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Related Experiment Video

Updated: Sep 12, 2025

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

Bridging Conflicting Views on Eye Position Signals: A Neurocomputational Approach to Perisaccadic Perception: Eye

Nikolai Stocks¹, Fred H Hamker¹

¹Chemnitz University of Technology, Department of Computer Science, Chemnitz, Germany.

The European Journal of Neuroscience

|August 4, 2025

Summary

Accurate eye position tracking during saccades is crucial for visual perception. This study models neural signals in the lateral intraparietal area (LIP), reconciling conflicting findings and showing how combined signals enable precise eye movement prediction.

Keywords:

computational neuroscience corollary discharge eye positions signals gain‐fields perisaccadic perception saccades

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Last Updated: Sep 12, 2025

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

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Published on: March 18, 2019

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How to Build a Dichoptic Presentation System That Includes an Eye Tracker

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Published on: September 6, 2017

Area of Science:

Neuroscience
Computational Neuroscience
Visual Perception

Background:

Saccades are essential for vision, but the brain's use of eye position signals is debated.
The classical model suggests imperfect eye position signals cause visual misperceptions during saccades.
Conflicting neurophysiological data exists regarding eye position signals in the lateral intraparietal area (LIP).

Purpose of the Study:

To reconcile conflicting neurophysiological findings on eye position signals in LIP.
To model how neural subpopulations in LIP contribute to accurate perisaccadic eye position decoding.
To investigate the roles of corollary discharge and proprioceptive signals in eye position representation.

Main Methods:

Computational modeling of two LIP neuron subpopulations.
Simulating inputs from corollary discharge and proprioceptive signals.
Decoding eye position from simulated neural activity.

Main Results:

The model successfully predicted and tracked perisaccadic eye position.
Decoding eye position was accurate using simulated neural activity.
A combination of signals explains previously conflicting experimental results.

Conclusions:

A unified framework for understanding LIP eye position signals is proposed.
Accurate perisaccadic eye position decoding relies on both predictive and proprioceptive inputs.
The findings clarify the neural basis of visual perception during eye movements.