The Research and Neuroscience of Dyscalculia

Table of contents:

1. Introduction
2. Early research foundations
3. Cognitive skill deficits
4. Brain imaging findings
5. Genetics and heritability
6. Neuroplasticity and intervention
7. Why this research matters
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1. Introduction

For much of the twentieth century, math difficulties were brushed aside as poor teaching, lack of practice, or low motivation. Children who failed at arithmetic were often labeled careless or lazy. Yet research has shown that, for some learners, the problem runs much deeper.

In recent decades, psychologists, neuroscientists, and geneticists have demonstrated that dyscalculia is a distinct learning disorder with identifiable cognitive and neural foundations. Brain imaging, twin studies, and experimental training programs have revealed why some children struggle profoundly with numbers while others excel.

Understanding these findings is not just an academic exercise. Research provides the evidence that dyscalculia is real, helps identify the cognitive skills most affected, and shows how intervention can harness the brain’s plasticity to improve outcomes. This article explores the key discoveries in research and neuroscience that have reshaped our understanding of dyscalculia.

2. Early research foundations

Although dyscalculia had been described earlier, the 1990s marked a turning point when systematic research began to frame it as a distinct learning disorder. Before this, math difficulties were typically attributed to poor teaching, lack of practice, or low motivation. Children who struggled were often described as careless or lazy, and their difficulties rarely attracted serious scientific study.

Psychologist David Geary (1993, 2004) advanced the field by proposing that math learning difficulties could be grouped into subtypes. He distinguished between:

• Procedural deficits — reliance on immature or error-prone problem-solving strategies.
• Semantic memory deficits — difficulty recalling arithmetic facts, such as multiplication tables.
• Visuospatial deficits — problems with aligning numbers, understanding place value, and spatial representation.

Epidemiological studies added weight to this emerging field. Shalev and Gross-Tsur (2001, 2004) conducted some of the first large-scale, longitudinal studies of developmental dyscalculia. They found that the condition affects between 3% and 6% of the population—similar to dyslexia—and that it tends to persist into adolescence and adulthood unless addressed.

At the same time, Brian Butterworth (1999) helped popularize the idea that dyscalculia stems from a deficit in number sense—the intuitive ability to understand quantities and their relationships. This perspective, later reinforced by neuroscience, would become one of the most influential theories in the field.

Together, these studies laid the groundwork for understanding dyscalculia as a genuine and widespread learning disorder, rather than a by-product of poor effort or inadequate teaching.

3. Cognitive skill deficits

Research has shown that dyscalculia is closely associated with weaknesses in a set of underlying cognitive processes that support mathematical learning. The most consistently identified include:

• Number sense: the intuitive grasp of quantity and numerical relationships (Butterworth, 1999). A child with poor number sense may not realize that 25 is closer to 30 than to 40, or may treat 4 and 40 as interchangeable.
• Working memory: the ability to hold and manipulate numbers in mind while solving problems (Geary, 2004). A child with weak working memory may lose track of steps in long division or forget the numbers they are carrying in addition.
• Visual-spatial skills: the capacity to align digits, understand place value, and interpret charts or graphs (Szűcs & Goswami, 2013). Deficits in this area can make column addition or reading a number line especially confusing.
• Processing speed: the efficiency with which a child retrieves math facts and recognizes numerical patterns (Ashcraft & Faust, 1994). Slow processing means they may know how to solve a problem, but run out of time in tests.

These difficulties appear across the IQ spectrum: a child may have strong reasoning or verbal skills but still lack number sense, or may have weaker general abilities yet display the same dyscalculia-specific profile.

Together, these findings helped shift the focus away from blaming poor teaching or effort, toward recognizing that mathematics depends on specific cognitive skills — and when those skills are weak, children are at risk of persistent difficulties.

4. Brain imaging findings

Neuroscience has played a central role in confirming dyscalculia as a genuine disorder rather than a product of poor teaching. One of the most consistent findings comes from studies of the intraparietal sulcus (IPS), a region of the parietal lobe involved in representing numbers and magnitudes. Functional MRI research shows that children with dyscalculia often display reduced activation in the IPS when performing even simple number tasks, such as comparing which of two digits is larger (Butterworth, Varma, & Laurillard, 2011).

Further evidence points to differences in connectivity. Kucian et al. (2006) demonstrated that children with dyscalculia show weaker communication between the parietal regions that process numbers and the frontal areas that support working memory and problem-solving. Structural studies add another layer: Rotzer et al. (2008) found reduced gray matter volume in parietal regions, suggesting that both the function and the anatomy of number-processing networks can be atypical.

At the same time, researchers caution against misinterpretation. Brain differences do not mean that children with dyscalculia are “brain disordered.” Protopapas and Parrila (2018), writing about dyslexia, argue that labeling a learning difficulty as a brain disorder risks pathologizing normal variation. Their reasoning applies equally to dyscalculia: brain imaging highlights group-level differences, but these should be understood as neurocognitive diversity, not as evidence of defect or disease.

Taken together, brain imaging confirms that dyscalculia has a biological basis while reminding us that it is not a unitary or pathological disorder. Instead, it reflects a spectrum of interacting neural and cognitive factors that influence how individuals learn mathematics.

5. Genetics and heritability

Family and twin studies show that both genetic and environmental factors influence mathematics ability. Kovas et al. (2007) studied 10-year-old twins and found substantial heritability for arithmetic skills, but also significant contributions from shared family and school environments.

This means that dyscalculia does not arise solely from genetic factors. While inherited factors contribute to vulnerabilities in number sense, memory, or attention, the environment in which a child grows up — including quality of instruction, parental attitudes toward math, and opportunities to practice — also shapes outcomes. Non-shared influences, such as individual teachers or interventions, can further tilt the balance.

