Fundamental Dimensions of Subjective State in PerformanceSettings: Task Engagement, Distress, and Worry

Gerald MatthewsUniversity of Cincinnati

Sian E. CampbellUniversity of Wolverhampton

Shona Falconer, Lucy A. Joyner, andJane Huggins

University of Dundee

Kirby GillilandUniversity of Oklahoma

Rebecca Grier and Joel S. WarmUniversity of Cincinnati

Subjective state constructs are defined within the traditional domains of affect,motivation, and cognition. Currently, there is no overarching state model thatinterrelates constructs within the different domains. This article reports 3 studiesthat provide converging evidence for 3 fundamental state dimensions labeled taskengagement, distress, and worry that integrate constructs across the traditionaldomains. Study 1 differentiated the state dimensions by factor analysis of the scalesof the Dundee Stress State Questionnaire (G. Matthews et al., 1999). Study 2showed differential state response to performance of tasks making different cog-nitive demands. Study 3 showed that states are correlated with differing patterns ofappraisal and coping. The 3 stress state dimensions provide a general descriptiveframework consistent with transactional accounts of stress and performance.

Much of psychology is shaped by the idea of a“trilogy of mind.” The idea originated in antiquity,but, in its modern form, divides psychological func-tioning into three domains of affect, conation (moti-

vation), and cognition (Hilgard, 1980). Conscious ex-perience may be shaped by all three domains orsystems, in that cognition, affect, and motivation all“feel” differently from one another (Mayer, FrasierChabot, & Carlsmith, 1997). At the same time, thesedifferent forms of subjective experience are interre-lated. Theories of basic emotions, for example, typi-cally link affects to characteristic cognitions and ac-tion tendencies (Plutchik, 1984). The interplaybetween the different domains is both a problem anda challenge for theory. The problem is that, in empiri-cal studies, it may be difficult to discriminate the ef-fects of affect from those of its cognitive and moti-vational concomitants. For example, effects of stateanxiety on behavior may be mediated by affect (e.g.,tension), by cognitions (e.g., intrusive thoughts andworries; Zeidner, 1998), or by motivations, such asthe urge to withdraw from the threatening situation(Geen, 1987). The overlap between domains presentsa challenge of integrating the different constructswithin a unified framework.

This article explores the interrelationships betweenaffective, cognitive, and motivational (or conative)states in the arena of subjective responses to demand-

Gerald Matthews, Rebecca Grier, and Joel S. Warm, De-partment of Psychology, University of Cincinnati; Sian E.Campbell, Department of Psychology, University ofWolverhampton, Wolverhampton, England; Shona Fal-coner, Lucy A. Joyner, and Jane Huggins, Department ofPsychology, University of Dundee, Dundee, Scotland;Kirby Gilliland, Department of Psychology, University ofOklahoma.

We gratefully acknowledge the financial support of theMedical Research Council (Grant G9510930) and the Car-negie Trust. We also thank Angela Tomlinson for her as-sistance with the test anxiety study. The Dundee StressState Questionnaire is available to qualified psychologistsvia e-mail from Gerald Matthews at matthegd@email.uc.edu

Correspondence concerning this article should be ad-dressed to Gerald Matthews, Department of Psychology,University of Cincinnati, Cincinnati, Ohio 45221. E-mail:matthegd@email.uc.edu

Emotion Copyright 2002 by the American Psychological Association, Inc.2002, Vol. 2, No. 4, 315–340 1528-3542/02/$5.00 DOI: 10.1037//1528-3542.2.4.315

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ing performance situations. A focus on performancesettings has several advantages. First, demandingtasks readily elicit a variety of “stress” responses suchas anxiety, fatigue, worry, and loss of motivation(Hockey, 1997). Cognitive psychology provides ra-tionales for sampling both subjective state constructsand the tasks that may generate qualitatively differentstress reactions (Matthews, Davies, Westerman, &Stammers, 2000). Second, self-regulative models ofstate provide a conceptual and theoretical frameworkthat is directly applicable to the performance context.Appraisal and coping processes may drive individualdifferences in both subjective response and objectivebehavior (Matthews & Wells, 1996). Third, perfor-mance challenges such as attaining proficiency in theworkplace and passing examinations are highly im-portant for people in industrialized societies, and sounderstanding the nature of subjective experience inthese settings is of both theoretical and practical rel-evance.

The remaining portion of this introduction is struc-tured as follows. First, we discuss how states are con-ceptualized, how states are distinct from processes,and how different state domains may be functionallyinterrelated as expressions of self-regulation. Next,we briefly review empirical studies of the dimensionalstructure of states, including a recent attempt to dif-ferentiate states within the different domains. Theoverarching aim of this research is to discriminatehigher order complexes of affect, motivation, andcognition that may reflect different modes of self-regulation, obtaining converging evidence from factoranalysis, experimental studies, and correlational data.

Conceptualizing States

A subjective state may be defined as a relativelytransient quality permeating conscious awarenesswhose representation is distributed across a variety ofmental processes or structures, and which has the po-tential to generalize across activities and contexts. Inthe affective domain, mood states1 provide a back-ground context for psychological functioning and im-pinge on different functions such as attention, judg-ment, and memory (Matthews, 1992). A mood state isthus an abstracted quality of mental function that can-not be reduced to any single cognitive or neural com-ponent (cf. Thayer, 1989, 1996). Cognitive dimen-sions must also represent generalized states, and notjust specific propositions, beliefs, or attitudes. Con-sider Sarason, Sarason, Keefe, Hayes, and Shearin’s(1986) task-irrelevant cognitive interference con-

struct. A high level of interference may be generatedby many different specific thoughts about personalconcerns. The state is defined by the overall level ofintrusive thoughts, not by the specific content of thethoughts. The state may persist across a change ofcontext: worries experienced at the breakfast tablemay transfer to state at the workplace, for example(with or without continuity of content). Similarly, amotivational state, such as an immediate urge toachieve, should be represented across a variety ofgoals and subgoals, which may vary with context.

A state is a hypothetical construct that is often in-ferred from verbal report, much like a physicist mayinfer the existence of a particle from trails in a cloudchamber. Thus, verbal reports provide indirect ratherthan direct indications of state (cf. Cattell’s, 1973,distinction between Q and Q� data). Experimental andcorrelational studies are required to build up a nomo-logical network that relates states, as operationallydefined constructs, to psychological theory. It is alsoimportant to distinguish states from traits, personalitydispositions that show much greater temporal stability(Matthews & Deary, 1998; Zuckerman, 1976).

Typically, theories of emotion claim that consciousfeeling states represent only a partial expression ofsome underlying physiological or psychological sys-tem. Questionnaire scales, such as those for state anxi-ety, do not measure emotion directly, but only itsexpression into consciousness. States are outcomevariables, to be distinguished from underlying cogni-tive and neural processes that may determine state butare not fully accessible to consciousness. The issue isthen how affective, cognitive, and motivational expe-riences relate to these underlying processes. Mayer etal.’s (1997) analysis of the trilogy of mind suggests a“separate-systems” approach that sees affect, motiva-tion, and cognition as reflecting separate, but interact-ing, systems. There may be a distinct subjectivity foreach domain, supported by a distinct set of underlyingprocesses. An alternative, “common-systems,” ap-proach sees underlying systems as simultaneouslygenerating affective, motivational, and cognitivestates. There may be multiple affective–motivational–cognitive systems, a view close to “basic emotions”theory (e.g., Plutchik, 1984), but giving affect, moti-

1 Emotions are often distinguished conceptually frommoods in being linked to some specific event or cognition.This article is concerned with basic dimensions of affectiveexperience that may be better labeled as “moods” ratherthan “emotions.”

MATTHEWS ET AL.316

vation, and cognition equal status as expressions ofthe latent system. Self-regulative theory provides anaccount of cognitive processes that may control allthree aspects of subjective state. Behavior is driven bygoal-directed attempts to reduce discrepancy betweenpreferred and actual state that are modified by feed-back (e.g., Carver & Scheier, 1990). States may thenindex the integrated functioning of the multiple dis-crete processes that support self-regulative functions.

