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视频片段内容触发的连续脑电图活动的分化分析。

Differentiation Analysis of Continuous Electroencephalographic Activity Triggered by Video Clip Contents.

机构信息

University of Liège.

University of Wisconsin at Madison.

出版信息

J Cogn Neurosci. 2018 Aug;30(8):1108-1118. doi: 10.1162/jocn_a_01278. Epub 2018 May 15.

Abstract

While viewing a video clip, we experience a wide variety of contents, from low-level features of the images to high-level ideas such as the storyline. Each change in our experience must be supported by some corresponding change in neurophysiological activity. Differentiation analysis, which quantifies the differences in brain activity by measuring the distances between observed brain states, was applied here to continuous high-density electroencephalographic data recorded while participants watched short video clips. These clips were manipulated in various ways to change the degree of meaningfulness of their contents. We found that neurophysiological differentiation mirrored that of phenomenal differentiation, being higher for meaningful clips and lower for phase-scrambled versions or random noise. The distinction between meaningful and meaningless clips was present even at the individual level, and moreover, differentiation values correlated with individual subjective reports of meaningfulness. Spatial and spectral breakdowns of the overall effect showed frontal and posterior ROIs and highlighted specific roles for different spectral bands. Comparing the results with a multivariate decoding approach reveals that the two methods are capturing different aspects of brain activity and highlights a crucial theoretical distinction between the level and pattern of activity. In future applications, differentiation analysis may be used to evaluate the subjective meaningfulness of stimuli when behavioral responses may be inadequate, as with disorders of consciousness.

摘要

在观看视频剪辑时,我们会体验到各种各样的内容,从图像的低级特征到情节等高级概念。我们体验中的每一次变化都必须得到一些相应的神经生理活动变化的支持。这里应用了分化分析,通过测量观察到的大脑状态之间的距离来量化大脑活动的差异,对参与者观看短视频剪辑时记录的连续高密度脑电图数据进行了分析。这些剪辑以各种方式进行了操作,以改变其内容的有意义程度。我们发现,神经生理分化反映了现象学分化,有意义的剪辑的分化更高,而相位打乱的版本或随机噪声的分化更低。有意义和无意义剪辑之间的区别即使在个体水平上也是存在的,此外,分化值与个体对有意义的主观报告相关。整体效应的空间和频谱分解显示了额区和枕区 ROI,并突出了不同频谱带的特定作用。将结果与多元解码方法进行比较表明,这两种方法捕捉到了大脑活动的不同方面,并强调了活动水平和模式之间的一个关键理论区别。在未来的应用中,当行为反应可能不充分时,如意识障碍,分化分析可用于评估刺激的主观意义。

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