In recent neuroscience research emerging from Stanford University, a groundbreaking study spearheaded by Hyesang Chang and colleagues offers new insights into the cognitive challenges faced by children who struggle with mathematics. Published in the esteemed journal JNeurosci, this work delves into the intricate relationship between children’s performance in numerical tasks and the brain mechanisms underlying their ability to adapt and learn. The investigation takes a comprehensive approach, integrating behavioral experiments with neuroimaging techniques to unravel why some children find math learning particularly arduous compared to their peers.
The study’s experimental design involved children engaging in tasks where they selected the larger number from pairs presented across successive trials. These numerical comparisons were represented in two distinct formats: symbolic, using Arabic numerals, and nonsymbolic, using clustered dots. This dual-format task paradigm allowed the researchers to probe the robustness of children’s numerical cognition across symbolic and nonsymbolic quantity representations. Through longitudinal tracking of performance over time, the researchers were able to develop a computational model that captured the dynamics of how children adjusted their decision-making strategies based on trial outcomes.
What emerges from the model is a critical aspect of cognitive adaptation that appears to differentiate children with typical mathematical development from those with learning difficulties. Children who had problems mastering math showed a marked deficit in updating their internal models or “belief states” as they experienced errors and feedback. Specifically, their cognitive systems failed to effectively modify expectations or decision strategies following unsuccessful trials, suggesting a rigidity in thought processes when confronted with new or conflicting numerical information. This inability to flexibly adjust learning in response to changing task demands is a key cognitive bottleneck for struggling learners.
To uncover neural correlates of this behavioral rigidity, the research team employed functional neuroimaging, including magnetic resonance imaging (MRI), to observe brain activity during task performance. They focused on brain regions known for their roles in monitoring and executive control, cognitive domains critical for error detection and adaptive behavior modification. Findings revealed attenuated activation in these monitoring hubs among children with atypical mathematical abilities. Such neural hypoactivity portends diminished capacity for behavioral adaptation, shedding light on the biological substrates that might impede learning.
Critically, the researchers demonstrated that the degree of weakened brain activity in these areas could reliably predict whether a child fell into the typical or atypical math ability category. This predictive power underscores the potential for neuroimaging biomarkers as diagnostic tools for early identification of math learning challenges. By linking brain function with computational signatures of learning adaptation, this integrative approach offers a novel framework for understanding individual differences in math acquisition.
These insights carry profound implications beyond mere number processing deficits. The study suggests that the educational difficulties observed may stem from more generalized cognitive impairments affecting adaptation and learning across contexts. As Chang notes, the observed impairments may not be exclusive to numerical cognition but could affect any cognitive operations that require monitoring ongoing performance and dynamically adjusting behavior. This reframing advocates for broader cognitive support interventions rather than narrowly focused math drills.
The methodology used in this work represents a cutting-edge fusion of behavioral modeling and high-resolution neuroimaging. By quantifying latent learning variables and mapping them to functional brain data, the study not only dissects the mechanisms of mathematical learning deficits but also advances precision neuroscience approaches. Such strategies bear potential to tailor educational strategies to individual neurocognitive profiles, potentially revolutionizing special education paradigms.
Moreover, this research paves the way for extending these computational and neuroimaging models to other developmental disorders characterized by learning deficits. Given the shared reliance on executive control and adaptive learning mechanisms across diverse cognitive domains, the model may have broader applicability. Future research may thus leverage this framework to design better tools for early diagnosis and intervention in conditions such as dyslexia, ADHD, and autism spectrum disorders.
This study’s findings contribute meaningfully to the discourse on developmental cognitive neuroscience, highlighting the interplay between brain function, computational learning dynamics, and behavioral outcomes in children. Understanding how the brain generates flexible, updated representations in the face of error feedback is crucial for decoding the biological bases of learning. Such knowledge holds promise not only for scientific advancement but also for practical application in educational neuroscience.
Intriguingly, the researchers’ approach emphasizes the importance of longitudinal data collection, capturing trial-by-trial variations over time rather than static snapshots of ability. This dynamism in experimental design mirrors real-world learning situations where adaptation occurs continuously. Capturing these nuances allows for deeper insights into the developmental trajectories of mathematical cognition and its disruption.
In summary, Chang and colleagues’ integrative investigation underscores that children struggling with math face multidimensional obstacles involving computational inflexibility and neural hypoactivity in executive monitoring regions. These dual insights unravel a more comprehensive picture of math learning disabilities, informing future research and educational interventions aimed at fostering adaptive learning skills crucial for academic success.
For educators, clinicians, and neuroscientists alike, this study signals a pivotal advancement toward personalized education grounded in the biological and cognitive realities of learners. By moving beyond surface-level performance measures to the neural algorithms that govern learning adaptation, we edge closer to transformative strategies that can empower children facing mathematical challenges and potentially reshape their educational trajectories for the better.
Subject of Research: People
Article Title: Neural and Computational Mechanisms Underlying Mathematical Learning Difficulties in Children
News Publication Date: 9-Feb-2026
Web References: http://dx.doi.org/10.1523/JNEUROSCI.2385-24.2025
References: Chang et al., 2025, JNeurosci
Image Credits: Chang et al., 2025
Keywords: Learning disabilities, Arithmetic, Numerical constants, Functional neuroimaging, Magnetic resonance imaging, Children, Cognitive development, Learning
