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Vision01:24

Vision

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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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Visual System01:26

Visual System

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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.
Once through the pupil, the light passes through the lens, a...
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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Anatomy of the Eyeball01:20

Anatomy of the Eyeball

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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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Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

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At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...
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Color Vision01:24

Color Vision

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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.
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Related Experiment Video

Updated: Jul 15, 2025

Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios
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Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios

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Toward biologically plausible artificial vision.

Mason Westfall1

  • 1Department of Philosophy, Philosophy-Neuroscience-Psychology Program, Washington University in St. Louis, St. Louis, MO, USA w.mason@wustl.eduhttp://www.masonwestfall.com.

The Behavioral and Brain Sciences
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

Deep convolutional neural networks (DCNNs) differ structurally from human vision. However, artificial vision in reinforcement-learning agents may be more human-like, especially with language representations supporting the language-of-thought hypothesis.

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Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Neuroscience

Background:

  • Deep convolutional neural networks (DCNNs) used in image classification show structural differences compared to human visual systems.
  • Artificial vision systems, particularly those used by reinforcement-learning agents, offer a potentially more analogous model to human vision.

Purpose of the Study:

  • To explore the structural similarities and differences between artificial vision systems and human vision.
  • To investigate the role of language-like representations in enhancing artificial vision performance and its implications for cognitive theories.

Main Methods:

  • Comparative analysis of deep convolutional neural networks (DCNNs) and human visual processing.
  • Evaluation of reinforcement-learning agents navigating 3D environments, focusing on performance with and without language-like representations.

Main Results:

  • DCNNs optimized for image classification exhibit significant structural disanalogies to human vision.
  • Reinforcement-learning agents demonstrate improved performance when utilizing language-like representations.
  • The enhancement of artificial agents' performance via language-like representations provides indirect support for the language-of-thought hypothesis (LoTH).

Conclusions:

  • Artificial vision systems, specifically those employing reinforcement learning in 3D environments, present a more promising avenue for modeling human-like vision than DCNNs.
  • Language-like representations are crucial for developing more human-like artificial vision and lend credence to the language-of-thought hypothesis.