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Choke or Thrive? The Relation Between Salivary Cortisol and Math Performance Depends on Individual Differences in Working Memory and Math-Anxiety


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In the current study, we explored how a person's physiological arousal relates to their performance in a challenging math situation as a function of individual differences in working memory (WM) capacity and math-anxiety. Participants completed demanding math problems before and after which salivary cortisol, an index of arousal, was measured. The performance of lower WM individuals did not depend on cortisol concentration or math-anxiety. For higher WM individuals high in math-anxiety, the higher their concentration of salivary cortisol following the math task, the worse their performance. In contrast, for higher WM individuals lower in math-anxiety, the higher their salivary cortisol concentrations, the better their performance. For individuals who have the capacity to perform at a high-level (higher WMs), whether physiological arousal will lead an individual to choke or thrive depends on math-anxiety.
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Choke or Thrive? The Relation Between Salivary Cortisol and Math
Performance Depends on Individual Differences in
Working Memory and Math-Anxiety
Andrew Mattarella-Micke and Jill Mateo
The University of Chicago Megan N. Kozak
Pace University
Katherine Foster and Sian L. Beilock
The University of Chicago
In the current study, we explored how a person’s physiological arousal relates to their performance
in a challenging math situation as a function of individual differences in working memory (WM)
capacity and math-anxiety. Participants completed demanding math problems before and after which
salivary cortisol, an index of arousal, was measured. The performance of lower WM individuals did
not depend on cortisol concentration or math-anxiety. For higher WM individuals high in math-
anxiety, the higher their concentration of salivary cortisol following the math task, the worse their
performance. In contrast, for higher WM individuals lower in math-anxiety, the higher their salivary
cortisol concentrations, the better their performance. For individuals who have the capacity to
perform at a high-level (higher WMs), whether physiological arousal will lead an individual to choke
or thrive depends on math-anxiety.
Keywords: math-anxiety, cortisol, working memory, individual differences
Math-anxiety is characterized as an adverse emotional reaction
to math or the prospect of doing math (Richardson & Suinn, 1972).
For math-anxious individuals, opening a math textbook or even
entering a math classroom can trigger a negative emotional re-
sponse. Despite normal performance in other academic areas,
people with math-anxiety perform poorly on measures of math
ability in comparison to their less-math-anxious peers (Hembree,
Why is math-anxiety tied to poor math performance? One
explanation is that math-anxious students are simply less skilled or
practiced at math than their non-math-anxious counterparts. After
all, individuals high in math-anxiety tend to avoid math classes and
receive lower grades in the math classes they do take (Ashcraft &
Kirk, 2001). However, there is an alternative explanation for how
math-anxiety compromises math performance. Namely, in math-
anxious individuals, the anxiety itself causes an online deficit in
math problem solving that contributes to poor math outcomes
(Ashcraft, Kirk, & Hopko, 1998).
Support for the view that people’s anxiety about doing math—
over and above their actual math ability—can impede their math
performance comes from work by Ashcraft and Kirk (2001). These
researchers examined low and high math-anxious individuals’
ability to simultaneously perform a mental addition task and a
memory task involving the short-term maintenance of random
letter strings for later recall. Difficulty levels of both the primary
math task and the secondary memory task were manipulated.
Performance was worst (mainly in the form of increased math task
error rates) in instances in which individuals, regardless of math-
anxiety, performed both a difficult math and memory task simul-
taneously. However, in comparison to less math-anxious individ-
uals, participants high in math-anxiety showed an exaggerated
increase in performance errors under the difficult math and mem-
ory task condition. The authors concluded that performance defi-
cits under demanding dual-task conditions were most pronounced
in high math-anxious individuals because their emotional reaction
diverted attention away from the content of the task. Similar to a
demanding secondary task, this process co-opted the working
memory capacity that might have otherwise been available for
math performance.
Working memory (WM) is a short-term system involved in
the control, regulation, and active maintenance of a limited
amount of information relevant to the task at hand (Miyake &
Shah, 1999). If anxiety has a disruptive effect on WM, then
performance should suffer when a task relies on this system.
This article was published Online First June 27, 2011.
Andrew Mattarella-Micke, Katherine Foster, and Sian L. Beilock, De-
partment of Psychology, The University of Chicago; Jill Mateo, Depart-
ment of Comparative Human Development, The University of Chicago;
Megan N. Kozak, Department of Psychology, Pace University.
This research was supported by NSF CAREER Grant DRL-0746970 to
Sian Beilock.
Correspondence concerning this article should be addressed to Sian L.
Beilock, Department of Psychology, 5848 South University Avenue, The
University of Chicago, Chicago, IL 60637. E-mail:
Emotion © 2011 American Psychological Association
2011, Vol. 11, No. 4, 1000–1005 1528-3542/11/$12.00 DOI: 10.1037/a0023224
Indeed, previous work supports this prediction. Beilock et al.
(2004) have shown that anxiety about performing at an optimal
level selectively affects performance on those math problems
that place the greatest demands on WM, such as problems that
involve a carry operation or the maintenance of large interme-
diate answers.
Moreover, individual differences in WM capacity also pre-
dict who will be most affected by stressful performance situa-
tions (Beilock & Carr, 2005). In particular, higher working
memory individuals (HWMs) are most susceptible to perfor-
mance decrements in stressful situations. This is because
HWMs tend to employ cognitively demanding strategies during
problem solving. These strategies allow HWMs to reach a
greater level of performance relative to lower working memory
individuals (LWMs) who employ cognitively leaner but less
accurate heuristics. Yet, these cognitively demanding strategies
fail when WM is compromised while heuristics yield a low but
consistent level of performance (Beilock & DeCaro, 2007).
Thus, there is considerable evidence to suggest that diversion of
WM, perhaps toward worries about the task, is one mechanism
behind the online deficits associated with math-anxiety
(Beilock, 2008).
