Department of Psychology.
Psychol Rev. 2017 Mar;124(2):115-129. doi: 10.1037/rev0000048. Epub 2017 Jan 5.
Viewers are highly accurate at recognizing sex and race from faces-though it remains unclear how this is achieved. Recognition of familiar faces is also highly accurate across a very large range of viewing conditions, despite the difficulty of the problem. Here we show that computation of sex and race can emerge incidentally from a system designed to compute identity. We emphasize the role of multiple encounters with a small number of people, which we take to underlie human face learning. We use highly variable everyday 'ambient' images of a few people to train a Linear Discriminant Analysis (LDA) model on identity. The resulting model has human-like properties, including a facility to cohere previously unseen ambient images of familiar (trained) people-an ability which breaks down for the faces of unknown (untrained) people. The first dimension created by the identity-trained LDA classifies both familiar and unfamiliar faces by sex, and the second dimension classifies faces by race-even though neither of these categories was explicitly coded at learning. By varying the numbers and types of face identities on which a further series of LDA models were trained, we show that this incidental learning of sex and race reflects covariation between these social categories and face identity, and that a remarkably small number of identities need be learnt before such incidental dimensions emerge. The task of learning to recognize familiar faces is sufficient to create certain salient social categories. (PsycINFO Database Record
观看者能够非常准确地从面部识别性别和种族——尽管目前尚不清楚这是如何实现的。尽管存在困难，但在非常广泛的观看条件下，对熟悉面孔的识别也非常准确。在这里，我们表明，性别和种族的计算可以从设计用于计算身份的系统中偶然出现。我们强调了与少数人多次接触的作用，这是我们认为的人类面部学习的基础。我们使用高度可变的日常“环境”图像来训练线性判别分析（LDA）模型的身份。由此产生的模型具有类似人类的属性，包括能够将以前从未见过的熟悉（训练过的）人的环境图像整合在一起的能力——这种能力对于未知（未受过训练的）人的面孔会失效。通过身份训练的 LDA 创建的第一个维度可以按性别对熟悉和不熟悉的面孔进行分类，第二个维度可以按种族对人脸进行分类——尽管在学习过程中并未明确对这些类别进行编码。通过改变进一步的 LDA 模型所训练的人脸身份的数量和类型，我们表明这种偶然的性别和种族学习反映了这些社会类别与面部身份之间的共变关系，并且在出现这种偶然维度之前，只需要学习非常少的身份。学习识别熟悉面孔的任务足以创建某些突出的社会类别。
