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Math Anxiety (MA) is characterized by a negative emotional response when facing math-related situations. MA is distinct from general anxiety and can emerge during primary education. Prior studies typically comprise adults and comparisons between high- versus low-MA, where neuroimaging work has focused on differences in network activation between groups when completing numerical tasks. The present study used voxel-based morphometry (VBM) to identify the structural brain correlates of MA in a sample of 79 healthy children aged 7–12 years. Given that MA is thought to develop in later primary education, the study focused on the level of MA, rather than categorically defining its presence. Using a battery of cognitive- and numerical-function tasks, we identified that increased MA was associated with reduced attention, working memory and math achievement. VBM highlighted that increased MA was associated with reduced grey matter in the left anterior intraparietal sulcus. This region was also associated with attention, suggesting that baseline differences in morphology may underpin attentional differences. Future studies should clarify whether poorer attentional capacity due to reduced grey matter density results in the later emergence of MA. Further, our data highlight the role of working memory in propagating reduced math achievement in children with higher MA.
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SCIEnTIFIC REpoRTS | (2018) 8:8500 | DOI:10.1038/s41598-018-26912-5
The Neurocognitive Architecture
of Individual Dierences in Math
Anxiety in Typical Children
Charlotte E. Hartwright
1,2, Chung Yen Looi2,3, Francesco Sella2, Alberto Inuggi
Flávia Heloísa Santos5, Carmen González-Salinas6, Jose M. García Santos7,
Roi Cohen Kadosh
2 & Luis J. Fuentes
Math Anxiety (MA) is characterized by a negative emotional response when facing math-related
situations. MA is distinct from general anxiety and can emerge during primary education. Prior studies
typically comprise adults and comparisons between high- versus low-MA, where neuroimaging work
has focused on dierences in network activation between groups when completing numerical tasks. The
present study used voxel-based morphometry (VBM) to identify the structural brain correlates of MA in
a sample of 79 healthy children aged 7–12 years. Given that MA is thought to develop in later primary
education, the study focused on the level of MA, rather than categorically dening its presence. Using
a battery of cognitive- and numerical-function tasks, we identied that increased MA was associated
with reduced attention, working memory and math achievement. VBM highlighted that increased
MA was associated with reduced grey matter in the left anterior intraparietal sulcus. This region was
also associated with attention, suggesting that baseline dierences in morphology may underpin
attentional dierences. Future studies should clarify whether poorer attentional capacity due to
reduced grey matter density results in the later emergence of MA. Further, our data highlight the role of
working memory in propagating reduced math achievement in children with higher MA.
Math anxiety (MA) is characterised by negative emotional response such as fear and tension when facing
math-related situations, which cannot be reduced to either general anxiety or test anxiety1. It disrupts mathemat-
ical performance irrespective of gender2, and can emerge in the primary school years3,4. Depending on the extent
of MA, the negative impact of MA could have far-reaching consequences beyond academic achievements5. Given
that a signicant variation in the level of MA is contributed by genetic factors6, understanding the neurocognitive
basis of individual dierences in MA may shed light on its causal pathway.
To date, there are a limited number of neuroimaging studies, which are mainly based on adults and compar-
isons of neural response between groups of high and low levels of MA. In adults, high- compared with low-level
MA has been shown to be associated with increased activity in bilateral posterior insula, brain areas linked with
threat and pain processing, when anticipating mathematical tasks7. High- compared to low-level MA has also
been associated with stronger deactivation within the default mode network during tasks that require additional
inhibitory functions, possibly reecting depletion of working memory resources4. Further, increased activity in
frontoparietal regions in high-level MA adults when anticipating mathematical tasks has been shown to corre-
spond with reduced performance decits, suggesting the role of cognitive control in MA8. In children9, high-level
MA has been associated with hyperactivity in the right amygdala when solving mathematical problems, and
increased connectivity between this and the ventromedial prefrontal cortex, areas implicated in the process-
ing and regulation of negative emotions. Compared with low-level MA, high-level MA has been linked with
decreased activity in brain areas involved in working memory and attention, including the dorsolateral prefron-
tal cortex, and reduced activity in posterior parietal areas, critical to numerical processing. Furthermore, brain
1Aston Brain Centre, School of Life and Health Sciences, Aston University, Birmingham, UK. 2Department of
Experimental Psychology, University of Oxford, Oxford, UK. 3School of Experimental Psychology, University
of Bristol, Bristol, UK. 4Istituto Italiano di Tecnologia, Genova, Italy. 5Departamento de Psicología Básica y
Metodología, Facultad de Psicología, Universidad de Murcia, Murcia, Spain. 6Departamento de Psicología Evolutiva
y de la Educación, Facultad de Psicología, Universidad de Murcia, Murcia, Spain. 7Servicio de Radiología, Hospital
Morales Meseguer, Murcia, Spain. Charlotte E. Hartwright and Chung Yen Looi contributed equally to this work.
Correspondence and requests for materials should be addressed to C.E.H. (email:
Received: 13 July 2017
Accepted: 22 May 2018
Published: xx xx xxxx
Correction: Publisher Correction
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stimulation over the dorsolateral prefrontal cortex has shown the ability to improve arithmetic performance
and reduced cortisol level in those with high-level MA10. To our knowledge, there are no prior published studies
assessing the link between brain structure and MA in children or adults.
A recent review11 highlighted that there are specic aspects of numerical and executive function that might
explain varying degrees of MA: lower working memory capacity, reduced attentional control, lower inhibitory
control and a decit in low-level numerical representations. Furthermore, increasing levels of MA may negatively
aect mathematical achievement, via disruption of core executive functions and/or decit in low-level numerical
representations. Using data collected from a larger study, we sought to test the strength of association made by
those predictions in that review, to better understand the neural and cognitive factors that are associated with the
degree of anxiety towards mathematics. We aimed to identify the structural brain correlates of the level of MA
in a typical school population, and to determine how brain structure mediates the relationship between those
cognitive functions that are most strongly predictive of the level of MA. Much of the prior literature has con-
sisted of between-group comparisons and, whilst these have provided important insights on the plausible neural
mechanisms of MA, the neural and cognitive architecture that contributes to individual dierences in MA, and
its association with mathematical achievement, remains unclear.
e present study comprises brain structure and MA data from 79 healthy, Spanish children. e Behavioural
Rating Inventory of Executive Function (BRIEF)12,13 was used to prole each child’s behavior in specic domains
of executive function. e BRIEF indices that were of primary interest for the present study were: inhibitory
control and impulsivity (INHBIT), ability to switch and alternate attention (SHIFT) and on-line, representational
memory (WORKING MEMORY). e BRIEF is regularly used in clinical and education settings, where higher
scores indicate greater diculty, and therefore lower capacity, in each domain. Numerical representation skills
were assessed using a number line task, consisting of positioning numbers on an analogue scale (PN)14, mathe-
matical achievement was determined using the Woodcock Johnson III Achievement (WJ)15 and the level of MA
was established via the Math Anxiety Scale (Math-AS)16. Voxel-based morphometry (VBM) was used to identify
brain-structure correlates of MA.
