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Related Experiment Video

Updated: May 19, 2026

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

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Published on: June 17, 2019

A bayesian optimal foraging model of human visual search.

Matthew S Cain¹, Edward Vul, Kait Clark

¹Duke University, USA. Matthew_Cain@brown.edu

Psychological Science

|August 8, 2012

Summary

People searching for multiple items adjust their search duration based on expected target numbers. However, their strategies suggest they perceive target distributions as more uniform than they are, indicating potential for improvement through training.

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Last Updated: May 19, 2026

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Published on: June 17, 2019

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Area of Science:

Cognitive psychology
Computational neuroscience
Ecological psychology

Background:

Real-world visual searches frequently involve an unknown quantity of targets.
This uncertainty poses metacognitive challenges, particularly in deciding when to cease searching, leading to frequent errors in multiple-target scenarios.

Purpose of the Study:

To quantify human search strategies in multiple-target visual searches using an ecological optimal foraging model.
To investigate if searchers adapt their strategies based on complex target-distribution statistics.

Main Methods:

Participants searched visual displays where the number of targets per trial was sampled from varying geometric distributions.
The study employed an optimal foraging model to analyze search behavior and decision-making processes.

Main Results:

Search duration was positively correlated with the expected number of targets, aligning with optimal foraging theory predictions.
Searchers dynamically adjusted their expectations based on higher-order, across-trial target distribution statistics.
Participants' search behavior indicated a tendency to perceive target distributions as more uniform than they actually were when compared to ideal observers.

Conclusions:

Human visual search strategies adapt to statistical regularities in target distributions.
A systematic deviation from optimal performance suggests that metacognitive strategies in multiple-target search can be enhanced through targeted training.