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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.
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Related Experiment Video

Updated: May 6, 2026

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Learning what to expect (in visual perception).

Peggy Seriès¹, Aaron R Seitz

¹Department of Informatics, University of Edinburgh Edinburgh, UK.

Frontiers in Human Neuroscience

|November 5, 2013

Summary

This summary is machine-generated.

Perceptual expectations, viewed as prior beliefs in Bayesian inference, shape our world experience. This review explores how these priors influence visual motion perception and their neural underpinnings.

Keywords:

Bayesian priors expectations perceptual learning probabilistic inference statistical learning

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Area of Science:

Computational neuroscience
Cognitive science
Neuroscience

Background:

Perception is significantly influenced by expectations, which can be modeled as prior beliefs in Bayesian inference.
The brain's potential "Bayes-optimal" processing under constraints highlights the importance of understanding prior beliefs.
Unresolved questions concern the dynamics, complexity, and neural basis of these priors.

Purpose of the Study:

To review recent research on Bayesian priors in visual motion perception.
To explore how expectations (priors) affect sensory experiences.
To discuss the neural substrates and learning mechanisms of perceptual priors.

Main Methods:

Review of experimental studies focusing on visual motion perception.
Analysis of data within the framework of Bayesian inference models.
Integration of findings with broader literature on perceptual learning and expectations.

Main Results:

Studies on motion perception provide insights into the nature and function of Bayesian priors.
Evidence suggests priors are dynamic and can be learned or unlearned.
The neural basis of these priors is an active area of investigation.

Conclusions:

Bayesian inference provides a powerful framework for understanding perception and expectations.
Visual motion perception serves as a key model for studying perceptual priors.
Further research is needed to fully elucidate the neural mechanisms and adaptive properties of priors.