Participants. Participants were recruited through two state primary schools in Murcia, Spain, as part of a
wider study1719. e primary sample comprised 137 Spanish children, aged 7–12 years (2nd – 6th grade). Written,
informed consent was obtained from parents prior to acquiring any data, and verbal consent reobtained imme-
diately prior to data acquisition. Parents were advised that they would be informed by the hospital Radiologist
if any clinically relevant abnormalities were detected. T1-weighted structural MRI data were acquired from an
initial sample comprising 110 children whose parents gave previous informed consent. Two were not included in
the current study as they were reported to be bilingual, which may aect the measurement of math anxiety and
numerical achievement. A further 7 were excluded due to learning disabilities. Note that children with learning
disabilities were diagnosed before our study and were receiving special education from their schools. We admin-
istered tests on all children to ensure that no one felt excluded. We only analysed data of children without learning
disabilities. Following exclusion due to unsatisfactory image quality resulting from movement- or other imaging
artefacts (n = 21) or neuro-incidental ndings (n = 1), the nal sample comprised 79 children (age M = 115.20,
SD = 14.13; males = 50.6%; right-handed = 88.6%). e study was approved by the University of Murcia Ethics
Committee, and it was conducted in accordance with the approved guidelines and the Declaration of Helsinki.
Materials. Measures that were modeled in the current study include math anxiety, mathematical abilities and
working memory.
Math Anxiety. MA was assessed using the Math-AS (known also as the EAM, Escala de Ansiedade a
Matemática16). It consists of 25 items that describe situations that are commonly experienced by elementary and
high school students during their math lessons. is scale measures the variations in the degrees of math anxiety,
from absence to extreme math anxiety. is task was translated from Portuguese into European Spanish by a
speaker uent in both languages (FHS) to enable cross-cultural adaptation. Children indicated the intensity of
their response to each item on a ve-point Likert scale by crossing out one of the following: (1) None (2) Low (3)
Moderate (4) High and (5) Very High. e score was the sum of all points from the 25 questions. is measure has
been shown to have accurate validity and reliability when used with children20. Note that we could not control for
general anxiety in our analyses however, as we did not have normative data for children below 9 years old using
the State-Trait Anxiety Inventory for Children (STAI-CH)21. To be condent that we measured MA and not gen-
eral anxiety trait, we ran a correlation analysis on the standardised scores of the 50 out of 79 children. We found a
lack of correlation between general anxiety (STAI-CH) and MA, rp = 0.07, p = 0.63.
Numerical Cognition. Math Achievement. Children’s math abilities were assessed using the Spanish ver-
sion of the Woodcock-Johnson III (WJ-III) Achievement (ACH) battery15, which has been validated for the use
of children aged 6–13 years in Spain22. It comprises 4 subtests: calculation, math uency, quantitative concepts
and applied problems (see Supplementary Information: Method). e raw scores of each subtest were transformed
into W scores23 following the Rasch’s measurement model24,25. We used the composite score of all 4 subtests in
our analyses.
Numerical Representation. Numerical representation was assessed using a number line task, consisting of
positioning numbers on an analogue scale (PN)14. e participants were required to map numbers on a vertical
line that was marked with “0” at the bottom and “100” at the top. In half of the trials, the line was further marked
with 4 horizontal lines at dierent locations to assist children with number mapping. Children were required
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to indicate the position of an Arabic numeral, orally or visually presented by the experimenter, by pointing to a
specic location on the line. ere were 12 trials in this task.
Executive Function. We used parents’ rating of childrens online memory (WORKING MEMORY), atten-
tion (SHIFT) and inhibitory control (INHIBIT) using the Spanish version of the Behavioural Rating Inventory of
Executive Function, BRIEFTM 12,13, which assesses the executive functioning of children between 5–18 years old.
It contains 86 items in eight non-overlapping clinical scales and two validity scales.
Procedure. Cognitive and Achievement Testing. Children’s performance on a range of behavioural, cognitive
and achievement tasks was assessed prior to collecting the structural MRI scans. Testing was conducted during
the Autumn term by six trained assistants with children in groups of two.
Analysis of Demographic, Cognitive and Achievement Data. Several of the measures resulted in positively
skewed data. Parametric statistics were combined with permutation testing as this approach, in contrast with
non-parametric analyses, has been shown to maximally reduce type I and type II errors26. Condence inter-
vals (CIs) were estimated using the bias-corrected and accelerated (BCa) percentile bootstrap method (10,000
e cognitive and achievement data were analysed using SPSS, version 22. e mediation analysis was con-
ducted using the Process macro for SPSS (v2.16.3), available from fol-
lowing a published analysis pipeline27. Ten-thousand bootstrap resamples were used to generate bias-corrected,
95% condence intervals.
Neuroimaging Acquisition. e participants were tted with ear plugs and so foam padding used to minimize
head movement during the scan. Participants were asked to remain as still as possible for the duration of the
scan, and a parent sat beside their child throughout. A T1-weighted image was acquired for each participant
using a 1.5 T GE HDX scanner with an 8-channel, phased array, transmit-receive head coil. A 3D FSPGR BRAVO
sequence was used to achieve whole brain coverage, composed of 142 axially oriented slices with a reconstructed
voxel size of 1 × 1 × 1 mm3, where TR = 12.4 ms, TE = 5.2 ms, ip angle = 12°.
Neuroimaging Analysis using Voxel-Based Morphometry. e MRI data were analyzed using the FMRIB Soware
Library (FSL, version 6.0.0; Non-brain tissue was removed from the struc-
tural images and an initial weak bias eld correction applied using FSL’s anatomy pipeline (
uk/fsl/fslwiki/fsl_anat). ese brain extracted, bias corrected images were then fed into the second and subsequent
stages of FSL-VBM28 (, an optimised VBM protocol29. e images
were grey matter-segmented and registered to the MNI-152 standard space using non-linear registration30. e
resulting images were averaged and ipped along the x-axis to create a le-right symmetric, study-specic grey
matter template. All native grey matter images were then non-linearly registered to this study-specic template
and “modulated” to correct for local expansion (or contraction) due to the non-linear component of the spatial
transformation. e modulated grey matter images were then smoothed with an isotropic Gaussian kernel with
a sigma of 3 mm, Full-Width-Half-Maximum (FWHM) ~7 mm. Finally, voxelwise general linear modelling was
applied using Randomise31 (, which permits permutation-based
non-parametric testing, correcting for multiple comparisons across space. Here, 10,000 permutations of the
data were generated to test against the null. reshold-free cluster enhancement (TFCE)32 was used to identify
cluster-like structures, taking family-wise error rate (FWE) corrected p-values < 0.05. To avoid any labelling bias,
probabilistic anatomical descriptors were determined using FSL Atlas Query (
Atlasquery). Anatomical labels were output for voxels that had survived multiple-comparison correction. Cluster
peak information was extracted using FSL’s Cluster tool. See Supplementary Information: Method for a detailed
description of the GLM analyses.
