[Paleopsych] Science: How "Competent" Faces Win Elections
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Science: How "Competent" Faces Win Elections
5.6.10
First, the summary from CHE:
Do physically attractive political candidates have an edge? The answer appears
to be yes, to judge from a paper by four researchers at Princeton University.
In their study, subjects were shown images of Congressional candidates for only
one second and were then asked which were more "competent," based solely on
their appearance. The subjects picked the actual election winners about 70
percent of the time, report Alexander Todorov, an assistant professor of
psychology, and three graduate students in his department.
The inferences also were directly proportional to the candidates' margins of
victory in races, the authors say. Their findings, they conclude, suggest that
rapid, unreflective impressions of candidates contribute to voting choices -- a
view that contradicts the common belief that votes are based primarily on
rational deliberations.
In a related article, Leslie A. Zebrowitz, a professor of psychology at
Brandeis University, and Joann M. Montepare, an associate professor of
psychology at Emerson College, say that voters appear to judge faces as not
competent based on whether, for example, candidates looked more "babyfaced"
than did their opponents. That trait, characterized by such features as round
faces and large eyes, is often interpreted by voters as a sign of being
submissive, naïve, and weak.
Mr. Todorov and his colleagues say that their findings suggest that
"consequential decisions can be more shallow than we would like to believe,"
and note that researchers have found "no good evidence that trait inferences
from facial appearance are accurate."
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Inferences of Competence from Faces Predict Election Outcomes
Alexander Todorov,1,2* Anesu N. Mandisodza,1 [{dagger}] Amir Goren,1 Crystal C.
Hall1
We show that inferences of competence based solely on facial appearance
predicted the outcomes of U.S. congressional elections better than chance
(e.g., 68.8% of the Senate races in 2004) and also were linearly related to the
margin of victory. These inferences were specific to competence and occurred
within a 1-second exposure to the faces of the candidates. The findings suggest
that rapid, unreflective trait inferences can contribute to voting choices,
which are widely assumed to be based primarily on rational and deliberative
considerations.
1 Department of Psychology, Princeton University, Princeton, NJ 08544, USA.
2 Woodrow Wilson School of Public and International Affairs, Princeton
University, Princeton, NJ 08544, USA.
[{dagger}] Present address: Department of Psychology, New York University, New
York, NY 10003, USA. <http://www.sciencemag.org/icons/back.gif>
* To whom correspondence should be addressed. E-mail: atodorov at princeton.edu
Faces are a major source of information about other people. The rapid
recognition of familiar individuals and communication cues (such as expressions
of emotion) is critical for successful social interaction (1). However, people
go beyond the inferences afforded by a person's facial appearance to make
inferences about personal dispositions (2, 3). Here, we argue that rapid,
unreflective trait inferences from faces influence consequential decisions.
Specifically, we show that inferences of competence, based solely on the facial
appearance of political candidates and with no prior knowledge about the
person, predict the outcomes of elections for the U.S. Congress.
In each election cycle, millions of dollars are spent on campaigns to
disseminate information about candidates for the U.S. House of Representatives
and Senate and to convince citizens to vote for these candidates. Is it
possible that quick, unreflective judgments based solely on facial appearance
can predict the outcomes of these elections? There are many reasons why
inferences from facial appearance should not play an important role in voting
decisions. From a rational perspective, information about the candidates should
override any fleeting initial impressions. From an ideological perspective,
party affiliation should sway such impressions. Party affiliation is one of the
most important predictors of voting decisions in congressional elections (4).
From a voter's subjective perspective, voting decisions are justified not in
terms of the candidate's looks but in terms of the candidate's position on
issues important to the voter.
Yet, from a psychological perspective, rapid automatic inferences from the
facial appearance of political candidates can influence processing of
subsequent information about these candidates. Recent models of social
cognition and decision-making (5, 6) posit a qualitative distinction between
fast, unreflective, effortless "system 1" processes and slow, deliberate,
effortful "system 2" processes. Many inferences about other people, including
inferences from facial appearance, can be characterized as system 1 processes
(7, 8). The implications of the dual-process perspective are that person
impressions can be formed "on-line" in the very first encounter with the person
and can have subtle and often subjectively unrecognized effects on subsequent
deliberate judgments.
