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Background factors predicting accuracy and improvement in micro expression recognition

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Abstract

Micro expressions are brief facial expressions displayed when people attempt to conceal, hide, or repress their emotions. They are difficult to detect in real time, yet individuals who can accurately identify micro expressions receive higher workplace evaluations and can better detect deception. Two studies featuring college students and security officers examined background factors that may account for accuracy differences when reading micro expressions, both before and after training. Study 1 revealed that college students who were younger and high in openness to experience were better at recognizing micro expressions. However, individual differences did not predict improvement in micro expression recognition gained through training. Study 2 revealed experiential factors such as prior facial expression training and lack of law enforcement experience were more predictive of micro expression recognition than personality or demographic factors. Individuals in both studies showed recognition improvement with training, and the implications of the ability to improve at micro expression recognition are discussed in the context of security and interpersonal situations.

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Notes

  1. A one-way analysis of variance of racial group on post-test accuracy was conducted, F (4, 226) = 4.045, p = 0.003. Tukey’s post hoc tests revealed the African American racial group was significantly different from the Caucasian group (p = 0.001) and the Asian group (p = 0.032).

  2. At the time of this study, the CD version of METTv2 (utilized in study one) was unavailable, as it had been revised into two web-based training tools (the METT Advanced, http://face.paulekman.com/, and the Micro Expression Recognition Training, http://www.humintell.com/).

  3. This training did not include use of either web-based training tool (METT Advanced, http://face.paulekman.com/or the Micro Expression Recognition Training, http://www.humintell.com).

  4. Although other studies tested ME recognition at different speeds and provided response scales with greater choices, which may affect ME score.

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Conflict of interest

Ashley E. Anker, Hyisung C. Hwang, Carolyn M. Hurley and Mark G. Frank have no conflict of interest. David Matsumoto is a co-author of the Micro Expression Training Tool used in both Studies. This tool is used commercially, and he receive a financial benefit from its sales.

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Authors and Affiliations

  1. University at Buffalo, Buffalo, NY, USA

    Carolyn M. Hurley, Ashley E. Anker & Mark G. Frank

  2. San Francisco State University and Humintell, San Francisco, CA, USA

    David Matsumoto & Hyisung C. Hwang

  3. University at Buffalo, Undergraduate Degree Programs in Singapore, 461 Clementi Rd, Singapore, 599491, Singapore

    Carolyn M. Hurley

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  1. Carolyn M. Hurley
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  2. Ashley E. Anker
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  5. Hyisung C. Hwang
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Correspondence to Carolyn M. Hurley.

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Part of this work was submitted in partial fulfillment of a Doctor of Philosophy degree at the University at Buffalo by the first author. The fourth author is a co-author of the Micro Expression Training Tool used in both studies. This tool is currently available from Humintell, which receives a financial benefit from its sales. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Transportation Security Administration, the Department of Homeland Security, or the United States of America.

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Hurley, C.M., Anker, A.E., Frank, M.G. et al. Background factors predicting accuracy and improvement in micro expression recognition. Motiv Emot 38, 700–714 (2014). https://doi.org/10.1007/s11031-014-9410-9

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