Department of Psychology, College of Science, National Taiwan University, Taipei City 10617, Taiwan.
Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei City 10051, Taiwan.
Sensors (Basel). 2021 Nov 14;21(22):7569. doi: 10.3390/s21227569.
Mind-wandering has been shown to largely influence our learning efficiency, especially in the digital and distracting era nowadays. Detecting mind-wandering thus becomes imperative in educational scenarios. Here, we used a wearable eye-tracker to record eye movements during the sustained attention to response task. Eye movement analysis with hidden Markov models (EMHMM), which takes both spatial and temporal eye-movement information into account, was used to examine if participants' eye movement patterns can differentiate between the states of focused attention and mind-wandering. Two representative eye movement patterns were discovered through clustering using EMHMM: centralized and distributed patterns. Results showed that participants with the centralized pattern had better performance on detecting targets and rated themselves as more focused than those with the distributed pattern. This study indicates that distinct eye movement patterns are associated with different attentional states (focused attention vs. mind-wandering) and demonstrates a novel approach in using EMHMM to study attention. Moreover, this study provides a potential approach to capture the mind-wandering state in the classroom without interrupting the ongoing learning behavior.
心流已被证明在很大程度上影响我们的学习效率，尤其是在当今数字化和分心的时代。因此，在教育场景中检测心流变得至关重要。在这里，我们使用可穿戴眼动仪记录持续注意反应任务期间的眼球运动。隐马尔可夫模型（EMHMM）的眼动分析考虑了空间和时间眼动信息，用于检查参与者的眼动模式是否可以区分专注和心流状态。通过 EMHMM 使用聚类发现了两种有代表性的眼动模式：集中模式和分散模式。结果表明，集中模式的参与者在检测目标方面表现更好，并且自我评估比分散模式的参与者更专注。这项研究表明，不同的眼动模式与不同的注意力状态（专注与心流）相关，并且展示了一种使用 EMHMM 研究注意力的新方法。此外，该研究提供了一种潜在的方法，可以在不中断正在进行的学习行为的情况下在课堂上捕捉心流状态。
