Anxiety and Working Memory Capacity: A Meta-Analysis andNarrative Review

Tim P. MoranGeorgia Institute of Technology and Michigan State University

Cognitive deficits are now widely recognized to be an important component of anxiety. In particular,anxiety is thought to restrict the capacity of working memory by competing with task-relevant processes.The evidence for this claim, however, has been mixed. Although some studies have found restrictedworking memory in anxiety, others have not. Within studies that have found impairments, there is littleagreement regarding the boundary conditions of the anxiety/WMC association. The aim of this reviewis to critically evaluate the evidence for anxiety-related deficits in working memory capacity. First, ameta-analysis of 177 samples (N � 22,061 individuals) demonstrated that self-reported measures ofanxiety are reliably related to poorer performance on measures of working memory capacity (g � �.334,p � 10�29). This finding was consistent across complex span (e.g., OSPAN; g � �.342, k � 30, N �3,196, p � .000001), simple span (e.g., digit span; g � �.318, k � 127, N � 17,547, p � 10�17), anddynamic span tasks (e.g., N-Back; g � �.437, k � 20, N � 1,318, p � .000003). Second, a narrativereview of the literature revealed that anxiety, whether self-reported or experimentally induced, is relatedto poorer performance across a wide variety of tasks. Finally, the review identified a number ofmethodological limitations common in the literature as well as avenues for future research.

Keywords: anxiety, meta-analysis, review, working memory capacity

Supplemental materials: http://dx.doi.org/10.1037/bul0000051.supp

“Anxiety . . . interferes with processing only in the articulatory loop.”—Ikeda et al. (1996)

“Anxiety impairs central executive functioning . . . and does notimpair functioning of the “slave” systems.”

—Eysenck et al. (2005)

“Anxiety selectively disrupts performance of spatial WM.”—Shackman et al. (2006)

“Anxiety was uncorrelated with WM capacity.”—Johnson and Gronlund (2009)

“Anxiety is positively associated with visual working memory.”—Moriya & Sugiura, 2012

Over the last 60 years, there has been a great deal of workdetailing the relationship between anxiety and performance onmeasures of cognitive abilities. For example, measures of anx-iety are reliably associated with increased interference fromdistractors in search tasks (e.g., Bar-Haim et al., 2007; Moser etal., 2012), poorer performance on measures of reading compre-hension (Calvo et al., 1992), and mathematical problem solving(e.g., Ashcraft & Kirk, 2001), as well as lower scores onstandardized tests of intelligence and general aptitude/achieve-ment (Hembree, 1988; see Eysenck et al., 2007 for a review).

Outside of the laboratory, national etiological studies havefound that individuals diagnosed with anxiety disorders are 1.4times more likely to drop out of high school (Kessler, Foster,Saunders & Stang, 1995), that approximately half of anxiouspatients fail to graduate high school because of anxiety (VanAmeringen, Mancini, & Farvolden, 2003), and that subclinicalanxiety negatively impacts academic performance (e.g., Crozier& Hostettler, 2003; Gumora & Arsenio, 2002).

In attempting to explain the widespread relationships betweenanxiety and cognition, researchers have long posited that anxietyrestricts the capacity of working memory (WM; e.g., Lewinski,1945; Moldawsky & Moldawsky, 1952; Rashkis & Welsh, 1946)and have recently begun to formally include WM as a key explan-atory mechanism in models of anxiety (e.g., Eysenck et al., 1992,2007; Ouimet et al., 2009; Robinson et al., 2013; Shackman et al.,2006; also see Posner & Rothbart, 2000). However, as the quotespresented above imply, several inconsistent findings have accu-mulated in the literature. A number of studies have indeed docu-mented impaired WMC in anxiety (e.g., Ashcraft & Kirk, 2001;Darke, 1988; Maloney et al., 2010; Shackman et al., 2006); amongthese studies, however, there is little agreement regarding whetheranxiety is related to WMC in a domain-specific way (e.g., phono-logical storage vs. domain-general attention) and which domainsare most closely related to anxiety (e.g., phonological, spatial,visual; cf. Ashcraft & Kirk, 2001; Markham & Darke, 1991;Moriya & Sugiura, 2012; Shackman et al., 2006). In contrast,several studies have found near-zero associations between anxietyand WMC (e.g., Johnson & Gronlund, 2009; Moritz et al., 2002;Salthouse, 2012) or have found improved WMC in anxiety (e.g.,Moriya & Sugiura, 2012, 2013).

Correspondence concerning this article should be addressed to Tim P.Moran, School of Psychology, Georgia Institute of Technology, 654Cherry Street, Atlanta, GA 30332. E-mail: timothy.moran@psych.gatech.edu

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Psychological Bulletin © 2016 American Psychological Association2016, Vol. 142, No. 5, 000 0033-2909/16/$12.00 http://dx.doi.org/10.1037/bul0000051

1

The aim of this review was to examine the evidence for im-paired working memory capacity in anxiety and provide a quan-titative assessment of this association. To provide some context,this review will begin with brief overviews of the measurement ofWMC as well as theories of anxiety and cognition. This is fol-lowed by a critical review of the literature on anxiety and workingmemory capacity.

Working Memory and Its Measurement

Working memory is generally described as a system that retainsaccess to a limited amount of information in the service of morecomplex cognitive operations. Although there exist many modelsof WM, the model most often identified in anxiety research is theinfluential multiple component model (Baddeley, 1986, 2007).This model partitions the WM system into separable domain-specific stores—that is, the phonological loop and visuospatialsketchpad—as well as a domain-general central executive. Thecentral executive acts as more of an attention system that is utilizedduring controlled processing—particularly in maintaining taskgoals and reducing interference from distraction and prepotentresponses. As will be discussed below, anxiety is most commonlyassociated with attentional control aspects of the central executive(although the multicomponent model has been the most widelyadopted model of WM by anxiety researchers, links betweenanxiety and other models of WM will become apparent throughoutthe review because of the focus on attentional control; e.g., Engle,Kane, & Tuholski, 1999; Engle, Tuholski, Laughlin, & Conway,1999; Engle, 2002).

A large body of research has been dedicated to studying indi-vidual differences in the capacity of working memory (WMC).The earliest attempts to measure WMC in anxiety utilized short-term memory tasks or “simple” measures of span—most often thedigit span—which only require the storage and rehearsal of theto-be-remembered items. For example, a participant might berequired to recall lists of digits in either the same order as pre-sented or in the reverse order. Span is typically quantified as theaverage number of items correctly recalled or as the longest listthat was recalled perfectly. More recently, studies have begunusing measures of “complex” span in which the presentation of thememoranda is interleaved with a demanding secondary task. Forexample, in Daneman and Carpenter’s (1980) reading span taskparticipants were required to recall a list of serially presentedwords. Between each word, participants were required to confirmthe veracity of a sentence (also see Turner & Engle, 1989). Thecrucial distinction between simple and complex span tasks appearsto be that the secondary task removes the memoranda from im-mediate awareness and controlled attention is necessary to main-tain access to it outside of awareness (e.g., Kane et al., 2007;Shipstead et al., 2014; see Unsworth & Engle, 2007a, 2007b for arelated account).

Research on individual differences in WMC implies both apractical and a structural distinction between measures of simpleand complex span. First, whereas complex span tasks reliablypredict abilities such as reading comprehension, emotion regula-tion, and resistance to distraction (among others; Kane et al., 2001;Schmeichel, Volokhov, & Demaree, 2008; Redick & Engle, 2006;Turner & Engle, 1989), relationships between simple span andhigher cognition are much less consistent (Carpenter, Miyake, &

Just, 1995; Daneman & Merikle, 1996). Second, Kane and col-leagues (2004) conducted a large psychometric investigation ofsimple and complex WMC tasks. They found (a) that complexspan tasks loaded on a domain-general “WMC” factor and (b) thatsimple span tasks loaded on domain-specific phonological andvisuospatial STM factors which were correlated with, but distinctfrom, the WMC factor.

Although it is clear that measures of simple and complex spanare psychometrically separable and differ in their ability to predictcomplex cognition, simple span measures have figured promi-nently in research on anxiety due to the fact that they are includedon standard neuropsychological batteries (e.g., the Wechsler Intel-ligence Scale). The current review will therefore include findingsfrom both complex and simple spans. Referring to simple spanmeasures as “working memory” should not be interpreted asendorsing a particular theoretical view; rather, it reflects the cur-rent state of how anxiety researchers view the WM system. Whenthese tasks must be distinguished, they will be referred to sepa-rately as measures of “simple” and “complex” span.

Recently, researchers have begun to favor more dynamic tasksas an index of individual differences in WMC. In such tasks,participants are required to monitor a continuous stream of to-be-remembered items and update the contents of WM in real time byadding new items as potential targets and dropping old items asthey become unnecessary for task performance. For example, inthe N-Back, participants monitor a continuous stream of items(typically letters or numbers) while maintaining the most recent“n” items (where n typically ranges between 0 and 4). The job ofthe participant is to determine whether the current item matchesthe one that was presented n items ago thereby requiring partici-pants to continuously update representations of the most recent nitems.

Because the level of load can be parametrically varied and thatphonological and spatial versions of the N-Back appear to haveequivalent psychometric properties (e.g., Shackman et al., 2006),the N-Back has recently become a popular alternative to simpleand complex WMC tasks among anxiety researchers (e.g., Shack-man et al., 2006; Vytal et al., 2012). However, the relationshipbetween the dynamic span tasks and other measures of WMCappears to be somewhat tenuous. For example, a recent meta-analysis (Redick & Lindsey, 2013) found that the correlationsbetween the N-Back and measures of complex span (r � .2) andsimple span (r � .25) were quite modest suggesting that they areunlikely to be measuring the same underlying WM operations.Similarly, Shipstead and colleagues (2014) conducted an analysisof potential mechanisms underlying individual differences inWMC. This study found that performance on complex span taskswas related to performance on measures of attentional controlwhereas running span tasks—that is, tasks in which a continuousstream of memoranda are presented and participants must reportthe most recent n items when the list is terminated—were muchmore strongly related to storage capacity (a latent variable derivedfrom simple span tasks). Although attentional control and storagecapacity were correlated, these results imply that individual dif-ferences in complex span and running span tasks rely on separablemechanisms.

As with simple span measures, these dynamic span measureswill also be included in the current review as they have been usedby many researchers to draw conclusions regarding WMC and

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2 MORAN

anxiety. When distinctions are drawn between types of tasks, thesewill be separated from simple and complex spans and referred toas “dynamic” spans.

Finally, a number of studies have imported the dual-task meth-odology from experimental studies on working memory (e.g.,Baddeley, 1986, 2007). In these studies, participants simultane-ously perform combinations of tasks that are thought to involve thephonological loop (e.g., articulatory suppression), visuospatialsketchpad (e.g., tapping simple spatial patterns), and central exec-utive (e.g., random generation) under the assumption that, if twotasks rely on the same underlying mechanisms, performance inthose tasks will suffer. In these studies, anxiety-related deficits ina WM process are then inferred from more pronounced dual-taskeffects for anxious individuals.

Theories of Anxiety and WMC

Cognitive theories of anxiety differ with respect to the degree ofdomain specificity of the anxiety/WMC relationship, the hypoth-esized level of analysis (e.g., psychological vs. biological) atwhich anxiety and WMC are related, and the direction of thecausal arrow. However, they tend to share two features. First,“anxiety” is considered to be a heterogeneous construct that can bepsychometrically and physiologically decomposed into at least twodimensions: worry/apprehension and arousal/emotionality (e.g.,Andrews & Borkovec, 1988; Barlow et al., 1996; Borkovec et al.,1983; Gorsuch, 1966; Heller & Nitschke, 1998; Heller et al.,1997a, b; Liebert & Morris, 1967; Nitschke et al., 2001; Sassen-rath, 1964). “Worry” refers to verbal rumination regarding thepotential negative outcomes of future events and is characteristicof generalized anxiety disorder. “Arousal”, on the other hand,refers to physiological hyperarousal and somatic tension (i.e.,dizziness, high heart rate, sweaty palms, hypervigilance etc.) and ischaracteristic of stressful circumstances and panic (e.g., Watson etal., 1995). Although most theories accept this distinction, there aredifferences with respect to the relative importance placed on worryand arousal.

Second, most theories propose that the anxiety/WMC relation-ship can be attributed to interference or competition betweenanxiety-related processes and task-related processes. For example,given that worry involves verbal rumination, it is often thought tointerfere with the maintenance of phonological information (e.g.,Eysenck & Calvo, 1992; Sarason, 1988). Along these lines, theo-ries differ with respect to the domain-specificity of the interferenceas well as at what level the interference occurs.

According to several theories, worry acts as a type of dual-taskthat interferes with cognitive task performance. For example, Sara-son (1988) proposed that anxiety is characterized by task-irrelevant thoughts and “proneness to self-preoccupation.” Theseworrisome thoughts were assumed to reduce the attention thatcould be allocated to task performance. Perhaps the most influen-tial accounts of the relationship between anxiety and performancehave been offered by Eysenck and colleagues’ (Eysenck, 1979,1982; Eysenck & Calvo, 1992; Eysenck et al., 2007) processingefficiency theory (PET) and attentional control theory (ACT).PET/ACT agree with Sarason (1988) insofar as worry is thought toaffect attentional processes; however, ACT further suggests thatanxiety affects specific executive processes such as inhibition. Inaddition to these effects on domain-general executive processes,

PET/ACT proposes that worry also has domain-specific effects.That is, based on research indicating that worry is primarilyrepresented as a phonological code (e.g., Borkovec, 1994; Bork-ovec et al., 1983), PET/ACT proposes that worry also preempts thecapacity of phonological storage.

Shackman and colleagues (2006) proposed a somewhat differentview of the anxiety/WMC association. Previous electroencephalo-graphic research conducted in healthy adults (Heller et al., 1997a,b; Muri et al., 2000) and neuropsychological research conducted inpsychiatric patients (Davidson et al., 2002), lesion patients(Bechara et al., 2000; Hornak et al., 2004), and nonhuman primates(Kalin et al., 1998) implicates regions in the right prefrontal cortexand right posterior-parietal cortex in supporting anxious arousal(see Murphy et al., 2003 for a meta-analysis). That is, arousingstates, as well as the trait tendency to experience physiologicalarousal under stress, are associated with increased activation inright PFC and PPC regions (Keller et al., 2000; Murphy et al.,2003; Nitschke et al., 2000). In addition to anxious arousal, theright mid- and ventrolateral-PFC have been associated with vigi-lance and spatial working memory processes (Lawrence et al.,2003; Manoach et al., 2004; Sturm & Willmes, 2001). Similarly,activity in the right PPC has been associated with spatial attentionand working memory (Bender, 1952; Corballis et al., 2002; Helleret al., 1997a, b; Muri et al., 2000). Based on these findings,Shackman et al. (2006) propose that anxious arousal competeswith other processes localized to right PFC and PPC, such asspatial working memory, for access to limited neural resources.Additionally, given that arousal is difficult to downregulate as itaids in the survival of an organism by preparing the organism todeal with potential threats (e.g., scanning the environment forthreats; Cornwell et al., 2011), it is assumed that this competitionwill generally be resolved in favor of processes supporting anxiousarousal rather than cognitive task performance. Thus, anxiousarousal is assumed to interfere with the maintenance of spatialinformation.

