Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

High-Level and Low-Level Awareness01:19

High-Level and Low-Level Awareness

384
Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...
384
Switching of BJT01:22

Switching of BJT

499
Switching behavior in Bipolar Junction Transistors (BJTs) is a fundamental aspect utilized in various electronic circuits, particularly for digital logic applications like switches and amplifiers. In a typical switching circuit, a BJT alternates between cut-off and saturation modes, corresponding to the "off" and "on" states, respectively, thus behaving like an ideal switch.
Cut-off Mode ("Off" State): In this state, both the emitter-base and collector-base junctions are...
499
Subconsciousness and No Awareness01:15

Subconsciousness and No Awareness

369
The concept of subconscious awareness refers to the processing of information below the level of conscious thought, which significantly influences both behaviors and decisions. It is also known as waking subconscious awareness. This complex level of cognition operates without the direct awareness of the individual, facilitating rapid and simultaneous handling of multiple information streams.
An illustrative example of subconscious processing is its role in problem-solving. Often, individuals...
369

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Learning shapes neural geometry in the primate prefrontal cortex.

Nature neuroscience·2026
Same author

External task switches activate default mode regions without enhanced processing of the surrounding scene.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Domain general frontoparietal regions show modality-dependent coding of auditory and visual rules.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Enablers and Barriers to Integrating Advance Care Planning in Chronic Kidney Disease Care in a Canadian Provincial Network.

Canadian journal of kidney health and disease·2025
Same author

Specialized response of default mode subnetworks and multiple-demand regions to changes in social content, place and time.

Neuropsychologia·2025
Same author

Genome Sequence of the Cluster EF Bacteriophage TinyMiny, Isolated using <i>Microbacterium foliorum</i>.

microPublication biology·2025
Same journal

Individualized mapping of functional brain networks in older adulthood.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

The language network responds robustly to sentences across tasks.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

Neighborhood disadvantage and brain myelination: Insights from infancy to childhood.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

Meditation and neurofeedback: A systematic scoping review, synthesis, and future directions.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

Interactive shape and color representation in visual working memory for colored objects in the human occipitotemporal cortex.

Imaging neuroscience (Cambridge, Mass.)·2026
查看所有相关文章

相关实验视频

Updated: Sep 11, 2025

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity
10:43

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Published on: July 1, 2014

15.3K

在任务交换机上的默认模式网络激活反映了心理任务集结构.

Ashley X Zhou1, John Duncan1, Daniel J Mitchell1

  • 1MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.

Imaging neuroscience (Cambridge, Mass.)
|August 13, 2025
PubMed
概括
此摘要是机器生成的。

默认模式网络 (DMN) 显示在任务切换期间活动增加. 它的反应取决于任务组的学习顺序,而不是任务的数量,这表明大脑中的等级组织.

关键词:
MVPA MVPA是什么意思认知 认知 认知默认模式网络模式 默认模式网络模式功能性核磁共振成像 (MRI) 功能性核磁共振成像切换的切换 切换的切换

更多相关视频

Measuring the Switch Cost of Smartphone Use While Walking
07:00

Measuring the Switch Cost of Smartphone Use While Walking

Published on: April 30, 2020

1.9K
Combining Transcranial Magnetic Stimulation and fMRI to Examine the Default Mode Network
11:02

Combining Transcranial Magnetic Stimulation and fMRI to Examine the Default Mode Network

Published on: December 28, 2010

13.1K

相关实验视频

Last Updated: Sep 11, 2025

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity
10:43

Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

Published on: July 1, 2014

15.3K
Measuring the Switch Cost of Smartphone Use While Walking
07:00

Measuring the Switch Cost of Smartphone Use While Walking

Published on: April 30, 2020

1.9K
Combining Transcranial Magnetic Stimulation and fMRI to Examine the Default Mode Network
11:02

Combining Transcranial Magnetic Stimulation and fMRI to Examine the Default Mode Network

Published on: December 28, 2010

13.1K

科学领域:

  • 神经科学是一个神经科学.
  • 认知神经科学 认知神经科学

背景情况:

  • 传统观点认为默认模式网络 (DMN) 是负任务的.
  • 最近的研究表明,在苛刻的任务切换过程中,DMN活动增加.
  • 在交换机期间调节DMN活动的因素仍然不清楚.

研究的目的:

  • 调查DMN对任务切换的响应是否取决于任务集的复杂性或基于学习顺序的抽象任务分组.
  • 探索底层任务切换和认知控制的神经机制.

主要方法:

  • 功能磁共振成像 (fMRI) 用于测量大脑活动.
  • 参与者执行了以不同的数量和学习顺序进行的以外部为重点的任务.
  • 应用多变量解码来分析DMN表示.

主要成果:

  • 在任务切换器上激活核心DMN独立于当前相关任务的数量.
  • 任务组学习的顺序显著影响了DMN响应.
  • 多变量解码在核心DMN中展示了任务,域和基于指令的分组的层次表示.

结论:

  • 在任务切换期间,默认模式网络的参与是由学习任务规则的层次结构调节的.
  • 随着任务复杂性的增加,DMN可能在管理更高层次的组织信息块方面发挥作用.
  • 这些发现挑战了DMN的纯任务负面模型,突出了它在认知灵活性和复杂任务管理中的作用.