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

Reinforcement Schedules01:24

Reinforcement Schedules

144
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
144
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

2.6K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
2.6K
Cognitive Learning01:21

Cognitive Learning

237
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...
237
Associative Learning01:27

Associative Learning

340
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
340
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

3.9K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
3.9K
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.1K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.1K

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

Physiological and transcriptomic analyses of Rosa persica in response to drought stress and functional validation of the transcription factor RpERF113-like.

BMC genomics·2026
Same author

Infant traumatic brain injury with a biphasic clinical course and late diffusion restriction: a case report.

Frontiers in neuroscience·2026
Same author

MCEPANet: A connectivity-edge guided attention network for robust medical image segmentation with multi-scale boundary preservation.

Biomedical physics & engineering express·2026
Same author

Line-of-sight stabilization enhancement via band combination feedforward control in a mobile platform-mounted optical communication telescope.

Applied optics·2026
Same author

Toward Fair Federated Graph Learning.

IEEE transactions on neural networks and learning systems·2026
Same author

From phytoremediation to renewable energy: sustainable upcycling of Fe-enriched peanut sprouts into single-atom catalysts for rechargeable zinc-air battery.

Bioresource technology·2026
Same journal

Blue Noise Dithering for Reservoir-based Spatio-temporal Importance Resampling.

IEEE transactions on visualization and computer graphics·2026
Same journal

ROS-GS: Relightable Outdoor Scenes With Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
查看所有相关文章

相关实验视频

Updated: Jun 25, 2025

An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
08:59

An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

Published on: March 3, 2023

2.1K

可见:以洞察力驱动的层次表可视化与强化学习.

Guozheng Li, Peng He, Xinyu Wang

    IEEE transactions on visualization and computer graphics
    |May 23, 2024
    PubMed
    概括
    此摘要是机器生成的。

    InsigHTable是一个新的系统,可以帮助用户创建有洞察力的层次表可视化. 它使用深度强化学习来有效地发现数据洞察力,减少可视化构建的复杂性.

    更多相关视频

    Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions
    07:09

    Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions

    Published on: May 2, 2019

    6.1K
    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.1K

    相关实验视频

    Last Updated: Jun 25, 2025

    An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
    08:59

    An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

    Published on: March 3, 2023

    2.1K
    Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions
    07:09

    Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions

    Published on: May 2, 2019

    6.1K
    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.1K

    科学领域:

    • 数据可视化 数据可视化
    • 人与计算机的交互
    • 机器学习 机器学习

    背景情况:

    • 层次表呈现复杂的数据,但可视化可以增加认知负载.
    • 创建层次表可视化的现有方法通常是繁和低效的.

    研究的目的:

    • 开发一个高效和洞察力驱动的系统,用于构建层次表可视化.
    • 为了应对庞大的设计空间和繁的建筑工艺所带来的挑战.

    主要方法:

    • 建议InsigHTable,一个混合倡议系统,集成数据洞察力和层次结构.
    • 使用深度强化学习框架与辅助奖励机制来进行顺序决策.
    • 定义数据洞察力,考虑表头的层次结构.

    主要成果:

    • InsigHTable有效地促进了层次表可视化的构建.
    • 具有辅助奖励的深度强化学习框架在发现数据洞察力方面被证明是有效的.
    • 案例研究和实验验证了系统的可用性和有效性.

    结论:

    • InsigHTable提高了创建层次表可视化的效率.
    • 该系统使用户能够更好地理解复杂的数据,并揭示隐藏的见解.
    • 混合主动和深度强化学习方法对于数据可视化工具的开发非常有价值.