# The Sense of Agency in Human-Machine Interaction Systems
## 5 Discussion
### The SoA as a Bond of HMC  
We posit that a potential solution for enhancing the SoA in HMI systems is enabling machines to obtain the operator's intentions and feelings without disrupting human activities. The machine can then dynamically adjust itself to align with the operator's needs and feelings. While explicit information about the operator can be obtained using sensors and data processing technology, capturing the full spectrum of the operator's intentions and emotions remains challenging. For instance, employing cameras and image processing techniques to recognize human expressions provides some explicit information, but it falls short of fully capturing the operator's nuanced feelings and intentions. Explicit information, while valuable, remains inherently limited in its ability to fully convey the operator's intricate feelings and intentions. The rapid advancements in HMC technology now enable machines to directly discern the operator's intentions and emotions through techniques like computer vision and speech recognition, significantly enhancing the SoA. However, during interactions with the machine, the operator must actively express their intentions and emotions, further amplifying the damage caused by the presence of machines to the SoA of humans [73; 74; 140]. [PERSON] et al. [117] and [PERSON] et al. [145] used the human experience to train robots so that machines could act like humans. The human experience is added to the robot's thoughts; therefore, the robot can think like a human. In addition, [PERSON] et al. [151] proposed a trajectory-planning algorithm for shared control, which can be regarded as the thought of a machine. [PERSON] et al. [32] proposed that a machine's understanding of its environment (the machine itself and its impacts on the environment included) requires self-awareness. When the machine has self-awareness, it can feel its behavior and the impact of its behavior on the environment, i.e., synthetic agency [153]. [PERSON] et al. [153] proposed a two-pronged perspective of the SoA in both humans and AI: first-person and second-person perspectives (\(F_{p}\) and \(S_{p}\), respectively). In \(S_{p}\), the AI (or human) can perceive the SoA of the other party (human or AI) through the model or mental representation of the other party's \(F_{p}\). In this way, the AI can understand and adapt its \(F_{p}\) to enhance, rather than undermine, the human operator's \(F_{p}\). So, the SoA serves as a bond for unconscious communication between humans and machines. By adjusting itself based on the operator's dynamic SoA, the machine can achieve unconscious HMI without disrupting the operator. To fully realize this vision, meticulous attention should be devoted to measuring the SoA in the HMI system and analyzing the influencing factors. The SoA can be integrated into HMIsystems as a communication channel, enabling more intuitive and effective interactions between humans and machines. To guide future research in this area, we propose the following theoretical framework:  
* Measure and analyze the SoA: Use tools to accurately measure the operator's SoA. This can include explicit measurement (e.g., questionnaires) [87] and implicit measurement [87] (e.g., intentional binding). Analyzing these data can help elucidate how different factors affect the SoA and improve the HMI system accordingly. * Implement advanced sensing technologies: Utilize sensors that can capture a variety of explicit information from operators, such as gestures, facial expressions, and voice intonation. These data can be processed using computer vision and speech recognition techniques to infer the operator's intent and emotions [113]. * Develop adaptive algorithms: Create algorithms that enable the machine to adjust its behavior in real time based on the operator's inferred intent and emotions. This may involve machine learning models trained on human experience data to dynamically predict and respond to the operator's needs [151].