Predicting task-general mind-wandering with EEG
Predicting task-general mind-wandering with EEG
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DOI：
10.3758/s13415-019-00707-1
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发表时间：
2019-08-01
影响因子：
2.9
通讯作者：
van Vugt, Marieke K.
中科院分区：
文献类型：
--
作者：
Jin, Christina Yi;Borst, Jelmer P.;van Vugt, Marieke K.
走神是指在执行任务时，产生与任务无关的想法的过程。走神的动力学仍然难以捉摸，因为很难仅仅根据行为来跟踪一个人的走神。这项研究的目标是开发一个机器学习分类器，可以使用脑电图（EEG）以一种概括任务的方式在线确定某人的走神状态。特别是，我们在EEG标记上训练了机器学习模型，将参与者的当前状态分类为走神或正在执行任务。为了能够检查分类器的任务通用性，本研究采用了两种不同的范式：持续注意反应任务（SART）和视觉搜索任务。在这两项任务中，随机插入了要求自我报告当时想法的探测问题，参与者对探测的反应被用来为分类器创建标签。在任务外反应之前的6次试验被标记为走神，而预测任务内反应的6次试验被标记为任务内。用作分类器特征的EEG标记包括单次试验P1、N1和P3，在PO 7、Pz、PO 8和Fz处的θ（4-8 Hz）和α（8.5-12 Hz）频带中的功率和相干性。我们使用支持向量机作为训练算法来学习EEG标记和当前走神状态之间的联系。我们能够区分任务中思维和任务外思维，准确率从0.50到0.85不等。此外，分类器是任务通用的：跨任务预测的平均准确率为60%，高于机会水平。在所有提取的EEG标记中，阿尔法功率最能预测走神。
Mind-wandering refers to the process of thinking task-unrelated thoughts while performing a task. The dynamics of mind-wandering remain elusive because it is difficult to track when someone's mind is wandering based only on behavior. The goal of this study is to develop a machine-learning classifier that can determine someone's mind-wandering state online using electroencephalography (EEG) in a way that generalizes across tasks. In particular, we trained machine-learning models on EEG markers to classify the participants' current state as either mind-wandering or on-task. To be able to examine the task generality of the classifier, two different paradigms were adopted in this study: a sustained attention to response task (SART) and a visual search task. In both tasks, probe questions asking for a self-report of the thoughts at that moment were inserted at random moments, and participants' responses to the probes were used to create labels for the classifier. The 6 trials preceding an off-task response were labeled as mind-wandering, whereas the 6 trials predicting an on-task response were labeled as on-task. The EEG markers used as features for the classifier included single-trial P1, N1, and P3, the power and coherence in the theta (4-8 Hz) and alpha (8.5-12 Hz) bands at PO7, Pz, PO8, and Fz. We used a support vector machine as the training algorithm to learn the connection between EEG markers and the current mind-wandering state. We were able to distinguish between on-task and off-task thinking with an accuracy ranging from 0.50 to 0.85. Moreover, the classifiers were task-general: The average accuracy in across-task prediction was 60%, which was above chance level. Among all the extracted EEG markers, alpha power was most predictive of mind-wandering.