Importantly, this interaction helps explain why dyscalculia often co-occurs with conditions like dyslexia and ADHD: overlapping genetic factors combine with environmental exposures to produce varied learning profiles.

Genetic studies, therefore, strengthen the case for dyscalculia as a genuine condition, while also highlighting the potential of high-quality teaching and targeted intervention to mitigate inherited risks.

6. Neuroplasticity and intervention

One of the most encouraging findings from modern neuroscience is the brain’s ability to adapt. Neuroplasticity refers to the capacity of neural networks to reorganize in response to experience and training. For children with dyscalculia, this means math difficulties are not fixed.

Brain imaging studies provide evidence of this adaptability. Kucian et al. (2011) showed that after intensive number line training, children with dyscalculia displayed increased activation in the intraparietal sulcus, the same region that is typically underactive in the disorder. Other studies demonstrate that targeted practice can strengthen connectivity between parietal and frontal brain regions, supporting improvements in working memory and calculation.

Cognitive training research has also shown promise. Holmes and Gathercole (2014) found that working memory interventions not only improved short-term memory but also led to gains in arithmetic performance. These findings suggest that addressing the cognitive foundations of math — rather than focusing solely on curriculum — can create lasting improvements.

Together, this body of evidence underscores that dyscalculia is not an unchangeable condition. With well-designed interventions that harness neuroplasticity, children can build new neural pathways and develop the skills they need to succeed in mathematics.

7. Why this research matters

Research and neuroscience have transformed dyscalculia from a misunderstood problem into a recognized learning disorder with clear cognitive and biological roots. This progress matters because it:

• Validates children’s struggles — confirming that math difficulties are real, not laziness or lack of effort.
• Identifies what to target — number sense, working memory, and other cognitive skills that underpin mathematics.
• Offers hope — demonstrating through neuroplasticity that the brain can change with the right kind of training.

The challenge is that most interventions focus only on the academic side — teaching math facts, procedures, and strategies. That is like weightlifting: it builds strength through practice. But when the underlying cognitive skills remain weak, the gains are limited.

This is where Edublox takes a different approach. By systematically strengthening the core cognitive skills that research has shown to be weak in dyscalculia — attention, memory, sequencing, processing speed, and number sense — Edublox provides the equivalent of steroids for academic training. It doesn’t replace weightlifting, but it multiplies the results.

In this way, Edublox embodies the central message of neuroscience: dyscalculia is not fixed. With the right kind of training, children can rewire their brains, overcome barriers, and succeed in mathematics.

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Edublox offers cognitive training and live online tutoring to help students overcome symptoms of dyscalculia. We work with families across the United States, Canada, Australia, and beyond. Book a free consultation to explore how we can support your child’s learning journey.

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References for The Research and Neuroscience of Dyscalculia:

• Ashcraft, M. H., & Faust, M. W. (1994). Mathematics anxiety and mental arithmetic performance: An exploratory investigation. Cognition and Emotion, 8(2), 97–125.
• Butterworth, B. (1999). The mathematical brain. London: Macmillan.
• Butterworth, B., Varma, S., & Laurillard, D. (2011). Dyscalculia: From brain to education. Science, 332(6033), 1049–1053.
• Geary, D. C. (1993). Mathematical disabilities: Cognitive, neuropsychological, and genetic components. Psychological Bulletin, 114(2), 345–362.
• Geary, D. C. (2004). Mathematics and learning disabilities. Journal of Learning Disabilities, 37(1), 4–15.
• Holmes, J., & Gathercole, S. E. (2014). Taking working memory training from the laboratory into schools. Educational Psychology, 34(4), 440–450.
• Kovas, Y., Haworth, C. M. A., Petrill, S. A., & Plomin, R. (2007). Mathematical ability of 10-year-old boys and girls: Genetic and environmental etiology of typical and low performance. Journal of Learning Disabilities, 40(6), 554–567.
• Kucian, K., Grond, U., Rotzer, S., Henzi, B., Schönmann, C., Plangger, F., Gälli, M., Martin, E., & von Aster, M. (2011). Mental number line training in children with developmental dyscalculia. NeuroImage, 57(3), 782–795.
• Kucian, K., Loenneker, T., Dietrich, T., Dosch, M., Martin, E., & von Aster, M. (2006). Impaired neural networks for approximate calculation in dyscalculic children: A functional MRI study. Behavioral and Brain Functions, 2(31).
• Protopapas, A., & Parrila, R. (2018). Is dyslexia a brain disorder? Brain Sciences, 8(4), 61.
• Rotzer, S., Kucian, K., Martin, E., von Aster, M., Klaver, P., & Loenneker, T. (2008). Optimized voxel-based morphometry in children with developmental dyscalculia. NeuroImage, 39(1), 417–422.
• Shalev, R. S., & Gross-Tsur, V. (2001). Developmental dyscalculia. Pediatric Neurology, 24(5), 337–342.
• Shalev, R. S., Manor, O., & Gross-Tsur, V. (2004). Developmental dyscalculia: A prospective six-year follow-up. Developmental Medicine & Child Neurology, 47(2), 121–125.
• Szűcs, D., & Goswami, U. (2013). Developmental dyscalculia: Fresh perspectives. Trends in Neuroscience and Education, 2(2), 33–37.

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• The Research and Neuroscience of Dyscalculia was authored by Sue du Plessis (B.A. Hons Psychology; B.D.), a dyscalculia specialist and with 30+ years of experience in learning disabilities.

• Edublox is proud to be a member of the Institute for the Advancement of Cognitive Education (IACE), an organization dedicated to improving learning through cognitive education and mediated learning approaches.