PDF(Pubmed)
Abstract：
Facial expression recognition (FER) is crucial for understanding the emotional state of others during human social interactions. It has been assumed that humans share universal visual sampling strategies to achieve this task. However, recent studies in face identification have revealed striking idiosyncratic fixation patterns, questioning the universality of face processing. More importantly, very little is known about whether such idiosyncrasies extend to the biological relevant recognition of static and dynamic facial expressions of emotion (FEEs). To clarify this issue, we tracked observers\' eye movements categorizing static and ecologically valid dynamic faces displaying the six basic FEEs, all normalized for time presentation (1 s), contrast and global luminance across exposure time. We then used robust data-driven analyses combining statistical fixation maps with hidden Markov Models to explore eye-movements across FEEs and stimulus modalities. Our data revealed three spatially and temporally distinct equally occurring face scanning strategies during FER. Crucially, such visual sampling strategies were mostly comparably effective in FER and highly consistent across FEEs and modalities. Our findings show that spatiotemporal idiosyncratic gaze strategies also occur for the biologically relevant recognition of FEEs, further questioning the universality of FER and, more generally, face processing.
摘要：
面部表情识别（FER）对于理解人类社交互动中他人的情绪状态至关重要。人们已经假设人类共享通用的视觉采样策略来实现这一任务。然而,最近的人脸识别研究揭示了惊人的特质固定模式，质疑面部处理的普遍性。更重要的是,对于这种特质是否扩展到静态和动态面部情感表情（FEE）的生物学相关识别，人们知之甚少。为了澄清这个问题，我们跟踪观察者的眼球运动，对显示六个基本FEE的静态和生态有效的动态人脸进行分类，全部归一化为时间表示(1s)，对比度和全局亮度在曝光时间。然后，我们使用了强大的数据驱动分析，将统计固定图与隐马尔可夫模型相结合，以探索跨FEEs和刺激模式的眼球运动。我们的数据揭示了FER期间三种在空间和时间上不同的相同发生的面部扫描策略。至关重要的是，这种视觉抽样策略在FER中大多比较有效,并且在FEEs和模式之间高度一致.我们的发现表明，时空特质凝视策略也发生在FEEs的生物学相关识别中，进一步质疑FER的普遍性，更一般地说,面部处理。