Although anxiety plays an important role in the expression of
poor performance, it may not be the only factor. For instance,
while math-anxious individuals report anxiety in math-related sit-
uations, they also exhibit intense physiological reactions, such as a
pounding heart, sweaty palms and even shaking hands (as in
Ashcraft, 2002), that may be related to their affective response. In
the current work we explore the relation between these physiolog-
ical reactions and math performance.
Two-Factor Theory of Emotion
Our approach is motivated by work in the social psychology
literature (Schachter & Singer, 1962). According to Schachter and
Singer’s two-factor theory, individuals perceive an emotional
event based on a cognitive interpretation of internal physiological
cues. For example, if a person experiences sweating palms and a
racing heart, the two-factor theory argues that one’s interpretation
of these cues discriminates between the subjective feeling of fear
and that of love (see also, Macdowell & Mandler, 1989). Given a
potentially stressful situation such as math problem solving,
whether an individual chokes or thrives may similarly depend on
their interpretation of their physiological state. While many indi-
viduals have heightened physiological responses in a math perfor-
mance situation, math-anxious individuals in particular may be
likely to interpret this physiological reaction negatively and
thus perform poorly. In contrast, nonanxious individuals might
even benefit from a heightened physiological state if they
interpret their physiological reaction to indicate a challenging
performance situation.
Present Research
In the current work, participants solved a set of difficult math
problems. To assess how our participants might construe this
potentially stressful situation, we also measured their trait math-
anxiety. Because math-anxiety taps into an individual’s explicit
anxiety about math, it is an appropriate gauge of how they would
react to a challenging math situation. We also asked participants to
complete a WM capacity measure. Last, we sampled salivary
cortisol concentrations in our participants both before and after the
math test as an index of their physiological response to performing
the task.
We selected the hormone cortisol because it is often associated
with stressors in humans and is thought to have effects on WM
(Duncko, Johnson, Merikangas, & Grillon, 2009; Elzinga & Ro-
elofs, 2005; Lupien, Gillin, & Hauger, 1999). Recent animal
research supports this idea (Roozendaal, McReynolds, &
McGaugh, 2004). In Sprague–Dawley rats, corticosterone (the
analogous hormone in rats) has been shown to act on the prefrontal
cortex to cause a deficit in performance on the delayed response T
maze (a putative measure of WM). Critically, this deficit depends
on input from the basolateral amygdala, a key region in emotional
processing (LeDoux, 2000). When input from this region is in-
terrupted by a lesion or blocked by a receptor antagonist, the
corticosterone-driven deficit disappears. This has lead to the
claim that the negative effects of corticosterone on WM depend
on emotional processing. Although not definitive, this work
suggests cortisol as a potential link between people’s anxiety
about a math situation, their WM capacity, and their math
We used modular arithmetic (Bogomolny, 1996) as our math
task. The object of modular arithmetic (MA) is to judge the
validity of problems such as 5119(mod 4). One way to solve
MA is to subtract the middle number from the first number (i.e.,
51–19) and then divide this difference is by the last number
(32/4). If the dividend is a whole number, the answer is “true.”
MA is a desirable math task because it is novel, challenging,
and its WM demands can be easily manipulated by varying the
size of the numbers and whether or not problems involve a
borrow operation.
In summary, the two-factor theory allows us to make specific
predictions about which individuals will choke and which will
thrive in our math performance situation. Individuals that in-
terpret the situation negatively (high math-anxious individuals)
will suffer as the intensity of their physiological response
increases. However, this same physiological intensity might
actually contribute to facilitated performance for those low in
Critically, the relationship between math-anxiety, cortisol, and
performance should depend on individual differences in WM. This
is because the demanding strategies that HMWs often apply in
math performance situations are compromised when WM is im-
paired. If problem solvers interpret their physiological response as
indicative of math-related distress, this interpretation may hinder
one’s ability to execute demanding computations in WM. In con-
trast, because HWMs’ demanding strategies should benefit from
increased availability of WM resources, HWMs may be in a
unique position to gain from a favorable emotional interpretation
of their physiological response.
Participants (N73; 29 male, 44 female) were recruited from
University of Chicago, Roosevelt University, and the local area
(age M23.03, SD 5.42, range 1842). Participants were
also screened for the use of psychiatric medications and adrenal
Working memory capacity. Participants’ performance on
the automated Reading Span (RSPAN; Conway et al., 2005), a
common WM measure, served as our measure of WM capacity.
In the RSPAN, participants read a series of sentences followed
by letters (e.g., “On warm sunny afternoons, I like to walk in the
park.R”), and judge whether each sentence makes sense by click-
ing either True or False. At the end of a series of two to five
sentence-letter sets, they recall the sequence of letters. Individuals
are tested on three series of each length, 12 in total.
RSPAN scores are calculated based on the total number of
letters recalled in order on any trial, regardless of whether the
entire sequence of letters was correct. This partial-credit scoring
shows high internal consistency and reduces skew (Conway et al.,
Participants performed within the normative range for the
RSPAN task (M59.59, SD 13, Range 19–75), but slightly
higher than reported in a recent latent-variable analysis (M
51.60; Unsworth et al., 2009). RSPAN scores did not differ as a
function of gender t(71) .19, p.49.
Math-anxiety. Math-anxiety was assessed using the short
Math-anxiety Rating Scale (sMARS). The sMARS (Alexander &
Martray, 1989) measures an individual’s level of anxiety concern-
ing math related situations. Across 25 items, participants rate how
anxious they would be during math activities (e.g., “Listening to
another student explain a math formula”) on a 1–5 scale. The
sMARS is a shortened version of the 98-item MARS (Richardson
& Suinn, 1972). It is highly correlated with the original MARS
(r.96) and exhibits acceptable test–retest reliability. The mean
sMARS score was 32.16 (SD 17.29), slightly lower than that
reported in Ashcraft & Kirk (2001; M36.3, SD 16.3).
