了解人类如何将儿童与成人区分开来,在法医和认知心理学的许多领域都具有有用的意义。然而,在这两个学科中,我们如何从面孔中提取年龄的研究令人惊讶地不足。这里,我们使用一种新颖的数据驱动的实验技术来客观地测量人类观察者用来对儿童和成人面部进行分类的面部特征。依靠35000多次试验,我们使用了一种反向相关技术,使我们能够揭示已知在面部感知位置中重要的特定特征,空间频率(SF),和取向-与准确的儿童和成人歧视有关。这表明人类观察者依靠鼻骨和眉毛区域的证据来进行准确的成人分类,而他们依靠眼睛和下巴区域来准确地对儿童面孔进行分类。对于定向结构,只有垂直方向的面部信息与面部成人分类相关,而水平和的特征,在较小程度上倾斜的方向,更多的是儿童脸的诊断。最后,我们发现SF诊断显示出面部年龄分类的U形模式,低SF和高SF中的信息可以诊断儿童面孔,和中期SFs诊断成人面孔。通过面部分类的面部特征的首次表征,我们表明,在一般的面部感知的心理物理学研究中发现的重要信息(即,眼睛区域,水平线,和中级SFs)对于面部年龄分类的实际背景至关重要,并提出了数据驱动的程序,通过这些程序可以针对现实世界的挑战实施面部年龄分类培训。
Knowing how humans differentiate children from adults has useful implications in many areas of both forensic and cognitive psychology. Yet, how we extract age from faces has been surprisingly underexplored in both disciplines. Here, we used a novel data-driven experimental technique to objectively measure the facial features human observers use to categorise child and adult faces. Relying on more than 35,000 trials, we used a reverse correlation technique that enabled us to reveal how specific features which are known to be important in face-perception - position, spatial-frequency (SF), and orientation - are associated with accurate child and adult discrimination. This showed that human observers relied on evidence in the nasal bone and eyebrow area for accurate adult categorisation, while they relied on the eye and jawline area to accurately categorise child faces. For orientation structure, only facial information of vertical orientation was linked to face-adult categorisation, while features of horizontal and, to a lesser extent oblique orientations, were more diagnostic of a child face. Finally, we found that SF diagnosticity showed a U-shaped pattern for face-age categorisation, with information in low and high SFs being diagnostic of child faces, and mid SFs being diagnostic of adult faces. Through this first characterisation of the facial features of face-age categorisation, we show that important information found in psychophysical studies of face-perception in general (i.e., the eye area, horizontals, and mid-level SFs) is crucial to the practical context of face-age categorisation, and present data-driven procedures through which face-age classification training could be implemented for real-world challenges.