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Related Experiment Video

Updated: Mar 19, 2026

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Learning What to See in a Changing World.

Katharina Schmack¹, Veith Weilnhammer¹, Jakob Heinzle²

¹Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin Berlin, Germany.

Frontiers in Human Neuroscience

|June 16, 2016

Summary

Visual perception relies on expectations, which are continuously updated from experience. This study shows Bayesian learning explains how visual perception dynamically incorporates changing expectations from associations and past outcomes.

Keywords:

Bayesian brain associative learning bistable perception hierarchical Gaussian filter sensory memory visual perception

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Area of Science:

Cognitive Neuroscience
Computational Vision
Psychology

Background:

Visual perception is influenced by expectations, but the learning mechanisms remain unclear.
Understanding how the brain learns and updates perceptual expectations in dynamic environments is crucial.

Purpose of the Study:

To investigate if perceptual expectations are continuously updated from ongoing experience.
To explore the role of associative learning and perceptual history in shaping visual perception.
To model the process of integrating expectations with sensory information.

Main Methods:

Applied a Bayesian framework to analyze visual perception.
Conducted two experiments with human observers performing an associative learning task.
Utilized computational modeling to explain perceptual outcomes.

Main Results:

Perception of ambiguous stimuli was biased by learned associations and prior perceptual outcomes.
A computational model best explained perception by continuously updating priors from associative learning and perceptual history.
Sensory information was combined probabilistically with updated priors.

Conclusions:

Visual perception is a dynamic process shaped by continuously updated expectations.
Bayesian learning and inference provide a framework for understanding how the brain integrates diverse sources of information for perception.
The findings highlight the adaptive nature of visual perception in response to changing environments.