Math-anxiety did not differ as a function of gender, t(71) .03,
Modular arithmetic. MA problems were always of the form
“x y(mod z)”. The left two operands were selected from num-
bers 2–98, with the constraint that the first number (x) was always
greater than the second number (y). The mod operand (z) ranged
from 2–9. Studies of mental arithmetic have determined that prob-
lems which involve the maintenance of information online, such as
a carry operation, place particular demand on WM (DeStefano &
LeFevre, 2004). In contrast, certain problems lend themselves to
solution via heuristics (e.g., “mod 2” problems which are always
false when the subtraction result is odd). These heuristic solutions
make few demands on WM (DeCaro, Wieth, & Beilock, 2007).
Based on these factors, problems in the math task were divided
into Low and High demand categories corresponding to their
relative recruitment of WM capacity. High Demand problems
always included a carry operation during the subtraction step and
could not be solved via simple heuristic. Low Demand problems
did not have a carry step or could be solved using heuristics (e.g.,
mod 2 problems with an odd subtraction or mod 5 problems
because they could be solved with the simple heuristic that only
subtractions ending in 0 or 5 were true).
Participants completed 30 practice trials, followed by three
experimental blocks of 70 problems, each separated by about a
minute rest. The critical trials consisted of 54 High-Demand prob-
lems, in addition to 186 Low-Demand problems. This proportion
of problems was selected such that participants were not overtaxed
by difficult problems, but had enough time on task for the sluggish
cortisol response to emerge. Order of blocks was counterbalanced
across participants.
Sessions were scheduled between 11:00 a.m. and 3:00 p.m. to
minimize circadian variation in cortisol concentrations across par-
ticipants. In order to collect proper measurements of salivary
cortisol, participants were instructed not to eat, drink, chew gum,
or brush their teeth for two hours before the session. Participants
were compensated for their involvement.
Participants began by signing informed consent. The first saliva
sample was collected by having individuals spit into a 12 75 mm
polypropylene tube, which was then capped (Fisher Science; IL,
U.S.A.). Next, all individuals were seated at a computer and
introduced to MA. Participants saw MA problems such as
71 23(mod 3) on the computer and were asked to judge whether
each problem was true or false as quickly and accurately as
possible. Each trial began with a 500-ms fixation point, screen-
center. This was replaced by a MA problem that remained on the
screen until the participant responded. After response, the word
Correct” (in black) or “Incorrect” (in red) appeared for 1,000 ms,
providing feedback. The screen then went blank for a 1,000-ms
intertrial interval.
After the MA task, a second saliva sample was obtained from
participants. This second sample was taken approximately 30
minutes after starting the math task, based on prior research
establishing that salivary cortisol peaks between 21 and 40 minutes
following stressor onset (Dickerson & Kemeny, 2004). Following
the second saliva sample, participants completed the WM tasks.
Last, participants filled out a short packet of questionnaires, in-
cluding sMARS. After the experiment, saliva samples were kept
frozen in the testing room for 2–3 weeks until transport to the lab,
where they were stored until assayed. Samples were assayed in
duplicate with
I-cortisol Corticote
radioimmunoassay kits
(MP Biomedicals, CA U.S.A.) and reassayed if the CV was
20%. The sensitivity of the assay is 0.07 g/dL.
Only individuals whose average MA accuracy and cortisol
concentration were within 2SD of the mean of the group were
included in the analyses. This resulted in the removal of four
participants due to accuracy and three participants based on cor-
tisol concentration. Sixty-six participants were retained in the
analyses below.
Self-report data concerning smoking behavior was also collected, how-
ever due to experimenter error this data was only collected for 42 subjects.
Nonetheless, smoking behavior did not correlate with math-anxiety, WM,
or salivary cortisol. Thus it was not included in further analysis.
We also collected Operation Span (OSPAN), a measure of WM that
incorporates math processing. For the purposes of studying the relation of
WM and math-anxiety, OSPAN was not included due to its necessary
relation to math processing.
Modular Arithmetic Accuracy
Overall, participants were fairly accurate on the MA problems
(M90% correct, SD 6%) and completed the problems in
about four seconds on average (M3981 ms, SD 1300). As
expected, the High-Demand problems were performed slower
(M6289 ms, SD 2218) and less accurately (M81%, SD
12%) than Low-Demand problems (M3232 ms, SD 1126;
M92%, SD 8%), t(65) 17.42, p.0001; t(65) ⫽⫺10.40,
To explore how math-anxiety, individual differences in WM,
and salivary cortisol related to low and high-demand problem
performance, we began by regressing both low and high-demand
math accuracy separately on math-anxiety, WM, post-MA cortisol
(log transformed to reduce skew) and their interactions.
regression approach is preferable to performing a median split and
dichotomizing continuous variables because the latter approach
reduces power (Cohen, 1983) and under certain conditions can
increase the probability of a Type I error (Maxwell & Delaney,
1993). Following Cohen and Cohen (2003), we only considered
regression coefficients as significant if the overall F-statistic was
significant. This procedure protects against Type-I error inflation
associated with testing multiple regression coefficients.
For each regression, the assumptions of normality, homogeneity
and error independence were verified through inspection of the
residuals and a normal q-q plot. Diagnostics of leverage, discrep-
ancy and influence were also considered for each regression to
confirm that the relationships were not the result of a few extreme
or influential cases. DFBETA, a measure of the effect an individ-
ual observation has on a particular beta did not exceed the thresh-
old of 1 (Cohen et al., 2003). Cook’s Distance, a measure of the
effect of a particular observation on overall fit, did not exceed one
for any observation and no residual reached significance as a
regression outlier using the Beckman and Cook (1983) procedure
(␣⫽.05). No leverage values (h*
) differed substantially from the
distribution of values and only four observations (6%) were iden-
tified for further examination (about 5% are expected on average).
