Facial Similarity Between Voters and Candidates Causes Influence

By Jeremy N. Bailenson, Shanto Iyengar, N. Yee, Nathan A. Collins. 2008

205 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/a74d725efcf60d5cf729c736c410f8de66f1547c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Christopher Y. Olivola
2. Ron Dotsch
3. Peter Mende-Siedlecki
4. Alexander Todorov
5. Milton Lodge
6. B. Verhulst
7. Howard G. Lavine
8. Jesse Fox

In Experimental and Cognitive Psychology

1. Christopher Y. Olivola
2. Ron Dotsch
3. Jesse Fox
4. Peter Mende-Siedlecki
5. Alexander Todorov
6. Milton Lodge
7. B. Verhulst
8. Howard G. Lavine

In Sociology and Political Science

1. Milton Lodge
2. Howard G. Lavine
3. Jesse Fox
4. B. Verhulst
5. Christopher Y. Olivola
6. Jeremy N. Bailenson
7. Peter Mende-Siedlecki
8. Leo Yeykelis

Citations in the corpus

• Social attributions from faces: determinants, consequences, accuracy, and functional significance. (2015, background): “Finally, when choosing between competing political candidates, participants were more likely to prefer individuals whose faces had been altered to resemble their own faces (Bailenson et al. 2008).”
• Fake images: The effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online (2018, background): “, 2002)— therefore further enhancing the credibility of these images—and even influence decision-making behaviors such as voting (Bailenson et al., 2008; Nash et al., 2009).”
• The Attractiveness Halo: Why Some Candidates are Perceived More Favorably than Others (2010, background): “In research in a related vein, familiarity with a political candidate was manipulated by blending a political candidate’s facial features with a participant’s facial features ( Bailenson et al. 2008 ).”
• INCREASING SAVING BEHAVIOR THROUGH AGE-PROGRESSED RENDERINGS OF THE FUTURE SELF (2011, background): “A morph is akin to a weighted average of two images and looks quite natural (Bailenson et al. 2008).”
• Elected in 100 milliseconds: Appearance-Based Trait Inferences and Voting (2010, result): “The correlation we find between familiarity and competence connects well with recent studies by Bailenson and his colleagues (Bailenson et al. 2008), who showed that voters were more likely to vote for politicians whose faces were morphed with their own faces.”