Why Some People Find Math Easier Than Others: New Research Reveals the Cognitive Factors

Key Points

Fluid reasoning and comprehension-knowledge were among the most consistent cognitive predictors of mathematics skills in a 2026 meta-analysis.
Researchers analyzed 47,231 correlations from 552 correlation matrices covering 122 standardized test batteries to examine the relationships between cognitive abilities and mathematics performance.
Number sense and mathematical fluency emerged as important foundations for more advanced skills, including calculation, mathematical knowledge and problem-solving.
Language, vocabulary, general knowledge, working memory and processing speed were also associated with particular aspects of mathematical performance.
The findings could help educators identify why students struggle with mathematics and tailor support to their specific difficulties, although the study does not establish that the identified relationships are causal.

New Research Examines Why Mathematical Ability Varies

Why do some people grasp mathematical concepts quickly while others struggle with calculations, equations or word problems? A study published in the Journal of Intelligence on July 6, 2026, offers a detailed examination of the cognitive abilities associated with differences in mathematics performance.

The research, titled Cognitive–Mathematics Relations: A Meta-Analysis of Norm-Referenced Standardized Test Batteries, was conducted by Christopher R. Niileksela and colleagues. It brings together a large body of information from standardized cognitive and academic assessments to investigate which mental abilities are most consistently associated with mathematical skills.

The researchers compiled 47,231 correlations from 552 correlation matrices across 122 standardized test batteries, drawing on 1,060 subtests. Rather than examining one group of students or one mathematics examination, the study combined data from test manuals to develop an integrated model of relationships between cognitive abilities and mathematics performance.

The central finding was that fluid reasoning and comprehension-knowledge were among the strongest and most consistent cognitive predictors of mathematics skills. The analysis also highlighted the importance of foundational mathematical abilities, particularly number sense and mathematical fluency, in understanding more advanced performance.

The findings offer a framework for examining why students experience different kinds of mathematical difficulties. However, they describe statistical relationships between abilities and test performance rather than proving that any single cognitive characteristic causes someone to succeed or struggle in mathematics.

Fluid Reasoning Helps People Work Through Unfamiliar Problems

One of the most important abilities identified in the research was fluid reasoning: the capacity to recognize relationships, apply logic and solve unfamiliar problems without relying entirely on previously learned information.

Mathematics frequently requires students to identify patterns, determine how quantities relate to one another and apply rules in situations they have not encountered before. Fluid reasoning is relevant to these tasks because it helps people work through new problems and determine which information matters.

Fluid reasoning was associated with performance across different mathematical tasks, including calculation, mathematical knowledge and problem-solving. In the integrated model, fluid reasoning had a negligible direct effect on mathematical problem-solving, but its total effect was moderate. This indicates that its relationship with problem-solving was reflected largely through indirect pathways in the model.

A supplementary analysis examined narrower cognitive abilities to explore these relationships in greater detail. General sequential reasoning, which involves applying logical rules and following relationships in a sequence, was consistently associated with mathematics skills. This suggests that the ability to apply known rules to unfamiliar problems may be particularly relevant to mathematical performance.

These findings do not mean that people who find reasoning difficult cannot learn mathematics. Instead, they suggest that differences in reasoning ability may help explain why some mathematical tasks are more challenging for certain learners than for others.

Language and General Knowledge Also Matter

Mathematics is often associated primarily with numbers and calculations, but the study identified another important contributor: comprehension-knowledge.

This broad cognitive ability includes acquired knowledge, vocabulary, language and verbal reasoning. These skills can help learners understand mathematical terminology, interpret instructions and connect new concepts with information they already know.

The researchers found that comprehension-knowledge was particularly relevant to mathematical knowledge and problem-solving. Its relationship with mathematics fluency was less consistent, indicating that different mathematical tasks place different demands on cognitive abilities.

The supplementary analysis also examined narrower abilities, including lexical knowledge and general knowledge. Lexical knowledge, which concerns knowledge of words and their meanings, was particularly associated with mathematical knowledge. General knowledge was related to several mathematical outcomes.

These findings help explain why a learner might perform differently on two tasks involving similar calculations. Solving a numerical expression may depend heavily on understanding the procedure, while solving a word problem can also require interpreting language, identifying relevant information and translating a written description into mathematical relationships.

Consequently, difficulty with a mathematical word problem does not necessarily indicate that a student cannot perform the calculations involved. The challenge may lie partly in understanding what the problem is asking.