Thus, there was no evidence for extreme or influential data in the
regression. Last, we tested for multicollinearity using the variance
inflation factor (VIF). A VIF of 10 is considered strong evidence
for multicollinearity (see Cohen et al., 2003). No predictor VIF
exceeded 1.7.
The regression equation predicting high-demand accuracy from
math-anxiety, salivary cortisol, WM and their interactions was
highly significant, F(7, 58) 3.10, p.01. High-demand MA
accuracy was negatively related to overall math-anxiety, ␤⫽
.493, t⫽⫺4.044, p.001. This main effect was qualified by
the predicted three-way interaction between salivary cortisol,
math-anxiety, and WM, ␤⫽⫺.260, t⫽⫺2.15, p.05. The main
effect for cortisol, WM and the two-way interactions did not reach
significance (see Table 1). To fully understand the regression, we
modeled one standard deviation above and below the mean for
WM and math-anxiety. This simplifies interpretation by charac-
terizing the data in terms of high and low WM and math-anxiety
“groups” without actually breaking up the continuous variables
(Aiken & West, 1991). The performance of these modeled groups
on high-demand MA problems is plotted as a function of salivary
cortisol concentration taken after MA in Figure 1.
As seen in Figure 1 (left panel), LWMs’ math accuracy did not
differ as a function of cortisol or math-anxiety. However, for
HWMs, the relation between accuracy and cortisol concentration
depended on their math-anxiety. For low math-anxious individu-
als, increasing cortisol was associated with better MA perfor-
mance. The opposite pattern was found for individuals high in trait
In terms of low-demand MA problems, the full regression model
was not significant, F(7, 58) 1.42, p.22. Because these
problems were specifically selected for their lesser reliance on
WM, this nonsignificant result is an important control. If math-
anxiety or cortisol predicted low-demand performance, this would
suggest that these variables affect performance through another
route apart from their effects on WM.
Modular Arithmetic Reaction Time
The same regressions performed on MA accuracy were also
performed on MA RTs for the high and low-demand problems.
Neither the full equation for high, F(7, 58) 1.54, p.17 nor
low-demand, F(7, 58) 2.01, p.07 reached significance (see
Table 1).
In the current study we explored the relation between an indi-
vidual’s physiological response and their performance on a chal-
lenging math task. We predicted this relation would depend on
whether a person was lower or higher in math-anxiety and thus
their positive or negative construal of the math situation. We
further suggested that this impact might be moderated by individ-
ual differences in WM. Our data showed strong support for these
hypotheses. The relation between cortisol concentration (our mea-
sure of physiological response) and math performance depended
on a participant’s math-anxiety and their WM capacity.
For high math-anxious individuals, increasing cortisol concen-
trations lead to worse math performance. But, for low math-
anxious individuals, this relationship was positive—increasing
concentration of cortisol lead to higher performance. This pattern
of results supports our claim that one’s interpretation of the math
situation helps to determine whether a physiological response will
be disruptive or beneficial.
The effect of cortisol in this scenario was qualified by individual
differences in WM capacity and the WM demands of the math
problems performed. As predicted by previous research (Beilock
& Carr, 2005), low-demand problems were not affected by the
interaction of math-anxiety and cortisol. Moreover, because low
working memory participants (LWMs) often do not rely heavily on
WM to solve mathematical computations (Beilock & DeCaro,
2007), their performance remained unchanged with increasing
concentrations of cortisol even on the high-demand problems. In
contrast, high working memory participants’ (HWMs) math accu-
racy was affected by an interaction between math-anxiety and
As mentioned above, we collected cortisol prior to the math task to
measure individual differences in baseline cortisol. For simplicity, this
variable was not reported in the analysis. However, results remained
significant when cortisol concentrations prior to the math task were in-
cluded as a covariate.
salivary cortisol on high-demand problems. HWMs that were also
high in math-anxiety tended to perform worse on the math task as
cortisol concentrations increased across individuals. However,
HWMs low in math-anxiety excelled on the math task as cortisol
concentrations increased. These results suggest that, given the
cognitive resources and the opportunity to interpret physiological
arousal as a motivational cue, individuals in a challenging envi-
ronment can push themselves to higher levels of performance.
Participants did also exhibit a global difference in performance as
a function of math-anxiety. This is consistent with claims that high
math-anxious individuals possess less experience with math over-
all, contributing to poor performance in addition to their online
affective response (Tobias, 1985).
This work relies on correlational data, thus we draw causal
conclusion cautiously. For instance, placement of the math-anxiety
measure after math performance allows the alternative hypothesis
that math accuracy affected reported math-anxiety (instead of vice
versa). This hypothesis is unlikely, however, because WM mea-
sures separated these two tasks by about 40 minutes. Further,
sMARS is a trait math-anxiety measure, with questions that con-
sider stable anxieties as opposed to one’s current affective state.
Last, an explanation which claims that performance affected re-
ported math-anxiety cannot account for the WM component of our
3-way interaction (i.e., that HWMs, but not LWMs, show a rela-
tionship between performance and math-anxiety).
Second, it is also possible that cortisol concentrations might not
affect performance, but instead may be a byproduct of perfor-
mance outcomes. For instance, perhaps when low math-anxious
individuals notice they are performing well, their cortisol concen-
trations increase; likewise, when high math-anxious individuals
Figure 1. Mean modular arithmetic accuracy on high-demand problems as a function of WM, Math-anxiety
and Cortisol.
