Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Visual System01:26

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 Processing01:20

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...
Vision01:24

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.
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category, whereas...

You might also read

Related Articles

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

Sort by
Same author

Image space opens up for visual neuroscience.

Neuron·2026
Same author

Reassessing Choice Probability: What 59 Macaque Studies Tell Us About Decision-Related Activity in Visual Cortex.

bioRxiv : the preprint server for biology·2026
Same author

Tracing aesthetic experience from perception and conception to appraisal using deep convolutional neural networks.

iScience·2026
Same author

From pixels to perception: A benchmark for human-like symmetry detection.

Vision research·2026
Same author

Finding Closure: A Closer Look at the Gestalt Law of Closure in Convolutional Neural Networks.

Computational brain & behavior·2026
Same author

Variability and predictability as key factors in a new approach to choreographic complexity in dance.

Cognition·2026
Same journal

Perception and action as one: Re-integrating research on human action through event files.

Psychological review·2026
Same journal

Associative learning explains "intuitive statistics" in animals.

Psychological review·2026
Same journal

A reciprocal model of practice and skill: Navigating between dropout and expertise.

Psychological review·2026
Same journal

The relative psychometric function: A general analysis framework for relating psychological processes.

Psychological review·2026
Same journal

A taxonomy of discriminatory behavior.

Psychological review·2026
Same journal

Extreme-value signal detection theory for recognition memory: The parametric road not taken.

Psychological review·2026
See all related articles

Related Experiment Video

Updated: May 9, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

A neural population model for visual pattern detection.

Robbe L T Goris1, Tom Putzeys, Johan Wagemans

  • 1Center for Neural Science, New York University, 4 Washington Place, Room 809, New York, NY 10003, USA. robbe.goris@nyu.edu

Psychological Review
|August 7, 2013
PubMed
Summary
This summary is machine-generated.

A new theory reinterprets pattern detection by modeling neural population activity in the primary visual cortex. This maximum-likelihood decoding approach explains classic detection results and offers a unified view of visual perception.

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns
09:42

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

Related Experiment Videos

Last Updated: May 9, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns
09:42

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

Area of Science:

  • Vision Science
  • Neuroscience
  • Computational Neuroscience

Background:

  • Traditional models of pattern detection link visual channels to behavior.
  • These models successfully explain threshold vision but conflict with current neurophysiology.
  • Existing theories fail to generalize to suprathreshold vision or non-detection tasks.

Purpose of the Study:

  • Propose an alternative theory for pattern detection.
  • Develop a model based on neurophysiologically inspired population activity.
  • Explain a wide range of classic detection phenomena.

Main Methods:

  • Constructed a neurophysiologically inspired model of population activity in the primary visual cortex.
  • Applied maximum-likelihood decoding for perceptual decision-making.
  • Tested the model against established psychophysical results.

Main Results:

  • The proposed theory successfully explains a broad range of classic detection results.
  • A single set of parameters accounts for summation, adaptation, and uncertainty effects.
  • The model provides a novel interpretation for psychophysical data on pattern detection.

Conclusions:

  • The maximum-likelihood decoding of neural population activity offers a more comprehensive theory of pattern detection.
  • This approach reconciles psychophysical findings with neurophysiological knowledge.
  • The theory unifies explanations for various visual perception phenomena.