How do personality traits modulate real-world gaze behavior? Generated gaze data shows situation-dependent modulations - Yamashita, Jumpei; Takimoto, Yoshiaki; Oishi, Haruo; Kumada, Takatsune (Frontiers in Psychology, 2024)
It has both scientific and practical benefits to substantiate the theoretical prediction that personality (Big Five) traits systematically modulate gaze behavior in various real-world (working) situations. Nevertheless, previous methods that required controlled situations and large numbers of participants failed to incorporate real-world personality modulation analysis. One cause of this research gap is the mixed effects of individual attributes (e.g., the accumulated attributes of age, gender, and degree of measurement noise) and personality traits in gaze data. Previous studies may have used larger sample sizes to average out the possible concentration of specific individual attributes in some personality traits, and may have imposed control situations to prevent unexpected interactions between these possibly biased individual attributes and complex, realistic situations. Therefore, we generated and analyzed real-world gaze behavior where the effects of personality traits are separated out from individual attributes. In Experiment 1, we successfully provided a methodology for generating such sensor data on head and eye movements for a small sample of participants who performed realistic nonsocial (data-entry) and social (conversation) work tasks (i.e., the first contribution). In Experiment 2, we evaluated the effectiveness of generated gaze behavior for real-world personality modulation analysis. We successfully showed how openness systematically modulates the autocorrelation coefficients of sensor data, reflecting the period of head and eye movements in data-entry and conversation tasks (i.e., the second contribution). We found different openness modulations in the autocorrelation coefficients from the generated sensor data of the two tasks. These modulations could not be detected using real sensor data because of the contamination of individual attributes. In conclusion, our method is a potentially powerful tool for understanding theoretically expected, systematic situation-specific personality modulation of real-world gaze behavior.