Deviations of attention from the task at hand are often associated with worse reading performance (Schooler, Reichle, & Halpern, 2004). Ironically, current methods for detecting these shifts of attention typically generate task interruptions and further disrupt performance. In the current study, we developed a method to (1) track shifts of attention away from the reading task by examining the similarity between 5 min of eyes-closed-resting-state EEG and 5 min reading EEG; and (2) investigate, during reading, how the ratio between attention shifts and focused reading relates to readers' comprehension. We performed a Spectral Similarity Analysis (SSA) that examined the spectral similarity between EEG recorded during reading and at rest on a moment-by-moment basis. We then recursively applied the algorithm to the resting-state data itself to obtain an individual baseline of the stability of brain activation recorded during rest. We defined any moment in which SSA during reading was greater than the mean correlation between resting-state EEG and itself as an "attentional shift." The results showed that the proportion of such attentional shifts recorded over the left visual region (O1) significantly predicted reading comprehension, with higher ratios (indicative of more frequent attentional shifts) relating to worse comprehension scores on the reading test. As a proof of its validity, the same measure collected during the reading comprehension test also predicted participants' Simon effect (incongruent - congruent response times) which is a common index of selective attention. This novel method allows researchers to detect attention shifts moments during reading without interrupting natural reading process.

中文翻译：

---

通过频谱相似性分析监测注意力振荡预测阅读理解

对手头任务的注意力偏差通常与较差的阅读表现有关（Schooler, Reichle, & Halpern, 2004）。具有讽刺意味的是，当前用于检测这些注意力转移的方法通常会导致任务中断并进一步破坏性能。在当前的研究中，我们开发了一种方法来 (1) 通过检查 5 分钟闭眼静息状态 EEG 和 5 分钟阅读 EEG 之间的相似性来跟踪注意力从阅读任务中的转移；(2) 调查在阅读过程中注意力转移和专注阅读之间的比例与读者理解的关系。我们进行了频谱相似性分析 (SSA)，逐时检查了阅读期间记录的 EEG 和静止时记录的 EEG 之间的频谱相似性。然后，我们将算法递归地应用于静息状态数据本身，以获得在静息期间记录的大脑活动稳定性的个体基线。我们将阅读期间 SSA 大于静息状态 EEG 与其自身之间的平均相关性的任何时刻定义为“注意力转移”。结果表明，在左侧视觉区域 (O1) 上记录的这种注意力转移的比例显着预测了阅读理解，较高的比率（表明注意力转移更频繁）与阅读测试中较差的理解分数有关。作为其有效性的证明，在阅读理解测试期间收集的相同测量值还预测了参与者的西蒙效应（不一致 - 一致响应时间），这是选择性注意的常见指标。