In the performance context, Hockey (1997) dis-criminated different control modes that illustrate howdifferent self-regulative functions may be expressedthrough characteristic patterns of subjective, behav-ioral, and physiological response. In an “overload”mode, the person maintains effort to compensate forprocessing inefficiency, generating emotional strain,loss of performance on low-priority task components,and physiological responses such as catecholamineexcretion. An alternate control mode is associatedwith fatigue. The person reduces the target level ofperformance so that less effort is required and theperson primarily experiences loss of motivation andenergy rather than strain. Hence, each control mode isassociated with a characteristic affective state, level ofmotivation and effort, and cognitions of task demandsand personal competence. However, it is unclear howstrong the linkages are between the subjective com-ponents of each control mode. Should we conceptu-alize states as tightly coupled packages of emotion,cognition, and motivation? Or is it more important torecognize that dimensions within these domains areoften dissociated?

Assessment of Subjective Experience

Assessment techniques that would differentiatestate dimensions as concomitants of self-regulationhave lagged behind advances in theory. The structureof subjective states is complex, in that states cannot bedescribed by a single dimension such as arousal oranxiety (Matthews, 1992), but there is no overarchingdimensional model that discriminates those dimen-sions that are fundamental to subjective experience.There are two principal strands of research on dimen-sional models of state. The first concerns the dimen-sionality of mood and subjective arousal. Thayer’s(1989) pioneering work identified fundamental di-mensions of energetic and tense arousal. Watson andTellegen (1985) proposed similar, but somewhatbroader dimensions of positive and negative affect(PA and NA). Energy–PA and tension–NA may berotated through 45° to give dimensions of arousal and

pleasure (Russell & Feldman Barrett, 1999). Severalauthors have proposed an alternative, three-factor di-mensional model, that originated with Wundt (1896)and distinguishes pleasantness of mood from ener-getic and tense arousal dimensions (Schimmack &Grob, 2000; Sjoberg, Svensson, & Persson, 1979).Matthews, Jones, and Chamberlain’s (1990) three-dimensional model added a hedonic tone dimension toThayer’s energy and tension axes, supported by con-firmatory factor analysis (Guàrdia & Adan, 1997).Both two- and three-factor models provide accountsof core affects that contribute to all emotions, al-though additional, more specific components are re-quired to fully explain variance in affect (Reisenzein,1994). Some rather separate research on anxiety statesdiscriminates emotions, such as feelings of tension,from disturbances of cognition, such as worry andinterfering thoughts (Zeidner, 1998). State worry hasbeen measured with the Cognitive Interference Ques-tionnaire (CIQ; Sarason et al., 1986). However, it isunclear how cognitive constructs may be integratedwith mood and motivation dimensions in a commonpsychometric model.

Recently, Matthews et al. (1999) developed a newquestionnaire (the Dundee Stress State Questionnaire;DSSQ) for comprehensive assessment of subjectivestates in performance contexts. Affective items re-ferred to diffuse moods, motivation items to the needsand pressures that initiate performance-related behav-ior, and cognitive items to self-referent beliefs andstyles of thought. Instructions for the DSSQ empha-size rating immediate rather than typical subjectiveexperience to ensure reporting of states rather thantraits. Items were sampled on the basis of a review ofthe main explanatory constructs used in studies ofstress, emotion, and performance (Matthews et al.,2000), as follows:

Affect: The UWIST Mood Adjective Checklist(UMACL; Matthews et al., 1990) was used to indexthree dimensions of energy, tension, and hedonictone. The checklist has been validated in both ex-perimental studies (e.g., Matthews, Davies, & Lees,1990) and field studies of real-world stressors (e.g.,Matthews, Dorn, & Glendon, 1991).

Motivation: Items were written to represent twomajor aspects of motivation in performance set-tings: task interest and strivings to achieve success-ful performance.

Cognition: Existing, validated measures were usedto assess cognitive interference from the task itself

FUNDAMENTAL DIMENSIONS OF STATE 317

and from personal concerns (Sarason et al., 1986),performance and social self-esteem (Heatherton &Polivy, 1991), and self-focus of attention (Fenig-stein, Scheier, & Buss, 1975). Fenigstein et al.’strait items were adapted for state use by minorchanges to items. Measures of concentration andperceived control were taken from ongoing workon driver stress (Matthews & Desmond, 1998,2002).

Matthews et al. (1999) reported item-based factoranalyses that discriminated 10 domain-specific fac-tors: the three Matthews et al. (1990) mood dimen-sions, a single motivation dimension, and six cog-nitive dimensions (see Table 1 for a summary).Hypothesized dimensions of Self-Focus of Attention,Concentration, and the two Sarason et al. (1986) Cog-nitive Interference dimensions were recovered as pre-dicted. A Self-Esteem factor was defined by both so-cial and performance items from the Heatherton andPolivy (1991) scale. A Confidence-Control factor wasdefined by perceived control items and by Heathertonand Polivy items describing confidence in perfor-mance. Scales were constructed by selecting 6–8items per factor. These scales met psychometric cri-teria for state measures (Zuckerman, 1976), for ex-ample, high internal consistency but lower test–retestreliability than traits. Age and gender differences instate were minor.

Aims of the ResearchThis article reports studies investigating how com-

ponents of affective, motivational, and cognitive statecohere around higher order constructs that may spanthe three traditional domains. Although domain-specific affective, motivational, and cognitive stateswere distinguished psychometrically (Matthews et al.,1999), the intercorrelations of these state dimensionsimplied the existence of higher order state factors.Hockey’s (1997) analysis of stress suggests that thesefactors may correspond to distinct control modes orself-regulative functions (see Hockey, 1997, p. 6), butdirect evidence is lacking. The studies reported hereaimed to explore the largely uncharted terrain of thesehigher order state factors by obtaining converging evi-dence from three complementary sources: factoranalysis, sensitivity of states to experimental stressmanipulations, and patterns of correlation with cog-nitive process measures.

Higher Order Factor Structure of State ScalesFactoring of the intercorrelated scales of the DSSQ

should reveal secondary factors integrating different

aspects of experience. Study 1 aimed to investigatefactor structure, using data pooled across multiplesamples to ensure adequate sample sizes. Some ofthese samples were also used in subsequent studies.

Sensitivity to Environmental Manipulations

In performance contexts, self-regulative processesare initiated by task demands that afford some per-ceived threat or challenge to the status of the self.Hence, qualitatively different types of task shouldelicit different modes of self-regulation. High work-load tasks whose demands exceed processing capaci-ties (e.g., working memory) should predominantlyelicit Hockey’s (1997) overload mode of control.Conversely, less demanding, but monotonous vigi-lance tasks should mainly elicit self-regulation in theform of effort reduction, associated with fatiguesymptoms. Hence, Study 2 tested for differential ef-fects of working memory and vigilance tasks on state.

Cognitive Stress Processes

If states index the status of self-regulative function,they should relate to specific processes supportingself-regulation. The present research focused on ap-praisal and coping processes as antecedents of sub-jective state, in line with cognitive theories of stressand emotion (Lazarus, 1999). Different modes of self-regulation should be associated with different patternsof appraisal and coping, as the person evaluates thesignificance of external demands and acts to minimizethreats and maximize potential gains from the situa-tion. Study 3 aimed to test whether different higherorder state syndromes correlated with differing pat-terns of task-related cognition, using occupationalsamples for ecological validity.

Study 1: Factor Structure and Dimensionalityof States

It was difficult to make strong predictions of higherorder factor structure because existing research ne-glects the issue. For example, anxiety research distin-guishes emotional and cognitive states, but it is un-clear whether they should be subsumed under a higherorder state anxiety factor or treated as distinct entities.With this proviso, two broad hypotheses may be dis-tinguished. First, PA and NA may be the major orga-nizing principles of subjective experience, with cog-nitive as well as affective expressions (Watson &Clark, 1984). This model has been criticized, but it isprobably the most widely accepted account of mood,especially in the United States (Schimmack & Grob,

MATTHEWS ET AL.318

2000). The similar dimensions of energetic and tensearousal are also seen as fundamental (Thayer, 1989).Low PA and high NA may be linked to Hockey’s(1997) control modes. Hockey’s “fatigued” mode ofstate control is defined subjectively by tiredness andloss of motivation, and so may correspond, approxi-mately, to low PA. Hockey’s “overload” mode is typi-fied by tension and subjective strain, and so resemblesNA.

