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.

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

Exposure to false cardiac feedback alters pain perception and anticipatory cardiac frequency.

eLife·2026

Same author

Gaze dynamics prior to navigation support hierarchical planning.

PloS one·2026

Same author

Limb apraxia and active inference in the visuomotor pathways.

Psychonomic bulletin & review·2026

Same author

The continuous dynamics of planning and acting in a virtually embodied task.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

False but phonologically plausible linguistic priors induce cross-linguistic auditory illusions and attenuate electrophysiological markers of surprise.

Imaging neuroscience (Cambridge, Mass.)·2026

Same author

Five Fristonian Formulae.

Entropy (Basel, Switzerland)·2025

Same journal

Multiphysics Investigation on Thermal Characteristics of Internal Bio-Inspired V-Ribbed Cooling Channels for Outer Rotor PMSM.

Biomimetics (Basel, Switzerland)·2026

Same journal

Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks.

Biomimetics (Basel, Switzerland)·2026

Same journal

A Systematic Taxonomy of the Sunflower Optimization Algorithm: Variants, Hybridization Strategies, Applications, and Research Directions.

Biomimetics (Basel, Switzerland)·2026

Same journal

Toward a Compositional Theory of Trust in Embodied Intelligence: A QNLP Framework for Modeling Context, Interaction, and Trustworthiness.

Biomimetics (Basel, Switzerland)·2026

Same journal

Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster Analysis.

Biomimetics (Basel, Switzerland)·2026

Same journal

A Modified Multi-Strategy Dhole Optimization Algorithm and Its Engineering Applications.

Biomimetics (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 15, 2025

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

Active Vision in Binocular Depth Estimation: A Top-Down Perspective.

Matteo Priorelli¹, Giovanni Pezzulo², Ivilin Peev Stoianov¹

¹Institute of Cognitive Sciences and Technologies, National Research Council of Italy, 35137 Padova, Italy.

Biomimetics (Basel, Switzerland)

|September 27, 2023

Summary

This summary is machine-generated.

This study proposes active inference for depth estimation, modeling the brain

Keywords:

action-perception cycles active inference active vision depth perception predictive coding

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

Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients

Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients

Published on: March 29, 2022

Related Experiment Videos

Last Updated: Jul 15, 2025

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition

Published on: July 21, 2020

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

Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients

Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients

Published on: March 29, 2022

Area of Science:

Computational neuroscience
Computer vision

Background:

Depth estimation is a complex problem due to visual ambiguity.
The brain uses various cues (monocular, binocular) for depth perception.
Current deep learning models treat the brain as a feature detector, lacking biological plausibility.

Purpose of the Study:

To propose a novel, biologically plausible approach to depth estimation.
To frame depth estimation as an active inference problem.
To investigate how the brain infers depth using generative models and predictive coding.

Main Methods:

Developed a hierarchical generative model for predicting eye projections.
Employed active inference principles and predictive coding for model inversion.
Incorporated a non-uniform fovea resolution assumption.

Main Results:

Demonstrated depth inference through biologically plausible homogeneous transformations.
Showed that active vision strategies, combining fixation and depth estimation, improve accuracy.
Validated the approach using local message passing, suitable for neural circuits.

Conclusions:

Active inference offers a biologically plausible alternative for neural depth estimation.
The proposed model integrates perception and action through iterative cycles.
This framework supports efficient, localized neural computations for depth perception.