Number Sense Provides a Foundation for Advanced Mathematics

Another major finding concerned number sense, a foundational ability to represent and compare quantities and understand how numbers relate to one another.

Number sense provides a basis for working with numerical information before, and alongside, the development of more formal mathematical skills. Understanding that one quantity is larger than another, for example, supports the development of more complex numerical knowledge.

In the researchers' integrated model, number sense was an important predictor of other mathematical abilities. Mathematical fluency was also associated with subsequent skills, including calculation, mathematical knowledge and problem-solving.

The model organized these relationships hierarchically. Number sense predicted mathematical fluency, while number sense and fluency were used to predict calculation. Those foundational abilities, together with calculation, contributed to the model of mathematical knowledge and problem-solving.

This pattern suggests that difficulties with advanced mathematics may sometimes reflect weaknesses in more basic skills. A student who struggles with a complex problem may need help with the underlying number relationships or calculation procedures, rather than only more practice with the final task.

There is an important qualification, however. The researchers had relatively few correlations available for number sense, and many came from the Woodcock–Johnson test batteries. They therefore cautioned that its estimated predictive importance should be examined using a broader range of assessments.

Working Memory and Processing Speed Play Different Roles

The study also found relationships between mathematics and other cognitive abilities, including short-term working memory and processing speed.

Working memory is the ability to hold and manipulate information temporarily. It can be relevant when a learner needs to remember intermediate results, keep track of several steps or work mentally with numbers while solving a problem.

In the study's model, working memory was associated with number sense and mathematical knowledge. The findings also suggested that specific aspects of working memory may relate differently to fluency and calculation.

Processing speed refers to the ability to perform relatively simple cognitive tasks quickly and accurately. It was particularly relevant to mathematical fluency, which involves carrying out mathematical procedures efficiently and accurately.

These abilities should not be treated as interchangeable. A student may understand a mathematical concept but need more time to complete calculations, while another may calculate familiar expressions efficiently but struggle to reason through a new problem.

The findings support examining the particular skill involved rather than assuming that all mathematics difficulties arise from the same underlying weakness. They do not establish that slower processing or limited working memory inevitably leads to poor mathematical achievement.

Mathematical Skills Build on One Another

The study's integrated model treats mathematics as a connected system of abilities rather than a single, uniform skill.

In this framework, number sense and mathematical fluency are foundational components, while calculation, mathematical knowledge and problem-solving involve additional demands. Cognitive abilities can be associated with these outcomes both directly and through their relationships with other mathematical skills.

In the hierarchical structural model, the combined direct and indirect pathways accounted for 67.4% of the statistical variance in mathematical problem-solving. The corresponding figure for mathematical knowledge was 65.5%. These figures describe the model's explanatory performance; they do not represent the percentage of an individual's mathematical ability that can be predicted with certainty.

The distinction matters because mathematical difficulties can appear in different forms. A learner may understand the concepts but make procedural errors, perform calculations accurately but misinterpret a word problem, or struggle to apply a familiar rule in an unfamiliar context.

Recognizing these differences could help educators investigate where a student's learning process breaks down. Rather than relying exclusively on an overall mathematics score, they can examine the types of errors a student makes and the underlying skills required for the task.

How the Findings Could Help Teachers Support Struggling Students

The findings have potential practical value for educational assessment. The researchers suggest that evaluating mathematics difficulties should consider cognitive abilities alongside performance in different mathematical domains.

For example, language and conceptual knowledge may warrant closer examination when a student struggles to understand mathematical terms or represent a word problem. Reasoning skills may be relevant when the learner has difficulty identifying relationships or applying rules to unfamiliar situations.

Working memory and processing speed may also deserve attention when problems involve multiple steps or efficient recall of mathematical facts. Examining number sense and mathematical fluency could help identify foundational difficulties that affect more advanced work.

The Phys.org report, published on October 7, 2026, also highlighted the researchers' interest in using error analysis to identify students' specific learning needs. Looking at the pattern of mistakes, rather than scores alone, may help teachers distinguish conceptual misunderstandings from reasoning or efficiency-related difficulties.

The study provides a framework for making those assessments more targeted, but it did not directly test a particular classroom intervention or demonstrate that one assessment approach produces better learning outcomes. The proposed educational applications should therefore be understood as implications of the findings, not as proven treatment effects.

Important Limitations and What Researchers Need to Examine Next

Although the meta-analysis brought together a substantial volume of data, its findings have important limitations.

