Adaptive Aiding for Symbiotic Human-Computer Control: Conceptual Model and Experimental Approach

Abstract

This report summarizes development of an experimental approach to investigation of benefits of adaptive aiding. A conceptual framework of the human-computer interface is presented, and its implications for the design of adaptive aids is discussed. The conceptual framework contains parallel elements for both the operator and the computer and emphasizes those factors expected to be important to successful cooperative task performance. A laboratory task for empirical investigation of some of the important design issues is presented. The task environment includes two competing tasks which must be performed simultaneously: a target spotting task and a tracking task. The target spotting task may be adaptively aided or not, depending on experimental conditions. This type of task was chosen for the investigation of adaptive aiding because it relies greatly on pattern recognition, an activity to which human operators and computers can be both make important contributions. The tracking task is included to provide a means of varying competing workload, a factor which is expected to alter the usefulness of the aid for the spotting task. Results of pilot testing with this task are presented. Additional keywords: Man machine systems.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1985
Accession Number
ADA153870

Entities

People

  • Nancy M. Morris
  • Paul R. Frey
  • William B. Rouse

Tags

Communities of Interest

  • Biomedical
  • C4I
  • Human Systems
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Cognitive Systems Engineering
  • Computer Communications
  • Computers
  • Environment
  • Graphics
  • Human Factors Engineering
  • Human-Computer Interaction
  • Human-Computer Interfaces
  • Human-Machine Interfaces
  • Information Processing
  • Information Transfer
  • Pattern Recognition
  • Recognition
  • Target Recognition
  • Task Performance And Analysis
  • Workload

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Software Engineering.
  • Systems Analysis and Design

Technology Areas

  • AI & ML