Susskind J M, Littlewort G, Bartlett M S, Movellan J, Anderson A K
Department of Psychology, University of Toronto, Canada.
Neuropsychologia. 2007 Jan 7;45(1):152-62. doi: 10.1016/j.neuropsychologia.2006.05.001.
Neuropsychological and neuroimaging evidence suggests that the human brain contains facial expression recognition detectors specialized for specific discrete emotions. However, some human behavioral data suggest that humans recognize expressions as similar and not discrete entities. This latter observation has been taken to indicate that internal representations of facial expressions may be best characterized as varying along continuous underlying dimensions. To examine the potential compatibility of these two views, the present study compared human and support vector machine (SVM) facial expression recognition performance. Separate SVMs were trained to develop fully automatic optimal recognition of one of six basic emotional expressions in real-time with no explicit training on expression similarity. Performance revealed high recognition accuracy for expression prototypes. Without explicit training of similarity detection, magnitude of activation across each emotion-specific SVM captured human judgments of expression similarity. This evidence suggests that combinations of expert classifiers from separate internal neural representations result in similarity judgments between expressions, supporting the appearance of a continuous underlying dimensionality. Further, these data suggest similarity in expression meaning is supported by superficial similarities in expression appearance.
神经心理学和神经影像学证据表明，人类大脑包含专门用于特定离散情绪的面部表情识别探测器。然而，一些人类行为数据表明，人类将表情识别为相似而非离散的实体。后一种观察结果被认为表明，面部表情的内部表征可能最好被描述为沿着连续的潜在维度变化。为了检验这两种观点的潜在兼容性，本研究比较了人类和支持向量机（SVM）的面部表情识别性能。分别训练支持向量机，以实时开发对六种基本情绪表情之一的全自动最优识别，且无需对表情相似性进行明确训练。结果显示，对表情原型具有较高的识别准确率。在没有对相似性检测进行明确训练的情况下，每个特定情绪支持向量机的激活程度捕捉到了人类对表情相似性的判断。这一证据表明，来自不同内部神经表征的专家分类器组合导致了表情之间的相似性判断，支持了连续潜在维度的出现。此外，这些数据表明，表情外观的表面相似性支持了表情意义的相似性。
