# Initial Student Attention-Allocation and Flight-Performance Improvements Based on Eye-Movement Data
## 1 Introduction  
### Research Background and Study Significance  
In recent years, flight training has emerged as a critical concern within the aviation community due to its pivotal role in aviation safety and pilot training. In order to improve the pilot's ability to monitor the flying environment, the French accident investigation agency has conducted a large number of accident investigations and suggested that various departments start to study the pilot's eye movement patterns. Among them, the research by [PERSON] et al. [1] showed that more than 900 pilots did not Knowing what the standard distribution of attention looks like requires a more detailed description of visual scanning. At the same time, the International Air Transport Association (IATA) also issued recommendations on strengthening cockpit monitoring and improving situational awareness inits 2016 report [2; 3]. According to a survey by [PERSON] [4] et al., nearly 75% of pilots felt that getting a standard scan pattern would allow them to further improve their level of attention allocation. Attention allocation, which is a key cognitive resource, has garnered attention from psychologists, who posit that consuming more than the available resources for a task may lead to performance decline, while appropriate attention allocation allows a person to flexibly adapt to changing task demands [5]. It is widely known that the human visual system plays a paramount role in cognitive processing; more than 80% of the information transmitted to the brain is gathered through vision [6; 7]. Eye-tracking technology, which was initially developed by [PERSON] et al. [8], can offer valuable insight into human cognition, learning, and decision-making processes by studying eye-movement behavior. A large amount of research has demonstrated that a strong correlation exists between eye-movement behavior and expertise across a wide variety of fields, which include driving [9], medicine [10; 11; 12], sports [13], and chess [14] applications. Specifically, these studies revealed that the gaze durations of experienced experts are shorter than those of inexperienced novices within the same field of study. In the aviation field, [PERSON] et al. [15] demonstrated the efficacy of studying the differences between eye-movement characteristics of novice and expert pilots by analyzing eye-movement indices for different flight scenarios. [PERSON] et al. [16] found that experts exhibited enhanced information-processing capabilities and made more accurate decisions during visual searching tasks. In their study regarding the saccade strategies of F-16 fighter pilots, [PERSON] et al. [17] discovered that expert pilots exhibited more stable scanning paths. [PERSON] et al. [18] employed the expert-novice experimental paradigm to collect eye-movement indicators from flight instructors and trainees during approach maneuvers. They used gaze entropy to quantify the gaze randomness within areas of interest (AOIs) and found that flight instructors exhibited more random and flexible eye-movement patterns during approach than novice pilots. [PERSON] et al. [19] investigated the eye-movement characteristics of pilots during instrument-approach flights and identified significant differences between the gaze and saccade patterns of experts and novices, which directly impacted the flight performance. Similar correlations between eye-movement data and job performance have also been observed in other fields. In the industrial production and manufacturing realm, [PERSON] et al. [20] determined that the use of color cues significantly influenced visual cognition during numerical comparison tasks. [PERSON] et al. [21] identified notable differences in visual behaviors that were based on various saccade modes and priority divisions in the engineering field. Their findings offer insight for optimizing human-computer interaction interfaces to improve practitioner performance. [PERSON] et al.