effect of this second-order belief may explain why the positive correlation between attractiveness and intelligence declines in early adulthood. Attractive people are shown to perform worse on an IQ test when they are sensitive to how their appearance is judged by others, but perform better when they are insensitive to others judgment. This suggests that second-order beliefs may override any intellectual advantages of attractiveness implied by the good genes theory.

3 Introduction Evolutionary theorists have long maintained that attractiveness is a signal of good genes (1, 2). Humans are thought to have evolved to prefer attractive people for their reproductive fitness (3). Previous research investigating the evolutionary benefit of attractiveness has primarily focused on its relationship to good health (4, 5). However, it has also been argued that attractive faces indicate greater intelligence because intelligent mates confer survival benefits to offspring through their ability to acquire resources (6). Yet, empirical support for the good genes theory has offered mixed findings regarding the relationship between attractiveness and intelligence. Although several early studies found a significant positive correlation between attractiveness and intelligence (7, 8), subsequent metaanalyses found that while attractive children, as judged by others, perform significantly better on intelligence tests, this relationship declines as people enter adulthood (9, 10). Consequently, studies conducted on both students and adults have found that attractiveness and IQ score are not significantly correlated (11, 12). We posit that one reason why this relationship declines is that attractive people develop beliefs about the social expectancies applied to them based on their appearance. As individuals identify with different social categories (e.g., attractive people), they begin to monitor the stereotypes that apply to those groups (13). Furthermore, individuals who identify with stereotyped groups tend to internalize social expectancies and conform their behavior to these stereotypes (14, 15). Although it is well documented that others expect attractive people to be more intelligent than unattractive people (16, 17), this does not necessarily mean that the person being judged believes this to be the case. This is because people s beliefs about the social expectancies that apply to them reflect second-order beliefs (i.e., what one believes others believe) (19). As second-order beliefs are largely derived from sociocultural influences (20), cultural depictions of attractiveness should exert a strong influence on these beliefs. Perhaps the most prominent depictions of attractiveness in modern culture are the ones that propagate the notion that attractive people are less intelligent than unattractive people. Whether it be through characters representing the dumb blonde or dumb jock, attractive people are often portrayed as unintelligent in the mass media (21, 22). Also, highly intelligent people are frequently represented as unattractive in cultural productions (23, 24). Given how often people are exposed to these depictions, their second-order beliefs about how their intelligence is judged based on their appearance should reflect these representations. Thus, the more closely individuals identify with the social category of attractive people, the less intelligent they should expect to be judged by others, which will lower their performance on intelligence tests. We tested this prediction using two complementary approaches. The first approach is a series of controlled experiments examining the relationship between self-perceived attractiveness and intelligence test performance. We initially focus on self-perceived attractiveness because it measures the extent to which individuals identify with the social category of attractive people. The second approach is an analysis of field data examining the relationship between otherperceived attractiveness and performance on an IQ test. Consistent with previous findings (9-12), we did not expect a significant correlation between other-perceived attractiveness and IQ score. However, given that people can internalize social expectancies, we hypothesized this relationship would be moderated by people s sensitivity to other s evaluation (i.e., self-monitoring). Selfmonitoring is a psychological trait that captures the degree to which people monitor and adjust

4 their behavior in social situations (25). Compared to low self-monitors, high self-monitors are more sensitive to how they are evaluated by others (26), particularly with regards to their physical appearance (27). Thus, we hypothesized that high self-monitors but not low selfmonitors who are judge by others to be an attractive person would be more likely to identify with this social category and conform their behavior to the belief that others expect attractive people to be less intelligent. This finding would suggest that one reason why the relationship between attractiveness and intelligence attenuates in early adulthood is that attractive individuals conform to a culturally acquired second-order belief. Table 1 provides an overview of the study designs, samples and measures. Methods and Results Overview. A pilot study and three preregistered replications of earlier studies were conducted on unique U.S. Amazon Mechanical Turk (Mturk) workers with a minimum HIT approval rate of 95%. The sample encompasses a diverse set of ages, education levels and incomes, with the median of Bachelor s level education and a lower bound of elementary school diploma, and the median household income at $50-99k with the lower bound of $0-9k. On average, the age across samples was 36.7 years, and 52.6% of participants reported to be female. We report the results of preregistered replications of original studies and the combined data (i.e., original and preregistered replications) below. Exact procedures and analyses on original studies, replications and combined data are available in the Supporting Information. Our studies received ethical approval from the Institutional Review Board at Columbia University. Informed consent was obtained and participants were told that they could opt out of the study at any time. Pilot Study. The pilot study confirmed that people who perceive themselves to be attractive expect others to judge them to be less intelligent. Participants (n = 195, 41.5% female, M age = 38.4) were asked to rank-order 10 personal traits according to what they expected other people would judge to be their strongest to weakest trait (second-order beliefs). They also rank-ordered the same traits according to what they personally believed were their strongest to weakest traits (first-order beliefs). The 10 traits, which were presented in a random order, were: attractive, extroverted, intelligent, popular, friendly, likable, positive, competent, assertive, and confident. The presentation order of the two sets of rankings was counterbalanced. There was a significant negative correlation between attractiveness and intelligence for second-order beliefs (r s (195) = , P = 0.034), but not for first-order beliefs (r s (195) = -0.09, P = 0.217). This indicates that the more attractive respondents believed they were, the less intelligent they expected to be judged by others. Study 1. Method. The first study (n = 209; 54.5% female, M age =35.9; osf.io/sq3ex) was designed to show that people who perceive themselves to be attractive perform worse on intelligence tests. We also tested an alternative explanation for any observed negative association between attractiveness and intelligence. Specifically, we tested whether attractive people perform worse because they are less motivated to do well on intelligence tests. This alternative explanation is based on findings that attractive people get more easily hired for jobs, earn higher incomes and find partners more easily (28, 29, 30). Such rewards of physical attractiveness do not require onerous

