Facial Stereotype Bias Is Mitigated by Training
Facial Stereotype Bias Is Mitigated by Training
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DOI：
10.1177/1948550620972550
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发表时间：
2020-11-28
影响因子：
5.7
通讯作者：
Freeman, Jonathan B.
中科院分区：
文献类型：
--
作者：
Chua, Kao-Wei;Freeman, Jonathan B.
人们会自动推断他人的个性（例如，可信度），并且这样的面部刻板印象偏见预测跨政治、法律的和商业领域的真实世界后果。本研究测试了这些偏见是否可以通过旨在重新配置特定面部外观和社会特征之间的关联的反刻板印象训练来减轻。在六项研究和一项复制中，行为反刻板印象训练在经济信任游戏、招聘决策甚至通过评估启动评估的自动评估的背景下，持续减少或消除了对白色男性面孔的刻板印象偏见。总之，这些结果表明，与可信度相关的面部刻板印象具有基本的可塑性，最低限度的培训能够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，面部印象比通常所理解的要灵活得多，它们为我们克服基于面部外观的根深蒂固的偏见提供了一个潜在的突破口。
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.