After I watched the serie "lie to me" many years ago I got all fired up, I instantly bought some book about body language and micro expressions. Then I started searching for youtube videos where I could practice to read micro expressions. But of course it wasn't that easy. Then I started looking for some micro expressions training tools but the only thing I could find was some training tool by Paul Ekman. Which I thought was way too expensive and I had also read some bad reviews about it. But a couple of months ago I again started to think about micro expressions. I did my research and found a training tool called METV.

The first month I practiced on METV for around 10 minutes a day on average. After a month I felt that I got really good at reading micro expressions. Before I found this tool I tried really hard to learn to read micro expressions but it didn't lead me anywhere. You're never going to learn this skill by reading books. You have to practice to get good at it and that is what this tool lets you do.

Micro Expression Training Tool Mett Download Free

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This profile (side-view) training is appropriate for people wishing to build on their frontal-view micro expression knowledge, and is appropriate for anybody who works in groups. Whenever multiple people are present, we often see only the profile view of some individuals. In addition, those working in surveillance can greatly benefit from this training.

Beginning the day you purchase your training tools package, you may access and begin your training. During that time, you are granted unlimited use of the tools in that package. You can (and we suggest you do) use your tools frequently for additional practice. Subscription lengths vary across packages, please review our Compare Features chart for more information.

All of our tools feature English, Spanish, and Portuguese caption capabilities. All users will be prompted at the beginning of the trainings to choose a language and will have the option to switch between languages throughout the tool.

The online training tools are interactive modules which help you learn to detect micro expressions and respond to emotional expressions. There are a total of seven training tools which are combined into three different packages. The format of each tool is a little different but generally follows a series of learning, practice, and test sections to measure your accuracy and improvement over time.

The online training tools are designed to teach you the everyday skills of reading and responding to micro expressions, whereas the FACS manual is a much more technical guide often used by researchers, animators, and in other various professional settings. Therefore, for anyone interested in learning about micro expressions, we generally recommend starting with the training tools on this page.

In the current study, we aimed to examine whether targeting tDCS over the rTPJ enhanced the effect of training on recognizing artificial and spontaneous micro-expressions using the Chinese version of METT. We also sought to understand the role of empathy in this process. We expected a significant training effect for both artificial and spontaneous micro-expression by using the Chinese version of METT. The anodal stimulation would enhance the effect of training on micro-expression recognition, especially for fear spontaneous ones. In light of prior findings regarding the role of rTPJ in facilitating the transformation of self to other representation, it was also hypothesized that anodal stimulation would enhance the effect of training in individuals with personal distress.

Figure 1. Experiment design. (A) Experimental procedure. Two visits were made. In the first visit, participants underwent anodal or sham stimulation and then completed the Chinese version of Micro-Expression Training Tool (METT); in the second visit 2 weeks later, they only finished the Chinese version of METT. The Chinese version of METT included five sections, pre-test, training, practice, review, and post-test. In the sections of pre-test and post-test, participants were asked to choose one of eight emotion labels after seeing the stimuli. The stimuli of pre-test included static expressions, artificial and spontaneous micro-expressions. The stimuli of post-test included artificial and spontaneous micro-expressions. (B) Placement of the anodal electrode for the right temporal parietal junction (rTPJ) between P6 and CP6 regions (top row) and the normalized electric field (NormE) derived from electric field modeling calculations using SimNIBS (bottom row). (C) The time series of artificial and spontaneous micro-expression in disgust. Source: L.F. Chen and Y.S. Yen, Taiwanese facial expression image database, Brain Mapping Laboratory, Institute of Brain Science, National Yang-Ming University, 2007.

The next section of the training included several Chinese versions of commentary videos in which the narrator emphasized critical facial features and explained how to recognize and distinguish confusing emotions accurately. In the practice section, participants practiced with feedback to ensure they understood and internalized the knowledge and skills learned in the earlier section. The review section repeated the training section. The final post-test followed the same form as the pre-test, but alternative materials were used to assess the ability of artificial and spontaneous micro-expression recognition after training.

