People automatically infer others’ personality (e.g., trustworthiness) based on facial appearance, and such facial stereotype biases predict real-world consequences across political, legal, and business domains. The present research tested whether these biases can be mitigated through counterstereotype training aimed at reconfiguring the associations between specific facial appearances and social traits. Across six studies and a replication, a behavioral counterstereotype training consistently reduced or eliminated facial stereotype biases for White male faces in the context of economic trust games, hiring decisions, and even automatic evaluations assessed via evaluative priming. Together, the results demonstrate a fundamental malleability in facial stereotyping related to trustworthiness, with a minimal training able to mitigate the tendency to activate and apply long-held, highly automatized facial stereotypes. These findings suggest that face impressions are more flexible than typically appreciated, and they provide a potential inroad toward combating our ingrained biases based on facial appearance.

人们根据面部表情自动推断他人的个性（例如，可信赖度），而这种面部刻板印象偏差会预测政治，法律和商业领域的现实后果。本研究测试了是否可以通过反刻板印象训练来缓解这些偏见，该刻板印象训练旨在重新配置特定面部表情和社会特征之间的关联。在六项研究和一项重复研究中，行为反刻板印象训练持续减少或消除了在经济信任博弈，雇用决策乃至通过评估启动进行自动评估的情况下对白人男性面孔的面部刻板印象偏差。在一起，结果表明与信任度有关的面部定型观念具有基本的延展性，只需进行最少的培训就能减轻激活和应用长期存在的高度自动化的面部刻板印象的趋势。这些发现表明，脸部表情比通常的欣赏方式更灵活，它们为打击基于面部表情的根深蒂固的偏见提供了潜在的机会。