Second, clinical studies of emotional disorders andanxiety make a fundamental distinction between NAand worry (Wells & Matthews, 1994). Worry and NAmay constitute distinct higher order factors, togetherperhaps, with an additional factor or factors for posi-tive aspects of experience. Other possibilities also ex-ist. Factors may be defined purely by dimensions ofone of the three domains, as a separate-systems modelwould suggest. Humphreys and Revelle (1984) pro-posed an influential model of stress and performancethat distinguished arousal (e.g., tense arousal, ener-getic arousal) from effort and its cognitive anteced-ents, implying a contrast between an affective factorand a cognitive–motivational factor. However, suchperformance models have inspired rather little workon subjective states. Thus, the present research repre-sents the first systematic attempt to investigate thehigher order structure of subjective states in the per-formance context.

Method

Data were pooled from a total of nine samples (seeTable 2),2 including both university students and non-students. The majority of respondents were British,but a smaller sample of North American participantswas included as a check on the generality of findings.Most of the studies used laboratory tasks, but Sample2 participants completed the DSSQ questionnaire im-mediately following a university examination. Partici-pants were tested in individual, sound-attenuated cu-bicles in Samples 1 and 3–5. Participants in Samples6 and 7 were tested in groups of 10–30 in universitylecture theaters, with task stimuli projected onto a

2 Some of these samples contributed to the initial psy-chometric analyses of the Dundee Stress State Question-naire scales reported by Matthews et al. (1999). These stud-ies were part of a large-scale project conducted over a3-year period, with the principal aim of identifying first-order scales through item factor analysis. This article addsnew data sets so as to present new analyses of second-orderfactor structure.T

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large screen. In Samples 8 and 9, participants weretested in groups of 5–10 in office space at their work-place. Table 2 also gives mean ratings for a standardworkload measure, the NASA-Task Load Index(NASA-TLX; Hart & Staveland, 1988), discussed inmore detail in Study 2 and scored here on a 0–10scale. Means for most tasks exceeded 5.0, a level thatis generally considered demanding (Hart & Staveland,1988; Temple et al., 2000). Data from Samples 4–9were analyzed further in subsequent studies, as indi-cated in Table 2. In each experimental study, partici-pants were instructed that the purpose of the studywas to assess people’s feelings before and after per-formance of mentally demanding tasks and that theyshould aim to perform as well as they could.

Because this study had a psychometric focus, onlybrief descriptions of tasks are given here. More de-tailed descriptions may be found below for visualworking memory (WM) and visual and auditory vigi-lance (see Study 2), and for auditory WM tasks (seeStudy 3). The visual WM task required respondents tocheck whether simple arithmetic problems were cor-rect while keeping concrete nouns in memory. Timepressure increased progressively during the task. Thevisual vigilance task required a line-length discrimi-nation, and the auditory vigilance task required ajudgment of the duration of tone stimuli. On the emo-tional Stroop task (Matthews & Harley, 1996), par-ticipants were required to name aloud the colors ofwords printed on 4 cards (96 words per card), admin-istered in counterbalanced order. Each card compriseddescriptors of a specific type: neutral (e.g., CAU-TIOUS, INOFFENSIVE), minor faults (e.g., FRIVO-LOUS, ECCENTRIC), major faults (e.g., DECEIT-FUL, HOSTILE), and negative emotion (e.g.,DEPRESSED, LONELY). There were 8 instances of12 trait descriptors on each card. The battery of visual,computer-controlled tasks used in Sample 2 com-prised (a) simple reaction time to a square stimulus,with a variable foreperiod of 1–10 s; (b) discrimina-tion of letter stimuli presented at a fixed location, inthe presence of distractor letters (focused attention);(c) discrimination of letter stimuli presented in anunpredictable location, in the presence of distractorletters (categoric search); and (d) a 12-min vigilancetask requiring detection of characterlike targets pre-sented against a masking stimulus (see Matthews,Warm, Dember, Mizoguchi, & Smith, 2001, for fulltask descriptions). All tasks required keypress re-sponses. Sample 2 were 2nd-year University of Dun-dee psychology students, who completed the DSSQafter an end-of-year university examination, compris-T

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ing three essay questions. Results were used to selectthe students allowed to continue their studies in psy-chology in the next academic year.

Results

Scale factor analyses. A total of 517 British par-ticipants (Samples 3–5, 7) completed the pretaskDSSQ, and 788 British participants (Samples 1, 3–5,7, 8) completed the posttask DSSQ. Table 3 showsthat most scales correlated to a moderate degree, typi-cally .2–.5, with the highest correlations between ten-sion and hedonic tone, and between low concentrationand task-irrelevant interference. We factored bothpre- and posttask scale scores. We also factored the“post–pre” difference scores (a dR-analysis), whichidentifies patterns of state change that are uncontami-nated by possible trait influences (Cattell, 1973), al-though factoring change scores may be risky becauseof increased measurement error (Zuckerman, 1976).Both DSSQ versions were also completed by 273North American participants. These data weresampled from two situations only, and so are treatedas subsidiary, but posttask factor structure was ana-lyzed to check cross-national stability.3

In each case, the following procedure was used,Horn’s (1965) parallel analysis was used to determinenumber of factors, because it is highly accurate instudies of artificial data sets in which the number offactors is known (Zwick & Velicer, 1986). Thirty ran-dom data sets with the same number of participantsand variables as the real data set were generated. Theeigenvalues for each data set were found and aver-aged, indicating the magnitude of the eigenvaluewhen correlations reflect chance alone. Factors wereextracted from the real data set until the factor eigen-value was less than the averaged eigenvalue for ran-dom data. An initial principal-components solutionwas extracted on this basis and was rotated using thedirect oblimin criterion, which gives oblique factors.The oblique rotation was used because it maximizessimple structure: Orthogonal rotation distorts factorstructures when the underlying dimensions are, infact, correlated (Cattell, 1973). In each case, parallelanalysis indicated that 3 second-order factors shouldbe extracted from the 10 scales, and all eigenvaluesexceeded 1.0. The percentages of variance explainedwere 62% (pretask), 66% (posttask), 58% (changescores), and 68% (U.S. posttask data).

Table 4 gives the pattern matrices from the fouranalyses. Factor loadings were similar in each case sothat corresponding factors were labeled Task Engage-ment, Distress, and Worry. In each matrix, Task En-

gagement was defined by the three scales of energy,motivation, and concentration, with hedonic tone andsuppression of task-irrelevant interference makinglesser contributions. The factor appears to contrastenthusiasm and interest in the task with fatigue andapathy. It is a cross-domain factor in that it relates tomood, motivation, and cognition. The Distress factorwas defined by mood and cognition scales. Its load-ings combined high tension with low hedonic tone(i.e., unhappiness) and low confidence-control, in linewith the theoretical importance of perceived control instates of affective distress. The Worry factor was ex-clusively cognitive. Its most consistent markers wereself-focus, low self-esteem, and task-irrelevant inter-ference, with poor concentration and task-related in-terference loading at >.3 on three out of the four ma-trices. Factor correlations were small (<.3). In theposttask data, they were .14 (Task Engagement–Distress), −.12 (Task Engagement–Worry), and .22(Distress –Worry). Table 4 also shows that British andNorth American posttask solutions were similar, ex-cept that self-esteem showed its highest loading(negatively) on the Distress factor in the North Ameri-can sample. Table 5 gives the communality (h2) ofeach scale in the British posttask data, representingthe proportion of total variance explained by all threefactors. Some of the unexplained variance representserror. Harman’s (1976) index of completeness, C, alsogiven in Table 5, expresses the reliable variance ex-plained by dividing h2 by the internal consistency ofthe scale (�) to correct for error. Typically, around80% of the reliable scale variance was attributable tothe three secondary factors.

Stability of the factor solution. Table 4 showssome variation in the loadings of scales on factorsacross the four matrices. Are these important? Wemay expect factor analyses of state scales to be lessconsistent than those of trait measures. The situationalinstability of states increases the likelihood of artifactdue to distributional variation, and factor structuremay change across situations and samples. In the pre-sent data, standard deviations were, fortunately, simi-lar in pre- and posttask data, reducing the likelihoodof artifact. Three further analyses were tested for con-sistency of factor structure. First, cross-occasion sta-bility was checked within the 517 British individualswho completed both pre- and posttask scales to con-

3 Variable distributions for each sample are availablefrom Gerald Matthews, as are the correlation matrices forthe analyses of U.S. data and change scores.

