Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany.
Neural Netw. 2011 Aug;24(6):652-64. doi: 10.1016/j.neunet.2011.03.006. Epub 2011 Mar 11.
Congenital prosopagnosia is a selective deficit in face identification that is present from birth. Previously, behavioral deficits in face recognition and differences in the neuroanatomical structure and functional activation of face processing areas have been documented mostly in separate studies. Here, we propose a neural network model of congenital prosopagnosia which relates behavioral and neuropsychological studies of prosopagnosia to theoretical models of information processing. In this study we trained a neural network with two different algorithms to represent face images. First, we introduced a predisposition towards a decreased network connectivity implemented as a temporal independent component analysis (ICA). This predisposition induced a featural representation of faces in terms of isolated face parts. Second, we trained the network for optimal information encoding using spatial ICA, which led to holistic representations of faces. The network model was then tested empirically in an experiment with ten prosopagnosic and twenty age-matched controls. Participants had to discriminate between faces that were changed either according to the prosopagnosic model of featural representation or to the control model of holistic representation. Compared to controls prosopagnosic participants were impaired only in discriminating holistic changes of faces but showed no impairment in detecting featural changes. In summary, the proposed model presents an empirically testable account of congenital prosopagnosia that links the critical features--a lack of holistic processing at the computational level and a sparse structural connectivity at the implementation level. More generally, our results point to structural differences in the network connectivity as the cause of the face processing deficit in congenital prosopagnosia.
先天性面容失认症是一种出生时就存在的选择性面部识别缺陷。此前，在识别面孔的行为缺陷以及面孔处理区域的神经解剖结构和功能激活方面的差异方面的研究大多是分开进行的。在这里，我们提出了一种先天性面容失认症的神经网络模型，将面容失认症的行为和神经心理学研究与信息处理的理论模型联系起来。在这项研究中，我们使用两种不同的算法来训练神经网络以表示面部图像。首先，我们引入了一种降低网络连通性的倾向，这种倾向表现为时间独立成分分析（ICA）。这种倾向以孤立的面部部分来表示面部特征。其次，我们使用空间 ICA 来训练网络以实现最佳信息编码，从而对面部进行整体表示。然后，我们在一项涉及 10 名面容失认症患者和 20 名年龄匹配的对照者的实验中对网络模型进行了实证检验。参与者必须在根据面容失认症的特征表示模型或控制的整体表示模型改变的面孔之间进行区分。与对照组相比，面容失认症患者仅在辨别整体变化的面孔方面受损，但在检测特征变化方面没有受损。总的来说，所提出的模型提出了一个可通过经验检验的先天性面容失认症解释，将关键特征——计算层面缺乏整体处理以及实现层面稀疏的结构连通性联系起来。更一般地说，我们的结果表明，网络连通性的结构差异是先天性面容失认症中面部处理缺陷的原因。