Data availability. e research meta-data supporting this publication are available on the Open Science
Framework repository, see DOI 10.17605/OSF.IO/PDFJE. e senior author, LJF, may be contacted regarding the
wider dataset.
Math Anxiety and its Association with Demographic, Cognitive and Numerical Factors. Table1 out-
lines the sample’s demographic, numerical and cognitive characteristics (see also Supplementary Information: TableS1
for a detailed breakdown by grade). Typically, the level of MA within the sample was low. An independent-sam-
ples t-test determined that there was no dierence in the mean level of MA between the sexes (t(65.569) = 1.063,
p = 0.292, 95% CI 13.21, 3.569). Age was positively associated with the level of MA (r(79) = 0.237, p = 0.035, CI 0.031,
0.433), consistent with previous studies33 (see Supplementary Information: Fig.S1).
To assess the relationship between MA, numerical- and cognitive-function we conducted a series of partial
correlations. Each of the three BRIEF indices of interest (INHIBIT, SHIFT, WORKING MEMORY), plus the
PN and WJ were correlated with the Math-AS score (controlling for age and biological sex). SHIFT, WORKING
MEMORY and WJ were statistically signicant aer applying a Bonferroni correction (Table2).
Math Anxiety and its Association with the Brain. VBM was used to identify the structural correlates
of MA. A general linear model (GLM) comprising Math-AS score and the nuisance variables, age, biological
sex, recruitment source and handedness was created. Contrasts for positive and negative associations between
grey matter volume (GMV) and MA were computed, where corrections were applied for multiple comparisons
across the brain, and adjustment to correct for running two contrasts using a Bonferroni correction. A whole
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brain analysis did not identify any regions that were positively associated with MA. Four clusters demonstrated a
negative association between GMV and MA, which encompassed only cortical grey matter (Table3 and Fig.1).
e largest cluster spanned both hemispheres across occipital and parietal cortices, including a section running
anterior-posterior along the le anterior intraparietal sulcus (IPS), in areas hIP1 and hIP3, as dened by the
Juelich Histological Atlas. A second, smaller cluster was identied in the right visual cortices, encompassing
extrastriate areas and additionally encompassing the le anterior IPS. Lastly, a small, le lateralized cluster was
identied in the inferior parietal lobule. e Bonferroni correction had split this latter cluster, resulting in a fur-
ther cluster in the lateral occipital cortex. As this comprised a single voxel (MNI: 18, 88, 42), this voxel was
excluded from subsequent analyses.
To understand the function of these structural correlates of MA, a further GLM was constructed comprising
the Math-AS scores and nuisance variables as outlined previously, plus those variables that had earlier shown
the most robust associations with the level of MA: attentional control (SHIFT), online memory (WORKING
MEMORY) and mathematical achievement (WJ). is GLM was applied to those voxels that were previously
identied as being negatively associated with MA.
Domain Scale/Index nmean SD min max
Age (months) 115.20 14.13 95.00 145.00
School grade — 4 — 2 6
Sex (M/F) 40/39 — — — —
Handedness (L/R) 9/70 — — — —
Numerical Cognition
Math Anxiety Scale 79 43.52 19.35 25 101
Woodcock-Johnson III Achievement 79 128.15 29.64 65 208
Number line task 73 7.51 2.18 2.00 10.50
Executive Function (BRIEF)
Initiate 71 12.92 2.99 8 22
Working Memory*71 17.49 4.62 10 27
Plan 71 19.87 5.43 12 33
Organization 71 10.75 3.28 6 18
Monitor 71 13.63 3.20 8 22
Inhibit*71 15.34 3.53 10 27
Shi*71 12.69 3.05 8 21
Emotional Control 71 16.49 4.21 10 28
Behavioral Regulation Index 71 43.96 9.41 18 69
Metacognition Index 71 74.69 16.70 48 118
Global Executive Composite 71 119.21 24.32 79 184
Table 1. Sample Characteristics. Note. * indicates BRIEF indices of primary interest due to prior published
associations with Math Anxiety.
Domain Math Anxiety
BRIEF indices
Inhibitory C ontrol
(INHIBIT) Attentional
Control (SHIFT) Online Memory
Math Anxiety (MA) Pe ar son’s r 0.112 0.302 0.278 0.320 0.313
p-value — 0.351 0.008*0.021 0.007*0.009*
Numerical Representation
Pe ar so n’s r 0.384 0.189 0.155 0.321
p-value 0.002 0.138 0.226 0.010
Math Achievement
Pe ar so n’s r 0.197 0.127 0.465
p-value 0.104 0.299 <0.001
Inhibitory C ontrol
Pe ar so n’s r 0.452 0.483
p-value <0.001 <0.001
Attentional Control
Pe ar so n’s r 0.602
p-value <0.001
Online Memory
Pe ar so n’s r
p-value —
Table 2. Association between Math Anxiety, Numerical and Cognitive Indices. Note. p-values reect two-tailed
partial correlation analyses, where age and biological sex was held constant. Degrees of freedom (df) = 69 for
MA * PN; df = 75 for MA * WJ; df = 67 for all BRIEF indices. * indicates associations that survive Bonferroni
corrected alpha for the 5 primary analyses of interest (rst row; Bonferroni corrected alpha, p < 0.01). B Ca
Bootstrapped 95% CI (10,000 samples) for statistically signicant, primary MA analyses: MA * WJ lower 0.455
upper 0.133; MA * SHIFT lower 0.074, upper 0.512; MA * WORKING MEMORY lower 0.052, upper 0.529.
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When controlling for attention, working memory and mathematical achievement, a large number of those
voxels initially found to be negatively associated with MA were no longer statistically signicant, particularly
within the IPS. Although this result alone is insucient to determine a dierence, the data suggest that the asso-
ciation between MA and GMV in these voxels may be mediated by one or more of the newly modelled variables.