More recent models (Robinson et al., 2013; Vytal et al., 2012,2013) have proposed an interaction between dimensions of anxiety(worry vs. arousal), working memory modality (phonological vs.spatial) and task difficulty. First, they note that both worry andphonological WMC engage regions in the dorsal, medial andprefrontal cortex. Additionally, anxious arousal and spatial work-ing memory both engage medial and ventral prefrontal regions(Clark et al., 2003; Dalton et al., 2005; D’Esposito et al., 1998;Engels et al., 2007; Kalisch et al., 2006; Paulesu et al., 2010; Silket al., 2010). Thus, like Shackman et al. (2006), Vytal and col-leagues propose domain-specific interference from anxiety (e.g.,that arousal interferes with spatial processes specifically and worryinterferes with phonological processes specifically). Second, theynote that worrisome thoughts are more amenable to down-regulation by top-down control than arousal (e.g., Kalisch et al.,2006; Ochsner, & Gross, 2005; Pessoa, Padmala, & Morland,2005). Given these findings, Vytal and colleagues propose thatworry interferes with phonological processes at low levels of taskdifficulty; at high levels of difficulty, however, task-relevant pro-cesses take precedence and high-worry individuals engage in somecompensatory effort or strategic shift to improve performance.Third, anxious arousal is thought to prime defensive mechanisms(e.g., hypervigilance) meant to protect the organism in the face ofpotential threats (Lang, Bradley, & Cuthbert, 1998). Importantly,

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3ANXIETY AND WORKING MEMORY

these arousal-related processes that subserve survival are lessamendable to top-down regulation than worry (Cornwell et al.,2011). Thus, arousal is expected to disrupt spatial WM across alllevels of task difficulty. This account is somewhat unique in thatit predicts domain-general effects to be produced in domain-specific ways. That is, both spatial and phonological storage areexpected to be impacted but by different mechanisms.

Whereas the preceding discussion has tended to focus on thedetrimental effects of anxiety on cognition, models emerging fromthe clinical literature have tended to hold the opposite assump-tion—namely, that cognitive deficits and biases are causal factorsin the development of anxiety (e.g., Beck, 1995; Beck & Clark,1997; Mathews & MacLeod, 2005; Ouimet et al., 2009; also seePosner & Rothbart, 2000). For example, it is widely believed thatthe negative attention bias characteristic of anxiety—that is, thetendency to focus attention on negatively valenced stimuli—is asignificant causal risk factor for anxious pathology (Broadbent &Broadbent, 1988; Beck, 1995; Beck & Clark, 1997; Mathews &MacLeod, 2005; McNally et al., 1994; Ouimet et al., 2009; van denHout et al., 1995). To test this, MacLeod et al. (2002) developedversions of a spatial cueing paradigm in which neutral and threat-ening words preceded a target. For one group, the target alwaysappeared following the threatening word; for the other group, thetarget always followed the neutral word. Following the spatialcueing task, the researchers induced stress by requiring partici-pants to attempt to solve unsolvable anagrams while being re-corded. Participants in the attend-threat group reported greaterincreases in negative mood in response to the stress induction.

Similarly, it is believed that negative interpretation biases (i.e.,the tendency to interpret ambiguous events as threatening), diffi-culty inhibiting intrusive thoughts, and difficulty regulating one’semotions contribute to the development of anxious pathology (e.g.,Bar-Haim et al., 2007; Campbell-Sills & Barlow, 2007; Ehlers &Clark, 2000; Mathews & MacLeod, 2005). Importantly, many ofthese processes are known to rely on WMC. For example, lowerworking memory has been shown to predict greater distractorinterference (Lavie, 2005; Lavie et al., 2004), reduced ability toregulate one’s emotions (Schmeichel, 2007; Schmeichel et al.,2008), and more frequent intrusive thoughts (Bomyea et al., 2012;Brewin & Smart, 2005). This has led some researchers to postulatethat restricted WMC itself may be a risk factor for later anxiety(e.g., Ouimet et al., 2009; also see Posner & Rothbart, 2000). Atpresent, no theoretical account makes clear predictions regardingdomain-general or domain-specific WM systems or which dimen-sion(s) of anxiety should be most sensitive to individual differ-ences in WMC.

Overall, there appears to be a great deal of agreement thatanxiety should be associated with impaired performance on work-ing memory tasks. Additionally, many theoretical approaches pro-pose that this relationship results from some type of competitionfor access to limited resources. However, there appears to beconsiderable disagreement regarding which dimension(s) of anxi-ety should be associated with which working memory domains(e.g., phonological vs. spatial) as well as the direction of the causalarrow. Additionally, the empirical findings have proven to besomewhat equivocal insofar as many studies have failed to findeffects and, of the studies that have found effects, there is littleagreement as to the circumstances in which task performancerelates to anxiety.

The review of the literature on anxiety and working memorywill be split into two broad sections. The first section will focus oncorrelational studies in which scores on measures of workingmemory were compared between high- and low-anxious individ-uals, as defined by scores on self-report measures of anxiety anddiagnostic assessments. The second section will focus on experi-mental studies in which either anxiety or working memory wasexperimentally manipulated. Sections will be further divided basedon methodology.

Review of the Literature on Anxiety and WorkingMemory Capacity

Correlational Studies

Studies of bivariate associations. The majority of studieshave involved comparing WM task performance between high-and low-anxious individuals or presenting bivariate correlationsbetween measures of anxiety and WMC. Given the large numberof studies that were identified (�100), these studies were synthe-sized meta-analytically rather than via a narrative review. Themeta-analytic methods are described in detail in the SupplementalMaterials.

The focal effect size for this analysis was Hedges’ g (Hedges,1981). Hedges’ g is computed from the more commonly usedCohen’s d and can be interpreted in the same way. Effect sizeswere coded such that a negative g value indicates that anxiousindividuals show reduced WMC whereas a positive value indicatesthat anxious individuals show greater capacities. A total of 177samples, consisting of 22,061 individuals, met criteria for inclu-sion. Effect sizes were pooled within the framework of a random-effects model.

In addition to examining the overall relationship between mea-sures of anxiety and WMC, several potential moderators were alsoexamined in order to evaluate the generality of the anxiety/WMCrelationship. Table 1 lists the moderator variables and describeshow they were defined.

Meta-analytic results. Across all 177 samples, measures ofanxiety were significantly associated with lower scores on measuresof WMC (g � �.334, k � 177, N � 22,061, 95% CI: �.392; �.276,p � 10�29). Analyses of the primary moderators of interest are shownin Table 2. Most population-related moderators (i.e., age, trait vs.state, worry vs. arousal) were not significant and all subcategorieswithin those moderators produced significant associations withWMC. Symptom severity did significantly moderate the relationshipbetween anxiety symptoms and WMC. Although effect sizes weresignificant in both clinical and subclinical samples, effect sizes forclinical populations were significantly larger.

To examine dimensions of anxiety, studies were grouped basedon whether they included measures of worry or arousal (note thatfew studies examined arousal directly; most of these studies ex-amined syndromes characterized by arousal symptoms such aspanic disorder). Interestingly, both worry and arousal predictedperformance on both phonological and spatial tasks. This findingwould seem to contradict the more domain-specific predictions ofexisting theories. However, it should be borne in mind that, al-though worry and arousal tend to form separate dimensions, theyare not uncorrelated (e.g., Nitschke et al., 2001). Thus, even whenone dimension of anxiety is examined directly, the measure may be

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4 MORAN

contaminated by another dimension (e.g., worry may influencescores on an arousal measure).

With respect to procedural moderators, the results were some-what more complex. The type of span used (i.e., simple, complexor dynamic) was not a significant moderator; effect sizes weresignificant for all subcategories. However, span domain (i.e., vi-sual vs. phonological vs. spatial) was a significant moderator;specifically, anxiety was negatively associated with scores onmeasures of phonological and spatial working memory but non-significantly positively associated with measures of visual workingmemory.

A more detailed examination of studies of visual WMC furthercomplicates these findings. The majority of the studies of visualWMC were conducted in socially anxious students attending EastAsian universities thus hinting at possible cultural differences,differences between types of anxiety, and/or a culture � anxietyinteraction. A series of additional analyses were conducted toattempt to test these possibilities. First, within studies of visualWMC, studies conducted in East Asia showed a significant posi-tive association (g � .704, k � 5, N � 201, p � .001), whereasstudies conducted within the United States and Europe showed asignificant negative association (g � �.317, k � 6, N � 1,029,p � .001; Q(1) � 29.05, p � .001). However, culture and type ofanxiety were nearly completely confounded in this analysis as the

American/European studies generally included state/trait anxietywhereas 4 of 5 Asian studies were conducted with socially anxiouscollege students. Within Asian studies of visual WMC, studiesinvolving socially phobic individuals showed significant positiveeffect sizes (g � .850, k � 4, N � 164, p � .001), whereas theeffect size for the study of trait anxiety was considerably smallerand nonsignificant (g � .190, N � 37, p � .56). Although theseresults are based on a very small number of studies and should beinterpreted cautiously, it suggests that this cultural influence maynot hold across the board; rather, it appears that it may be specificto social anxiety.

The possibilities of a main effect of type of anxiety and a maineffect of culture were tested. First, to test the possibility that socialanxiety, regardless of culture, is associated with greater workingmemory capacity, the effect sizes for Asian social phobics (g �.850, k � 4, N � 164, p � .001) was compared against that forEuropean/American social phobics (g � �.269, k � 7, N � 341,p � .01). Contrary to this notion, these effect sizes were signifi-cantly different (Q(1) � 30.47, p � .001). Second, the possibilitythat anxiety predicts greater WMC across all modalities in Asianpopulations was tested. However, the results did not support thispossibility. Anxiety predicted poorer performance for both spatial(g � �.502, k � 1, N � 65, p � .19) and phonological(g � �.523, k � 4, N � 258, p � .01) material and these were

Table 1Coding Definitions for Moderators in the Meta-Analysis

Moderator and subcategories Coding definition

AgeAdults The study included participants age 18 and overChildren The study included participants under 18

Anxiety dimensionArousal The study included an explicit measure of anxious arousal (e.g. the Mood

and Anxiety Symptom Questionnaire) or included clinically diagnoseddisorders characterized by arousal symptoms (e.g. panic, phobia etc.)

Worry The study included an explicit measure of worry (e.g. the Penn State WorryQuestionnaire) or included clinically diagnosed disorders characterized byworry symptoms (e.g. generalized anxiety disorder)

Other The study did not include an explicit measure of arousal or worryTrait vs. State

Trait The Study included an explicit measure of trait anxiety (e.g. the STAI-Trait)State The Study included an explicit measure of state anxiety (e.g. the STAI-State)

SeverityClinical The study included clinically diagnosed anxiety disorders using the DSM or

ICD classification systemsNonclinical The study included self-reported anxiety using a questionnaire

WM Span typeComplex The study used a WM task that combined storage with an interleaved

processing task (e.g. OSPAN)Simple The study included a WM task that included temporary storage only (e.g.

Digit Span)Dynamic The study included a WM task that required continually updating WM

representations (e.g. N-Back)WM Span domain

Spatial The study included a WM task that requires storing spatial locationsPhonological The study included a WM task that requires storing phonologically-

rehearsable material (e.g. letters, digits)Visual The study included a WM task that requires storing non-spatial visual

features (e.g. color)Manuscript type

Peer-reviewed The study was published in a peer-reviewed journalThesis/dissertation The study was conducted as part of a thesis or dissertation but was not

published in a peer-reviewed journal.

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5ANXIETY AND WORKING MEMORY

significantly different than the effect sizes reported for visualmaterial, Q(2) � 20.12, p � .001. Thus, these results point to aspecific culture � anxiety type � WM domain interaction (seeSupplemental Materials for a reanalysis with outlying studiesremoved).

Finally, to determine whether published studies systematicallyproduced larger effect sizes than unpublished work, studies pub-lished in peer-reviewed journals were compared to otherwise un-published theses and dissertations. As shown in Table 2, peer-reviewed studies and theses/dissertations produced very similarresults.

The results of this meta-analysis provide evidence for a robustassociation between anxiety symptoms and performance on mea-sures of WMC. Although it appears to hold under a variety of typesof tasks, anxiety subtypes as well as levels of symptom severity,this effect appears to be of moderate magnitude. Interestingly, theresults of this analysis suggest that both worry and arousal symp-toms predict impaired performance on tasks using both phonolog-ical and spatial material. This may point to more domain-generaldeficits than some theorists have suggested, although more careful

analyses are required to partition the effects of anxiety domain andWMC domain.

The most unexpected result of this meta-analysis was the find-ing that anxiety predicted greater capacity for nonspatial visualfeatures in East Asian populations. These results hint at a culture �type of anxiety � WM domain interaction. This will be returned toin the discussion.

Dual-task studies. Studies of anxiety and dual-tasking haveproceeded under the assumption that if a given task relies on acomponent of WM, then performance on that task will suffer whenthe participant is required to simultaneously perform an additionaltask which also relies on that WM component. Importantly, ifanxiety is associated with restricted working memory capacity, theadditional load should impact anxious individuals’ performance toa greater degree than nonanxious individuals.

