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.

社交互动是动态的并随着时间的推移而展开。为了理解社会互动，人们必须将连续的信息汇总成概括的、全局的评估。但人们如何做到这一点呢？为了解决这个问题，我们使用不同的刺激进行了九项研究（ N = 1,583）。我们关注的焦点是社交互动的一个核心方面，即评估他人的情绪反应。结果表明，当聚合表达不同程度情感的图像和视频序列时，感知者会高估该序列的平均情感强度。这种高估的倾向是由对更多情绪表达的更强记忆所驱动的。计算模型支持了这一说法，并表明放大不能仅通过个体样本的非线性感知来解释。我们的结果表明，连续情绪信息的感知存在放大效应，这可能对涉及重复情绪估计的多种类型的社交互动产生影响。