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

Team Workload in Action Teams: A Review of Empirical Research.

Human factors·2026
Same author

(Some) Benefits in Operator Decisions to Use AI After Experiencing Optimal Outcomes.

Human factors·2025
Same author

Information Access Costs With an Augmented Reality Head-Mounted Display.

Human factors·2025
Same author

The effect of stress on prospective memory in robotic command and control.

Cognitive research: principles and implications·2025
Same author

Clutter costs in head-mounted displays: a study examining trade-offs between overlay and adjacent presentation of information.

Cognitive research: principles and implications·2025
Same author

Effects of Physiological Loading from Patient-Derived Activities of Daily Living on the Wear of Metal-on-Polymer Total Hip Replacements.

Bioengineering (Basel, Switzerland)·2025

Related Experiment Video

Updated: Mar 27, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.4K

Time Sharing Between Robotics and Process Control: Validating a Model of Attention Switching.

Christopher Dow Wickens1, Robert S Gutzwiller2, Alex Vieane3

  • 1Alion Science and Technology, Boulder, Colorado cwickens@alionscience.com.

Human Factors
|January 17, 2016
PubMed
Summary

The strategic task overload management (STOM) model accurately predicts how people switch tasks under high workload. Difficult tasks are performed longer, while priority does not influence task switching decisions.

Keywords:
attentional processescognitiondual taskhuman performance modelingmanufacturingmethods and skillsprocess controlprocess control systemsroboticstask switchingtime sharing

More Related Videos

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments
09:43

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Published on: April 15, 2014

11.1K
A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets
08:45

A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets

Published on: December 5, 2014

9.7K

Related Experiment Videos

Last Updated: Mar 27, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.4K
A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments
09:43

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Published on: April 15, 2014

11.1K
A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets
08:45

A Dual Task Procedure Combined with Rapid Serial Visual Presentation to Test Attentional Blink for Nontargets

Published on: December 5, 2014

9.7K

Area of Science:

  • Cognitive psychology
  • Human-computer interaction
  • Human factors engineering

Background:

  • Task switching is a critical aspect of human performance in complex environments.
  • Existing models often struggle to predict task allocation under cognitive overload.
  • The strategic task overload management (STOM) model proposes task switching is influenced by task attributes like difficulty, priority, interest, and salience.

Purpose of the Study:

  • To validate the strategic task overload management (STOM) model's predictive accuracy for task switching behavior.
  • To investigate the influence of task attributes (difficulty, priority, interest, salience) on task switching under overload.
  • To assess the STOM model's utility in predicting cognitive tunneling in human-in-the-loop simulations.

Main Methods:

  • Experiment 1: Participants performed tasks from the Multi-Attribute Task Battery to assess the impact of difficulty and priority on task switching.
  • Experiment 2: Participants concurrently managed an environmental control task and a robotic arm simulation, with workload manipulated via automation and decision support.
  • Attention allocation in Experiment 2 was measured using head tracking, and data were compared against STOM model predictions.

Main Results:

  • Task difficulty significantly influenced task switching, with more difficult tasks being performed longer.
  • Task priority, interest, and salience did not significantly influence task switching decisions.
  • The STOM model accurately predicted attention allocation, accounting for over 95% of the variance in task allocation across conditions.

Conclusions:

  • The STOM model provides a robust framework for predicting task switching and cognitive tunneling.
  • The model's accuracy in predicting attention allocation highlights its utility for designing and evaluating complex human-machine systems.
  • Findings suggest that while difficulty is a key factor, task priority may play a lesser role in dynamic task switching under overload.