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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- 1 School of Life Sciences, Beijing University of Chinese Medicine, Beijing, Beijing, China
- 2 College of Information Engineering, Huzhou University, Huzhou, China
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
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