Building and applying a human cognition model for visual analytics

  • Authors:

  • Tera Marie Green;William Ribarsky;Brian Fisher

  • Affiliations:

  • Charlotte Visualization Center, University of North Carolina at Charlotte;Charlotte Visualization Center, University of North Carolina at Charlotte;School of Interactive Arts and Technology, Simon Fraser University, Canada

  • Venue:
  • Information Visualization
  • Year:
  • 2009

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Abstract

It is well known that visual analytics addresses the difficulty of evaluating and processing large quantities of information. Less often discussed are the increasingly complex analytic and reasoning processes that must be applied in order to accomplish that goal. Success of the visual analytics approach will require us to develop new visualization models that predict how computational processes might facilitate human insight and guide the flow of human reasoning. In this paper, we seek to advance visualization methods by proposing a framework for human 'higher cognition' that extends more familiar perceptual models. Based on this approach, we suggest guidelines for the development of visual interfaces that better integrate complementary capabilities of humans and computers. Although many of these recommendations are novel, some can be found in existing visual analytics applications. In the latter case, much of the value of our contribution lies in the deeper rationale that the model provides for those principles. Lastly, we assess these visual analytics guidelines through the evaluation of several visualization examples.