Abstract
The EMERT (ecological microexpression recognition test) by Zhang et al. (2017) used between-subjects Latin square block design for backgrounds; therefore, participants could not get comparable scores. The current study used within-subject pseudorandom design for backgrounds to improve EMERT to PREMERT (pseudorandom EMERT) and used eyes-closed and eyes-open resting-state functional magnetic resonance imaging to detect relevant brain activity of PREMERT for the first time. The results showed (1) two new recapitulative indexes of PREMERT were adopted, such as microexpression M and microexpression SD. Using pseudorandom design, the participants could effectively identify almost all the microexpressions, and each microexpression type had significant background effect. The PREMERT had good split-half reliability, parallel-forms reliability, criterion validity, and ecological validity. Therefore, it could stably and efficiently detect the participants’ microexpression recognition abilities. Because of its pseudorandom design, all participants did the same test; their scores could be compared with each other. (2) amplitude of low-frequency fluctuations (ALFF; 0.01–0.1 Hz) in both eyes-closed and eyes-open resting states and ALFF difference could predict microexpression M, and the ALFF difference was less predictive. The relevant resting-state brain areas of microexpression M were some frontal lobes, insula, cingulate cortex, hippocampus, amygdala, fusiform gyrus, parietal lobe, caudate nucleus, precuneus, thalamus, putamen, temporal lobe, and cerebellum. (3) ALFFs in both eyes-closed and eyes-open resting states and ALFF difference could predict microexpression SD, and the ALFF difference was more predictive. The relevant resting-state brain areas of microexpression SD were some frontal lobes, central anterior gyrus, supplementary motor area, insula, hippocampus, amygdala, cuneus, occipital lobe, fusiform gyrus, parietal lobe, caudate nucleus, pallidum, putamen, thalamus, temporal lobe, and cerebellum. (4) There were many similar relevant resting-state brain areas, such as brain areas of expression recognition, microexpressions consciousness and attention, and the change from expression backgrounds to microexpression, and some different relevant resting-state brain areas, such as precuneus, insula, and pallidum, between microexpression M and SD. The ALFF difference was more sensitive to PREMERT fluctuations.
Highlights
The Ecological Microexpression Recognition TestMicroexpressions are very transitory expressions lasting about 1/25 to 1/2 s, which can reveal people’s true emotions they try to hide or suppress (Ekman and Friesen, 1975; Porter et al, 2012). Matsumoto et al (2000) developed the Japanese and Caucasian Brief Affect Recognition Test (JACBART, classical microexpression recognition) to measure microexpression recognition
(3) Sphericity test of backgrounds × microexpressions showed the variance was not homogeneous, p < 0.05, and we performed Greenhouse correction and found that background expressions and microexpressions had significant interaction effect, F(14.16,53) = 15.26, p < 0.001, ηp2 = 0.227, which meant that background expressions and microexpressions influenced each other. Those results indicated that the ecological validity of PREMERT was good that it could detect the differences among different microexpressions and among different expression backgrounds (Zhang et al, 2017; Yin et al, 2019)
Pearson correlation analysis was made between amplitude of low-frequency fluctuations (ALFF) of resting-state and microexpression SD (Table 5 and Figure 3). (1) In the eyes-closed resting state, ALFFs in frontal lobe, hippocampus, occipital lobe, parietal lobe, caudate nucleus, pallidum, temporal lobe, and cerebellum were significantly correlated with some microexpression SD. (2) In the eyesopen resting state, ALFFs in central anterior gyrus, frontal lobe, supplementary motor area, insula, hippocampus, occipital lobe, fusiform gyrus, parietal lobe, caudate nucleus, pallidus, temporal lobe, and cerebellum were significantly correlated with some microexpression SD. (3) In the ALFF difference of eyes-open minus eyes-closed resting states, ALFF differences in frontal lobe, hippocampus, amygdala, wedge, occipital lobe, parietal lobe, caudate nucleus, putamen, thalamus, temporal lobe, and cerebellum were significantly correlated with some microexpression SD
Summary
The Ecological Microexpression Recognition TestMicroexpressions are very transitory expressions lasting about 1/25 to 1/2 s, which can reveal people’s true emotions they try to hide or suppress (Ekman and Friesen, 1975; Porter et al, 2012). Matsumoto et al (2000) developed the Japanese and Caucasian Brief Affect Recognition Test (JACBART, classical microexpression recognition) to measure microexpression recognition. The participants would first see a neutral expression for 2,000 ms, and a microexpression was presented for a little time, followed by the neutral expression for 2,000 ms again. Participants needed to check out the microexpression type. The neutral expression backgrounds could eliminate the visual aftereffects of the microexpression. It did not examine the influence of backgrounds with emotional expressions. The research has broken through the JACBART paradigm. It was very instructive, it needed to be further developed. (1) It did not explore either all backgrounds or all microexpressions. It was very instructive, it needed to be further developed. (1) It did not explore either all backgrounds or all microexpressions. (2) It did not either reveal that the microexpressions in different backgrounds were ecological microexpressions or set up ecological microexpression recognition test to test reliability and validity
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