Predicting political elections from rapid
and unreflective face judgments
Charles C. Ballew II* and Alexander Todorov*†‡
*Department of Psychology and†Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ 08540
Edited by Edward E. Smith, Columbia University, New York, NY, and approved September 25, 2007 (received for review June 10, 2007)
the facial appearance of candidates predicted the outcomes of
gubernatorial elections, the most important elections in the United
States next to the presidential elections. In all experiments, par-
ticipants were presented with the faces of the winner and the
runner-up and asked to decide who is more competent. To ensure
that competence judgments were based solely on facial appear-
ance and not on prior person knowledge, judgments for races in
which the participant recognized any of the faces were excluded
from all analyses. Predictions were as accurate after a 100-ms
exposure to the faces of the winner and the runner-up as exposure
after 250 ms and unlimited time exposure (Experiment 1). Asking
participants to deliberate and make a good judgment dramatically
increased the response times and reduced the predictive accuracy
of judgments relative to both judgments made after 250 ms of
exposure to the faces and judgments made within a response
deadline of 2 s (Experiment 2). Finally, competence judgments
collected before the elections in 2006 predicted 68.6% of the
gubernatorial races and 72.4% of the Senate races (Experiment 3).
These effects were independent of the incumbency status of the
candidates. The findings suggest that rapid, unreflective judg-
ments of competence from faces can affect voting decisions.
face perception ? social judgments ? voting decisions
United States. American states are significant political and eco-
nomic entities, with some being larger and economically more
powerful than many foreign countries. In addition to wielding
to ascend to the presidency. For example, 17 of 43 presidents have
and potential of a governorship comes at a cost. In 1998, the 36
gubernatorial races averaged $14.1 million in expenses each (1). By
comparison, the average Senate campaign cost was $3.3 million in
Despite the significance of gubernatorial races, we show that
rapid, unreflective judgments of competence based solely on facial
appearance and made after as little as 100 ms of exposure to the
our prior work on forecasting the outcomes of Senate elections (3),
we showed that people believe that competence is the most
important attribute for a politician and that trait inferences of
competence from faces but not other traits (e.g., trustworthiness,
attractiveness, likeability, etc.) predict election outcomes. We ar-
gued that these inferences are rapid, intuitive, and unreflective, but
we did not provide direct evidence for this assumption.
The first objective of the current research was to provide such
evidence. The second objective was to replicate the Senate findings
for gubernatorial races, which are arguably more important. The
third objective was to test whether the effect of competence
incumbency status of the candidates. In the Senate and House of
Representatives elections, there are no terms limits, and incum-
bents have overwhelming odds of being reelected (4). In contrast,
ith the exception of the president, state governors are
arguably among the most powerful elected officials in the
many states have term limits for governors, and, correspondingly,
there are fewer incumbents in gubernatorial races.
Faces are a rich source of social information, and trait judgments
from faces can be made after minimal time exposure (5). For
example, we have shown that 100 ms of exposure to a face is
sufficient for people to make a variety of trait judgments, including
competence, and that additional time only increases confidence in
judgments (6). In Experiment 1, we tested whether competence
judgments made after 100 ms of exposure to the faces of the
candidates predict the outcomes of gubernatorial elections better
than chance and whether additional time exposure (250 ms and
unlimited time) improves the accuracy of prediction.
In our previous work (3) and Experiment 1, participants were
asked to rely on their ‘‘gut’’ feeling or first impression when making
on judgments. Deliberating about judgments that are unreflective
and not easy to articulate can interfere with the quality (7) and
consistency (8) of the judgments. If trait judgments from faces are
unreflective, instructions to deliberate and make a good judgment
should not improve the predictive accuracy of judgments. In
Experiment 2, we tested whether deliberation judgments are less
accurate in predicting the election outcomes than judgments made
after 250 ms of exposure to the faces and judgments made under
response deadline of 2 s, forcing participants to rely on quick
In Experiment 3, we collected competence judgments for both
gubernatorial and Senate races in 2006 before the actual election.
We tested whether these judgments based solely on facial appear-
ance would predict the election outcomes better than chance, as we
did in our previous work on predicting the Senate elections
prospectively in 2004 (3).