HO Anatomical Region Hemi
(L/R) Cluster Size
(voxels) p-value t-value
MNI Coordinates
of Cluster Peak
Lingual Gyrus*R 217 0.006 5.62 12 66 6
Intracalcarine Cortex R
Occipital Fusiform Gyrus R
Temporal Occipital Fusiform Cortex R
Precuneous Cortex R
Cuneal Cortex*L 1446 0.002 5.11 882 20
Cuneal Cortex R
Lateral Occipital Cortex sup div (inc. anterior intraparietal sulcus hIP1&3a) L
Precuneous Cortex L & R
Occipital Pole L & R
Supracalcarine Cortex L & R
Intrcalcarine Cortex L
Superier Parietal Lobule (inc. anterior intraparietal sulcus hIP1&3a) L
Angular Gyrus L
Lingual Gyrus L
Lateral Occipital Cortex* (inc. anterior intraparietal sulcus hIP1&3a) L 76 0.017 4.91 42 64 34
Angular Gyrus L
Supramarginal Gyrus, posterior division L
Table 3. Probabilistic Labels for Brain Regions where Grey Matter Volume is Negatively Associated with Math
Anxiety. Note. Anatomical labels taken from the Harvard-Oxford (HO) Atlas bundled with FSL 6.0.0. *Cluster
peak. aReects anatomical label from Jeulich Histological Atlas available with FSL 6.0.0. p- and t-values reect
cluster peak. Labels are only reported from regions which survived correction at the voxel level, and subsequent
Bonferroni correction.
Figure 1. Grey matter correlates of Math Anxiety. Surface rendered image reects a t-statistic cluster map
rendered onto a template brain. All coloured areas reect those grey matter voxels that were signicantly
negatively associated with MA aer multiple comparison corrections as outlined in the method. e top right
panel illustrates the results transformed and rendered onto a single participant’s annoymised T1-structural
image. All images are presented in neurological convention, where the le of the image reects the le of the
brain. Surface rendering created using Surf Ice42; Individual subject rendering created using Mango43.
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To assess this possibility, the linear directional contrasts for each newly modelled variable werecomputed. ese
data suggested that, of the additional variables added, attentional control was negatively associated with a large
proportion of those voxels that were no longer associated with MA (see Supplementary Information: Fig.S2).
However, this result did not survive correction for multiple-comparisons. Working memory and math achieve-
ment did not explain the association with MA (see Supplementary Information for detailed modelling procedure).
To further assess this, the mean GMV was extracted for each of the 3 clusters identied in the earlier VBM
analysis. ough large, the spatial arrangement of these clusters may demarcate dierentiation of function, as
each comprised relatively distinct anatomical regions: lingual gyrus, cuneal cortex and the intraparietal sulcus.
SHIFT, WORKING MEMORY and WJ were entered into a series of partial correlations to examine their rela-
tionship with GMV within each of the 3 clusters. When controlling for age, biological sex and Math-AS score,
attentional control (SHIFT) was shown to be negatively associated with GMV across the IPS, although this result
did not survive a Bonferroni correction for 9 tests. All other tests were non-signicant at the uncorrected level
(see Table4).
The Cognitive Architecture of MA and Resultant Outcomes in Numerical Achievement.
Suárez-Pellicioni and colleagues11 outline a model, the processing eciency theory34 in which intrusive thoughts
resultant from MA consume working memory. This, in turn, is argued to expend already limited attentional
resources in high MA individuals, leading to diminished performance when complex mathematical operations are
performed. Such a relationship provides a causal explanation for the association between MA (Math-AS) and mathe-
matical achievement (WJ). We tested this theoretical model using mediation analysis. When controlling for working
memory and the nuisance variables age and biological sex, the relationship between MA and math achievement was
no longer signicant (c) (see Table5 and Fig.2). e overall mediation model found that MA, working memory, age
and biological sex explained approximately 55% of the variance in math achievement, (R2 = 0.5527, F(4, 66) = 20.39,
p < 0.0001). Consistent with this model, the mediation analysis suggested that higher levels of MA (Math-AS) were
associated with slightly elevated diculty with holding appropriate information in mind (WORKING MEMORY),
which in turn resulted in reduced math achievement (WJ).
e current study sought to examine the neurocognitive bases of MA in typically developing children. Unlike
most prior work, however, the analyses focused on the level of MA, which is a more rigorous approach than
making comparisons based on the presence or absence or of it35. is work therefore provides both a descrip-
tion of how MA might manifest itself across a cohort of typical children, as well as a detailed evaluation of
theoretically-driven factors that might inuence the degree of MA.
Most of the children in the sample demonstrated low-levels of MA. Around 10% of the entire sample reported
moderate to high-levels of MA, and prevalence increased linearly with age. Aside from numerical representations,
all other cognitive variables that were of theoretical interest were associated with the degree of MA: the more
math anxious children demonstrated reduced inhibitory and attentional control, as well as lower working mem-
ory capacity and math achievement. ough MA appears to be associated with dierences in baseline executive
functions in children with MA, working memory was also shown to mediate the relationship between MA and
math achievement suggesting that, possibly in addition to baseline dierences, capacity issues may be exacerbated
‘online’ when working with mathematics by the physiological response to anxiety, which in turn leads to poorer
learning and performance in mathematics.
Contrary to previous studies36,37, we found no association between MA and low-level numerical representa-
tions. ere has been considerable debate regarding the presence of a low-level decit in math ability in MA11.
It may be that the task used here was not ne-grained enough to highlight any association, although note that
the data from the task used did correlate with the measure of math achievement, suggesting that the task was
tapping into numerical cognition to some degree. e current pattern of results is also indicative of experiential
dierences between high and low math anxious individuals, as MA may result in avoidance of exposure to math
Domain Math Achievement
Control (SHIFT) Online Memory
Lingual Gyrus*Pe ar so n’s r 0.086 0.070 0.051
p-value 0.460 0.572 0.678
Cuneal Cortex*Pe ar so n’s r 0.168 0.123 0.086
p-value 0.148 0.318 0.487
Lateral Occipital Cortex*Pe ar son ’s r 0.112 0.318 0.066
/anterior Intraparietal Sulcus hIP1&3** p-value 0.334 0.008 0.592
Table 4. Partial Correlation Results for Regional Grey Matter Volume, Numerical and Cognitive Indices. Note.
p-values reect two-tailed partial correlation analyses, where MAS score, age and biological sex is held constant.
Degrees of freedom (df) = 74 for WJ; df = 66 for BRIEF indices. BCa Bootstrapped 95% CI (10,000 samples)
for SHIFT * IPS, lower 0.513, upper 0.108. No analyses survive Bonferroni corrected alpha for 9 tests of
interest (corrected alpha, p < 0.0056). *Anatomical descriptors reect VBM cluster peak and are taken from the
Harvard-Oxford (HO) Atlas bundled with FSL 6.0.0. **Reects anatomical label from Jeulich Histological Atlas
available with FSL 6.0.0.
