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Related Concept Videos

Concepts and Prototypes01:24

Concepts and Prototypes

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The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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Color Vision01:24

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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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The Representativeness Heuristic02:13

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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Visual Agnosia01:12

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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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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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Models of visual categorization.

Thomas Serre1

  • 1Cognitive, Linguistic & Psychological Sciences Department, Institute for Brain Sciences, Brown University, Providence, RI, USA.

Wiley Interdisciplinary Reviews. Cognitive Science
|March 22, 2016
PubMed
Summary
This summary is machine-generated.

Visual categorization helps us organize the world, distinguishing important items like food or threats. Computational models are key to understanding the neural and behavioral processes involved in this essential visual skill.

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Area of Science:

  • Neuroscience
  • Computer Science
  • Cognitive Science

Background:

  • Visual categorization is fundamental for survival, enabling rapid identification of objects and scenes.
  • Bridging the gap between neural mechanisms, information processing, and behavior is crucial for understanding visual categorization.
  • Computational models offer a powerful framework for integrating multi-level analyses of visual categorization.

Purpose of the Study:

  • To review recent advancements in understanding the computational mechanisms of visual categorization.
  • To discuss the challenges and future directions in visual categorization research.

Main Methods:

  • Review of computational modeling approaches in visual categorization.
  • Analysis of research integrating neural, computational, and behavioral data.

Main Results:

  • Computational models have significantly advanced the understanding of how the visual system categorizes information.
  • Progress has been made in linking neural network activity to behavioral outcomes in categorization tasks.

Conclusions:

  • Computational modeling is essential for a comprehensive understanding of visual categorization.
  • Further research is needed to address remaining challenges in bridging different levels of analysis for visual categorization.