## 5 Discussion  
In today's era of rapid technological progress, the interactions between humans and artificial intelligence (AI) are intensifying. At the heart of this interaction lies trust, a crucial determinant of the relationship's success. Our research delves into the dynamic nature of trust in human-robot interaction (HRI), revealing its nonlinear relationship with task difficulty. This insight is crucial for understanding and optimizing the role of robots in practical scenarios. Our findings indicate that trust in robots tends to increase during tasks that are either straightforward or highly complex. This pattern can be attributed to the robot's predictable performance in these scenarios, allowing users to accurately assess its capabilities and, consequently, establish a more informed level of trust. However, an intriguing phenomenon emerges with tasks of intermediate complexity, where uncertainties about the robot's performance become more pronounced. This can lead to a fluctuation in trust levels, reflecting a \"V-shaped\" pattern akin to the one observed in previous research [56; 57]. Notably, this pattern resonates with the principles of flow theory in human factors, which posits that optimal challenges are key to maintaining engagement and satisfaction in activities [56]. According to this theory, tasks that align well with an individual's skills foster a state of flow, leading to higher engagement and satisfaction. In contrast, tasks that are misaligned with one's skills can result in anxiety and frustration. Our study's observations suggest that tasks of either low or high complexity tend to align more effectively with users' expectations and abilities when interacting with robots, thereby fostering a higher level of trust. Low-complexity tasks align well, maybe because they often fall within the participant's comfort zone, requiring a level of skill that most  
Figure 8: Average trust and its changes across different task difficulties at various stages. In this figure, the \(x\)-axis denotes the levels of task difficulty, categorized as “Easy”, “Normal”, and “Hard”, which correspond to simple, moderate, and difficult tasks, respectively. The \(y\)-axes in subfigures (**a–c**) illustrate the average trust values at different stages. Meanwhile, the \(y\)-axis in subfigure (**d**) depicts the variations in trust throughout the different stages. The labels “Step1”, “Step2”, and “Step3” represent the Training, Accompanying, and Automated stages, in that order. users possess or can easily acquire. This familiarity and ease of operation lead to a sense of confidence and control, enhancing trust in the robotic system. On the other hand, high-complexity tasks, while challenging, are typically approached with a clear understanding of the need for advanced AI assistance. Participants engaging in these tasks are generally prepared for, and perhaps even expect, the sophisticated capabilities of robots, which aligns with their heightened skill levels or understanding necessary for such tasks. This acknowledgment of the robot's role in complex scenarios can lead to a more trusting relationship, as users rely on and value the advanced support the robot provides. However, intermediate-complexity tasks pose unique challenges. They often fall outside the user's comfort zone and do not necessitate advanced AI intervention, leading to ambiguity about the robot's role and capabilities. This uncertainty makes it difficult for users to predict or comprehend robotic behavior, destabilizing the trust dynamic. Our findings with regard to trust dynamics echo [PERSON]'s research [57] on robotic user preferences and autonomy, highlighting the impact of task difficulty on trust. [PERSON]'s OODA (Observe, Orient, Decide, Act) model sheds light on the different stages of trust development, correlating with a user's cognitive, emotional, and behavioral responses. The initial phase, 'observe', gathers environmental and robotic data, followed by the 'orient' phase, which interprets this data.