September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Open vs Closed Shapes: A Dimension of Perceptual Awareness?
Author Affiliations
  • David Burlinson
    University of North Carolina at Charlotte
  • Kalpathi Subramanian
    University of North Carolina at Charlotte
  • Paula Goolkasian
    University of North Carolina at Charlotte
Journal of Vision August 2017, Vol.17, 1381. doi:
  • Views
  • Share
  • Tools
    • Alerts
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      David Burlinson, Kalpathi Subramanian, Paula Goolkasian; Open vs Closed Shapes: A Dimension of Perceptual Awareness?. Journal of Vision 2017;17(10):1381. doi:

      Download citation file:

      © ARVO (1962-2015); The Authors (2016-present)

  • Supplements

Effective communication using visualization relies in part on the use of viable visual encoding strategies. For example, a viewer's ability to rapidly and accurately discern between two or more categorical variables in a chart or figure is contingent upon the distinctiveness of the encodings applied to each variable. Research in perception suggests that color is a more salient visual feature when compared to shape and, although that finding is confirmed by visualization studies, their data suggest characteristics of shape also yield meaningful differences in distinctiveness. We propose that open or closed shapes (that is, whether it is composed of line segments that are bounded across a region of space or not) represent a salient characteristic that influences perceptual awareness. Two experiments were designed to test the reliability of the open/closed dimension. In the first, a flanker paradigm was used to test whether perceptual load and open/closed feature dimension would modulate the effect of the flanker on target processing. Results showed an influence of both variables. The second experiment used a Same/Different RT task to replicate and extend those findings. Results from both show that responses are faster and more accurate when closed rather than open shapes are processed as targets; and there is more processing interference when two competing shapes come from the same rather than different open/closed feature dimensions. Our results bear implications for the visualization community and can inform recommendations in automated charting software.

Meeting abstract presented at VSS 2017


This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.