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Inferences of Competence from Faces Predict Election Outcomes

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Science  10 Jun 2005:
Vol. 308, Issue 5728, pp. 1623-1626
DOI: 10.1126/science.1110589

Abstract

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.

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 (911). 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]

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 χ2 statistic tests the proportion of correctly predicted races against the chance level of 50%.

Election Correctly predicted χ2
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

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.

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.

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 (3439). 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.”

Supporting Online Material

www.sciencemag.org/cgi/content/full/308/5728/1623/DC1

Materials and Methods

SOM Text

Fig. S1

Table S1

References

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