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Updated: Feb 1, 2026

Eye Movement Monitoring of Memory

Eye Movement Monitoring of Memory

Published on: August 15, 2010

Predicting eye movement patterns from fMRI responses to natural scenes.

Thomas P O'Connell¹, Marvin M Chun^2,3

¹Department of Psychology, Yale University, New Haven, 06520, USA. thomas.oconnell@yale.edu.

Nature Communications

|December 6, 2018

Summary

Researchers used functional magnetic resonance imaging (fMRI) to predict eye movements from brain activity, linking neural representations, deep learning models, and behavior for the first time.

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Area of Science:

Neuroscience
Cognitive Science
Computer Vision

Background:

Eye tracking measures spatial attention and predicts eye movements to natural images.
Noninvasive techniques to access brain representations guiding eye movements are lacking.

Purpose of the Study:

To predict human eye movement patterns using functional magnetic resonance imaging (fMRI).
To reconstruct spatial representations in the brain that guide eye movements.
To establish a link between brain activity, deep neural network models, and behavior.

Main Methods:

Utilized fMRI to reconstruct spatial representations from neural activity evoked by natural scenes.
Developed a model-based decoding pipeline aligning fMRI activity with deep convolutional neural network (CNN) activity.
Reconstructed spatial priority maps to predict eye movements in a zero-shot manner.

Main Results:

Successfully predicted human eye movement patterns from fMRI responses to natural scenes.
Demonstrated that visual representations of scenes and objects map onto neural representations that predict eye movements.
Identified a novel three-way link between brain activity, deep neural network models, and behavioral data.

Conclusions:

fMRI can be used to predict eye movement patterns by reconstructing spatial representations.
Neural representations underlying visual perception are linked to the guidance of eye movements.
This study establishes a significant connection between brain activity, computational models, and observable behavior.