Learning in Virtual Reality VR Study Suggests Movement Can Shape How People Learn

Punching a virtual response box or swinging a virtual lightsaber can dramatically improve learning on certain tasks, according to new research from Florida Atlantic University’s College of Engineering and Computer Science, which challenges long-standing assumptions about how people learn.

Deciding whether a road is slippery, recognizing a potentially dangerous lesion on a medical scan or determining whether a piece of fruit is ripe may seem like tasks of the mind alone. But in everyday life, categorizing what we see is often closely tied to how we move and interact with the world around us.

Despite this connection, decades of research have largely treated category learning as a cognitive process, with physical movement serving only to express a decision after it has been made. Surprisingly, little research has examined whether the physical actions people make while categorizing information can actually influence how well they learn.

Now, Florida Atlantic and Brown University researchers have found that larger movements can significantly improve learning. However, the benefit depends on the type of category being learned.

The findings, published in Science Advances, challenge traditional theories of category learning, which generally focus on how people mentally represent and recognize objects, treating movement as the final step in the process. Instead, the research suggests that physical responses may play an active role in shaping what people learn.

To investigate, the researchers developed an immersive virtual reality experiment in which participants learned to categorize visual patterns using either simple button presses or larger arm movements. Participants either swung a virtual lightsaber to hit a target or punched a response box without directly interacting with the visual stimulus. Researchers tested two types of category learning. One relies on explicit rules, such as identifying an object based on specific features. The other requires people to recognize patterns and integrate multiple features to make a judgment, often without being able to articulate exactly how they reach a decision.

The results revealed a striking difference. Participants who used larger arm movements learned the similarity-based categories more effectively and achieved greater accuracy than those who responded with simple button presses. However, larger movements provided no meaningful benefit for learning categories based on explicit rules, even when the researchers made that task more challenging.

“What surprised me most was how selective the effect was,” said Luke Rosedahl, Ph.D., corresponding author, an assistant professor in FAU’s Department of Biomedical Engineering, a faculty fellow of FAU’s Institute for Smarter Cities, Spaces, and Health, and a member of the FAU Stiles-Nicholson Brain Institute. “We found that larger movements substantially improved learning when people had to recognize patterns by integrating multiple features, but provided no meaningful benefit when people learned categories using explicit rules. Even more intriguing, the benefit occurred whether participants swung at the target or punched a separate response box. The movement itself appeared to matter, not whether it directly interacted with what they were learning.”

The researchers conducted additional experiments to determine whether the improvement could be explained by other factors. They controlled the amount of time participants viewed each image, accounted for differences in response speed and made the rule-based task more complex. The advantage for larger movements remained specific to similarity-based learning.