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Related Concept Videos

Visual System01:26

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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.
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Vision01:24

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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.
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Parallel Processing01:20

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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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...
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Related Experiment Video

Updated: Apr 23, 2026

Visualizing Visual Adaptation
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Distinct system-level computations underlie perceptual variation across the visual field.

Shutian Xue1, Antoine Barbot1, Jared Abrams2

  • 1Department of Psychology, New York University, New York, NY 10003.

Proceedings of the National Academy of Sciences of the United States of America
|April 21, 2026
PubMed
Summary
This summary is machine-generated.

Human visual perception varies across the visual field. System-level computations like gain and internal noise explain these differences, linking perception to neural architecture.

Keywords:
eccentricityequivalent noisegaininternal noisevisual field asymmetries

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Area of Science:

  • Neuroscience
  • Visual Perception
  • Computational Neuroscience

Background:

  • Human visual perception of basic dimensions is not uniform across the visual field.
  • This heterogeneity impacts daily activities like reading and scene perception.
  • Understanding the underlying system-level computations is crucial.

Purpose of the Study:

  • To investigate how system-level computations explain variations in visual perception across eccentricity and polar angle.
  • To estimate gain, internal noise, and nonlinearity in orientation discrimination.
  • To link perceptual heterogeneity to neural architecture.

Main Methods:

  • Employed the equivalent noise method and perceptual template model.
  • Measured orientation discrimination of Gabor stimuli across different visual field locations (fovea, parafovea, perifovea) and polar angles.
  • Quantified perceptual parameters: gain, internal noise, and nonlinearity.

Main Results:

  • Visual performance decreased with eccentricity due to reduced gain and nonlinearity, and increased internal noise.
  • Gain, but not internal noise or nonlinearity, varied with polar angle, showing specific patterns along horizontal and vertical meridians.
  • Individual differences in eccentricity effects correlated with gain decrease.

Conclusions:

  • Distinct system-level computations underlie eccentricity effects and polar angle asymmetries in visual perception.
  • Findings connect perceptual field variations with underlying neural architecture and constraints.
  • Provides insights into how the human brain encodes visual information under neural limitations.