Recent research published in journals cerebral cortex This suggests that adults who are good at math tend to rely less on brain areas associated with body movement when processing numbers. These findings provide evidence that as people acquire advanced mathematical skills, their brains shift to a more automatic and abstract way of thinking about numbers.
Numerical processing relies on multiple mental forms. Scientists describe the linguistic form of number words, the visual form of written numbers, and the semantic form of actual meanings or quantities. In recent years, scientists have proposed that an embodied form also exists, where physical experiences such as counting on fingers help shape how the brain understands volume.
To explore how these mental forms interact at different life stages, the authors aimed to understand how physical representations of numbers relate to formal mathematical abilities in both children and adults. Shuane Leng, a postdoctoral fellow in psychology and human development at Vanderbilt University’s Peabody College, explained the motivation behind the study.
“We know that number processing is the basis of mathematical ability, but the underlying brain mechanisms remain hotly debated,” Ren said. “The theory of embodied cognition suggests that our abstract understanding of mathematics is initially rooted in physical, sensory, and motor experiences, such as counting on our fingers in infancy. We use fMRI to study both children and adults. We wanted to take a closer look at how sensorimotor regions of the brain are recruited during number processing, and how that neural involvement actually interlocks with real-world math abilities across different stages of development.”
Functional magnetic resonance imaging (fMRI) is a type of brain scan that measures blood flow to detect active areas of the brain. To conduct the study, researchers collected imaging data from 104 adults with an average age of about 23 years. They also tested 88 fourth-grade children with an average age of nearly 10 years.
While inside the scanner, participants completed a numerical comparison task and a speech-based task. During the task, participants saw two different images on the screen. One type was a symbolic Arabic numeral, like the visual number 4. The other type was figurative representations, which consisted of color photographs of human hands with varying numbers of fingers raised.
In the numerical task, participants had to decide whether a number displayed on the screen was greater or less than a specific target number. Participants pressed a button to answer as quickly as possible. In the sound-based phonology task, participants had to decide whether the onset sound of a number matched the onset sound of a cartoon object, such as a fan or the sun.
The researchers also measured the participants’ overall math abilities outside of the scanner using a standardized assessment called the Woodcock-Johnson Third Edition Achievement Test. This assessment included three specific mathematics tests. The calculation subtest measured basic calculation skills across different types of mathematics. The math fluency subtest measured how many simple arithmetic problems participants could solve in 3 minutes.
Finally, the Applied Problems subtest measured the ability to analyze and solve spoken language problems. To confirm that the brain activity was specifically related to math, the scientists also tested basic reading comprehension. They used two reading comprehension subtests to measure letter identification and the ability to hear unknown words. By comparing math scores and reading scores with brain scans, researchers were able to isolate specific neural networks responsible for numerical cognition.
Looking at brain scans, the scientists observed that adults utilize a broader network of brain regions when processing numbers compared to when processing sounds. These regions include the occipital, temporal, parietal, and insular regions of the brain. The children activated a smaller, more localized set of brain regions during the same task.
“What surprised us most was that as we grow up, the way our brains are recruited to process numbers changes dramatically,” Ren told Cypost. “When you look at the global brain map, adults engage in a more extensive and extended network of regions across the brain than children.”
“But within that broader adult network, those with high math proficiency actually showed reduced activation across sensorimotor and attentional areas, a pattern that is completely absent in children. This reveals an interesting paradox: As the brain gains years of experience, actual math proficiency is marked not by making the brain work harder, but by a shift toward incredible neural efficiency and automaticity.”
In adults, lower activity in the somatosensory and motor cortices during counting tasks was associated with higher math skills. These cortices are the parts of the brain responsible for processing the sensation of physical touch and voluntary body movements. The authors also found that adults with better math skills had reduced activation in the right insular cortex.
The insular cortex is a brain region that detects highly demanding cognitive tasks and sends signals to the brain to exert more effort. Low activation in this region suggests that math-proficient adults perceive basic number tasks to be less mentally demanding. These adults operate on a kind of cognitive autopilot, and it doesn’t take much conscious effort to process quantities.
“The key point is that skilled math performance in adulthood is characterized by fundamental neural changes toward efficiency and automaticity,” Ren said. “While children rely heavily on basic number processing and sensory fundamentals to make sense of numbers, our results show that adults with higher math skills actually show reduced activation in sensorimotor and attentional brain areas. This suggests that the rise in higher math proficiency with experience is not about working the brain harder, but rather a shift from physical ‘scaffolding’ to more abstract and automatic mental representations.” ”
The researchers also examined the left intraparietal sulcus, a brain region known for handling numbers. In adults, less activity in this region correlates with better math performance, supporting the neural efficiency hypothesis. For children, exactly the opposite was true. Higher activity in the left intraparietal sulcus predicted better mathematics performance in fourth grade, indicating that young learners still rely heavily on basic quantity processing to succeed in mathematics.
None of these brain activity patterns correlated with participants’ reading scores. This lack of correlation provides evidence that the reduced dependence on motor and quantity processing areas is highly specific to mathematical skills. It does not simply reflect general intelligence or advanced reading comprehension.
A potential misconception about these findings is that physical methods such as finger counting are useless for learning mathematics. The authors note that physical representations often serve as necessary scaffolding for young learners to understand basic number concepts.
“An important caveat is that our findings do not suggest that children’s sensorimotor strategies, such as counting with their fingers, are bad or should be stopped early,” Ren says. “Sensorimotor experience serves as an essential adaptive scaffold when we first learn mathematical concepts. Importantly, this relationship changes over time. Although physical grounding is essential for early learning, our long-term mathematical proficiency ultimately relies on the brain learning to offload its labor-intensive physical processing to achieve automaticity.”
A limitation of this study is that the adult and child data were collected using two different brain scanners. This was partially due to scheduling constraints due to the global pandemic. Differences between scanners typically affect overall signal strength rather than specific operational correlations, but future studies should use consistent equipment to eliminate potential interference.
“Because this study involved separate groups of adults and fourth graders, one important next step will be to utilize a longitudinal design to track these neural transitions within the same individuals over time,” Ren said. “It is interesting and important to identify exactly when and how the brain transitions away from reliance on sensorimotor scaffolding. Ultimately, understanding this developmental trajectory will help us design more appropriate and tailored educational strategies and interventions for individuals who face persistent challenges in learning mathematics.”
These findings highlight broader trends in brain development and cognition. “Overall, I think this study nicely illustrates a broader tenet of cognitive neuroscience: learning and high-level expertise are often characterized by adaptive reductions in activity as the brain’s workload decreases and effortful control is replaced by smooth automaticity,” Ren said.
The study, “Reduced reliance on sensorimotor processing in the brain is associated with higher math skills in adults,” was authored by Xueying Ren, Marc N. Coutanche, Julie A. Fiez, and Melissa E. Libertus.
