Abstract:
The existing microexpression recognition tests only measured the static microexpression recognition ability, and neither of them investigated the dynamic microexpression recognition ability. Therefore, in this study, the transient static expressions with multiple emotional arousal gradients were used as the approximation of dynamic microexpression, and the dynamic microexpression recognition ability test DMERT was established. The experiment adopted 7 (background expressions: sadness vs. disgust vs. fear vs. anger vs. anger vs. Surprised vs. happy vs. calm) ×2 (background expression arousal: 3 vs. 5) ×6 (dynamic microexpression: sad vs. disgust vs. fear vs. anger vs. surprise vs. pleasure) ×2 (arousal of dynamic microexpression: 1→2→3→2→1 vs. 3→4→5→4→3). The results show that DMERT has good partition-half reliability, calibration validity, discriminative validity and ecological validity. The subjects had a certain degree of dynamic microexpression recognition ability, but the level was low. The DMERT established in this study can stably and effectively measure the dynamic microexpression recognition ability, and provides an operational definition for dynamic microexpressions.
已有微表情识别测验只测量静态微表情识别能力,较少考察动态微表情识别能力。因此,本研究采用多个情绪唤醒度渐变的短暂静态表情作为动态微表情的近似,建立动态微表情识别能力测验DMERT(DynamicMicroExpressionRecognitionTest)。实验采用7(背景表情:悲伤vs.厌恶vs.恐惧vs.愤怒vs.惊讶vs.愉快vs.平静)×2(背景表情唤醒度:3vs.5)×6(动态微表情:悲伤vs.厌恶vs.恐惧vs.愤怒vs.惊讶vs.愉快)×2(动态微表情唤醒度:1→2→3→2→1vs.3→4→5→4→3)被试内设计。结果发现:DMERT具有良好的分半信度、校标效度、区分效度和生态效度;被试具有一定程度的动态微表情识别能力但水平较低。本研究建立的DMERT能稳定有效地测量到动态微表情识别能力,为动态微表情提供操作性定义。