Competence emerges as one of the most important trait attributes on which
people evaluate politicians (9–11). If voters evaluate political candidates on
competence, inferences of competence from facial appearance could influence
their voting decisions. To test this hypothesis, we asked na"ve participants to
evaluate candidates for the U.S. Senate (2000, 2002, and 2004) and House (2002
and 2004) on competence (12). In all studies, participants were presented with
pairs of black-and-white head-shot photographs of the winners and the
runners-up (Fig. 1A) from the election races. If participants recognized any of
the faces in a race pair, the data for this pair were not used in subsequent
analyses. Thus, all findings are based on judgments derived from facial
appearance in the absence of prior knowledge about the person.
[ ] Fig. 1. (A) An example of a pair of faces used in the experiments: the 2004
U.S. Senate race in Wisconsin. In all experiments, the positions of the faces
were counterbalanced. (B) Scatterplot of differences in proportions of votes
between the winner and the runner-up in races for the Senate as a function of
inferred competence from facial appearance. The upper right and lower left
quadrants indicate the correctly predicted races. Each point represents a
Senate race from 2000, 2002, or 2004. The competence score on the x axis ranges
from 0 to 1 and represents the proportion of participants judging the candidate
on the right to be more competent than the one on the left. The midpoint score
of 0.50 indicates that the candidates were judged as equally competent. The
difference in votes on the y axis ranges from –1 to +1 [(votes of candidate on
the right – votes of candidate on the left)/(sum of votes)]. Scores below 0
indicate that the candidate on the left won the election; scores above 0
indicate that the candidate on the right won the election. [Photos in (A):
Capitol Advantage] [View Larger Version of this Image (48K GIF file)] As shown
in Table 1, the candidate who was perceived as more competent won in 71.6% of
the Senate races and in 66.8% of the House races (13). Although the data for
the 2004 elections were collected before the actual elections (14), there were
no differences between the accuracy of the prospective predictions for these
elections and the accuracy of the retrospective predictions for the 2000 and
2002 elections (15). Inferences of competence not only predicted the winner but
also were linearly related to the margin of victory. To model the relation
between inferred competence and actual votes, we computed for each race the
difference in the proportion of votes (16). As shown in Fig. 1B, competence
judgments were positively correlated with the differences in votes between the
candidates for Senate [r(95) = 0.44, P < 0.001] (17, 18). Similarly, the
correlation was 0.37 (P < 0.001) for the 2002 House races and 0.44 (P < 0.001)
for the 2004 races. Across 2002 and 2004, the correlation was 0.40 (P < 0.001).
Table 1. Percentage of correctly predicted races for the U.S. Senate and House
of Representatives as a function of the perceived competence of the candidates.
The percentages indicate the races in which the candidate who was perceived as
more competent won the race. The [{chi}] 2 statistic tests the proportion of
correctly predicted races against the chance level of 50%.
Election Correctly predicted [{chi}] 2
U.S. Senate
2000 (n = 30) 73.3% 6.53 (P < 0.011)
2002 (n = 33) 72.7% 6.82 (P < 0.009)
2004 (n = 32) 68.8% 4.50 (P < 0.034)
Total (n = 95) 71.6% 17.70 (P < 0.001)
U.S. House of Representatives
2002 (n = 321) 66.0% 33.05 (P < 0.001)
2004 (n = 279) 67.7% 35.13 (P < 0.001)
Total (n = 600)
66.8%
68.01 (P < 0.001)
In the previous studies, there were no time constraints on the participants'
judgments. However, system 1 processes are fast and efficient. Thus, minimal
time exposure to the faces should be sufficient for participants to make
inferences of competence. We conducted an experiment in which 40 participants
(19) were exposed to the faces of the candidates for 1 s (per pair of faces)
and were then asked to make a competence judgment. The average response time
for the judgment was about 1 s (mean = 1051.60 ms, SD = 135.59). These rapid
judgments based on minimal time exposure to faces predicted 67.6% of the actual
Senate races (P < 0.004) (20). The correlation between competence judgments and
differences in votes was 0.46 (P < 0.001).
