UTA researchers leverage AI to rethink navigation skills

University of Texas at Arlington researcher Stephen Weisberg found that advanced artificial intelligence tools fail to reveal a clear link between brain structure and navigational abilities in healthy young people, challenging long-held ideas about how the brain helps us find our way.

For decades, many in the scientific community believed that people with good navigation skills, such as learning and recalling complex routes quickly, may have larger or differently shaped brain areas than other people. For example, a famous study of London taxi drivers suggested that intensive navigation training can increase “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 these assumptions using new analytical techniques, including deep convolutional neural networks and other machine learning models that can go beyond simple size measurements to detect subtle patterns in brain scans. Despite these advanced methods, researchers failed to find a measurable link between brain structure and navigation ability in healthy young people.

Understanding navigation is important given its practical impact on daily life, including independence, memory, and risk of dementia.

“Given the quality of the data obtained from MRI scans and healthy young adult populations, there appears to be no detectable signal using these advanced metrics,” said Professor Weisberg, who conducted the study at the University of Florida before joining UT Arlington last fall as part of the RISE 100 initiative.

This study was published in a peer-reviewed journal neuropsychologyanalyzed data from 90 participants with an average age of 23.1 years. Participants learned two routes using a virtual environment. The results showed that there was little difference in navigation performance when comparing two brain regions: the thalamus, which acts as a control region, and the hippocampus, an area traditionally associated with navigation and memory.

Although this finding shows that there are limits to 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 picture of what AI can tell us about how brain structure and function maps to behavior,” Weisberg said. “Machine learning and AI have been fairly successful in predicting disease states. What we’re interested in is whether these models can help with behavioral functions such as cognitive training and education.”

Future studies will focus on larger samples and older populations, Weisberg said.

“Our ability to move allows us to do basically everything we do. Studying how the brain supports navigation can help us understand what we need when things go well and what we lack when things go wrong.”