We’ve all suffered the frustration of being lost.

We’ve searched for something familiar to help us find our way—maybe a sign, a landmark or perhaps we tried to retrace our steps. According to research at The University of Texas at Austin, there may be a better way to navigate.

Dr. Brian Stankiewicz, assistant professor in the Department of Psychology and Center for Perceptual Systems, studies spatial navigation and object recognition. His research uses virtual reality environments to help pinpoint the information and process needed to navigate through large-scale spaces such as a building or city.

Dr. Brian Stankiewicz stands in front of a scene from a virtual reality environment.

“Everyday we are able to navigate from one location to another,” Stankiewicz said. “But how? Do we carry a mental map in our heads? Do we know what a particular street corner looks like or do we simply remember something like a road map? What I’m particularly interested in is what we remember and store about an environment so that when we return we are able to way-find and move to a particular destination.”

Just for fun, close your eyes and try to remember your last commute. Maybe you went to work, school or a friend’s house—perhaps it’s a place you travel to daily. What is it you remember about your surroundings along the way?

A series of experiments compared the importance of visual cues such as pictures on the walls, doors and signs, versus topological structures such as the way roads or hallways interconnect.

“We found that people learned the topological structure more rapidly,” Stankiewicz said.

So next time you’re in a building or an area of town that you frequently find yourself lost in, try paying more attention to how the hallways or roads connect. Maybe they form a T- or L-shape. Try building your own “mental map” of the location and see if things improve on your next visit. You’ll be one step closer to being a more efficient navigator.

Stankiewicz has put this to the test by creating human and robot models to predict human behavior in different navigational situations and to measure efficiency. If navigation was a video game, think of the robot model as the competitor that always makes the perfect score you’re trying to beat.

Imagine being dropped off in random locations within a building and asked to find your way to a target location. All the walls are identical. The only difference is the way they connect. This scenario is created in a virtual environment for the study.

Dr. Stankiewicz takes a sneak peak at virtual reality.

“Humans take about twice as many actions as it does the robot model to complete this task,” Stankiewicz said.

He attributes this to a breakdown in a process called hypothesis maintenance. In the building, as the person looks around, he or she is constantly making new observations. Maintenance occurs when the person uses this new information to update his or her beliefs. It is a type of mental checklist that allows a person to confirm a location or rule out others.

During a second phase of the experiment, this information was maintained on the subject’s behalf. Arrows were displayed in a viewfinder to help the subject keep track of where he or she had been.

“When they no longer have to keep track of this information in their heads, we can get them to navigate almost optimally,” Stankiewicz said. “When a subject is lost, they are most likely stressed and frustrated, which can make the hypothesis maintenance process even more difficult.”

Another series of experiments is the development of a training regimen for low-vision navigation. Low vision means the subject can see, but his or her perception is greatly hindered as a result of a variety of diseases, disorders and injuries that affect the eye. Many people with low vision have age-related macular degeneration, cataracts, glaucoma or diabetic retinopathy. According to “The Lighthouse National Survey on Vision Loss: The Experience, Attitudes, and Knowledge of Middle-Aged and Older Americans,” about 14 million Americans, about one out of every 20 people, have low vision, and about 135 million people around the world have low vision.

Dr. Stankiewicz models the viewfinder used to experience the virtual reality environment.

“Most people with low vision can navigate through familiar environments just fine,” Stankiewicz said. “It is when they have to go to a novel environment that they may have trouble.”

This led to the idea that if people could access a corresponding virtual environment via their computer before visiting the real location they could familiarize themselves before having to truly navigate.

“Our first question was, ‘How well matched does the virtual environment have to be to the real one?’” Stankiewicz said.

Stankiewicz’s team ran a number of conditions. One example included a low-resolution environment that was very sparse. Researchers constructed walls, the bare minimum. The second was a high-resolution environment using detailed video footage.

“When we took the subjects to the real environment they were nearly as good after training on the low-resolution environment as they were after training in the high-resolution one,” he said.

Once again, the emphasis is on learning the topological structure. If the subjects were able to learn the basic layout, they were able to navigate. Having more details in the environment didn’t make a big difference.

Another piece of the low-vision training research is creating a small handheld navigation device. For example, a person about to visit the Empire State Building could download a map of that building to the device. Once in the building, the device would collect and process information to determine its user’s location. It would then give directions to his or her desired location.

Stankiewicz’s future research will examine the differences in people’s navigational strategies and the way they acquire information.

“Once we understand the parameters of what makes someone a good or bad navigator, we can ultimately teach someone the strategies to become a more efficient navigator,” he said.

Michelle Bryant

Photos: Marsha Miller