Early studies of dual-tasking (Macleod & Donnellan, 1993;Derakshan & Eysenck, 1998) tended to find preserved WMC inanxious participants. In these studies, participants completed agrammatical reasoning task while under conditions of varyingload. For example, in Macleod and Donnellan (1993), participants

Table 2Meta-Analytic Results for High-Anxiety Versus Low-Anxiety Comparisons

Moderator g (95% CI) k N p Qa (df) pa

Age 1.82 (1) .18Adults �.351 (�.414; �.287) 152 19,989 �.001 — —Children �.236 (�.390; �.083) 25 2,072 .003 — —

Anxiety dimension .89 (1) .35Arousal �.306 (�.436; �.175) 52 2,711 �.001 — —Worry �.446 (�.707; �.185) 11 1,105 .001 — —

Trait/State .46 (1) .50Trait �.292 (�.424; �.161) 26 10,967 �.001 — —State �.210 (�.408; �.013) 11 1,590 .03 — —

Severity 10.13 (1) .001Clinical �.424 (�.505; �344) 100 5,720 �.001 — —Nonclinical �.235 (�.319; �.151) 76 15,819 �.001 — —

WM Span type 1.47 (2) .48Complex �.342 (�.479; �.204) 30 3,196 �.001 — —Simple �.318 (�.387; �.248) 127 17,547 �.001 — —Dynamic �.437 (�.620; �.255) 20 1,318 �.001 — —

WM Span domain 10.32 (2) .006Spatial �.410 (�.519; �.300) 54 4,310 �.001 — —Phonological �.336 (�.411; �.261) 110 16,350 �.001 — —Visual .008 (�.222; .238) 11 1,230 .95 — —

WM Span type � Severity: Clinical 2.73 (2) .26Complex �.561 (�.857; �.265) 8 503 �.001 — —Simple �.393 (�.491; �.296) 81 4,531 �.001 — —Dynamic �.591 (�.856; �.327) 11 686 �.001 — —

WM Span type � Severity: Nonclinical .34 (2) .84Complex �.266 (�.418; �.113) 22 2,693 .001 — —Simple �.215 (�.321; �.109) 45 12,494 �.001 — —Dynamic �.265 (�.525; �.005) 9 632 .04 — —

Anxiety dimension � Domain: Arousal 8.99 (2) .01Spatial �.388 (�.644; �.132) 15 793 .003 — —Phonological �.376 (�.549; �.204) 31 1,650 �.001 — —Visual .271 (�.130; .672) 6 268 .19 — —

Anxiety dimension � Domain: Worry 2.44 (1) .12Spatial �.676 (�1.024;�.327) 3 258 �.001 — —Phonological �.339 (�.579; �.099) 7 647 .006 — —

Manuscript type .02 (1) .90Peer-reviewed �.335 (�.397; �.273) 155 18,808 �.001 — —Thesis/dissertation �.324 (�.479; �.168) 22 3,253 �.001 — —

a Q statistic and p value for comparison between subcategories of a moderator.

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6 MORAN

were presented with a letter pair (e.g., YX) and a sentence describ-ing the pair (e.g., Y is preceded by X) and participants wererequired to judge the veracity of the sentence. This was interleavedwith a secondary task in which participants maintained either sixzeroes (i.e., low load) or six random digits (i.e., high load). In thesestudies, the high load condition reduced accuracy, but this effectwas not modulated by anxiety. Richards et al. (2000), on the otherhand, found that test-anxious individuals performed less accuratelyon the reasoning task. However, this finding was independent ofload suggesting it did not result from restricted WMC.

Interestingly, all of those studies paired a fairly demanding task(e.g., grammatical reasoning) with a relatively undemanding task(e.g., rehearsing digits) that relied on phonological storage. Thistype of secondary task probably does not require the involvementof attentional processes (e.g., Baddeley, 2007; Cowan, 2005).Indeed, Eysenck, Payne and Derakshan (2005) examined this issueby varying task difficulty as well as task modality (i.e., phonolog-ical vs. visuospatial). In this study, participants completed theCorsi Blocks task (Corsi, 1972) which requires participants toreproduce a spatial sequence that has been recently produced bythe experimenter. Given that Corsi Block performance is impairedwhen it is presented concurrently with a random number genera-tion task (Vecchi & Richardson, 2001), Eysenck et al. (2005)assumed that performing this task would require executive in-volvement. The Corsi Block task was paired with secondary tasksthat varied in difficulty and involved either phonological or spatialprocesses. When the secondary task was articulatory suppression(easy/phonological) which, like the string of digits used by Ma-cLeod and Donnellan (1993), presumably relied on phonologicalstorage, high- and low- trait-anxious individuals performed equallywell. Similarly, high- and low- trait-anxious participants showedequivalent performance during both spatial-task conditions. In thedifficult/phonological condition, however, high-anxious individu-als committed more errors on both tasks than did low-anxiousindividuals.

Another set of studies have taken an approach to dual-taskingthat might be considered more ecologically valid. In all of thesestudies, participants completed a version of a random generationtask—a task in which participants produce random sequences ofitems (e.g., digits). This task is thought to be highly attentionallydemanding and reliant on frontal lobe-mediated executive pro-cesses (Baddeley et al., 1998; Baddeley et al., 1984; Milner, 1982)given that it requires avoiding stereotyped and repetitious re-sponses. Importantly, the secondary task in each of these studieswas worry itself. That is, participants were instructed to completethe random generation task while either worrying about a self-relevant topic or under some control condition. The advantage ofthis method is that it maps onto some of the predictions of theory(e.g., Sarason, 1988) far more directly than other dual-task meth-odologies. This methodology also allows for a more direct test ofthe causal role of worry than dual-task studies in which worry isnot explicitly manipulated.

In Hayes et al. (2008), participants completed a random gener-ation task while either worrying about an important negative topicor thinking about a positive topic. Trait-worriers in the worrycondition produced less random responses (Towse & Neil, 1998)than both trait worriers in the positive-thought condition andnonworriers. Stefanopoulou et al. (2014) replicated these findingsin a sample of patients suffering from generalized anxiety disorder,

a condition primarily characterized by persistent and uncontrolla-ble worry (APA, 2013). Interestingly, these findings appear to bespecific to worry in a phonological form as negative visual imag-ery does not seem to have these effects (Leigh & Hirsch, 2011).Although, given that worry appears to be inherently phonological(Borkovec, 1994; Borkovec et al., 1983), one could argue thatparticipants in this study were simply less capable of engaging in“visual worry.”

Rapee (1993), like Eysenck et al. (2005), took a more compre-hensive approach by evaluating the generality of these findings tomore domain-specific stores. In this study, participants completedtwo worry sessions wherein they were asked to worry about apersonally relevant topic. In the single task condition, worryingwas accomplished in isolation. In the dual-task condition, partic-ipants worried while simultaneously completing either easy (artic-ulatory suppression/finger tapping) or difficult (spoken randomgeneration/random key pressing) phonological and spatial tasks.Participants reported fewer thoughts judged to be “worry-related”in the spoken random number generation condition than any othercondition.

Studies of dual-tasking seem to suggest that anxiety is mostclosely related to deficits in domain-general attentional processes.That is, when studies pair a primary task with a secondary loadingtask that primarily relies on phonological storage, anxious individ-uals are equally as accurate as nonanxious individuals (althoughthey are slowed; Derakshan & Eysenck, 1998; Macleod & Don-nellan, 1993). However, when two attentionally demanding tasksare paired (i.e., Eysenck et al., 2005), anxious individuals begin toshow impaired performance. Note that, in Eysenck et al. (2005),the largest performance decrements were observed when the CorsiBlocks were paired with counting backward—that is, when avisuospatial task was paired with a phonological task. Thus, thisfinding cannot be attributed to interference produced by superfi-cially similar tasks or “overloading” a storage buffer. Additionally,a number of studies (Hayes et al., 2008; Leigh & Hirsch, 2011;Stefanopoulou et al., 2014) have found that worry interferes withperformance in a random generation task. Given the relative ho-mogeneity of methods used in random generation tasks, effectsizes were pooled to yield a moderate, but significant, decrease inrandomness while engaging in worry (g � �.377, k � 3, N � 127,p � .001).

Finally, dual-tasking studies have provided some evidence ofdomain-specificity as well. For example, Leigh and Hirsch (2011)found that phonological worry, but not negative visual imagery,affected random generation performance. Similarly, Rapee (1993)found that only spoken random generation affected worry, whichinvolved speaking aloud, whereas random key pressing did not.Given that the studies showing the strongest evidence for domain-specificity examined worry specifically, it is possible that worryhas both domain-general and domain-specific effects on the WMsystem. However, this has not been shown unambiguously. Forexample, the meta-analysis found that anxiety in general, andworry in particular, predicted (a) both simple and complex spansand (b) memory for both phonological and spatial material. Giventhat tasks measuring domain-general processes as well as domain-specific stores are highly correlated (e.g., Kane et al., 2004), it ispossible that worry also predicts the variance that is shared be-tween these types of tasks. Alternately, these differences may beattributable to the tasks themselves. As noted earlier, given that

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7ANXIETY AND WORKING MEMORY

worry tends to take a phonological form (Borkovec, 1994; Bork-ovec et al., 1983), participants may have found it difficult tocomply with the task demands when asked to engage in negativevisual imagery.

Prospective studies. The obvious limitation of these cross-sectional studies is that one cannot draw clear causal inferences.Although most studies have assumed that interference caused byanxiety is the causal agent, this need not be the case. Deficits inWMC could represent a risk factor for later anxiety (e.g., Ouimet,Gawronski, & Dozois, 2009). A small number of studies haveattempted to address this limitation by employing longitudinaldesigns under the assumption that if one variable is the causalfactor, it should predict the other variable at a later date.

Along these lines, Macklin et al. (1998) examined generalintelligence and the development of subsequent posttraumaticstress disorder. In this study, Wechsler intelligence scale scoreswere recorded from U.S. army recruits before and after tours ofduty in Vietnam. Individuals who developed posttraumatic stressdisorder performed worse on the intelligence measures both priorto and following deployment (ds � �.84 and �.64, respectively).Interestingly, symptom severity did not predict postdeploymentintelligence and differences in postdeployment intelligence werenonsignificant when premorbid test scores were controlled sug-gesting that developing PTSD did not produce any further declinesin intelligence scores. The findings of this study make it one of thefew to provide evidence that individual differences in cognitiveabilities can act as a risk factor for later emotional disturbances. Itshould be noted, however, that premorbid intelligence also pre-dicted the amount and severity of combat exposure. This leavesopen the possibility that trauma severity can account for thisrelationship. Additionally, although measures of intelligence andmeasures of working memory are highly related, they are notisomorphic (Ackerman et al., 2005).

To the author’s knowledge, there are only two studies that haveexamined WMC, specifically, and anxiety prospectively. First,Bredemeier and Berenbaum (2013) used a phonological/spatialN-Back composite to predict self-reported worry at a later timepoint. In this study, Time 1 N-Back significantly predicted Time 2worry scores; interestingly, this effect persisted even when Time 1worry was controlled suggesting that N-Back scores weren’t sim-ply tapping into some underlying emotional vulnerability. Visu-Petra and colleagues (2014), on the other hand, attempted topredict later WM scores using earlier anxiety scores. This studyfound that trait anxiety predicted both concurrent digit span as wellas digit span at a nine month follow-up (but not visuospatial tasks)in young children.

The work by Macklin et al. (1998) and Bredemeier and Beren-baum (2013) has provided some of the most direct evidence thatcognitive abilities can act as a risk factor for later anxiety thatcurrently exists in the literature. However, the inferences that canbe drawn from these studies are limited by the fact that neitherpresented fully crossed lagged correlations. For example, measuresof worry and WM are often highly reliable over short periods oftime (Hockey & Geffen, 2004; Meyer et al., 1990; Redick et al.,2012; Stöber, 1998; however, see Jaeggi et al., 2010); althoughBredemeier and Berenbaum (2013) showed that Time 1 N-Backpredicted Time 2 worry, it is quite plausible that Time 1 worrywould have predicted Time 2 N-Back had it been measured.Indeed, Visu-Petra et al. (2014) found that Time 1 anxiety pre-

dicted Time 2 digit span. A stronger test of the causal relationshipwould involve cross-lagged correlations. In such designs, anxietyand working memory would be measured at two disparate timepoints. If anxiety is the causal factor, anxiety at Time 1 would beexpected to predict working memory at Time 2 but workingmemory at Time 1 should be significantly less predictive ofanxiety at Time 2 (or vice versa if WMC is the causal factor;Campbell & Stanley, 1963; Kenny, 1975). Unfortunately, it doesnot appear as if any such studies have been conducted.

WMC as a mediator. A handful of studies have gone beyonddemonstrating bivariate associations between anxiety and mea-sures of WM and have begun to examine the mediational role ofWM in linking anxiety to real-world outcomes such as academicperformance and treatment response. Although many of thesestudies were included in the meta-analysis, their findings are worthreviewing here.

Academic performance. As noted earlier, anxious individualsexperience academic difficulties such as lower GPAs, scores onstandardized tests, and graduation rates. Recently, research hasbegun to link these difficulties to anxiety-related impairments incognitive abilities. For example, in Owens et al. (2008), 11-year-old children completed measures of trait anxiety, backward digitand spatial spans, and the National Curriculum Standard Assess-ment Test—a standardized academic achievement test in the U.K.Trait anxiety predicted poorer performance on both the simplespans and the standardized test. Importantly, the association be-tween anxiety and academic achievement was fully accounted forby individual differences in simple span scores. Similar findingswere reported by Vukovic et al. (2013) who examined mathemat-ical problem solving in math anxious children. A follow-up studyby Owens and colleagues (2014) has provided the most thoroughexploration of the interrelationships between anxiety, WMC andacademic performance. They conducted path models includinganxiety/depression, worry, the composite score computed frombackward digit and spatial span tasks, and academic performance.The indirect path leading from anxiety/depression to academicperformance through worry and working memory was significantbut the direct path was not (also see Ganley & Vasilyeva, 2014).In other words, the tendency to worry mediated the associationbetween trait anxiety and impaired WMC and this impaired WMCmediated the association between worry and academic difficulties.

Interestingly, each of the child studies in this section found thatanxiety predicts performance on backward simple span measuresand those that examined forward span measures found near-zerocorrelations (often � .1). Although work conducted with adultshas typically found that backward simple spans load with forwardsimple span tasks and not with complex span tasks (e.g., OSPAN;Engle, Tuholski, et al., 1999), this may not be the case for children.Some work suggests that the requirements of backward span tasksare sufficiently difficult to challenge the limits of attention inchildren (Alloway et al., 2004). It is also interesting to note thatOwens et al. (2014) found that the composite computed fromphonological and spatial measures of simple span correlated withworry and mediated the association with academic performance—suggesting fairly domain-general deficits. These results, then, areconsistent with the idea that anxiety is most closely coupled withattentionally demanding tasks and that this deficit can account foranxiety’s association with academic deficits.

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8 MORAN

Treatment response. In addition to mediating the link be-tween anxiety and academic performance, some work suggests thatdomain-general abilities may be an important determinant of one’sresponse to treatment. For example, Mohlman (2005) proposedthat the successful completion of CBT may be reliant on executivefunctions due to the fact that patients are often required to increasethe extent to which they regulate their negative emotions and wellas increase their use of logic and reasoning to combat negativemoods and beliefs.

To test this, Mohlman and Gorman (2005) examined the effec-tiveness of CBT for GAD in patients with high and low executivefunctioning (EF; defined by a composite score of several EF tasks).This study produced several results worth noting: first, althoughhigh- and low-EF individuals completed the same amount ofCBT-related homework, the high-EF individuals’ produced signif-icantly higher quality homework. Second, high-EF individualsshowed significantly greater gains on self-report measures of anx-iety. Third, the rate of GAD was not significantly different be-tween high- and low-EF individuals at the posttreatment assess-ment or at the 12-month follow-up. However, this latter findingmay be the result of low statistical power (N � 13 and 12 at theposttreatment assessment and 12-month follow-up, respectively);the difference in rates of GAD was quite large both at posttreat-ment (37% vs. 80%; d � 1.05) and at the follow-up (29% vs. 80%;d � 1.27). Similarly, D’Alcante and colleagues (2012) examinedthe effectiveness of both CBT and fluoxetine, a common SSRI, fortreating OCD. This study found that pretreatment intelligencepredicted greater reductions in the Y-BOCS for patients receivingCBT but not for patients receiving fluoxetine.