5 investments in intellectual training. Consequently, attractive people may consider the benefits they receive as a result of their physical attractiveness sufficient, and be less motivated to signal their intelligence. We measured participants performance motivation (e.g., how much effort they put into the test). To ensure that any observed negative effect of attractive on test performance was not contingent on making attractiveness salient before the test, half the participants rated their attractiveness before performing the test, while the remaining half rated their attractiveness after the test (randomly assigned). For those in the attractiveness measurement before test condition, participants first rated themselves in the following10 traits from 1 (not at all descriptive of me) to 7 (very much descriptive of me): intelligent, loyal, attractive, athletic, resourceful, critical, passive, stubborn, good looking and agreeable. Some of the items (e.g., athletic) were more commonly associated with attractiveness than others in order to reduce participants suspicion. Responses to the items attractive and good looking (r = 0.92) were averaged to create an index of attractiveness (M = 4.53, SD = 1.52). They then completed a filler task that required them to unscramble five sentences that were unrelated to attractiveness or intelligence. Next, participants were told that they would take an intelligence test, and that solving the problems requires the ability to deduce information from abstract rules. In addition, they were told that they would receive a score at the end of the test. They were further instructed that they have 30 seconds to complete each question on the test. Participants were first presented with a sample question and an answer to the sample question, and then proceeded to the actual questions. The test consisted of 10 items from the Raven s Progressive Matrices (31), which is a common component of a nonverbal IQ test that measures fluid intelligence (32). Each question presented a 3 3 matrix in which the bottom right entry was missing. Participants had to uncover the underlying rules that explained the sequence of shapes in the matrix, and select the correct part of the missing matrix from eight suggested answers. The screen displayed one question at a time, and it moved to the next question automatically after 30 seconds, counting any questions unanswered as zero. Fort those in the test before attractiveness measurement condition, the procedure was identical to that of the measurement before test condition, except participants completed the filler task and rated themselves on the 10 personal traits immediately after the test. Upon completing the test, all participants indicated their performance motivation on three seven-point scales (α =.76) by answering how important was it for them to do well on the test, how motivated they were to do well on the test, and how much effort they put into the test. Distributions of participants self-attractiveness rating and performance are available in Supporting Information (Figs. S1, S2). Results. As expected, attractiveness negatively predicted intelligence test performance (β = , SE = 0.13, t(205) = -2.72, P = 0.007; βcombined = -0.30, SE = 0.09, t(435) = -3.46, P < 0.001), regardless of the order in which attractiveness was measured (attractiveness order interaction: β = 0.01, SE = 0.13, t(205) = 0.08, P = 0.936; βcombined = 0.05, SE = 0.09, t(435) = 0.58, P = 0.563). Thus, consistent with our theory, the more closely people identified with the social category of attractive people the worse they performed on the intelligence test. Contrary to what one would expect if the effect were explained by low motivation, attractiveness positively predicted performance motivation (β = 0.15, SE = 0.05, t(205) = 2.97, P = 0.003; βcombined = 0.13, SE = 0.03, t(435) = 3.82, P < 0.001). The interaction between attractiveness and measurement order on performance motivation was not significant (β = -0.02, SE = 0.05, t(205)

