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BRIEF REPORT

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,

1990).

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: beilock@uchicago.edu

Emotion © 2011 American Psychological Association

2011, Vol. 11, No. 4, 1000–1005 1528-3542/11/$12.00 DOI: 10.1037/a0023224

1000

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

performance.

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 51⬅19(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

math-anxiety.

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.

Method

Participants

Participants (N⫽73; 29 male, 44 female) were recruited from

University of Chicago, Roosevelt University, and the local area

1001

MATH-ANXIETY

(age M⫽23.03, SD ⫽5.42, range ⫽18–42). Participants were

also screened for the use of psychiatric medications and adrenal

dysfunction.

1

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.,

2005).

Participants performed within the normative range for the

RSPAN task (M⫽59.59, 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.

2

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; M⫽36.3, SD ⫽16.3).

Math-anxiety did not differ as a function of gender, t(71) ⫽.03,

p⫽.98.

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.

Procedure

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

125

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.

Results

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.

1

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.

2

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.

1002 MATTARELLA-MICKE, MATEO, KOZAK, FOSTER, AND BEILOCK

Modular Arithmetic Accuracy

Overall, participants were fairly accurate on the MA problems

(M⫽90% correct, SD ⫽6%) and completed the problems in

about four seconds on average (M⫽3981 ms, SD ⫽1300). As

expected, the High-Demand problems were performed slower

(M⫽6289 ms, SD ⫽2218) and less accurately (M⫽81%, SD ⫽

12%) than Low-Demand problems (M⫽3232 ms, SD ⫽1126;

M⫽92%, SD ⫽8%), t(65) ⫽17.42, p⬍.0001; t(65) ⫽⫺10.40,

p⬍.0001.

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.

3

The

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*

ii

) 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

math-anxiety.

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).

Discussion

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

3

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.

1003

MATH-ANXIETY

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

Predictor

High Demand Low Demand

Accuracy RT Accuracy RT

(t)(t)(t)(t)

sMARS ⴚ.49 (ⴚ4.04

ⴱⴱ

).11 ( 0.88) ⫺.34 (⫺2.60

ⴱ

).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

ⴱ

)

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 ⴚ.26 (ⴚ2.15

ⴱ

)⫺.20 (⫺1.50) ⫺.21 (⫺1.58) ⫺.20 (⫺1.56)

Adjusted R

2

.19 .07 .05 .07

F3.11

ⴱⴱ

1.54 1.42 2.01

Note. Adjusted R

2

, 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

2

and Fstatistics.

ⴱ

p⬍.05.

ⴱⴱ

p⬍.01.

1004 MATTARELLA-MICKE, MATEO, KOZAK, FOSTER, AND BEILOCK

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.

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Received May 21, 2010

Revision received December 13, 2010

Accepted January 5, 2011 䡲

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