Overall, these studies, though there are few of them, suggest thatdomain-general abilities play a role in the successful completion ofCBT. Although the underlying mechanism is currently unclear—perhaps high-EF individuals already possess many of the skillstaught by CBT, or they are more compliant with the treatmentprotocols (e.g., Mohlman & Gorman, 2005)—it appears that EF isan important determinant of treatment response. It should be noted,however, that neither study examined WMC directly. Mohlmanand Gorman (2005) used a composite of several executive functiontasks rather than measures of WM span. Executive functions referto a heterogeneous set of abilities which generally include controlfunctions (e.g., inhibition), updating, maintenance of relevant in-formation, planning, and reasoning as well as others. Thus, find-ings regarding EFs may be highly dependent on the particularabilities that were measured. This is not to say that these findingswould not extend to measures of WMC. Indeed, McCabe andcolleagues (2010) found that EF tasks and complex spans sharedconsiderable variance—which they considered to reflect domain-general attention. However, these findings should be replicatedwith measures of WMC.

Experimental Studies

This section of the review focuses on studies that have experi-mentally manipulated either anxiety or working memory capacity.As with the correlational studies, this section is divided intosubsections based on methodology.

Ego-threat. One of the earliest lines of research investigatingthe effects of anxiety on working memory has been characterizedas “ego-threat.” In studies of ego-threat, the cognitive tests them-

selves are used as a threat to participants’ self-image and self-esteem. Participants are typically informed that the task is animportant measure of intelligence or that they are doing poorly ona task at which most participants excel.

Studies by Moldawsky and Moldawsky (1952) and Hodges andSpielberger (1969) were among the first to explore working mem-ory under ego-threat conditions. Undergraduates were first as-sessed with the digit span. Following this baseline assessment,participants were assigned to either an ego-threat condition or acontrol condition and readministered the digit span. Althoughperformance for the control participants did not change betweenassessments, participants in the ego-threat condition recalled fewercorrect digit series during the second assessment. Similar findingswere reported by Hodges and Durham (1972) who recorded digitspans at baseline and then again after participants were informedthat they appeared to be experiencing much more difficulty thanother participants (however, see Steyaert & Snyder, 1985).Walker, Sannito, & Firetto (1970) and Firetto and Davey (1971)attempted to confirm that the ego-threat effect was due to anincrease in anxiety. As these studies were conducted before thedevelopment of statistical mediation techniques, they rated partic-ipants on the extent to which they “appeared anxious” and dem-onstrated that the ego-threat manipulation reduced digit spans onlyfor those participants who appeared anxious.

Coy et al. (2011) took a slightly more detailed approach byexamining the effect of ego-threat on both phonological and visu-ospatial storage. Participants in the ego-threat condition performedworse on phonological measures but not on the spatial measures.Interestingly, path analytic results indicated that self-evaluativenegative thoughts (similar to worry) was a mediator between threatcondition and digit span performance which is consistent withnotions of domain-specific interference.

Another set of studies of have discussed ego-threat in terms ofthreats to one’s social image. Perhaps the most well-known ma-nipulation is the Trier Social Stress Task in which participantscomplete a videotaped speech and a mental arithmetic task in frontof an evaluative audience (Kirschbaum et al., 1993). Using thismethod, Schoofs and colleagues (2008) found that participants inthe ego-threat condition were less accurate on a phonological 2-and 3-Back tasks relative to controls. Oei and colleagues (2006)used a similar anxiety induction but required participants to com-plete the Sternberg memory task (Sternberg, 1966). In this task,participants must memorize a set of 1 to 4 target items. Then, aftera short delay, a new set of items are presented and participantsmust determine if one of the target items is an element in this newdisplay. This study found that performance was impaired in thestress condition but only for high levels of load (i.e., 4 items).Although the number of studies is quite small, these results suggestthat the anxiety elicited by the Trier Social Stress Task can impairperformance in WM measures that are superficially quite dissim-ilar (i.e., N-Back and Sternberg); however, it is interesting thatthese studies differed with respect to the condition � load inter-action. Whereas Schoofs and colleagues (2008) found group ef-fects for both 2- and 3-back trials, Oei found that the group effectwas specific to high load trials. One could posit that maintainingand continually updating even 2 items is more demanding that thansimply storing 1 to 3 items. This difference could, thus, reflectdifferences in task demands.

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9ANXIETY AND WORKING MEMORY

Overall, the ego-threat manipulation appears to reliably affectWM scores—simple span scores, in these studies. To get a senseof the extent to which the ego-threat manipulation affects WMperformance, effect sizes were pooled across known studies. Thisresulted in a significant aggregate effect (g � �.618, k � 9, N �493, p � .001) such that participants under ego-threat conditionstended to perform worse on simple span measures.

Affective video clips. The manipulations described in thissection involve presenting participants with film clips that aremeant induce an anxious state (e.g., clips from Scream, Halloweenetc.) or are meant to be affectively neutral. Following the induc-tion, participants in all studies completed a variant of the N-Backtask.

Results from this line of research have been quite mixed. Somestudies have found no effect on accuracy or RTs (e.g., Fales et al.,2008; Qin et al., 2009; however, if one examines Qin et al.’s data,there appears to be a notable, albeit nonsignificant, dip in accuracyfor the anxiety condition). As with ego-threat studies, it is possiblethat changes in N-Back performance were only apparent in partic-ipants for whom the manipulation was effective. For example, Qinet al. (2009) measured self-report and physiological measures ofanxiety and found that these measures predicted the degree towhich the manipulation affected N-Back RTs (r2’s ranged between.29 and .42); correlations with accuracy were not reported.

On the other hand, some work has found that anxiety-inducingfilm clips impair phonological 3-Back performance (Gray et al.,2002) and may improve spatial 3-Back performance (Gray, 2001).One should note, however, that Gray (2001, Gray, Braver, &Raichle, 2002) also included pleasant film clips. The analyses thatwere presented make it difficult to determine whether the anxietycondition differed from the neutral baseline or only from thepleasant conditions.

In evaluating these studies, it would be useful to pool effectsizes to determine whether any overall effect is present. However,in general, there was not enough information available to do so.Possible explanations for these mixed results will be returned totoward the end of the section on experimental studies.

Threat-of-shock studies. The logic underlying threat-of-shock studies is quite straightforward. In these studies, participantscomplete a measure of working memory in one of two conditions.In the threat condition, a randomly timed electrical shock is de-livered via an electrode affixed to the participant’s arm. In the safecondition, participants are informed that no shock will be deliv-ered.

The first to use this methodology were Pyke and Agnew (1963),who recorded digit spans under threat and safe conditions. Thethreat condition reduced spans relative to the safe condition. How-ever, this effect was notably larger in participants who completedthe threat condition first (relative to those who completed the safecondition first) possibly suggesting that anxiety disrupted taskperformance when the task was more novel and less so when it hadbeen learned.

Shackman and colleagues (2006) used threat-of-shock to exam-ine the relationship between anxiety and performance in phono-logical and spatial versions of the 3-Back. Although phonological3-Back accuracy was unaffected by the threat-of-shock manipula-tion, there was a decrease in accuracy for the spatial 3-Back.Lavric et al. (2003) used the same methodology with one excep-tion—shocks were threatened but never actually delivered. The

findings of this study were consistent with those of Shackman et al.(2006). This ensures that the results of Shackman et al. (2006)cannot be attributed to some distraction caused by the delivery ofthe shock.

In a series of similar studies, Vytal and colleagues (Vytal et al.,2012, 2013; see also Robinson et al., 2013) have complicated theview presented by Shackman and Lavric. These studies also ex-amined the effect of threat-of-shock on the N-Back; however, theyemployed 1-, 2-, and 3-Back designs. Vytal et al. (2012), using aphonological N-Back, replicated the findings of Shackman et al.(2006; i.e., that threat-of-shock did not affect 3-back performance).In the 1- and 2-Back conditions, however, accuracy was reducedunder threat.

Using both a phonological and spatial N-Back, Vytal et al.(2013) have provided the most comprehensive examination of theeffect of shock on N-Back performance. Participants completed 1-,2-, and 3-back conditions for both a phonological and spatialN-Back. Results were consistent with previous studies. For thephonological N-Back, the threat condition reduced accuracy in the1- and 2-back but not the 3-back. For the spatial N-Back, the threatcondition impaired performance at all levels of load. Thus, this lineof work suggests a modality � load interaction. That is, anxiety, asinduced by threat-of-shock, restricts spatial storage at all levels ofload but only restricts phonological storage at lower levels of load.This finding, however, has not been completely consistent. Kalischet al. (2006) examined phonological 2-Back performance using athreat-of-shock design as well. Unlike Vytal et al. (2012, 2013),2-Back performance was unaffected (also see Hansen et al., 2009).As the methodology of this study was quite similar to the otherstudies in this section and there are no clear flaws in the design, itis unclear why this study failed to find an effect. One could positthat low power (N � 14) resulted in the null finding but this is notclear. More work will need to be conducted in order to determineif there are boundary conditions on when the modality � loadinteraction is expected—this issue will be returned to later.

Overall, the threat-of-shock appears to reliably reduce accuracy(g � �.681, k � 3, N � 115, p � .001) for spatial 3-Back tasks.However, the pooled effect sizes for phonological 3-Back perfor-mance (g � .010, k � 4, N � 141, p � .92) and phonological2-Back performance (g � �.200, k � 4, N � 125, p � .42) werenonsignificant. One-Backs and spatial 2-Backs were not pooled asthey have been less frequently studied; however, the extant evi-dence suggests that accuracy is reliably reduced by threat-of-shockin these tasks (e.g., Vytal et al., 2012).

Treatment studies. Although not explicitly intended to testthe causal relationship between anxiety and WMC, another class ofstudies may nonetheless provide additional insights. These studiesfocused on the effectiveness of anxiety treatments while alsorecording measures from neuropsychological batteries, such asworking memory. The logic of these designs is straightforward; ifanxiety causally influences working memory capacity, then thesuccessful alleviation of anxiety symptoms would be expected toincrease scores on measures of working memory.

Mataix-Cols and colleagues (2002) compared phonological andspatial simple spans between medicated and unmedicatedobsessive–compulsive patients. However, no reliable differencesemerged on any tests. Similarly, Butters et al. (2011) examined theeffects of a pharmacological treatment for geriatric generalizedanxiety disorder. In this study, digit span scores did not differ at

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10 MORAN

baseline and showed no improvement over the course of treatment.In a third study (Nielen & Den Boer, 2003) obsessive–compulsivepatients were treated with fluoxetine. Participants completed avisual and spatial version of the change detection task (Luck &Vogel, 1997) before and after treatment. In the change detectiontask, participants view a memory array consisting of several (typ-ically 2–12) discrete shapes. After a brief delay (�1 sec), partic-ipants view a test array and must determine if it is identical to thememory array. In visual version of the task, participants saw apattern of colored items and had to determine whether the memoryarray and the test array included the same colors. In the spatialtask, participants had to determine whether the spatial locations ofthe items on the screen matched the previous presentation. In thevisual task, patients were equally accurate as controls at baselineand did not improve over the course of treatment. With respect tothe spatial task, obsessive–compulsive patients performed worseat baseline, relative to controls, and performed worse followingtreatment, relative to pretreatment. However, controls who did notreceive treatment also performed worse at the posttest suggestingthat this is unlikely to be attributable to the treatment.

At an initial glance, the results of these studies fail to provideany support for a causal role for anxiety. Of the three studiesreviewed above, none found evidence of increased WMC follow-ing treatment. However, the interpretation of these results is com-plicated by several factors. First, it should be noted that Mataix-Cols et al. (2002) did not randomly assign patients to treated anduntreated groups; patients were grouped based on preexistingtreatment regimens. It is thus difficult to rule out any number ofpreexisting group differences which may have masked an effect oftreatment. Second, the efficacy of the treatment regimens appearsto be somewhat dubious. In Butters et al. (2011) the authors failedto provide evidence that the treatment successfully reduced anxietysymptoms. In Nielen and Den Boer (2003), the authors did presentgroup-level evidence that the fluoxetine treatment reduced symp-toms relative to baseline; however, only 63% of patients respondedto the treatment. Thus, it is possible that either the treatmentregimens were ineffective at reducing anxiety symptoms (i.e.,Butters et al., 2011) or that gains made by responsive patients weremasked patients who did not respond to treatment (Nielen & DenBoer, 2003).

Work by van der Wee and colleagues (2007) has helped clear upissues surrounding treatment response. In this study, obsessive–compulsive patients were treated with paroxetine and venlafaxine, aselective serotonin reuptake inhibitor and serotonin-norepinephrinereuptake inhibitor, respectively. After treatment, participants weregrouped based on whether they showed evidence of treatment re-sponse. Results revealed that responders committed fewer errors ona spatial 3-Back posttreatment, relative to pretreatment. For nonre-sponders, performance did not significantly improve.

Overall, studies of treatment response have produced mixedresults. The combined effect size for the four pharmaceuticalstudies suggests modest gains from treatment but is statisticallyunreliable (g � .280, k � 4, N � 292, p � .27). The study by vander Wee and colleagues (2007) has lent some credence to thenotion that anxiety causally influences WMC by demonstratingthat N-Back scores increased in treatment-responders but not non-responders. It should be noted, however, that the division ofparticipants into “responders” and “nonresponders” was somewhatquestionable. The criteria by which this distinction was made was

not provided; additionally, nonresponders showed significant im-provement on one of the three measures of obsessive–compulsivesymptoms. In conjunction with the small sample size (7 respondersand 7 nonresponders), this would seem to warrant some cautionwhen interpreting these findings. It is also worth noting that thesestudies represent a very limited sample of possible anxiety treat-ments. That is, it is possible that cognitive/behaviorally basedinterventions would be more effective than pharmacological ones;however, it does not seem that this has been tested.

Recently, studies have taken a different approach to the treat-ment methodology by examining whether adaptive working mem-ory training (e.g., Cogmed, 2006) improves scores on measures ofanxiety. In adaptive training programs, the set size of the to-be-remembered material gradual increases as the participants achievea certain level of accuracy. This ensures that the training programconstantly challenges the boundaries of the participant’s WMC.The results of these studies have been quite mixed. In Åkerlund etal. (2013), patients with acquired brain injuries completed anadaptive training regimen as well as a measure of anxiety devel-oped for hospital use (Zigmond & Snaith, 1983). Although anxietyscores improved for the control group, there were no significanteffects for the trained group at the 6- or 18-week follow-upsessions. Roughan and Hadwin (2011) examined adaptive trainingin young adolescents. This study provided somewhat more mixedresults. Although there was some evidence of improvement onmeasures of test and trait anxiety at the 3-week follow-up, thesegains were not maintained at the 3-month follow-up. Althoughthere was not enough information to pool effect sizes, these find-ings suggest that adaptive WMC training does not causally affectanxiety. However, as with the pharmacological studies, WMCtraining may be more effective for “responders”—that is, individ-uals who are more engaged by the task. Indeed, Sari and col-leagues (in press) examined adaptive WMC training in individualshigh in self-reported trait anxiety. This study found that training-related gains in WMC predicted reduced anxiety at the posttestsession—that is, individuals who benefited from the WMC train-ing showed reduced anxiety following training.

