Discussions in social psychology overlook an important way in which biases can be encoded in conceptual representations. Most accounts of implicit bias focus on ‘mere associations’ between features and representations of social groups. While some have argued that some implicit biases must have a richer conceptual structure, they have said little about what this richer structure might be. To address this lacuna, we build on research in philosophy and cognitive science demonstrating that concepts represent dependency relations between features. These relations, in turn, determine the centrality of a feature f for a concept C: roughly, the more features of C depend on f, the more central f is for C. In this paper, we argue that the dependency networks that link features can encode significant biases. To support this claim, we present a series of studies that show how a particular brilliance-gender bias is encoded in the dependency networks which are part of the concepts of female and male academics. We also argue that biases which are encoded in dependency networks have unique implications for social cognition.

中文翻译：

---

刻板印象、概念中心性和性别偏见：一项实证调查

社会心理学中的讨论忽略了一种重要的方式，在这种方式中，偏见可以被编码到概念表征中。大多数隐性偏见的解释都集中在社会群体的特征和表征之间的“纯粹关联”上。虽然有些人认为某些隐性偏见必须具有更丰富的概念结构，但他们几乎没有说明这种更丰富的结构可能是什么。为了解决这个空白，我们建立在哲学和认知科学的研究基础上，证明概念代表特征之间的依赖关系。这些关系反过来决定了概念 C 的特征 f 的中心性：粗略地说，C 的特征依赖于 f 的越多，f 对 C 的中心就越多。 在本文中，我们认为链接特征的依赖网络可以编码显着的偏差。为了支持这一主张，我们展示了一系列研究，展示了特定的才华横溢的性别偏见是如何在依赖网络中编码的，这些网络是女性和男性学者概念的一部分。我们还认为，在依赖网络中编码的偏见对社会认知具有独特的影响。