Du Shichuan, Martinez Aleix M
The Ohio State University, Columbus, OH, USA.
J Vis. 2013 Mar 18;13(4):13. doi: 10.1167/13.4.13.
Facial expressions of emotion are essential components of human behavior, yet little is known about the hierarchical organization of their cognitive analysis. We study the minimum exposure time needed to successfully classify the six classical facial expressions of emotion (joy, surprise, sadness, anger, disgust, fear) plus neutral as seen at different image resolutions (240 × 160 to 15 × 10 pixels). Our results suggest a consistent hierarchical analysis of these facial expressions regardless of the resolution of the stimuli. Happiness and surprise can be recognized after very short exposure times (10-20 ms), even at low resolutions. Fear and anger are recognized the slowest (100-250 ms), even in high-resolution images, suggesting a later computation. Sadness and disgust are recognized in between (70-200 ms). The minimum exposure time required for successful classification of each facial expression correlates with the ability of a human subject to identify it correctly at low resolutions. These results suggest a fast, early computation of expressions represented mostly by low spatial frequencies or global configural cues and a later, slower process for those categories requiring a more fine-grained analysis of the image. We also demonstrate that those expressions that are mostly visible in higher-resolution images are not recognized as accurately. We summarize implications for current computational models.
情绪的面部表情是人类行为的重要组成部分，但对于其认知分析的层次组织却知之甚少。我们研究了在不同图像分辨率（240×160至15×10像素）下成功分类六种经典情绪面部表情（喜悦、惊讶、悲伤、愤怒、厌恶、恐惧）以及中性表情所需的最短曝光时间。我们的结果表明，无论刺激的分辨率如何，对这些面部表情都存在一致的层次分析。即使在低分辨率下，幸福和惊讶在非常短的曝光时间（10 - 20毫秒）后也能被识别。恐惧和愤怒的识别速度最慢（100 - 250毫秒），即使在高分辨率图像中也是如此，这表明其计算过程较晚。悲伤和厌恶的识别时间介于两者之间（70 - 200毫秒）。成功分类每个面部表情所需的最短曝光时间与人类受试者在低分辨率下正确识别它的能力相关。这些结果表明，对于主要由低空间频率或全局配置线索表示的表情，存在快速、早期的计算过程，而对于那些需要对图像进行更精细分析的类别，则存在较晚、较慢的过程。我们还证明，那些在高分辨率图像中最明显的表情并不能被准确识别。我们总结了对当前计算模型的启示。
