Effects of eye movement modeling examples on adaptive expertise in medical image diagnosis

Abstract

Research indicates that expert performance is domain specific and hardly transfers to novel tasks or domains. However, due to technological changes in dynamic work settings, experts sometimes need to adapt and transfer their skills to new task affordances. The present mixed method study investigates whether eye movement modeling examples (EMME) can promote adaptive expertise in medical image diagnosis. Performance, eye tracking, and think-aloud protocol data were obtained from nine medical experts and fourteen medical students. Participants interpreted dynamic visualizations before (baseline) and after (retention, transfer) viewing an expert model's eye movements. Findings indicate that studying eye movement modeling examples had positive effects on performance, task-relevant fixations, and the use of cognitive and metacognitive comprehension strategies. Effects were stronger for the retention than for the transfer task. Medical experts benefitted more from the modeling examples than did medical students. Directions for future research and implications for related domains are discussed.