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

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...

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.

Perceptual Constancy

Perceptual Constancy

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...

You might also read

Related Articles

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

Sort by

Same author

Rod Pathway Dysfunction in Early-Stage Diabetic Retinopathy Assessed by ERG and Pupillometry.

Investigative ophthalmology & visual science·2026

Same author

Correction: ISCEV extended protocol for the photoreceptor directed ERG using full-field silent substitution stimuli.

Documenta ophthalmologica. Advances in ophthalmology·2026

Same author

Exploring metabolomic clues in diabetic retinopathy: a pilot study.

Acta diabetologica·2026

Same author

ISCEV extended protocol for the photoreceptor directed ERG using full-field silent substitution stimuli.

Documenta ophthalmologica. Advances in ophthalmology·2026

Same author

A practical introduction to wavelet analysis in electroretinography.

Documenta ophthalmologica. Advances in ophthalmology·2025

Same author

A rapid pupillometry protocol for clinical use: effect of age and test-retest repeatability.

Documenta ophthalmologica. Advances in ophthalmology·2025

Search research articles

Related Experiment Video

Updated: Jun 10, 2026

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Spatial contrast sensitivity in dynamic and static additive luminance noise.

J Jason McAnany¹, Kenneth R Alexander

¹Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, 1855 W. Taylor St., Chicago, IL 60612, USA.

Vision Research

|July 20, 2010

Summary

This summary is machine-generated.

This study reveals how temporal noise characteristics quantitatively affect spatial contrast sensitivity function (CSF) properties. Different noise types bias visual pathways, influencing band-pass or low-pass CSF characteristics, predictable by the linear amplifier model (LAM).

More Related Videos

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

Published on: April 11, 2025

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Published on: March 18, 2019

Related Experiment Videos

Last Updated: Jun 10, 2026

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss

Published on: April 11, 2025

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Published on: March 18, 2019

Area of Science:

Visual Neuroscience
Psychophysics
Image Perception

Background:

The spatial contrast sensitivity function (CSF) describes visual system's ability to detect patterns of varying spatial frequencies.
Understanding how noise, a ubiquitous visual stimulus component, influences CSF is crucial for visual perception models.
Previous research suggests different noise types may differentially engage visual processing pathways.

Purpose of the Study:

To quantitatively define the relationship between temporal characteristics of additive luminance noise and spatial contrast sensitivity function (CSF) properties.
To investigate how different noise conditions (dynamic/static, synchronous/asynchronous) modulate CSF.
To test the applicability of the linear amplifier model (LAM) across various noise conditions.

Main Methods:

Obtained CSFs from two observers using short-duration Gabor patch targets.
Added white luminance noise with varying root-mean-square contrasts (c(rms)).
Manipulated noise temporal characteristics: dynamic vs. static, and synchronous vs. asynchronous presentation relative to the target.

Main Results:

CSFs exhibited band-pass properties with increasing c(rms) in asynchronous dynamic, synchronous dynamic, and synchronous static noise.
CSFs remained low-pass across all c(rms) levels in asynchronous static noise.
The linear amplifier model (LAM) accurately predicted CSF properties across all noise conditions.

Conclusions:

Asynchronous static noise uniquely alters CSF, likely by biasing sensitivity towards transient (magnocellular) mechanisms.
Other noise conditions (asynchronous dynamic, synchronous dynamic, synchronous static) appear to favor sustained (parvocellular) mechanisms.
The temporal properties of luminance noise significantly modulate spatial contrast sensitivity, with implications for understanding visual processing pathways.