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Association Areas of the Cortex

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Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
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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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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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Updated: May 20, 2025

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
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Covariance-based decoding reveals a category-specific functional connectivity network for imagined visual objects.

Francesco Mantegna1, Emanuele Olivetti2, Philipp Schwedhelm3

  • 1Department of Psychology, New York University, New York, NY 10003, USA; Department of Engineering Science, Oxford University, Oxford, Oxfordshire, United Kingdom; CIMeC - Center for Mind and Brain Sciences, Mattarello, TN 38100, Italy.

Neuroimage
|March 26, 2025
PubMed
Summary
This summary is machine-generated.

Visual imagery of objects like faces and places involves specific brain networks. Researchers decoded these mental images using functional connectivity patterns, showing distinct brain activity for different object categories.

Keywords:
CovarianceDecodingFaceImageryMagnetoencephalographyPlace

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Area of Science:

  • Cognitive Neuroscience
  • Neuroimaging

Background:

  • Visual imagery of complex objects relies on coordinated brain activity.
  • Both short-range sensory and long-range control-sensory connections are crucial for mental image construction and maintenance.

Purpose of the Study:

  • To investigate if distinct, category-specific brain networks exist for visual imagery of different object types (faces vs. places).
  • To determine if functional connectivity patterns can differentiate between face and place imagery.

Main Methods:

  • Used magnetoencephalography (MEG) with a minimally constrained paradigm, using only visual word cues to prompt imagery.
  • Analyzed functional connectivity patterns, estimated via spatial covariance across brain regions, to decode face versus place imagery.
  • Performed subnetwork analysis to examine contributions of different connections.

Main Results:

  • Face and place imagery were successfully decoded using functional connectivity data.
  • Both short-range and long-range brain connections were found to be involved in distinguishing imagery categories.
  • Category-specific network dynamics were observed even with purely endogenous brain signals.

Conclusions:

  • Visual imagery of different object categories (faces, places) can be distinguished by unique functional connectivity patterns within the brain.
  • These category-specific brain network dynamics can be identified using internal brain signals, without external stimuli.
  • The findings support the existence of specialized neural networks for processing distinct visual imagery targets.