Elected in 100 milliseconds: Appearance-Based Trait Inferences and Voting

By Christopher Y. Olivola, Alexander Todorov. 2010

559 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/0965a50373ff2e38a05a3646867890d5f1069ae9

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Citations in the corpus

Social attributions from faces: determinants, consequences, accuracy, and functional significance. (2015, background): “(b) Judgments of political affiliation (study 2 in Olivola & Todorov 2010a), where the base-rates were experimentally manipulated (between participants) and known to participants, so that either Democrats were the dominant category (blue), Republicans were the dominant category (red ), or both…”
The influence of clothing on first impressions: Rapid and positive responses to minor changes in male attire (2013, background): “It is now well documented that people can form snap judgements, with an above chance level of accuracy, and can do so from a single static picture (Olivola and Todorov, 2010a).”
Vote-earning strategies in flexible list systems: Seats at the price of unity (2013, background): “Somewhat less superficially, “facial competence” too has been shown to give candidates an advantage (Olivola and Todorov, 2010).”
Body weight, not facial width-to-height ratio, predicts aggression in pro hockey players (2012, background): “Facial characteristics are known to influence observers’ predictions of behavior in domains ranging from criminal sentencing (Eberhardt, Davies, Purdie-Vaughns, & Johnson, 2006) to government elections (Olivola & Todorov, 2010).”
The Attractiveness Halo: Why Some Candidates are Perceived More Favorably than Others (2010, methodology): “In their target article featured in this volume, Olivola and Todorov (2010) argue that it is competence judgments in particular that predict electoral preferences, and they find that competence is more strongly associated with actual election outcomes than are other facial cues such as…”
Image Counterfactual Sensitivity Analysis for Detecting Unintended Bias (2019, background): “For example, this includes perceptions of age and gender presentation (e.g., [31, 25]).”