August 2014
Volume 14, Issue 10
Vision Sciences Society Annual Meeting Abstract  |   August 2014
Properties of Spatiotemporal Boundary Formation
Author Affiliations
  • Gennady Erlikhman
    Department of Psychology, University of California, Los Angeles
  • Gideon Caplovitz
    Department of Psychology, University of Nevada, Reno
  • Philip Kellman
    Department of Psychology, University of California, Los Angeles
Journal of Vision August 2014, Vol.14, 61. doi:
  • Views
  • Share
  • Tools
    • Alerts
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Gennady Erlikhman, Gideon Caplovitz, Philip Kellman; Properties of Spatiotemporal Boundary Formation. Journal of Vision 2014;14(10):61.

      Download citation file:

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

  • Supplements

Background: Spatiotemporal boundary formation (SBF) is the perception of contours, global form, and global motion from discrete transformations of sparse textural elements of which gradual accretion and deletion of texture is a special case (Shipley & Kellman, 1994, 1997). While some aspects of SBF are understood, little work has been done to uncover the underlying computational and neural mechanisms. Research Questions: What are the conditions (texture element and global shape transformations) that support SBF? How can the process be modeled? What are the neural mechanisms that support global shape perception in SBF? Design: We conducted several experiments in which transformations of sparsely distributed, circular texture elements or Gabor patches resulted in percepts of clear illusory boundaries and surfaces. An invisible, virtual object moved along a circular path in the display. Elements that fell within the boundary of the object changed color, position, or orientation. Virtual objects transformed in size, orientation, velocity, or shape. Texture element density was also manipulated. Subjects performed a 10-AFC task in which they matched the perceived object to one of 10 possible shapes. Results and Conclusions: Identification accuracy improved with increasing element density. SBF supported shape identification even for non-rigid, transforming virtual objects. All texture element transformations except for isoluminant color changes resulted in SBF, suggesting that the process depends on luminance changes at an early input stage. A computational model that integrates local signals was capable of extracting illusory edges from SBF displays and accurately predicted human performance across several experiments. Multi-voxel pattern analysis of neuroimaging data and source localization from EEG recordings were consistent with other findings that global shape representations emerge early in higher-level visual areas and feed back onto earlier ones. A convergence of behavioral, computational, and biological evidence indicates that SBF is a robust process that relies on several processing stages.

Meeting abstract presented at VSS 2014


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.