Some Studies on Estimation Using Biological Information for Achievement Levels of Iterative Learning

我们的目标是通过使用生物信息来理解迭代学习的客观结果，重点是事件相关电位(ERPs)。事件相关电位是通过“数据选择和平均法”(Funada等人，2010年)测量的。证实通过迭代学习可以观察到问题显示后脑电波形的一些变化。我们推导了一个合适的模型来反映迭代学习的效果，并在此基础上提出了一种迭代学习成绩水平的估计方法。我们发现，不同形状的事件相关电位主要是由个体之间不同的熟练程度引起的。

We are aiming at understanding the objective achievement of the iterative learning by using biological information with a focus on event-related potentials (ERPs). ERPs are measured by the “Data Selecting and Averaging Method” (Funada, et al., 2010). It was confirmed that we could observe some changes in the waveforms of EEG after the display of aproblem by iterative learning. That is, the latencies of ERPs became shorter by iterative learning.We derived a suitable model that reflects the effect of iterative learning, and then proposed a method of how the achievement level by iterative learning is estimated. We showed that different shapes of ERPs are mainly caused by different levels of proficiency among individuals.