Predicting political elections from rapid and unreflective face judgments

Ballew and Todorov. 10.1073/pnas.0705435104.

Supporting Information

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SI Figure 5
SI Text
SI Figure 6
SI Table 3

SI Figure 5

Fig. 5. Scatter plot of the two-party vote share for the candidates and their perceived competence (Experiment 1). Each point represents a gubernatorial race. The line represents the best fitting linear curve.

SI Figure 6

Fig. 6. Scatter plots of predictive accuracy of competence judgments and response times for judgments. Each point represents a gubernatorial race. (A) Judgments after unlimited time exposure to faces. (B) Deliberation judgments. The y axis crosses the x axis at the point of correct prediction (>0.50). The line represents the best fitting quadratic curve.

Table 3. Partial correlations between vote share and judgments made after unlimited time exposure controlling for intuitive judgments

Controlling for judgments	Partial correlation
100-ms exposure (Exp. 1)	0.15
250-ms exposure (Exp. 1)	0.06
Averaged across 100 and 250 ms (Exp. 1)	0.06
250-ms exposure (Exp. 2)	-0.02
250-ms exposure (averaged across both experiments)	-0.04

SI Text

Preliminary Analysis and Analysis of Continuous Competence Judgments (Experiment 1). Analyses were conducted at both (i) the level of participants on the proportion of correctly predicted races and (ii) at the level of races on the proportion of participants choosing the winner as more competent. In the latter analysis, races in which a majority of participants judged the winner as more competent were classified as correctly predicted. For each race the binary competence judgments were combined across participants, after controlling for recognition. This yielded a mean competence with a range from 0 to 1. For example, if 24 of 36 participants judged the winner as more competent and none of the participants recognized any of the faces, the mean would be 0.67. A mean over 0.50 signified that a majority of participants judged the winner as more competent, and thus the race was classified as correctly predicted.

In addition to the mean competence obtained from the forced choice judgments, we also obtained a second competence measure by aggregating the responses on the nine-point scale competence judgment presented after the binary judgment. For each race, the summed ratings for the runner-up (when the runner-up was chosen as more competent) were subtracted from the summed ratings for the winner (when the winner was chosen as more competent) to obtain a measure of differences in competence. At the level of participants, the continuous competence judgments were submitted to a 2 (candidate: winner vs. runner-up) ´ 3 (time exposure: 100 ms vs. 250 ms vs. unlimited time) mixed-subjects ANOVA. The only significant effect was the effect of candidate [F(1, 117) = 9.86, P < 0.002, h² = 0.078]. Participants were more likely to judge the winner as more competent (M = 3.93, SE = 0.11) than the runner-up (M = 3.82, SE = 0.11), although the effect was relatively small. At the level of the races, the measures of competence obtained from the competence ratings did not contribute any additional information over the information gained from the simple binary competence judgments. The correlation between the average competence aggregated across the binary judgments and the competence aggregated across the nine-point scale judgments (the difference between the ratings for the winner and the runner-up) was above 0.95 in all three conditions.

Technical Note for Fig. 4. The scatter plots in Fig. 4 show the relation between the nonshared variance of unreflective judgments and vote share, and the nonshared variance of deliberation judgments and vote share. The corresponding correlations were 0.34 and -0.18. Technically, these correlations are slightly different from the partial correlations, because for the computation of the latter, the shared variance between vote share and the controlled variable is also removed. However, the presentation in Fig. 4 is more intuitive and because vote share is not highly correlated with judgments, the correlations depicted in Fig. 4 are practically identical to the partial correlations.

Partial Correlation Analysis Across both Experiments. In the section on Analysis across both experiments in Experiment 2, we reported that the correlation between the time unconstrained judgments (obtained in Experiment 1) and vote share was eliminated after the analysis controlled for unreflective judgments. The measure for the unreflective judgments was obtained from judgments made after 250 ms of exposure and response deadline judgments, both judgments obtained in Experiment 2. For consistency of presentation, we reported the partial correlation correcting for the latter judgments in the main text. However, an argument can be made that the proper control should be judgments obtained in Experiment 1. As shown in the SI Table 3, the results are identical. In all cases, the correlation was reduced (from 0.27) and was not significant. The correlation was highest when the analysis controlled for the judgments made after 100 ms of exposure, suggesting that judgments may be improving with exposures longer than 100 ms but not with exposures longer than 250 ms.

Analysis of Response Times. For the analysis of response times, for each participant we excluded response times that were more than 3 standard deviations above their mean response time within each experimental condition (with the exception of the response deadline condition in Experiment 2).

The findings of Experiments 1 and 2 suggest that longer response times are not necessarily associated with less predictive judgments. Although the response times for the judgments in the unlimited time condition and the deliberation judgments were practically identical, the predictive accuracy of judgments was reduced only in the deliberation condition.

As shown in SI Fig. 6, the relation between response times and predictive accuracy of judgments is best described by a quadratic function. The higher the consensus in the judgment, the faster was the judgment. This was the case for both judgments correctly predicting the outcomes of the races and judgments incorrectly predicting these outcomes. For both deliberation and time-unconstrained judgments, the quadratic models accounted for significantly more variance than the linear models [F(1, 52) = 14.83, P < 0.001, and F(1, 52) = 32.94, P < 0.001, respectively].

Incumbency Status and Competence Judgments. Although we showed that the effect of competence judgments was independent of incumbency status for Senate races in our prior work (3), this was not the case for the House races. For these races, competence judgments predicted the winner only in races in which the incumbents won. There are a number of differences between House and Senate races and it is not clear how to interpret the latter finding. There is less media exposure to House candidates than to Senate candidates, and it is likely that many voters are unfamiliar with the faces of their House candidates, a possibility that suggests different accounts of voting decisions in House and Senate races. It was also impossible to obtain pictures of both candidates for all House races and this may have introduced unknown biases in the sample of these races.