It should be noted, however, that the efficacy of adaptive WMCtraining programs is currently disputed (e.g., Shipstead et al., 2010;Redick et al., 2013; see Au et al. [2015] and Melby-Lervåg &Hulme [2013] for conflicting meta-analyses). For example, Åker-lund et al. (2013) found that the trained group did not significantlydiffer from controls at the 6 or 18 week follow-up on any measureof working memory (forward/backward digit and spatial span andWAIS-III WM scale). For Roughan and Hadwin (2011), trainedparticipants did show increased WMC scores (as measured by acomposite of digit and spatial spans) relative to controls at the3-week and 3-month follow-up sessions. However, this study alsoincluded a measures of response inhibition and general intelli-gence. Although no effects were found for inhibition, generalintelligence showed increases relative to controls at the 3-weekfollow-up but not the 3-month follow-up. These findings suggestthat, although performance improved on superficially similar tasksfollowing training, far-transfer effects were not achieved (e.g.,Shipstead et al., 2010). Finally, Sari et al. (in press) found thatWMC training transferred to the flanker task but transfer to theantisaccade task was less clear despite the fact that both tasksostensibly measured attentional control/inhibition. Overall then,the evidence suggests that WMC training may reduce anxiety for

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11ANXIETY AND WORKING MEMORY

individuals who strongly engage in the task; however, given someof these limitations as well as the broader debate regarding theefficacy of WMC training, these results warrant future replicationattempts.

Other experimental approaches. Finally, this section willdescribe manipulations that have only appeared in a small numberof studies. The first of these is the set of studies that used the ColdStressor task. This involves immersing the participant’s hand inpainfully cold water or in water at a comfortable temperature.Following the stressor, participants then complete a version of theSternberg task. The results of these studies have been quite mixed.Porcelli et al. (2008; Exp. 1) found a nearly significant condition �load interaction such that the manipulation reduced accuracy forlow load trials only. Their second study, however, did not replicatethis effect. Duncko et al. (2009), on the other hand, found that thestress condition impaired Sternberg performance only at moderatelevels of load although it should be noted that the overall condi-tion � load interaction was nonsignificant. It is not quite clear whythe findings of these studies diverge (also see Oei et al., 2006,described above, which used the same WM task but found effectsonly at high levels of load). It does not appear that task difficultycan explain these findings. The high load used by Porcelli et al.(2008) included 6 items whereas Duncko and Oei only used 4. Onepossibility is that all of these studies differed with respect to theiroutcome measures (e.g., overall accuracy vs. hit rate vs. falsealarm rate). It may be that anxiety differentially affects processesthat contribute to these measures (see below); however this iscurrently unclear. When these results are considered in conjunctionwith threat-of-shock studies, there appears to be an important,albeit inconsistent, role played by the level of cognitive load usedin a study. This issue will be returned to later.

Sorg and Whitney (1992) took a somewhat unique approach byrequiring high- and low- trait-anxious participants to play videogames for 10 min before completing simple and complex measuresof working memory. For the simple span task, no effects weresignificant. For the more demanding complex span task, trait-anxious individuals in the stress condition showed impaired spanscores relative to anxious controls.

Finally, the study by Robinson and colleagues (2008) involvedparticipants engaging in a simulated underwater helicopter evacua-tion. In their first study, OSPAN scores were recorded two daysbefore the stress manipulation as well as immediately before it.Participants in the stress group scored lower on the OSPAN, relativeto a control group, immediately before the simulated evacuation.However, they also scored lower at the baseline assessment. Therewas no within-subjects effect of time. The baseline difference makesthese findings difficult to interpret but the nonsignificant effect of timemakes it unlikely that OSPAN scores were affected by the stressor. Intheir second study, the stress group again performed more poorly—this time immediately following the stressor; however no baselineassessment was presented.

Summary of experimental findings. Overall, experimentalstudies of anxiety and WMC have painted a very mixed picture.Some inductions, such as threat-of-shock and ego-threat seem toreliably affect WM performance. Other manipulations, such asaffective film clips, have failed to provide consistent results. Thereseem to be two main factors responsible for these inconsistentfindings: (a) many studies seemed to suffer from problems ofinternal validity insofar as it is unclear whether the manipulation

had the desired effect, and (b) even when anxious affect waselicited, it may not have lasted throughout the task.

For example, fatigue seems to be a viable alternative explanationfor the findings of Sorg and Whitney (1992). That is, the stressmanipulation (i.e., video games) consisted of relatively a demandingtask. Participants in the experimental condition later performed worseon the demanding complex span task but not on the simple span task.Similarly, although the stressor used by Robinson et al. (2008) waslikely successful at eliciting lasting affect, the sample consisted ofstudents at a nautical academy for whom this stressor was a requiredtraining exercise. Thus, this manipulation may have been interpretedas “challenge” rather than as a stressor by this sample.

Perhaps less intuitively, ego-threat studies also suffer frominternal validation issues. While all of these studies utilized sometype of ego-threat manipulation, there were subtle differencesbetween studies that made it difficult to determine what wasinduced by the manipulation and whether the various manipula-tions were equivalent. For example, Hodges and Durham (1972)“directly” threatened participants’ self-image by informing themthat they appeared to be experiencing more difficulty than average.Coy et al. (2011), on the other hand, informed participants that themeasures used in that study were highly indicative of intelligencethereby rendering the threat to the participant’s self-image contin-gent on potential later failures (i.e., completing an intelligence testonly threatens one’s ego if they perform poorly)—perhaps bettercharacterized as the “threat-of-ego-threat.” In addition to this vari-ation in how ego-threat is operationalized, these manipulationsalso conflate threats to the self-image with threats to their social-image. That is, in most of these manipulations, participants knewthat the experimenter was aware of their performance. These issuesare not necessarily problematic assuming that all of these manip-ulations induce some manipulation-independent self-evaluative ortest anxiety. However, this is not clear. Finally, some researchers(e.g., Chalus, 1976) have used the “indirect” form of ego-threat asa control condition under the assumption that the mere potentialfor failure would not have the same effect as informing partici-pants that they had actually failed. Thus, the extent to which thistask induces anxiety, which processes other than anxiety are im-pacted, and the conditions under which this manipulation is effec-tive have not been well-documented.

This would be much less problematic if these studies had (a)provided evidence that manipulation had the intended effect (e.g.,an increase on a measure of state anxiety) and (b) shown that theincrease in anxiety mediated the group effect. In general, however,studies did not provide evidence that the manipulation successfullyinduced anxiety and no study ego-threat demonstrated mediation.Some studies (e.g., Walker et al., 1970; Firetto & Davey, 1971)rated participants’ anxiety; however, a fairly serious challenge tothis manipulation-check was posed by Walker et al. (1970). As inthe other studies, participants who were rated as anxious recalledfewer digit strings than those who did not appear anxious. Whatseparates this study is that they collected scale scores from theWechsler Intelligence test as well and found that participants whoappeared anxious performed worse on almost all subscales. Thisfinding allows for the possibility that the ego-threat manipulationincreased anxiety in participants with lower baseline intelligence.If this is the case, then this manipulation-check actually demon-strates that digit span scores vary as a function of individualdifferences in general intelligence.

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12 MORAN

The second problem—that is, that the induced affect may havebeen short-lived—affects all of the studies in which the affect-induction ended prior to the WM task (e.g., Affective Film Clips,Trier Social Stress Task, Cold Stressor, etc.). Given that induced-affect can be very short-lived (Davidson, Ekman, et al., 1990;Garrett & Maddock, 2001; Gross, 1998) and can be down-regulated by demanding tasks (such as a WM task; Erber & Erber,2000), it is possible that the anxious affect did not persist throughthe WM task. Many studies did record self-reported and physio-logical measures of anxiety; however, these were often recordeddirectly before or after the induction rather than during the WMtask (see Shackman et al., 2006 for a good discussion of this issue).

Among the experimental studies discussed in this review, thethreat-of-shock studies have been the most thorough in terms ofestablishing the internal validity of their manipulation. In all cases,anxiety was manipulated within-subjects in a counterbalanceddesign and the authors demonstrated the presence of anxiety dur-ing task performance. For Lavric et al. (2003), participants woreheart rate monitors during N-Back performance. Both heart rateand self-reports indicated greater anxiety in the threat condition;additionally, these measures predicted N-Back performance in thethreat condition. Shackman et al. (2006) took a different approachand presented acoustic startle probes (i.e., loud bursts of whitenoise), which is widely considered to be a valid measure of arousaland defensive motivation (e.g., Lang et al., 1998), at randomintervals throughout the N-Back. Not only were startle reflexeslarger during the threat condition but the change in startle reflexbetween the threat and control condition predicted the change inaccuracy between threat and safety. Vytal and colleagues (2012,2013) found the same results using startle and self-report.

However, it is worth noting that anxiety indicators only ac-counted for up to about 44% of the variance in the change inN-Back performance in these studies. Given the fact that most ofthe variance in the change in N-Back scores appears to be unac-counted for and that even effective manipulations are likely tohave a few unintended effects (Shadish et al., 2001), it would beworth demonstrating that indices of anxiety fully mediate thedifference in N-Back scores as a function of condition. Indeed,Shackman and colleagues (2006) found that changes in corrugatoractivity mediated the group effect on spatial N-Back scores andthat changes in startle scores nearly did as well. Based on thesefindings, it seems safe to conclude that threat-of-shock can caus-ally affect performance on the N-Back under certain circumstancesand that this effect can be attributed to increases in anxiety.

General Discussion

The aim of the present review was to determine whether anxietyis related to reduced working memory capacity. Following both ameta-analysis and a narrative review, the main conclusion of thepresent review appears to be a qualified yes. The meta-analysisrevealed a moderate, yet robust, association (g � �.334) betweenmeasures of anxiety and measures of working memory capacity.Additionally, the studies reviewed above suggest both that anxietycan causally influence performance on WMC tasks and that themost pronounced effects of anxiety are on measures tappingdomain-general attentional processes rather than on domain-specific stores. The “yes” is qualified as the review also identifieda number of methodological limitations that have made drawing

firm conclusions difficult. The following sections review the pri-mary findings of this review as well as discuss methodologicallimitations, their implications, and ways to move forward.

Correlational Studies

The findings that (a) anxiety is related to poorer performance onmeasures of complex span and (b) dual-task studies suggest thatanxiety—and worry in particular—is most strongly associatedwith performance on attentionally demanding tasks are readilyinterpretable within attentional accounts of working memory andanxiety. According to the attentional account of WMC, individualdifferences in WMC—particularly complex span task perfor-mance—are driven by the ability to select relevant information,resist distraction and maintain access to relevant information out-side of immediate awareness (e.g., Engle, 2002).

This dovetails nicely with attentional/inhibition accounts ofanxiety which propose that worry is attentionally demanding andacts as a dual-task during cognitive test performance (e.g., Eysencket al., 1992, 2007; Sarason, 1988). Specifically, Eysenck andcolleagues (see Derakshan & Eysenck, 2009; Eysenck et al., 2007;Eysenck & Derakshan, 2011 for reviews) have proposed thatanxiety interferes with the ability to inhibit irrelevant stimuli andresponses. Indeed, a large body of research indicates that anxietyis associated with deficits in attentional control even in the absenceof a storage component. For example, Moser and colleagues(Moser et al., 2012; Moran & Moser, 2015) have conducted aseries of studies in which participants viewed circular searcharrays consisting of 10 circles and diamonds. On half of trials, allshapes were presented in the same color (either red or green); onthe other half of trials, a distractor item was presented in theopposing color (e.g., one green item among 9 red items). Traitanxiety was found to predict the degree of slowing produced by thediscrepant item. Using the same search task (i.e., Moser et al.,2012; Moran & Moser, 2015), Esterman et al. (2013) replicatedthis finding in a sample of veterans suffering from PTSD. Giventhe relatively low informational load of these tasks, these resultsimply aberrant attentional control—in particular, inhibiting inter-ference from the distractor—in anxiety rather than deficits intemporary storage alone (see Berggren & Derakshan, 2013 for areview of anxiety-related attentional impairments).

Recent work has extended this line of thinking to WMC. In thisview, anxiety impairs the ability to inhibit irrelevant informationfrom gaining access to WM and, as a result, impairs the ability tomaintain relevant information. Much of this research has beenconducted using a variant of Vogel and colleagues’ (Luck &Vogel, 1997; Vogel, Woodman, & Luck, 2001) change detectiontask that includes three conditions: two relevant items that need tobe stored, four relevant items that need to be stored, or tworelevant items along with two irrelevant distractors. Vogel, Mc-Collough, and Machizawa (2005) were the first to examine “fil-tering efficiency” (the ability to filter or inhibit irrelevant infor-mation from gaining access to WM) using this method. This studyfound that, for high-span individuals, neural activity on distractortrials was identical to neural activity when two relevant items werepresented without distractors. For low-span individuals, neuralactivity on distractor trials was identical to neural activity whenfour relevant items were presented without distractors. In otherwords, high-span individuals were able to successfully filter out

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13ANXIETY AND WORKING MEMORY

the irrelevant distractors whereas low-span individuals stored ir-relevant items along with relevant items. Thus, the logic of filter-ing efficiency studies is as follows: the extent to which irrelevantdistractors items affects performance—both behavioral and phys-iological measures of filtering have been studied—can be taken asan indicator of the extent to which individuals fail to filter thosedistractors from WM.

To the author’s knowledge, Stout and Rokke (2010) were thefirst to examine the association between filtering efficiency andanxiety. This study found that self-report measures of state anxi-ety, rumination, and depression predicted poorer filtering effi-ciency in the change detection task as measured by behavioralperformance. Interestingly, further analyses suggested that thiswas only true for low-span individuals. For high-span individuals,these measures were unrelated to filtering efficiency. Moriya andSugiura (2012) extended this work by finding that trait and socialanxiety also predicted poorer filtering efficiency. However, it isunclear whether anxiety interacted with WMC in this study. Morerecently, Qi and colleagues (2014) examined neural measures offiltering efficiency as a function of trait anxiety. This study re-ported poorer filtering efficiency for high trait anxious individualsand that WMC predicted filtering efficiency only for low traitanxious individuals (also see Stout, Shackman, Johnson, & Larson[2015] and Stout, Shackman, & Larson [2013] who examinedfiltering efficiency using threatening stimuli).

