Selecting One Among the Many: A Simple Network Implementing Shifts in Selective Visual Attention.

Abstract

This study addresses the question of how simple networks can account for a variety of phenomena associated with the shift of a specialized processing focus across the visual scene. We address in particular aspects of the dichotomy between the preattentive-parallel and the attentive-serial modes of visual perception and their hypothetical neuronal implementations. Specifically, we propose the following; (1) A number of elementary features, such as color, orientation, direction of movement, disparity etc. are represented in parallel in different topographical maps, called the early representation. (2) There exists a selective mapping from this early representation into a more central representation, such that at any instant the central representation contains the properties of only a single location in the visual scene, the selected location. (3) We discuss some selection rules that determine which location will be mapped into the central representation. The major rule, using the saliency or conspicuity of locations in the early representation, is implemented using a so-called Winner-Take-All network. A hierarchical pyramid-like architercture is proposed for this network. We suggest possible implementations in neuronal hardwre, including a possible role for the extensive back-projection from the cortex to the LGN.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1984
Accession Number
ADA148989

Entities

People

  • C. Koch
  • S. Ullman

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence
  • Brain
  • Cognitive Science
  • Computations
  • Data Displays
  • Disparities
  • Information Processing
  • Information Systems
  • Massachusetts
  • Orientation (Direction)
  • Perception
  • Physiology
  • Psychology
  • Psychophysiology
  • Visual Cortex
  • Visual Perception

Readers

  • Artificial Intelligence
  • Image Processing and Computer Vision.
  • Neural Network Machine Learning.