当前位置: X-MOL 学术Cogn. Neurodyn. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Switch or stay? Automatic classification of internal mental states in bistable perception
Cognitive Neurodynamics ( IF 3.7 ) Pub Date : 2019-07-19 , DOI: 10.1007/s11571-019-09548-7
Susmita Sen 1 , Syed Naser Daimi 1 , Katsumi Watanabe 2 , Kohske Takahashi 3 , Joydeep Bhattacharya 4 , Goutam Saha 1
Affiliation  

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.

中文翻译:

切换还是停留?在双稳态感知中自动分类内部心理状态

人脑会经历多种认知状态,其中大多数在执行任务时会被隐藏或隐含,理解它们具有重大的现实意义。但是,识别内部心理状态非常具有挑战性,因为这些状态很难标注,通常是短暂的,并且通常与其他任务重叠。一个这样的问题与双稳态知觉有关,我们认为它由两个内部心理状态组成,即过渡和维持。过渡状态是短暂的,代表感知的变化,而维持状态则相对较长,代表稳定的感知。在这项研究中,我们提出了一种表征过渡和维持状态持续时间的新方法,并根据神经磁脑反应对它们进行了分类。向参与者展示各种类型的歧义视觉刺激,他们在其上指示知觉转换的时刻,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,并在逐个试验的基础上对过渡和维护状态进行了分类。使用SVM和ANN分类器,我们分别获得了79.58%和78.40%的分类精度。接下来,我们研究了分类器模型捕获的这些内部心理表征的时间波动,发现随着维护状态移向下一个过渡状态,准确性显示出下降的趋势。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,枕叶和小脑的来源参与了区分过渡和维持状态的过程。交叉条件分类分析建立了小波特征的泛化潜力。总而言之,这项研究通过在单次试验中建立脑与行为的关系,提出了一种涉及双稳态感知的内源性精神状态的自动分类。
更新日期:2019-07-19
down
wechat
bug