five

Table_2_Effects of Male Defendants’ Attractiveness and Trustworthiness on Simulated Judicial Decisions in Two Different Swindles.XLSX

收藏
NIAID Data Ecosystem2026-03-11 收录
下载链接:
https://figshare.com/articles/dataset/Table_2_Effects_of_Male_Defendants_Attractiveness_and_Trustworthiness_on_Simulated_Judicial_Decisions_in_Two_Different_Swindles_XLSX/9905726
下载链接
链接失效反馈
官方服务:
资源简介:
The present study aimed to examine the effects of male defendants’ facial appearance (attractiveness and trustworthiness) on judicial decisions in two different swindles. We selected the following four categories of faces by manipulating facial attractiveness and trustworthiness simultaneously: the attractive and trustworthy face; the attractive but untrustworthy face; the unattractive but trustworthy face; and the unattractive and untrustworthy face. A total of six hundred and sixty-three participants across two studies were asked to make conviction-related judgments and penalty-related decisions for the defendants after they were randomly assigned to one of the four categories of faces. In Experiment 1, we used a blind-date swindle and found a “beauty penalty” for physically attractive defendants among females. Specifically, female participants were more likely to issue a guilty verdict to better-looking male defendants. Additionally, this “beauty-penalty effect” was merely observed in the untrustworthy condition. In Experiment 2, we used a telecommunication swindle, and the results showed that facial trustworthiness significantly predicted punishment magnitude and sentence decisions. Moreover, an exploratory analysis revealed that the disgust evoked by the faces partially mediated the relationship between facial trustworthiness and the assignment of criminal penalties. Taken together, these findings indicated that facial attractiveness and trustworthiness played different roles in judicial decisions. Importantly, the effect of facial attractiveness on judicial decisions differed as the detailed criminal circumstances of the offenses changed.
创建时间:
2019-09-26
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作