The establishment of dynamic microexpression training tool

The existing microexpression training tool METT only trains the static microexpression recognition ability under neutral background, but does not train the dynamic microexpression recognition ability under expression backgrounds. Therefore, in the current study, the dynamic microexpression recognition ability test DMERT was used as pretest and posttest, the METT recognition techniques were guided and trained, and the dynamic microexpression training tool DMETT was established as a training tool for dynamic microexpression recognition ability. The experiment was conducted in 3 stages (pretest vs. training vs. posttest) × 7 (background expression: sadness vs. Disgust vs. fear vs. anger vs. surprise vs. happiness vs. Neutral) × 2 (background expression emotional arousal: 3 vs. 5) × 6 (dynamic microexpression: sadness vs. Disgust vs. fear vs. anger vs. surprise vs. happiness) × 2 (emotional arousal of dynamic microexpression: 1 → 2 → 3 → 2 → 1 vs. 3 → 4 → 5 → 4 → 3). The results showed that: (1) The DMETT could effectively improve dynamic microexpression recognition ability with good reliability and validity. (2) The METT recognition techniques were suitable for training dynamic microexpression recognition ability. (3) The DMETT could be used as a training tool of dynamic microexpression recognition ability, and can be further verified to serve as a measurement tool of microexpression learning ability in the future. (4) There was no gender difference in general dynamic microexpression recognition ability, but females outperformed males in general dynamic microexpression recognition learning with small effect sizes.

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Publication Details

Journal
Frontiers in Psychology
Published
2026-01-29
DOI
https://doi.org/10.3389/fpsyg.2025.1676880
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
0.00

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article

The establishment of dynamic microexpression training tool

Yichen Li, Lingfei Xu, Jing Liu, Ming Yin et al.
Frontiers in Psychology
Emotion and Mood Recognition
article

The establishment of dynamic microexpression training tool

Yichen Li, Lingfei Xu, Jing Liu, Ming Yin, Ning Cai, Jianxin Zhang, Xiaodan Wang
article en

Abstract

The existing microexpression training tool METT only trains the static microexpression recognition ability under neutral background, but does not train the dynamic microexpression recognition ability under expression backgrounds. Therefore, in the current study, the dynamic microexpression recognition ability test DMERT was used as pretest and posttest, the METT recognition techniques were guided and trained, and the dynamic microexpression training tool DMETT was established as a training tool for dynamic microexpression recognition ability. The experiment was conducted in 3 stages (pretest vs. training vs. posttest) × 7 (background expression: sadness vs. Disgust vs. fear vs. anger vs. surprise vs. happiness vs. Neutral) × 2 (background expression emotional arousal: 3 vs. 5) × 6 (dynamic microexpression: sadness vs. Disgust vs. fear vs. anger vs. surprise vs. happiness) × 2 (emotional arousal of dynamic microexpression: 1 → 2 → 3 → 2 → 1 vs. 3 → 4 → 5 → 4 → 3). The results showed that: (1) The DMETT could effectively improve dynamic microexpression recognition ability with good reliability and validity. (2) The METT recognition techniques were suitable for training dynamic microexpression recognition ability. (3) The DMETT could be used as a training tool of dynamic microexpression recognition ability, and can be further verified to serve as a measurement tool of microexpression learning ability in the future. (4) There was no gender difference in general dynamic microexpression recognition ability, but females outperformed males in general dynamic microexpression recognition learning with small effect sizes.

Frontiers in PsychologyVol. 16
Jiangnan University (CN), Protein Express (United States) (US), Digital Science (United States) (US), Jiangsu Police Officer College (CN)
Ministry of Education of the People's Republic of China
Gender equality
Openalex Percentile: Top 79%
Emotion and Mood Recognition
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