Citation

Jin CY, Borst JP, van Vugt MK. Eur. J. Neurosci. 2020; ePub(ePub): ePub.

Copyright

(Copyright © 2020, Federation of European Neuroscience Societies, Publisher John Wiley and Sons)

DOI

10.1111/ejn.14863

PMID

32538509

Abstract

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 shared neural structures among 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.

Language: en

Keywords

support vector machine; vigilance; alpha oscillation; independent component analysis; mind-wandering; task demands