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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., Competing Interests: The authors have declared that no competing interests exist.

Background: When interpreting medical images such as dental panoramic radiographs (Orthopanthomogram, OPT), errors are frequent. Previous research has shown that eye movement modelling examples (EMME) are a supportive training method for medical image interpretation to reduce errors. To date, EMME support for OPTs has not been verified. Objectives: We investigated whether a training with EMME and verbal explanations supports dental students in evaluating OPTs. Methods: Dental students were randomly assigned to an intervention (N = 42) or a control group (N = 41). The intervention group received the EMME between pre- and post-test. In a laboratory study, we measured students' gaze behaviour during evaluating OPTs and the detection rate of anomalies. Results and conclusions: The training led to fewer, shorter, and later fixations on anomalies and no difference in visual coverage of the OPT. The detection rate of anomalies did not improve. We replicated the latter finding in an online study (N = 31). Students may not have been able to apply the information from the EMME to detect anomalies. The image reading processes changed to more efficient rather than deeper visual search. Major takeaways: This study evaluated a training method with EMME for anomaly detection of OPTs. EMMEs did not improve the anomaly detection of dental students and changed their visual search process toward a more efficient rather than a deeper search.