Category: Education, Simulation & Virtual Reality
Introduction & Objective :
Practice is generally considered to be the single most important factor responsible for the permanent improvement in the ability to perform a motor skill i.e. motor learning. However, when task demands exceed the available attention resources learning becomes hindered. Increased cognitive load (CL) during surgery has been associated with inferior task performance, a higher likelihood of errors and the possibility of an incomplete skill transfer to the clinical environment. Task-evoked pupillary responses (TEPRs) that include changes in pupil diameter and patterns in eye movement fixation have been found to relate to CL that can now be recorded in real-time using newly developed portable devices. We aim to demonstrate the feasibility of real-time CL assessment utilizing TEPRs during Virtual reality (VR) robot-assisted simulation training.
Motor learning is dependent on three major factors; task complexity, learning conditions, and skill level of the learner. Fluctuations in TEPRs were recorded using portable eyewear while varying these 3 conditions. 15 participants with varying robotic experience volunteered in the study.TEPRs were recorded while participants completed; a sequence of VR suturing tasks to assess task complexity and then the same task while completing a 2ry task to assess learning conditions. Finally to assess the impact of learners’ skills the participants were quasi-randomly assigned to 3 groups that completed a series of 9 trials on one of the 3 suturing tasks. Differences in CL through TEPRs and degree of motor learning (improved performance) were measured between in the 1st and last repetition.
Pupillary eye movements (diameter, blinks, rapid eye movements and fixations) and a calculated index of cognitive activity (ICA) were successfully recorded in real time during each task using the portable eyewear. Changes in TEPRs and calculated ICA incrementally increased with the advancement in difficulty of both 1ry and 2ry tasks difficulty. Participants with no prior robotic experience displayed the least ICA and maximal learning (maximal change in performance) in the simplest suturing task, while demonstrating the least learning and maximal ICA in the most complex suturing task. Experts displayed the reverse results.
Knowledge of CL during surgical training offers the ability to optimize motor learning (e.g. instructors can make practice conditions more difficult if motor learning is impeded by low task difficulty (task is too easy for additional information to be processed). The real-time measurement of CL during simulation training would facilitate the development of individualized training curricula.
Ahmed Ghazi– Assistant professor of Urology , Director of Simulation Innovation Laboratory (SIL) , Co-Director-Fellowship In Endourology and Robotic Surgery, University of Rochester, Rochester, New York
Tyler Holler– Research Coordinator, University of Rochester, Rochester, New York
Jean Joseph– W.W. Scott Professor and Chairman Departement of Urology Professor of Oncology, University of Rochester, Rochester, New York
Assistant professor of Urology , Director of Simulation Innovation Laboratory (SIL) , Co-Director-Fellowship In Endourology and Robotic Surgery
University of Rochester
Rochester, New York
Ahmed Ghazi MD, MSc, FEBU
Assistant professor of Urology
Director of Simulation Innovation Laboratory (SIL)
Co-Director-Fellowship In Endourology and Robotic Surgery
University of Rochester Medical Center