Date of Award

12-2021

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Department of Systems Engineering and Management

First Advisor

Michael E. Miller, PhD

Abstract

Automation employed in information fusion systems is designed to help humans combine information derived from multiple sources to form a cohesive assessment of the situation. Research using the Levels of Automation model (Parasuraman, Wickens, and Sheridan, 2000) have produced conflicting results, which Patterson (2017) posited was because it focused solely on analytical processing while neglecting the effects of intuitive cognition. The present study examined how information acquisition automation affects the human’s ability to detect patterns in data needed to reach higher levels of information fusion. Results showed that when information acquisition was performed through manual operations, pattern recognition performance was similar for both perceptual and symbolic tasks. However, when information acquisition was automated, pattern recognition was better for the perceptual task than for the symbolic task. The results of this research can inform guidelines for the design of common workspaces to support human-machine teaming in future information fusion systems.

AFIT Designator

AFIT-ENV-DS-21-D-062

DTIC Accession Number

AD1157185

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