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Related Concept Videos

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

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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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The cerebral cortex, the brain's outermost layer, is pivotal in processing complex cognitive tasks, emotions, and various sensory inputs and executing voluntary motor activities. This intricate structure is divided into three primary functional areas: the motor areas, sensory areas, and association areas.
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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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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...
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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

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

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Visual scenes are categorized by function.

Michelle R Greene1, Christopher Baldassano1, Andre Esteva2

  • 1Department of Computer Science, Stanford University.

Journal of Experimental Psychology. General
|December 29, 2015
PubMed
Summary
This summary is machine-generated.

Scene categorization relies on understanding a place's function, not just its objects or visual features. This study shows functional understanding is key to how humans categorize visual scenes.

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Area of Science:

  • Cognitive Science
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Traditional scene categorization models focus on object recognition and visual features.
  • There is ongoing debate regarding the primary drivers of human scene categorization.

Purpose of the Study:

  • To test the hypothesis that scene categories are determined by their functions (possibilities for actions).
  • To compare the predictive power of functional models against object-based and visual feature models.

Main Methods:

  • Collected a large-scale scene category distance matrix from human observers (5 million trials).
  • Mapped actions from the American Time Use Survey onto scenes (1.4 million trials).
  • Utilized hierarchical linear regression to analyze variance explained by different models.

Main Results:

  • A strong correlation was found between ranked category distance and functional distance (r = .50).
  • The function model significantly outperformed object-based (r = .33), visual feature (r = .39), and lexical models (r = .27).
  • Functions accounted for 85.5% of explained variance, with unique contributions.

Conclusions:

  • Scene categorization is primarily driven by functional understanding, challenging traditional object-and-feature-based models.
  • The predictive power of other models is largely due to shared variance with the function-based model.
  • Human scene perception emphasizes action possibilities over mere visual properties.