Participants were presented with the faces of the winner and the
runner-up for 89 gubernatorial races and asked to judge which
1). In the third condition the faces were presented until the
participant responded, with no time constraints. For each race,
participants made three consecutive judgments: a binary choice of
who was more competent, a continuous judgment (on a nine-point
scale) of how much more competent the chosen person was§, and
a recognition judgment. If participants recognized either of the
faces for a given race, their responses for that race were not
Author contributions: C.C.B. and A.T. designed research; C.C.B. performed research; A.T.
analyzed data; and A.T. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
‡To whom correspondence should be addressed. E-mail: firstname.lastname@example.org.
§This measure did not contribute any additional information over the information gained
from the binary competence judgments. Details are provided in SI Text.
This article contains supporting information online at www.pnas.org/cgi/content/full/
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faces were displayed along with a binary competence judgment
measure (‘‘Which person is more competent?’’). In the timed
conditions, the faces and letters were displayed for 100 or 250 ms
cloud filter that occupied the same area as the images (Fig. 1). The
mask remained up along with the A/B letters and the binary
B tabs were placed over the ‘‘w’’ and ‘‘p’’ keys on the keyboard,
respectively. Thus, pressing the A tab always corresponded with
choosing the candidate on the left (marked with an A) as more
competent, and vice versa.
screen (1,000 ms) and fixation cross (500 ms). The faces were
displaying the faces with a scaled continuous competence measure
presented below the faces: ‘‘On a scale of 1 to 9, how much more
whom they chose as more competent on the preceding trial using
the 1 through 9 keys on the top of the keyboard. In the timed
with masks when the question was displayed.
Finally, the faces were presented again and participants were
asked whether they recognized either of the faces from outside the
study. Large neon ‘‘NO’’ and ‘‘YES’’ tabs were placed over the ‘‘z’’
and ‘‘/’’ keys, respectively, to collect this response. In all conditions,
faces were presented for an unlimited time to ensure the most
conservative measure of recognition.
Preliminary analyses. To ensure that competence judgments were
based solely on facial appearance and not on prior person knowl-
edge, judgments for races in which the participant recognized any
of the faces were excluded from all analyses. This procedure
resulted in the exclusion of 4.4% of the judgments.
To test whether the difference in competence between the two
candidates was linearly related to the difference in votes between
them, we used a measure of the two-party vote share. In this
analysis, both vote share (e.g., the vote for the Democratic candi-
date out of the total votes for Republican and Democratic candi-
dates) and competence (e.g., the perceived competence of the
Democratic candidate relative to the Republican candidate) are
conditioned on the candidate’s party. The analysis is the same
whether it is conditioned on the Republican or Democratic candi-
date, because the measures for the candidates are perfectly nega-
Experiment 2. Participants. One hundred and ten Princeton Univer-
sity undergraduate students participated in the studies in exchange
for payment or partial course credit. Participants were randomly
position of the images).
Procedures. In both the 250-ms and the response deadline condi-
tions, the instructions were the same as those in Experiment 1. In
the deliberation condition, participants were told that we were
interested in thoughtful reactions and that they should think
randomized for each participant. The procedures were the same as
any additional information over the binary competence judgments
in Experiment 1. The faces in the deliberation and the response
However, in the latter condition participants had only 2 s to
respond. After 2 s, the images were replaced by a blank screen (1
s) and a fixation point (500 ms) signaling the beginning of the next
Experiment 3. Sixty-four Princeton University undergraduate stu-
dents participated in the studies in exchange for partial course
credit. Participants were randomly assigned to one of two experi-
mental conditions (counterbalancing of the position of the images
of Republican and Democratic candidates). The procedures were
the same as in the unconstrained time condition in Experiment 1.
Participants made judgments for 35 gubernatorial races and 29
Senate races. The order of the races was randomized for each
participant. We excluded one gubernatorial race, because the
incumbent was famous (Arnold Schwarzenegger in California) and
would have been recognized by most participants; we also excluded
four Senate races, because two races included famous incumbents
(Hillary Clinton in New York and Joe Lieberman in Connecticut),
collection, and pictures were unavailable.
We thank Amir Goren and Crystal Hall for comments on an earlier
version of this paper, and Manish Pakrashi and Valerie Loehr for their
help in running the experiments.
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