Department of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom.
Department of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom
Proc Natl Acad Sci U S A. 2014 Aug 12;111(32):E3353-61. doi: 10.1073/pnas.1409860111. Epub 2014 Jul 28.
First impressions of social traits, such as trustworthiness or dominance, are reliably perceived in faces, and despite their questionable validity they can have considerable real-world consequences. We sought to uncover the information driving such judgments, using an attribute-based approach. Attributes (physical facial features) were objectively measured from feature positions and colors in a database of highly variable "ambient" face photographs, and then used as input for a neural network to model factor dimensions (approachability, youthful-attractiveness, and dominance) thought to underlie social attributions. A linear model based on this approach was able to account for 58% of the variance in raters' impressions of previously unseen faces, and factor-attribute correlations could be used to rank attributes by their importance to each factor. Reversing this process, neural networks were then used to predict facial attributes and corresponding image properties from specific combinations of factor scores. In this way, the factors driving social trait impressions could be visualized as a series of computer-generated cartoon face-like images, depicting how attributes change along each dimension. This study shows that despite enormous variation in ambient images of faces, a substantial proportion of the variance in first impressions can be accounted for through linear changes in objectively defined features.
人们可以可靠地从面部判断出社交特征（如值得信赖或支配力）的第一印象，尽管这些印象的有效性值得怀疑，但它们可能会对现实世界产生重大影响。我们试图利用基于属性的方法来揭示驱动这些判断的信息。从具有高度可变性的“环境”人脸照片数据库中的特征位置和颜色中客观测量属性（身体面部特征），然后将其用作神经网络的输入，以对潜在的社交归因进行建模的因素维度（亲和力、年轻吸引力和支配力）。基于此方法的线性模型能够解释评分者对以前未见的面孔的印象差异的 58%，并且可以使用因素-属性相关性来根据每个因素的重要性对属性进行排序。然后，神经网络被用于根据特定的因子得分组合预测面部属性和相应的图像属性。通过这种方式，可以将驱动社交特征印象的因素可视化为一系列计算机生成的类似卡通的人脸图像，描绘出属性如何沿每个维度变化。这项研究表明，尽管人脸的环境图像存在巨大差异，但通过客观定义特征的线性变化，可以解释第一印象差异的很大一部分。