FUNDAMENTAL DIMENSIONS OF STATE 321

trol for sample. Everett’s (1983) factor-score methodwas used to check the comparabilities of the factorsolutions obtained from pre- and posttask data. Re-gression model factor scores were calculated using thetwo sets of coefficients provided by the two solutions.Factor score correlations were as follows for pre- andposttask data, respectively: .98 and .99 (Task Engage-ment), .95 and .96 (Distress), and .94 and .92 (Worry).Second, to check cross-sample stability, the 788 Brit-ish individuals who completed the posttask DSSQwere divided into two arbitrary halves, and factorscore coefficients were found for each subsample.Factor score correlations were .96 (Task Engage-ment), .99 (Distress), and .97 (Worry). A third analy-sis checked cross-national stability. Factor scoreswere estimated from the posttask data for British (n �788) and North American (n � 274) samples; thefactor score correlations in the North Americansample were .98 (Task Engagement), .97 (Distress),and .97 (Worry).

Discussion

The factor solution identified broad cross-domainsyndromes that neatly reconcile two major distinc-tions made by state theorists. One hypothesized factorsolution, based on the two-factor model of affect(Watson & Clark, 1984), distinguished energy–PAfrom tension–NA. Energy and tension had high load-ings on the Task Engagement and Distress factors,respectively, supporting Thayer’s (1989) distinctionbetween energy and tension as fundamental aspects ofmood. However, this contrast was not simply betweentwo mood dimensions. Instead, both mood dimen-sions participated in broader state syndromes of taskengagement and distress, which related to cognitionand motivation as well as to affect. A second hypoth-esis distinguished emotionality and worry as funda-mental axes. Consistent with this hypothesis, separatefactors were found for Distress and Worry factors,although the loadings of confidence and control ondistress showed that distress is intimately related tothis aspect of cognition as well as to negative emo-tion. Thus, previous models of state dimensions werepartially supported by the data, but no single modelprovided a comprehensive account of state.

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to Hockey’s overload mode, in binding together ten-sion, negative mood, and cognitions of lack of confi-dence and control. The emergence of the third factor,labeled Worry, extends Hockey’s conceptual schemein demonstrating that self-referent, predominantlynegative thoughts about performance and personalconcerns should be distinguished from emotionalstrain, consistent with theories of worry (e.g., Wells &Matthews, 1994).

Study 2: Sensitivity of States to Task Stressors

The next study aimed to test whether tasks impos-ing qualitatively different kinds of demand and self-regulative challenge elicited different patterns of statechange. Specifically, we expected that WM taskswould elicit Hockey’s (1997) overload mode of state

control, and hence distress, because of their high cog-nitive demands and time pressure. By contrast, thestress of vigilance tasks relates not just to workload,but also to difficulties in sustaining attention to mo-notonous tasks (Scerbo 1998). Such tasks should pro-voke Hockey’s (1997) fatigued control mode in whichloss of performance is associated with aversion toeffort, and hence task disengagement. We used be-tween-groups design so that baseline pretask stateswere similar in each group, facilitating cross-groupcomparisons of task-induced state change. All groupsused student participants. Both British and NorthAmerican samples were used: This variation insample composition is justified by the similarity offactor structure of state in the two nationalities. Statechange was assessed in six groups: two control groupsperforming undemanding tasks, two groups perform-ing time-pressured WM tasks, and two groups per-forming vigilance tasks. We used different instancesof understanding and vigilance tasks, but the two WMtasks were similar.

We also tested the association between workloadand posttask state in four of the groups. Workload isdefined as the investment of cognitive resources in-duced by task demands (Wickens & Hollands, 1999).It may be assessed either through self-report orthrough objective means, such as psychophysiologicalrecording. Subjective and objective workload nor-mally concur quite well, but may dissociate undercertain conditions that relate primarily to the extentthat changes in resource investment actually affectperformance (Wickens & Hollands, 1999). TheNASA-TLX (Hart & Staveland, 1988) is a subjectivescale and one of the most widely used workload in-

Table 5Communalities (h2) and Indices of Completeness (C) forScales in the Factor Analysis of British Posttask Data

Scale � h2 C

Energetic Arousal .82 .58 71Motivation .80 .71 89Concentration .89 .75 84Tense Arousal .87 .72 83Hedonic Tone .88 .76 86Confidence-Control .84 .58 69Self-Focus .87 .70 80Self-Esteem .89 .65 73CI-TR .77 .60 78CI-TI .85 .60 71

Note. CI-TR � task-relevant cognitive interference; CI-TI �task-irrelevant cognitive interference.

Table 4Secondary Factor Structure of the DSSQ, in British Pretask (Pre), Posttask (Post), and Change Score Data, and inAmerican Posttask Data (Pattern Matrices)

Scale

Task engagement Distress Worry

Pre Post Change U.S. Pre Post Change U.S. Pre Post Change U.S.

Energetic Arousal 67 71 78 82 −18 −22 −02 −04 02 −02 07 −06Motivation 85 84 78 82 24 02 −21 01 −03 17 18 16Concentration 50 68 71 57 −18 −02 02 −16 −54 −46 −26 −46Tense Arousal −01 29 29 12 77 82 78 88 −01 03 11 00Hedonic Tone 52 34 28 36 −46 −75 −77 −71 −13 −01 −08 −16Confidence-Control 31 29 23 45 −66 −67 −60 −66 32 05 −13 14Self-Focus 15 −03 −32 03 11 −23 −38 09 80 85 51 72Self-Esteem −23 −26 −07 −11 −63 −24 −18 −63 −35 −71 −77 −49CI-TR 11 01 07 20 54 31 12 13 28 64 66 77CI-TI −17 −47 −53 −29 −07 −07 −32 −37 83 58 36 73

Note. Loadings exceeding ±0.4 are in bold. DSSQ � Dundee Stress State Questionnaire (Matthews et al., 1999); U.S. � American data;CI-TR � task-relevant cognitive interference; CI-TI � task-irrelevant cognitive interference.

FUNDAMENTAL DIMENSIONS OF STATE 323

struments (Wickens & Hollands, 1999). Followingperformance, the person rates six sources of workloadfor the particular task performed, which are aggre-gated to give an overall workload score. Hart andStaveland (1988) confirmed that ratings were highlysensitive to objective task demands, suggesting sub-jective bias was minor. Hockey’s (1997) linkage ofoverload to emotional strain implies that high work-load should relate primarily to distress. In addition,rating scales that relate specifically to involvementwith the task, namely effort and self-rated perfor-mance, should correlate with task engagement.

Method

Six groups of university students were tested (n �618: 265 men and 353 women), with a mean age of20.8. Two groups were control groups, performingundemanding tasks. Group 1 comprised 16 men and16 women (mean age � 18.7), and Group 2 com-prised 18 men and 32 women (mean age � 19.4). Theparticipants exposed to demanding, potentially stress-ful tasks (Groups 3–6) were previously included in thefactor analysis (see Table 2). Age and gender data forthese groups can be found in Table 2. In each case,participants were informed that the study was con-cerned with people’s performance, and their feelingsduring performance, on the task concerned. Partici-pants were instructed to respond as quickly and accu-rately as possible (except for Group 2). Debriefingsfor Groups 3–6 stated that the tasks were intended tobe demanding and it was normal to experience somenegative moods during testing.

Group 1 (n � 32). Participants were tested inindividual cubicles at the Department of Psychology,University of Cincinnati. They were asked to sortstandard decks of cards into the four suits for a periodof 5 min. A computerized metronome presented au-ditory click stimuli at a rate of 1 per second to pacethe sorting. This task has previously been used as alow-workload reference condition in studies of atten-tion and performance: Mean workload on a 0–100scale is about 20 (Temple et al., 2000).

Group 2 (n � 50). Participants were tested inindividual cubicles at the Department of Psychology,University of Cincinnati. They were asked to selectand read a magazine, choosing from popular titles oncurrent affairs and entertainment, for a duration of 15min. Afterward, they completed a short questionnaireabout their enjoyment of the magazine, ostensibly formarket research purposes.4

Group 3 (n � 114). Participants were tested inindividual cubicles at the Department of Psychology,

University of Dundee.5 They performed a 24-min au-ditory vigilance task, similar to that used by Lieber-man, Wurtman, Emde, Roberts, and Coviella (1987).Participants were presented with a series of 70 dBtones through headphones, at a rate of 30 per minute.They were required to press a key in response to tonesshorter in duration than the standard duration of 700ms (target probability: .044). Target duration was in-dividually set to a value that gave an initial detectionrate of approximately 90%; typically 550–600 ms.

Group 4 (n � 115). Participants performed two12-min visual vigilance tasks (Matthews, Davies, &Holley, 1993) at the University of Dundee. Partici-pants watched a series of pairs of horizontal flickeringlines, presented for 300 ms each at a rate of 60 perminute, and pressed a key in response to a targetstimulus 15% longer than the standard length (targetprobability: .25). In one version (comparative judg-ment), only one of the pair of lines was longer thanstandard length, whereas in the second version (abso-lute judgment), both lines were longer. Order of ad-ministration of the two versions was counterbalanced.

Group 5 (n � 137). A visual WM task (Mat-thews & Campbell, 1999) was used. Participants weretested in groups and presented with sets of five itemsin succession on a projection screen in a University ofDundee lecture theater. Each item had an arithmeticand word-recall component. Each item presented a

4 Group 2 completed a new version of the DSSQ con-taining a revised set of motivation items, with a changedresponse format. Data from another sample (n � 468),which completed both original and revised Motivationscales, showed that factor score estimates calculated usingthe two versions of the Motivation scales correlated at .96(task engagement), 1.00 (distress), and 1.00 (worry).

5 Groups 3 and 4 participated in a balanced-placebo studyof effects of tea ingestion on mood and performance, inwhich expectancy (caffeinated or decaffeinated) and lowdosages of caffeine (0, 50, 100 mg) were manipulated in-dependently. Effects of beverage ingestion were of smallmagnitude compared with the effects of performing thetasks and did not influence the second-order DSSQ factors.These results are not discussed further in this article. Allparticipants performed the following sequence: DSSQ 1,Vigilance Task 1; DSSQ 2, Vigilance Task 2; DSSQ 3.Vigilance Tasks 1 and 2 were the auditory and visual tasks,whose ordering was counterbalanced across the sample.Group 3 performed the auditory task first, whereas Group 4performed the visual tasks first. In each case, DSSQ 1 istaken as the pretask DSSQ, and DSSQ 2 as the posttaskDSSQ.

MATTHEWS ET AL.324

simple calculation such as “(9/3) + 4 � 7?” Partici-pants were required to check a box on an answer formif the calculation was correct and to ignore incorrectcalculations. A high-frequency concrete noun (e.g.,WINDOW) was printed above the calculation. Aftereach set of five items, participants were required towrite down, in the correct order, the noun presentedsimultaneously with each calculation: 15 s was al-lowed for this part of the task. There were 16 sets of5 arithmetic problems, so participants had to recallfive nouns at a time. Time pressure was imposed byprogressively reducing the time available for eachproblem from 6 s for the 1st set to 3 s for the 16th set.Participants were required to maintain silence duringthe task; all groups met this requirement.

Group 6 (n � 170). A further study of visualWM was conducted at the University of Oklahoma,using the same task as for Sample 3 and a singletesting session. Total duration was longer becauseparticipants were given longer to write down the wordsets from memory (25 s).

Groups 2–5 completed a modified form of theNASA-TLX (Hart & Staveland, 1988) posttask,which comprised six rating scales for different aspectsof workload: Mental Demands, Physical Demands,Temporal Demands, Poor Performance, Effort, andFrustration. Participants were required to give a nu-merical rating from 0 to 10 rather than use the 0–100visual analogue scales of the original form. Overallworkload was computed by using a simple un-weighted mean.

Results

To compare states and state changes across differ-ent samples, tasks, and studies, a common metric isrequired. However, it is difficult to provide meaning-ful normative data for states, because the distributionsof state variables differ across situations. The best wecan do is to take some arbitrary reference point as abaseline, against which data may be compared. Re-search using the DSSQ takes as an arbitrary baselinethe typical states of persons at the beginning of anexperiment by using a laboratory task of no overtpersonal significance (Matthews et al., 1999). Some-what similarly, the boiling point of water was onceused as an arbitrary reference for temperature mea-surement, although it has no intrinsic significance andvaries with extraneous variables such as atmosphericpressure. Hence, the “norms” for the DSSQ are themeans and standard deviations for the sample of 517British participants completing the pretask measure,which were fairly stable across different samples.

Posttask standard deviations were similar to pretaskstandard deviations: typically, task-induced stress in-fluenced central tendency rather than variability ofscores.

Initially, pre- and posttask scores were calculatedfor the 10 first-order DSSQ scales for each partici-pant. Scale values were transformed to standardizedzs, using the British norms. Next, regression modelfactor scores were calculated for each of the 3 second-order factors as weighted sums of the zs for the 10first-order scales. The regression coefficients for theweighting were derived from the posttask British fac-tor solution (Study 1). Calculation of regressionmodel factor scores maximizes the correlation be-tween the factor scores and the underlying factor. Fac-tor scores are distributed with a mean of 0 and stan-dard deviation of 1. As this study focuses on task-induced state change, we report, as a descriptivestatistic, the sample means for difference scores (i.e.,posttask score–pretask score). The difference scoreexpresses state change in units representing the nor-mative standard deviation, and so it may be seen as az (see Cohen, 1988). A positive z indicates that scoresincreased as a consequence of task performance. Thecalculation of difference scores provides a convenientway of comparing state change magnitudes acrossscales and samples, but the zs should be interpretedwith caution because the normative baseline is arbi-trary.

Cohen (1988) suggested that z values of .2, .5, and.8 correspond to small, medium, and large effect sizes,respectively. In looking at effect sizes for compari-sons between multiple means, derived from analysesof variance (ANOVAs), we used the partial eta-squared index which estimates the proportion of vari-ance explained by the effect of interest. Conventionaleta-squared values for small, medium, and large ef-fects are approximately .01, .06, and .14 (these valuescorrespond to the squared point biserial rs for themean differences represented by z).

Patterns of task-induced state change. An initialmultivariate ANOVA used a 3 × 6 × 2 (State Factor ×Task × Time of Administration) design to test fordifferential state change across tasks. Time of admin-istration was a within-subjects factor contrasting pre-and posttask assessments of state. The State Factor ×Task × Time of Administration interaction was sig-nificant (Pillai’s trace � 0.53), F(10, 1226) � 43.64,p < .01), confirming that state change (i.e., effects oftime) varied with both tasks and state factors. Uni-variate analyses were conducted to test that (a) eachstate factor was sensitive to effects of task type, (b)

FUNDAMENTAL DIMENSIONS OF STATE 325

effects of WM and vigilance tasks both differed fromthe control conditions, and (c) the nature of statechange for each of the three task types. Sums ofsquares for effects were adjusted for each other byusing a regression approach to maintain invariancewith respect to the cell frequencies, which were un-equal.

The first set of analyses used 6 × 2 (Task × Time ofAdministration) mixed-model ANOVAs, with each ofthe three DSSQ factor score estimates as the depen-dent variable. Interaction between task and time dem-onstrates differential state change across tasks. Thisinteraction was significant for task engagement,F(5, 612) � 179.59, p < .01, �2 � .422; distress,F(5, 612) � 31.49, p < .01, �2 � .204; and worry,F(5, 612) � 22.37, p < .01, �2 � .154. Next, thecontrast between (a) the two control tasks and (b) thetwo WM tasks were analyzed within a 4 × 2 (Task ×Time of Administration) design. The Task × Timeinteraction, for the control versus WM contrast, wassignificant for distress, F(1, 386) � 101.69, p < .01,�2 � .209; and task engagement, F(1, 386) � 10.12,p < .01, �2 � .025; but not for worry. WM induceshigher distress and task engagement than the controlconditions. We performed a similar analysis for thecontrast between (a) the two control tasks and (b) thetwo vigilance tasks. The Task × Time interaction forthe contrast was significant for distress, F(1, 307) �14.79, p < .01, �2 � .046; task engagement, F(1, 307)� 101.53, p < .01, �2 � .145; and worry F(1, 307) �

63.65, p < .01, �2 � .172. Relative to the controlconditions, the vigilance tasks elicited higher distressand worry but lower task engagement. Both types oftask produced state changes that differed from thoseseen in the control condition. Figure 1 shows patternsof state change for each factor, expressed as differ-ence scores standardized against the normative pre-task standard deviation (a full table of means andstandard deviations is given in Appendix A).

