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...

You might also read

Related Articles

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

Sort by

Same author

Recovering stereo vision by squashing virtual bugs in a virtual reality environment.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences·2016

Same author

Learning and inference using complex generative models in a spatial localization task.

Journal of vision·2016

Same author

Unifying account of visual motion and position perception.

Proceedings of the National Academy of Sciences of the United States of America·2015

Same author

Stereopsis and amblyopia: A mini-review.

Vision research·2015

Same author

Kinesthesis can make an invisible hand visible.

Psychological science·2013

Same author

The brain uses adaptive internal models of scene statistics for sensorimotor estimation and planning.

Proceedings of the National Academy of Sciences of the United States of America·2013

Same journal

Analysis of human visual experience data.

Journal of vision·2026

Same journal

Pyramid-based Bayesian modeling for high-resolution behavioral analysis.

Journal of vision·2026

Same journal

Sensation without perception: The white whale effect and perceptual blindness in autonomous vehicles.

Journal of vision·2026

Same journal

Gaze behavior during closed-captioned movie viewing adapts to absent audio through more frequent switching between text and scene.

Journal of vision·2026

Same journal

In pursuit of saccade awareness: Limited volitional control and minimal conscious access to catch-up saccades during smooth pursuit eye movements.

Journal of vision·2026

Same journal

Dissociable effects of element-lifetime and stimulus-duration on local and global motion processing: An equivalent noise study.

Journal of vision·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 13, 2026

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Adapting internal statistical models for interpreting visual cues to depth.

Anna Seydell¹, David C Knill, Julia Trommershäuser

¹Department of General and Experimental Psychology, University of Giessen, Germany. aseydell@gmail.com

Journal of Vision

|May 15, 2010

Summary

This summary is machine-generated.

The brain rapidly adjusts its interpretation of visual depth cues based on recent environmental statistics. It can also use different statistical models for different object categories, demonstrating flexible Bayesian learning.

More Related Videos

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Published on: December 4, 2013

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

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

Published on: May 2, 2019

Related Experiment Videos

Last Updated: Jun 13, 2026

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues

Published on: December 4, 2013

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

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

Published on: May 2, 2019

Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Visual Perception

Background:

Environmental statistical regularities are crucial for interpreting sensory cues.
These regularities differ across object categories and environments, posing a challenge for perception.

Purpose of the Study:

To investigate if and how the brain modifies prior assumptions about scene statistics for interpreting visual depth cues when stimulus statistics change.
To determine the brain's capacity for rapid adaptation and category-specific statistical modeling in visual perception.

Main Methods:

Subjects judged surface slant using stereoscopic depth cues and a figural compression cue (aspect ratio).
The reliability of the figural compression cue was manipulated by altering the distribution of stimulus aspect ratios.
Stimuli from different shape categories (ellipses, diamonds) with varying statistical properties were interleaved.

Main Results:

Subject reliance on the figural compression cue shifted in response to changes in stimulus statistics.
When presented with interleaved categories, subjects de-emphasized the compression cue for categories with more random aspect ratios.
Behavior aligned with Bayesian learning models, indicating rapid adaptation of cue weighting.

Conclusions:

Relative cue weights dynamically adjust based on recently experienced stimulus statistics.
The brain employs distinct statistical models for different object categories, showcasing perceptual flexibility.
Findings support the role of Bayesian inference in adapting visual perception to environmental variability.