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problems38. Moreover, an experientially-driven reduction in math achievement does not discount a primary,
causal decit in numerical representation; it may be that more extreme levels of MA reect both causal (rep-
resentational) and aectual (experiential) mechanisms. Indeed, cross-sectional work strongly suggests the pres-
ence of a less precise representation of numerical representation in adults with high MA37.
e present study also identied that reduced grey matter volume in the IPS, lingual gyrus and cuneal
cortex was associated with increased MA. e identication of a structural correlate in relatively young chil-
dren suggests that there may be dierences in early brain structure that underpin the development of MA.
us, whilst prior functional imaging studies demonstrate how network functionality can explain dierences
in mathematical performance in people with math anxiety8,9, these data provide the rst evidence of a possible
underlying structural basis. Genetic modelling suggests that around 40% of variance in MA6 can be explained
Path Estimate p-value
BCa 95% CI
Lower Upper
Total eect (c)0.3639 0.0058 0.6188 0.1091
Direct eect (c’)0.2247 0.0743 0.4721 0.0227
a 0.0758 0.0088 0.0197 0.1318
b1.8378 0.0006 2.8603 0.8154
Indirect eect
ab 0.1392 — 0.3074 0.0282
Table 5. Mediation Path Coecients and Condence Intervals for Math Anxiety Predicting Math
Achievement. Note. BCa condence intervals (CI) reect 10,000 samples.
Figure 2. Summary coecients for mediation model. Note. Path a = unstandardised IV to mediator; path
b = unstandardised mediator to DV; path c = unstandardised total eect (IV to DV); path c = unstandardised
direct eect. Coecient values rounded to 2 decimal places; full values reported in accompanying table (3).
ns = non-signicant *p < 0.01; **p < 0.001. Note. Higher working memory values indicate lower working
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SCIEnTIFIC REpoRTS | (2018) 8:8500 | DOI:10.1038/s41598-018-26912-5
by heredity, thus, a neurodevelopmental precursor is not implausible. Prior work has implicated the IPS in a
network of regions showing aberrant activity in young, math anxious children, where these patterns of activa-
tion were unrelated to intelligence, general anxiety, reading ability or working memory9. Our data suggest that
the IPS region identied in the current study might serve an attentional function, where children with reduced
grey matter in this area had lower reported attentional resources, and this reduced attentional capacity was
associated with increased MA. is is consistent with the assumption of the processing eciency theory34 that
high MA individuals may already have a limited attentional resource, and MA would further consume work-
ing memory, contributing to lower performance on complex mathematical operations. Future studies could
investigate whether this reduced grey matter in the IPS is a structural adaptation due to reduced attentional
capacity or compensatory strategies associated with MA39, or a potential biomarker on the causal pathway of
the development of MA. One model that should be examined in future research is that children with MA, or
who go on to develop MA, start o with dierences in IPS structure, which translate into a decit in baseline
attentional capacity. According to this view, a limited ability to attend to stimuli, particularly mathematical
stimuli – where demands on attentional resources are oen high due to the nature of arithmetic problems –
could result in general feelings of anxiety, which later become habitually associated with doing math, causing
the development of MA.
In addition to providing a rich description of the neurocognitive bases of the level of math anxiety, the current
study provides testable hypotheses regarding the emergence and maintenance of MA. Having a more nuanced
understanding of the neurocognitive prole of MA, including any impairments that may cause, contribute to,
or result from MA, has important implications for the development of targeted, individualized intervention.
Longitudinal and cross-sectional work to prole MA against developmental dyscalculia, which may appear qual-
itatively similar40, is required, however, to assess the validity of these assertions.
Limitations and Suggested Future Directions. e review by Suárez-Pellicioni and colleagues11 out-
lines three core explanations of MA: (1) Task-related competition for working memory resources (2) A decit
in low-level numerical representation (3) Math anxiety as an inhibition/attentional-control decit. Using data
collected from a wider study, we evaluated each of these, combining paediatric MRI data from typically develop-
ing children to advance a neurocognitive model of the level of MA. Whilst our results address some of the core
areas of interest in the cognitive literature, the present study does not, however, provide an assessment across
all cognitive domains, so cannot be considered an exhaustive evaluation of the neurocognitive architecture of
MA. Moreover, our executive function measures were derived from parental report. Although the measure of
executive function administered is used extensively in education and clinical settings, a standardized, automated
neurocognitive test battery may provide more valid data regarding performance and capacity.
Future work should include measures sampled ‘online’ whilst performing numerical tasks, based on
event-related potentials, functional magnetic resonance imaging or autonomic measurement, for example. Whilst
such work has been conducted with adults, there has been little progress towards developing a dynamic view of
how individual dierences in children’s physiological responses to numerical stimuli vary as a function of MA.
Importantly, such an approach could identify further potential antecedents of MA; thus, permitting directed
early intervention ideally to prevent, or at least reduce, the negative consequences of MA. Similarly, multimodal
approaches could achieve deeper evidence regarding the functional properties of MA and, if approached longi-
tudinally, its emergence could be evaluated. Still, though longitudinal approaches permit causal inference, they
attract signicant technical and practical challenges41.
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Authors CEH FS, and RCK were supported by the European Research Council (Learning and Achievement,
award number 338065). Authors LJF, FHS, JMGS and CGS were supported by grant PSI2017-84556-P from
Ministry of Economy, Industry and Competitiveness (FEDER funding).
Author Contributions
L.J.F. conceived and designed the over-arching study. All authors contributed to the math anxiety study concept.
F.H.S. translated the Math-AS into Spanish and organized and interpreted the Math-AS scores. C.G.S. organized
and interpreted the BR.IEF questionnaire. L.J.F., A.I. and J.M.G.S. designed and implemented the MRI protocol.
L.JF., A.I., F.H.S., C.G.S. and J.M.G.S. oversaw the testing and data collection. C.E.H., C.Y.L., F.S. and R.C.K.
were given access to the subset of data described in this article to examine math anxiety, following L.J.F. taking a
sabbatical within RCK’s laboratory at the Department of Experimental Psychology, University of Oxford. C.Y.L.
contributed to the organization of test measures and raw data. C.E.H., C.Y.L. and F.S. conducted the behavioural
analysis. C.E.H. conducted the neuroimaging analyses. C.E.H. and C.Y.L. draed the manuscript. L.J.F., R.C.K.,
F.S., A.I., F.H.S., C.G.S. and J.M.G.S. provided critical revisions of the manuscript. All authors approved the nal
version of the manuscript for submission.
Additional Information
Supplementary information accompanies this paper at
Competing Interests: e authors declare no competing interests.
