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

Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

4.0K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
4.0K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

1.4K
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 the...
1.4K
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.3K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.3K
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

783
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
783
Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

940
Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
940

You might also read

Related Articles

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

Sort by
Same author

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity.

Journal of visualized experiments : JoVE·2025
Same author

Semantic Consistency Reasoning for 3-D Object Detection in Point Clouds.

IEEE transactions on neural networks and learning systems·2023
Same author

Decreasing dissolved oxygen enhances in situ curtailment of intermediate Cr(VI) during photo-oxidative decomplexation of Cr(III)-EDTA.

Environmental science and pollution research international·2023
Same author

The promotion of neural regeneration in an extreme rat spinal cord injury model using a collagen scaffold containing a collagen binding neuroprotective protein and an EGFR neutralizing antibody.

Biomaterials·2010
Same author

Significant evidence of association between polymorphisms in ZNF533, environmental factors, and nonsyndromic orofacial clefts in the Western Han Chinese population.

DNA and cell biology·2010
Same author

[Surgical outcomes of pediatric symptomatic epilepsy and their influencing factors].

Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics·2010

Related Experiment Video

Updated: May 6, 2026

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

13.1K

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task.

Xiangyi Lyu1, Jun Wu2, Zhigang Ma3

  • 1School of Economics and Management, Jiangsu University of Science and Technology.

Journal of Visualized Experiments : Jove
|December 22, 2025
PubMed
Summary

This study used electrocardiographic (ECG) data to objectively measure cognitive load during human-AI collaboration. Findings reveal how task difficulty and delegation authority impact cognitive load in real-time.

More Related Videos

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task
13:18

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task

Published on: May 24, 2020

8.2K
Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
07:08

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Published on: December 5, 2025

146

Related Experiment Videos

Last Updated: May 6, 2026

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

13.1K
Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task
13:18

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task

Published on: May 24, 2020

8.2K
Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
07:08

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Published on: December 5, 2025

146

Area of Science:

  • Human-Computer Interaction
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Cognitive load significantly impacts workplace performance and safety.
  • Real-time, objective measurement of cognitive load is crucial for human-AI collaboration.
  • Traditional cognitive load assessment methods (e.g., questionnaires) lack real-time accuracy.

Purpose of the Study:

  • To investigate cognitive load dynamics during human-AI collaboration using physiological data.
  • To examine how task difficulty and delegation authority influence cognitive load.
  • To explore internal state dynamics during human-AI collaboration.

Main Methods:

  • Implemented a generalizable human-AI collaborative task with varying difficulty levels.
  • Participants could choose to complete tasks themselves or delegate to an AI.
  • Electrocardiographic (ECG) data were collected for continuous, objective cognitive load monitoring.

Main Results:

  • Physiological signals, specifically ECG, provide a more objective measure of real-time cognitive load compared to traditional methods.
  • Task difficulty and the authority to delegate tasks to AI influence participants' cognitive load.
  • Distinct internal state dynamics were identified through physiological data during collaboration.

Conclusions:

  • ECG data can effectively track cognitive load fluctuations in human-AI collaborative tasks.
  • Understanding these dynamics is essential for optimizing human-AI collaboration models.
  • Findings support enhanced efficiency and resource integration by leveraging human and AI strengths.