Implementation of a Human Information Processing Model for Task Network Simulation

Abstract

Task network simulation is an analytical technique that is widely used to predict operator performance and/or workload during early stages of systems design. Task network simulation is based on traditional time-line analysis methods, but allows possibility of non-deterministic task characteristics such as completion times, sequences, outcomes etc. Many simulation environments allow task parameters to vary with various network states, which supports complex logical relationships, and the varying network behaviours. This report outlines the implementation of a theoretical framework for a new model of the human information processor for using task network simulation. The development and validation of the Information Processing (IP) model is described in detail elsewhere This report deals only with those aspects that are necessary to take the ideas of the IP Model and adapt them for direct application task network simulation. The material contained in this report provides the bridge between the conceptual descriptions of the Model, and the software requirements necessary to put that concept into practice. As part of this process, many parameters defined and assigned tentative values so that the model can be run within the task network simulation environment.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1994
Accession Number
ADA283903

Entities

People

  • Keith C. Hendy

Organizations

  • DRDC Toronto

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Classification
  • Cognition
  • Cognitive Systems Engineering
  • Cognitive Workload
  • Databases
  • Engineering
  • Human Factors Engineering
  • Human Systems Integration
  • Human-Machine Systems
  • Information Processing
  • New York
  • Psychology
  • Simulations
  • Systems Engineering
  • Task Performance And Analysis
  • Word Processors

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Parallel and Distributed Computing.
  • Systems Analysis and Design