Blauch Nicholas M, Behrmann Marlene, Plaut David C
Program in Neural Computation, Carnegie Mellon University, United States of America; Neuroscience Institute, Carnegie Mellon University, United States of America.
Neuroscience Institute, Carnegie Mellon University, United States of America; Department of Psychology, Carnegie Mellon University, United States of America.
Cognition. 2021 Mar;208:104341. doi: 10.1016/j.cognition.2020.104341. Epub 2020 Jun 23.
Humans are generally thought to be experts at face recognition, and yet identity perception for unfamiliar faces is surprisingly poor compared to that for familiar faces. Prior theoretical work has argued that unfamiliar face identity perception suffers because the majority of identity-invariant visual variability is idiosyncratic to each identity, and thus, each face identity must be learned essentially from scratch. Using a high-performing deep convolutional neural network, we evaluate this claim by examining the effects of visual experience in untrained, object-expert and face-expert networks. We found that only face training led to substantial generalization in an identity verification task of novel unfamiliar identities. Moreover, generalization increased with the number of previously learned identities, highlighting the generality of identity-invariant information in face images. To better understand how familiarity builds upon generic face representations, we simulated familiarization with face identities by fine-tuning the network on images of the previously unfamiliar identities. Familiarization produced a sharp boost in verification, but only approached ceiling performance in the networks that were highly trained on faces. Moreover, in these face-expert networks, the sharp familiarity benefit was seen only at the identity-based output probability layer, and did not depend on changes to perceptual representations; rather, familiarity effects required learning only at the level of identity readout from a fixed expert representation. Our results thus reconcile the existence of a large familiar face advantage with claims that both familiar and unfamiliar face identity processing depend on shared expert perceptual representations.
人们通常认为人类在人脸识别方面是专家，然而与熟悉的面孔相比，对不熟悉面孔的身份感知出奇地差。先前的理论工作认为，不熟悉面孔的身份感知存在问题，因为大多数身份不变的视觉变异性对于每个身份来说都是独特的，因此，每个面孔身份基本上都必须从头开始学习。我们使用一个高性能的深度卷积神经网络，通过检查视觉经验在未训练的、物体专家和面部专家网络中的影响来评估这一说法。我们发现，只有面部训练在新的不熟悉身份的身份验证任务中导致了实质性的泛化。此外，泛化随着先前学习的身份数量的增加而增加，突出了面部图像中身份不变信息的普遍性。为了更好地理解熟悉度是如何建立在通用面部表征之上的，我们通过在先前不熟悉身份的图像上微调网络来模拟对面孔身份的熟悉过程。熟悉过程在验证方面产生了大幅提升，但只有在对面部进行高度训练的网络中才接近上限性能。此外，在这些面部专家网络中，明显的熟悉度优势只在基于身份的输出概率层出现，并且不依赖于感知表征的变化；相反，熟悉度效应只需要在从固定专家表征中读取身份的层面上进行学习。因此，我们的结果调和了熟悉面孔的巨大优势与熟悉和不熟悉面孔身份处理都依赖于共享专家感知表征这一说法的存在。
