对人脸判断的共同贡献和特异贡献的决定因素。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-11-01 Epub Date: 2024-09-19 DOI:10.1037/xhp0001239
Daniel N Albohn, Joel E Martinez, Alexander Todorov
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引用次数: 0

摘要

最近的研究表明,观察者的特异性比人脸的特征对人脸的社会判断差异的贡献更大。然而,目前还不清楚是什么条件决定了共同变异和特异变异的相对贡献。在这里,我们研究了两个条件:判断类型和人脸刺激的多样性。首先,我们表明,对于较简单的、可直接观察到的、不同观察者之间一致的判断(如男性气质),共享方差超过了特异方差;而对于较复杂的、可直接观察到的较少的判断(如可信度),特异方差超过了共享方差。其次,我们表明,对更多样化的人脸图像的判断会增加共享方差的数量。最后,我们使用机器学习方法,研究了刺激(如偶然的情感相似性、皮肤亮度)和观察者变量(如种族、年龄)如何导致判断的共享方差和特异方差。总之,我们的研究结果表明,在目前的研究中,观察者的年龄是对人脸判断的特异性差异贡献最一致和最好的预测因素。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
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Determinants of shared and idiosyncratic contributions to judgments of faces.

Recent work has shown that the idiosyncrasies of the observer can contribute more to the variance of social judgments of faces than the features of the faces. However, it is unclear what conditions determine the relative contributions of shared and idiosyncratic variance. Here, we examine two conditions: type of judgment and diversity of face stimuli. First, we show that for simpler, directly observable judgments that are consistent across observers (e.g., masculinity) shared exceeds idiosyncratic variance, whereas for more complex and less directly observable judgments (e.g., trustworthiness), idiosyncratic exceeds shared variance. Second, we show that judgments of more diverse face images increase the amount of shared variance. Finally, using machine-learning methods, we examine how stimulus (e.g., incidental emotion resemblance, skin luminosity) and observer variables (e.g., race, age) contribute to shared and idiosyncratic variance of judgments. Overall, our results indicate that an observer's age is the most consistent and best predictor of idiosyncratic variance contributions to face judgments measured in the current research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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