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

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

254
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
254
Types of Selection01:46

Types of Selection

41.4K
Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
41.4K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

149
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
149
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

86
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
86
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
101
Scaling01:26

Scaling

317
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
317

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

Building bridges between brain and behavior: An open-source toolbox for joint modeling with fMRI.

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

Positive Bias in Value-Based Decision Making: Neurocognitive Associations with Resilience.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026
Same author

Effector-specific corticospinal modulation is preserved in older adults during proactive stopping: A novel Bayesian approach.

Neurobiology of aging·2026
Same author

An illustrative guide to expressing cognitive theories using evidence accumulation modelling.

Behavior research methods·2026
Same author

Joint Cognitive Models Reveal Sources of Robust Individual Differences in Conflict Processing.

Computational brain & behavior·2026
Same author

The diffusion model's drift rate parameter primarily reflects efficiency, rather than speed, of evidence accumulation.

Psychonomic bulletin & review·2026
Same journal

Perception and action as one: Re-integrating research on human action through event files.

Psychological review·2026
Same journal

Associative learning explains "intuitive statistics" in animals.

Psychological review·2026
Same journal

A reciprocal model of practice and skill: Navigating between dropout and expertise.

Psychological review·2026
Same journal

The relative psychometric function: A general analysis framework for relating psychological processes.

Psychological review·2026
Same journal

A taxonomy of discriminatory behavior.

Psychological review·2026
Same journal

Extreme-value signal detection theory for recognition memory: The parametric road not taken.

Psychological review·2026
查看所有相关文章

相关实验视频

Updated: Sep 11, 2025

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

7.7K

解释多尺度选择动态.

Steven Miletić1, Niek Stevenson2, Ami Eidels3

  • 1Cognitive Psychology Unit, Institute of Psychology, Leiden University.

Psychological review
|August 11, 2025
PubMed
概括
此摘要是机器生成的。

选择响应时间显示复杂的多层次动态. 独特的学习和控制机制通过更新对环境的表示和决策者指导选择的能力来解释这些动态.

更多相关视频

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

7.6K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.3K

相关实验视频

Last Updated: Sep 11, 2025

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

7.7K
Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

7.6K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.3K

科学领域:

  • 认知心理学 认知心理学
  • 计算神经科学是一种计算神经科学.
  • 决策科学科学 决策科学

背景情况:

  • 选择响应时间表现出普遍的多尺度动态,表明跨各种时间尺度的顺序依赖.
  • 驱动这些复杂动态的基本机制在决策研究中仍然不太了解.

研究的目的:

  • 解释在选择反应时间中观察到的多尺度动态.
  • 确定并将特定的学习和控制机制与决策中的已知顺序效应联系起来.

主要方法:

  • 模拟不同学习和控制机制的叠加.
  • 代表环境结构和决策者能力.
  • 调节证据积累过程,基于学习到的表征.

主要成果:

  • 秒到分钟范围内的动态是由多个学习和控制机制的叠加解释的.
  • 这些机制在每次选择后更新了选择环境和/或决策者能力的表示.
  • 该模型成功地将这些机制与刺激历史,与错误相关,以及选择序列中的难易效应联系起来.

结论:

  • 一个统一的帐户解释了选择序列和关键实验效应的多尺度动态.
  • 拟议的机制提供了一个计算框架,用于理解在顺序依赖下决策.
  • 该模型为观察到的选择行为提供了群体和个人层面的解释.