Table 1
Regression Analyses for Modular Arithmetic Reaction Time and Accuracy
High Demand Low Demand
Accuracy RT Accuracy RT
sMARS .49 (4.04
).11 ( 0.88) .34 (2.60
).09 ( 0.66)
RSPAN .09 ( 0.72) .06 (0.43) .16 ( 1.08) .05 (0.34)
Cortisol .18 ( 1.51) .18 (1.39) .06 ( 0.48) .27 (2.15
Cortisol sMARS .17 (1.46) .14 ( 1.07) .02 (0.18) .19 ( 1.54)
sMARS RSPAN .01 (0.06) .01 (0.34) .02 (0.11) .07 ( 0.50)
RSPAN Cortisol .11 ( 0.91) .23 ( 1.76) .03 (0.25) .17 ( 1.37)
sMARS RSPAN Cortisol .26 (2.15
).20 (1.50) .21 (1.58) .20 (1.56)
Adjusted R
.19 .07 .05 .07
1.54 1.42 2.01
Note. Adjusted R
, adjusted coefficient of determination; , standardized regression coefficient. Regression
coefficients that exceed ␣⫽.05 for both the overall Ftest and the individual tare indicated in bold along with
their respective adjusted R
and Fstatistics.
notice they are making mistakes, their cortisol increases. However,
a meta-analysis of cortisol studies points to uncontrollable situa-
tions which include potential social-evaluative stress as eliciting
the strongest cortisol response (Dickerson & Kemeny, 2004).
These criteria are consistent with failure on a difficult math task
and thus could explain the experience and performance of highly
math-anxious individuals, but not those lower in math-anxiety.
When considered in tandem with previous experimental manip-
ulations of situation-induced anxiety (Beilock and DeCaro, 2007;
Beilock & Carr, 2005 Dickerson & Kemeny, 2004), these data
support the claim that anxiety affects performance through its
impact on the WM system. The results also suggest that explicit
measures of anxiety alone cannot account for the full impact of
stress on performance. Physiological factors such as cortisol also
play a role. In sum, the results suggest future avenues of research
toward isolating a cognitively (Beilock & Carr, 2005) and biolog-
ically (Roozendaal et al., 2004) plausible mechanism for online
math performance decrements related to anxiety.
Last, the essential role of affect in this ostensibly “cold” cog-
nitive task is of special note. Math performance in adults is most
often studied from a purely cognitive approach (Ashcraft, 1992;
DeStefano & LeFevre, 2004), in which differences in affective
processes are accepted as a necessary source of random variation.
Yet, in the current study, the interaction of affective processes with
cognitive ability account for 25% of the variance in accuracy. This
argues strongly that a cold cognitive task such as math problem
solving can only be understood through a theoretical lens that
includes both affective and cognitive sides of the theoretical coin.
Aiken, L. S., & West, S. G. (1991). Multiple Regression: Testing and
interpreting interactions. Newbury Park, CA: Sage.
Alexander, L., & Martray, C. (1989). The development of an abbreviated
version of the Mathematics Anxiety Rating Scale. Measurement and
Evaluation in Counseling and Development, 22, 143–150.
Ashcraft, M. H. (1992). Cognitive arithmetica review of data and theory.
Cognition, 44(1–2):75–106.
Ashcraft, M. H. (2002). Math anxiety: Personal, educational, and cognitive
consequences. Current Directions in Psychological Science, 11, 181–
Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among working
memory, math anxiety, and performance. Journal of Experimental
Psychology-General, 130, 224–237.
Ashcraft, M. H., Kirk, E. P., & Hopko, D. (1998). On the cognitive
consequences of mathematics anxiety. In C. Dolan (Ed.), The develop-
ment of mathematical skills. Hove, UK: Psychology Press.
Beilock, S. L. (2008). Math performance in stressful situations. Current
Directions in Psychological Science, 17, 339–343.
Beckman, R. J., & Cook, R. D. (1983). Outlier...s. Technometrics, 25,
Beilock, S. L., Kulp, C. A., Holt, L. E., & Carr, T. H. (2004). More on the
fragility of performance: Choking under pressure in mathematical prob-
lem solving. Journal of Experimental Psychology-General, 133, 584
Beilock, S. L., & Carr, T. H. (2005). When high-powered people
failWorking memory and “choking under pressure” in math. Psycho-
logical Science, 16, 101–105.
Beilock, S. L., & DeCaro, M. S. (2007). From poor performance to success
under stress: Working memory, strategy selection, and mathematical
problem solving under pressure. Journal of Experimental Psychology-
Learning Memory & Cognition, 33, 983–998.
Bogomolny, A. (1996). Modular arithmetic. Retrieved from http://
Cohen, J. (1983). The Cost of Dichotomization. Applied Psychological
Measurement, 7, 249–253.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple
regression/correlation analysis for the behavioral sciences (3rd ed.):
Mahwah, NJ: Erlbaum Publishers.
Conway, A. R. A., Kane, M. J., Bunting, M. F., Hambrick, D. Z., Wilhelm,
O., & Engle, R. W. (2005). Working memory span tasks: A method-
ological review and user’s guide. Psychonomic Bulletin & Review, 12,
DeCaro, M. S., Wieth, M., & Beilock, S. L. (2007). Methodologies for
examining problem solving success and failure. Methods, 42, 5867.
DeStefano, D., & LeFevre, J. A. (2004). The role of working memory
in mental arithmetic. European Journal of Cognitive Psychology, 16,
Dickerson, S. S., & Kemeny, M. E. (2004). Acute stressors and cortisol
responses: A theoretical integration and synthesis of laboratory research.
Psychological Bulletin, 130, 355–391.
Duncko, R., Johnson, L., Merikangas, K., & Grillon, C. (2009). Working
memory performance after acute exposure to the cold pressor stress in
healthy volunteers. Neurobiology of Learning and Memory, 91, 377–
Elzinga, B. M., & Roelofs, K. (2005). Cortisol-Induced Impairments of
Working Memory Require Acute Sympathetic Activation. Behavioral
Neuroscience, 119, 98–103.
Hembree, R. (1990). The nature, effects, and relief of mathematics anxiety.
Journal for Research in Mathematics Education, 21, 33–46.
LeDoux J. E. (2000). Emotion Circuits in the Brain. Annual Review of
Neuroscience, 23, 155–184.
