Psychonomic Bulletin & Review

, Volume 1, Issue 1, pp 29–55 | Cite as

Rethinking perceptual organization: The role of uniform connectedness

  • Stephen PalmerEmail author
  • Irvin Rock


A principle of perceptual organization, calleduniform connectedness (UC), is described, and a theoretical approach to perceptual organization is proposed in which this principle plays a fundamental role. The principle of UC states that closed regions of homogeneous properties—such as lightness, chromatic color, texture, and so forth—tend to be perceived initially as single units. We demonstrate its effects and show that they occur even when opposed by powerful grouping principles such as proximity and similarity. We argue that UC cannot be reduced to such grouping principles, because it is not a form of grouping at all. We then propose a theoretical framework within which UC accounts for the initial (orentry level) organization of the visual field into primitive units. Classical principles of grouping operate after UC, creating superordinate units consisting of two or more basic-level units. Parsing processes also operate after UC, dividing basic-level units into subordinate parts. UC in the retinal image is proposed to be a necessary, but not a sufficient, condition for unit formation, since connected elements on the retina that are perceived to lie in separate depth planes fail to be perceived as units. This fact, together with other evidence that the Gestalt principles of grouping are based onperceived (rather than retinal) relations, suggests that the organization of visual stimulation into UC objects is ultimately achieved within a relatively late, postconstancy representation of environmental surfaces. The implications of this possibility are discussed in light of present theories of visual perception.


Retinal Image Perceptual Organization Binocular Disparity Element Connectedness Common Fate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Psychonomic Society, Inc. 1994

Authors and Affiliations

  1. 1.Psychology DepartmentUniversity of CaliforniaBerkeley

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