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Related Experiment Video

Updated: May 10, 2026

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

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Published on: August 25, 2020

Learning where to look for a hidden target.

Leanne Chukoskie¹, Joseph Snider, Michael C Mozer

¹Institute for Neural Computation, University of California at San Diego, La Jolla, CA 92093, USA.

Proceedings of the National Academy of Sciences of the United States of America

|June 12, 2013

Summary

This summary is machine-generated.

Humans learn to efficiently search novel environments by using past experience to guide their gaze. A reinforcement-learning model accurately predicts how individuals update their search strategies based on rewards and avoid unrewarded areas.

Keywords:

ideal observer oculomotor reinforcement learning saccades

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Area of Science:

Cognitive Science
Neuroscience
Computational Biology

Background:

Humans forage for both food and information, requiring complex visual search behaviors.
Understanding how individuals learn to direct gaze in novel contexts is crucial but not well understood.

Purpose of the Study:

To investigate how humans learn to direct visual search in a novel environment.
To model the learning process using computational approaches and compare it to human performance.

Main Methods:

Participants searched for a hidden target in a novel scene, with target locations varying stochastically.
A reinforcement-learning (RL) model was used to simulate and analyze the learning trajectories.
Performance was compared against ideal-observer theory.

Main Results:

Participants rapidly learned to find hidden targets by focusing on previously rewarded locations and avoiding unrewarded ones.
The RL model successfully characterized human learning trajectories and predicted sensitivity to recent experience.
Both human and model performance approached optimal levels predicted by ideal-observer theory.

Conclusions:

Experience in novel environments significantly drives human visual search.
Reinforcement learning provides a robust framework for understanding gaze control and learning in visual search.
These findings may extend to other foraging behaviors in both humans and animals.