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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. [ABSTRACT FROM AUTHOR]

Expert behavior extends from rapid information processing abilities [1], which relates to more structured working memory designated for their domain-specific tasks [2] and reflects cognitive load. Much of the literature has used pupil dilation changes as an indicator of cognitive load [3] in an n-back task. Our aim was to see how generalizable the pupil diameter is to cognitive load for expert dentists and students during dental X-Ray examination. First year dental students (n = 50) and experts (n = 26) in the university clinic viewed panoramic radiographs with instructions to find areas that could be indicative of pathologies (see [4] for methods). We measured percent pupil dilation change from baseline from both groups as well as fixation and saccade behavior from the left eye after comprehensive pre-processing. Median pupil percent change and variance were then used as features for 10 different machine learning classifiers with a cost function for expert misclassification . With pupil information alone, the average accuracy for all classifiers was 64.93%. However, for the same classifiers with the gaze feature in combination with the pupil dilation change were trained, the average accuracy was 80.52%, with one exception . Therefore, using pupil dilation alone was not able to distinguish experts from students with high accuracy. These results stress that pupil diameter alone may not be a strong indicator or cognitive load [5], and that other features may be needed for better classification. [ABSTRACT FROM AUTHOR]