Arch Laboratory and Department of Psychology, George Mason University, Fairfax, VA 22030, USA.
Neuroimage. 2012 Jan 2;59(1):48-56. doi: 10.1016/j.neuroimage.2011.07.047. Epub 2011 Jul 30.
Adaptive training using neurophysiological measures requires efficient classification of mental workload in real time as a learner encounters new and increasingly difficult levels of tasks. Previous investigations have shown that artificial neural networks (ANNs) can accurately classify workload, but only when trained on neurophysiological exemplars from experienced operators on specific tasks. The present study examined classification accuracies for ANNs trained on electroencephalographic (EEG) activity recorded while participants performed the same (within task) and different (cross) tasks for short periods of time with little or no prior exposure to the tasks. Participants performed three working memory tasks at two difficulty levels with order of task and difficulty level counterbalanced. Within-task classification accuracies were high when ANNs were trained on exemplars from the same task or a set containing the to-be-classified task, (M=87.1% and 85.3%, respectively). Cross-task classification accuracies were significantly lower (average 44.8%) indicating consistent systematic misclassification for certain tasks in some individuals. Results are discussed in terms of their implications for developing neurophysiologically driven adaptive training platforms.
使用神经生理测量进行适应性训练需要实时高效地对精神工作负荷进行分类，因为学习者会遇到新的、越来越困难的任务级别。先前的研究表明，人工神经网络 (ANN) 可以准确地分类工作负荷，但前提是仅在对特定任务的有经验操作员的神经生理样本进行训练时。本研究检查了在参与者在短时间内进行相同（任务内）和不同（跨任务）任务时记录的脑电图 (EEG) 活动上进行训练的 ANN 的分类准确率，这些参与者对任务的了解很少或没有。参与者以两种难度水平执行三个工作记忆任务，任务顺序和难度级别平衡。当 ANN 从相同任务或包含要分类任务的集合的示例中进行训练时，分类准确率很高（分别为 87.1%和 85.3%）。跨任务分类准确率显着降低（平均为 44.8%），这表明某些个体中某些任务的分类存在一致的系统错误。结果从开发神经生理驱动的自适应训练平台的角度进行了讨论。
