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

Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

453
Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
453
Cognitive Enhancers: Cholinesterase Inhibitors and NMDA Receptor Antagonists01:30

Cognitive Enhancers: Cholinesterase Inhibitors and NMDA Receptor Antagonists

110
Cognitive enhancers, also known as "smart drugs," are substances used to enhance memory, mental alertness, and concentration. These can be natural or synthetic and improve cognition in conditions like Alzheimer's disease (AD) and other neurodegenerative diseases. Some common examples include caffeine, amphetamines, methylphenidate, modafinil, arecoline, donepezil, vortioxetine, and piracetam. These enhancers work on the principle of synaptic plasticity and altered circuit function.
110
Cognitive Learning01:21

Cognitive Learning

233
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
233
Survival Tree01:19

Survival Tree

73
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
73
Role of Cerebellum and Prefrontal Cortex in Memory01:14

Role of Cerebellum and Prefrontal Cortex in Memory

396
The cerebellum, while traditionally associated with motor control, also plays a crucial role in memory, particularly in procedural memory, which involves learning motor tasks that become automatic through repetition. For example, studies have shown that when the cerebellum is damaged, individuals or animals lose the ability to learn conditioned motor responses, such as the conditioned eye-blink response in classical conditioning experiments with rabbits. This study demonstrates the...
396

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

Prediction of cognitive performance by demographics, sleep, and brain morphometry: machine learning findings from ENIGMA-Sleep Working Group.

Research square·2026
Same author

Global prevalence and disability burden of brain disorders: Impact of neurological, mental, and substance use disorders.

Neuroscience and biobehavioral reviews·2026
Same author

Widespread use of invalid statistical tests in biomedical machine learning.

bioRxiv : the preprint server for biology·2026
Same author

Biological brain aging, cognitive-motor decline and vascular risk: a multivariate imaging analysis of 40,579 individuals.

Frontiers in aging neuroscience·2026
Same author

Linking human brain functional connectivity to underlying neurotransmission.

bioRxiv : the preprint server for biology·2026
Same author

Test-retest reliability of meta analytic networks during naturalistic viewing.

PloS one·2026
Same journal

Somatosensory cortex shapes perceptual decision bias via the superior colliculus.

Research square·2026
Same journal

Combinatorial Targeting of Avapritinib-Driven MAP Kinase Activation in High-Grade Glioma.

Research square·2026
Same journal

Supporting Implementation of the National Standards for Cancer Survivorship Care: Development of the Cancer Survivorship Maturity Model (CSMM).

Research square·2026
Same journal

Operationalizing a walking exercise prescription based on 6-minute walk test results.

Research square·2026
Same journal

Age but not sex modifies lymphoid immune responses in murine sepsis.

Research square·2026
Same journal

Indirect effect, through aspects of neighborhood affluence and racial/ethnic composition, of receiving a Section 8 voucher on the prevalence of psychiatric disorders among boys and girls in the Moving to Opportunity study.

Research square·2026
查看所有相关文章

相关实验视频

Updated: Jun 15, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

使用ML来预测认知功能的性能使用ML的陷.

Gianna Kuhles, Sami Hamdan, Stefan Heim

    Research square
    |August 26, 2024
    PubMed
    概括
    此摘要是机器生成的。

    机器学习对认知能力的预测可能会因为混变量而导致误导. 这项研究表明,这些因素如何膨胀准确性,强调需要在分析中仔细控制.

    更多相关视频

    Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
    10:13

    Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

    Published on: February 14, 2014

    13.7K
    Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
    10:28

    Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

    Published on: July 24, 2019

    15.2K

    相关实验视频

    Last Updated: Jun 15, 2025

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    7.5K
    Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach
    10:13

    Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

    Published on: February 14, 2014

    13.7K
    Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
    10:28

    Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

    Published on: July 24, 2019

    15.2K

    科学领域:

    • 认知神经科学 认知神经科学
    • 计算语言学 计算语言学
    • 心理测量 心理测量 心理测量

    背景情况:

    • 机器学习 (ML) 经常用于从各种数据源预测认知能力.
    • 在ML实施和结果解释方面存在潜在的陷,特别是涉及混变量.
    • 使用语音特征进行执行功能 (EF) 性能预测是一个相关的应用领域.

    研究的目的:

    • 为了突出ML预测中由于混变量导致错误结论的风险.
    • 用一个案例来说明这些风险,该案例预测了使用prosodic特征预测EF性能.
    • 强调在ML管道中控制混杂物的重要性.

    主要方法:

    • 健康的参与者 (n=231) 完成了语言任务和EF测试.
    • 使用ML模型,从264个体特征中预测EF性能.
    • 控制了年龄,性别和教育的混影响.

    主要成果:

    • 最初观察到一个合理的模型适合预测 EF 性能在 Trail 制作测试.
    • 深入的分析显示了混泄漏,导致预测准确度膨胀.
    • 混和目标之间的强烈关系被确定为膨胀精度的原因.

    结论:

    • 混变量可以显著影响ML模型的性能和准确性.
    • 如果未能充分控制混因素,可能会导致认知预测任务中的错误结论.
    • 研究人员必须保持谨慎,并实施可靠的方法来控制ML分析中的混变量.