Repeated ANOVA indicated a training effect on artificial micro-expression recognition. The mean accuracy of artificial micro-expression recognition for each condition is displayed in Figure 2A. The main effect of the testing stage was significant for the first visit [F(1,56) = 146.91, pcorrected < 0.001, 2p = 0.72] and second visit [F(1,56) = 21.47, pcorrected < 0.001, 2p = 0.28], indicating that the accuracy of artificial micro-expression recognition was higher post-test than pre-test in both the first and the second visits (first visit: 81.96  12.48 vs. 57.39  13.79; second visit: 80.40  16.53 vs. 69.46  14.77). However, during these two training programs, both the main effect of stimulation [first visit: F(1,56) = 0.99, pcorrected = 0.33, 2p = 0.02; second visit: F(1,56) = 0.01, pcorrected = 0.94, 2p = 0.00] and stimulation  testing stage interaction [first visit: F(1,56) = 1.11, pcorrected = 0.30, 2p = 0.02; second visit: F(1,56) = 0.98, pcorrected = 0.33, 2p = 0.17] did not reach significance.

Similar training effects were found in spontaneous micro-expression recognition (see Figure 2B). The main effect of the testing stage was significant (first visit: F(1,56) = 23.78, pcorrected < 0.001, 2p = 0.30, post-test accuracy: 53.35  17.25 vs. pre-test accuracy: 40.71  18.01; second visit: F(1,56) = 10.76, pcorrected < 0.01, 2p = 0.16, post-test accuracy: 55.90  16.48 vs. pre-test accuracy: 47.41  15.23). This noteworthy improvement was highly consistent with our observations in the artificial micro-expression recognition test, although it was much more difficult to identify a spontaneous micro-expression correctly. Furthermore, this training effect can be sustained for a significant period, as the accuracy of the pre-test in the second visit after 2 weeks was higher than the baseline created during the first visit [t(57) = 2.40, pcorrected < 0.05]. The main effect of stimulation was not significant [F(1,56) = 2.59, pcorrected = 0.11, 2p = 0.04]. Of note, stimulation  testing stage interaction was significant in the first visit [F(1,56) = 4.17, pcorrected < 0.05, 2p = 0.07]. A simple effect analysis showed a higher accuracy of spontaneous micro-expression recognition for the anodal condition (M = 58.80, SD = 15.74) compared with the sham condition (M = 47.52, SD = 17.14) in the post-test (pcorrected < 0.05). Meanwhile, there was no significant difference between the anodal (M = 41.11, SD = 18.94) and sham (M = 40.28, SD = 17.28) groups in the pre-test (pcorrected = 0.86). We also found marginally significant interaction in the second visit [F(1,56) = 3.11, pcorrected = 0.08, 2p = 0.05]. The simple effect analysis showed an improvement in the accuracy of spontaneous micro-expression recognition through training in the anodal group even if no stimulation was applied in the second visit (anodal group: post-test accuracy: 59.11  15.45 vs. pre-test accuracy: 46.30  17.04, pcorrected < 0.01; sham group: post-test accuracy: 52.47  17.12 vs. pre-test accuracy: 48.61  13.23, pcorrected = 0.30). As all the results until this stage showed no stimulation effect from training on artificial micro-expression, we did not consider it in the next analysis.

Table 2. Correlations between empathic traits and mean improved accuracy of overall spontaneous micro-expressions, and mean improved accuracy of spontaneous micro-expression of fear and sadness in the anodal and sham group.

We found significantly higher improved accuracy of fear spontaneous micro-expression in the anodal group than in the sham group. Fear is a visual cue associated with fast-approaching danger and requires an immediate response so that fear always attracts salient attention (Jack et al., 2014). The rTPJ engaged in attentional orienting toward threat information could contribute to the increased training effect of stimulation on the spontaneous micro-expressions of fear (Sagliano et al., 2019). This finding was consistent with previous research. Donaldson et al. (2019) found that the rTPJ anodal HD-tDCS could influence fear facial emotion recognition. Besides, they suggested that the rTPJ involvement in facial emotion processing depended on the intensity and salience/valence (negativity/threat) of the emotion. Surprisingly, we also found an increased training effect from anodal stimulation over the rTPJ for the spontaneous micro-expression of sadness. When recognizing expressions of sadness, eye cues had greater significance than other cues and tended to capture most attentional resources (Eisenbarth and Alpers, 2011). rTPJ was proved to be linked to eye gaze and sensitive to eye information (Kelley et al., 2021), and might therefore lead to an improved effect of training on spontaneous micro-expressions of sadness. 589ccfa754

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