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Three pre-registered studies find that perpetrators who commit moral transgressions are judged less harshly when their transgressions impact individuals who have committed immoral actions (bad victims). In Studies 1 and 2, we used between- and within-participant comparisons to show that perpetrators, and the moral transgressions they committed, were judged less harshly when they impacted bad (vs. neutral) victims. In Study 3, contrary to predictions derived from both karma theory and dyadic morality, we did not find evidence that perpetrators who transgressed against highly moral individuals (good victims) were seen differently than those who transgressed against neutral victims. The increased leniency towards perpetrators who transgressed against bad victims occurred even though perpetrators in our studies did not know who their victims were, meaning this leniency did not result from the perception that perpetrators were intentionally punishing bad victims. Instead, consistent with research on dehumanization of immoral individuals, bad victims were seen as having less capacity for emotional experience (relative to both good and neutral victims). Thus, our findings suggest that moral transgressions against bad victims are viewed as less wrong because these transgressions are perceived to cause less harm to their victims.

The emotion of disgust has been claimed to affect a diverse array of social judgments, including moral condemnation, inter-group prejudice, political ideology, and much more. We attempt to make sense of this large and varied literature by reviewing the theory and research on how and why disgust influences these judgments. We first describe two very different perspectives adopted by researchers on why disgust should affect social judgment. The first is the pathogen-avoidance account, which sees the relationship between disgust and judgment as resulting from disgust’s evolved function as a pathogen-avoidance mechanism. The second is the extended disgust account, which posits that disgust functions much more broadly to address a range of other threats and challenges. We then review the empirical evidence to assess how well it supports each of these perspectives, arguing that there is more support for the pathogen-avoidance account than the extended account. We conclude with some testable empirical predictions that can better distinguish between these two perspectives.

We use a distributed language model to examine the moral language used by U.S. political elites. In Study 1, we analyze 687,360 Twitter messages (“tweets”) posted by accounts belonging to Democratic and Republican members of Congress from 2016-2018. In Study 2, we analyze 2,630,688 speeches given on the floor of the House and Senate from 1981-2017. We find that partisan differences in moral language use shift over time as the parties gain or lose political power. Overall, lower political power was associated with greater use of moral language for both Democrats and Republicans. On Twitter, Democrats used more moral language in the period after Donald Trump won the 2016 presidential election. In Congressional transcripts, both Democrats and Republicans used more of most kinds of moral language when they were in the minority.

Food crops produced by new technologies such as genetic engineering are widely opposed (Gaskell, Bauer, Durant & Allum, 1999; Scott, Inbar, Wirz, Brossard & Rozin, 2018). Here, we examine one reason for this opposition: recency. More recently-developed crops are evaluated less favorably, whether they are produced by artificial selection (i.e., conventional breeding), natural or man-made irradiation, or genetic engineering. Negative effects of recency persist in a within-subjects design where people are able to explicitly compare crops developed at different times, and an internal meta-analysis shows that the negative effect of recency is robust across measures and stimuli. These results have implications for the evaluation of crops produced using new modification techniques, including the widespread opposition to genetic engineering.