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.

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...

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

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.

You might also read

Related Articles

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

Sort by

Same author

CNN-based search model fails to account for human attention guidance by simple visual features.

Attention, perception & psychophysics·2023

Same author

What Is Wrong With the Current Evaluative Bibliometrics?

Frontiers in research metrics and analytics·2022

Same author

Crowding and attention in a framework of neural network model.

Journal of vision·2020

Same author

What limits search for conjunctions of simple visual features?

Journal of vision·2019

Same author

Discrimination of spatial phase: The roles of luminance distribution and attention.

Vision research·2018

Same author

Combining local and global limitations of visual search.

Journal of vision·2017

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

Search research articles

Related Experiment Video

Updated: Aug 28, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Capacity Limitations of Visual Search in Deep Convolutional Neural Networks.

¹Institute of Psychology, University of Tartu, 50409 Tartu, Estonia endel.poder@ut.ee.

Neural Computation

|September 16, 2022

Summary

This summary is machine-generated.

Deep neural networks (DNNs) mimic human vision but show different visual search patterns. Unlike humans, DNNs exhibit similar capacity limitations for both simple and complex visual features.

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Published on: June 17, 2019

Related Experiment Videos

Last Updated: Aug 28, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Published on: June 17, 2019

Area of Science:

Computer Vision
Cognitive Neuroscience
Artificial Intelligence

Background:

Deep convolutional neural networks (CNNs) are inspired by biological visual systems.
CNNs achieve human-level performance in object classification tasks.
Understanding CNNs' visual search capabilities is crucial for AI development.

Purpose of the Study:

To compare the visual search performance of deep neural networks (DNNs) with human observers.
To investigate capacity limitations in DNNs for simple features versus feature configurations.
To identify qualitative differences in visual processing between AI and humans.

Main Methods:

Three pretrained deep neural networks were utilized.
Networks were tested on visual search tasks involving simple features.
Networks were also tested on visual search tasks involving feature configurations.

Main Results:

DNNs demonstrated qualitative differences compared to human performance.
No significant difference was found in DNNs' performance between simple feature search and feature configuration search.
Both simple and complex visual stimuli revealed comparable capacity limitations in the tested DNNs.

Conclusions:

DNNs exhibit distinct visual search mechanisms compared to humans.
Capacity limitations in DNNs do not differentiate between 'pop-out' simple features and complex configurations.
Further research is needed to fully understand the nuances of AI visual perception.