Modeling Human Attention Allocation Strategies in Situations with Competing Criteria.

Abstract

In supervisory control situations involving multiple human operators, proper cooperation and coordination is essential. In addition to their individual tasks, the operators are jointly responsible for certain tasks. It is usually not clear when and who should take responsibility for the joint tasks. Proper scheduling of tasks and appropriate allocation of resources are necessary for optimal performance, and in some instances, for overall safety. Understanding how multiple operators interact requires the understanding of single operator performance. A model based on Pareto Optimality and Fuzzy Set theory was developed for the human operator. An experimental paradigm had been developed earlier to study the human monitor. The scenario used was similar to monitoring the spread of forest fires, and timely identification of threatening conditions. Experiments were conducted based on the paradigm. Results showed that the operators used updates to reduce the uncertainty to a sufficiently low level before starting threat classification. Sites where the probability of damage was close to 0.5 were more difficult to classify. Early decisions resulted in more errors. Heuristics proposed earlier appeared to be relevant. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1981
Accession Number
ADA108509

Entities

People

  • Thiruvenkatasamy Govindaraj

Organizations

  • Purdue University

Tags

Communities of Interest

  • Biomedical
  • Human Systems
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Air Force Facilities
  • Artificial Intelligence
  • Biomedical Research
  • Computers
  • Engineering
  • Environment
  • Forest Fires
  • Fuzzy Sets
  • Industrial Engineering
  • Mathematical Models
  • Monitoring
  • Scientific Research
  • Set Theory
  • Supervisory Control
  • Threat Evaluation
  • United States

Readers

  • Instructional Design and Training Evaluation.
  • Neural Network Machine Learning.
  • Operations Research