Humans readily form social impressions, such as attractiveness and trustworthiness, from a stranger’s facial features. Understanding the provenance of these impressions has clear scientific importance and societal implications. Motivated by the efficient coding hypothesis of brain representation, as well as Claude Shannon’s theoretical result that maximally efficient representational systems assign shorter codes to statistically more typical data (quantified as log likelihood), we suggest that social “liking” of faces increases with statistical typicality. Combining human behavioral data and computational modeling, we show that perceived attractiveness, trustworthiness, dominance, and valence of a face image linearly increase with its statistical typicality (log likelihood). We also show that statistical typicality can at least partially explain the role of symmetry in attractiveness perception. Additionally, by assuming that the brain focuses on a task-relevant subset of facial features and assessing log likelihood of a face using those features, our model can explain the “ugliness-in-averageness” effect found in social psychology, whereby otherwise attractive, intercategory faces diminish in attractiveness during a categorization task.

人类很容易从陌生人的面部特征中形成社会印象，例如吸引力和可信赖性。了解这些印象的来源具有明显的科学重要性和社会意义。受大脑表示的有效编码假设以及克劳德·香农（Claude Shannon）的理论结果（即最大效率的表示系统将较短的代码分配给统计上更典型的数据（量化为对数似然））的驱动，我们建议，社交的“喜好”随着统计上的典型性而增加。结合人类行为数据和计算模型，我们显示，人脸图像的吸引力，可信度，支配地位和价数随其统计典型性（对数似然性）线性增加。我们还表明统计的典型性可以至少部分解释对称性在吸引力感知中的作用。此外，通过假设大脑专注于与任务相关的面部特征子集并使用这些特征评估面孔的对数似然性，我们的模型可以解释社会心理学中发现的“平均程度丑陋”效应，从而具有吸引力，类别任务期间类别间的吸引力会降低。