6 = -0.34, P = 0.738; βcombined = 0.02, SE = 0.03, t(435) = 0.68, P = 0.497). This suggests that the negative relationship between self-perceived attractiveness and performance cannot be explained by a lack of motivation. Study 2. Method. The second study (n = 223; 53.8% female, M age =36.9; osf.io/sqf9n) manipulated whether respondents believed the test was a measure of intelligence. We hypothesized that if participants are conforming to the belief about how their intelligence is judged by others, the negative relationship between self-perceived attractiveness and performance would only emerge when the test is framed as a measure of intelligence. To examine this prediction, we had participants perform a task that does not actually measure intelligence, but could be credibly framed as a measure of intelligence. All participants first rated themselves on 17 personal traits: competitive, intelligent, loyal, conservative, attractive, athletic, resourceful, critical, passive, stubborn, friendly, good looking, agreeable, humble, gullible, calm and patient. Their responses to attractive and good looking (r = 0.90) were averaged to form an attractiveness index (M = 4.51, SD = 1.56). After a filler task, participants answered 20 items of a numeric version of the Stroop test (33), which measures attentional control, not intelligence (34). In this test, participants were presented with blocks of digits where the numerosity of the digits and the numerical values were incongruent (e.g., four 3s: 3333, or five 6s: 66666). They had to then pick out of four options an answer that correctly indicates the numerosity of the digits, rather than the numerical value of the digits. For example, 4 is the correct answer in the case of 3333, and 5 in the case of This test, identical in content, was described as an intelligence test to half the participants, or a number recognition test to the other half (randomly assigned). Participants were presented with two trials and the corresponding answers before beginning the actual task. For each question, participants were given 2 seconds to click on the correct quantity of the digits. Any question left unanswered was counted as zero. After the test, participants answered the same measures of performance motivation as in Study 1 (α = 0.74). As a manipulation check, participants indicated (ranging from 1 to 7) how much they thought the test was measuring their intelligence among other things, such as impulse control, motor skills, attention, focus, and ability to recognize numbers. Distributions of participants self-attractiveness rating and performance are available in Supporting Information (Figs. S3, S4). Results. The results of the manipulation check confirmed that those in the intelligence condition believed the test was measuring their intelligence (M = 5.05, SD = 1.80) more strongly than those in the non-intelligence condition (M = 3.69, SD = 1.90; F 1,221 =30.36, P < 0.001). Participants beliefs about whether the test was measuring other abilities (e.g., impulse control) did not differ by condition (Ps > 0.110). When participants were told that the test measures intelligence, attractiveness negatively predicted performance (β = -0.77, SE = 0.24, t(219) = -3.16, P = 0.002; βcombined = -0.75, SE = 0.20, t(405) = -3.83, P < 0.001). However, when they were told that the test measures attention, the relationship was not significant (β = -0.21, SE = 0.23, t(219) = -0.93, P = 0.354; βcombined = , SE = 0.20, t(405) = -0.45, P = 0.651). The interaction between test type and attractiveness was significant at alpha = 0.1 (β = -0.28, SE = 0.17, t(219) = -1.67, P = 0.096; βcombined = -0.33, SE = 0.14, t(405) = -2.35, P = 0.019; see Fig. 1). This suggests that attractive individuals are conforming to a particular belief about their intelligence because they performed worse only when the test, which did not actually measure intelligence, was framed as an intelligence test.

7 Similar to the findings of Study 1, attractiveness positively predicted performance motivation (β = 0.14, SE = 0.05, t(219) = 3.11, P = 0.002; βcombined = 0.19, SE = 0.03, t(405) = 5.35, P < 0.001). The interaction between attractiveness and test framing was not significant (β = 0.06, SE = 0.05, t(219) = 1.23, P = 0.219; βcombined = 0.04, SE = 0.03, t(405) = 1.23, P = 0.219). Study 3. Method. The third study (n = 245; 51.4% female, M age =37.1; osf.io/phaz7) was designed to show that the observed negative relationship between self-perceived attractiveness and performance on intelligence tests is due to second-order beliefs. If this relationship is due to second-order beliefs, having people focus on their personal, first-order beliefs regarding their intelligence prior to the test would attenuate the effect. Participants first rated themselves on 12 personal traits: energetic, competitive, intelligent, loyal, conservative, attractive, athletic, critical, passive, stubborn, good looking and patient. Responses to good looking and attractive (r = 0.92) were averaged to form an attractiveness index (M = 4.30, SD = 1.32). They then completed the same filler task as in previous studies. Before taking the test, participants were told to either focus on what they believe other people think about their intelligence (second-order beliefs) or what they personally think about their intelligence (first-order beliefs). Specifically, those in the second-order condition were told, Before beginning the test, we would like you to take a moment to think about how other people judge your intelligence. How do you think others would expect you to perform on the test? How smart do others believe you to be? Those in the firstorder beliefs condition participants were told, Before beginning the test, we would like you to take a moment to think about your intelligence. How do you think you will perform on the test? How smart do you believe yourself to be? They then completed the Raven s test from Study 1. After the test, participants indicated their performance motivation (α = 0.77) and completed a manipulation check by indicating the extent to which they thought about their own beliefs about their intelligence versus what other people think about their intelligence prior to the test (1 = how intelligent I think I am, 7 = how intelligent other people think I am). Distributions of participants self-attractiveness rating and performance are available in Supporting Information (Figs. S5, S6). Results. The results of the manipulation check confirmed that those in the first-order belief condition thought more about how intelligent they (versus others) consider themselves to be (M

