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The diversity rank-score function for combining human visual perception systems.

Christina Schweikert1, Darius Mulia2, Kilby Sanchez2

  • 1Division of Computer Science, Mathematics and Science, St. John's University, Queens, NY, USA. schweikc@stjohns.edu.

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Summary
This summary is machine-generated.

Combining individual observations improves decision-making when diversity is considered. This study introduces a diversity rank-score function to identify optimal pairs for enhanced decision fusion.

Keywords:
Cognitive diversityCombinatorial fusion analysisDiversity rank-score functionMultiple scoring systemsRank-score characteristic (RSC) function

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Area of Science:

  • Cognitive Science
  • Decision Analysis
  • Information Fusion

Background:

  • Joint decision-making is often desired but challenging to optimize.
  • The performance of fused decisions depends on the diversity between individual systems.

Purpose of the Study:

  • To evaluate the role of cognitive diversity in improving joint decisions.
  • To develop a method for identifying optimal pairs of observers for decision fusion.

Main Methods:

  • Treated human observers as visual perception systems, using confidence levels to create scoring systems.
  • Calculated a diversity rank-score function for pairs of observations within a combinatorial fusion framework.
  • Utilized a graph to visualize diversity variations among system pairs.

Main Results:

  • The diversity rank-score function effectively quantifies cognitive diversity between observers.
  • Visualizations revealed variations in diversity among different observer pairs.
  • Identified specific pairs likely to benefit from combined decision-making.

Conclusions:

  • Cognitive diversity is a critical factor in successful information fusion.
  • The proposed diversity rank-score function and its graphical representation are valuable tools for selecting optimal systems for joint decisions.
  • This framework aids in enhancing the performance of combined decision systems.