A Deep Learning Approach for Expertise Classification using Saccade Behavior
Eye movements reflect the cognitive advantage of experts over novices in a domain specific task. Current literature focuses on fixations but leaves out saccades. This research investigates the gaze behavior of dentistry students and expert dentists viewing orthopantomograms (OPTs). All proposed Long Short-Term Memory (LSTM) models were able to distinguish expert and novice gaze behavior by saccade features above guess chance, with the best performing feature having an accuracy of 77.1%. The results provide further evidence for the holistic model of image perception, which proposes that experts initially analyze an image globally, and then proceed with a focal analysis. Further, our results show that saccade features are important to understand expert gaze behavior, and therefore should get integrated into current theories on expertise.