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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.

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Related Experiment Video

Updated: Jan 8, 2026

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

What if eye...? Computationally recreating vision evolution.

Kushagra Tiwary¹, Aaron Young¹, Zaid Tasneem²

¹Camera Culture, MIT Media Laboratory, Cambridge, USA.

Science Advances

|December 17, 2025

Summary

This summary is machine-generated.

Computational evolution simulates eye and behavior development, revealing task-specific selection drives eye diversity and optical innovations. It uncovers scaling laws between visual acuity and neural processing for vision science discovery.

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Last Updated: Jan 8, 2026

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Published on: April 24, 2017

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Published on: March 25, 2011

Using Looming Visual Stimuli to Evaluate Mouse Vision

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Published on: June 13, 2019

Area of Science:

Evolutionary Biology
Computational Neuroscience
Artificial Intelligence

Background:

Natural selection has shaped diverse vision systems.
Computational evolution provides a method to test hypotheses in vision.
Understanding the evolutionary pressures on vision is crucial.

Purpose of the Study:

To computationally recreate and analyze the evolution of vision.
To investigate the principles shaping vision across different levels of Marr's hierarchy.
To use embodied artificial intelligence (AI) as a tool for hypothesis testing in vision science.

Main Methods:

Co-evolving eyes and behaviors in embodied agents.
Utilizing computational evolution to simulate evolutionary outcomes.
Analyzing the emergence of optical innovations and their trade-offs.

Main Results:

Task-specific selection drives the bifurcation of eye evolution.
Optical innovations emerge to balance light collection and spatial precision.
Identified scaling laws between visual acuity and neural processing.

Conclusions:

Computational evolution offers a powerful paradigm for understanding vision.
Embodied AI can accelerate scientific discovery in vision science.
The study provides insights into the evolution of eye and brain size.