Training of surgeons is essential for safe and effective use of robotic surgery, yet current assessment tools for learning progression are limited. The objective of this study was to measure changes in trainees’ cognitive and behavioral states as they progressed in a robotic surgeon training curriculum at a medical institution. Seven surgical trainees in urology who had no formal robotic training experience participated in the simulation curriculum. They performed 12 robotic skills exercises with varying levels of difficulty repetitively in separate sessions. EEG (electroencephalogram) activity and eye movements were measured throughout to calculate three metrics: engagement index (indicator of task engagement), pupil diameter (indicator of mental workload) and gaze entropy (indicator of randomness in gaze pattern). Performance scores (completion of task goals) and mental workload ratings (NASA-Task Load Index) were collected after each exercise. Changes in performance scores between training sessions were calculated. Analysis of variance, repeated measures correlation, and machine learning classification were used to diagnose how cognitive and behavioral states associate with performance increases or decreases between sessions. The changes in performance were correlated with changes in engagement index ($r_{rm}=-.25,p<.001$) and gaze entropy ($r_{rm}=-.37,p<.001$). Changes in cognitive and behavioral states were able to predict training outcomes with 72.5% accuracy. Findings suggest that cognitive and behavioral metrics correlate with changes in performance between sessions. These measures can complement current feedback tools used by medical educators and learners for skills assessment in robotic surgery training.

外科医生的培训对于安全有效地使用机器人手术至关重要，但目前用于学习进展的评估工具是有限的。本研究的目的是衡量受训者在医疗机构的机器人外科医生培训课程中取得进展时认知和行为状态的变化。七名没有正式机器人培训经验的泌尿外科外科学员参加了模拟课程。他们在不同的课程中重复进行了 12 项难度不同的机器人技能练习。整个过程中测量 EEG（脑电图）活动和眼球运动以计算三个指标：参与指数（任务参与的指标）、瞳孔直径（心理工作量的指标）和注视熵（注视模式的随机性指标）。每次练习后收集表现分数（完成任务目标）和心理工作量评级（NASA-任务负荷指数）。计算训练课程之间的表现分数变化。方差分析、重复测量相关性和机器学习分类用于诊断认知和行为状态如何与会话之间的性能增加或减少相关联。绩效的变化与敬业度指数的变化相关（机器学习分类用于诊断认知和行为状态如何与会话之间的性能增加或减少相关联。绩效的变化与敬业度指数的变化相关（机器学习分类用于诊断认知和行为状态如何与会话之间的性能增加或减少相关联。绩效的变化与敬业度指数的变化相关（$r_{r米}=-.25,p<.001$) 和注视熵 ($r_{r米}=-.37,p<.001$）。认知和行为状态的变化能够以 72.5% 的准确率预测训练结果。研究结果表明，认知和行为指标与会话之间的表现变化相关。这些措施可以补充医学教育者和学习者用于机器人手术培训技能评估的当前反馈工具。