The Fusiform Face Area Responds Automatically to Statistical Regularities Optimal for Face Categorization
The Fusiform Face Area Responds Automatically to Statistical Regularities Optimal for Face Categorization
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DOI：
10.1002/hbm.20626
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发表时间：
2009-05-01
影响因子：
4.8
通讯作者：
Seghier, Mohamed L.
中科院分区：
文献类型：
--
作者：
Caldara, Roberto;Seghier, Mohamed L.
统计学充斥着我们的感知世界。假设人类大脑被调整以满足视觉环境的约束，视觉系统计算应该被优化以处理这样的干扰。一个与社会相关的、高度重复的、大脑已经发展出敏感性的同质模式肯定是人脸。然而，面部敏感区域被调整为哪种统计参数，以及它们的检测自动发生的程度在很大程度上是未知的。使用功能磁共振成像，我们测量激活内的面部敏感区域的非面部对称和不对称的曲线图案，无论是在上部或下部的高对比度元素。在扫描仪外进行的面部评价表明，这些模式不被视为示意性面部。值得注意的是，对称性侵犯扰乱了对面孔的感知，尽管客观的图像相似性测量显示这些模式的高面孔值。在面部敏感区域中，只有右侧梭状面区（FFA）对对称性敏感。这个区域也表现出更大的反应模式与更多的元素在上半部分。重要的是，FFA的反应更强烈地与物理客观的面孔属性的刺激比观察员的主观面孔评级。这些发现提供了直接的证据，表明右FFA的神经计算被调整为曲线对称模式，上部具有高对比度元素，最适合人脸的物理结构。FFA可能会使用这种低级几何图形来自动将视觉形状分类为面部。《脑地图》30：1615-1625，2009年。(C)2008 Wiley-Liss，Inc.
Statistical regularities pervade our perceptual world. Assuming that the human brain is tuned for satisfying the constraints of the visual environment, visual system computations should be optimized for processing such regularities. A socially relevant and highly recurrent homogenous pattern for which the brain has developed sensitivity is certainly the human face. Yet, for which statistical regularities the face sensitive regions are tuned for, and to what extent their detection occurs automatically is largely unexplored. Using fMRI we measured activations within the face sensitive areas for nonface symmetrical and asymmetrical curvilinear patterns with either more high-contrast elements in the upper or in the lower part. Faceness evaluation performed outside of the scanner showed that these patterns were not perceived as schematic faces. Noticeably, symmetry violations disrupted perception of faceness, despite objective image similarity measures showing high faceness values for those patterns. Among the faces sensitive regions, only the right Fusiform Face Area (FFA) showed sensitivity to symmetry. This region showed also greater responses to patterns with more elements in the upper part. Critically, the FFA's responses were more strongly correlated with the physical objective faceness properties of the stimuli than the perceived subjective faceness ratings of the observers. These findings provide direct evidence that the neural computations of the right FFA are tuned to curvilinear symmetrical patterns with high-contrasted elements in the upper part, which fit best with the physical structure of human faces. Such low-level geometrical regularities might be used by the FFA to automatically categorize visual shapes as faces. Hum Brain Mapp 30:1615-1625, 2009. (C) 2008 Wiley-Liss, Inc.