Cookies Policy
X

This site uses cookies. By continuing to browse the site you are agreeing to our use of cookies.

I accept this policy

Find out more here

From stereogram to surface: how the brain sees the world in depth

No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.

Brill’s MyBook program is exclusively available on BrillOnline Books and Journals. Students and scholars affiliated with an institution that has purchased a Brill E-Book on the BrillOnline platform automatically have access to the MyBook option for the title(s) acquired by the Library. Brill MyBook is a print-on-demand paperback copy which is sold at a favorably uniform low price.

Access this article

+ Tax (if applicable)
Add to Favorites
You must be logged in to use this functionality

image of Spatial Vision
For more content, see Multisensory Research and Seeing and Perceiving.

When we look at a scene, how do we consciously see surfaces infused with lightness and color at the correct depths? Random-Dot Stereograms (RDS) probe how binocular disparity between the two eyes can generate such conscious surface percepts. Dense RDS do so despite the fact that they include multiple false binocular matches. Sparse stereograms do so even across large contrast-free regions with no binocular matches. Stereograms that define occluding and occluded surfaces lead to surface percepts wherein partially occluded textured surfaces are completed behind occluding textured surfaces at a spatial scale much larger than that of the texture elements themselves. Earlier models suggest how the brain detects binocular disparity, but not how RDS generate conscious percepts of 3D surfaces.

This article proposes a neural network model that predicts how the layered circuits of visual cortex generate these 3D surface percepts using interactions between visual boundary and surface representations that obey complementary computational rules. The model clarifies how interactions between layers 4, 3B and 2/3A in V1 and V2 contribute to stereopsis, and proposes how 3D perceptual grouping laws in V2 interact with 3D surface filling-in operations in V1, V2 and V4 to generate 3D surface percepts in which figures are separated from their backgrounds. The model explanations of 3D surface percepts raised by various RDS are demonstrated by computer simulations. The model hereby unifies the explanation of data about stereopsis and data about 3D figure–ground separation and completion of partially occluded object surfaces. It shows how these model mechanisms convert the complementary rules for boundary and surface formation into consistent visual percepts of 3D surfaces.

Affiliations: 1: Department of Cognitive and Neural Systems, Center for Adaptive Systems and Center of Excellence for Learning in Education, Science, and Technology, Boston University, 677 Beacon St., Boston, MA 02215, USA

10.1163/156856809786618484
/content/journals/10.1163/156856809786618484
dcterms_title,pub_keyword,dcterms_description,pub_author
6
3
Loading
Loading

Full text loading...

/content/journals/10.1163/156856809786618484
Loading

Data & Media loading...

http://brill.metastore.ingenta.com/content/journals/10.1163/156856809786618484
Loading

Article metrics loading...

/content/journals/10.1163/156856809786618484
2009-01-01
2016-12-04

Sign-in

Can't access your account?
  • Tools

  • Add to Favorites
  • Printable version
  • Email this page
  • Subscribe to ToC alert
  • Get permissions
  • Recommend to your library

    You must fill out fields marked with: *

    Librarian details
    Your details
    Why are you recommending this title?
    Select reason:
     
    Spatial Vision — Recommend this title to your library
  • Export citations
  • Key

  • Full access
  • Open Access
  • Partial/No accessInformation