8 = 2.75, SD = 1.63) compared to those in the second-order belief condition (M = 3.72, SD = 1.92; F 1,243 = 18.1, P < 0.001). When individuals focused on their second-order beliefs, attractiveness negatively predicted performance (β = -0.54, SE = 0.18, t(241) = -3.01, P = 0.003; βcombined = -0.59, SE = 0.13, t(476) = -4.56, P < 0.001). However, when they focused on their first-order beliefs, the relationship was not significant (β = -0.06, SE = 0.18, t(241) = -0.30, P = 0.761; βcombined = , SE = 0.13, t(476) = -0.99, P = 0.321). The interaction between belief focus (first-order or second-order) and self-perceived attractiveness was significant at alpha = 0.1 (β = -0.24, SE = 0.13, t(241) = -1.89, P = 0.060; βcombined = -0.23, SE = 0.09, t(476) = -2.51, P = 0.012; see Fig. 2). The findings support our theory that individuals who identify with the social category of attractive people test perform worse on intelligence tests because they conform to a second-order belief regarding their intelligence. As in previous studies, the analysis of performance motivation showed that attractiveness positively predicted performance motivation (β = 0.14, SE = 0.06, t(241) = 2.37, P = 0.019; βcombined = 0.10, SE = 0.04, t(476) = 2.37, P = 0.018). The interaction between attractiveness and beliefs was not significant in the replication study (β = -0.02, SE = 0.06, t(241) = -0.42, P = 0.677; βcombined = -0.09, SE = 0.04, t(476) = -2.13, P = 0.034). However, there was a significant interaction observed in the combined data due to a significant interaction in the original study that was not observed in the preregistered replication. Because of this, we are hesitant to speculate on this finding (see Supplementary Methods for further discussion). Nevertheless, the results of this study are consistent with those in previous studies in that people who perceive themselves to be attractive are not less motivated to perform intelligence tests. Next, we analyzed field data to complement the experiments by investigating the relationship between otherperceived attractiveness and IQ score. Field Study 4. Method. This study examines the relationship between other-perceived attractiveness and intelligence test performance as a function of people s self-monitoring level. The data were

9 obtained from the mypersonality Facebook application (35). This application allowed users to take an IQ test and several personality measures, including a self-monitoring scale (26). Users received feedback on their responses and a subsample of respondents voluntarily granted the application access to their Facebook profile. Amongst other information, the Facebook profiles included users profile pictures, which enabled us to study the relationship between otherperceived attractiveness and IQ score as a function of users self-monitoring level. Users whose profile pictures could not be rated in terms of attractiveness were excluded. These included photos with more than one individual, photos where users were wearing costumes or sunglasses, photos that were not of the user (e.g., celebrities, pets, or sceneries), and otherwise imperceptible photos (e.g., blurry or doctored images). For users who had more than one profile picture, we chose a picture at random to be rated by non-acquainted judges. There were 1,138 users (44.4% female, M age =24.7) in the dataset for which IQ score, self-monitoring score and pictures were available. Ten judges who were unfamiliar with the users and blind to our hypotheses rated the attractiveness of each user (1 = very unattractive, 10 = very attractive). We averaged the coders attractiveness ratings to obtain a measure of other-perceived attractiveness. The raters exhibited good inter-rater agreement with an average Intraclass Correlation Coefficient (ICC) of 0.85 (out of 1), suggesting that a person s attractiveness could be judged with a considerable degree of reliability. Results. We analyzed the relationship between other-perceived attractiveness and IQ score at different levels of self-monitoring. Consistent with previous research (36), we examined the scores of individuals (n = 971; 41.6% female, M age =24.6) within the standard range of IQ (between 70 and 130; 37) since scores below 70 (mentally disabled) and above 130 (intellectually gifted) are atypical. As expected, the overall effect of attractiveness on IQ score was not significant (β = -0.03, SE = 0.39, t(967) = -0.09, P = 0.932). However, in line with our prediction, there was a significant interaction between attractiveness and self-monitoring (β = , SE = 0.37, t(967) = -3.00, P = 0.003; see Fig. 3). This interaction remains significant when the respondents beyond the aforementioned range of IQ are included in the data (see Supporting Information). A Johnson-Neyman analysis (38) found that for self-monitoring scores above 0.83 standard deviations above the mean (i.e., high self-monitors), the correlation between attractiveness and IQ score is negative and significant. Thus, a negative relationship between other-perceived attractiveness on intelligence emerged among people who are highly sensitive to how their appearance is evaluated by others. In contrast, for self-monitoring scores below 0.98 standard deviations below the mean (i.e., low self-monitors) the correlation is positive and significant. This latter finding suggests that when individuals are less sensitive to how their appearance is evaluated by others, the relationship between attractiveness and intelligence is consistent with the good genes theory.

10 Discussion The present findings demonstrate that attractive people s performance on intelligence tests can suffer in light of their second-order beliefs about how others judge their intelligence. The more attractive people perceive themselves to be, the worse they perform on intelligence tests because they believe that others expect them to be less intelligent. The negative relationship between self-perceived attractiveness and test performance diminishes when people focused on their personal belief (rather than what other people think) about their intelligence. The analysis of field data shows that attractive people, as perceived by others, perform worse on an IQ test when they are sensitive to how their appearance is judged by others (i.e., high self-monitors), but perform better when they are insensitive to others judgment (i.e., low self-monitors). Our findings have implications for evolutionary account of the benefits of attractiveness. Previous research suggests that the influence of evolution is so profound that attractiveness can predict positive health outcomes (39) as well as election results (40). However, evidence for the good genes theory regarding the relationship between attractiveness and intelligence has been primarily observed in childhood and puberty and shown to attenuate in adulthood (9, 10). We postulate one reason for this pattern is that cultural depictions that deride attractive people s intelligence forge a corresponding second-order belief in early adulthood. Thus, attractive people who identify with this social category conform to this culturally-shaped belief, which reduces their intelligence test performance. It should be noted that research using archival data from studies conducted in first half of the 20 th century found a significant positive relationship between other-rated attractiveness and intelligence in adulthood (41). This result, however, is not necessarily inconsistent with our theory because the data was collected before the advent of television programming. Consequently, it is unlikely that cultural depictions of attractive people as unintelligent were widely disseminated in society at that time. As such, one would not expect respondents in these studies to hold a second-order belief that attractive people are unintelligent. The results of our investigation suggest that physical attractiveness invites a gap in our thinking such that when we become the target of attractiveness, we fail to consider the innately positive response to attractive people and expect the opposite to occur in a way that negates our own intellectual competence. Why is it that we grant halo to others attractiveness, but don t expect the same to occur to us? This may happen because people are poor judges of their