First, the analysis used normative and concurrent-validity samples from standardized test batteries rather than clinical samples of students diagnosed with specific mathematics learning disabilities. The results therefore do not establish whether the same relationships apply to every group of learners who experience serious mathematical difficulties.

Second, the study included English-speaking people in the United States. It remains uncertain whether the same patterns would generalize across different languages, cultures and educational environments.

Third, the researchers found substantial variation among the correlations. Different test batteries, subtests, samples and measurement approaches produced different estimates, meaning that the observed relationships should not be treated as universal or identical in every setting.

The analysis relied on correlations drawn from standardized test manuals rather than longitudinal data tracking how cognitive abilities and mathematical skills change over time.
Although the statistical model examined indirect relationships between cognitive abilities and advanced mathematics skills, it could not establish the timing of those relationships or prove that foundational skills causally mediate later performance.

The limited number of measures of number sense was another important concern. Future research using a wider variety of assessments could help determine how consistently number sense predicts other mathematics skills.

The researchers also identified the need to examine developmental differences and investigate whether the model applies to clinical populations. Longitudinal research would be particularly useful for testing how cognitive abilities and mathematical skills influence one another over time.

For now, the study provides a broad synthesis of evidence linking cognitive abilities with mathematical performance. Its main contribution is a more detailed account of how reasoning, language-related knowledge and foundational numerical skills are associated with different parts of mathematics, offering educators a basis for investigating learning difficulties more precisely.

Key Points Summary

A 2026 meta-analysis identified fluid reasoning and comprehension-knowledge as two of the most consistent cognitive predictors of mathematics skills.
The researchers analyzed 47,231 correlations from 122 standardized test batteries.
Number sense and mathematical fluency were important foundations for more advanced mathematical skills.
Working memory and processing speed were associated with specific aspects of mathematics performance.
The findings may help educators identify individual learning needs, but the study does not establish causal relationships.
The results need further investigation in different languages, cultures and clinical populations.

What This Means

Why it matters: The research suggests that mathematical ability involves several interconnected cognitive and academic skills, rather than one single ability.

Who may be affected: Students who struggle with mathematics, teachers, educational psychologists and professionals responsible for assessing learning difficulties may benefit from a more detailed understanding of these relationships.

What to watch next: Further research could establish whether the findings generalize to different educational settings and clinical populations. Longitudinal studies could also clarify how foundational mathematical skills and cognitive abilities influence learning over time.

Frequently Asked Questions (FAQ)

1. Why are some people better at math than others?

The study found that differences in fluid reasoning, comprehension-knowledge and foundational mathematical skills were associated with differences in mathematics performance. Other abilities, including working memory and processing speed, were also relevant to particular mathematical tasks.

2. What is fluid reasoning in mathematics?

Fluid reasoning is the ability to recognize relationships, apply logic and solve unfamiliar problems without relying entirely on previously learned information.

3. Why is language important for mathematics?

Language and acquired knowledge help people understand mathematical terminology, interpret instructions and connect mathematical concepts. The study identified comprehension-knowledge as an important predictor, particularly for mathematical knowledge and problem-solving.

4. What is number sense, and why does it matter?

Number sense involves understanding and comparing quantities and recognizing relationships between numbers. In the study's model, it was an important predictor of other mathematical skills, although the researchers cautioned that relatively few number-sense assessments contributed to the analysis.

5. Does the study prove that mathematical ability is determined by intelligence?

No. The research examined statistical relationships between cognitive abilities and mathematics skills. It did not establish that intelligence alone determines mathematical performance or that the observed relationships are causal.

6. Can the findings help teachers support students who struggle with math?

Potentially. The researchers suggest examining specific errors and assessing foundational mathematical and cognitive skills to identify learning needs. However, this study did not directly test whether a particular intervention improves achievement.

7. What were the main limitations of the research?

The analysis used English-speaking U.S. samples, excluded clinical samples, found substantial variation among correlations and relied on cross-sectional data. The researchers also noted that the number of assessments available for number sense was limited.

Sources

Original research paper —Niileksela, C. R., Hajovsky, D. B., Cocar-Montenegro, F. P., Schneider, W. J., Flanagan, D. P., and Alfonso, V. C. (2026). Cognitive–Mathematics Relations: A Meta-Analysis of Norm-Referenced Standardized Test Batteries. Journal of Intelligence, 14(7), 142. Published July 6, 2026.
Read the original research paper on MDPI
— Phys.org
Lawson, E. (October 7, 2026). Why some people are good at math while others struggle.
Read the Phys.org report

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