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切换还是保持?双稳态感知中内部心理状态的自动分类。

Switch or stay? Automatic classification of internal mental states in bistable perception.

作者信息

Sen Susmita, Daimi Syed Naser, Watanabe Katsumi, Takahashi Kohske, Bhattacharya Joydeep, Saha Goutam

机构信息

1Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721 302 India.

2Department of Intermediate Art and Science, Waseda University, Tokyo, Japan.

出版信息

Cogn Neurodyn. 2020 Feb;14(1):95-113. doi: 10.1007/s11571-019-09548-7. Epub 2019 Jul 19.

Abstract

The human brain goes through numerous cognitive states, most of these being hidden or implicit while performing a task, and understanding them is of great practical importance. However, identifying internal mental states is quite challenging as these states are difficult to label, usually short-lived, and generally, overlap with other tasks. One such problem pertains to bistable perception, which we consider to consist of two internal mental states, namely, transition and maintenance. The transition state is short-lived and represents a change in perception while the maintenance state is comparatively longer and represents a stable perception. In this study, we proposed a novel approach for characterizing the duration of transition and maintenance states and classified them from the neuromagnetic brain responses. Participants were presented with various types of ambiguous visual stimuli on which they indicated the moments of perceptual switches, while their magnetoencephalogram (MEG) data were recorded. We extracted different spatio-temporal features based on wavelet transform, and classified transition and maintenance states on a trial-by-trial basis. We obtained a classification accuracy of 79.58% and 78.40% using SVM and ANN classifiers, respectively. Next, we investigated the temporal fluctuations of these internal mental representations as captured by our classifier model and found that the accuracy showed a decreasing trend as the maintenance state was moved towards the next transition state. Further, to identify the neural sources corresponding to these internal mental states, we performed source analysis on MEG signals. We observed the involvement of sources from the parietal lobe, occipital lobe, and cerebellum in distinguishing transition and maintenance states. Cross-conditional classification analysis established generalization potential of wavelet features. Altogether, this study presents an automatic classification of endogenous mental states involved in bistable perception by establishing brain-behavior relationships at the single-trial level.

摘要

人类大脑会经历无数种认知状态,其中大部分在执行任务时是隐藏的或隐含的,而理解这些状态具有重大的实际意义。然而,识别内部心理状态颇具挑战性,因为这些状态难以标记,通常持续时间很短,而且一般会与其他任务重叠。一个这样的问题与双稳态感知有关,我们认为它由两种内部心理状态组成,即转换和维持。转换状态持续时间很短,代表感知的变化,而维持状态相对较长,代表稳定的感知。在本研究中,我们提出了一种新颖的方法来表征转换和维持状态的持续时间,并根据脑磁图大脑反应对它们进行分类。向参与者呈现各种类型的模糊视觉刺激,他们要指出感知切换的时刻,同时记录他们的脑磁图(MEG)数据。我们基于小波变换提取了不同的时空特征,并逐次试验地对转换和维持状态进行分类。使用支持向量机(SVM)和人工神经网络(ANN)分类器,我们分别获得了79.58%和78.40%的分类准确率。接下来,我们研究了我们的分类器模型所捕捉到的这些内部心理表征的时间波动,发现随着维持状态向下一个转换状态转变,准确率呈下降趋势。此外,为了识别与这些内部心理状态相对应的神经源,我们对MEG信号进行了源分析。我们观察到顶叶、枕叶和小脑的神经源参与了区分转换和维持状态。交叉条件分类分析确立了小波特征的泛化潜力。总之,本研究通过在单次试验水平上建立脑-行为关系,提出了一种对双稳态感知中涉及的内源性心理状态进行自动分类的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a4/6973829/9abcdb70de45/11571_2019_9548_Fig1_HTML.jpg

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