11 physical attractiveness and often use harsher standards when evaluating themselves than others (42). In addition, there may be differences in the focus of second-order beliefs at the time of judgment. When judging others, our perceptions of them are more salient than the stereotypes about their social group, especially because the cultural learning of stereotypes is subtle (20, 43) and reliance on stereotype is considered negative (44). In contrast, when drawing inferences about how others might see us, the process of self-categorization requires us to examine secondorder beliefs, often generated and propagated by sources like the mass media. Our results also shed new light on past attempts to reconcile the bright and dark sides of being physically attractive (45, 46). Past research has separated various personality dimensions to show that attractiveness is a positive signal for certain traits (e.g., popularity) but not others (e.g., egotism) (47). Yet, this research assumes that physical attractiveness is associated with a given trait in either a positive or negative way. In our research, we examine how attractiveness affects intelligence test performance differentially as a function of people s focus on social (vs. personal) expectations about this trait. By demonstrating that having people focus on their second-order beliefs leads to a negative effect of self-perceived attractiveness on performance, but not when they focus on their personal beliefs (Study 3), we demonstrate that attractiveness can have different associations with the same trait. Our findings suggest that second-order beliefs regarding social norms and expectancies can influence not only a public behavior but also a private behavior that does not bear social consequences. Recent studies showed that attempts to alter people s personal beliefs about a stereotype can be less effective than influencing people s beliefs about what others think in initiating behavioral change (48). People have also been found to follow cultural norms to the extent they expected others in society to hold up the same norms (49). However, previous research on second-order beliefs have focused on large-scale collective behaviors, such as social cooperation and sustainable behaviors like energy conservation (50, 51). This makes intuitive sense because it is important to think about the community s expectations and the potential cost of norm violation when doing something as part of a community. Our research contributes to this research stream by exploring the importance of second-order beliefs in predicting a private behavior (i.e., test taking). Uncovering the pervasive impact of cultural stereotypes can be troubling, but our results suggest that the effects on intellectual behavior are neither fixed nor unpredictable. Our finding that heightening individuals focus on their personal beliefs can mitigate the negative effect of attractiveness should inspire cautious optimism among researchers and policy makers alike. Furthermore, based on previous findings that second-order beliefs are more amenable to change than first-order beliefs (52), challenging the existing portrayals of attractive people in the mass media can prove useful in ameliorating the expectations for attractive people s intellectual abilities. Footnotes 1 Author contributions: All authors contributed to the data curation, analysis, methodology, and writing. Y.J. and K.W. prepared the study materials and conducted the studies. Y.J., K.W., and S.M. analyzed the data and wrote the manuscript. All authors provided feedback at different stages of the research, reviewed and approved the manuscript.

12 Acknowledgments The data that support the findings of this paper are available on the OSF website (osf.io/za3t2/). Anonymized data from the field study will be made available upon request. The authors declare no competing interests. We thank Gita Johar, Alice Moon, and Ruth Appel for their helpful comments. References 1. Thornhill R, Gangestad SW (1993) Human facial beauty: Averageness, symmetry, and parasite resistance. Hum Nature, 4(3): Buss DM, Schmitt DP (1993) Sexual strategies theory: An evolutionary perspective on human mating. Psycho Rev, 100(2): Rhodes G (2006) The evolutionary psychology of facial beauty. Ann Rev Psychol, 57: Kalick SM, Zebrowitz, LA, Langlois JH, Johnson RM (1998) Does human facial attractiveness honestly advertise health? Longitudinal data on an evolutionary question. Psychol Sci, 9(1): Shackelford TK, Larsen RJ (1999) Facial attractiveness and physical health. Evol Hum Behav, 20(1): Buss DM (1985) Human mate selection. Am Sci, 73: Mohr A, Lund FH (1933) Beauty as related to intelligence and educational achievement. J Soc Psychol, 4: Anderson LD (1921) Estimating intelligence by means of printed photographs. J App. Psychol, 5: Jackson LA, Hunter JE, Hodge CN (1995) Physical attractiveness and intellectual competence: A meta-analytic review. Soc Psychol Quart, 58(2): Langlois JH, Kalakanis L, Rubenstein AJ, Larson A, Hallam M, Smoot M (2000) Maxims or myths of beauty? A meta-analytic and theoretical review. Psychol Bull, 126(3): Feingold A (1992) Good looking people are not what we think. Psychol Bull, 111(2): Kleisner K, Chvátalová V, Flegr J (2014) Perceived Intelligence is associated with measured intelligence in men but not women. PLOS. One, 9: e Darley JM, Fazio RH (1980) Expectancy confirmation processes arising in the social interaction sequence. Am Psychol, 35(10): Swann WB (1984) Quest for accuracy in person perception: A matter of pragmatics. Psychol Rev, 91(4): Spencer SJ, Steele CM, Quinn DM (1999) Stereotype threat and women's math performance. J Exp Soc Psychol, 35: Jackson LA, Hunter JE, Hodge CN (1995) Physical attractiveness and intellectual competence: A meta-analytic review. Soc Psychol Quart, 58(2): Thornhill R, Gangestad SW (1993) Human facial beauty: Averageness, symmetry, and parasite resistance. Hum Nature, 4(3): Feingold A (1988) Matching for attractiveness in romantic partners and same-sex friends: A meta-analysis and theoretical critique. Psychol Bull, 104(2):

