The discovery could have implications beyond everyday life. It may help scientists better understand how habits form, why some behaviors are difficult to change, and how future artificial intelligence systems could become better at building new skills from previous learning.

"We have another stepping stone in our understanding of how the brain learns," said senior author Maximilian Riesenhuber, PhD, a professor of neuroscience at Georgetown University School of Medicine, and co-director of the Center for Neuroengineering. "The encouraging part is that you really can learn to multitask. There is actually a way to remodel your brain architecture and use other parts of your brain."

How the Brain Automates Learned Skills

The research expands on decades of work exploring how the brain acquires new abilities. While scientists have learned a great deal about the early stages of learning, much less has been known about what happens after a skill has been practiced extensively and becomes almost effortless.

Driving is a familiar example, Riesenhuber explained. Learning to drive initially demands constant attention, but years of experience allow many people to carry on a conversation, listen to music, or think through a problem while still driving safely.

"The question is: how does your brain do that?" Riesenhuber said.

Brain Scans Reveal a Shift in Neural Circuits

To investigate, the research team asked volunteers to sort morphed images of cars into two categories by identifying subtle visual differences. Participants completed more than 30,000 sorting trials over a period of 5 to 10 weeks using a smartphone app designed as a game.

The researchers examined participants' brains with fMRI and EEG scans before training began and again after the practice period ended.

Early in learning, the sorting task primarily activated the prefrontal cortex, the region responsible for executive functions such as planning, reasoning, and conscious decision-making. Because this part of the brain generally handles one demanding task at a time, it has long been viewed as a major limit on multitasking.

After weeks of practice, however, brain activity had shifted. The same categorization task was now being handled mainly by the temporal cortex, a region involved in memory and recognizing complex objects.

"Previous studies have shown that parts of the temporal cortex can be activated by particular object categories in experienced observers, birds, cars, even Pokémon, but a limitation of all of those studies is that they only looked after people became experts. The strength of this study is that it is longitudinal; we measure before and after training, so we can see that extensive training essentially put a category-selective area in the temporal lobe that was not there before," said first author Patrick Cox, PhD, who began the study as a graduate student in Riesenhuber's lab and is now an assistant professor of psychology at Lehigh University.

"This has implications for critical real-world scenarios, like when a radiologist can accurately classify masses on an X-ray as benign or malignant fairly automatically, often without extensive deliberation, thanks to years of training," Cox said.

How Brain Rewiring Enables Multitasking

The researchers found that information from the newly developed car selective area in the temporal cortex could bypass the prefrontal cortex and travel directly to brain regions responsible for producing responses.

"Experience remodels the brain to bypass that frontal bottleneck. The prefrontal cortex then stays free for whatever else you want to do, increasing your capacity," Riesenhuber explained.

The team also found that the more the car sorting task was "offloaded" from the prefrontal cortex, the better participants performed a second task at the same time.

That result challenges the long-accepted belief that people cannot truly multitask. Instead, many scientists have argued that the brain simply alternates attention between tasks so quickly that it creates the illusion of doing both at once.

"What we show is that the circuitry actually changes so the brain can do two things at once," Riesenhuber said. "This really is true multitasking."

What the Findings Mean for Habits and AI

The results may also provide new insight into compulsive behaviors. Because well-learned behaviors move into brain circuits that are less dependent on conscious control, simply trying to think about something else may not be enough to break an unwanted habit.

"The first step to unlearning something is understanding where it is actually happening in the brain," Riesenhuber said. "This shows why strategies like telling someone to think of something else don't really help, because they don't really have the behavior under conscious control."

The researchers also believe the findings may help explain why humans continue building new abilities throughout life while current AI systems still struggle to learn continuously without disrupting previously acquired knowledge.

According to Riesenhuber, transferring a well-learned skill into the temporal cortex frees the prefrontal cortex to focus on new challenges, allowing existing knowledge to serve as the foundation for future learning. Today's AI systems generally lack that kind of flexible architecture.

The team now plans to investigate exactly what signals move learning from one brain region to another and to determine which kinds of tasks can eventually be performed in parallel.

"Another really interesting question is what kinds of tasks can be learned well enough to do in parallel," Cox said. "We can walk and chew gum at the same time, but looking at our phones to text while driving will never be safe, because we take our eyes away from the road. It comes down to being able to train fully separate neural circuits for two tasks to become compatible."

The study, "Extensive Experience Remodels Neural Task Circuitry to Escape the Frontal Bottleneck and Increase Automaticity of Categorization," was published June 4 in the Journal of Cognitive Neuroscience.

In addition to Riesenhuber and Cox, the research team included Clara A. Scholl, Marissa L. Laws, Nelson E. Jaimes, and Xiong Jiang of Georgetown University. The work was supported by the National Science Foundation (BCS-1232530), the ARCS Foundation, and the Army Research Laboratory (W911NF-24-1-0097). The authors reported no personal financial interests related to the study.