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Stereotypes, Conceptual Centrality and Gender Bias: An Empirical Investigation
Author(s) -
Del Pinal Guillermo,
Madva Alex,
Reuter Kevin
Publication year - 2017
Publication title -
ratio
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.475
H-Index - 29
eISSN - 1467-9329
pISSN - 0034-0006
DOI - 10.1111/rati.12170
Subject(s) - centrality , dependency (uml) , focus (optics) , epistemology , dependency theory (database theory) , feature (linguistics) , cognition , psychology , social psychology , cognitive psychology , sociology , computer science , functional dependency , artificial intelligence , linguistics , philosophy , mathematics , combinatorics , physics , database , neuroscience , relational database , optics
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.[Note 1. This paper benefited greatly from discussions with audiences at ...]