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Related Experiment Video

Updated: Feb 6, 2026

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

How Do Expectations Shape Perception?

Floris P de Lange¹, Micha Heilbron¹, Peter Kok²

¹Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands; All authors contributed equally.

Trends in Cognitive Sciences

|August 21, 2018

Summary

This summary is machine-generated.

Prior knowledge significantly aids perception and decision-making by leveraging the world's probabilistic structure. This review explores neural mechanisms and Bayesian frameworks for integrating prior expectations with sensory data in perception.

Keywords:

Bayesian inference perception perceptual inference prediction predictive coding sensory processing

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Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Psychophysics

Background:

Perception and perceptual decision-making rely heavily on prior knowledge of the environment's probabilistic structure.
Understanding how prior expectations are implemented neurally is crucial for advancing cognitive science.

Purpose of the Study:

To review recent advancements in the neural basis of expectation in perception.
To discuss Bayesian theories of perception and their implications for computational modeling.
To explore how empirical data can refine computational frameworks for probabilistic integration.

Main Methods:

Literature review of recent neuroscientific and computational studies.
Discussion of Bayesian inference principles applied to perception.
Analysis of empirical data's role in constraining computational models.

Main Results:

Identified key neural sources and targets involved in expectation generation and modulation.
Highlighted the utility of Bayesian frameworks for understanding prior-sensory integration.
Emphasized the iterative relationship between empirical findings and computational model development.

Conclusions:

Prior knowledge significantly shapes perception through neural mechanisms and probabilistic computations.
Bayesian theories provide a robust framework for modeling perceptual integration.
Future research should integrate neuroscientific data with computational models to elucidate expectation's role in perception.