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... This increased IPS activity could be derived from earlier modulation in neurotransmitters that are associated with neuroplasticity, such as GABA and glutamate (Werker and Hensch 2015). Taken together, these findings raise the possibility that IPS is a central hub for TA, and it was the case in the context of maths anxiety in children (Young et al. 2012;Hartwright et al. 2018). Since no previous studies have established a top-down modulation of IPS on the amygdala, this set of findings suggests that TA is unlikely to be underpinned by the amygdala-based canonical fear conditioning network. ...
... Since no previous studies have established a top-down modulation of IPS on the amygdala, this set of findings suggests that TA is unlikely to be underpinned by the amygdala-based canonical fear conditioning network. Instead, state anxiety was shown to be underpinned by the frontoparietal network, in which the IPS and the dorsolateral prefrontal cortex are key seeds (Arsalidou and Taylor 2011;Moeller et al. 2015;Balderston et al. 2017;Arsalidou et al. 2018;Hartwright et al. 2018;Popescu et al. 2019). Furthermore, the welldocumented involvement of the frontoparietal regions in cognition (Dumontheil and Klingberg 2011;Kaufmann et al. 2011;Menon 2016;Sokolowski et al. 2017) makes them excellent candidates for the investigation of TA. ...
Full-text available
The increased prevalence of test anxiety in our competitive society makes it a health issue of public concern. However, its neurobiological basis, especially during the years of formal education, is currently scant. Previous research has highlighted the association between neural excitation/inhibition balance and psychopathology and disease. We examined whether the glutamate/GABA profile tracks test anxiety levels in development, using a cross-sectional and longitudinal design in a cohort spanning from early childhood to early adulthood (N = 289), reassessed approximately 21 months later (N = 194). We used magnetic resonance spectroscopy to noninvasively quantify glutamate and gamma-Aminobutyric acid (GABA) levels in the intraparietal sulcus (IPS) and the middle frontal gyrus. We show that the glutamate/GABA balance within the IPS relates to current individual variation in test anxiety levels and predict future test anxiety approximately 21 months later. Critically, this relationship was observed during early childhood but not during the later developmental stages. Our results extend the use of the excitation/inhibition balance framework to characterize the psychopathology mechanisms of test anxiety, an underexplored yet widespread and debilitating condition that can impact early child development. Our findings provide a better understanding of the neurotransmitter basis underlying the emergence of anxiety disorders during development.
... Findings on young children are somewhat mixed. A majority of studies have not shown any gender differences (Ching, 2017;Dowker et al., 2012;Harari et al., 2013;Hartwright et al., 2018;Kucian et al., 2018;Primi et al., 2020;Ramirez et al., 2013;Wu et al., 2014), while in some studies, girls have reported experiencing more MA than boys (Gunderson et al., 2018;Lauer et al., 2018;Sorvo et al., 2017), and sometimes also boys have been found to display higher MA than girls (Dowker et al., 2019). One possible explanation for these progressively more systematic gender differences in MA draws on students' increasing awareness of gender stereotypes about academic abilities (McKown & Weinstein, 2003). ...
... As to the level of MA, we found no gender differences, which is in line with the majority of previous research on young children (e.g. Ching, 2017;Hartwright et al., 2018;Wu et al., 2014), nor did we detect gender effects on the changes in MA. Given the assumption of gender stereotypes explaining gender differences in MA (Bieg et al., 2015;Steffens et al., 2010;Watt, 2004), this would suggest either that such stereotypes are not (yet) present in this particular context or that the children are not (yet) aware of those (McKown & Weinstein, 2003). ...
We investigated the levels of and changes in mathematics anxiety (MA), symbolic numerical magnitude processing (SNMP) and arithmetic skills, and how those changes are linked to each other. Children’s (n = 264) MA, SNMP and arithmetic skills were measured in Grade 1, and again in Grade 2, also including a mathematics performance test. All three constructs correlated significantly within each time point, and the rank-order stability over time was high, particularly in SNMP and arithmetic skills. By means of latent change score modelling, we found overall increases in SNMP and arithmetic skills over time, but not in MA. Most interestingly, changes in arithmetic skills and MA were correlated (i.e. steeper increase in arithmetic skills was linked with less steep increase in MA), as were changes in SNMP and arithmetic skills (i.e. improvement in SNMP was associated with improvement in arithmetic skills). Only the initial level of arithmetic skills and change in it predicted mathematics performance. The only gender difference, in favour of boys, was found in SNMP skills. The differential effects associated with MA (developmentally only linked with arithmetic skills) and gender (predicting only changes in SNMP) call for further longitudinal research on the different domains of mathematical skills.
... Studies providing converging evidence from psychometric, neurophysiological, and cognitive data remain scarce. [37][38][39][40][41][42][43] Despite their relative difficulty, in comparison to questionnaires, such studies might bring new insights into the understanding of MA; they might prove particularly useful because they allow the tracing of neurophysiological/cognitive responses while performing anxiety-inducing mathematics tasks. Nevertheless, the evidence so far shows that questionnaires remain a more reliable measure of MA than implicit MA measures. ...
Full-text available
In this paper, we discuss several largely undisputed claims about mathematics anxiety (MA) and propose where MA research should focus, including theoretical clarifications on what MA is and what constitutes its opposite pole; discussion of construct validity, specifically relations between self-descriptive, neurophysiological, and cognitive measures; exploration of the discrepancy between state and trait MA and theoretical and practical consequences; discussion of the prevalence of MA and the need for establishing external criteria for estimating prevalence and a proposal for such criteria; exploration of the effects of MA in different groups, such as highly anxious and high math-performing individuals; classroom and policy applications of MA knowledge; the effects of MA outside educational settings; and the consequences of MA on mental health and well-being. Where are we? Over the past seven decades, 1 mathematics anxiety (MA) has been investigated to understand relevant psychometric, experimental, behavioral, and neu-rophysiological aspects. As one might expect, studies conducted over such a long period of time have brought several insights into the nature of MA. Most of these developments have been summarized in several review papers and meta-analyses. 2-8 We do not aim here to duplicate this vast amount of work on research synthesis but rather direct an interested reader to specific papers. We limit ourselves to relatively undisputed claims about MA (Box 1) and attempt to point to open questions and areas that, in our opinion, deserve more attention in future research.
... At the same time, studies using other measures [(neuro)physiological, cognitive] in that context are relatively scarce and rather focus on group level differences. Studies providing converging evidence from psychometric, (neuro)physiological, and cognitive data remain scarce [28][29][30][31][32][33][34] . Despite their relative difficulty in comparison to questionnaire administration, such studies can bring new insights into our understanding of MA. ...
