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ObjectiveWe examine how human operators adjust their trust in automation as a result of their moment-to-moment interaction with automation. BackgroundMost existing studies measured trust by administering questionnaires at the end of an experiment. Only a limited number of studies viewed trust as a dynamic variable that can strengthen or decay over time. MethodSeventy-five participants took part in an aided memory recognition task. In the task, participants viewed a series of images and later on performed 40 trials of the recognition task to identify a target image when it was presented with a distractor. In each trial, participants performed the initial recognition by themselves, received a recommendation from an automated decision aid, and performed the final recognition. After each trial, participants reported their trust on a visual analog scale. ResultsOutcome bias and contrast effect significantly influence human operators’ trust adjustments. An automation failure leads to a larger trust decrement if the final outcome is undesirable, and a marginally larger trust decrement if the human operator succeeds the task by him/herself. An automation success engenders a greater trust increment if the human operator fails the task. Additionally, automation failures have a larger effect on trust adjustment than automation successes. ConclusionHuman operators adjust their trust in automation as a result of their moment-to-moment interaction with automation. Their trust adjustments are significantly influenced by decision-making heuristics/biases. ApplicationUnderstanding the trust adjustment process enables accurate prediction of the operators’ moment-to-moment trust in automation and informs the design of trust-aware adaptive automation. 

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. 

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. 

This study examined the impact of experience on individuals’ dependence behavior and response strategies when interacting with imperfect automation. 41 participants used an automated aid to complete a dual-task scenario comprising of a compensatory tracking task and a threat detection task. The entire experiment was divided into four quarters and multi-level models (MLM) were built to investigate the relationship between experience and the dependent variables. Results show that compliance and reliance behaviors and perfor- mance scores significantly increased as participants gained more experience with automation. In addition, as the experiment progressed, a significant number of participants adapted to the automation and resorted to an extreme use response strategy. The findings of this study suggest that automation response strategies are not static and most individual operators eventually follow or discard the automation. Understanding individual response strategies can support the development of individualized automation systems and improve operator training. 

Trust has been identified as a central factor for effective human-robot teaming. Existing literature on trust modeling predominantly focuses on dyadic human-autonomy teams where one human agent interacts with one robot. There is little, if not no, research on trust modeling in teams consisting of multiple human agents and multiple robotic agents. To fill this research gap, we present the trust inference and propagation (TIP) model for trust modeling in multi-human multi-robot teams. We assert that in a multi-human multi-robot team, there exist two types of experiences that any human agent has with any robot: direct and indirect experiences. The TIP model presents a novel mathematical framework that explicitly accounts for both types of experiences. To evaluate the model, we conducted a human-subject experiment with 15 pairs of participants (N=30). Each pair performed a search and detection task with two drones. Results show that our TIP model successfully captured the underlying trust dynamics and significantly outperformed a baseline model. To the best of our knowledge, the TIP model is the first mathematical framework for computational trust modeling in multi-human multi-robot teams. 

We conducted a meta-analysis to determine how people blindly comply with, rely on, and depend on diagnostic automation. We searched three databases using combinations of human behavior keywords with automation keywords. The period ranges from January 1996 to June 2021. In total, 8 records and a total of 68 data points were identified. As data points were nested within research records, we built multi-level models (MLM) to quantify relationships between blind compliance and positive predictive value (PPV), blind reliance and negative predictive value (NPV), and blind dependence and overall success likelihood (OSL).Results show that as the automation’s PPV, NPV, and OSL increase, human operators are more likely to blindly follow the automation’s recommendation. Operators appear to adjust their reliance behaviors more than their compliance and dependence. We recommend that researchers report specific automation trial information (i.e., hits, false alarms, misses, and correct rejections) and human behaviors (compliance and reliance) rather than automation OSL and dependence. Future work could examine how operator behaviors change when operators are not blind to raw data. Researchers, designers, and engineers could leverage understanding of operator behaviors to inform training procedures and to benefit individual operators during repeated automation use.