Summary: A new study has shattered the long-held scientific consensus regarding the human brain’s capacity to engage in true multitasking. The study demonstrates how the brain physically remodels its underlying architecture after extensive experience to automate learned tasks.
By utilizing functional MRI (fMRI) and EEG technologies, investigators proved that continuous training forces complex processing tasks to migrate out of the bottlenecked prefrontal cortex and into the temporal cortex, bypassing executive deliberation entirely and leaving the frontal networks clear to handle parallel operations.
Key Facts
- The Frontal Bottleneck Overpass: Early stages of skill acquisition rely heavily on the prefrontal cortex, the region governing executive function and thinking, which historically acts as a strict cognitive bottleneck capable of handling only one demanding task at a time.
- The Temporal Offloading Discovery: Following weeks of extensive training, the neural circuitry physically shifts, offloading the automated task to the temporal cortex, a brain region optimized for object recognition and memory encoding.
- The 30,000-Trial Longitudinal Audit: Researchers tracked participants who completed more than 30,000 image-sorting trials over a 5-to-10-week span via a smartphone app game, allowing researchers to capture structural brain scans both before and after expertise was achieved.
- Dismantling the Task-Switching Myth: The findings directly challenge the traditional neurological theory that human multitasking is an illusion made up of rapid, back-and-forth task-switching. Instead, the study proves the brain can physically build distinct, separate neural circuits to execute two tasks simultaneously.
- The Unlearning and Compulsion Metric: Because automated behaviors move into circuits less accessible to conscious thought, the research reveals why cognitive strategies like “thinking of something else” fail to curb compulsive behaviors, providing a new anatomical map to guide addiction therapies.
- The Human Continuous Learning Blueprint: Moving automated skills into the temporal cortex frees up the prefrontal cortex to use old information as a modular building block for new skills—a major breakthrough that explains human continuous learning efficiency compared to current artificial intelligence models.
- Circuit Compatibility Horizons: Senior author Dr. Maximilian Riesenhuber and first author Dr. Patrick Cox note that future research will focus on the exact signals that trigger this neural migration and define the limits of parallel processing, noting that tasks remain dangerous if they compete for the same physical sensory mechanics.
Source: Georgetown University
New research by Georgetown scientists shows how the brain rewires itself to automate learned tasks. The findings challenge a long-held understanding of how humans master complex skills, suggesting that true multitasking is really possible.
Beyond offering encouragement to busy people that they really can do two things at once, the study also has important implications for the development of artificial intelligence capable of building on prior learning as the brain does.
“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.”
The new study builds on decades of research on how learning occurs in the brain.
Scientists wanted to understand the mechanisms behind automation, and how the brain shifts from learning a new task into a way of executing that task more unconsciously after extensive experience.
A good example is driving, Riesenhuber said. When someone first learns to drive, it requires their full concentration. But after driving for many years, most people can talk, listen to music, or consider a problem without having to focus completely on operating the vehicle.
“The question is: how does your brain do that?” Riesenhuber said.
Most previous research on learning has focused on the early stages, but what happens to the brain long-term is harder to study and less understood.
For the new study, researchers trained people to sort morphed images of cars into two categories, learning to spot subtle differences to tell them apart. Participants completed more than 30,000 trials over 5 to 10 weeks, using an app that allowed them to sort the images as a game on their phone. Researchers used fMRI and EEG to conduct brain scans on the participants before and after they completed the trials.
They found that after people had initially learned to sort the images, the task activated their prefrontal cortex. This area of the brain is responsible for executive function and thinking, but can typically only handle one task at a time.
However, when researchers scanned the brains of participants who had been practicing the sorting task for weeks, they found that the categorization was now happening in the temporal cortex, a part of the brain involved in encoding 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 Pokemon, 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.
Category information from the car-selective area in the temporal cortex bypassed the prefrontal cortex and connected directly to output parts of the brain.
“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. Indeed, the researchers found that the more the car task was “offloaded” from the prefrontal cortex, the better people were able to do another task in parallel to the car task.
