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Evaluating the evidence for expectation suppression in the visual system.

Daniel Feuerriegel¹, Rufin Vogels², Gyula Kovács³

¹Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia.

Neuroscience and Biobehavioral Reviews

|April 9, 2021

Summary

This summary is machine-generated.

Expectation suppression in visual perception is challenged by confounding factors like surprise and attention. Genuine expectation suppression is only reliably found in specific long-term statistical learning designs.

Keywords:

Attention Electrophysiology Expectation suppression Prediction Repetition suppression Vision fMRI

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Area of Science:

Cognitive Neuroscience
Visual Perception
Computational Neuroscience

Background:

Predictive coding theories of visual perception heavily rely on expectation suppression.
Recent studies suggest confounding factors may inflate or mimic expectation suppression.
Key confounds include surprise, attention, stimulus repetition/adaptation, and novelty.

Purpose of the Study:

To critically review evidence for expectation suppression in various experimental designs.
To identify and discuss confounding factors influencing expectation suppression findings.
To propose improved experimental designs for future research on expectation suppression.

Main Methods:

Systematic review of studies investigating expectation suppression.
Analysis of designs including probabilistic cueing, statistical learning, oddball, action-outcome learning, and apparent motion.
Evaluation of evidence in light of identified confounding factors.

Main Results:

Consistent evidence for expectation suppression was found only in specific statistical learning designs requiring extensive sequence learning.
Across other designs with shorter learning periods (1-2 sessions), evidence for genuine expectation suppression was inconsistent.
Confounding factors like surprise, attention, and adaptation significantly impact observed effects.

Conclusions:

The prevalence and interpretation of expectation suppression require re-evaluation due to confounding factors.
Absence of expectation suppression in some contexts offers insights into predictive processing and perceptual decision-making models.
Future research should employ rigorous designs to isolate true expectation suppression effects.