13 19. Stangor C, Schaller M (1996) in Stereotypes and stereotyping, eds. Macrae, C. N., Stangor, C. & Hewstone M. (Guilford, New York), pp Stangor C (2000) Stereotypes and prejudice (Psychology Press, Philadelphia, PA). 21. Eagly AH, Ashmore RD, Makhijani MG, Longo LC (1991) What is beautiful is good, but : A meta-analysis of research on the physical attractiveness stereotype. Psychol Bull, 110(1): Sailes GA (1993) An investigation of campus stereotypes: the myth of Black athletic superiority and the dumb jock stereotype. Sociol Sport J, 10: Kendall L (1999) Nerd nation: Images of nerds in US popular culture. Int J Cultural Stud, 2(2): Margolis J, Fisher A (2002) Unlocking the clubhouse: Women in computing (MIT Press, Cambridge, MA). 25. Snyder M (1974) Self-monitoring of expressive behavior. J Pers Soc Psychol, 30(4): Snyder M, Gangestad S (1986) On the nature of self-monitoring: Matters of assessment, matters of validity. J Pers Soc Psychol, 51: Sullivan LA, Harnish RJ (1983) Body image: Differences between high and low selfmonitoring males and females. J Res Pers, 24: Hamermesh DS, Biddle JE (1994) Beauty and the labor market. Am Econ Rev, 84(5): Hosoda M, Stone-Romero EF, Coats G (2003) The effects of physical attractiveness on job-related outcomes: a meta-analysis of experimental studies. Pers Psychol, 56: Judge TA, Hurst C, Simon LS (2009) Does it pay to be smart, attractive, or confident (or all three)? Relationships among general mental ability, physical attractiveness, core selfevaluations, and income. J Appl Psychol, 94: Raven J, Raven JC, Court JH (1998) Manual for Raven s Progressive Matrices and Vocabulary Scales (Oxford Psychologists Press Ltd., Oxford). 32. Motta RW, Joseph JM (1999) Group Intelligence Tests. in Handbook of Psychological Assessment, eds. Goldstein, G., Hersen, M. (Pergamon, Oxford). 33. Windes JD (1968) Reaction time for numerical coding and naming of numerals. J Exp Psychol, 78: Gailliot MT, Baumeister RF, DeWall CN, Maner JK, Plant EA, Tice DM, et al. (2007) Self-control relies on glucose as a limited energy source: Willpower is more than a metaphor. J Pers Soc Psychol, 92(2): Kosinski M, Matz SC, Gosling SD, Popov V, Stillwell D (2015) Facebook as a research tool for the social sciences: Opportunities, challenges, ethical considerations, and practical guidelines. Am Psychol, 70: Flynn JR (1984) The mean IQ of Americans: Massive gains 1932 to Psychol Bull, 95(1): Peyre H, Charkaluk ML, Forhan A, Heude B, Ramus F (2017) Do developmental milestones at 4, 8, 12 and 24 months predict IQ at 5 6 years old? Results of the EDEN mother-child cohort. Eur J Paediatr Neuro, 21(2): Spiller SA, Fitzsimons GJ, Lynch JG, McClelland GH (2013) Spotlights, floodlights, and the magic number zero: Simple effects tests in moderated regression. J Marketing Res, 50(2):

14 39. Henderson JJ, Anglin JM (2003) Facial attractiveness predicts longevity. Evol Hum Behav, 24: White AE, Kenrick DT, Neuberg SL (2013) Beauty at the ballot box: Disease threats increase preferences for physically attractive leaders. Psychol Sci, 24: Zebrowitz LA, Hall JA, Murphy NA, Rhodes G (2002) Looking smart and looking good: Facial cues to intelligence and their origins. Pers Soc Psychol B, 28(2): Murstein B. I (1972) Physical attractiveness and marital choice. J Pers Soc Psychol, 22: Tajfel H (1981) Human groups and social categories: Studies in social psychology (Cambridge University Press, Cambridge, U.K.). 44. Plant EA, Devine PG (1998) Internal and external motivation to respond without prejudice. J Pers Soc Psychol, 75(3): Griffin AM, Langlois JH (2006) Stereotype directionality and attractiveness stereotyping: Is beauty good or is ugly bad? Soc Cognition, 24(2): Gheorghiu AI, Callan MJ, Skylark WJ (2017) Facial appearance affects science communication. P Natl Acad Sci USA, 114: Dermer M, Thiel DL (1975) When beauty may fail. J Pers Soc Psychol, 31(6): Paluck EL, Shepherd H (2012) The salience of social referents: a field experiment on collective norms and harassment behavior in a school social network. J Pers Soc Psychol, 103(6): Stangor C, Sechrist G, Jost JT (2001) Changing racial beliefs by providing consensus information. Pers Soc Psychol B, 27(4): Rand DG, Nowak MA (2013) Human cooperation. Trends Cogn Sci, 17: Jachimowicz JM, Hauser OP, O Brien JD, Sherman E, Galinsky AD (2018) The critical role of second-order normative beliefs in predicting energy conservation. Nat Hum Behav, 2: Paluck EL (2009) What s in a norm? Sources and processes of norm change. J Pers Soc Psychol, 96(3): Figure Legends Fig. 1 Performance (i.e., number of items answered correctly out of 20) on the Numeric Stroop test as a function of self-perceived attractiveness, when participants were told the test was measuring intelligence (Intelligence) or not (Non-Intelligence) in Study 2. Fig. 2 Performance (i.e., number of items answered correctly out of 10) on the Raven s matrices test as a function of self-perceived attractiveness, when participants thought of what others think about their intelligence (Second-Order Belief) or what they personally think about their intelligence (First-Order Belief) in Study 3. Fig. 3 Regression coefficient with other-perceived attractiveness predicting IQ score as a function of people s self-monitoring score from Correlational Field Study 4. Shaded area represents confidence interval.