Effect sizes were pooled across the five studies examining theassociation between anxiety and filtering efficiency. Overall, anx-iety predicted poorer gating of irrelevant information from WM(g � �.700, k � 5, N � 229, p � .001). This finding implies thatanxiety is robustly associated with poorer inhibition of irrelevantinformation and may be a mechanism by which anxiety restrictsavailable WM. Thus, broadly speaking, the findings of this reviewcan be considered consistent with an inhibition account of anxiety.That is, anxiety appears to interfere with inhibitory control overattention. This impaired inhibition, in turn, leaves anxious individ-uals less capable of preventing irrelevant information from gainingaccess to WM and reducing the WMC available for task perfor-mance.

Interestingly, the current meta-analysis did not support the moredomain-specific predictions of any theory. Anxiety, broadly de-fined, predicted WM performance for both phonological and spa-tial material. Additionally, the meta-analysis found that the rela-tionship between worry and WMC was statistically equivalentbetween phonological and spatial material and, if anything, wasgreater for spatial material.

How should these findings be accounted for? First, it is possiblevisual forms of worry play a larger role in disrupting cognitionthan believed by theorists. This would imply that worry is associ-ated with separable deficits in phonological and spatial storagedepending on which type of worry the individual in engaging in.However, this seems unlikely given the relatively large literaturesuggesting that worry is primarily phonological in nature (e.g.,Borkovec, 1994; Borkovec et al., 1983; Rapee, 1993). A morelikely possibility is that anxiety is associated with the varianceshared between the phonological and spatial tasks. Latent variableanalyses suggest that complex span tasks involving phonologicaland spatial material share substantial variance and load on a single,domain-general factor which has been interpreted as executiveattention (e.g., Engle, Tuholski, et al., 1999; Kane et al., 2004).

The results of this meta-analysis allow for the possibility thatanxiety predicts this domain-general variance. In other words,anxiety would be associated with other processes to the extent thatthey require controlled attention.

Even within the context of this controlled attention/inhibitionview of anxiety and WMC, one might wonder why the associationbetween worry and WMC was much larger in the spatial domain(�.676 vs. �.339). There would seem to be two possibilities. First,it should be noted that the difference between these effect sizeswas not significant (p � .12); it is possible that this differencewould not persist in a larger sample of studies. However, giventhat the magnitude of the effect size for spatial WMC is nearlytwice that of the effect size for phonological tasks, it is alsopossible that this nonsignificant finding is simply due to low power(only 10 studies contributed to this comparison). This leads to thesecond possibility—that phonological storage is more dissociablefrom executive processes than is visuospatial storage. For exam-ple, Kane et al. (2004) found that residual variance from visuospa-tial simple span tasks predicted general fluid intelligence whereasresidual variance from phonological simple span tasks did not(also see Baddeley, 2000, 2007; Engle, Kane, et al., 1999; Engle,Tuholski, et al., 1999; Logie, 1995 for similar findings). Baddeley(2000, 2007) suggested that this is due to the fact that phonologicalmemoranda (e.g., letters, words, digits etc.) tend to be highlyoverlearned and can be reproduced by the rehearsal process. Visu-ospatial material, on the other hand, is more likely to requirecontinued attention in order to be maintained. This issue will needto await further research as no studies have examined associationsbetween anxiety, phonological WMC and spatial WMC at the levelof latent variables.

However, the inhibition interpretation should be considered inthe light of two caveats. First, there is a larger disagreement in theliterature regarding whether executive attention or inhibition is themore fundamental ability on which the other relies (cf. Engle,2002; Engle, Kane, et al., 1999; Engle, Tuholski, et al., 1999;Hasher & Zacks, 1988; Lavie et al., 2004; Zacks & Hasher, 1994).For example, Hasher and Zacks (e.g., Hasher & Zacks, 1988;Zacks & Hasher, 1994) proposed that individual differences inWMC can be attributed to individual differences in the ability toinhibit the no-longer-relevant contents of WMC. In this view, then,inhibition is the more fundamental ability. Engle and colleagues,on the other hand, have proposed that the ability to inhibit inter-ference is reliant on available WMC (e.g., Engle, 2002; Engle,Kane, et al., 1999; Engle, Tuholski, et al., 1999; see Lavie et al.,2004 for a related account). This view is based on findings indi-cating that (a) distracter interference is greater both for low-spanindividuals (Kane & Engle, 2003) and that WM load manipula-tions are capable of influencing distractor interference and (b)high- and low-span individuals appear to be equally able to resistinterference when placed under conditions of WM load (Kane &Engle, 2000; Rosen & Engle, 1997). This latter finding indicatesthat high-span individuals’ ability to resist distraction is reliant onavailable WMC. It would, therefore, also be consistent with theexisting literature to suggest that restricted WMC in anxiety resultsin impaired inhibition.

Second, some of the findings of the current review hint at moregeneral deficits than inhibition. While the dual-tasking studiestended to find that anxiety interfered with demanding tasks (e.g.,random generation) but not tasks that relied more on domain-

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14 MORAN

specific stores (e.g., digit span), the meta-analysis found thatanxiety is associated with poorer performance on simple spantasks.1 Given that (a) simple span tasks tend primarily reflectdomain-specific storage (e.g., Kane et al., 2004) and that (b) thevariance shared between simple and complex spans seems toreflect storage whereas the variance that is unique to complexspans appears to reflect executive processes (e.g., Engle, Kane, etal., 1999; Engle, Tuholski, et al., 1999), the fact that anxietypredicts simple span performance could suggest that anxiety is alsoassociated with impaired cognition in the absence of an inhibitorycomponent.

Indeed, this would be consistent with a growing literature thatsuggests anxiety is related to very broad deficits in fluid cognition.For example, Hembree (1988) meta-analyzed over 500 studies onanxiety and cognition and found that anxiety predicted poorerperformance on measures of general intelligence. Similarly, Salt-house (2012) examined inductive reasoning, spatial visualization,verbal episodic memory, perceptual speed and vocabulary at thelevel of latent variables. Trait anxiety was related to the varianceshared between all five abilities and, once that shared variance wasaccounted for, did not relate to the individual abilities. That is, inthese studies, anxiety predicted highly general abilities rather thanmore specific functions/abilities.

Thus, the existing literature also allows for another interpreta-tion—namely, that anxiety is not related to specific executivefunctions, such as inhibition, but, instead, is related to general fluidcognition at a highly domain-general level. Figure 1 presents onepotential model describing the relationship between anxiety andfluid cognition. In this model, a “cognitive hierarchy” is adaptedfrom Salthouse and colleagues (2008, 2012; note that this ispresented for demonstrative purposes and is not meant to be anexhaustive list of all relevant abilities). At the lower end of thehierarchy are more specific abilities such as memory, vocabularyand so forth (see Calvo et al. [1992] for evidence that anxietypredicts poorer performance on measures of vocabulary). Thesespecific abilities are defined by individual cognitive tasks whichare not depicted in Figure 1. At the top of the hierarchy is “fluidcognition” which is defined as the variance that is shared betweenthe more specific abilities. In this model, anxiety is assumed tohave its effects at the top of the hierarchy (solid black arrow). Oncethe shared variance is accounted for, anxiety is not expected topredict the individual abilities (broken gray arrows).

If this is the case, one could reasonably wonder why anxietypredicts certain abilities, such as inhibition, far more reliably thanother abilities (e.g., Derakshan & Eysenck, 2009; Eysenck &Derakshan, 2011; Eysenck et al., 2007 for reviews). In accountingfor this, at least two facts must be borne in mind. First, large scalestudies of anxiety and cognition have tended to reveal modesteffect sizes. For example, the current meta-analysis found anaggregate effect of g � �.334 for the relationship between anxietyand measures of WMC. Hembree (1988) reported aggregate cor-relations ranging between r � �.10 and r � �.31 in a meta-analysis on the relationship between test anxiety and intelligenceand academic performance. Similarly, the association between traitanxiety and general fluid cognition in Salthouse (2012) was� � �.11 (N � 1,000). Given these modest effects, it may not besurprising that many studies fail to find associations betweenanxiety and cognitive performance—especially when consideringthe relatively small samples employed in many of these studies.

Indeed, the median power to detect an effect of g � �.334 forstudies in the current meta-analysis was 0.23.

Second, there is variability in terms of the extent to whichindividual tasks rely on specific abilities. For example, Miyake andcolleagues (e.g., Miyake et al., 2000; Friedman et al., 2008) haveconducted a number of latent variable analyses of tasks thought tomeasure executive functions (e.g., inhibition, shifting and updat-ing). These studies reported factor loadings between .33 and .63(Miyake et al., 2000) and .43 and .72 (Friedman et al., 2008). Thatis, certain tasks were much better representations of those func-tions (i.e., they were more reliant on those functions) than othertasks. With this in mind, it is possible that some failures to findassociations between anxiety and certain executive functions areattributable to the fact that some tasks are poor representations oftheir purported function. A similar argument can be applied at thelevel of latent variables. Work by Miyake and colleagues (Fried-man et al., 2008, 2011; Miyake & Friedman, 2012) suggests thatinhibition, shifting, and updating are not equally related to “com-mon executive functioning” (i.e., the variance shared betweeninhibition, shifting, and updating). Specifically, whereas loadingsfor shifting and updating tend to be between .6 and .7, the loadingfor inhibition tends to be 1. That is, inhibition, at least as it wasmeasured in these samples, is indistinguishable from the higherorder, more domain-general ability (however, also see Friedman etal., 2006; Shipstead et al., 2014). Thus, anxiety may be morereliably related to inhibition than other executive functions be-cause inhibition, as it is commonly measured, is difficult or im-possible to distinguish from higher order abilities.

Regardless of the specific model, the literature supports of fairlydomain-general account of the association between anxiety andWMC. Anxiety is most strongly related to attentionally demandingtasks and is also related to both attentional performance in theabsence of a storage component and storage in the absence of anobvious attentional component. However, there are still a numberof outstanding questions. As noted above, ample evidence supportsa link between anxiety and inhibitory control. However, it iscurrently unclear whether this an example of a specific deficit ininhibition or the result of deficits in higher order fluid cognition.

In addition to being related to measures of simple and complexspan, the current review found that anxiety—both experimentallyinduced and measured as an individual difference—was related todynamic span measures of WMC (e.g., the N-Back). As noted inthe introduction, although most anxiety researchers assume that theN-Back and other measures of WMC are measuring the sameunderlying WM system (e.g., Robinson et al., 2013; Shackman etal., 2006; Vytal et al., 2012, 2013), mounting evidence suggeststhat this is not the case. A recent meta-analysis found low corre-lations between the N-Back and measures of simple and complexspan (Redick & Lindsey, 2013). Similarly, Kane et al. (2007)demonstrated that the N-Back and OSPAN accounted for indepen-dent variance in general fluid intelligence. Results such as theseimply that these tasks are not simply interchangeable and may bemeasures of separate underlying processes.

1 This discrepancy is likely to be attributable to statistical power. Nearlyall dual-task studies were conducted in undergraduates and the meta-analysis revealed that the effect size for subclinical samples was g � �.215(see Table 1). Achieving a reasonable level of statistical power (� .80)would require more than 500 participants.

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15ANXIETY AND WORKING MEMORY

In attempting to explain the relationship between anxiety andthe N-Back, there would seem to be several possibilities. First,while complex and dynamic spans are only weakly related, they doshare some variance and it is currently unclear exactly what thisvariance represents. The current review cannot rule out the possi-bility that anxiety is associated with the variance that is sharedbetween complex and dynamic spans. This possibility would beconsistent with the model depicted in Figure 1. That is, that anxietyis associated with broad deficits in fluid cognition which manifestin the performance of a number of different tasks, including theN-Back. Second, Shipstead and colleagues (2014) analyzed can-didate mechanisms underlying individual differences in WMC.They reported that, whereas complex span tasks related to perfor-mance on measures of attentional control, what they called “run-ning span tasks” were related to overall storage capacity (e.g.,Cowan, 2005). Importantly, running span tasks are superficiallyquite similar to the N-Back insofar as they both require attendingto serially presented memoranda, maintaining the most recent nitems and discarding items that are no longer relevant. This findingmay imply that anxiety is associated with a limited capacity storethat is even more restrictive than the typical 3- to 5-item limit (e.g.,Cowan, 2001, 2005; Luck & Vogel, 1997; Rouder et al., 2011).This possibility is quite intriguing given that it implies morefundamental capacity limits beyond the attentional limitations typ-ically associated with anxiety (e.g., Eysenck et al., 1992, 2007).However, it seems unlikely that a short-term anxiety induction(Robinson et al., 2013; Shackman et al., 2006; Vytal et al., 2012,2013) would alter some fundamental capacity of the workingmemory system. Rather, it is more likely that anxiety-relatedprocesses interfered with active maintenance in some way.

The third explanation regards the differing task demands ofcomplex/simple spans and the N-Back. Complex/simple tasks re-quire participants to serially recall items with no external cues andthus require explicit recollection of an item in the context in whichit was encountered. The N-Back, on the other hand, involvesrecognizing recently presented items and possibly discriminating

them from recent nontargets. Thus, in addition to tapping recol-lection, the N-Back also involves familiarity-based processes thatmight obscure the relationship between the N-Back and complexspans (Oberauer, 2005; Szmalec, Verbruggen, Vandierendonck, &Kemps, 2011; also see Yonelinas, 2002 for a review). Indeed, thisdifference in retrieval demands appears to play a large role inpsychometrically dissociating these tasks. In particular, Sheltonand colleagues (Shelton et al., 2007, 2009) required participants tocomplete a modified N-Back that involved free recall of a subset ofthe most recent items. Items were serially presented as in theN-Back; however, when the list was complete, participants wererequired to recall the item that appeared 0-, 1-, 2-, or 3-back.Participants did not know which item they were required to recalluntil after the list was complete. Although the correlation betweenthe standard recognition N-Back and complex span tasks tends tobe a modest .20 (Redick & Lindsey, 2013), Shelton and colleaguesfound correlations as large as about .50.

What does this mean for anxiety? It is possible that anxiety isdifferentially associated with recollection and familiarity-basedprocesses. For example, tagging an item as familiar is thought tobe a relatively automatic and undemanding process whereas ex-plicit recollection is more effortful (e.g., Yonelinas, 2002) and agreat deal of research suggests that anxious individuals are morelikely to be reliant on less demanding strategies (see above; also,Eysenck et al., 2007). Alternately, it is, perhaps, unsurprising thatanxious individuals tend to believe that their environment is lesspredictable and amenable to control relative to low-anxious indi-viduals (e.g., APA, 2013; Archer, 1979; Judge et al., 2002; Wat-son, 1967; note that much of the experimental work involving theN-Back placed participants in a situation in which they lackedcontrol over their environment—i.e., by delivering randomly timedelectric shocks—e.g., Shackman et al., 2006). Some work suggeststhat when individuals lack control over their environment they aremore likely to falsely recognize patterns and rate novel stimuli asfamiliar in a presumed attempt to gain control over uncertainenvironments (e.g., Shermer, 2011; Simonov et al., 1977; Whitson

Figure 1. A potential model describing the relationship between anxiety and cognition. Anxiety is assumed toimpair fluid cognition—that is, domain-general abilities that are common to the lower order abilities. Black,unbroken arrows are meant to depict significant causal paths. Gray, broken arrows are meant to depictnonsignificant paths.

