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 Experiment Videos

Adaptive automation of human-machine system information-processing functions.

David B Kaber1, Melanie C Wright, Lawrence J Prinzel

  • 1North Carolina State University, Department of Industrial Engineering, 328 Riddick Labs, Raleigh, NC, 27695-7906, USA. dbkaber@ncsu.edu

Human Factors
|March 24, 2006
PubMed
Summary
This summary is machine-generated.

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

Driver behavior analysis at alternative intersection corridors through driving simulator.

Accident; analysis and prevention·2026
Same author

Models of automation proportion in human-in-the-loop systems and operator situation awareness responses.

Ergonomics·2025
Same author

Information displays for automated surveillance algorithms of in-hospital patient deterioration: a scoping review.

Journal of the American Medical Informatics Association : JAMIA·2023
Same author

Preventing patient violence in hospitals: Applying critical decision method interviews to understand how skilled staff think and act differently.

Applied nursing research : ANR·2022
Same author

User interface approaches implemented with automated patient deterioration surveillance tools: protocol for a scoping review.

BMJ open·2022
Same author

A Cognitive Modeling Approach to Decision Support Tool Design for Anesthesia Provider Crisis Management.

International journal of human-computer interaction·2021
Same journal

Effects of Task Priority and Difficulty in Multitasking Across Screens.

Human factors·2026
Same journal

Compatibility Effects With Simple Lever Tools: A Replication and Extension Beyond Simple Button Responses.

Human factors·2026
Same journal

Effects of Egocentric and Exocentric Supervisor Viewpoint Perspectives on Motion Plan Legibility and Decision Support in Automated Spacecraft Docking Maneuvers.

Human factors·2026
Same journal

System-Wide Trust (SWT) Versus Component-Specific Trust (CST) in Multi-Agent Human-Agent Teams: Individual Variability in Trust Bias.

Human factors·2026
Same journal

Driver Adaptation to Partially Automated Driving in Urban Environments: Effects of Repeated Exposure and System Capabilities on Drivers' Trust, Monitoring, and Response.

Human factors·2026
Same journal

Modeling Human Expertise in a Sanding Task.

Human factors·2026
See all related articles

Humans adapt better to adaptive automation (AA) supporting physical tasks than cognitive ones in air traffic control. AA generally outperforms manual control, aiding system design.

Area of Science:

  • Human-Computer Interaction
  • Cognitive Systems Engineering
  • Automation and Control

Background:

  • Complex systems require effective human-automation interaction for optimal performance.
  • Adaptive automation (AA) dynamically adjusts assistance based on operator workload, but its impact across different processing stages is not fully understood.

Purpose of the Study:

  • To investigate human operators' ability to interact with adaptive automation (AA) across various information processing stages.
  • To compare the effectiveness of AA with manual control in a simulated air traffic control task.

Main Methods:

  • Forty participants operated a simulated air traffic control task under adaptive automation or manual control conditions.
  • AA was applied to information acquisition, analysis, decision-making, and action implementation, adapting to operator workload measured via a secondary task.

Related Experiment Videos

  • Performance was evaluated across two 20-minute trials.

    • Main Results:

    • Adaptive automation significantly impacted operator performance, especially during periods of manual control within adaptive conditions.
    • Human operators demonstrated better adaptation to AA supporting sensory and psychomotor functions (action implementation) compared to cognitive functions (information analysis, decision-making).
    • Adaptive automation proved superior to completely manual control.

    Conclusions:

    • Human-automation interaction models are crucial for understanding adaptive automation's role in complex systems.
    • Findings suggest designing AA to support cognitive functions requires careful consideration of human adaptation limits.
    • This research provides valuable insights for designing automation to enhance air traffic controller performance.