Humans face a fundamental challenge of how to balance selfish interests against moral considerations. Such trade‐offs are implicit in moral decisions about what to do; judgments of whether an action is morally right or wrong; and inferences about the moral character of others. To date, these three dimensions of moral cognition–decision‐making, judgment, and inference–have been studied largely independently, using very different experimental paradigms. However, important aspects of moral cognition occur at the intersection of multiple dimensions. This talk will demonstrate the advantages of investigating these three dimensions of moral cognition within a unified experimental framework. A core component of this framework is harm aversion, a moral sentiment defined as a distaste for harming others. The framework integrates economic utility models of harm aversion with Bayesian reinforcement learning models describing beliefs about others’ harm aversion. Examples from several studies will show how this framework can provide novel insights into the mechanisms of moral decision‐making, judgment, and inference.

People form instantaneous impressions from facial appearance, agree on these impressions, and act on these impressions. We have introduced data-driven computational methods that allow us to visualize the configurations of facial features that lead to specific social inferences (e.g., trustworthiness). That is, we are able to visualize appearance stereotypes and identify their perceptual basis. However, perceptual determinants are insufficient to account for the full content of impressions. In fact, for almost any impression, statistical modeling shows that stable idiosyncratic preferences contribute to more than 50% of the variance. These preferences originate in one’s learning history of faces and partially explain why the evidence for accuracy of first impressions is weak. Recently, we have started studying other mechanisms that can lead to the perpetuation of false appearance stereotypes. Preferences for specific faces (e.g., trustworthy-looking) lead to biased sampling of observations that confirm stereotypes even when the stereotypes are completely inaccurate.