Lupien, S. J., Gillin, C. J., & Hauger, R. L. (1999). Working memory is
more sensitive than declarative memory to the acute effects of cortico-
steroids: A dose-response study in humans. Behavioral Neuroscience,
113, 420430.
Macdowell, K. A., & Mandler, G. (1989). Constructions of EmotionDis-
crepancy, Arousal, and Mood. Motivation and Emotion, 13, 105–124.
Miyake, A., & Shah, P. (1999). Models of working memory: Mechanisms
of active maintenance and executive control. New York: University
Richardson, F. C., & Suinn, R. M. (1972). The Mathematics Anxiety
Rating Scale: Psychometric data. Journal of Counseling Psychology, 19,
Roozendaal, B., McReynolds, J. R., & McGaugh, J. L. (2004). The baso-
lateral amygdala interacts with the medial prefrontal cortex in regulating
glucocorticoid effects on working memory impairment. Journal of Neu-
roscience, 24, 1385–1392.
Schachter, S., & Singer, J. (1962). Cognitive, Social, and Physiological
Determinants of Emotional State. Psychological Review, 69, 379–399.
Tobias, S. (1985). Test anxiety: Interference, defective skills and cognitive
capacity. Educational Psychologist, 20, 135–142.
Unsworth, N., Redick, T. S., Heitz, R. P., Broadway, J., & Engle, R. W.
(2009). Complex working memory span tasks and higher-order cogni-
tion: A latent variable analysis of the relationship between processing
and storage. Memory, 17, 635–654.
Received May 21, 2010
Revision received December 13, 2010
Accepted January 5, 2011
... Moreover, it is not even clear whether WM plays a role in determining the interaction or the relationship between MA and math performance. While some studies reported that better WM allows individuals to master mathematical performance in spite of high math anxiety [15,19], other studies showed the opposite pattern of results with individuals with higher WM being more prone to math failures caused by anxiety [11,[22][23][24]. Finally, a recent meta-analysis questioned whether WM would play a role at all in mediating the relationship between MA and math performance [25]. ...
... For instance, Ashcraft and Kirk [15] found that MA correlated with working memory only when the task used to measure WM involved arithmetic or math-related stimuli (computation-based working memory) but not verbal stimuli. Similar results were found by other groups that used computation-based WM [23,85,86]. Here, we tested participants' VSWM using a number-free task and did not find a significant relationship between MA and VSWM, as well as between VSWM and math performance. ...
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Many individuals, when faced with mathematical tasks or situations requiring arithmetic skills, experience exaggerated levels of anxiety. Mathematical anxiety (MA), in addition to causing discomfort, can lead to avoidance behaviors and then to underachievement. However, the factors inducing MA and how MA deploys its detrimental effects are still largely debated. There is evidence suggesting that MA affects working memory capacity by further diminishing its limited processing resources. An alternative account postulates that MA originates from a coarse early numerical cognition capacity, the perception of numerosity. In the current study, we measured MA, math abilities, numerosity perception and visuo-spatial working memory (VSWM) in a sample of neurotypical adults. Correlational analyses confirmed previous studies showing that high MA was associated with lower math scores and worse numerosity estimation precision. Conversely, MA turned out to be unrelated to VSWM capacities. Finally, partial correlations revealed that MA fully accounted for the relationship between numerosity estimation precision and math abilities, suggesting a key role for MA as a mediating factor between these two domains.
... Beyond interfering with working memory itself, as discussed above, high pressure might also activate the stress response, a distracting experience in itself that adds to the attentional load required to complete the task and decreases an individual's ability to perform using working memory 8 . Human studies suggest that cortisol levels interact with cognitive traits such as working memory capacity in these tasks, such that high working memory individuals are more likely to fall prey to the choking phenomenon in cognitive tasks with higher increases in cortisol over the course of the task at hand 12,20,21 . Because the stress response has been well-conserved 3 , if animals are also sensitive to pressure, we have reason to expect that cortisol would be related to their responses as well. ...
... Nonetheless, future work should explore tasks that use other methods for involving working memory to ensure that these results generalize. Additionally, future work might explore how working memory capacity is related to 14 performance under pressure, as shown in humans 20 . ...
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Humans often experience striking performance deficits when their outcomes are determined by their own performance, colloquially referred to as “choking under pressure.” Physiological stress responses that have been linked to both choking and thriving are well-conserved in primates, but it is unknown whether other primates experience similar effects of pressure. Understanding whether this occurs and, if so, its physiological correlates, will help clarify the evolution and proximate causes of choking in humans. To address this, we trained capuchin monkeys on a computer game that had clearly denoted high- and low-pressure trials, then tested them on trials with the same signals of high pressure, but no difference in task difficulty. Monkeys significantly varied in whether they performed worse or better on high-pressure testing trials and performance improved as monkeys gained experience with performing under pressure. Baseline levels of cortisol were significantly negatively related to performance on high-pressure trials as compared to low-pressure trials. Taken together, this indicates that less experience with pressure may interact with long-term stress to produce choking behavior in early sessions of a task. Our results suggest that performance deficits (or improvements) under pressure are not solely due to human specific factors but are rooted in evolutionarily conserved biological factors.
... Previous approaches taken from the field of stress research, although limited in number, encompass the use of cortisol secretion (e.g., Refs. [40][41][42][43] and autonomic measures, such as heart rate, blood pressure, as well as SC, that seem to be associated with increased arousal during a mathematical task (e.g., Refs. [44][45][46][47]. ...
