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首页> 外文期刊>The European Journal of Neuroscience >Distinguishing vigilance decrement and low task demands from mind-wandering: A machine learning analysis of EEG
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Distinguishing vigilance decrement and low task demands from mind-wandering: A machine learning analysis of EEG

机译：显着的警惕减少和低任务需求从思维 - 徘徊：脑电图的机器学习分析

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摘要

Mind-wandering is a ubiquitous mental phenomenon that is defined as self-generated thought irrelevant to the ongoing task. Mind-wandering tends to occur when people are in a low-vigilance state or when they are performing a very easy task. In the current study, we investigated whether mind-wandering is completely dependent on vigilance and current task demands, or whether it is an independent phenomenon. To this end, we trained support vector machine (SVM) classifiers on EEG data in conditions of low and high vigilance, as well as under conditions of low and high task demands, and subsequently tested those classifiers on participants' self-reported mind-wandering. Participants' momentary mental state was measured by means of intermittent thought probes in which they reported on their current mental state. The results showed that neither the vigilance classifier nor the task demands classifier could predict mind-wandering above-chance level, while a classifier trained on self-reports of mind-wandering was able to do so. This suggests that mind-wandering is a mental state different from low vigilance or performing tasks with low demands-both which could be discriminated from the EEG above chance. Furthermore, we used dipole fitting to source-localize the neural correlates of the most import features in each of the three classifiers, indeed finding a few distinct neural structures between the three phenomena. Our study demonstrates the value of machine-learning classifiers in unveiling patterns in neural data and uncovering the associated neural structures by combining it with an EEG source analysis technique.
机译：心不在焉是一种普遍存在的心理现象，被定义为与正在进行的任务无关的自我产生的思想。当人们处于低警觉状态或执行一项非常简单的任务时，往往会出现心不在焉的情况。在目前的研究中，我们调查了思维偏离是否完全依赖于警惕性和当前任务需求，或者它是否是一种独立的现象。为此，我们在低警惕性和高警惕性条件下，以及在低任务要求和高任务要求条件下，对EEG数据训练支持向量机（SVM）分类器，然后在参与者自我报告的思维游荡上测试这些分类器。参与者的瞬时精神状态是通过间歇性思维探测来测量的，在这种探测中，他们报告了自己当前的精神状态。结果表明，无论是警惕性分类器还是任务需求分类器都不能预测高于机会水平的思维游荡，而根据思维游荡自我报告训练的分类器能够预测。这表明，思维游荡是一种不同于低警惕性或低要求执行任务的精神状态，这两种精神状态都可以与脑电图区分开来。此外，我们使用偶极子拟合来定位三个分类器中最重要特征的神经相关性，确实在这三种现象之间找到了一些不同的神经结构。我们的研究证明了机器学习分类器在揭示神经数据中的模式以及通过将其与EEG源分析技术相结合来揭示相关神经结构方面的价值。

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