15 Table. 1 Overview of the methods used in Studies 1 4, including samples, design, predictor variables, and dependent variables (DVs). Method Study 1 Study 2 Study 3 Study 4 Sample size N = 209 N = 223 N = 245 N = 971 Design Experimental Experimental Experimental Correlational Predictors DV -Self-rated Attractiveness -Test Order (Before vs. After attractiveness rating) Test Performance -Self-rated Attractiveness -Test Frame (Intelligence vs. non- Intelligence test) Test Performance -Self-rated Attractiveness -Belief Focus (Second- vs. First-order belief) Test Performance -Other-rated Attractiveness -Selfmonitoring Score IQ Score

16 Supplementary Materials for Second-Order Beliefs Lower the Performance of Attractive People on Intelligence Tests Youjung Jun, Keith Wilcox*, Sandra Matz *Correspondence to: This PDF file includes: Supplementary Methods Correlational Field Study 4 Supplementary Analyses Figs. S1 to S6

17 Supporting Information Supplementary Methods Overview Our studies received ethical approval from the Institutional Review Board at Columbia University. Informed consent for the pilot and other studies was obtained and participants were told that they could opt out of the study at any time. Participants in these studies were unique U.S. Amazon Mechanical Turk (Mturk) workers with a minimum HIT approval rate of 95%. In all studies reported here, our samples encompass diverse education levels and incomes, with the median of Bachelor s level education and a lower bound of elementary school diploma, and the median household income at $50-99k with the lower bound of $0-9k. On average, the age across samples was 36.7 years, and 52.6% of participants reported to be female. In all studies, we excluded participants who did not pass attention checks. In Studies 2 and 3, we included a question that asked participants whether they had experienced any technical malfunction (e.g., page not loading, freezing) while taking a focal test because performance on the test was our key dependent measure. Since it was important that participants completed the test without disruption, we removed participants who indicated that they experienced a technical malfunction. In Study 3, we added a question that asked respondents whether they had completed the focal test before. We included this question because the study was conducted on the same Mturk panel from a new Requester account, which made it difficult to screen out respondents who had taken earlier studies. Thus, to ensure that our respondents were unique, we screened out respondents who answered yes to this question. Across the pilot study and all studies reported here, we removed 15% of the sample on average. The results reported in the main manuscript are from preregistered replications of original studies and combined data (i.e., pooled data from replication and original studies). For the convenience of the reader we have included results from replications, original studies and combined data in this Supplementary Information. Consequently, some of the findings are duplicated in the main manuscript. Pilot Study The objective of the pilot test was to show that the more attractive people perceive themselves to be, the less intelligent they expect to be judged by others. Out of 210 participants who completed the pilot (39.5% female, M age = 38.0), 15 (7%) who failed at least one of the two attention checks were removed from analyses, resulting in the final sample of 195 participants (41.5% female, M age = 38.4). Participants median education level was a Bachelor s degree and the median annual income was 50-99k. The study was conducted in October Participants were asked to rank-order 10 personal traits according to what they expected other people to judge to be their strongest to weakest trait (second-order beliefs). They also rankordered the same traits according to what they personally believed were their strongest traits (first-order beliefs). The 10 traits, which were presented in a random order, were: attractive, extroverted, intelligent, popular, friendly, likable, positive, competent, assertive, and confident. The presentation order of the two sets of rankings was counterbalanced. This was followed by the first attention check item that required participants to ignore what was being asked in three

