September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Towards a Unifying Model of Crowding: Model Olympics
Author Affiliations
  • Adrien Doerig
    1 Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
  • Aaron Clarke
    Laboratory of Computational Vision, Psychology Department, Bilkent University, Ankara, Turkey
  • Greg Francis
    Department of Psychological Sciences, Purdue University, USA
  • Michael Herzog
    1 Laboratory of Psychophysics, Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Journal of Vision August 2017, Vol.17, 399. doi:
  • Views
  • Share
  • Tools
    • Alerts
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Adrien Doerig, Aaron Clarke, Greg Francis, Michael Herzog; Towards a Unifying Model of Crowding: Model Olympics. Journal of Vision 2017;17(10):399. doi:

      Download citation file:

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

  • Supplements

In crowding, perception of an object deteriorates in the presence of nearby elements. Obviously, crowding is a ubiquitous phenomenon since elements are rarely seen in isolation. Up to date, there exists no consensus on how to model crowding. In previous experiments, it was shown that the global configuration of the entire stimulus needs to be taken into account. These findings rule out simple pooling or substitution models and favor models sensitive to global spatial aspects. In order to further investigate how to incorporate these aspects into models, we tested a large number of models, using a database of about one hundred stimuli. As expected, all local models fail. Further, capturing basic regularities in the stimulus does not suffice to account for global aspects, as illustrated by the failures of Fourier analysis and textural models. Our results highlight the importance of grouping to explain crowding. Specifically, we show that a two-stage model improves performance strongly. In this model, first, elements are segregated into groups and, second, only elements in the same group interfere with each other. The model must integrate information across large parts of the visual field.

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.