Neural Network Based Human Performance Modeling

Abstract

Neural networks provide an alternative method of building models of human performance. They can learn behavior from examples, reducing the need for many identical repetitions and intensive analysis. A properly trained net can be very robust in its response to a novel stimulus. This opens the door to modeling performance in the presence of an interactive stimulus. Neural networks provide the possibility of robust models that can operate interactively in real time, depending on the size and architecture of the net and the application. A neural network architecture derived from recurrent back propagation is presented which learns to mimic human behavior and performance in a sample task. It shows operating characteristics similar to those of human subjects, and even makes the same kinds of mistakes. Possible applications are discussed.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1990
Accession Number
ADA578289

Entities

People

  • Edward Fix
  • Harry G. Armstrong

Organizations

  • Armstrong Laboratory

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Air Defense
  • Biomedical Research
  • Computers
  • Computing System Architectures
  • Defense Systems
  • Errors
  • Expert Systems
  • Human Behavior
  • Motor Skills
  • Network Architecture
  • Neural Networks
  • Reaction Time
  • Recurrent Neural Networks
  • Simulations
  • Simulators
  • Standards
  • Training

Fields of Study

  • Computer science

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks