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Related Experiment Video

Updated: Jul 17, 2026

The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

Published on: February 19, 2018

Where's Waldo, Ohio? Using Cognitive Models to Improve the Aggregation of Spatial Knowledge.

Lauren E Montgomery¹, Charles M Baldini¹, Joachim Vandekerckhove¹

¹Department of Cognitive Sciences, University of California Irvine, Irvine, CA 92697-5100 USA.

Computational Brain & Behavior

|July 16, 2026

Summary

Cognitive modeling enhances group accuracy in spatial knowledge tasks by accounting for individual expertise and task difficulty. This approach improves upon simple statistical averages for collective intelligence.

Keywords:

Cognitive modeling Expertise Spatial knowledge Wisdom of the crowd

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Last Updated: Jul 17, 2026

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Published on: October 13, 2023

Area of Science:

Cognitive science
Human-computer interaction
Geographic information science

Background:

The wisdom of the crowd phenomenon suggests group judgments can be more accurate than individual ones.
Traditional aggregation methods like averaging may not fully leverage individual expertise or account for task variability.

Purpose of the Study:

To improve the accuracy of collective intelligence in spatial knowledge tasks using cognitive modeling.
To develop and evaluate models that incorporate individual expertise and task difficulty.

Main Methods:

Participants estimated locations and confidence radii for US cities.
Cognitive models were developed to infer group estimates, considering individual expertise and city difficulty.
Models were applied to US city data and replicated with European city data.

Main Results:

Statistical averages (simple and radius-weighted) outperformed individual majority judgments.
Model-based estimates generally surpassed statistical averages in accuracy.
Models allowing for varying individual expertise across cities showed the highest accuracy.

Conclusions:

Cognitive modeling offers a superior approach to aggregating judgments in spatial tasks compared to simple statistical methods.
Accounting for individual differences in expertise and task difficulty is crucial for maximizing collective intelligence.
The developed models show promise for enhancing wisdom of the crowd applications in various domains.