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Related Experiment Video

Updated: Nov 5, 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

Saccade Landing Point Prediction Based on Fine-Grained Learning Method.

Aythami Morales^1,2, Francisco M Costela^2,3, Russell L Woods^2,3

¹BiDA-Lab, Department of Electrical Engineering, Universidad Autonoma de Madrid, 28049 Madrid, Spain.

IEEE Access : Practical Innovations, Open Solutions

|May 13, 2021

Summary

This summary is machine-generated.

This study introduces a novel algorithm using LSTM networks to predict the exact landing point of saccadic eye movements early on. This advancement significantly improves accuracy in real-world gaze-contingent systems.

Keywords:

LSTM Saccade eye movement fine-grained learning gaze-contingent recurrent neural networks

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Last Updated: Nov 5, 2025

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Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

Area of Science:

Neuroscience
Computer Science
Human-Computer Interaction

Background:

Saccades are rapid eye movements crucial for visual attention.
Predicting saccade landing points is vital for gaze-contingent systems to mitigate display-update latency.
Current prediction methods struggle with the high speed and complexity of naturalistic viewing.

Purpose of the Study:

To develop and evaluate a novel algorithm for early saccade landing point prediction.
To improve the accuracy and reliability of eye-tracking-based gaze-contingent systems.
To analyze factors influencing saccade landing point prediction errors.

Main Methods:

Utilized Long Short-Term Memory (LSTM) networks for predictive modeling.
Implemented a fine-grained loss function tailored for saccade prediction.
Evaluated the algorithm on a large dataset of nearly 220,000 saccades from 75 participants during natural video viewing.
Compared performance against state-of-the-art saccade landing point prediction algorithms.

Main Results:

The proposed LSTM-based algorithm demonstrated superior performance compared to existing methods.
Achieved up to a 50% reduction in prediction error.
Identified key factors affecting prediction accuracy, including saccade duration, length, participant age, and intrinsic user characteristics.

Conclusions:

Early prediction of saccade landing points is feasible with advanced deep learning techniques.
The developed algorithm offers a significant improvement for real-time gaze-contingent applications.
Further research into user-specific characteristics can enhance prediction accuracy.