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Related Experiment Video

Updated: Nov 1, 2025

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning.

Gregory J Zelinsky^1,2, Yupei Chen¹, Seoyoung Ahn¹

¹Department of Psychology, Stony Brook University, Stony Brook, NY, 11794, USA.

Neurons, Behavior, Data Analysis, and Theory

|June 24, 2021

Summary

This summary is machine-generated.

Machine learning, using inverse-reinforcement learning (IRL), successfully predicted human visual search behavior for specific objects like clocks and microwaves. The model learned target-specific attention patterns, demonstrating how goals guide visual exploration.

Keywords:

attention models eye behavior fixation prediction reinforcement learning top-down attention visual search

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Area of Science:

Cognitive Science
Computer Science
Neuroscience

Background:

Understanding goal-directed behavior is crucial for cognitive science.
Machine learning methods, particularly inverse-reinforcement learning (IRL), offer new tools for analyzing complex behavioral data.
Large, annotated datasets are essential for training machine learning models.

Purpose of the Study:

To develop and validate a machine learning model for predicting human visual search behavior.
To investigate how target goals influence attention and fixation patterns during visual search.
To identify target-specific and context-dependent visual features guiding attention.

Main Methods:

Collected 16,184 search fixations from participants viewing images of microwaves and clocks.
Utilized inverse-reinforcement learning (IRL) on a large-scale annotated image dataset (MS-COCO).
Trained IRL models to learn reward functions and policies, then predicted fixations of new searchers.

Main Results:

The IRL model accurately predicted behavioral search efficiency and fixation density maps.
Learned reward maps revealed target-specific attention patterns.
Identified scene context, such as wall fixations for clock searches, influencing attention.

Conclusions:

Machine learning, specifically IRL, can effectively model goal-directed visual attention.
Attention is guided not only by target features but also by scene context.
This approach provides insights into the visual features underlying goal-directed attention control.