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Facilitating Conditional Probability Problems with Visuals

  • Vince Kellen
  • Susy Chan
  • Xiaowen Fang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4551)

Abstract

In tasks such as disease diagnosis, interpretation of evidence in criminal trials and management of security and risk data, people need to process conditional probabilities to make critical judgments and decisions. As dual-coding theory and the cognitive theory of multimedia learning (CTML) would predict, visual representations (VRs) should aid in these tasks. Conditional probability problems are difficult and require subjects to build a mental model of set inclusion relationships to solve them. Evidence from neurological research confirms that mental model construction relies on visual spatial processing. Prior research has shown conflicting accounts of whether visuals aid in these problems. Prior research has also revealed that individuals differ in their ability to perform spatial processing tasks. Do visuals help solve these problems? Do visualization interface designers need to take into account the nuances of spatial processing and individual differences? This study uses a 3x2 factorial design to determine the relationship between subject’s spatial abilities (high or low) and visual and text representations on user performance and satisfaction.

Keywords

Information visualization Bayesian reasoning conditional probabilities dual-coding cognitive theory of multimedia learning mental models individual differences spatial ability 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Vince Kellen
    • 1
  • Susy Chan
    • 1
  • Xiaowen Fang
    • 1
  1. 1.DePaul University, School of Computer Science, Telecommunications and Information Systems, Chicago, ILUnited States of America

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