Nonaccidental properties underlie shape recognition in Mammalian and nonmammalian vision.

Academic Article


  • An infinite number of 2D patterns on the retina can correspond to a single 3D object. How do visual systems resolve this ill-posed problem and recognize objects from only a few 2D retinal projections in varied exposure conditions? Theories of object recognition rely on the nonaccidental statistics of edge properties, mainly symmetry, collinearity, curvilinearity, and cotermination. These statistics are determined by the image-formation process (i.e., the 2D retinal projection of a 3D object ); their existence under a range of viewpoints enables viewpoint-invariant recognition. An important question in behavioral biology is whether the visual systems of nonmammalian animals have also evolved biases to utilize nonaccidental statistics . Here, we trained humans and pigeons to recognize four shapes. With the Bubbles technique, we determined which stimulus properties both species used to recognize the shapes. Both humans and pigeons used cotermination, the most diagnostic nonaccidental property of real-world objects, despite evidence from a model computer observer that cotermination was not the most diagnostic pictorial information in this particular task. This result reveals that a nonmammalian visual system that is different anatomically from the human visual system is also biased to recognize objects from nonaccidental statistics.
  • Authors

  • Gibson, Brett
  • Lazareva, Olga F
  • Gosselin, Frédéric
  • Schyns, Philippe G
  • Wasserman, Edward A
  • Status

    Publication Date

  • February 20, 2007
  • Published In

  • Current Biology  Journal
  • Keywords

  • Adult
  • Animals
  • Columbidae
  • Female
  • Form Perception
  • Humans
  • Linear Models
  • Male
  • Pattern Recognition, Visual
  • Photic Stimulation
  • Recognition, Psychology
  • Species Specificity
  • Vision, Ocular
  • Digital Object Identifier (doi)

    Pubmed Id

  • 17275301
  • Start Page

  • 336
  • End Page

  • 340
  • Volume

  • 17
  • Issue

  • 4