The finding challenges a longstanding theory that humans are not capable of true multitasking. Instead, it was thought that the brain rapidly switched back and forth between two tasks.
“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.”
The findings can also have implications for understanding compulsive behaviors, because they demonstrate that learned behaviors move into brain circuits that are less accessible to conscious thought or executive function.
“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.”
It also helps explain why humans are so good at continuous learning, or building skills upon skills — something that AI still struggles with.
Moving a learned skill into the temporal cortex and freeing space in the prefrontal cortex could allow the brain to use the old information as a building block to learn something new, Riesenhuber said. Current AI models don’t have that same capability, he noted.
Next, researchers want to study the mechanisms or signals involved in moving learning from one part of the brain to another and to figure out what the limits of multitasking are.
“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.”
Funding: Funding for this study was provided by the National Science Foundation (BCS-1232530) and the ARCS Foundation, and the Army Research Laboratory (W911NF-24-1-0097). The authors report having no personal financial interests related to the study.
Key Questions Answered:
A: By building an entire category-selective area where one didn’t exist before. The Georgetown University study showed that practicing a skill tens of thousands of times rewires the brain, allowing the task to migrate out of the crowded prefrontal cortex and relocate to the temporal lobe, creating a permanent, automated circuit.
A: Because deeply learned habits migrate to brain regions that completely bypass your conscious control center. Since these automated behaviors are handled in the temporal cortex rather than the prefrontal executive network, simply trying to “think of something else” is ineffective because the behavior is running on a circuit separate from conscious thought.
A: How to master continuous learning without wiping out past data. Freeing up space in the prefrontal cortex by offloading mastered habits to the temporal cortex allows humans to use old memories as modular building blocks to learn new things, a structural trick current AI models still struggle to replicate.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- Journal paper reviewed in full.
- Additional context added by our staff.
About this neuroscience and neurotech research news
Author: Karen Teber
Source: Georgetown University
Contact: Karen Teber – Georgetown University
Image: The image is credited to Neuroscience News
Original Research: Closed access.
“Extensive Experience Remodels Neural Task Circuitry to Escape the Frontal Bottleneck and Increase Automaticity of Categorization” by Patrick H. Cox, Clara A. Scholl, Marissa L. Laws, Nelson E. Jaimes, Xiong Jiang, and Maximilian Riesenhuber. Journal of Cognitive Neuroscience
DOI:10.1162/JOCN.a.2618
Abstract
Extensive Experience Remodels Neural Task Circuitry to Escape the Frontal Bottleneck and Increase Automaticity of Categorization
Object category learning is a foundational cognitive process. Most human category learning studies involve brief paradigms lasting a few hours and show increased shape tuning in visual areas and task-dependent responses in pFC.
Other studies also identify a “frontal bottleneck” that limits multitasking. However, real-world categorization often involves months or years of practice, potentially producing qualitative shifts toward automaticity. We tested the hypothesis that extensive training causes a spatio-temporal shift in the neural circuitry supporting categorization.
Participants were trained over >30,000 trials across 5–10 weeks to categorize novel morphed car stimuli via a mobile app. We used fMRI and EEG rapid adaptation techniques to examine neural responses after initial learning (∼4 hr in 1–2 weeks) and after extensive training (∼16 additional hours over another 4–8 weeks).
Converging fMRI and EEG results showed that extensive training fundamentally remodeled task-related circuitry: Visual areas in ventral occipito-temporal cortex (vOTC) were initially shape-selective, but category-selective responses emerged in the vOTC after extensive training. The vOTC also showed decreased functional connectivity with the pFC and increased connectivity with motor output areas.
This supports the hypothesis that extensive experience enables category decisions to occur outside of the “frontal bottleneck.” Critically, the decrease in connectivity between vOTC and pFC was associated with improved categorization performance while dual-tasking, indicating increased automaticity.
These findings demonstrate that prolonged training reshapes the neural basis of categorization, shifting it from a flexible but attentionally controlled process to a more streamlined, automatic process.