The final step of the analysis investigated statechange within each of the three task types (control,vigilance, WM). For each task type, a 2 × 2 (TaskType × Time of Administration) mixed-modelANOVA was conducted for each DSSQ state factor.The within-subjects time factor gives an estimate ofthe state change effect size for that task type. Thiseffect size tests the predictions made in the introduc-tion of Study 2; that is, that WM tasks should increasedistress, and vigilance tasks should decrease task en-gagement. The Task × Time factor gives an effect sizefor the difference in state change for the two taskscomposing that category. Because each pair of taskswas broadly similar, we expected that these effectsizes would be small.

In the control conditions, the main effect of timewas significant only for worry, F(1, 80) � 37.14, p <.01, �2 � .629 and there was no significant Time ×Task interaction (i.e., cardsort vs. reading tasks) forany of the three dependent variables (all �2s < .050).That is, worry showed a large-magnitude decline in

Figure 1. Task-induced changes in DSSQ (Dundee Stress State Questionnaire; Matthews etal., 1999) second-order factors in six participant groups. (All changes are significant exceptthose marked with a pound sign [“#”].) Visual WM � Visual working memory; UK �British participants; USA � American participants.

MATTHEWS ET AL.326

undemanding conditions, but distress and task en-gagement did not change. For WM, the effect of timewas significant for task engagement, F(1, 305) �116.88, p < .01, �2 � .276; distress, F(1, 305) �399.92, p < .01, �2 � .567; and worry, F(1, 305) �314.19, p < .01, �2 � .507. Task performance in-creased task engagement and distress but decreasedworry. In addition, the Time × Task interaction wassignificant for distress, F(1, 305) � 6.87, p < .01, �2

�.022 . Both samples showed increased distress, butstate change was greater in the British sample. How-ever, the magnitude of the interaction was small com-pared with that of the main effect. For vigilance, theeffect of time was significant for task engagement,F(1, 227) � 281.27, p < .01, �2 � .553; distress, F(1,227) � 34.28, p < .01, �2 � .131; and worry F(1,227) � 12.51, p < .01, �2 � .052. The Time × Taskinteraction was significant only for task engagement,F(1, 227) � 11.66, p < .01, �2 � .049. The principaleffect of performance was to reduce task engagement,with smaller magnitude increases in distress and de-creases in worry. The decline in task engagement wasgreater for the visual task (perhaps because eyestrainis a source of fatigue in vigilance; Temple et al.,2000), but the effect size was small compared with theoverall effect of task. Figure 1 also shows which spe-cific state changes failed to reach significance at p <.05, on the basis of post hoc t tests, using the Bonfer-roni correction to control for Type I error.

Workload. The effect of task on mean workloadwas significant in a one-way ANOVA, F(3, 412) �64.40, p < .01, �2 � .319. Workload was higher forWM (British sample: M � 6.59) than for visual vigi-lance (M � 5.39), auditory vigilance (M � 4.78) orthe reading task (M � 4.02). Workload derived moststrongly from mental demands (mean rating � 6.47)and least from physical demands (mean rating �2.44). Task differences for the rating scales were gen-erally similar to those for overall workload. For ex-ample, means for Mental Demands were 8.05 (WM),6.50 (visual vigilance), 5.57 (auditory vigilance), and4.12 (reading). Table 6 gives correlations betweenposttask DSSQ factor scores and both overall work-load and the individual rating scales. Overall work-load was most strongly correlated with distress, butalso related to higher task engagement and worry. Infact, distress was correlated with all workload ratings,but task engagement showed a more differentiatedpattern of correlation. Higher task engagement wasassociated with all three forms of task demand (men-tal, physical, and temporal) and higher effort, but alsowith reduced workload with respect to higher perfor-

mance ratings and low frustration. Overall workloadwas also analyzed with a modified index, the mean ofthe first 5 ratings that excluded the frustration item.Although the NASA-TLX rationale is that the respon-dent is rating “the workload due to dealing with frus-tration” rather than frustration itself, the content of therating appears similar to DSSQ content, leading to apossibly artifactual correlation. Table 6 shows thatdropping the frustration rating from the overall indexhad little effect on correlations.

DiscussionDemanding tasks do not elicit some generalized

stress or arousal response. Instead, qualitatively dif-ferent task demands lead to different patterns of statechange. Performance of undemanding tasks eliciteddecline in worry; vigilance produced task disengage-ment and moderate distress; and WM evoked highlyelevated distress, decreased worry, and moderately el-evated task engagement.6 The state changes resultingfrom performing WM and vigilance tasks resembleHockey’s (1997) descriptions of overload and fa-tigued self-regulative control modes, respectively.NASA-TLX data confirmed that the WM task im-posed the highest workload. Overload of capacity to

6 Analyses of the 10 first-order DSSQ scales (availablefrom Gerald Matthews) show that the primary scales asso-ciated with each second-order factor illustrate a coherentpattern of task-induced state change. For example, vigilanceproduced concurrent declines in energy, motivation, andconcentration (loss of task engagement). Working memoryproduced two coherent patterns: (a) reduced confidence andhedonic tone, and increased tension (increased distress); and(b) reduced self-focus and cognitive interference, and in-creased self-esteem (reduced worry).

Table 6NASA-TLX Correlates of Posttask Stress State,Aggregated Across Four Samples (n = 416)

VariableTask

engagement Distress Worry

Mental Demands .27** .34** .10*Physical Demands .26** .33** .15**Temporal Demands .20** .43** .09Poor Performance −.26** .41** −.07Effort .35** .18** .05Frustration −.18** .53** .19**Overall workload .21** .54** .18**Overall workload

(excluding frustration).24** .51** .14**

Note. NASA-TLX � NASA-Task Load Index (Hart & Stave-land, 1988).* p < .05. **p < .01.

FUNDAMENTAL DIMENSIONS OF STATE 327

manage task demands may elicit both the affective(e.g., tension) and cognitive (e.g., low confidence)aspects of the distress response. The WM task alsoelicited somewhat increased levels of task engage-ment: the overloaded person may strive to maintainperformance despite feelings of emotional strain. Cor-relational data suggested that overall workload corre-lates most highly with posttask distress, but perceiveddemands also relate to task engagement.

The vigilance tasks were lower in workload butrequire the performer to cope with the monotony ofthe task (Scerbo, 1998). Effort reduction is a commonresponse to tasks that have little-perceived value(Hockey, 1997). Hence, reduced motivation, energy,and concentration may all be expressions of the fa-tigued control mode. Likewise, both low demands andlow effort on the NASA-TLX correlated with taskdisengagement. The medium-magnitude increases indistress seen in vigilance might reflect the substantialworkload of the tasks, although lower than for WM.Hence, in performance settings, the distinction be-tween overload (distress) and effort reduction (taskdisengagement) may be more fundamental than thedistinction between NA and PA. Tasks that are bothhighly demanding and monotonous may elicit bothtypes of state change. Matthews and Desmond (2002)induced task fatigue by having participants perform aprolonged high-workload drive on a simulator. Thefatigue manipulation produced both increased distressand decreased task engagement.

