Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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.

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...

Gestalt Principles of Perception

Gestalt Principles of Perception

Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...

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...

Perceptual Constancy

Perceptual Constancy

Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Stage-specific priming of pop-out effects in the target-feature memory encoding and retrieval.

Acta psychologica·2026

Same author

Duration reproduction under memory pressure: Modeling the roles of visual memory set size in duration encoding and reproduction.

Cognition·2026

Same author

Predictive Processing in Autism Spectrum Disorder: The Atypical Iterative Prior Updating Account.

Biological psychiatry global open science·2025

Same author

Poster Session: Characteristic differences in eye movements in people with Parkinson's disease.

Journal of vision·2025

Same author

Context-based guidance versus context suppression in contextual learning: Role of un-/certainty in the target-context relations in visual search.

Journal of experimental psychology. Human perception and performance·2025

Same author

Distractor-response binding influences visual search.

Attention, perception & psychophysics·2025

Same journal

Hunting ecology predicts eye arrangements in the modular visual system of spiders.

Current biology : CB·2026

Same journal

Sub-second fluctuations between top-down and bottom-up modes distinguish diverse human brain states.

Current biology : CB·2026

Same journal

Queen bees offload pesticide burden to eggs when social buffering is overwhelmed.

Current biology : CB·2026

Same journal

Pitch selectivity in ferret auditory cortex.

Current biology : CB·2026

Same journal

A cell size-dependent competition between geometry and polarity governs nuclear and spindle positioning in early embryos.

Current biology : CB·2026

Same journal

Trophic cascades drive sustainability in the agricultural heritage rice-fish coculture system.

Current biology : CB·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 10, 2026

How to Build a Dichoptic Presentation System That Includes an Eye Tracker

How to Build a Dichoptic Presentation System That Includes an Eye Tracker

Published on: September 6, 2017

Visual perception: one world from two eyes.

Jenny Read¹, Fredrik Allenmark

¹Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK. jenny.read@newcastle.ac.uk

Current Biology : CB

|June 8, 2013

Summary

This summary is machine-generated.

Computational models explain how the brain matches visual input from both eyes for binocular vision. Recurrent connections in the primary visual cortex are key to preventing incorrect visual matches.

More Related Videos

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

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

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

Published on: April 16, 2014

Related Experiment Videos

Last Updated: May 10, 2026

How to Build a Dichoptic Presentation System That Includes an Eye Tracker

How to Build a Dichoptic Presentation System That Includes an Eye Tracker

Published on: September 6, 2017

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

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

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

Published on: April 16, 2014

Area of Science:

Neuroscience
Computational Vision
Visual Processing

Background:

Binocular vision relies on integrating distinct visual information from each eye.
The primary visual cortex (V1) initiates the complex process of visual matching.
Understanding neural mechanisms for accurate visual perception is crucial.

Purpose of the Study:

To investigate the role of computational models in understanding binocular vision.
To explore how recurrent connections in the primary visual cortex contribute to visual matching.
To elucidate the neural basis for suppressing false matches in visual perception.

Main Methods:

Development and analysis of computational models of the primary visual cortex.
Simulations incorporating recurrent neural connections.
Analysis of model responses to varying visual inputs.

Main Results:

Models successfully replicate early stages of visual matching in binocular vision.
Recurrent connections demonstrate a significant role in reducing responses to erroneous matches.
The findings provide insights into the neural computation underlying stereopsis.

Conclusions:

Recurrent connections are essential for accurate binocular visual processing.
Computational models offer a valuable framework for studying visual cortex function.
This research advances our understanding of how the brain achieves robust visual perception.