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16 MORAN

& Galinksy, 2008). Both of these possibilities suggest that anxiousindividuals are falsely determining N-Back nontargets to be famil-iar and predict that anxious individuals’ impaired performance onthe N-Back results from an increase in false alarms specifically.However, this is difficult to test as most studies have reportedeither overall accuracy or sensitivity (i.e., d=) thereby obscuring thedistinction between hits and false alarms.

To examine the relationship between anxiety and false alarms, Icollected N-Back and anxiety scores from 197 undergraduates.Participants completed a letter version of the 3-Back as well as thePenn State Worry Questionnaire and Anxious Arousal Subscale ofthe Mood and Anxiety Symptoms Questionnaire (see Supplemen-tal Materials for a detailed description of the methods). As can beseen in Table 3, both the Anxious Arousal subscale and the PSWQpredicted the false alarm rate but not the hit rate. To furtherexamine these relationships, a regression was conducted in whichboth self-report measures were used to predict the false alarm rate.The overall regression was significant, F(2, 194) � 9.82, p � .001.Interestingly, the Anxious Arousal subscale remained a significantpredictor in this regression (� � .28, t � 3.77, p � .001) but thePSWQ did not (� � .07, t � 0.87, p � .38).

Finally, response bias, C, was computed using Snodgrass andCorwin (1988)’s formula:

C � .5�LN�(1 � FA)(1 � H)((FA)(H)) ��

where LN is the natural logarithm, H is the proportion of hits, andFA is proportion of false alarms. H and FA values equal to 0 or 1were adjusted by .01. Positive values of C indicate a conservative“no” bias whereas negative values indicate a liberal “yes” bias.Higher Anxious Arousal scores predicted a “yes” bias, r � �.24,p � .001. This finding suggests that anxiously aroused individualstend to set a response criterion such that items are more likely tobe tagged as familiar even if they are nontargets (e.g., MacMillan& Creelman, 2005).

It should, however, be noted that Hansen and colleagues (2009)found that participants in the “safe” condition were more likely tocommit false alarms in a threat-of-shock design. It is unclear whyresults of Hansen et al. (2009) differ so dramatically from thosepresented above (also see Shin et al., 2006). Perhaps it resultedfrom the cognitive load used in that study (Hansen used a 2-Backwhereas the current study used a 3-Back). Alternately, the currentstudy utilized a lower target to nontarget ratio (see SupplementalMaterials) than did Hansen et al. (2009). Varying the proportion oftargets present in a task may differently affect anxious and non-anxious individuals’ response criteria. In any case, these resultssuggest that the relationship between anxiety and the N-Back is

likely to be somewhat nuanced. Fully understanding it will requirea detailed understanding of the effects of different types of anxiety(e.g., worry/arousal), different operationalizations of anxiety (e.g.,self-report/induction) and N-Back task parameters.

To thoroughly test whether anxiety increases reliance on famil-iarity processes, performance on lure sequences would need to beexamined (e.g., Yonelinas, 2002). In the N-Back, lure sequencesare sequences wherein an item matches a recent but irrelevant item(e.g., L-F-X-L in a 2-Back task) and are likely to be flagged asfamiliar due to their recent appearance (e.g., Szmalec et al., 2011).Unfortunately, few studies included in this review presented datafor lure sequences or even indicated whether lure sequences wereincluded in the task. Fales et al. (2008) did examine lure trials butfound no effects. However, as noted above, it is unclear whetherthis design successfully induced lasting affect. Overall, it seemsthat anxiety is robustly and causally (see below) related to perfor-mance on the N-Back. But fully understanding this relationship isgoing to require both a more detailed account of how the N-Backrelates to other measures of working memory as well as morethorough descriptions of anxious individuals’ performance.

Perhaps the most surprising finding of the meta-analysis wasthat social anxiety appears to be either unrelated to WMC orrelated to increased visual WMC in East Asian populations (e.g.,Moriya & Sugiura, 2012, 2013). This is quite unexpected insofaras no existing theory makes specific predictions for visual WMC,for cultural differences, or for social anxiety specifically. Moriyaand Sugiura (2012) speculate that socially anxious individualswork to maintain a large number of visual features so as to keeptrack of audience member responses in social situations. However,this study only examined students attending East Asia universities.It is true that social phobia is among the most culturally sensitiveanxiety disorders insofar as it manifests as a fear of negativeevaluation in Western cultures and as a fear of offending others inEast Asian cultures (e.g., Chang, 1997; Okazaki et al., 2002;Okazaki, 1997). However, in both cases, socially anxious individ-uals must keep track of others’ reactions. Additionally, Westernand East Asian students do not appear to differ in the amount ortype of information that is used when evaluating faces (e.g.,Caldara et al., 2010). At present, it seems likely that this findingcan be attributed to measurement issues. For example, all studiesdemonstrating this enhanced visual WMC used the same simplespan measure (i.e., change detection) as well as the same measureof social anxiety. One cannot rule out the possibility that thesefindings reflect more task-specific factors rather than a true asso-ciation between social anxiety and WMC for visual features.Alternately, it is possible that the psychometric properties of thesocial anxiety measure are not invariant across cultures—that is, itmay not be measuring the same construct in both cultures (Chen,2008). This possibility is especially likely given cultural differ-ences in social anxiety. In any case, given that this finding persistsacross several samples and is not predicted by any existing theory,it would seem to warrant future attention.

Limitations and Future Directions forCorrelational Studies

The meta-analysis, along with studies of dual-tasking, revealeda robust relationship between measures of anxiety and WMC.However, the most common method of demonstrating a relation-

Table 3Descriptive Statistics and Correlations for the 3-Back Task(N � 197)

Variable M SD 1 2 3

1. Anxious arousal 1.58 .51 —2. PSWQ 3.31 .96 .35�� —3. Hits .67 .18 .13 .12 —4. False alarms .19 .13 .30�� .16� .09

� p � .05. �� p � .001.

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17ANXIETY AND WORKING MEMORY

ship between anxiety and WMC consisted of presenting scores ona single measure of WMC as a function of a single measure ofanxiety. Although this has undoubtedly contributed to our under-standing, the aim of this line of inquiry is not just to show that testscores are related. Rather, the aim is to understand the relationshipbetween the emotional state and the cognitive abilities that under-lie these test scores. One of the most prominent threats to under-standing the anxiety/WMC relationship at the level of abstractconstructs has been the tacit assumption that scores on a single testare process-pure indicators of that construct.

It is generally understood that scores on a given measure can bepartitioned into at least two sources of variance (e.g., Kim &Mueller, 1978; Shadish et al., 2001). The first is “true score”variance which represents variance attributed to the constructunder investigation (e.g., anxiety). Statistically, true score varianceis instantiated as “valid” variance or variance that is shared withother measures of the same construct. The second source of vari-ance is error variance which can be further partitioned into twosources. The first of these sources is random error that arisesduring measurement; importantly, this random error is unrelated toother sources of variance and tends to average to zero in largesamples. Random error can be statistically estimated as the pro-portion of unreliable variance. Finally, the second source of errorvariance is systematic error. Systematic error refers to some con-struct that is being reliably measured but differs from the charac-teristic of interest (e.g., scores on a measure of anxiety may beinfluenced by variance related to depression). Such variance isstatistically reliable but is not necessarily shared with other mea-sures of the same construct.

For example, the reliability coefficient of the State–Trait Anx-iety Inventory (STAI) tends to be approximately .902 (e.g.,Nitschke et al., 2001). With respect to validity, correlations be-tween the STAI and other anxiety measures tend to range between.35 and .70—I will assume .70, but any reasonable validity esti-mate will yield the same conclusions. Such combinations of highreliability coefficients and relatively low validity coefficients leadto interpretive difficulties that are often unappreciated.

Based on those estimates, one can infer that about 81% of thevariance of STAI scores can be considered reliable or “true score”variance whereas the remaining 19% can be attributed to measure-ment error. Approximately half (49%) of the total variance can beconsidered valid. Thus, approximately 32% of the variance in theSTAI is reliable but not valid. That is, it reliably measures some-thing other than anxiety as defined by the STAI. What, then, doesthis mean for the study of anxiety and working memory? If onewere to conduct a study using the STAI and a single measure ofworking memory,3 it would be impossible to unambiguously at-tribute a significant correlation to trait-anxiety, per se. Rather, anycombination of the valid variance and the unaccounted for reliablevariance could be driving this relationship.

This has indeed been the case. Nearly all correlational studiespresented bivariate associations between single measures of work-ing memory and anxiety. Although a few studies did presentinterrelationships between several constructs (e.g., measures oftrait anxiety, worry and working memory), this is not the same asa true multimeasure assessment. In these studies, multiple mea-sures were recorded to demonstrate a mediational effect or to showsome degree of specificity (e.g., that a measure of WMC predictedworry but not state anxiety); however, each construct was still

represented by a single measure. To show a relationship at theconstruct level, multiple measures of each construct must be in-cluded and aggregated. Thus, although it seems fairly clear thatmeasures of anxiety and WMC do interrelate, future research willbenefit from showing that this relationship holds at the constructlevel.

This leads to a second, related limitation. One of the primarydimensions along which theories differ is the extent to which theypredict domain-specific versus domain-general effects. That is,theories differ both with respect to the roles they posit for theworry and arousal dimensions of anxiety as well as with respect tothe WM domain(s) most strongly related to anxiety. In general, thenotions that worry impacts phonological storage and arousal im-pacts spatial storage have found some support in this review; whatis missing is strong evidence for the predicted degree of specific-ity. That is, demonstrating that one dimension of anxiety predictsperformance in a WMC task is not the same thing as demonstratingthat this dimension of anxiety uniquely predicts performance inthat task. A good example of this comes from the work of Owenset al. (2014). In this study, worry and WMC were found to mediatethe association between trait anxiety and academic performance inadolescents. However, this study did not include a measure ofarousal thus allowing for the possibility that this finding is notspecific to worry (while they did include a measure of depression,this was analyzed separately and not controlled). Indeed, work byPutwain, Connors, and Symes (2010) has found that both worryand arousal symptoms independently predict academic perfor-mance in adolescents. Similarly, although the discussion thus farhas focused on measures of anxiety, the same could be said forboth simple and complex measures of span. For example, phono-logical and spatial storage tend to be highly related and both arestrongly correlated with domain-general WMC (i.e., complexspans; Kane et al., 2004).

The facts that (a) all measures of anxiety appear to predict taskperformance and (b) and all of the WMC measures included in thisreview appear to be related to anxiety lead to an interpretativedifficulty. Namely, although it is clear from the meta-analysis andreview that measures of anxiety predict measures of WMC, it isunclear what these correlations represent. For example, the meta-analysis found that worry predicts WMC for both phonologicaland spatial material. It may be that worry involves both specificphonological and specific spatial deficits, that worry predicts onlythe shared variance (i.e., more domain-general variance), or thatworry predicts the shared variance as well as specific phonologicaldeficits once the shared variance has been accounted for. Thislatter possibility would be broadly consistent with theorizing onworry (Eysenck et al., 1992; Sarason, 1988). Though it is lessfrequently studied, the same could be said of arousal-based mea-sures. For example, it may be that arousal predicts the domain-general variance was well as specific spatial deficits once theshared variance has been accounted for (e.g., Robinson et al.,2013; Vytal et al., 2012, 2013).

2 For the sake of simplicity, I will focus on internal consistency. But thesame conclusions will be reached if other types of reliability are consid-ered.

3 Working memory measures tend to show reliability � .7 and tend tocorrelate with other WMC measures between .3 and .75 (e.g., Kane et al.,2004). Thus, similar arguments could be adduced for measures of WMC.

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18 MORAN

Figure 2 presents a potential model that would be consistentwith existing research. Given that worry and arousal seem topredict WMC domains broadly, the shared variance between worryand arousal is assumed to predict domain-general processes. Vari-ance specific to worry and arousal, on the other hand, predictprocesses specific to phonological and spatial storage, respectively(the WM component of the model was based on Kane et al., 2004).Given the importance of this issue for existing theories, there is aclear need to examine the interrelationships between worry,arousal, phonological storage, spatial storage, and domain-generalprocesses in the same participants. Such detailed studies wouldallow researchers to examine the structure of the interrelationshipsbetween anxiety and cognition and adjudicate between competingmodels in ways that previous research has not. It should be notedthat this limitation is also true of constructs, such as depression,which are closely related to anxiety. Although some studies didexamine both anxiety and depression (e.g., Stout & Rokke, 2010;Owens et al., 2014), most studies analyzed these measures sepa-rately. Given that depression is also related to executive function-ing (e.g., Snyder, 2013), this means the current review cannot ruleout the possibility that the anxiety/WMC association is attributableto shared variance with depression.

Finally, the relationship between trait and state anxiety hasreceived very little attention in the literature. The current reviewsuggests that state anxiety, whether induced or measured viaself-report, is detrimental to complex cognition. Recent work byGrillon and colleagues (Grillon, Robinson, Mathur, & Ernst, 2015;Robinson, Krimsky, & Grillon, 2013), on the other hand, has foundthat the threat-of-shock improves performance on response inhi-bition tasks. It is true that these studies did not measure WMCdirectly; however, as noted above, work by Friedman and Miyake(Friedman et al., 2008, 2011) has suggested that, once the variancecommon to all of the executive functions—thought to reflect

domain general attention (McCabe et al., 2010)—is accounted for,there is no inhibition-specific variance. That is, inhibition appearsto be a highly domain-general process.

Such findings might be best understood in terms of the differ-ences between trait and state anxiety. Indeed, researchers havelong posited that state anxiety is nonlinearly related to task per-formance and that it interacts with trait anxiety to affect perfor-mance whereas trait anxiety is often thought to uniformly impaircognitive task performance (Easterbrook, 1959; Eysenck, 1979;Eysenck et al., 1992, 2007; Yerkes & Dodson, 1908). For example,trait anxiety may interfere with WMC by predisposing one toworry whereas the effects of state anxiety may be mediatedthrough an arousal mechanism which is curvilinear and/or depen-dent on the level of trait anxiety. However, this possibility isdifficult to evaluate in the current review. The majority of studiesin this review have not explicitly evaluated the trait-by-state anx-iety interaction and, among those that have, the results have beenquite mixed. Hodges and Durham (1972) found no trait � ego-threat interaction on digit span performance whereas Walker andSpence (1964) found a significant interaction such that trait anx-iety predicted digit span performance only in the control condition.More recently, Sorg and Whitney (1992) found that a stress in-duction impaired complex span performance only for trait-anxiousindividuals suggesting that the stress induction impaired perfor-mance only for those already predisposed to anxiety (also see Shinet al., 2006). Similarly, Salthouse (2012) found a curvilinearrelationship between trait anxiety and several measures of cogni-tive ability (also see Walkenhorst & Crowe, 2009). However, traitand state anxiety are often highly correlated and this relationshipmay have been attributed to individual differences in state anxiety.Overall, the current review was able to find very little evidencebearing on the relationship between state and trait anxiety. Giventhe historical and theoretical importance of the distinct and inter-

Figure 2. A potential model describing the relationship between anxiety and WMC. In this model, processesspecific to worry are assumed to impair phonological storage whereas processes specific to arousal are assumedto impair spatial storage. Processes that are shared between worry and arousal are assumed to impair domain-general abilities. Singled-headed arrows depict assumed causal pathways whereas doubled-headed arrows depictcorrelations between constructs.

