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

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.

Racemic Mixtures and the Resolution of Enantiomers

Racemic Mixtures and the Resolution of Enantiomers

A racemic mixture, or racemate, is an equimolar mixture of enantiomers of a molecule that can be separated using their unique interaction with chiral molecules or media. Racemic mixtures are denoted by the (±)- prefix. This ‘optical rotation descriptor’ applies to the whole solution of a racemic mixture rather than a specific stereoisomer. Enantiomers typically have the same physical and chemical properties. Hence, they are not easily separable. However, enantiomers can exhibit different...

You might also read

Related Articles

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

Sort by

Same author

Pseudosparse neural coding in the visual system of primates.

Communications biology·2021

Same author

Face Representations via Tensorfaces of Various Complexities.

Neural computation·2019

Same author

Extrinsic and intrinsic representations.

The Behavioral and brain sciences·2019

Same author

Representation of shape, space, and attention in monkey cortex.

Cortex; a journal devoted to the study of the nervous system and behavior·2019

Same author

Organization of Binocular Pathways: Modeling and Data Related to Rivalry.

Neural computation·2019

Same author

Attention Effects on Neural Population Representations for Shape and Location Are Stronger in the Ventral than Dorsal Stream.

eNeuro·2018

Same journal

Role of AQP4 in ameliorating heat stress-induced cellular injury in a cell line model through active heat acclimation.

Frontiers in human neuroscience·2026

Same journal

Correction: Cognitive state monitoring for neuroadaptive information visualization.

Frontiers in human neuroscience·2026

Same journal

The synthetic self-hypothesis: dopaminergic redirection through self-face recognition in stuttering therapy.

Frontiers in human neuroscience·2026

Same journal

A randomised, placebo-controlled, triple-blind clinical trial to investigate the efficacy of <i>Ginkgo biloba</i> extract EGb 761<sup>®</sup> in cognitive impairment associated with post COVID-19 syndrome-the EGb COCOS protocol.

Frontiers in human neuroscience·2026

Same journal

Examining the independent and combined effects of autistic and ADHD traits on multisensory integration.

Frontiers in human neuroscience·2026

Same journal

Prediction of hormone receptor status in breast cancer brain metastases using an MRI-based multimodal deep learning framework.

Frontiers in human neuroscience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 29, 2026

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

Unmixing binocular signals.

Sidney R Lehky¹

¹Computational Neuroscience Laboratory, The Salk Institute La Jolla, CA, USA.

Frontiers in Human Neuroscience

|September 3, 2011

Summary

This summary is machine-generated.

Binocular rivalry, where perception alternates between two images, can occur at higher brain levels. A new algorithm, non-negative matrix factorization, shows how images can be separated after mixing, explaining this phenomenon.

Keywords:

binocular rivalry blind source separation independent component analysis non-linear dynamical systems non-negative matrix factorization

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

How to Create and Use Binocular Rivalry

How to Create and Use Binocular Rivalry

Published on: November 10, 2010

Related Experiment Videos

Last Updated: May 29, 2026

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

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

How to Create and Use Binocular Rivalry

How to Create and Use Binocular Rivalry

Published on: November 10, 2010

Area of Science:

Neuroscience
Computational Vision
Perception

Background:

Binocular rivalry involves alternating perception of incompatible images presented to each eye.
Early models suggested rivalry occurs at early visual stages via reciprocal inhibition.
Recent findings indicate rivalry can also happen at higher, abstract levels, dissociated from eye-of-origin.

Purpose of the Study:

To investigate the mechanism allowing separate image identities to be maintained after binocular mixing for higher-level rivalry.
To demonstrate how image unmixing can occur at later visual stages.

Main Methods:

Utilized non-negative matrix factorization (NMF), a non-linear signal-processing algorithm.
Applied NMF to model the unmixing of visual information after binocular mixing.

Main Results:

Demonstrated that NMF can unmix left and right eye images after they have been mixed.
Showed that image unmixing is possible at any stage subsequent to binocular mixing.
Identified a potential neural mechanism for late-stage binocular representation and interaction.

Conclusions:

The ability to unmix images post-mixing provides a mechanism for generating diverse binocular representations in different cortical areas.
Non-linear algorithms like NMF can explain complex neural processing and non-intuitive perceptual phenomena like higher-level binocular rivalry.