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

Modeling and Similitude01:12

Modeling and Similitude

268
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
268
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

673
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.
673
Gestalt Principles of Perception01:21

Gestalt Principles of Perception

314
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...
314
Topographic Surveying and Contours01:29

Topographic Surveying and Contours

99
Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
99
Line, Surface, and Volume Integrals01:15

Line, Surface, and Volume Integrals

2.4K
A line integral for a vector field is defined as the integral of the dot product of a vector function with an infinitesimal displacement vector along a prescribed path. If the prescribed path is closed, the integrals reduce to a closed-line integral. The closed-contour integral of the vector field is referred to in terms of the circulation of the vector field around the closed path. A vector with zero circulation around every closed path is called a conservative field, while one with non-zero...
2.4K
Perceptual Constancy01:12

Perceptual Constancy

402
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...
402

You might also read

Related Articles

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

Sort by
Same author

A Language Model for Pediatric Occupational Therapy Documentation: Model Development and Pilot Study.

JMIR AI·2026
Same author

U<b>se of benzodiazepines and medications for opioid use disorder for withdrawal management in a hospital-based setting for individuals exposed to benzodiazepine-contaminated fentanyl: A case serie</b>s.

Drug and alcohol dependence reports·2026
Same author

Point-of-care ultrasound in Canadian anesthesiology residency programs: a follow-up survey.

Canadian journal of anaesthesia = Journal canadien d'anesthesie·2026
Same author

Physically Informed 3D Food Reconstruction: Methods and Results.

IEEE journal of biomedical and health informatics·2026
Same author

Human Papillomavirus Self-Sampling Attitudes Amongst Women Living with HIV Prior to a Self-Sampling Intervention.

Cancers·2026
Same author

Expanded CD16<sup>+</sup>CD56<sup>+</sup>Granzyme B<sup>+</sup> NK like CD8<sup>+</sup> T cells an off target effect of bruton's tyrosine kinase inhibitors in Waldenström macroglobulinemia.

Scientific reports·2025
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Related Experiment Video

Updated: Jul 9, 2025

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions
07:09

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions

Published on: May 2, 2019

6.1K

Modeling the Role of Contour Integration in Visual Inference.

Salman Khan1,2,3, Alexander Wong2,4, Bryan Tripp1,2,5

  • 1Centre for Theoretical Neuroscience, Department of System Design Engineering.

Neural Computation
|December 5, 2023
PubMed
Summary
This summary is machine-generated.

The brain

More Related Videos

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

9.0K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

Related Experiment Videos

Last Updated: Jul 9, 2025

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions
07:09

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions

Published on: May 2, 2019

6.1K
Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

9.0K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Visual perception

Background:

  • The brain's visual system uses recurrent mechanisms to enhance processing under challenging conditions.
  • Contour integration in the primary visual cortex (V1) strengthens neural responses to aligned edges.
  • Understanding the perceptual role of contour integration requires computational modeling.

Purpose of the Study:

  • To investigate how contour integration is optimized for high-level visual tasks.
  • To explore the role of contour integration in visual perception using artificial neural networks.
  • To compare model performance with brain function and behavior.

Main Methods:

  • Embedded a biologically grounded contour integration model into a task-driven artificial neural network.
  • Trained the network using a gradient-descent variant on synthetic and natural image tasks.
  • Compared a recurrent model with a parameter-matched non-recurrent control network.

Main Results:

  • The recurrent model successfully mimicked brain behavior, neural responses, and lateral connections on a synthetic contour detection task.
  • On natural image tasks, the model enhanced weak contours and differentiated points on the same vs. different contours.
  • A non-recurrent network performed as well as or better than the recurrent model on natural image tasks, suggesting recurrence is not essential.
  • A modified model without distinguishing excitatory/inhibitory neurons achieved the best performance across all tasks.

Conclusions:

  • Contour integration mechanisms may not be essential for all naturalistic contour-related visual tasks.
  • Recurrent processing might be more critical for specific tasks or under more challenging viewing conditions.
  • Optimizing models by removing distinctions between neuron types can improve performance.