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

Cognitivism01:17

Cognitivism

1.7K
Cognitive psychology emerged as a significant field in the mid-20th century. It focused on understanding humans' internal mental processes. This approach emphasizes how people perceive, remember, think, and solve problems—elements critical to human cognition.
Previously dominated by behaviorism, which prioritized observable behaviors and largely ignored mental processes, psychology transformed in the 1950s. Cognitive psychologists argue that understanding how we think and process...
1.7K
Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

1.2K
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...
1.2K
Data Validation01:03

Data Validation

5.5K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
5.5K
Steps in the Modeling Process01:14

Steps in the Modeling Process

336
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
336
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

132
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
132
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

107
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...
107

You might also read

Related Articles

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

Sort by
Same author

The role of goals, actions, and outcomes in the sense of agency.

Acta psychologica·2026
Same author

Opportunities and risks of artificial intelligence in patient portal messaging in primary care.

NPJ digital medicine·2025
Same author

Technology use among the nation's medical examiner and coroner offices: Data from the 2018 Census of Medical Examiner and Coroner Offices.

Forensic science international. Synergy·2024
Same author

Understanding Is a Process.

Frontiers in systems neuroscience·2022
Same author

Evaluation of Goal Recognition Systems on Unreliable Data and Uninspectable Agents.

Frontiers in artificial intelligence·2022
Same author

Accessibility and Usability of State Health Department COVID-19 Vaccine Websites: A Qualitative Study.

JAMA network open·2021
Same journal

Limits to Language Prediction: Findings From Diverse Populations.

Topics in cognitive science·2026
Same journal

There Is More Than Meets the Eye: The Dual Role of Perception in Shaping Color Lexicons.

Topics in cognitive science·2026
Same journal

Inference and Imagination.

Topics in cognitive science·2026
Same journal

Gesture Use Across Different Concepts: Focusing on Cross-Linguistic Diversity.

Topics in cognitive science·2026
Same journal

Exploring Amazonian Cognitive Diversity at Chana Research Station.

Topics in cognitive science·2026
Same journal

Do (We Think That) Plants Have Agency?

Topics in cognitive science·2026
See all related articles

Related Experiment Video

Updated: Sep 22, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

829

Validating and Refining Cognitive Process Models Using Probabilistic Graphical Models.

Laura M Hiatt1, Connor Brooks2, J Gregory Trafton1

  • 1Navy Center for Applied Research in Artificial Intelligence, US Naval Research Laboratory.

Topics in Cognitive Science
|May 24, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for refining cognitive process models. By comparing graphical models derived from human data and simulated data, researchers can improve model accuracy.

Keywords:
ACT-RCognitive modelsGraphical models

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

604
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Related Experiment Videos

Last Updated: Sep 22, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

829
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

604
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Cognitive process models are crucial for understanding human behavior.
  • Validating these models against empirical data is essential but challenging.
  • Existing methods may not fully capture the nuances of cognitive processes.

Purpose of the Study:

  • To present a new methodology for developing and validating cognitive process models.
  • To enhance the fidelity of cognitive models to empirical data.
  • To provide a systematic approach for identifying and correcting model imperfections.

Main Methods:

  • Utilizing graphical models, specifically hidden Markov models.
  • Developing models from both human empirical data and synthetic data traces.
  • Employing differences between graphical models to guide iterative model refinement.

Main Results:

  • The methodology effectively uncovers subtle imperfections in cognitive process models.
  • Iterative refinement based on model comparison enhances model fidelity.
  • The approach allows for addressing nuanced discrepancies between models and data.

Conclusions:

  • The proposed method offers a robust framework for cognitive model development and validation.
  • This approach leads to more accurate and reliable cognitive process models.
  • It facilitates a deeper understanding of cognitive mechanisms through improved model fidelity.