People act upon their desires, but often, also act in adherenceto implicit social norms. How do people infer these unstatedsocial norms from others’ behavior, especially in novel so-cial contexts? We propose that laypeople have intuitive the-ories of social norms as behavioral constraints shared acrossdifferent agents in the same social context. We formalize in-ference of norms using a Bayesian Theory of Mind approach,and show that this computational approach provides excellentpredictions of how people infer norms in two scenarios. Ourresults suggest that people separate the influence of norms andindividual desires on others’ actions, and have implications formodelling generalizations of hidden causes of behavior.

Observers often judge agents who miss a desired outcome by a small, compared to a large, margin to be less happy. This nearmiss effect has typically been examined in situations where the agents have control over outcomes (e.g., missing a flight). Here, we extend this work in three ways. First, we show that near-miss effects play into observers’ intuitive theories of emotion even for randomly-determined outcomes over which agents demonstrably have no control. Second, we find data consistent with a hypothesis in which—even in randomly determined cases—near-miss effects reflect an illusion of control over those events. Finally, we integrate near-miss effects into a broader model of affective cognition, and quantify the psychological cost of a missing a desired outcome by relatively little distance, relative to winning or losing that outcome

Social interactions are dynamic and unfold over time. To make sense of social interactions, people must aggregate sequential information into summary, global evaluations. But how do people do this? Here, to address this question, we conducted nine studies (N = 1,583) using a diverse set of stimuli. Our focus was a central aspect of social interaction-namely, the evaluation of others' emotional responses. The results suggest that when aggregating sequences of images and videos expressing varying degrees of emotion, perceivers overestimate the sequence's average emotional intensity. This tendency for overestimation is driven by stronger memory of more emotional expressions. A computational model supports this account and shows that amplification cannot be explained only by nonlinear perception of individual exemplars. Our results demonstrate an amplification effect in the perception of sequential emotional information, which may have implications for the many types of social interactions that involve repeated emotion estimation.