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19ANXIETY AND WORKING MEMORY

active roles of trait and state anxiety (e.g., Easterbrook, 1959;Eysenck, 1979; Eysenck et al., 1992, 2007; Yerkes & Dodson,1908), there would seem to be a clear need for more detailed studyin this area.

The correlational studies reviewed here provide clear evidencefor an association between anxiety symptoms and WMC. That is,a meta-analysis of 177 effect sizes, as well as a narrative review ofdual-tasking studies, indicate that anxiety is related to poorerperformance on measures of WMC. Additional evidence suggeststhat WMC is capable of mediating the relationship between anx-iety and important real-world outcomes, such as academic perfor-mance. However, the review also identified a number of limita-tions to be addressed by future research: (a) there is a clear need toconduct multimeasure assessments that are capable of decompos-ing variance associated with measures of anxiety and WMC intotheoretically meaningful partitions and to examine their interrela-tions (e.g., worry, arousal and depression for anxiety and domain-general executive attention and modality-specific storage forWMC); (b) determine the nature of the association between anx-iety and the N-Back task; (c) explore the independent and inter-active effects played trait and state anxiety and evaluate the pos-sibility of curvilinear effects; and (d) determine what role, if any,culture plays in the anxiety/WMC relationship.

Experimental Studies and the Causal Relationship

There currently exist competing hypotheses regarding the natureof anxiety-related deficits in cognitive performance. Many moderntheories propose that processes related to anxiety interfere withcognition in some way—for example, by demanding attention,consuming phonological resources, or by competing with storage-related processes for neural representation (Eysenck et al., 1992,2007; Robinson et al., 2013; Shackman et al., 2006). Such frame-works are generally agnostic with respect to the etiology of anx-ious symptoms but suggest that, once present, these symptomsproduce a number of downstream effects on measures of cognitiveability. Other, more clinically focused theories (e.g., Mathews &MacLeod, 2005; Ouimet et al., 2009) have tended to shift thecausal burden to cognitive biases and deficits. In these theories,preexisting individual differences in cognitive abilities may act asa risk factor which, when paired with subsequent stress, predisposean individual for later anxiety symptoms. Thus, in these theories,WMC is one of many causal factors contributing to the etiology ofanxiety.

Unfortunately, the current review can only offer a partial reso-lution to this question. The strongest evidence bearing on thisquestion comes from threat-of-shock studies which have shown,fairly unequivocally, that performance on at least one measure ofWMC can be impaired by an anxiety induction. What makes thesestudies such strong demonstrations is their focus on internal va-lidity. First, these studies were some of the few that experimentallymanipulated anxiety thereby helping to rule out preexisting indi-vidual differences. Second, these studies used a well-validated andwidely studied method of inducing anxiety (e.g., Davis et al., 2010;Schmitz & Grillon, 2012). Third, they provided both self-reportedand physiological evidence that the induction indeed modulatedparticipants’ anxiety and Shackman et al. (2006) provided evi-dence that this increase in anxiety measures mediated the threat-of-shock effect.

The review presented above indicates that ego-threat manipula-tions are also capable of affecting performance on WM measures.Early studies, in which participants were faced with either actualfailure or the threat-of-failure, found robust effects of the ego-threat manipulation on digit span performance. More sociallyfocused manipulations, such as the Trier Social Stress Task, ap-pears to affect performance in the phonological 2- and 3-Back(Schoofs et al., 2008) and to some extent in the Sternberg task (Oeiet al., 2006).

These findings are quite promising insofar as they suggest thatat least two manipulations are capable of affecting performance ona number of WM tasks (i.e., N-Back, digit span, and Sternbergtask). However, these results should be interpreted in the light ofa number of caveats. First, these studies have generally not shedlight on the underlying mechanisms. Threat-of-shock paradigmsrobustly increase physiological symptoms of anxiety (Grillon,2008), increase vigilance (Cornwell et al., 2007), and activate theamygdala (Phelps et al., 2001), which are symptoms that mostclosely map onto the arousal dimension of anxiety (e.g., Nitschkeet al., 2001; Heller, Nitschke, Etienna, & Miller, 1997; Heller,Nitschke, & Lindsay, 1997). However, it cannot be said withcertainty that the threat-of-shock manipulation did not induceworry. Similarly, ego-threat studies place participants in a stress-ful, evaluative situation which is often associated with arousalsymptoms. For example, Beilock and colleagues (2007) found thatthe effects of stereotype threat—a manipulation that is conceptu-ally very similar to ego-threat—had its largest effects on tasksinvolving phonological material. These studies would be aided byincluding even a self-reported measure of worry to examine vari-ance that is specific to worry. Additionally, the literature wouldbenefit from studies that attempt to experimentally induce orreduce worry. A number of dual-tasking studies did attempt to dothis; however, it is unclear whether asking people to engage inworry in a laboratory setting mimics the process of worry in reallife. Expressive writing may offer one avenue forward—althoughthere are likely to be many. Expressive writing has shown somepromise with respect to temporarily alleviating worry and itseffects on WMC (e.g., Ramirez & Beilock, 2011; also see Klein &Boals, 2001a). Thus, although there does appear to be clear evi-dence that certain anxiety manipulations are capable of affectingtests scores, there is still much to be learned about the underlyingmechanisms.

The second caveat is that anxiety inductions and measures ofWMC were often confounded. For example, all studies using thestandard ego-threat manipulation measured WMC with a simplespan task. Similarly, nearly all of the threat-of-shock studies in-ferred working memory deficits from versions of the N-Back.Given that simple spans, complex spans, and the N-Back appear toindex processes that are at least partially separable (e.g., theOSPAN; Kane et al., 2004, 2007; Redick & Lindsey, 2013), it isunclear whether these effects would generalized to other WMmeasures. The results of Pyke and Agnew (1963) suggest that it isunlikely that the threat-of-shock design only affects processesspecific to the N-Back; however generalizing these results willrequire the use of multiple measures of WMC.

Finally, there seems to be a WM load � WM modality � affectinteraction that inconsistently appears across studies. Threat-of-shock studies have generally found that anxiety impaired perfor-mance on all levels of load for a spatial N-Back but only for low

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20 MORAN

levels of load for a phonological N-Back (however, see Kalisch etal., 2006). Conversely, Schoofs et al. (2008) found that the TrierSocial Stress Task affected both phonological 2- and 3-Backperformance. Using the Sternberg task, studies have found thatanxiety inductions impair performance at low (Porcelli et al., 2008;Exp. 1; however see Porcelli et al., 2008; Exp. 2), moderate load(Duncko et al., 2009) and high load (Oei et al., 2006).

Taking an individual differences approach, Stefanopoulou et al.(2014) failed to find the expected group � load interaction in aphonological N-Back in patients with generalized anxiety disorder.Klein and Boals (2001b) reported that correlations between ameasure of state-anxiety and OSPAN scores were fairly reliableacross set sizes. Similarly, Schweizer and Dalgleish (2011) pre-sented reading span scores as a function of set size for a sample ofPTSD patients. Although the interaction term was not presented,and inspection of their Table 1 suggests that the interaction waslikely nonsignificant for affectively neutral material. Lastly, thisreview presented novel data indicating that self-reported arousalpredicts poorer performance on a phonological 3-Back.

One could rebut this limitation by proposing (a) that some ofthese manipulations were of questionable effectiveness, (b) thatsymptoms were not provoked in Stefanopoulou et al. (2014); Kleinand Boals (2001b) and Schweizer and Dalgleish (2011) and it istherefore unclear whether participants were actually symptomaticduring the task, or (c) it may be that these different versions of theN-Back were not well-matched or that the variable requirements ofthe N-Back, OSPAN and Sternberg tasks make it impossible todirectly compare performance. Such rebuttals would certainly befair. However, these points do suggest that at the very least thereare subtle differences between self-report measures, manipulationsand WM measures that have not been accounted for. There wouldseem to be a clear need to use well-characterized measures andmanipulations as well as to understand the boundary conditions onthe effects of anxiety.

With respect to models that consider low WMC to be a riskfactor for anxiety (e.g., Ouimet et al., 2009; also see Posner &Rothbart, 2000), the current review found mixed evidence relevantto the causal status of WMC. There appear to be two primaryfactors responsible for limiting the conclusions that can be drawnregarding WMC as a risk factor. First, although anxiety can bereadily manipulated in a laboratory setting, it is unclear whetherinterventions are capable of manipulating WMC (e.g., Redick etal., 2013; Shipstead et al., 2010). This situation severely limits thecausal inferences that can be drawn for WMC and anxiety. A fewstudies found that adaptive working memory training programsfailed to reduce anxiety symptoms (Åkerlund et al., 2013;Roughan & Hadwin, 2011). On the other hand, Sari and colleagues(in press) demonstrated that the degree to which anxious individ-uals engaged in the training task predicted decreased anxietyfollowing training. It is currently unclear why these findings di-verge. It may be that participants in Åkerlund et al. (2013) andRoughan and Hadwin (2011) were less engaged in the training taskthan in Sari et al. (in press) or that some methods of WMC training(e.g., CogMed vs. dual N-Back) might be more effective thanothers. Alternately, training may be more effective for some pop-ulations than others. For example, Sari et al. (in press) includedparticipants preselected for trait anxiety. Similarly, Studer-Luethiet al. (2012) and Jaeggi et al. (2014) have shown that individualdifferences in conscientiousness, mindset and neuroticism moder-

ate the effectiveness of WMC training (however, see Thompson etal. [2013] for a failure to replicate some of these findings). In anycase, Sari et al. (in press) provides some of the most directevidence that reduced WMC can act as a risk factor for anxiety;however, given some of the inconsistencies in the literature, aswell as the debate regarding WMC training, replicating thesefindings and determining under what circumstances WMC trainingaffects anxiety seem to warrant further work.

Second, the majority of the studies in the current review havefocused on adult populations. In the absence of novel, prolongedstressors (e.g., combat; Macklin et al., 1998), adults are somewhatunlikely to show large increases in anxiety or rates of diagnosisover a short period of time (Bredermeier & Berenbaum [2013]proposed that their effect resulted from upcoming midterm exams).In general, adult-like anxiety, as well as the stress responses whichpredispose one to anxiety, begin to appear during pubertal devel-opment (e.g., Gunnar & Vazquez, 2006; Reardon et al., 2009;Sumter et al., 2010; also see APA, 2013). With respect to childstudies, the present meta-analysis found that effect sizes werestatistically equivalent for adults and children. However, therewere relatively few studies conducted in child populations andmany of the child studies included somewhat wide age ranges(e.g., 6–13 years old). This means that the current meta-analysislacked the sensitivity to detect the developmental course ofanxiety-related impairments in WMC.

Conducting longitudinal/prospective studies would seem to rep-resent a fruitful avenue for moving forward. The small number ofprospective correlational studies also hint at the possibility thatlower WMC may act as a risk factor for later anxiety. Specifically,Macklin et al. (1998) and Bredemeier and Berenbaum (2013) haveshown that lower general intelligence and N-Back scores, respec-tively, predict later anxiety. However, as noted earlier, the infer-ences that can be drawn from these studies is limited by severalfactors including (a) the use of intelligence scores rather thanWMC scores, (b) confounding factors such as combat severity, (c)designs that were not fully crossed, and (d) the fact that thesestudies tended to be conducted in adults. Both the risk factors fordeveloping anxiety as well as the downstream effects of anxietyare likely going to be most easily detected in longitudinal studieswhich follow children and adolescents through puberty. Movingforward, then, it seems that there is much to be gained fromconducting lagged cross-correlational studies in children. If WMCis indeed a risk factor for later anxiety, then measures of WMCrecorded prior to puberty would be expected to predict post-pubertal anxiety; additionally, measures of anxiety recorded priorto puberty should not predict post-pubertal measures of WMC.This design has the additional advantage of being able to evaluatemore sophisticated models.

For example, it is possible that the relationship between anxietyand WMC is more cyclical than many theories propose. Perhapsindividual differences in WMC increase the likelihood of lateranxiety which, in turn, interferes with working memory (as de-picted in Figure 3; see Van Bockstaele et al., 2014 for a similarproposal regarding attentional biases). This possibility would bebroadly consistent with the work of Hirsch and Mathews’ (2012)proposal that the control of worrisome thoughts is reliant ondomain-general attentional control abilities. Importantly, worryitself is assumed to impair attentional control abilities in thismodel. Thus, in this model, worry and WMC are capable of

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21ANXIETY AND WORKING MEMORY

bidirectionally affecting each other. Hallion and colleagues (2014)have provided some initial support of this model. In this study,2-Back performance suffered when participants attempted to con-trol worries relative to neutral thoughts suggesting that the controlof worry is reliant on available WMC (also see Gorlin & Teach-man, 2015).

Although there has not been as much experimental work as therehas been correlational work, a few conclusions regarding thecausal status of anxiety and WMC seem warranted. Most impor-tantly, work using the threat-of-shock method has provided fairlystrong evidence that an anxiety induction is capable of impairingperformance on at least one measure of WMC—the N-Back.Additionally, ego-threat studies, while suffering from some limi-tations, appear capable of impairing performance on simple spanmeasures. However, the review also identified a number of limi-tations to be addressed by future research: (a) experimental studieshave often paired a particular anxiety induction with a particularWMC task rather than examining the generality of their findings,(b) lagged longitudinal studies can help establish the temporalprecedence of one construct over the other, (c) determine theconditions under which cognitive load affects the anxiety/WMCassociation, and (d) further explore the effects of WMC-training ofanxiety symptoms using well-validated training methods.

Conclusions

Over the last 60 years, a great deal of research has beenconducted with the intent of examining the relationship betweenanxiety and WM. It seems clear form this review that (a) measuresof anxiety predict poorer performance on measures of WMC, (b)impaired WMC mediates the association between anxiety andsome real-world outcomes such as academic performance and

treatment response, and (c) anxiety inductions adversely affectperformance on WM measures at least under certain circum-stances. However, there are still basic questions regarding the WMprocesses most affected by anxiety. Future work using multim-ethod assessments and well-characterized experimental manipula-tions will help to answer these questions.

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Received February 9, 2015Revision received February 1, 2016

Accepted February 4, 2016 �

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34 MORAN