The factor analyses suggested that Hockey’s (1997)account of control modes was incomplete in neglect-ing worry responses. If worry and distress are trulydistinct responses, they should sometimes be dissoci-ated, that is, one should change while the other re-mains constant. In fact, the control conditions pro-duced lower worry, with no change in distress. TheWM task elicited changes in opposite directions: in-creased distress and decreased worry. Distress andworry were also differentiated by the considerablyweaker correlations obtained between worry andNASA-TLX workload. As described by self-regulative theory (Matthews & Wells, 1999; Wells &Matthews, 1994), worry may be linked to general so-cial-evaluative concerns, such as appearing competentto the experimenter rather than to concerns about cop-ing with the specific demands of the task. Worry gen-erally declined over time. Participants attending labo-ratory experiments probably have concerns aboutwhether they will cope adequately with task require-ments, but, typically, these concerns are reduced bysuccessful compliance with the experimental proto-

col. Poor performance on an artificial task of no di-rect, personal significance may not threaten self-worth. Directing attention toward the task may oftensuppress worry, as in the control conditions and WMtasks outlined in this study. However, when the task ismonotonous, the person may be more inclined to al-locate attention to some aspects of self-evaluation, aswith the vigilance tasks. In a study of simulated driv-ing, Matthews (1996) had participants drive in a“black ice” condition, in which the car skidded fre-quently and uncontrollably. This failure experienceinduced a large increase in interference (in addition todistress), suggesting that threats to personal compe-tence may have a powerful effect on worry (whichcorrelates strongly with cognitive interference).

Study 3: States and Cognitive Stress Processes

Self-regulation is supported by the multiple ap-praisal and coping processes described by the trans-actional model of stress (Lazarus, 1999). A self-regulative control loop (Carver & Scheier, 1990)requires evaluation of the status of the self with re-spect to personal goals (appraisal) and formulation ofcorrective responses to reduce discrepancy betweencurrent and preferred status (coping). For example,self-regulation in anxiety is directed toward the re-moval of threats to personal safety or self-worth. It isaccompanied by appraisals of threat and lack of per-sonal control and coping efforts that include activemonitoring for threat and self-critical emotion-focused strategies that maintain awareness of threat(Wells & Matthews, 1994). Hence, subjective statesshould be systematically related to these cognitivestress processes.

Study 3 aimed to test whether a person’s typicalstyle of coping and appraisal predicted state beforeand after performance of tasks requiring WM, withinan occupational context. Measures were taken as fol-lows. First, we assessed three fundamental categoriesof coping: task-focus (also described as problem-focus), emotion-focus, and avoidance. Psychometricevidence, conceptual analyses, and empirical studiesconverge in discriminating these categories (Endler &Parker, 1990; Matthews & Wells, 1996), although itmay also be important to distinguish different sub-types of coping within each category. Second, weused two widely used job perception questionnaires toindex appraisal of working conditions (i.e., context-specific appraisals rather than more abstract catego-

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ries of appraisal such as threat and challenge; Lazarus,1991) to test whether coping influences stress stateover and above effects of the work environment.Third, we used the NASA-TLX workload scale as anindex of appraisal of immediate task demands. Fourth,we assessed the basic personality traits of neuroticism(N) and extraversion (E) to verify that correlationsbetween coping and state were not an artifact of con-founding by personality. These traits may correspondto dispositional NA and PA, respectively (Watson &Clark, 1992), and correlate with coping measures inboth general and occupational settings (Costa, Somer-field, & McCrae, 1996; Matthews & Deary, 1998).

We outline below several distinct pathways throughwhich cognitive processes may influence state, espe-cially in real-world settings in which the person actsautonomously.

Direct effects: Appraisal theories of emotion (e.g.,van Reekum & Scherer, 1997) claim that appraisalsare a direct, proximal influence on affective state. Therole of appraisal in cognitive and motivational stateshas attracted less interest, although negative self-evaluations are a precursor of cognitive interference(Sarason et al., 1986), and self-beliefs influence mo-tivation in performance settings (Dweck, 2000). Simi-larly, some coping is directly targeted at mood change(Thayer, 1996).

Internal feedback: Coping and appraisal are in dy-namic interaction (Lazarus & Folkman, 1984). Forexample, appraisals of lack of personal competencetend to lead to the use of emotion-focused rather thantask-focused coping strategies. However, choice ofcoping strategy also feeds back into appraisal. In atask environment, for example, formulating a work-able plan of action is likely to increase appraisal ofself-competence.

External feedback: Coping processes that influencebehavior produce immediate or delayed changes inthe external world. Over time, appraisal of thesechanges will feed back into cognitive stress processes.In performance environments, task-focused coping islikely to be more successful in dealing with task de-mands than emotion-focused or avoidance strategies(Matthews & Wells, 1996). Assuming the person ismotivated to perform, the latter two strategies aremore likely to be associated with poor performance,and hence with worry and distress.

Environmental selection: Coping may influence theenvironments to which a person is exposed. For ex-ample, task-focus may encourage a person at work tobehave proactively in taking on additional responsi-bilities, seeking out coworkers, and so forth. In social

environments, others’ appraisals of a person’s copingmay also influence environmental exposure. At work,difficult tasks may be assigned to those people whoappear to be more task-focused.

Hence, causal processes are likely to be complexand dynamic. Thus, this study aimed to establish somebroad relationships between cognitive process mea-sures and stress states in a controlled setting, ratherthan to test causal models. The basis for prediction isthat the various causal pathways are likely to generatesome consistency in patterns of appraisal and copingwithin the workplace, and these patterns of cognitionshould correlate with state. Dynamic interaction be-tween appraisal, coping and interaction with the ex-ternal world is likely to propagate interindividual con-sistency (Matthews, 1999). For example, individualdifferences in threat appraisal may generate individualdifferences in coping behavior (e.g., emotion-focus)and environmental exposure (e.g., avoidance of po-tentially threatening situations). In addition, personal-ity factors such as N may introduce bias into multipleappraisal and coping processes (Costa et al., 1996;Matthews, Derryberry, & Siegle, 2000). Hence, al-though cognitions and appraisals vary across time,depending on external events, people are likely toshow consistent biases in patterns of appraisal, cop-ing, and exposure to situational stressors.

We tested participants in experimentally controlledsettings in the workplace during a normal workingday, performing either an auditory WM task or asimulated customer service task that required WM, inthat the participant had to keep an inquiry in memorywhile formulating a vocal response. Both types of taskplaced taxing cognitive demands on the participant,requiring attempts at coping. Individual differences incoping were predicted to influence subjective stateresponse as follows: Task-focused coping is related toapproach tendencies and active, effective involvementin the work environment (e.g., Bhagat, Allie, & Ford,1995). Hence, task-focused coping should relate pri-marily to higher task engagement. As task-focus mayalso maintain adequate performance, it may also cor-relate with reduced distress and worry. Emotion-focus, as operationalized by Endler and Parker (1990),refers to strategies such as self-criticism that tend toactivate self-discrepancies, elevating both distress andworry (Matthews & Wells, 1996). Avoidance copinginvolves withdrawal from task-related activities, shift-ing attention from external stimuli to personal con-cerns (Matthews & Wells, 1996). Hence, it was pre-dicted that this form of coping would relate primarilyto worry.

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Method

Participants. This study included four groups ofparticipants. The total N of 328 was made up of 89men and 239 women (mean age � 33.1). Groups 1and 2 were included in the factor analyses of Study 1;demographic information on these participants isgiven in Table 2 (i.e., as Samples 8 and 9). Groups 3and 4 were made up of additional participants whoperformed simulations of customer service work (51men and 126 women; mean age � 30.8).

Group 1 (n � 83) comprised social workers em-ployed at a Scottish regional council. They advisedclients with various life problems, either in residentialsettings or in homes for people with difficulties incommunity living. They were tested in their offices atwork in groups of 5–10. They performed an auditoryWM task, based on a task used by Yuill, Oakhill, andParkin (1989). Participants were played a tape of thetask stimuli. The task required ordered recall of everythird digit in a 15-digit sequence, spoken aloud, at arate of 1 digit per second. Participants also had tocount the number of occurrences of the digit 5 in thesequence. There were 18 sequences of this kind. Awritten response was given at the end of each se-quence. Group 2 (n � 68) comprised employees of amajor British telecommunications company employedin customer service positions. They were required toanswer phone calls from customers, frequently aboutaccounts and billing. They performed the same audi-tory WM task as Group 1. Group 3 (n � 86) wereemployees of a large British retail organization. Theirjob was to answer calls from customers. They weretrained both in knowledge of company products andin the social skills required to handle customers whomay be irate. A simulation of the customer servicetask was developed. Each participant was tested indi-vidually in a cubicle. Participants were presented withauditory stimuli by telephone through a headset.Stimuli comprised a total of 32 representative inqui-ries and complaints about company products. A vocalresponse was required, which was tape-recorded foranalysis of performance (not reported here). Group 4(n � 91) was a different set of employees working incustomer service for the same telecommunicationscompany as Group 2. They were tested in individualcubicles, wearing headsets through which the auditorystimuli were delivered. They performed a simulationin two phases, designed to separate declarative fromprocedural knowledge of the customer service task.Ten sample inquiries about billing and accounts wereconstructed. Each one required the employee to elicit

relevant information from the customer (e.g., accountnumber), make changes in a database, and inform thecustomer of what was being done. In the first phase ofthe simulation, the employees described in generalterms how they would handle the inquiry. In the sec-ond phase, they were required to handle the call asthey would at work, asking for the customer’s accountnumber, searching a computerized database for thecaller’s account, and making changes to the databaserecords as appropriate.