The findings show that 1-s judgments of competence suffice to predict the
outcomes of actual elections, but perhaps people are making global inferences
of likability rather than specific inferences of competence. To address this
alternative hypothesis, we asked participants to make judgments on seven
different trait dimensions: competence, intelligence, leadership, honesty,
trustworthiness, charisma, and likability (21). From a simple halo-effect
perspective (22), participants should evaluate the candidates in the same
manner across traits. However, the trait judgments were highly differentiated.
Factor analysis showed that the judgments clustered in three distinctive
factors: competence (competence, intelligence, leadership), trust (honesty,
trustworthiness), and likability (charisma, likability), each accounting for
more than 30% of the variance in the data (table S1). More important, only the
judgments forming the competence factor predicted the outcomes of the
elections. The correlation between the mean score across the three judgments
(competence, intelligence, leadership) and differences in votes was 0.58 (P <
0.001). In contrast to competence-related inferences, neither the trust-related
inferences (r = –0.09, P = 0.65) nor the likability-related inferences (r =
–0.17, P = 0.38) predicted differences in votes. The correlation between the
competence judgment alone and differences in votes was 0.55 (P < 0.002), and
this judgment correctly predicted 70% of the Senate races (P < 0.028). These
findings show that people make highly differentiated trait inferences from
facial appearance and that these inferences have selective effects on
decisions.
We also ruled out the possibility that the age, attractiveness, and/or
familiarity with the faces of the candidates could account for the relation
between inferences of competence and election outcomes. For example, older
candidates can be judged as more competent (23) and be more likely to win.
Similarly, more attractive candidates can be judged more favorably and be more
likely to win (24). In the case of face familiarity, though unrecognized by our
participants, incumbents might be more familiar than challengers, and
participants might have misattributed this familiarity to competence (25).
However, a regression analysis controlling for all judgments showed that the
only significant predictor of differences in votes was competence (Table 2).
Competence alone accounted for 30.2% of the variance for the analyses of all
Senate races and 45.0% of the variance for the races in which candidates were
of the same sex and ethnicity. Thus, all other judgments combined contributed
only 4.7% of the variance in the former analysis and less than 1.0% in the
latter analysis.
Table 2. Standardized regression coefficients of competence, age,
attractiveness, and face familiarity judgments as predictors of differences in
proportions of votes between the winner and the runner-up in races for the U.S.
Senate in 2000 and 2002. Matched races are those in which both candidates were
of the same sex and ethnicity.
Predictor Differences in votes between winner and runner-up
All races Matched races
Competence judgments 0.49 (P < 0.002) 0.58 (P < 0.002)
Age judgments 0.26 (P < 0.061) 0.07 (P = 0.62)
Attractiveness judgments 0.07 (P = 0.63) 0.08 (P = 0.62)
Face familiarity judgments -0.05 (P = 0.76) 0.03 (P = 0.86)
Accounted variance (R2) 34.9% 45.8%
Number of races
63
47
[ ] Fig. 2. Scatterplot of simulated voting preferences as a function of
inferred competence from facial appearance. Each point represents a U.S. Senate
race from 2000 or 2002. One group of participants was asked to cast
hypothetical votes and another group was asked to judge the competence of
candidates. Both the competence score and the voting preference score range
from 0 to 1. The competence score represents the proportion of participants
judging the candidate on the right to be more competent than the one on the
left. The preference score represents the proportion of participants choosing
the candidate on the right over the one on the left. The midpoint score of 0.50
on the x axis indicates that the candidates were judged as equally competent.
