Modeling eye movement indicative of expertise behavior is decisive in user evaluation. However, it is indisputable that task semantics affect gaze behavior. We present a novel approach to gaze scanpath comparison that incorporates convolutional neural networks (CNN) to process scene information at the fixation level. Image patches linked to respective fixations are used as input for a CNN and the resulting feature vectors provide the temporal and spatial gaze information necessary for scanpath similarity comparison. We evaluated our proposed approach on gaze data from expert and novice dentists interpreting dental radiographs using a local alignment similarity score. Our approach was capable of distinguishing experts from novices with 93% accuracy while incorporating the image semantics. Moreover, our scanpath comparison using image patch features has the potential to incorporate task semantics from a variety of tasks.

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Expert behavior is characterized by rapid information processing abilities, dependent on more structured schemata in long-term memory designated for their domain-specific tasks. From this understanding, expertise can effectively reduce cognitive load on a domain-specific task. However, certain tasks could still evoke different gradations of load even for an expert, e.g., when having to detect subtle anomalies in dental radiographs. Our aim was to measure pupil diameter response to anomalies of varying levels of difficulty in expert and student dentists' visual examination of panoramic radiographs. We found that students' pupil diameter dilated significantly from baseline compared to experts, but anomaly difficulty had no effect on pupillary response. In contrast, experts' pupil diameter responded to varying levels of anomaly difficulty, where more difficult anomalies evoked greater pupil dilation from baseline. Experts thus showed proportional pupillary response indicative of increasing cognitive load with increasingly difficult anomalies, whereas students showed pupillary response indicative of higher cognitive load for all anomalies when compared to experts.

The interpretation of medical images is an error-prone process that may yield severe consequences for patients. In dental medicine panoramic radiography (OPT) is a frequently used diagnostic procedure. OPTs typically contain multiple, diverse anomalies within one image making the diagnostic process very demanding, rendering students’ development of visual expertise a complex task. Radiograph interpretation is typically taught through massed practice; however, it is not known how effective this approach is nor how it changes students’ visual inspection of radiographs. Therefore, this study investigated how massed practice–an instructional method that entails massed learning of one type of material–affects processing of OPTs and the development of diagnostic performance. From 2017 to 2018, 47 dental students in their first clinical semester diagnosed 10 OPTs before and after their regular massed practice training, which is embedded in their curriculum. The OPTs contained between 3 to 26 to-be-identified anomalies. During massed practice they diagnosed 100 dental radiographs without receiving corrective feedback. The authors recorded students’ eye movements and assessed the number of correctly identified and falsely marked low- and high prevalence anomalies before and after massed practice. Massed practice had a positive effect on detecting anomalies especially with low prevalence (p < .001). After massed practice students covered a larger proportion of the OPTs (p < .001), which was positively related to the detection of low-prevalence anomalies (p = .04). Students also focused longer, more frequently, and earlier on low-prevalence anomalies after massed practice (ps < .001). While massed practice improved visual expertise in dental students with limited prior knowledge, there is still substantial room for improvement. The results suggest integrating massed practice with more deliberate practice, where, for example, corrective feedback is provided, and support is adapted to students’ needs.