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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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Somatosensation01:33

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The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.
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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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Motor and Sensory Areas of the Cortex01:14

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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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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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Related Experiment Video

Updated: Jun 28, 2025

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
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Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

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Dampened sensory representations for expected input across the ventral visual stream.

David Richter1, Micha Heilbron1,2, Floris P de Lange1

  • 1Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6500 HB Nijmegen, The Netherlands.

Oxford Open Neuroscience
|April 10, 2024
PubMed
Summary
This summary is machine-generated.

Expectations enhance perception by suppressing neural responses to expected stimuli. This study supports the "dampening" model, showing expectations reduce sensory cortex redundancy for better processing of surprising information.

Keywords:
dampeningexpectation suppressionperceptionpredictive processingsharpeningvision

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Perception

Background:

  • Perceptual expectations improve perception speed, reliability, and informativeness.
  • Expectation suppression, an attenuated neural response to expected stimuli, is a key neural signature.
  • The underlying neural modulation of expectation suppression (sharpening vs. dampening) remains debated.

Purpose of the Study:

  • To investigate the neural mechanisms of expectation suppression.
  • To differentiate between sharpening and dampening models of expectation suppression.
  • To utilize forward models to bridge neural-level inferences with voxel-level neuroimaging data.

Main Methods:

  • Employed forward models to simulate sharpening and dampening mechanisms.
  • Mapped neural modulations to voxel-level functional magnetic resonance imaging (fMRI) data.
  • Analyzed empirical fMRI data to determine the best fit for either sharpening or dampening models.

Main Results:

  • A feature-specific gain modulation, suppressing neurons tuned to expected stimuli, best explained the fMRI data.
  • Results provide strong support for the dampening account of expectation suppression.
  • Demonstrated that opposite neural modulations can yield similar population-level signals.

Conclusions:

  • Expectations reduce redundancy within the sensory cortex.
  • This neural dampening mechanism facilitates the updating of internal models when encountering surprising information.
  • The findings clarify the neural basis of expectation suppression and its role in predictive coding.