The midpoint score of 0.50 on the y axis indicates lack of preference for
either of the candidates. [View Larger Version of this Image (10K GIF file)]
Actual voting decisions are certainly based on multiple sources of information
other than inferences from facial appearance. Voters can use this additional
information to modify initial impressions of political candidates. However,
from a dual-system perspective, correction of intuitive system 1 judgments is a
prerogative of system 2 processes that are attention-dependent and are often
anchored on intuitive system 1 judgments. Thus, correction of initial
impressions may be insufficient (26). In the case of voting decisions, these
decisions can be anchored on initial inferences of competence from facial
appearance. From this perspective, in the absence of any other information,
voting preferences should be closely related to such inferences. In real-life
voting decisions, additional information may weaken the relation between
inferences from faces and decisions but may not change the nature of the
relation.
To test this hypothesis, we conducted simulated voting studies in which
participants were asked to choose the person they would have voted for in a
political election (27). If voting preferences based on facial appearance
derive from inferences of competence, the revealed preferences should be highly
correlated with competence judgments. As shown in Fig. 2, the correlation was
0.83 (P < 0.001) (28). By comparison, the correlation between competence
judgments and actual differences in votes was 0.56 (P < 0.001). These findings
suggest that the additional information that voters had about the candidates
diluted the effect of initial impressions on voting decisions. The simulated
votes were also correlated with the actual votes [r(63) = 0.46, P < 0.001] (29,
30). However, when controlling for inferences of competence, this correlation
dropped to 0.01 (P = 0.95), which suggests that both simulated and actual
voting preferences were anchored on inferences of competence from facial
appearance.
Our findings have challenging implications for the rationality of voting
preferences, adding to other findings that consequential decisions can be more
"shallow" than we would like to believe (31, 32). Of course, if trait
inferences from facial appearance are correlated with the underlying traits,
the effects of facial appearance on voting decisions can be normatively
justified. This is certainly an empirical question that needs to be addressed.
Although research has shown that inferences from thin slices of nonverbal
behaviors can be surprisingly accurate (33), there is no good evidence that
trait inferences from facial appearance are accurate (34–39). As Darwin
recollected in his autobiography (40), he was almost denied the chance to take
the historic Beagle voyage—the one that enabled the main observations of his
theory of evolution—on account of his nose. Apparently, the captain did not
believe that a person with such a nose would "possess sufficient energy and
determination."
References and Notes
1. J. V. Haxby, E. A. Hoffman, M. I. Gobbini, Trends Cognit. Sci. 4, 223
(2000).[CrossRef][ISI][Medline]
2. R. Hassin, Y. Trope, J. Pers. Soc. Psychol. 78, 837
(2000).[CrossRef][ISI][Medline]
3. L. A. Zebrowitz, Reading Faces: Window to the Soul? (Westview, Boulder, CO,
1999).
4. L. M. Bartels, Am. J. Polit. Sci. 44, 35 (2000).[ISI]
5. S. Chaiken, Y. Trope, Eds., Dual Process Theories in Social Psychology
(Guilford, New York, 1999).
6. D. Kahneman, Am. Psychol. 58, 697 (2003).[CrossRef][ISI][Medline]
7. A. Todorov, J. S. Uleman, J. Exp. Soc. Psychol. 39, 549
(2003).[CrossRef][ISI]
8. J. S. Winston, B. A. Strange, J. O'Doherty, R. J. Dolan, Nat. Neurosci. 5,
277 (2002).[CrossRef][ISI][Medline]
9. D. R. Kinder, M. D. Peters, R. P. Abelson, S. T. Fiske, Polit. Behav. 2, 315
(1980).[CrossRef]
10. In one of our studies, 143 participants were asked to rate the importance
of 13 different traits in considering a person for public office. These traits
included competence, trustworthiness, likability, and 10 additional traits
mapping into five trait dimensions that are generally believed by personality
psychologists to explain the structure of personality: extraversion,
neuroticism, conscientiousness, agreeableness, and openness to experience (11
). Competence was rated as the most important trait. The mean importance
assigned to competence was 6.65 (SD = 0.69) on a scale ranging from 1 (not at
all important) to 7 (extremely important). The importance assigned to
competence was significantly higher than the importance assigned to any of the
other 12 traits (Ps < 0.005).
