Chauhan Vassiki, Visconti di Oleggio Castello Matteo, Taylor Morgan, Gobbini Maria Ida
Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA.
Department of Neuroscience and Behavior, Barnard College, Columbia University, New York, NY 10027, USA.
Brain Sci. 2023 Mar 18;13(3):509. doi: 10.3390/brainsci13030509.
Personal familiarity facilitates rapid and optimized detection of faces. In this study, we investigated whether familiarity associated with faces can also facilitate the detection of facial expressions. Models of face processing propose that face identity and face expression detection are mediated by distinct pathways. We used a visual search paradigm to assess if facial expressions of emotion (anger and happiness) were detected more rapidly when produced by familiar as compared to unfamiliar faces. We found that participants detected an angry expression 11% more accurately and 135 ms faster when produced by familiar as compared to unfamiliar faces while happy expressions were detected with equivalent accuracies and at equivalent speeds for familiar and unfamiliar faces. These results suggest that detectors in the visual system dedicated to processing features of angry expressions are optimized for familiar faces.
个人熟悉度有助于快速且优化地检测面孔。在本研究中，我们调查了与面孔相关的熟悉度是否也能促进对面部表情的检测。面孔加工模型提出，面孔识别和面部表情检测由不同的通路介导。我们使用视觉搜索范式来评估当情绪性面部表情（愤怒和高兴）由熟悉面孔产生时是否比由不熟悉面孔产生时能被更快地检测到。我们发现，与不熟悉面孔相比，当愤怒表情由熟悉面孔产生时，参与者检测的准确率高11%，速度快135毫秒，而对于高兴表情，熟悉面孔和不熟悉面孔的检测准确率和速度相当。这些结果表明，视觉系统中专门用于处理愤怒表情特征的检测器针对熟悉面孔进行了优化。
