Target Identification Predictor Study: Visual, Cognitive, and Training Variables.

Abstract

The Target Identification Predictor Study (TIPS) was designed to determine the predictive utility of visual, cognitive, and training variables upon tracked vehicle identification performance within an operational context and to provide a reliable and valid basis for a model to select antitank gunner trainees. Regression analyses performed on the scores of 208 junior enlisted soldiers showed that scores from classroom vehicle identification training and scores on the Group Embedded Figures Test were significant predictors of identification skills. Thirty-three percent of the variability was predicted by this regression. A discriminant analysis showed that these three scores could be used to classify the soldiers into good or poor target identification groups. Results from a cross validation analysis correctly classified %8.9 percent of the soldiers, indicating that this classification scheme was highly reliable. Since target identification is a critical initial task within the engagement acquisition performance complex for many weapon systems, identification of predictors of superior target identification skills (and the selection of individuals with these skills) could be an important means of enhancing target acquisition effectiveness both directly and indirectly.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1999
Accession Number
ADA361986

Entities

People

  • Richard R. Levine
  • Robert M. Wildzunas

Organizations

  • United States Army Aeromedical Research Lab

Tags

Communities of Interest

  • Biomedical
  • Human Systems

DTIC Thesaurus Topics

  • Acquisition
  • Artillery
  • Classification
  • Color Vision
  • Combat Vehicles
  • Data Science
  • Detection
  • Discriminant Analysis
  • Human Factors Engineering
  • Human Systems Integration
  • Identification
  • Information Science
  • Regression Analysis
  • Target Acquisition
  • Training
  • Weapon Systems
  • Weapons

Readers

  • Computer Vision.
  • Instructional Design and Training Evaluation.
  • Regression Analysis.