Modeling the Dynamics of Mental Workload and Human Performance in Complex Systems

Abstract

This program studied the relationship between subjective workload and human behavior and proposed a model of the dynamics of this relationship. Results of three simulation experiments are detailed in this report and show that simple linear identification algorithms are robust in online identification of noisy, nonlinear versions of the model. This model and the associated algorithms have the potential to enable online inferences of workload and could be used to prompt/invoke human aiding or automated systems to help reduce workload. Applications for such systems exist in aiding aircraft pilots; command, control, communication decision makers; and other personnel in dynamic, time constrained environments.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1992
Accession Number
ADA258553

Entities

People

  • John M. Hammer
  • Sharon L. Edwards
  • William B. Rouse

Tags

Communities of Interest

  • Air Platforms
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Aircraft Models
  • Aircrafts
  • Algorithms
  • Cognitive Workload
  • Complex Systems
  • Cross Correlation
  • Environment
  • Frequency
  • Identification
  • Information Processing
  • Measurement
  • Noise
  • Resonant Frequency
  • Simulations
  • Standards
  • Workload

Fields of Study

  • Computer science
  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Fully Networked C3
  • Fully Networked C3 - Command and Control