Dynamic Generalizations of Systems Factorial Technology for Modeling Perception of Fused Information

Abstract

Models are a fundamental part of understanding cognition. The advantages of cognitive modeling are particularly clear when attempting to understand how changes in a cognitive task lead to changes in performance. Systems factorial technology (SFT) can be used to explain and understand why there are differences in performance, not just that there is a difference. In this project, we have extended the applicability of SFT to more complex environments than the basic perceptual experiments to which it has been previously applied. This included extensions of the statistical analyses to include hierarchical parametric Bayesian modeling and semi- and non-parametric modeling. We then applied SFT in both basic visual search studies and in task requiring the use of multispectral imagery.

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Document Details

Document Type
Technical Report
Publication Date
Jan 11, 2017
Accession Number
AD1032105

Entities

People

  • Joseph W. Houpt

Organizations

  • Wright State University

Tags

Communities of Interest

  • Advanced Electronics
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Bayesian Networks
  • Cognition
  • Computational Science
  • Data Displays
  • Data Mining
  • Data Science
  • Detection
  • Experimental Design
  • Information Processing
  • Information Science
  • Monte Carlo Method
  • Psychology
  • Statistical Algorithms
  • Statistical Analysis
  • Surveys
  • Three Dimensional
  • Visible Spectra

Fields of Study

  • Computer science

Readers

  • Regression Analysis.
  • Systems Analysis and Design
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML