Learning Anatomical Structures: a Reinforcement-Based Learning Approach

Abstract

Given the reduced formal instruction time for many of the basic sciences within medical curricula, educators are searching for instructional methods that ensure students have the necessary foundational knowledge. The objective of this study was to design an anatomical structure identification reinforcement learning task for participants with minimal prior neuroanatomical knowledge. We predicted that the provision of immediate feedback would activate reinforcement learning mechanisms within the brain thus enhancing knowledge acquisition in novice learners such that performance accuracy (correct identification of neuroanatomical structures) improves from approximately 50 % (guessing) to 90 % by task completion. Ten participants learned to identify 10 neuroanatomical structures shown using two-dimensional (2D) coronal brain images over the course of 320 trials (20 trials per experiment block with 16 blocks total). An analysis of behavioural learning curves demonstrated the progression of learning, and each participant achieved a 90–100 % accuracy by block 13 (260 trials) for each of the 10 structures. The total task duration was approximately 30–35 min with all participants reaching proficiency by 25–30 min. Importantly, there was a significant increase in performance on a post-task knowledge identification test. Our results highlight the key role of reinforcement learning approaches to establishing foundational knowledge in the pre-clinical sciences, specifically anatomy, in a time-efficient manner. Further, progression of learning can be assessed through examination of learning curves. Designing effective pre-class exercises that make use of reinforcement learning theory as a means to promote learning may be an effective method to build base knowledge prior to classroom interactions in anatomy education.