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

You might also read

Related Articles

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

Sort by
Same author

Unraveling the Unexpected: How Pilots Can Successfully Manage Unexpected Events.

Human factors·2026
Same author

The role of ADHD in aggressive driving behavior among young adult drivers: effects of traffic aggressiveness and roadway environments.

Accident; analysis and prevention·2026
Same author

Human vigilance in the age of intelligent machines: Challenges and prospects.

Ergonomics·2026
Same author

Training for vigilance on the move using knowledge of results: The effects of feedback type on performance and subjective response.

Ergonomics·2025
Same author

Correction: How and why humans trust: A meta-analysis and elaborated model.

Frontiers in psychology·2025
Same author

Can ergonomics/human factors survive?

Ergonomics·2025
Same journal

Identification of systemic barriers, facilitators and adaptations to effective record-keeping: a South African primary healthcare clinic case study.

Ergonomics·2026
Same journal

Layer-specific facial soft-tissue thickness in 1174 Chinese adults: Implications for finite-element headforms and ergonomic design.

Ergonomics·2026
Same journal

The dual effects of information presentation speed on operator performance in dynamic tasks: a study in supervisory control and data acquisition interfaces.

Ergonomics·2026
Same journal

Evaluating generative AI teaching assistants in simulated learning environments: how instructor type and support type affect students' perceptions.

Ergonomics·2026
Same journal

Swipe smart, not hard: hand health of smartphone users in a university population.

Ergonomics·2026
Same journal

Couriers' work-related musculoskeletal disorders and psychological distress: Insights for work errors and traffic safety.

Ergonomics·2026
See all related articles

Related Experiment Video

Updated: May 8, 2026

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

Task partitioning effects in semi-automated human-machine system performance.

P A Hancock1

  • 1a Department of Psychology , University of Central Florida , Orlando , FL , 32826 , USA.

Ergonomics
|September 12, 2013
PubMed
Summary
This summary is machine-generated.

Pilots performing flight simulations showed that partial automation, where pilots retain weapon release control, improved performance over full automation. Effective task partitioning in human-machine systems is crucial for optimal outcomes.

More Related Videos

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

Evaluating Tests of Cognition using a Computerized Touch-Sensitive Tablet, Eye Tracking, and Functional Magnetic Resonance Imaging
10:10

Evaluating Tests of Cognition using a Computerized Touch-Sensitive Tablet, Eye Tracking, and Functional Magnetic Resonance Imaging

Published on: January 30, 2026

Related Experiment Videos

Last Updated: May 8, 2026

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

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

Evaluating Tests of Cognition using a Computerized Touch-Sensitive Tablet, Eye Tracking, and Functional Magnetic Resonance Imaging
10:10

Evaluating Tests of Cognition using a Computerized Touch-Sensitive Tablet, Eye Tracking, and Functional Magnetic Resonance Imaging

Published on: January 30, 2026

Area of Science:

  • Human-Computer Interaction
  • Aerospace Engineering
  • Cognitive Psychology

Background:

  • Complex human-machine systems require effective task allocation between pilots and automation.
  • Previous research suggests automation can enhance performance but may introduce new challenges.
  • Understanding the impact of different automation levels on pilot performance is critical for system design.

Purpose of the Study:

  • To investigate the impact of varying levels of automation on pilot performance in a simulated flight task.
  • To evaluate the effectiveness of task partitioning between human and automated control in targeting sub-tasks.
  • To determine optimal design principles for human-machine systems based on performance outcomes.

Main Methods:

  • Twelve professional pilots performed a flight simulation with tracking, monitoring, and targeting sub-tasks.
  • Three conditions were tested: fully manual, partial automation, and fully automated targeting.
  • Automated assistance was limited to the targeting sub-task; tracking and monitoring were always manual.

Main Results:

  • Significantly higher tracking errors were observed in manual conditions compared to both automated conditions.
  • Partial automation resulted in faster monitoring response times than full automation.
  • Pilots retaining control over final weapon release in partial automation showed improved performance outcomes.

Conclusions:

  • Task partitioning, where pilots retain control over critical decisions like weapon release, is a key design principle for effective human-machine systems.
  • Full automation of the targeting sub-task can lead to performance decrements compared to partial automation.
  • The design of automation should consider the context of performance and the specific way tasks are partitioned to avoid negative impacts.