Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

57
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...
57
Reason and Intuition01:37

Reason and Intuition

6.5K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
6.5K
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

89
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
89
Principle of Virtual Work: Problem Solving01:13

Principle of Virtual Work: Problem Solving

1.2K
The principle of virtual work is an essential concept in the field of mechanics and engineering. This is used to solve problems related to the equilibrium of a structure or system. It is based on the assumption that if a system is in equilibrium, the work done by all the forces during a virtual displacement is zero. This principle is applied by considering virtual displacements of the system and the corresponding work done by internal and external forces.
To apply the principle of virtual work,...
1.2K
Modeling and Similitude01:12

Modeling and Similitude

270
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
270
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

643
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
643

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Factors influencing duration between non-fatal and fatal crashes among high-risk drivers.

International journal of injury control and safety promotion·2026
Same author

Methadone Maintenance Treatment vs. Long-Term Abstinence Without Opioid Agonist: Epigenome-Wide Study of DNA Methylation.

Epigenomes·2026
Same author

Podium Abstracts Presented at the 2025 Annual Meeting of the Arthroscopy Association of North America.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association·2026
Same author

A self-filtering liquid acoustic sensor for voice recognition.

Nature electronics·2026
Same author

Relationship between social deprivation, CEAP score and time to varicose vein surgery: A single-centre retrospective analysis.

Phlebology·2026
Same author

Evaluating the feasibility of a co-produced, bespoke dementia education programme for formal caregivers of individuals with intellectual disability.

Journal of intellectual disabilities : JOID·2026

Related Experiment Video

Updated: Jul 11, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

9.9K

A rational analysis and computational modeling perspective on IAM and déjà vu.

Justin Li1, Steven Jones2, John Laird2

  • 1Departments of Cognitive Science and Computer Science, Occidental College, Los Angeles, CA, USA justinnhli@oxy.edu https://www.oxy.edu/academics/faculty/justin-li.

The Behavioral and Brain Sciences
|November 14, 2023
PubMed
Summary

This study suggests enhancing a proposed memory architecture by analyzing involuntary autobiographical memory and déjà vu. Computational modeling can clarify these memory phenomena and refine the architecture.

More Related Videos

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm
06:35

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

Published on: April 28, 2016

34.1K
Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
09:49

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Published on: April 16, 2014

25.5K

Related Experiment Videos

Last Updated: Jul 11, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

9.9K
Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm
06:35

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

Published on: April 28, 2016

34.1K
Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
09:49

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Published on: April 16, 2014

25.5K

Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Computational Modeling

Background:

  • Barzykowski and Moulin proposed a novel memory architecture.
  • Existing memory models may not fully account for subjective experiences like déjà vu.
  • Involuntary autobiographical memory plays a crucial role in memory recall.

Purpose of the Study:

  • To enhance the proposed memory architecture by integrating a functional analysis of involuntary autobiographical memory and déjà vu.
  • To address ambiguities in the current memory architecture proposal through computational modeling.
  • To provide a more precise description of memory phenomena using existing research.

Main Methods:

  • Rational analysis of the functional roles of involuntary autobiographical memory and déjà vu.
  • Computational modeling of memory phenomena.
  • Review and synthesis of relevant past research on memory and subjective experiences.

Main Results:

  • Identified areas for improvement in the proposed memory architecture.
  • Demonstrated the utility of computational modeling in clarifying theoretical proposals.
  • Provided examples of precise descriptions for memory-related phenomena.

Conclusions:

  • Integrating functional analysis and computational modeling can significantly refine memory architecture proposals.
  • A deeper understanding of involuntary autobiographical memory and déjà vu is essential for comprehensive memory models.
  • The proposed enhancements offer a path toward more robust and precise computational memory architectures.