Jiangsu Police Institute, Nanjing, China.
School of Humanities, Jiangnan University, Wuxi, China.
PLoS One. 2020 Dec 22;15(12):e0241681. doi: 10.1371/journal.pone.0241681. eCollection 2020.
Zhang, et al. (2017) established the ecological microexpression recognition test (EMERT), but it only used white models' expressions as microexpressions and backgrounds, and there was no research detecting its relevant brain activity. The current study used white, black and yellow models' expressions as microexpressions and backgrounds to improve the materials ecological validity of EMERT, and it used eyes-closed and eyes-open resting-state fMRI to detect relevant brain activity of EMERT for the first time. The results showed: (1) Two new recapitulative indexes of EMERT were adopted, such as microexpression M and microexpression SD. The participants could effectively identify almost all the microexpressions, and each microexpression type had a significantly background effect. The EMERT had good retest reliability and calibration validity. (2) ALFFs (Amplitude of Low-Frequency Fluctuations) in both eyes-closed and eyes-open resting-states and ALFFs-difference could predict microexpression M. The relevant brain areas of microexpression M were some frontal lobes, insula, cingulate cortex, hippocampus, parietal lobe, caudate nucleus, thalamus, amygdala, occipital lobe, fusiform, temporal lobe, cerebellum and vermis. (3) ALFFs in both eyes-closed and eyes-open resting-states and ALFFs-difference could predict microexpression SD, and the ALFFs-difference was more predictive. The relevant brain areas of microexpression SD were some frontal lobes, insula, cingulate cortex, cuneus, amygdala, fusiform, occipital lobe, parietal lobe, precuneus, caudate lobe, putamen lobe, thalamus, temporal lobe, cerebellum and vermis. (4) There were many similarities and some differences in the relevant brain areas between microexpression M and SD. All these brain areas can be trained to enhance ecological microexpression recognition ability.
张等人（2017 年）建立了生态微表情识别测试（EMERT），但它仅使用白色模型的表情作为微表情和背景，并且没有研究检测其相关的大脑活动。本研究使用白色、黑色和黄色模型的表情作为微表情和背景，以提高 EMERT 的材料生态有效性，并首次使用闭眼和睁眼静息态 fMRI 检测 EMERT 的相关大脑活动。结果表明：（1）采用了两个新的 EMERT 综合指标，如微表情 M 和微表情 SD。参与者可以有效地识别几乎所有的微表情，并且每种微表情类型都有显著的背景效应。EMERT 具有良好的重测信度和校准有效性。（2）闭眼和睁眼静息状态下的 ALFFs（低频波动幅度）和 ALFFs 差异可以预测微表情 M。微表情 M 的相关脑区为一些额叶、脑岛、扣带回、海马、顶叶、尾状核、丘脑、杏仁核、枕叶、梭状回、颞叶、小脑和蚓状核。（3）闭眼和睁眼静息状态下的 ALFFs 和 ALFFs 差异可以预测微表情 SD，并且 ALFFs 差异更具预测性。微表情 SD 的相关脑区为一些额叶、脑岛、扣带回、楔前叶、杏仁核、梭状回、枕叶、顶叶、楔前叶、尾状核、壳核、丘脑、颞叶、小脑和蚓状核。（4）微表情 M 和 SD 的相关脑区既有相似之处，也有一些差异。所有这些脑区都可以被训练来增强生态微表情识别能力。
