Department of Psychology, University of Illinois, Champaign, IL 61801, USA.
Psychophysiology. 2012 Dec;49(12):1558-70. doi: 10.1111/j.1469-8986.2012.01474.x. Epub 2012 Oct 23.
We hypothesized that control processes, as measured using electrophysiological (EEG) variables, influence the rate of learning of complex tasks. Specifically, we measured alpha power, event-related spectral perturbations (ERSPs), and event-related brain potentials during early training of the Space Fortress task, and correlated these measures with subsequent learning rate and performance in transfer tasks. Once initial score was partialled out, the best predictors were frontal alpha power and alpha and delta ERSPs, but not P300. By combining these predictors, we could explain about 50% of the learning rate variance and 10%-20% of the variance in transfer to other tasks using only pretraining EEG measures. Thus, control processes, as indexed by alpha and delta EEG oscillations, can predict learning and skill improvements. The results are of potential use to optimize training regimes.
我们假设，控制过程（通过脑电图（EEG）变量进行测量）会影响复杂任务的学习速度。具体来说，我们在太空堡垒任务的早期训练期间测量了阿尔法功率、事件相关频谱扰动（ERSP）和事件相关脑电位，并将这些测量结果与后续的学习速度和转移任务中的表现相关联。一旦排除初始分数的影响，最佳预测指标是额叶阿尔法功率以及阿尔法和德尔塔 ERSP，而不是 P300。通过结合这些预测指标，我们仅使用预训练 EEG 测量结果就可以解释大约 50%的学习速度变化和 10%-20%的转移到其他任务的变化。因此，由阿尔法和德尔塔 EEG 振荡表示的控制过程可以预测学习和技能的提高。这些结果对于优化训练方案具有潜在的用途。
