People are able to quickly and automatically evaluate faces on different traits, such as trustworthiness. There is a growing literature demonstrating that factors such as learning and experience play a role in shaping these judgments. In the current work, we assess the malleability of our trait evaluations by associating arbitrary facial features with trustworthy or untrustworthy behaviors. Across five studies, we demonstrate that this learning can impact trait evaluation and effectively form novel facial stereotypes, which exert effects on evaluations as strong as intrinsic facial trustworthiness. With only a brief training, participants' rapidly acquired novel facial stereotypes, which were activated automatically and early on in processing, and which biased participants' trust behavior and hiring decisions. These results suggest that our trait evaluations of faces are shaped by an implicit learning mechanism that abstracts the co-occurrence between facial features and trait-related behaviors, resulting in the creation of novel facial stereotypes.

人们能够快速、自动地评估不同特征的面孔，例如可信度。越来越多的文献表明，学习和经验等因素在塑造这些判断方面发挥着作用。在目前的工作中，我们通过将任意面部特征与可信或不可信行为相关联来评估我们的特征评估的延展性。在五项研究中，我们证明这种学习可以影响特征评估并有效地形成新的面部刻板印象，这对评估的影响与内在面部可信度一样强。仅通过简短的培训，参与者就迅速获得了新的面部刻板印象，这些刻板印象在处理过程中被自动激活，并且会影响参与者的信任行为和招聘决定。