18 questions and to simply respond to the questions by entering the word survey three times into text boxes. Respondents then indicated their age, gender, education level, proficiency in English (1 = beginner, 5 = native speaker), race/ethnicity, and household income. Education level was measured by having participants indicate the highest level of formal education completed, using the following categories: None, Elementary School, Middle School, Some High School, High School, Some College, Associates, Bachelor s, Graduate Degree, or Doctorate. Income level (i.e., annual household income) was measured using the following categories: 0-9k, 10-19k, 20-49k, 50-99k, k, k, 500k+. Finally, respondents completed some general questions about the survey. Included in these questions was a second manipulation check that asked respondents to check and write the word yes in a text box to show that they were paying attention. Participants who did not follow these instructions were excluded from the analyses. We tested the relationship between the rank-orders of attractiveness and intelligence for second-order beliefs using Spearman s rank-order correlation coefficient (i.e., Spearman s rho). Spearman s rho revealed a significant negative relationship between attractiveness and intelligence (r s (195) = -0.15, P = 0.034). This indicates that the more attractive respondents believed they were to others the less intelligent they expected to be judged by others. Importantly, our analysis of first-order beliefs did not find a significant correlation between attractiveness and intelligence (r s (195) = -0.09, P = 0.217). Study 1 (Preregistered replication) The objective of Study 1 was to examine the relationship between self-perceived attractiveness and intelligence test performance. Two hundred sixty-nine participants (52.4% female, M age = 35.7) completed the study. We set a target sample of at least 200 respondents. Sixty participants (22%) who failed at least one attention check were excluded from the analyses. This resulted in a final sample of 209 participants (54.5% female, M age =35.9). Participants median education level was a Bachelor s degree and the median annual income was 50-99k. The study was conducted in May The order in which participants rated their attractiveness and completed the intelligence test was randomized across participants. Before beginning the study, all participants completed an attention check that required them to enter the sixth word in this sentence into a text box. Participants who did not enter the correct word (i.e., this ) were excluded from analysis. Afterwards, participants were randomly assigned to either the measurement before test or measurement after test condition. In the before test condition, participants first rated themselves on 10 personal traits (1 = not at all descriptive of me, 7 = very much descriptive of me): intelligent, loyal, attractive, athletic, resourceful, critical, passive, stubborn, good looking and agreeable. Some of the items (e.g., athletic) were more commonly associated with attractiveness than others in order to reduce participants suspicion. Responses to the items attractive and good looking (r = 0.92) were averaged to create an index of attractiveness (M = 4.53, SD = 1.52). Afterwards, participants completed a filler task that required them to unscramble five sentences that were unrelated to attractiveness or intelligence. Next, participants were told that they would take an intelligence test, and that solving the problems requires the ability to deduce information from abstract rules. In addition, they were told that they would receive a score at the end of the test. Importantly, they were told that their performance would be judged not only on how many questions they answered correctly, but also

19 on how much time they took to complete the test, highlighting the importance of both accuracy and speed. They were further instructed that they would have 30 seconds to complete each questions on the test. The test contained 10 questions from the Raven s Progressive Matrices Test, which is a standard measure of IQ. Participants were first presented with a sample question, an answer to the sample question, and then proceeded to the actual questions. For each question, participants were presented with a 3 3 matrix in which the bottom right entry was missing. Participants had to uncover the underlying rules that explained the sequence of shapes in the matrix, and select the correct part of the missing matrix from eight suggested answers. The screen displayed one question at a time, and it moved to the next question automatically after 30 seconds, counting any questions unanswered as zero. Our focal dependent measure was the number of items answered correctly. The procedure for those in the measurement after test condition was identical to that of the before test condition, except respondents completed the filler task and rated themselves on the 10 personal traits immediately after the test. We manipulated the measurement order to examine whether the relationship between self-perceived attractiveness and intelligence test performance is contingent on participants rating of their attractiveness prior to the test. We also wanted to rule out the possibility that people who perceive themselves to be attractive perform worse on intelligence tests because they place less value on intelligence, which could lower their motivation to perform the test. Thus, after the test, we asked respondents about their performance motivation on a three-item scale (α =.76) which included questions related to how important was it for them to do well on the test (1 = not at all important, 7 = extremely important), how motivated they were to do well on the test (1 = not at all motivated, 7 = extremely motivated) and how much effort they put into the test (1 = not much effort at all, 7 = a lot of effort). These items were included in a set of questions with other filler items that asked them how confident they were in their performance (1 = not at all confident, 7 = extremely confident), how bad they would feel if they did not perform well on the test (1 = not bad at all, 7 = extremely bad) and how enjoyable they found the test (1 = not enjoyable at all, 7 = extremely enjoyable). Also, included in these items was a second attention check that asked respondents to check a specific response on a seven-point scale to show that they are paying attention. Afterwards, participants received their score and answered some questions about their reaction to the score including how satisfied they were with their score (1 = not satisfied at all, 7 = extremely satisfied) and how well they thought the score represented their ability (1 = not well at all, 7 = extremely well). Participants answers to these items are not further discussed because they did not consistently vary by conditions and were not relevant to our findings. Respondents then indicated their age, gender, education level, proficiency in English (1 = beginner, 5 = native speaker), race/ethnicity, and household income. Education level was measured by having participants indicate the highest level of formal education completed, using the following categories: None, Elementary School, Middle School, Some High School, High School, Some College, Associates, Bachelor s, Graduate Degree, or Doctorate. Income level (i.e., annual household income) was measured using the following categories: 0-9k, 10-19k, 20-49k, 50-99k, k, k, above 500k. Finally, respondents completed a number of general questions about the survey. Included in these questions was a third manipulation check that asked respondents to check and write the word yes in a text box to show that they are paying attention. Participants who did not follow these instructions were excluded from analysis.