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Related Experiment Video

Updated: Mar 25, 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

Introducing context-dependent and spatially-variant viewing biases in saccadic models.

Olivier Le Meur¹, Antoine Coutrot²

¹IRISA University of Rennes 1, Campus Universitaire de Beaulieu, 35042 Rennes, France.

Vision Research

|February 23, 2016

Summary

Viewing behavior during scene exploration is not universal. Saccade distributions vary by visual category, leading to improved saliency and saccadic models that better predict human scanpaths.

Keywords:

Saliency Viewing biases Visual attention Visual scanpath

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Last Updated: Mar 25, 2026

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Area of Science:

Cognitive Science
Computer Vision
Neuroscience

Background:

Systematic viewing tendencies exist during scene exploration, with saccades often skewed and horizontally/vertically oriented.
Previous models assumed universal viewing biases, potentially limiting their accuracy.

Purpose of the Study:

To investigate if viewing biases are modulated by the semantic visual category of a stimulus.
To develop improved saliency and saccadic models that incorporate category-specific viewing patterns.

Main Methods:

Analyzed joint distributions of saccade amplitudes and orientations across different visual categories.
Developed a saliency model leveraging category-specific viewing biases, independent of visual information.
Created a saccadic model incorporating low-level features and spatially-variant, context-dependent viewing biases.

Main Results:

Joint saccade distributions significantly vary across visual categories and are spatially variant within scenes.
A saliency model based on these biases outperformed existing models.
A new saccadic model demonstrated superior performance and produced scanpaths closely matching human behavior.

Conclusions:

Viewing biases are not universal but are modulated by visual category and scene context.
Incorporating these nuanced biases significantly enhances visual attention models.
Findings have implications for human-computer interfaces, medical diagnosis, and image/video processing.