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

Steps in the Modeling Process01:14

Steps in the Modeling Process

290
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...
290
Modeling in Therapy01:26

Modeling in Therapy

139
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
139
Modeling and Similitude01:12

Modeling and Similitude

323
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...
323
Typical Model Studies01:30

Typical Model Studies

426
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
426
Observational Learning01:12

Observational Learning

285
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
285
Molecular Models02:00

Molecular Models

40.0K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
40.0K

You might also read

Related Articles

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

Sort by
Same author

Exploring farmers' perceptions of the value and management of dairy-bred calves in block calving dairy systems.

Journal of dairy science·2026
Same author

Neural field modeling and analysis of consciousness states in the brain.

Neuroscience of consciousness·2025
Same author

Generation of surrogate brain maps preserving spatial autocorrelation through random rotation of geometric eigenmodes.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Empirical estimation of the eigenmodes of macroscale cortical dynamics: Reconciling neural field eigenmodes and resting-state networks.

Neuroimage. Reports·2025
Same author

Electrical circuit model of spatiotemporal trade dynamics: Foundations and derivation of the gravity model.

PloS one·2025
Same author

Unification of alpha, mu, and tau rhythms and their beta-band harmonics via eigenmodes: spectral peaks, topography, and reactivity.

Journal of theoretical biology·2025
Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026
Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026
Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026
Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026
Same journal

Neural responses to equity and inequity when receiving vicarious rewards for self and charity during adolescence.

NeuroImage·2026
Same journal

Cognitive Strategy-based neuromodulation optimizes neural communication to improve working memory.

NeuroImage·2026
See all related articles

Related Experiment Video

Updated: Aug 29, 2025

Corticospinal Excitability Modulation During Action Observation
12:33

Corticospinal Excitability Modulation During Action Observation

Published on: December 31, 2013

9.0K

Ten rules for effective modeling.

P A Robinson1

  • 1School of Physics, The University of Sydney, Sydney, NSW 2006, Australia.

Neuroimage
|September 12, 2022
PubMed
Summary
This summary is machine-generated.

This study presents ten rules for effective brain modeling, ensuring valid and well-supported outcomes. These guidelines help researchers integrate complex phenomena into a coherent picture for deeper understanding.

Keywords:
Brain modelingMethods

More Related Videos

Construction of a Realistic, Whole-Body, Three-Dimensional Equine Skeletal Model using Computed Tomography Data
11:09

Construction of a Realistic, Whole-Body, Three-Dimensional Equine Skeletal Model using Computed Tomography Data

Published on: February 25, 2021

3.3K
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

1.8K

Related Experiment Videos

Last Updated: Aug 29, 2025

Corticospinal Excitability Modulation During Action Observation
12:33

Corticospinal Excitability Modulation During Action Observation

Published on: December 31, 2013

9.0K
Construction of a Realistic, Whole-Body, Three-Dimensional Equine Skeletal Model using Computed Tomography Data
11:09

Construction of a Realistic, Whole-Body, Three-Dimensional Equine Skeletal Model using Computed Tomography Data

Published on: February 25, 2021

3.3K
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

1.8K

Area of Science:

  • Neuroscience
  • Computational Biology
  • Systems Biology

Background:

  • Modeling natural systems, like the brain, is crucial for understanding complex phenomena.
  • Integrated modeling requires careful planning of aims, methods, and analysis.

Purpose of the Study:

  • To provide ten rules for integrated modeling of the brain and other systems.
  • To ensure valid and well-supported outcomes in scientific modeling.
  • To assist researchers and referees in assessing modeling validity.

Main Methods:

  • Development of a ten-rule framework for integrated modeling.
  • Focus on clear aims, coordinated selection of model components, and analysis methods.
  • Emphasis on distillation and presentation of results for well-supported conclusions.

Main Results:

  • A structured approach to brain modeling is presented.
  • The ten rules facilitate the integration of multiple phenomena into a coherent model.
  • The framework supports the achievement of specific modeling aims.

Conclusions:

  • Adherence to these ten rules enhances the validity and success of brain modeling projects.
  • The proposed framework aids in achieving a deeper understanding of complex systems.
  • These guidelines serve as a valuable tool for both modelers and manuscript reviewers.