一个专门的面部处理网络与猴子面部斑块的代表性几何一致
A specialized face-processing network consistent with the representational geometry of monkey face patches
摘要：
大量证据表明，人类和非人类灵长类动物的面部处理与其他物体相比是不同的。从生理学和心理学的角度来看，各种研究报告都表明，人脸是在大脑中专门的神经网络中进行处理的--比如猴子的面部斑块和人类的梭形面部区域（FFA）。我们都是面部处理的专家，能够识别出面部类别中非常细微的差异，尽管视觉和特征有很大的相似性。在查看整个面部之后，快速且准确地执行识别，而如果操纵某些面部配置（例如，倒置、未对准）或者如果由于遮挡而示出面部的部分视图，则识别显著下降。这指的是一个备受争议但得到高度支持的概念，称为整体面部处理。我们建立了一个层次的人脸处理的计算模型的基础上，从最近的神经元和行为的研究在灵长类动物的面孔加工的证据。模型的最后三层的代表性几何形状具有与在猴子面部贴片（后、中、前贴片）中观察到的特征类似的特征。此外，在文献中报道的几个面孔加工相关的现象自动出现的属性，这个模型。通过几个计算层，使用生物学上合理的学习规则的表示。该模型满足面孔反转效应、复合面孔效应、其他种族效应、视角和身份选择性以及规范面孔视角。据我们所知，迄今为止还没有提出具有这种性能并与生物学数据一致的模型。
英文摘要：
Ample evidence suggests that face processing in human and non-human primates is performed differently compared with other objects. Converging reports, both physiologically and psychophysically, indicate that faces are processed in specialized neural networks in the brain -i.e. face patches in monkeys and the fusiform face area (FFA) in humans. We are all expert face-processing agents, and able to identify very subtle differences within the category of faces, despite substantial visual and featural similarities. Identification is performed rapidly and accurately after viewing a whole face, while significantly drops if some of the face configurations (e.g. inversion, misalignment) are manipulated or if partial views of faces are shown due to occlusion. This refers to a hotly-debated, yet highly-supported concept, known as holistic face processing. We built a hierarchical computational model of face-processing based on evidence from recent neuronal and behavioural studies on faces processing in primates. Representational geometries of the last three layers of the model have characteristics similar to those observed in monkey face patches (posterior, middle and anterior patches). Furthermore, several face-processing-related phenomena reported in the literature automatically emerge as properties of this model. The representations are evolved through several computational layers, using biologically plausible learning rules. The model satisfies face inversion effect, composite face effect, other race effect, view and identity selectivity, and canonical face views. To our knowledge, no models have so far been proposed with this performance and agreement with biological data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
