Hung Shao-Min, Hsieh Po-Jang
Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
Neurosciences, Huntington Medical Research Institutes, Pasadena, CA, USA.
Neuroimage Rep. 2021 Dec 17;2(1):100073. doi: 10.1016/j.ynirp.2021.100073. eCollection 2022 Mar.
The recent task-free approach in Cognitive Neuroscience has sparked interest in understanding the brain's default mode network (DMN). One particular mental activity that has been identified to recruit such a network is mind-wandering, which points to the functional aspect of mind-wandering as a default system. However, the phenomenological aspect of mind-wandering has been missing in the literature on brain imaging. To tackle this issue, we adopted online thought sampling while participants underwent a simple fixation task over multiple sessions in the scanner. During 10 h of scanning of each participant, over 200 mind wander episodes were labelled in each participant. With linear support vector machine classification on mind-wandering episodes with exclusive sensory content, we found that decoding accuracy in content-corresponding sensory cortices was significantly higher, indicating the neural bases of the phenomenology of mind-wandering. Unique patterns in classification were revealed in different individuals, pointing to individual variances in our phenomenal experiences.
认知神经科学中最近出现的无任务方法引发了人们对理解大脑默认模式网络（DMN）的兴趣。一种已被确定会激活该网络的特定心理活动是走神，这表明走神作为一种默认系统的功能方面。然而，在脑成像文献中，走神的现象学方面一直缺失。为了解决这个问题，我们在参与者在扫描仪中进行多个会话的简单注视任务时采用了在线思维抽样。在对每个参与者进行10小时的扫描过程中，每个参与者身上标记了超过200次走神事件。通过对具有独特感官内容的走神事件进行线性支持向量机分类，我们发现内容对应的感觉皮层中的解码准确率显著更高，这表明了走神现象学的神经基础。在不同个体中揭示了分类中的独特模式，这表明我们的现象学体验存在个体差异。
