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The Intergroup Bias in the Effects of Facial Feedback on the Recognition of Micro-expressions

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Artificial Intelligence in Education Technologies: New Development and Innovative Practices (AIET 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 154))

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Abstract

Micro-expression is the facial expression that is extremely quick and lasts less than half a second. As a spontaneous expression, it is usually produced when people try to suppress their emotions and can reveal the true emotions of human beings. It plays an important role in lie detection. In recent years, with the progress of neural network technology, the research on micro-expression recognition has made significant progress. However, because our understanding of the psychological process of micro-expression recognition is far from complete, the existing method of recognizing micro-expression still cannot meet the standard of practical application. In the present research, we investigated the effects of facial feedback and social identity of the expresser on the recognition of micro-expressions by one behavioral experiment. The results showed that facial feedback can moderate the intergroup bias in micro-expression recognition, which suggests that humans will imitate other people's facial expressions to different degrees in the recognition of micro-expression. At shorter duration, facial feedback has a stronger effect on the recognition of micro-expression of outgroup members. And at longer duration, facial feedback has a stronger effect on the recognition of micro-expressions of ingroup members. This further suggests that we need to consider the identity of the model and the identity of the coder to obtain more accurate and effective data coding when establishing the micro-expression database.

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Acknowledgement

This work was supported by the Outstanding Young Scientific Research Project of Hunan Provincial Department of Education (19B361).

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Authors and Affiliations

  1. Department of Psychology, School of Educational Sciences, Hunan Normal University, Changsha, 410006, Hunan, China

    Kunling Peng, Yaohan Wang & Qi Wu

  2. Cognition and Human Behavior Key Laboratory of Hunan Province, Hunan Normal University, Changsha, 410006, Hunan, China

    Kunling Peng, Yaohan Wang & Qi Wu

Authors
  1. Kunling Peng
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  2. Yaohan Wang
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Corresponding author

Correspondence to Qi Wu .

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Editors and Affiliations

  1. Department of Curriculum and Instruction, The Education University of Hong Kong, Tai Po, Hong Kong

    Eric C. K. Cheng

  2. Swinburne University of Technology, Melbourne, VIC, Australia

    Tianchong Wang

  3. IU International University of Applied Sciences, Erfurt, Germany

    Tim Schlippe

  4. Department of Food Science & Technology, University of Patras, Agrinio, Greece

    Grigorios N. Beligiannis

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Peng, K., Wang, Y., Wu, Q. (2023). The Intergroup Bias in the Effects of Facial Feedback on the Recognition of Micro-expressions. In: Cheng, E.C.K., Wang, T., Schlippe, T., Beligiannis, G.N. (eds) Artificial Intelligence in Education Technologies: New Development and Innovative Practices. AIET 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 154. Springer, Singapore. https://doi.org/10.1007/978-981-19-8040-4_9

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  • DOI: https://doi.org/10.1007/978-981-19-8040-4_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8039-8

  • Online ISBN: 978-981-19-8040-4

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