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Related Concept Videos

Color Vision01:24

Color Vision

Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
Vision01:24

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
Perceptual Constancy01:12

Perceptual Constancy

Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
Visual System01:26

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
Visual Agnosia01:12

Visual Agnosia

Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round end"...
Anatomy of the Eyeball01:20

Anatomy of the Eyeball

The eye is a spherical, hollow structure composed of three tissue layers. The outer layer — the fibrous tunic, comprises the sclera — a white structure — and the cornea, which is transparent. The sclera encompasses some of the ocular surface, most of which is not visible. However, the 'white of the eye' is distinctively visible in humans compared to other species. The cornea, a clear covering at the front of the eye, enables light penetration. The eye's middle layer, the vascular tunic,...

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Related Experiment Video

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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

The limits of feedforward vision: recurrent processing promotes robust object recognition when objects are degraded.

Dean Wyatte1, Tim Curran, Randall O'Reilly

  • 1University of Colorado Boulder, Boulder, CO, USA. dean.wyatte@colorado.edu

Journal of Cognitive Neuroscience
|August 22, 2012
PubMed
Summary

The brain uses recurrent processing to recognize objects despite visual degradation like occlusion and low contrast. This feedback mechanism strengthens weakened signals, aiding object recognition in challenging conditions.

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Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
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Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

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Related Experiment Videos

Last Updated: May 19, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
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Published on: December 15, 2023

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
07:08

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Published on: August 1, 2018

Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Visual Perception

Background:

  • Everyday vision must overcome environmental challenges like foreground clutter and poor lighting.
  • Object recognition struggles with degraded visual stimuli, including occlusion and reduced contrast.
  • Existing models suggest recurrent processing may enhance recognition of low-quality inputs.

Purpose of the Study:

  • To investigate the role of recurrent processing in robust object recognition under visual degradation.
  • To test the hypothesis that recurrent feedback strengthens weakened bottom-up signals for degraded objects.
  • To compare experimental findings with predictions from a computational model of object recognition.

Main Methods:

  • Utilized backward masking to interrupt visual processing of partially occluded and contrast-reduced images.
  • Conducted a categorization experiment to assess object recognition performance.
  • Employed a computational model of object recognition with excitatory feedback for simulations.

Main Results:

  • Found significant interactions between masking, occlusion, and contrast reduction, impairing recognition of heavily degraded stimuli.
  • The computational model accurately predicted experimental results in an isomorphic simulation.
  • Masking specifically interfered with recurrent processing needed for highly degraded inputs, less so for clear inputs.

Conclusions:

  • Recurrent processing is crucial for robust object recognition, particularly when visual input is degraded.
  • Object recognition is a dynamic, interactive process, not solely feedforward.
  • The findings highlight the importance of feedback mechanisms in overcoming visual challenges.