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信息论分析多认知模式下大脑中知识和意义创造周期

Information-Theoretical Analysis of the Cycle of Creation of Knowledge and Meaning in Brains under Multiple Cognitive Modalities.

机构信息

Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Physics & Ian Kirk's Lab., Centre for Brain Research, The University of Auckland, Auckland 1142, New Zealand.

The Embassy of Peace, Whitianga, Coromandel 3591, New Zealand.

出版信息

Sensors (Basel). 2024 Feb 29;24(5):1605. doi: 10.3390/s24051605.

DOI:10.3390/s24051605
PMID:38475141
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10935065/
Abstract

It is of great interest to develop advanced sensory technologies allowing non-invasive monitoring of neural correlates of cognitive processing in people performing everyday tasks. A lot of progress has been reported in recent years in this research area using scalp EEG arrays, but the high level of noise in the electrode signals poses a lot of challenges. This study presents results of detailed statistical analysis of experimental data on the cycle of creation of knowledge and meaning in human brains under multiple cognitive modalities. We measure brain dynamics using a HydroCel Geodesic Sensor Net, 128-electrode dense-array electroencephalography (EEG). We compute a pragmatic information (PI) index derived from analytic amplitude and phase, by Hilbert transforming the EEG signals of 20 participants in six modalities, which combine various audiovisual stimuli, leading to different mental states, including relaxed and cognitively engaged conditions. We derive several relevant measures to classify different brain states based on the PI indices. We demonstrate significant differences between engaged brain states that require sensory information processing to create meaning and knowledge for intentional action, and relaxed-meditative brain states with less demand on psychophysiological resources. We also point out that different kinds of meanings may lead to different brain dynamics and behavioral responses.

摘要

开发先进的感官技术，允许在人们执行日常任务时对认知处理的神经相关性进行非侵入性监测，这是非常有趣的。近年来，在使用头皮 EEG 阵列的这个研究领域已经取得了很多进展，但电极信号中的高噪声带来了很多挑战。本研究对人类大脑在多种认知模式下创造知识和意义周期的实验数据进行了详细的统计分析。我们使用 HydroCel Geodesic Sensor Net，128 电极密集型脑电图（EEG）来测量大脑动态。我们通过对 20 名参与者在六种模式下的 EEG 信号进行希尔伯特变换，计算出一个源于分析幅度和相位的实用信息（PI）指数，这些模式结合了各种视听刺激，导致不同的心理状态，包括放松和认知参与的状态。我们根据 PI 指数得出了几个相关的测量方法，以对不同的大脑状态进行分类。我们证明了需要感官信息处理才能为有意行动创造意义和知识的参与性大脑状态与对心理生理资源需求较少的放松冥想性大脑状态之间存在显著差异。我们还指出，不同的意义可能导致不同的大脑动态和行为反应。

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5
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6
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8
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