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Related Experiment Video

Updated: Mar 13, 2026

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

Apparent Motion Suppresses Responses in Early Visual Cortex: A Population Code Model.

Nathalie Van Humbeeck¹, Tom Putzeys¹, Johan Wagemans¹

¹Laboratory of Experimental Psychology, Department of Brain & Cognition, University of Leuven, Leuven, Belgium.

Plos Computational Biology

|October 27, 2016

Summary

This summary is machine-generated.

Apparent motion (AM) does not increase early visual cortex activation. Instead, our model shows AM suppresses sensory responses, aligning with predictive coding theories for visual masking.

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Published on: November 9, 2011

Area of Science:

Neuroscience
Computational Vision
Psychophysics

Background:

Apparent motion (AM) is perceived movement between discrete stimuli.
AM masking suggests V1 activation, but predictive coding offers an alternative explanation.
Existing evidence for AM-related V1 activation remains inconclusive.

Purpose of the Study:

To investigate the neural mechanisms of apparent motion (AM) masking.
To test whether AM increases primary visual cortex (V1) activation.
To evaluate the role of predictive coding in AM masking.

Main Methods:

Developed a V1-like population code model based on contrast normalization.
Analyzed the effect of AM on target grating detection.
Compared model predictions with psychophysical masking data.

Main Results:

AM limits target detection performance, especially at high contrasts and with matching orientations.
The V1-like model showed insufficient AM-related activation to explain masking.
Model simulations predicted strong suppression of early sensory responses during AM.

Conclusions:

AM masking is better explained by predictive coding-induced response suppression than by increased V1 activation.
Early visual cortex activation is not the primary driver of AM masking.
Predictive coding provides a viable framework for understanding AM-related neural processing.