December 2001
Volume 1, Issue 3
Vision Sciences Society Annual Meeting Abstract  |   December 2001
Bubbles: A new technique to reveal the use of information in recognition tasks
Author Affiliations
  • F. Gosselin
    University of Glasgow, Glasgow, Scotland, UK
  • P. G. Schyns
    University of Glasgow, Glasgow, Scotland, UK
Journal of Vision December 2001, Vol.1, 333. doi:
  • Views
  • Share
  • Tools
    • Alerts
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      F. Gosselin, P. G. Schyns; Bubbles: A new technique to reveal the use of information in recognition tasks. Journal of Vision 2001;1(3):333.

      Download citation file:

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

  • Supplements

Everyday, people flexibly perform different categorizations of common faces, objects and scenes. Intuition and scattered evidence suggest that these categorizations require the use of different visual information from the input. However, there is no unifying method, based on human categorization performance, which isolates the information used. To this end, we developed Bubbles, a general technique that can assign the credit of a human categorization performance to specific visual information. Bubbles starts with specifying an ‘image generation space’ (e.g., the 2D image plane, or the 3D space of 2D image locations x n spatial scales). Stimuli are designed to randomly and sparsely sample this space with “bubbles” of information of Gaussian shape. Subjects are instructed to recognize these sparse stimuli. The number of bubbles is adjusted to maintain performance at a set criteria. Subjects succeed when the bubbles reveal enough information; the locations of these bubbles are recorded in a CorrectSpace and a TotalSpace. When subjects cannot identify the sparse stimuli, the bubbles are not sufficiently informative, and we only add their locations to the TotalSpace. We then divide CorrectSpace by TotalSpace to derive a ProportionSpace that weighs the significance of each region of the image generation space for the task at hand. We applied Bubbles to human and ideal observers resolving different tasks of face recognition (identity, gender, expression) and object recognition (basic vs. subordinate categorizations) to isolate the specificity of human feature extraction.

Gosselin, F., Schyns, P.G.(2001). Bubbles: A new technique to reveal the use of information in recognition tasks [Abstract]. Journal of Vision, 1( 3): 333, 333a,, doi:10.1167/1.3.333. [CrossRef]

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.