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

383
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...
383
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

3.1K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
3.1K
Cognitive Learning01:21

Cognitive Learning

1.5K
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...
1.5K
Purposive Learning01:22

Purposive Learning

563
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
563
Observational Learning01:12

Observational Learning

1.2K
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...
1.2K
Principle of Virtual Work: Problem Solving01:13

Principle of Virtual Work: Problem Solving

1.8K
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,...
1.8K

You might also read

Related Articles

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

Sort by
Same author

Digital Health Monitoring and Intervention Suite for Stress in Frontline Nurses: Prospective Cohort Trial.

JMIR formative research·2026
Same author

Using Sound to Simulate Tactile Cues: AI-Generated Audio and Pseudo-Haptics in Medical Simulation.

Cureus·2026
Same author

Numeracy in Adolescents With Type 1 Diabetes: Assessment and Application of a Video Game Intervention.

Cureus·2026
Same author

Shaping the Future: Scaling Entrustable Professional Activities to Nonclinical Simulation Operations Specialist Training.

Simulation in healthcare : journal of the Society for Simulation in Healthcare·2026
Same author

Examining the Validity of the Implementation Quality Rubric for Simulation (IQR-SIM) for Assessing Implementation Quality of Simulation-based Programs.

Simulation in healthcare : journal of the Society for Simulation in Healthcare·2026
Same author

Communication and Medication-Related Deprescribing for Healthcare Professionals: A Rapid Review of the Literature.

Worldviews on evidence-based nursing·2026
Same journal

Correspondence: Peer support is not a substitute for institutional reform in mental health disclosure amongst medical students.

Medical education·2026
Same journal

When I say validity.

Medical education·2026
Same journal

Channelling Socrates to re-imagine asynchronous online learning.

Medical education·2026
Same journal

Moving beyond tokenism: A structured and intentional approach to embedding health advocacy in medical education.

Medical education·2026
Same journal

When I say … 'in situ simulation'.

Medical education·2026
Same journal

Examiner training and calibration for simulated clinical examinations: A scoping review.

Medical education·2026
See all related articles

Related Experiment Video

Updated: Mar 15, 2026

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
20:12

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Published on: October 8, 2011

31.2K

Thrive or overload? The effect of task complexity on novices' simulation-based learning.

Faizal A Haji1,2,3, Jeffrey J H Cheung1,2, Nicole Woods1

  • 1Wilson Centre, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.

Medical Education
|August 27, 2016
PubMed
Summary
This summary is machine-generated.

Reducing task complexity in simulation training improves novice performance and lowers cognitive load during skill acquisition and retention. However, this benefit may not fully transfer to more complex tasks, highlighting task complexity’s role in learning outcomes.

More Related Videos

Simulator Training for Endovascular Neurosurgery
08:08

Simulator Training for Endovascular Neurosurgery

Published on: May 6, 2020

4.2K
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

5.1K

Related Experiment Videos

Last Updated: Mar 15, 2026

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
20:12

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Published on: October 8, 2011

31.2K
Simulator Training for Endovascular Neurosurgery
08:08

Simulator Training for Endovascular Neurosurgery

Published on: May 6, 2020

4.2K
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

5.1K

Area of Science:

  • Medical Education
  • Simulation-based Training
  • Cognitive Load Theory

Background:

  • High-fidelity simulation is often linked to improved learning transfer.
  • However, increased fidelity can elevate task complexity, potentially overloading novice learners.
  • Understanding the interplay between task complexity and cognitive load is crucial for effective simulation design.

Purpose of the Study:

  • To investigate how varying task complexity affects novices' cognitive load and learning in simulation-based procedural skills training.
  • To examine the impact of task complexity on skill acquisition, retention, and transfer of learning.

Main Methods:

  • Thirty-eight medical students were randomized to practice a simple or complex lumbar puncture (LP) simulation.
  • Participants underwent four practice trials for skill acquisition, followed by retention and transfer trials after a 10-day interval.
  • Performance and cognitive load were assessed using multiple measures across all trials.

Main Results:

  • The simple task group consistently showed superior LP performance and lower cognitive load during acquisition and retention.
  • While the simple task group experienced performance decline and increased cognitive load between retention and transfer, the complex task group remained stable.
  • No significant differences in performance or cognitive load were observed at the transfer phase, except for fewer sterility breaches in the simple task group.

Conclusions:

  • Reduced task complexity enhances initial learning and retention in simulation but yields mixed results for transfer to more complex procedures.
  • Cognitive overload, mediated by task complexity, appears to influence the relationship between simulation design and learning outcomes.
  • Task complexity is a critical factor to consider in simulation instructional design to optimize learning for novices.