Differences between experts and novices in decision-making processes

Experts and novices differ with respect to the use of intuition and deliberation in their decision-making processes, which affects the quality of decisions they make. We often ask ourselves: where does expert intuition and deliberation come from? If the progression from novice to expert is made through learning experiences, what should we provide novices during training? Identifying the discrepancies between experts and novices is essential for developing a computational learning system that simulates the human decision-making process. In this paper, we investigate the difference between individuals operating Unmanned Aerial Vehicle (UAV) missions, collected in a dataset called the Supervisory Control Operations User Test bed (SCOUT), by analyzing their computational models. For the computational models, deep neural networks (DNNs) and double transition models (DTMs) were employed. A set of DNNs was constructed from biometric information about eye movements, and a set of DTMs was built from event-driven data associated with actions taken by the individuals. For investigating DNNs, we examined how much improvement was obtained during training and validation, while for DTMs, we concentrated on the reward distributions of trajectories derived through inverse reinforcement learning (IRL). We classified SCOUT subjects into three levels of expertise according to their selfassessment and the maximum score achieved: novice, intermediate and expert. By analyzing these models, we identified differences between the expert and novice groups. In particular, the accuracy of the expert DNNs improved more effectively than that of novice DNNs, and the reward distributions of the expert DTMs were more closely clustered than those of novice DTMs.