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Gaze Control as Prediction.

John M Henderson¹

¹Center for Mind and Brain and Department of Psychology, University of California, Davis, 267 Cousteau Place, Davis, CA, USA.

Trends in Cognitive Sciences

|December 10, 2016

Summary

This summary is machine-generated.

This study proposes knowledge-driven prediction as a new model for understanding gaze control during scene viewing. This approach integrates viewer intentions and scene understanding, unifying diverse research findings in active scene viewing.

Keywords:

attention eye movements gaze prediction scene perception

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Area of Science:

Cognitive Science
Neuroscience
Computer Vision

Background:

Current models of overt attention focus on image properties and salience.
These models often neglect viewer intentions and scene understanding.
This overlooks crucial factors in complex scene viewing.

Purpose of the Study:

To propose an alternative framework for gaze control in natural scenes.
To characterize gaze behavior as knowledge-driven prediction.
To integrate disparate phenomena in active scene viewing.

Main Methods:

Theoretical analysis and synthesis of existing research.
Conceptual framework development.
Integration of computational and neurobiological perspectives.

Main Results:

Gaze control can be explained by knowledge-driven predictive mechanisms.
This approach unifies various aspects of active scene viewing.
It bridges behavioral and neurobiological studies of eye movements.

Conclusions:

Knowledge-driven prediction offers a unified theory of gaze control.
This framework facilitates integration across perception and cognition.
It provides a basis for future research in computational and neurobiological domains.