Full-text available
In this paper we list ten fairly undisputed claims about Mathematics Anxiety (MA) and propose where MA research should focus on. The areas future MA research should focus on comprise (a) theoretical clarifications on what MA is, and what constitutes its opposite pole, (b) construct validity – specifically relations between self-descriptive, (neuro)physiological and cognitive measures, (c) discrepancy between state and trait MA and its theoretical and practical consequences, (d) prevalence of MA and call for establishing external criteria for estimating prevalence and proposal of such criteria, (e) exploring role of MA in different groups, e.g., highly anxious and high match performing individuals, (f) classroom applications of MA knowledge, (g) effects of MA outside educational settings, (h) consequences of MA on mental health and well-being.
... Students may develop mathematics anxiety for different reasons (Hartwright et al., 2018;McDonough and Ramirez, 2018). For example, some students may develop mathematics anxiety due to having general trait anxiety (Baloglu, 1999;Kazelskis et al., 2000;Cipora et al., 2015;O'Leary et al., 2017;Paechter et al., 2017;Lauer et al., 2018), which is a relatively a stable individual disposition and related to feeling anxiety across different situations and in different environments. ...
... Nonetheless, not all individuals with high levels of MA have poor mathematics performance; multiple cognitive and contextual factors could affect the potential detrimental effects of MA on academic achievement (for a review, see [42]). Among these, the following factors have been identified: working memory [27,43], metacognition [40], the type of problem-solving strategy used [31], parental involvement [44], and parents' math anxiety [45]. Taken together, the above findings revealed that the MAmathematics achievement link is quite complex, which underscores the need to better understand the nature of MA and the cognitive and contextual factors that may moderate or mediate the association with mathematics achievement. ...
Full-text available
Math anxiety (MA) affects students of all age groups. Because of its effects on children’s academic development, the need to recognize its early manifestations has been highlighted. We designed a European-Spanish version of the Scale for Early Mathematics Anxiety (SEMA; Wu et al. (2012)), and assessed its psychometric properties in a sample of children aged 7 to 12 years. The participants (967 typically developing children) were elementary school students recruited from ten schools. Children reported their general and math anxiety levels in an individual session and performed nonverbal IQ and math abilities subtests in a group session. Teachers reported the final math grades. The psychometric indices obtained, and the resulting factor structure revealed that the European-Spanish version of the SEMA developed in this study is a reliable and valid measure to evaluate MA in children from 3 rd to 6 th grade. Moreover, we explored gender differences, that resulted in small effect sizes, which disappeared when controlling for trait anxiety. Differences across grades were found for both global MA and the numerical processing anxiety factor but not for the situational and performance anxiety factor. Finally, MA was negatively associated with students’ math achievement, although the strength of the associations varied with the MA measure selected, the kind of math achievement analyzed, and the school stage considered. Our findings highlight the relevance of MA in elementary school and highlight the need for an early identification of students at risk of suffering MA to palliate the negative consequences of MA in children’s cognitive and academic development.
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Uzun yıllar, öğrenmede yoğunluklu olarak bilişsel süreçler üzerinde yoğunlaşılmıştır. 1950’li yıllardan bu yana yapılan araştırmalarda öğrenmede bilişsel süreçlerle birlikte duyuşsal ve psiko-motor süreçlerin de araştırmalara dâhil edildiği görülmektedir. Öğrenmedeki bilişsel olmayan süreçler içerisinde kaygı, tutum, motivasyon, öz-yeterlik ve akademik benlik gibi bileşenler yer almaktadır. Bu bileşenlerden araştırmalarda en fazla ele alınanı kaygıdır. Matematik biliminin günümüzdeki önemine ek olarak, özellikle matematik dersinin ülkemizde başarının anahtarı olarak görülmesi, bu derse karşı politika yapıcıların, öğrencilerin, öğretmenlerin ve ebeveynlerin beklentilerini farklılaştırabilmektedir. Bu beklentiler eğitimle ilişki tüm paydaşlar üzerinde matematiksel kaygılara neden olabilmektedir. Matematik kaygısı çevresel, durumsal ve psikolojik ve duygusal sebeplerden kaynaklı oluşabilmekte ve gelişebilmektedir. Bu nedenle, matematik kaygısının çok yönlü ele alınması gerekir. Bu düşüncelerden yola çıkarak yazılan kitap ülkemizde matematik kaygısını kapsamlı bir şekilde ele alan ilk kitap olma özelliği taşımaktadır. Matematik kaygısı üzerine yapılmış güncel araştırmalarla kitap dokuz bölümden oluşmaktadır. Bölüm yazarlarının hepsi matematik kaygısı üzerine araştırma deneyimlerine sahiptir. Kitabın özellikle politika yapıcılara, öğretmenlere, öğretmen adaylarına ve ebeveynlere matematik kaygısının tüm yönlerini anlama konusunda ışık tutacağına inancındayız. Kitabın ortaya çıkmasındaki katkılarından dolayı bölüm yazarlarına ve yayınevine teşekkürlerimizi sunarız. Keyifli okumalar…
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Neste artigo analisamos os efeitos do exercício físico no cérebro, no desempenho cognitivo/escolar e saúde mental a partir da perspectiva das Neurociências, relacionando com o estado emocional da Ansiedade Matemática (AM). O estado da arte aponta que o exercício físico traz benefícios estruturais e funcionais ao sistema nervoso, melhora a aptidão cardiorrespiratória, o desempenho cognitivo e promove modulação da ansiedade. Essas evidências dão suporte para a formulação de um modelo que postula os benefícios da prática do exercício físico no controle da ansiedade matemática nas populações jovens em idade escolar. Nossas discussões caminham na perspectiva de fomentar a implementação, a curto e médio prazos, de estratégias metodológicas viáveis ao sistema educacional para a melhoria da saúde física, cognitiva e do desempenho matemático dos estudantes
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It is well established that math anxiety has a negative relationship with math performance (MP). A few theories have provided explanations for this relationship. One of them, the Attentional Control Theory (ACT), suggests that anxiety can negatively impact the attentional control system and increase one's attention to threat-related stimuli. Within the ACT framework, the math anxiety (MA)—working memory (WM) relationship is argued to be critical for math performance. The present meta-analyses provides insights into the mechanisms of the MA—MP relation and the mediating role of WM. Through database searches with pre-determined search strings, 1,346 unique articles were identified. After excluding non-relevant studies, data from 57 studies and 150 effect sizes were used for investigating the MA—MP correlation using a random-effects model. This resulted in a mean correlation of r = −0.168. The database search of WM as a mediator for the MA—MP relation revealed 15 effects sizes leading to a descriptive rather than a generalizable statistic, with a mean indirect effect size of −0.092. Overall, the results confirm the ACT theory, WM does play a significant role in the MA—MP relationship.