11. S. D. Gosling, P. J. Rentfrow, W. B. Swan Jr., J. Res. Pers. 37, 504
(2003).[CrossRef][ISI]
12. See supporting data on Science Online.
13. For the House races in 2002, we were able to obtain pictures of both the
winner and the runner-up for 321 of the 435 races. For the House races in 2004,
we were able to obtain pictures for 279 of the 435 races (12).
14. In the studies involving these races, we used photographs of the Democratic
and Republican candidates (12).
15. In addition, the accuracy of the predictions was not affected by the race
and sex of the candidates. This is important because participants might have
used race and sex stereotypes to make competence judgments for contests in
which the candidates were of different sexes and races. For example, in such
contests Caucasian male candidates were more likely to win. However, if
anything, competence judgments predicted the outcomes of elections in which the
candidates were of the same sex and race (73.1% for the Senate and 68.5% for
the House) more accurately than elections in which they were of different sexes
and races (67.9% and 64.3%, respectively). This difference possibly reflects
participants' social desirability concerns when judging people of different
race and sex.
16. For races with more than two candidates, we standardized this difference so
that it was comparable to the difference in races with two candidates.
Specifically, the difference between the votes of the winner and those of the
runner-up was divided by the sum of their votes.
17. From the scatterplot showing the relation between competence judgments and
votes for Senate (Fig. 1B ), seven races (three in the lower right quadrant and
four in the upper left quadrant) could be identified as deviating from the
linear trend. It is a well-known fact that incumbents have an advantage in U.S.
elections (18 ). In six of the seven races, the incumbent won but was judged as
less competent. In the seventh race (Illinois, 2004) there was no incumbent,
but the person who won, Barack Obama, was the favorite long before the
election. Excluding these seven races, the correlation between competence
judgments and differences in votes increased to 0.64 (P < 0.001). Although
incumbent status seemed to affect the strength of the linear relation between
inferences of competence and the margin of victory, it did not affect the
prediction of the outcome. Competence judgments predicted the outcome in 72.9%
of the races in which the incumbent won, in 66.7% of the races in which the
incumbent lost, and in 68.8% of the cases in which there was no incumbent (
[{chi}] 2 < 1.0 for the difference between these percentages; P = 0.89).
18. A. D. Cover, Am. J. Polit. Sci. 21, 523 (1977).[ISI]
19. A bootstrapping data simulation showed that increasing the sample size to
more than 40 participants does not improve the accuracy of prediction
substantially (12) (fig. S1).
20. Given the time constraints in this study, to avoid judgments based on
salient differences such as race and sex, we used only Senate races (2000,
2002, and 2004) in which the candidates were of the same sex and race.
21. For this study, we used the 2002 Senate races. The judgments in this and
the subsequent studies were performed in the absence of time constraints (12).
22. H. H. Kelley, J. Pers. 18, 431 (1950).[ISI][Medline]
23. J. M. Montepare, L. A. Zebrowitz, Adv. Exp. Soc. Psychol. 30, 93
(1998).[ISI]
24. T. L. Budesheim, S. J. DePaola, Pers. Soc. Psychol. Bull. 20, 339
(1994).[ISI]
25. C. M. Kelley, L. L. Jacoby, Acta Psychol. (Amsterdam) 98, 127 (1998).
26. D. T. Gilbert, in Unintended Thought, J. S. Uleman, J. A. Bargh, Eds.
(Prentice-Hall, Englewood Cliffs, NJ, 1989), pp. 189–211.
27. For these studies, we used the 2000 and 2002 Senate races (12).
28. An additional analysis from a study in which participants made judgments of
the candidates for the Senate (2000 and 2002) on 13 different traits [see (10 )
for the list of traits] provided additional evidence that inferences of
competence were the key determinants of voting preferences in this situation.
We regressed voting preferences on the 13 trait judgments. The only significant
predictor of these preferences was the judgment of competence [ [{bullet}] =
0.67, t(49) = 4.46, P < 0.001].
29. A similar finding was obtained in an early study conducted in Australia (30
). Hypothetical votes based on newspaper photographs of 11 politicians were
closely related to the actual votes in a local government election. Moreover,
both hypothetical and actual votes correlated with inferences of competence.