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The present study aimed to analyze the different components of state mathematics anxiety that students experienced while solving calculation problems by manipulating their stress levels. A computerized mathematical task was administered to 165 fifth-graders randomly assigned to three different groups: positive, negative, and control conditions, in which positive, negative, or no feedback during the task was given, respectively. Behavioral (task performance), emotional (negative feelings), cognitive (worrisome thoughts and perceived competence), and psychophysiological responses (skin conductance and vagal withdrawal) were analyzed. Behavioral responses did not differ in the positive and negative conditions, while the latter was associated with children's reportedly negative emotional states, worries, and perceived lack of competence. The stress induced in the negative condition led to an increase in skin conductance and cardiac vagal withdrawal in children. Our data suggest the importance of considering students' interpretation of mathematics-related experiences, which might affect their emotional, cognitive, and psychophysiological responses.
... Similar relationship between math anxiety and working memory was also found such that those with average and high working memory performed poorer in math at higher levels of math anxiety but this was only true for those not suspected of dyscalculia. This supported the depleted cognitive capacity theory such that the cognitive capacity for those with higher working memory could be overloaded with worry which in turn affects math performance (Beilock & Carr, 2005;Kellogg et al., 1999;Mattarella-Micke et al., 2011). ...
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Math anxiety negatively relates to math performance. This negative relationship may be exacerbated in low-progress math learners. However, there are limited studies on math anxiety among low progress learners in a paradoxically high performing education system like Singapore. To fill this research gap, this research analysed the anxiety profiles of 151 students who were in the math learning support intervention program administered by the Ministry of Education, Singapore (MOE). We examined the complex relationship centred in math anxiety with relevant variables such as demographic characteristics, working memory and math performance. Limitations and future directions are discussed.
... As with most self-report measures, the accuracy of these questionnaires are subjected to the participants' truthfulness and the accuracy of their own self-perceptions (Dowker et al., 2016). To counteract this problem, some studies utilized physiological measures to determine the individual's level of MA, such as the level of cortisol secretion (Mattarella-Micke et al., 2011). Neurological measures such as EEG recordings (Núñez-Peña & Suárez-Pellicioni, 2015) and functional MRI scans have also been used to measure and map out MA (Pletzer et al., 2015). ...
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Math anxiety is a highly prevalent problem in education that has consistently shown to lead to poor math performance. This study sought to investigate whether certain behaviours are predictive of math anxiety among students. This study involved elementary school students who were low-progressing in math, and is part of an educational intervention program. Ten classifications types of behavioural indicators were identified, such as counting out loud. A multiple linear regression was conducted, identifying three behavioural observations that were positively and significantly associated with their math anxiety. Implications and limitations are discussed.
... Previous studies have examined emotional perception of math-related information using several types of physiological metrics, including: blood pressure (Hunt et al. 2017), salivary cortisol (Mattarella-Micke et al. 2011), skin conductance and heart rate (Qu et al. 2020), and amplitude in frontal and parietal areas using EEG (Núñez-Peña and Suárez-Pellicioni 2015) and MRI (Lyons and Beilock 2012). ...
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Background: Emotional perception of math-related information can affect an individual's attitude and professional choices, especially in the area of science, technology, engineering, and math (STEM) professions. Method: The study compared the processing of math-related words, words with negative emotional valence, and words with neutral valence, using the physiological measure of pupil dilation on a random sample of 30 adults. Pupil responses were examined during a lexical decision task (LDT). We sought to show that exposure to math-related stimuli would cause arousal of the sympathetic system leading to an increase in pupil dilation, similar to that caused by exposure to negative stimuli. Results: pupillary responses were sensitive to words with emotional valence; exposure to math-related words led to increased pupil dilation compared to neutral words; exposure to words with negative valence led to increased pupil dilation compared to neutral words; exposure to math-related words and words with negative valence led to similar pupil dilation. The study concludes math-related textual stimuli lead to increased pupil dilation, similar to negative affective valence textual stimuli. Conclusion: These findings create new possibilities for studying the cognitive and emotional effort required to process math-related information using pupillary response, with implications for researchers, educators, and leaders in the field.
... In high math-anxious students, excessive rumination about failure consumes this system, significantly depletes the mental bandwith necessary for executing mathematical problems at hand, and results in poor performance. Furthermore, research by Mattarella-Micke et al. (2011) suggests that performance in high math-anxious students with high working memory capacity is compromised based on students' interpretation of their physiological arousal when presented with difficult math problems to solve. Their interpretation of the math situation as panic-inducing, causes them to "choke" or "blank out," resulting in higher error rates. ...
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Math anxiety has become an alarming social justice concern, as it results in negative academic consequences, contributes to disinterest and lack of persistence in STEM programs for underrepresented students, and limits their opportunities in STEM careers. According to research, this fear of math occurs long before students begin working on math problems. When high-math anxious students encounter math situations, anticipation anxiety consumes working memory capacity, inhibits learning, and causes them to severely underperform on mathematical tasks. However, very few studies have been conducted to embed psychological interventions in the classroom in an effort to mitigate both anticipation and execution anxiety. Findings from preliminary research suggest that a combined mindfulness and growth mindset intervention, designed to address both anticipation and execution anxiety, was effective in reducing math anxiety in students in a semester-long statistics course. The current research, a replication of the successful pilot study, investigated the generalizability of the mindfulness and growth mindset approach (MAGMA) in decreasing math anxiety in students in various STEM-related courses, and with different instructors. Results indicate that overall, students’ self-perceived math anxiety decreased significantly compared to their control counterparts. Furthermore, considerable anxiety reduction was found for female students. However, no differences were found for final exam scores between the intervention and control group. Nevertheless, the MAGMA intervention appears to be an effective, inexpensive approach in alleviating math anxiety, and increasing mathematical resilience in community college students as they take STEM-related courses.