Strong foundational skills in mathematical problem solving, acquired in early childhood, are critical not only for success in the science, technology, engineering, and mathematical (STEM) fields but also for quantitative reasoning in everyday life. The acquisition of mathematical skills relies on protracted interactive specialization of functional brain networks across development. Using a systems neuroscience approach, this review synthesizes emerging perspectives on neurodevelopmental pathways of mathematical learning, highlighting the functional brain architecture that supports these processes and sources of heterogeneity in mathematical skill acquisition. We identify the core neural building blocks of numerical cognition, anchored in the posterior parietal and ventral temporal-occipital cortices, and describe how memory and cognitive control systems, anchored in the medial temporal lobe and prefrontal cortex, help scaffold mathematical skill development. We highlight how interactive specialization of functional circuits influences mathematical learning across different stages of development. Functional and structural brain integrity and plasticity associated with math learning can be examined using an individual differences approach to better understand sources of heterogeneity in learning, including cognitive, affective, motivational, and sociocultural factors. Our review emphasizes the dynamic role of neurodevelopmental processes in mathematical learning and cognitive development more generally.
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Given the considerable prevalence of math anxiety, it is important to identify the factors contributing to it in order to improve mathematical learning. Research on math anxiety typically focusses on the effects of more complex arithmetic skills. Recent evidence, however, suggests that deficits in basic numerical processing and spatial skills also constitute potential risk factors of math anxiety. Given these observations, we determined whether math anxiety also depends on the quality of spatial-numerical associations. Behavioral evidence for a tight link between numerical and spatial representations is given by the SNARC (spatial-numerical association of response codes) effect, characterized by faster left-/right-sided responses for small/large digits respectively in binary classification tasks. We compared the strength of the SNARC effect between high and low math anxious individuals using the classical parity judgment task in addition to evaluating their spatial skills, arithmetic performance, working memory and inhibitory control. Greater math anxiety was significantly associated with stronger spatio-numerical interactions. This finding adds to the recent evidence supporting a link between math anxiety and basic numerical abilities and strengthens the idea that certain characteristics of low-level number processing such as stronger number–space associations constitute a potential risk factor of math anxiety.
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The construct of mathematics anxiety has been an important topic of study at least since the concept of 'number anxiety' was introduced by Dreger & Aiken (1957), and has received increasing attention in recent years. This paper focuses on what research has revealed about mathematics anxiety in the last 60 years, and what still remains to be learned. We discuss what mathematics anxiety is; how distinct it is from other forms of anxiety; and how it relates to attitudes to mathematics. We discuss the relationships between mathematics anxiety and mathematics performance. We describe ways in which mathematics anxiety is measured, both by questionnaires, and by physiological measures. We discuss some possible factors in mathematics anxiety, including genetics, gender, age and culture. Finally, we describe some research on treatment. We conclude with a brief discussion of what still needs to be learned.
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This work sought to investigate the specific contribution of two different components of Effortful Control (EC)-attentional focusing (AF) and inhibitory control-to children's mathematics achievement. The sample was composed of 142 children aged 9–12 year-old. EC components were measured through the Temperament in Middle Childhood Questionnaire (TMCQ; parent's report); math achievement was measured via teacher's report and through the standard Woodcock–Johnson test. Additionally, the contribution of other cognitive and socio-emotional processes was taken into account. Our results showed that only AF significantly contributed to the variance of children's mathematics achievement; interestingly, mediational models showed that the relationship between effortful attentional self-regulation and mathematics achievement was mediated by academic peer popularity, as well as by intelligence and study skills. Results are discussed in the light of the current theories on the role of children's self-regulation abilities in the context of school.
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Although many children encounter difficulties in arithmetic, the underlying cognitive and emotive factors are still not fully understood. This study examined verbal and visuospatial short-term memory (STM) and working memory (WM) performance in children with developmental dyscalculia (DD) and high mathematics anxiety (MA) compared with typically developing (TD) children. Groups were matched on reading comprehension performance and IQ as well as on general anxiety. We aimed to test whether children with DD and MA were differently impaired in verbal and visuospatial STM and WM. Children were individually tested with four computerized tasks: two STM tasks (forward verbal and visuospatial recall) and two WM tasks (backward verbal and visuospatial recall). Relative to children with TD, those with DD did not show impairments on the forward or backward verbal tasks, but showed specific impairments in the visuospatial WM task. In contrast, children with MA were particularly impaired in the verbal WM task. Knowing the underlying cognitive processes that differentiate why children with DD and MA fail in math could have both educational and clinical implications.
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A decade has passed since the last published review of math anxiety, which was carried out by Ashcraft and Ridley (2005). Given the considerable interest aroused by this topic in recent years and the growing number of publications related to it, the present article aims to provide a full and updated review of the field, ranging from the initial studies on the impact of math anxiety on numerical cognition to the latest research exploring its electrophysiological correlates and brain bases from a cognitive neuroscience perspective. Finally, this review describes the factors and mechanisms that have been claimed to play a role in the origins and/or maintenance of math anxiety, and examines in detail the main explanations proposed to account for its negative effects on performance: competition for working memory resources, deficit in a low-level numerical representation, and inhibition/attentional control.
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Mathematics anxiety is negatively related to mathematics performance, thereby threatening the professional success. Preoccupation with the emotional content of the stimuli may consume working memory resources, which may be reflected in decreased deactivation of areas associated with the default mode network (DMN) activated during self-referential and emotional processing. The common problem is that math anxiety is usually associated with poor math performance, so that any group differences are difficult to interpret. Here we compared the BOLD-response of 18 participants with high (HMAs) and 18 participants with low mathematics anxiety (LMAs) matched for their mathematical performance to two numerical tasks (number comparison, number bisection). During both tasks, we found stronger deactivation within the DMN in LMAs compared to HMAs, while BOLD-response in task-related activation areas did not differ between HMAs and LMAs. The difference in DMN deactivation between the HMA and LMA group was more pronounced in stimuli with additional requirement on inhibitory functions, but did not differ between number magnitude processing and arithmetic fact retrieval.
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Previous studies have suggested that numerical processing relates to mathematical performance, but it seems that such relationship is more evident for intentional than for automatic numerical processing. In the present study we assessed the relationship between the two types of numerical processing and specific mathematical abilities in a sample of 109 children in grades 1-6. Participants were tested in an ample range of mathematical tests and also performed both a numerical and a size comparison task. The results showed that numerical processing related to mathematical performance only when inhibitory control was involved in the comparison tasks. Concretely, we found that intentional numerical processing, as indexed by the numerical distance effect in the numerical comparison task, was related to mathematical reasoning skills only when the task-irrelevant dimension (the physical size) was incongruent; whereas automatic numerical processing, indexed by the congruency effect in the size comparison task, was related to mathematical calculation skills only when digits were separated by small distance. The observed double dissociation highlights the relevance of both intentional and automatic numerical processing in mathematical skills, but when inhibitory control is also involved.