30. D. S. Martin, Aust. J. Psychol. 30, 255 (1978).[ISI]
31. G. A. Quattrone, A. Tversky, Am. Polit. Sci. Rev. 82, 719 (1988).[ISI]
32. J. R. Zaller, The Nature and Origins of Mass Opinion (Cambridge Univ.
Press, New York, 1992).
33. N. Ambady, F. J. Bernieri, J. A. Richeson, Adv. Exp. Soc. Psychol. 32, 201
(2000).[CrossRef][ISI]
34. There is some evidence that judgments of intelligence from facial
appearance correlate modestly with IQ scores (35). However, these correlations
tend to be small [e.g., <0.18 in (35 )], they seem to be limited to judgments
of people from specific age groups (e.g., puberty), and the correlation is
accounted for by the judges' reliance on physical attractiveness. That is,
attractive people are perceived as more intelligent, and physical
attractiveness is modestly correlated with IQ scores.
35. L. A. Zebrowitz, J. A. Hall, N. A. Murphy, G. Rhodes, Pers. Soc. Psychol.
Bull. 28, 238 (2002).[Abstract/Free Full Text]
36. Mueller and Mazur (37 ) found that judgments of dominance from facial
appearance of cadets predicted military rank attainment. However, these
judgments did not correlate with a relatively objective measure of performance
based on academic grades, peer and instructor ratings of leadership, military
aptitude, and physical education grades.
37. U. Mueller, A. Mazur, Soc. Forces 74, 823 (1996).[ISI]
38. There is evidence that trait inferences from facial appearance can be
wrong. Collins and Zebrowitz [cited in (23 ), p. 136] showed that baby-faced
individuals who are judged as less competent than mature-faced individuals
actually tend to be more intelligent. There is also evidence that subtle
alterations of facial features can influence the trait impressions of highly
familiar presidents such as Reagan and Clinton (39).
39. C. F. Keating, D. Randall, T. Kendrick, Polit. Psychol. 20, 593
(1999).[CrossRef][ISI]
40. F. Darwin, Ed., Charles Darwin's Autobiography (Henry Schuman, New York,
1950), p. 36.
41. Supported by the Department of Psychology and the Woodrow Wilson School of
Public and International Affairs at Princeton University. We thank M. Savard,
R. Hackell, M. Gerbasi, E. Smith, B. Padilla, M. Pakrashi, J. Wey, and R. G.-L.
Tan for their help with this project and E. Shafir, D. Prentice, S. Fiske, A.
Conway, L. Bartels, M. Prior, D. Lewis, and two anonymous reviewers for their
comments on previous drafts of this paper.
----------------
Appearance DOES Matter
Leslie A. Zebrowitz and Joann M. Montepare*
Take a look at these two snapshots (see the figure). Which man is more
babyfaced? Most viewers would say it's the person on the right. And that's the
person who lost a 2004 U.S. congressional election to his more mature-faced and
competent-looking opponent. In fact, about 70% of recent U.S. Senate races were
accurately predicted based on which candidates looked more competent from a
quick glance at their faces. This remarkable effect, reported by Todorov et al.
on page 1623 of this issue, likely reflects differences in "babyfacedness" (1
). A more babyfaced individual is perceived as less competent than a more
mature-faced, but equally attractive, peer of the same age and sex (2, 3 ).
Although we like to believe that we "don't judge a book by its cover,"
superficial appearance qualities such as babyfacedness profoundly affect human
behavior in the blink of an eye (4).
[Figure 1] Which person is more babyfaced?
What facial qualities make someone look more babyfaced and less competent?
Facial measurements and computer modeling reveal that babies and babyfaced
adults of all ages share such features as a round face, large eyes, small nose,
high forehead, and small chin (2, 3, 5). So a babyish face is not synonymous
with age, which Todorov et al. (1 ) eliminated as an explanation for their
findings. This general quality also seems to be racially universal and evident
in both sexes (2, 3 ). However, a woman's facial anatomy tends to be more
neotenous than a man's, which may be a disadvantage for women when vying for
leadership positions (6 ). The association between facial maturity and
perceived competence is ubiquitous: Babyfaced individuals within various
demographic groups are perceived as less competent, whether by their own or
another group. Its impact can be seen even for famous politicians: When images
of former U.S. presidents Reagan and Kennedy were morphed to increase
babyfacedness, their perceived dominance, strength, and cunning decreased
significantly (7).
