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.

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...

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...

You might also read

Related Articles

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

Sort by

Same author

Brain Activity Underlying Mental Imagery: Event-related Potentials During Mental Image Generation.

Journal of cognitive neuroscience·2013

Same author

Perseverative and semantic influences on visual object naming errors in optic aphasia: a connectionist account.

Journal of cognitive neuroscience·2013

Same author

Savings in relearning face-name associations as evidence for "covert recognition" in prosopagnosia.

Journal of cognitive neuroscience·2013

Same author

Mechanisms of spatial attention: the relation of macrostructure to microstructure in parietal neglect.

Journal of cognitive neuroscience·2013

Same author

Early commitment of neural substrates for face recognition.

Cognitive neuropsychology·2010

Same author

Prosopamnesia: a selective impairment in face learning.

Cognitive neuropsychology·2010

Same journal

Sensorimotor Adaptation of Vocal Pitch Is Impaired in Cerebellar Ataxia.

Journal of cognitive neuroscience·2026

Same journal

Memory in the Palm of Your Hand: Smartphone-based Methods for Measuring Memory in the Wild.

Journal of cognitive neuroscience·2026

Same journal

Processing Asymmetry in Object-modifying Relative Clauses: Evidence from Functional Connectivity.

Journal of cognitive neuroscience·2026

Same journal

Extensive Experience Remodels Neural Task Circuitry to Escape the Frontal Bottleneck and Increase Automaticity of Categorization.

Journal of cognitive neuroscience·2026

Same journal

Investigating the Effects of Acute Stress on Neural Mechanisms of Self-controlled Decision-making.

Journal of cognitive neuroscience·2026

Same journal

Distilling the Neurophenomenological Signatures of Pure Awareness during Transcendental Meditation.

Journal of cognitive neuroscience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 8, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Visual Object Representation: Interpreting Neurophysiological Data within a Computational Framework.

D C Plaut¹, M J Farah

¹School of Computer Science, Carnegie Mellon University.

Journal of Cognitive Neuroscience

|August 23, 2013

Summary

This summary is machine-generated.

Computational and neuroscientific approaches have advanced vision research, but primarily for low-level processing. This study integrates high-level vision findings using a computational framework for object recognition and representation.

More Related Videos

Functional Magnetic Resonance Imaging (fMRI) of the Visual Cortex with Wide-View Retinotopic Stimulation

Functional Magnetic Resonance Imaging (fMRI) of the Visual Cortex with Wide-View Retinotopic Stimulation

Published on: December 8, 2023

Related Experiment Videos

Last Updated: May 8, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Functional Magnetic Resonance Imaging (fMRI) of the Visual Cortex with Wide-View Retinotopic Stimulation

Functional Magnetic Resonance Imaging (fMRI) of the Visual Cortex with Wide-View Retinotopic Stimulation

Published on: December 8, 2023

Area of Science:

Neuroscience
Computer Vision
Cognitive Science

Background:

Vision research has progressed by integrating computational and neuroscientific methods.
Current integrative approaches largely focus on low-level visual processing.
Recent advances in high-level vision in separate disciplines necessitate integration.

Purpose of the Study:

To bridge the gap between computational and neuroscientific findings in high-level vision.
To extend understanding of object representation and recognition.
To utilize a computational framework to organize and interpret neuroscientific data.

Main Methods:

Employing a computational framework from computer vision research.
Organizing and interpreting human and primate neurophysiology data.
Analyzing neuropsychological findings within the computational framework.

Main Results:

The computational framework provides a structure for understanding high-level visual processing.
Integration of computational and neuroscientific data offers new insights into object recognition.
Neurophysiological and neuropsychological data are interpreted through the lens of computational models.

Conclusions:

An integrated approach using computational frameworks can advance the study of high-level vision.
This work contributes to a unified understanding of object representation and recognition.
Future research should continue to leverage interdisciplinary methods for vision science.