Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Principle of Virtual Work: Problem Solving

Principle of Virtual Work: Problem Solving

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,...

Biot-Savart Law: Problem-Solving

Biot-Savart Law: Problem-Solving

The magnitude and direction of a magnetic field created by a steady current can be calculated using the Biot-Savart law.
Consider a mobile phone battery bank as a source of steady current, which flows through the wire connected between the two. What is the magnitude of the magnetic field created by this current at a field point P?
To estimate the magnitude of the total magnetic field, we first consider a small current element of length dl, at a distance r from the field point. Now the following...

Ampere-Maxwell's Law: Problem-Solving

Ampere-Maxwell's Law: Problem-Solving

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...

Cognitive Learning

Cognitive Learning

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...

You might also read

Related Articles

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

Sort by

Same author

Evaluating the Effectiveness of VR Rehabilitation Systems for ASD: A Focus on Embodied Interaction and Longitudinal Impact.

IEEE transactions on visualization and computer graphics·2026

Same author

Transcriptional Response of Rice Mesocotyl Elongation to Sowing Depth and Identification of Key Regulatory Factors.

Genes·2026

Same author

Carbapenem-resistant Acinetobacter baumannii bloodstream infections and specific phages isolation, analysis and application.

Frontiers in cellular and infection microbiology·2026

Same author

Dual modal data-driven virtual reality-based mild cognitive Impairment assessment using MCIformer.

Computer methods and programs in biomedicine·2026

Same author

Identification of potentially deleterious mutations in gastric cancer using patient-derived xenograft models.

Frontiers in genetics·2026

Same author

Dynamic Association between Stressful Life Events and Social Anxiety in University Students: Within-Person Longitudinal Mediation by Rumination.

Journal of youth and adolescence·2025

Search research articles

Related Experiment Video

Updated: Oct 13, 2025

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Functional Brain Network Analysis of Knowledge Transfer While Engineering Problem-Solving.

Fuhua Wang¹, Zuhua Jiang¹, Xinyu Li^2,3

¹Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, China.

Frontiers in Human Neuroscience

|November 11, 2021

Summary

This study reveals how functional connectivity (FC) in the brain reflects knowledge transfer during engineering problem-solving. Prior knowledge and transfer distance significantly alter brain network dynamics, impacting cognitive structure.

Keywords:

brain network cognitive structure functional connectivity functional near-infrared spectroscopy knowledge transfer wavelet phase coherence

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

Related Experiment Videos

Last Updated: Oct 13, 2025

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Published on: August 9, 2016

Area of Science:

Neuroscience
Cognitive Science
Engineering Psychology

Background:

Knowledge transfer is a complex cognitive process vital for engineering problem-solving, involving working memory, behavior control, and decision-making.
Existing research often overlooks the neurophysiological mechanisms underlying knowledge transfer and its impact on cognitive structure.
Understanding the dynamic brain network alterations during knowledge transfer is crucial.

Purpose of the Study:

To investigate the neurophysiological mechanisms of knowledge transfer during engineering problem-solving using functional connectivity (FC).
To evaluate how prior cognitive level, knowledge transfer distance, and transfer performance affect brain network dynamics.
To explore the role of the prefrontal cortex and specific brain regions in knowledge transfer.

Main Methods:

Utilized a modified Wisconsin Card-Sorting Test (M-WCST) with 31 participants.
Recorded neural activation in the prefrontal cortex using functional near-infrared spectroscopy (fNIRS).
Analyzed functional connectivity (FC) through wavelet amplitude and phase coherence.

Main Results:

Prior cognitive level and transfer distance were found to significantly impact functional connectivity (FC).
Wavelet amplitude and phase coherence demonstrated significant correlations with prefrontal cortex cognitive function.
The dorsolateral prefrontal cortex (DLPFC) and occipital face area (OFA) showed distinct patterns during the experiment.

Conclusions:

Functional connectivity (FC) provides a viable method for assessing cognitive structure alterations in knowledge transfer.
The findings offer neurophysiological evidence for the functional brain network supporting knowledge transfer in engineering problem-solving.
This study contributes to NeuroManagement by elucidating the brain mechanisms of knowledge transfer.