The consequences of being anxious towards mathematics can be broad and long-lasting. They include the avoidance of mathematics, the limitation in selecting higher education courses and careers and negative feelings of guilt and shame. Several causes for mathematics anxiety have been reported with past educational experiences, and particularly primary school teachers, taking a sizeable amount of blame. As mathematics anxiety has been described as a wide-spread, detrimental emotion in the classroom, it is pertinent for primary school teachers to be confident in mathematics and well-prepared to be effective teachers of the subject. However, high incidences of mathematics anxiety have been repeatedly reported among in-service and pre-service teachers, and negative correlations found between mathematics anxiety and effectiveness when teaching mathematics. In particular, mathematics anxious female teachers have been found to influence girls’ gender-related beliefs about who is good at mathematics, which in turn negatively affects girls’ mathematics achievement. Given that females make up the majority of the primary school teaching profession in the United Arab Emirates, the context for this study, this is of concern. This chapter looks at the history of mathematics anxiety, and how it is defined and measured. The causes and consequences of mathematics anxiety, and the mathematics anxiety of UAE national pre-service teachers are discussed, and the perpetual cycle of anxiety which must be broken if we want more females in mathematics-related professions. Recommendations for breaking the cycle are made in this chapter.
How people perceive and value negative affective states is associated with physiological responses to stressful events and moderates the association between negative feelings and physiological and behavioral outcomes. However, previous studies on valuation of negative affective states have been conducted mostly in Western cultures. Different cultural backgrounds shape how people view negative emotions as well as how people attend to internal emotional states, which may change the effects of valuing negative emotions. The present study thus examined whether valuation of nervousness was associated with the magnitude and duration of cortisol responses to a standardized laboratory stressor and task performance in East Asian and European American students. Two hundred undergraduate students were recruited through a large pool of students taking psychology courses. They engaged in demanding speech and arithmetic tasks as part of the Trier Social Stress Test (TSST). European American participants who had a higher valuation of nervousness showed lower cortisol reactivity. Valuing nervousness was associated with better speech performance in students from both cultural backgrounds, and the strength of this association was moderated by cortisol level. Our findings call attention to the importance of considering whether negative emotions are viewed as beneficial or an impediment, as well as the cultural context when responding to demanding and threatening situations.
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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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Individuals with high math anxiety demonstrated smaller working memory spans, especially when assessed with a computation-based span task. This reduced working memory capacity led to a pronounced increase in reaction time and errors when mental addition was performed concurrently with a memory load task. The effects of the reduction also generalized to a working memory-intensive transformation task. Overall, the results demonstrated that an individual difference variable, math anxiety, affects on-line performance in math-related tasks and that this effect is a transitory disruption of working memory. The authors consider a possible mechanism underlying this effect - disruption of central executive processes - and suggest that individual difference variables like math anxiety deserve greater empirical attention, especially on assessments of working memory capacity and functioning.
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Highly math-anxious individuals are characterized by a strong tendency to avoid math, which ultimately undercuts their math competence and forecloses important career paths. But timed, on-line tests reveal math-anxiety effects on whole-number arithmetic problems (e.g., 46 + 27), whereas achievement tests show no competence differences. Math anxiety disrupts cognitive processing by compromising ongoing activity in working memory. Although the causes of math anxiety are undetermined, some teaching styles are implicated as risk factors. We need research on the origins of math anxiety and on its “signature” in brain activity, to examine both its emotional and its cognitive components.
The primary purpose of this study was to develop an abbreviated version of the Math Anxiety Rating Scale (MARS). The result was an internally consistant and reliable 25-item scale.
In the article by S. Schachter and J. Singer, which appeared in Psychological Review (1962, 69(5), 379-399) the following corrections should be made: The superscript "a" should precede the word "All" in the footnote to Table 2. The superscript "a" should appear next to the column heading "Initiates" in Table 3. The following Tables 6-9 should be substituted for those which appeared in print. (The following abstract of this article originally appeared in record 196306064-001.) It is suggested that emotional states may be considered a function of a state of physiological arousal and of a cognition appropriate to this state of arousal. From this follows these propositions: (a) Given a state of physiological arousal for which an individual has no immediate explanation, he will label this state and describe his feelings in terms of the cognitions available to him. (b) Given a state of physiological arousal for which an individual has a completely appropriate explanation, no evaluative needs will arise and the individual is unlikely to label his feelings in terms of the alternative cognitions available. (c) Given the same cognitive circumstances, the individual will react emotionally or describe his feelings as emotions only to the extent that he experiences a state of physiological arousal. An experiment is described which, together with the results of other studies, supports these propositions. (PsycINFO Database Record (c) 2006 APA, all rights reserved).
Results of 151 studies were integrated by meta-analysis to scrutinize the construct mathematics anxiety. Mathematics anxiety is related to poor performance on mathematics achievement tests. It relates inversely to positive attitudes toward mathematics and is bound directly to avoidance of the subject. Variables that exhibit differential mathematics anxiety levels include ability, school grade level, and undergraduate fields of study, with preservice arithmetic teachers especially prone to mathematics anxiety. Females display higher levels than males. However, mathematics anxiety appears more strongly linked with poor performance and avoidance of mathematics in precollege males than females. A variety of treatments are effective in reducing mathematics anxiety. Improved mathematics performance consistently accompanies valid treatment.
We reviewed the literature on the role of working memory in the solution of arithmetic problems such as 3 + 4 or 345 + 29. The literature was neither comprehensive nor systematic, but a few conclusions are tenable. First, all three components of the working memory system proposed by Baddeley (i.e., central executive, phonological loop, and visual-spatial sketchpad) play a role in mental arithmetic, albeit under different conditions. Second, mental arithmetic requires central executive resources, even for single-digit problems. Third, further progress in understanding the role of working memory in arithmetic requires that researchers systematically manipulate factors such as presentation conditions (e.g., operand duration, format), problem complexity, task requirements (e.g., verification vs production), and response requirements (e.g., spoken vs written); and that they consider individual differences in solution procedures. Fourth, the encoding-complex model (Campbell, 1994) seems more likely to account for the variability observed in arithmetic solutions than other models of numerical processing. Finally, working memory researchers are urged to use mental arithmetic as a primary task because the results of the present review suggest that solution of problems that involve multiple digits are likely to involve an interaction of all the components of the working memory system.