Why do we think babyfaced people are less competent, at first glimpse?
According to the ecological theory of social perception, our ability to detect
the attributes of age, health, identity, and emotion has evolutionary and
social value. Thus, we have a strong, built-in, predisposition to respond to
facial qualities that reveal these characteristics. Moreover, our responses can
be overgeneralized to people who look like individuals who actually have the
attributes. In this case, our impressions of babies (submissive, naïve, and
weak) are extended to babyfaced adults who are consequently perceived as less
competent than their more mature-faced peers. On the other hand, we get a more
warm and honest impression from a babyface (2, 3, 5).
So what are the social--even political--consequences of our behavior? One must
consider the context. Just as competentlooking, mature-faced individuals are
favored as congressional leaders, so are they favored for other occupations
requiring leadership and intellectual competence. However, those occupations
requiring warmth, such as nursing, are most likely assumed by babyfaced adults
(2, 3 ). Contextual effects are also seen in judicial decisions. Judges are
more apt to believe denials of negligent acts by mature-faced defendants, whose
competent appearance is inconsistent with carelessness. In contrast, they
believe denials of intentional transgressions by babyfaced defendants, whose
warm and honest appearance is not compatible with such malfeasance (2, 3 ).
Shifts in the popularity of American actresses tell a similar tale regarding
contextual relevance of perceived competence. Actresses with mature faces are
favored during times of social and economic hardship. But in prosperous times,
we turn our preference toward those with a baby?s glow (8).
When does perceived competence fail to predict election outcomes? Todorov et
al. found that more competent-looking candidates were defeated in 30% of races.
One possible explanation is that face biases could have favored babyfaced
candidates in those particular contests. It would be interesting to determine
whether babyfaced candidates have the edge in races where polls show that
integrity is a highly relevant trait. Like competence, perceived integrity is
an important quality used to judge politicians, and it favors babyfaced
individuals (9, 10 ). The more competent-looking candidates also had only a
small advantage in contests between candidates of different sexes. This was
attributed to people's reluctance to judge the relative competence of male
versus female opponents (1 ). Such concerns should be minimal when judging
babyfacedness. Thus, we may better predict outcomes in mixed-sex contests if
babyfacedness is used as a proxy for perceived competence.
Are we far from predicting the winner of an election based on voters' responses
to a candidate's appearance? Unfortunately, the Todorov et al. study shows that
this reality may be all too near. The study has important implications for
political marketing, social decision-making, and the democratic process. It
also highlights unanswered questions about appearance biases at both the
neuroscience and social science levels. What brain mechanisms underlie
automatic reactions to superficial qualities such as facial appearance? How can
we inoculate people against biased reactions to such qualities? The latter
question is particularly important given that more competent-looking victors in
congressional elections are not likely to be smarter or bolder than babyfaced
losers. Indeed, Todorov et al. noted that more babyfaced men tend to be
slightly more intelligent (1 ). They also tend to be more highly educated,
contrary to impressions of their naïveté, and more assertive and more likely to
earn military awards, contrary to impressions of their submissiveness and
weakness (2, 3 ). Understanding the nature and origins of appearance biases has
real-world value, not the least of which may be identifying electoral reforms
that could increase the likelihood of electing the most qualified leaders
rather than those who simply look the part.
References
1. A. Todorov, A. N. Mandisodza, A. Goren, C. C. Hall, Science 308, [1623]
(2005).
2. L.A. Zebrowitz, Reading Faces (Westview, Boulder, CO, 1997). [publisher's
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L. A. Zebrowitz is in the Department of Psychology, Brandeis University,
Waltham, MA 02454, USA. J. M. Montepare is in the Department of Marketing
Communication, Emerson College, Boston, MA 02116, USA. E-mail:
zebrowit at brandeis.edu
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