Facial movement optimizes part-based face processing by influencing eye movements
Facial movement optimizes part-based face processing by influencing eye movements
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DOI：
10.1167/14.10.565
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发表时间：
2014-08
影响因子：
1.8
通讯作者：
Naiqi G. Xiao;P. Quinn;Qiandong Wang;Genyue Fu;Kang Lee
中科院分区：
文献类型：
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作者：
Naiqi G. Xiao;P. Quinn;Qiandong Wang;Genyue Fu;Kang Lee
我们对人脸处理的理解大多来自于使用静态人脸图片作为刺激的研究。目前尚不清楚我们目前关于面部处理的知识在多大程度上可以推广到面部移动的现实世界中。最近的研究表明，面部运动促进了部分而非整体的面部处理。本研究采用高频眼动追踪和复合面部效应范式，探讨了面部运动对部分加工影响的显性视觉注意机制。在移动面部条件下，参与者首先记住了一段2秒钟的无声视频中的一张脸，视频中是一张咀嚼和眨眼的脸。然后用静态合成人脸对他们进行测试。合成面的上半部和下半部来自不同的模型，它们显示为对齐或不对齐。参与者判断合成脸的上半部分是否和他们刚刚看到的是同一个人。静态人脸条件与移动人脸条件相同，只是待学习的人脸是静态图片。参与者在学习和测试过程中的眼球运动被记录下来。与之前的研究结果一致，学习移动的面孔比学习静态的面孔导致的复合效应更小，这表明面部运动促进了基于部分的面部加工。此外，在学习相对于静止面孔的移动面孔时，参与者在每次注视时表现出更长的注视时间（即更深层次的处理）。此外，每个参与者在学习移动时相对于静态面部的上脸看时间优势正预测了面部运动产生的基于部分的面部加工增加。这种关联仅在对齐条件下观察到，而在未对齐条件下没有观察到，这表明固定移动的上半面对减少来自对齐的下半面的干扰是特异性的。这些结果表明，面部运动通过影响眼球运动来优化基于部分的面部加工。
Much of our understanding about face processing has been derived from studies using static face pictures as stimuli. It is unclear to what extent our current knowledge about face processing can be generalized to real world situations where faces are moving. Recent studies have shown that facial movements facilitate part-based, not holistic, face processing. The present study, using high-frequency eye tracking and the composite face effect paradigm, examined the overt visual attention mechanisms underlying the effect of facial movements on part-based processing. In the moving face condition, participants first remembered a face from a 2-second silent video depicting a face chewing and blinking. They were then tested with a static composite face. The upper and lower halves of the composite face were from different models, which were displayed either aligned or misaligned. Participants judged whether the upper half of the composite face was the same person as the one they just saw. The static face condition was identical to the moving face condition except that the to-be-learned faces were static pictures. Participants eye movements during learning and testing were recorded. Consistent with previous findings, learning moving faces led to a smaller composite effect than learning static faces, suggesting that facial movements facilitated part-based face processing. In addition, participants exhibited longer looking time for each fixation (ie, deeper processing) while learning the moving relative to the static faces. Further, each participants upper face looking time advantage while learning moving relative to static faces positively predicted the part-based face processing increase engendered by facial movements. The association was only observed in the aligned but not the misaligned condition, indicating that fixating the moving upper face half was specific to reducing the interference from the aligned lower face half. These results indicate that facial movement optimizes part-based face processing by influencing eye movements.