Steven Weisberg, a researcher at The University of Texas at Arlington, found that advanced artificial intelligence tools could not uncover a clear link between brain structure and navigation ability in healthy young adults—challenging long-standing ideas about how the brain helps us find our way.
For decades, many in the scientific community believed that people with elite navigation skills—such as quickly learning and recalling complex routes—might have larger or differently shaped brain regions than others. Famous studies of London taxi drivers, for example, suggested that intense navigation training could lead to more “real estate” in certain parts of the brain.
In the new study, Dr. Weisberg and his team, including University of Florida Ph.D. candidate Ashish Sahoo, tested those assumptions using newer analytic techniques, including deep convolutional neural networks and other machine-learning models capable of detecting subtle patterns in brain scans beyond simple size measurements. Despite these advanced methods, the researchers found no measurable connection between brain structure and navigation performance in healthy young adults.
Understanding navigation is important given its real-world implications for daily life, including independence, memory, and dementia risk.
“With the quality of data we have from MRI scans and this healthy young adult population, there does not appear to be a detectable signal using these advanced metrics,” said Weisberg, who conducted the study at the University of Florida before joining UT Arlington last fall as part of the RISE 100 initiative.
The study, published in the peer-reviewed journal Neuropsychologia , analyzed data from 90 participants with an average age of 23.1 years. Participants learned two routes using a virtual environment. Results showed little difference in navigation performance when comparing two brain regions: the thalamus, which served as the control region, and the hippocampus, a region traditionally linked to navigation and memory.
While the findings point to limits in what AI can currently reveal about everyday cognitive skills, the technology remains a powerful research tool. Weisberg said more robust models could detect differences in future studies.
“Our study should be one data point in a larger landscape of what AI can tell us about how brain structure and function map onto behavior,” Weisberg said. “Machine learning and AI have been pretty successful at predicting disease states. What we’re interested in is whether these models have utility for behavioral function—things like cognitive training or education.”
Future research will focus on larger samples and older populations, Weisberg said.
“Our ability to get around enables basically everything we do. Studying how the brain supports navigation helps us understand what is needed when it goes well and what is lacking when it doesn’t.”
Neuropsychologia
10.1016/j.neuropsychologia.2025.109352
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The authors have no relevant financial or non-financial interests to disclose.