Abudarham Naphtali, Yovel Galit
School of Psychological Sciences, Tel Aviv University, Israel.
School of Psychological Sciences, Tel Aviv University, Israel.
Vision Res. 2019 Apr;157:105-111. doi: 10.1016/j.visres.2018.01.002. Epub 2018 Feb 1.
Many studies have shown better recognition for faces we have greater experience with, relative to unfamiliar faces. However, it is still not clear if and how the representation of faces changes during the process of familiarization. In a previous study, we discovered a subset of facial features, for which we have high perceptual sensitivity (PS), that were critical for determining the identity of unfamiliar faces. This was done by assigning values to 20 different facial features based on perceptual rating, converting faces into feature-vectors, and measuring the correlations between face similarity ratings and distances between feature-vectors. In the current study, we examined the contribution of high and low-PS features to face identity after familiarization. To familiarize participants with unfamiliar faces, we used an individuation training protocol that was found to be effective in previous studies, in which different names are assigned to different faces and participants are asked to learn the face-name association. Our findings show that even after repeated exposure to the same image of each identity, which allows close examination of all facial features, only high-PS features contributed to face identity, while low-PS features did not. This subset of high-PS features includes both internal and external features and part and configuration features. We therefore conclude that identification of familiarized and unfamiliar faces may rely on the same subset of critical features. These findings further support a new categorization of facial features according to their perceptual sensitivity.
许多研究表明,相较于不熟悉的面孔,我们对更熟悉的面孔识别能力更强。然而,面孔表征在熟悉过程中是否以及如何变化仍不清楚。在之前的一项研究中,我们发现了一部分面部特征,我们对其具有较高的感知敏感性(PS),这些特征对于确定不熟悉面孔的身份至关重要。这是通过基于感知评分给20种不同的面部特征赋值,将面孔转换为特征向量,并测量面孔相似度评分与特征向量之间的距离的相关性来实现的。在当前研究中,我们考察了熟悉后高PS特征和低PS特征对面孔识别的贡献。为了让参与者熟悉不熟悉的面孔,我们使用了一种个性化训练方案,该方案在之前的研究中被发现是有效的,即给不同的面孔赋予不同的名字,并要求参与者学习面孔-名字的关联。我们的研究结果表明,即使在反复接触每个身份的相同图像后,这使得可以仔细检查所有面部特征,但只有高PS特征对面孔识别有贡献,而低PS特征则没有。这个高PS特征子集包括内部和外部特征以及部分和构型特征。因此,我们得出结论,熟悉面孔和不熟悉面孔的识别可能依赖于相同的关键特征子集。这些发现进一步支持了根据面部特征的感知敏感性进行新的分类。