Why Some Kids Struggle With Math: Scientists Identify Key Brain Differences

For decades, educators have observed that some children consistently struggle with math despite normal intelligence and access to instruction. Now, new neuroscience research is offering deeper insight into why math can be more difficult for some children than others.

A recent study published February 9 in the Journal of Neuroscience identifies specific thinking processes and brain regions that may explain these differences, particularly when children work with symbolic numbers.

It’s Not Just About Getting the Right Answer

In the study, researchers examined second- and third-grade students with and without math learning disabilities. The children were shown two numbers between 1 and 9 and asked to quickly choose the larger one while undergoing MRI brain scans.

Interestingly, researchers weren’t primarily focused on whether the children answered correctly. Instead, they analyzed how the children approached the task, measuring response speed, caution, and behavioral changes after mistakes using advanced mathematical modeling.

The results revealed subtle but important differences.

Children with math learning disabilities:

• Were less cautious when responding
• Did not slow down after making mistakes

By contrast, children with typical math skills tended to slow down following errors, a behavior linked to performance monitoring and learning.

The Brain Regions Involved

MRI data showed that these behavioral differences corresponded to reduced activity in two critical brain areas:

• Middle Frontal Gyrus — associated with numerical processing, attention, impulse control, and adaptive thinking
• Anterior Cingulate Cortex — involved in detecting mistakes and regulating performance

Lower activation in the middle frontal gyrus was linked to reduced cautiousness when answering. Meanwhile, lower activity in the anterior cingulate cortex was associated with failing to slow down after errors.

In simple terms: the challenge may not be raw number understanding alone, it may involve executive control and error monitoring systems in the brain.

The Symbol Problem

Here’s where it gets more interesting.

When researchers replaced Arabic numerals with dot patterns representing quantities, the behavioral and brain differences disappeared. Children with math learning disabilities showed similar brain activity to their peers.

This suggests that the difficulty lies primarily in symbolic number processing, not in understanding quantity itself.

Educational neuroscientist Bert De Smedt of KU Leuven, who was not involved in the study, notes that symbolic processing has long been recognized as a major hurdle for children with math difficulties. This study strengthens that theory by showing how brain activity and decision-making behavior differ during symbolic tasks.

Why This Matters

The findings suggest that math difficulties are not caused by a single “math center” malfunctioning in the brain. Instead, they may involve a network of regions responsible for attention, impulse control, and performance monitoring.

Developmental cognitive neuroscientist Marie Arsalidou of York University emphasizes that math relies on multiple brain systems working together. This research reinforces that complexity.

Importantly, the study does not establish cause and effect. It remains unclear whether reduced activity in these brain regions causes math difficulties or results from them.

What This Means for Education

One of the most promising implications of this research is intervention strategy.

Lead researcher Hyesang Chang suggests that future approaches might focus less on drilling calculations and more on teaching students how to think about problem-solving. Strengthening metacognitive skills, such as self-monitoring, slowing down after mistakes, and applying structured strategies, could potentially help students overcome symbolic math challenges.

The takeaway is clear: math struggles are not simply about intelligence or effort. They may reflect hidden cognitive mechanisms that shape how children process symbols, monitor errors, and regulate their thinking.

Understanding those mechanisms is the first step toward designing smarter educational solutions.