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Vision

Vision

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.

Visual System

Visual System

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...

Motor and Sensory Areas of the Cortex

Motor and Sensory Areas of the Cortex

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.
Motor Areas
The motor areas located in the frontal lobe are central to controlling voluntary movements. This region is further subdivided into the primary motor cortex and the premotor cortex....

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

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.

Parallel Processing

Parallel Processing

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...

Association Areas of the Cortex

Association Areas of the Cortex

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:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...

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Related Experiment Video

Updated: Jul 6, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Human Visual Cortex and Deep Convolutional Neural Network Care Deeply about Object Background.

Jessica Loke¹, Noor Seijdel¹, Lukas Snoek¹

¹University of Amsterdam.

Journal of Cognitive Neuroscience

|January 2, 2024

Summary

This summary is machine-generated.

Deep convolutional neural networks (DCNNs) and human brains prioritize object background processing over object category in early visual stages. Figure-ground segregation is key for object recognition in both DCNNs and the visual cortex.

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Area of Science:

Neuroscience
Computer Vision
Cognitive Science

Background:

Deep convolutional neural networks (DCNNs) partially predict brain activity in object categorization.
The factors driving DCNN predictive power remain unclear.
Figure-ground segregation is crucial for human object recognition.

Purpose of the Study:

Investigate factors influencing DCNN predictive power in object categorization.
Determine if figure-ground segregation explains DCNNs' ability to predict brain activity.
Compare DCNN and human EEG responses to object background versus category.

Main Methods:

Compared four DCNN architectures with EEG data from 62 human participants.
Used stimuli with identical objects in varied backgrounds to isolate background influence.
Analyzed early EEG activity (<100 ms) and early DCNN layers.

Main Results:

Early EEG activity and DCNN layers process object background, not category.
DCNN prediction of EEG activity is driven by background processing.
Trained DCNNs show distinct activations compared to untrained networks, highlighting figure-ground segregation's role.

Conclusions:

Both human visual cortex and DCNNs prioritize figure-ground segregation for object categorization.
Object background processing is a fundamental mechanism shared by biological and artificial visual systems.
Figure-ground segregation may be a prerequisite for object feature recognition.