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Previous research into trust dynamics in human-autonomy interaction has demonstrated that individuals exhibit specific patterns of trust when interacting repeatedly with automated systems. Moreover, people with different types of trust dynamics have been shown to differ across seven personal characteristic dimensions: masculinity, positive affect, extraversion, neuroticism, intellect, performance expectancy, and high expectations. In this study, we develop classification models aimed at predicting an individual’s trust dynamics type–categorized as Bayesian decision-maker, disbeliever, or oscillator–based on these key dimensions. We employed multiple classification algorithms including the random forest classifier, multinomial logistic regression, Support Vector Machine, XGBoost, and Naive Bayes, and conducted a comparative evaluation of their performance. The results indicate that personal characteristics can effectively predict the type of trust dynamics, achieving an accuracy rate of 73.1%, and a weighted average F1 score of 0.64. This study underscores the predictive power of personal traits in the context of human-autonomy interaction. 

Understanding the impact of operator characteristics on human-automation interaction (HAI) is crucial as automation becomes pervasive. Despite extensive HAI research, the association between operator characteristics and their dependence on automation has not been thoroughly examined. This study, therefore, examines how individual characteristics affect operator dependence behaviors when interacting with automation. Through a controlled experiment involving 52 participants in a dual-task scenario, we find that operators’ decision-making style, risk propensity, and agreeableness are associated with their dependence behaviors when using automation. This research illuminates the role of personal characteristics in HAI, facilitating personalized team interactions, trust building, and enhanced performance in automated settings.