Data from: The morphometrics of “masculinity” in human faces

In studies of social inference and human mate preference, a wide but inconsistent array of tools for computing facial masculinity has been devised. Several of these approaches implicitly assumed that the individual expression of sexually dimorphic shape features, which we refer to as maleness, resembles facial shape features perceived as masculine. We outline a morphometric strategy for estimating separately the face shape patterns that underlie perceived masculinity and maleness, and for computing individual scores for these shape patterns. We further show how faces with different degrees of masculinity or maleness can be constructed in a geometric morphometric framework. In an application of these methods to a set of human facial photographs, we found that shape features typically perceived as masculine are wide faces with a wide inter-orbital distance, a wide nose, thin lips, and a large and massive lower face. The individual expressions of this combination of shape features—the masculinity shape scores—were the best predictor of rated masculinity among the compared methods (r = 0.5). The shape features perceived as masculine only partly resembled the average face shape difference between males and females (sexual dimorphism). Discriminant functions and Procrustes distances to the female mean shape were poor predictors of perceived masculinity.在社会推断与人类择偶偏好的相关研究中，学界已开发出一系列用于计算面部男性化特征（facial masculinity）的工具，但这类工具的应用结果存在广泛的不一致性。其中部分方法暗含了一项假设：我们将其称之为男性特质（maleness）的性二态形状特征的个体表达，与被感知为具有男性化特征的面部形状特征相一致。本研究提出了一种形态测量策略，可分别估算支撑感知男性化与男性特质的面部形状模式，并为这些形状模式计算个体得分。本研究进一步展示了如何在几何形态测量（geometric morphometric）框架下，构建具有不同程度男性化特征或男性特质的面部图像。在将这些方法应用于一组人类面部照片的实验中，我们发现，通常被感知为具有男性化特征的形状模式为：宽脸型、较宽的眶间距、宽鼻梁、薄嘴唇，以及宽大厚重的下面部。这种形状特征组合的个体表达——即男性化形状得分（masculinity shape scores）——是所有对比方法中，对主观评分的男性化特征的最佳预测指标（相关系数r=0.5）。被感知为具有男性化特征的形状模式，仅部分契合男性与女性之间的平均面部形状差异（性二态性，sexual dimorphism）。判别函数（discriminant functions）以及与女性平均面部形状的普氏距离（Procrustes distances），均无法很好地预测感知到的男性化特征。