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ORIGINAL RESEARCH article

Front. Psychol.
Sec. Emotion Science
Volume 15 - 2024 | doi: 10.3389/fpsyg.2024.1397340

Detecting Five-Pattern Personality Traits Using Eye Movement Features for Observing Emotional Faces

Provisionally accepted
Ying Yu Ying Yu 1Qingya Lu Qingya Lu 1Xinyue Wu Xinyue Wu 1Zefeng Wang Zefeng Wang 2Chenggang Zhang Chenggang Zhang 1Xuanmei Wu Xuanmei Wu 1Cong Yan Cong Yan 1*
  • 1 School of Life Sciences, Beijing University of Chinese Medicine, Beijing, Beijing, China
  • 2 College of Information Engineering, Huzhou University, Huzhou, China

The final, formatted version of the article will be published soon.

    The five-pattern personality traits rooted in the theory of traditional Chinese medicine have promising prospects for clinical application. However, they are currently assessed using a self-reporting scale, which may have certain limitations. Eye tracking technology, with its non-intrusive, objective, and culturally neutral characteristics, has become a powerful tool for revealing individual cognitive and emotional processes. Therefore, applying this technology for personality assessment is a promising approach. In this study, participants observed five emotional faces (anger, happy, calm, sad, and fear) selected from the Chinese Facial Affective Picture System. Utilizing artificial intelligence algorithms, we evaluated the feasibility of automatically identifying different traits of the five-pattern personality traits from participants' eye movement patterns. Based on the analysis of five supervised learning algorithms, we draw the following conclusions: The Lasso feature selection method and Logistic regression achieved the highest prediction accuracy under all five traits. This study develops a framework for predicting five-pattern personality traits using eye movement behavior, offering a novel approach for personality assessment in TCM.

    Keywords: eye tracking, five-pattern personality traits, machine learning, emotional faces, Personality Prediction

    Received: 10 Apr 2024; Accepted: 04 Sep 2024.

    Copyright: © 2024 Yu, Lu, Wu, Wang, Zhang, Wu and Yan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Cong Yan, School of Life Sciences, Beijing University of Chinese Medicine, Beijing, 100029, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.