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Related Experiment Video

Updated: Mar 3, 2026

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Published on: March 18, 2019

Saccadic suppression as a perceptual consequence of efficient sensorimotor estimation.

Frédéric Crevecoeur^1,2, Konrad P Kording³

¹Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université catholique de Louvain, Louvain-la-Neuve, Belgium.

|May 3, 2017

Summary

This summary is machine-generated.

The brain suppresses visual information during saccadic eye movements to optimize sensorimotor control. This perceptual suppression arises from efficient computations linking perception and motor control.

Keywords:

computational biology eye movements neuroscience none optimal control saccadic suppression signal-dependent noise state estimation systems biology

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Last Updated: Mar 3, 2026

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Published on: March 18, 2019

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The Measurement and Treatment of Suppression in Amblyopia

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Published on: December 14, 2012

Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Humans execute saccadic eye movements frequently, involving significant suppression of sensory feedback.
The precise reasons for this extensive discarding of visual information remain unclear.

Purpose of the Study:

To investigate the link between perception and motor control in the context of saccadic eye movements.
To theoretically explain the phenomenon of perceptual suppression during saccades.

Main Methods:

Developed a theoretical model of a Bayesian estimator.
Analyzed the impact of signal-dependent noise and sensorimotor delays on sensory information weighting.

Main Results:

The Bayesian estimator model predicts a reduction in sensory information weighting around saccade execution.
This theoretical reduction aligns with observed behavioral suppression and neural response changes during saccades.
Demonstrated that efficient sensorimotor processing can account for saccadic suppression.

Conclusions:

Saccadic suppression is likely a consequence of efficient sensorimotor computations.
Perception and motor control may share neural resources within the brain.