Questionnaire measures. Job perceptions wereassessed with six scales that discriminate the main jobcharacteristics associated with stress (Sims, Szilagyi,& Keller, 1976) and with a questionnaire measure ofintrinsic and extrinsic job satisfaction (Warr, Cook, &Wall, 1979). These measures were substantially inter-correlated, so the eight scales were subjected to aprincipal-components analysis. Two rotated factors,correlated at .23, were extracted. The first factor (la-beled Job Rewards) was defined by the followingfactor pattern loadings >.4: friendship opportunities(loading of .82), variety (.82), identity or job coher-ence (.81), working with others (.69), personal au-tonomy (.67), and intrinsic job satisfaction (.62). JobRewards relates mainly to various attributes of the jobitself, including its social and intrinsic components.The second factor Work Environment was defined byfeedback on job performance (.88), extrinsic job sat-isfaction (.69), and intrinsic job satisfaction (.53).This factor seems to relate to appraisals of employees’treatment by management. Regression model factorscores were analyzed subsequently. Coping was as-sessed with a shortened version of the Coping Inven-tory for Stressful Situations (CISS; Endler & Parker,1994), comprising the 20 items with the highest load-ings on the CISS Task-Focus, Emotion-Focus, andAvoidance Scales (6–7 items/scale). Previous re-search verified that the short scales were highly cor-related with the originals, and scale reliabilities ex-ceeded 0.75 (Mohamed, 1996). Instructions weremodified so that respondents were asked specificallyhow they dealt with work demands. Instructions forjob auditory perception and coping measures empha-sized reporting typical responses. The study also in-cluded the Eysenck Personality Questionnaire—Revised (Eysenck, Eysenck, & Barrett, 1985) as ameasure of the neuroticism (N) and extraversion (E)personality dimensions.

Procedure. In each study, job perception, coping,and personality scales were completed in the respon-dent’s own time, before the experimental session, tominimize any contamination by current state. On the

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day of testing, participants completed a pretaskDSSQ, the experimental task, and the posttask DSSQand NASA-TLX.

Results

We calculated factor scores by using the normativeregression weights, as in Study 1. Figure 2 shows theprofiles of state change for each group (see AppendixB for pre- and posttask means).7 An initial multivari-ate test, using a 3 × 4 × 2 (State Factor × Task × Timeof Administration) design, showed a significant inter-action between the three factors (Pillai’s trace �0.10), F(6, 648) � 5.51, p < .01; the tasks eliciteddifferent patterns of state change across the three fac-tors. Univariate ANOVAs used a 2 × 4 (Time of Ad-ministration × Group) design. The main effect of timewas significant for task engagement, F(1, 324) �27.36, p < .01, �2 � .078; distress, F(1, 324) �161.33, p < .01, �2 � .332; and worry, F(1, 324) �256.60, p < .01, �2 � .442. These tasks elicited apattern of state change characterized by two largemagnitude effects, increased distress, and decreasedworry, together with a smaller increase in task en-gagement. The Sample × Time interaction was sig-nificant for task engagement, F(3, 324) � 5.53, p <.01, �2 � .049; and for worry, F(3, 324) � 8.33, p <.01, �2 � .072; but not for distress, F(3, 324) � 1.86.Further analysis showed some variation in the size ofchanges in task engagement across the sample, whichwere significant (p < .01) only in Samples 1 and 4.Values of �2 for Samples 1–4 were .164, .053,

.003, and .265. The Sample × Time interaction forworry reflected a greater decline in worry for auditoryWM than for simulated customer service. The statechange was independently significant for the two WMgroups, F(1, 150) � 159.94, p < .01, �2 � .515; andfor the two simulation task groups, F(1, 176) �89.98, p < .01, �2 � .338.

Table 7 gives means for the workload, job percep-tion, coping, and personality variables and their cor-relations with the DSSQ state factors assessed pre-and posttask. Overall workload was analyzed by usingthe modified index, excluding the frustration rating, asdiscussed in Study 2. The effect of group on workloadwas significant in a one-way ANOVA, F(3, 320) �10.01, p < .01, �2 � .086. Workload was higher forthe two customer service tasks (M � 6.09 and 6.06)than for the auditory WM task (M � 5.36 and 5.24).The workload data replicated the significant relation-ships found between overall workload and posttaskengagement, distress, and worry in Study 2. The cor-relations between pretask state and workload ratedposttask suggest that states may bias workload ap-

7 The two simulation groups showed elevated levels oftask engagement before performance, which carried throughto posttask testing (see Appendix B). These participantsappeared to be excited and motivated by the opportunity todemonstrate their work skills. However, the baseline differ-ence appeared to have little systematic effect on the task-induced engagement response.

Figure 2. Task-induced changes in DSSQ (Dundee Stress State Questionnaire; Matthews etal., 1999) second-order factors in four participant groups. (All changes are significant exceptthose marked with a pound sign [“#”].) Auditory WM � Auditory working memory; Gp �group.

FUNDAMENTAL DIMENSIONS OF STATE 331

praisal to a modest degree. The most robust predictorsof state would appear to be those that emerge in bothpre- and posttask data. On this basis, task engagementrelated most consistently to perceptions of a support-ive work environment; distress to high emotion-focus,low task-focus, and more neurotic personality; worryrelated most consistently to high emotion-focus, highavoidance, and neuroticism. Some predictors weresignificant only in the pretask data. For example, atthis time, task engagement was associated with bothJob Perception factors, high task-focus and avoidance,E, and low N.

Table 7 suggests that stress states relate to multiplepredictors, but these predictors were intercorrelated.For example, N was correlated with lower perceptionsof job rewards (r � −.37, p < .01), lower externalsupport (r � −.21, p < .01), lower task-focus (r �−.30, p < .01), and higher emotion-focus (r � −.41, p< .01). Perhaps the correlations between job percep-tion, coping, and stress state were artifacts of mutualconfounding by N. To test whether there were mul-tiple, independent predictors of stress states, we con-ducted multiple regressions.

We used posttask DSSQ factors as criteria becausethey index state in the experimentally controlled en-vironment of task performance. We emphasize thatthese analyses were not intended to test causal mod-els, which would entail modeling of (a) workplacestressors influencing posttask state indirectly, via pre-task state; and (b) situational cognitions mediating

effects of more distal variables. The aim here is sim-ply to test whether the states, as experienced duringthe controlled task, relate to distinct patterns of cog-nition. We conducted hierarchical multiple regres-sions with variables entered in blocks, whose order ofentry reflected proximity of the predictors to subjec-tive state. The first block comprised task type, WM,or simulation (effect-coded as “1” or “−1”) to controlfor differential effects of the two task types. The sec-ond block was made up of the workload index, thethird block comprised the five cognitive process vari-ables (two Job Perception factors + three CopingScales), and the fourth block consisted of the person-ality traits of E and N. The significance of the addi-tional variance contributed to the equation by eachblock on entry was tested. In the regression equation,with worry as a criterion, posttask distress was alsoincluded in the first block of predictors. Distress andworry were positively correlated in this sample (r �.40, p < .01), and so there was a concern that corre-lates of worry might reflect this association. Control-ling for distress identifies unique predictors of worry.

The final equations, including all predictors, weresignificant for task engagement (R � .39), F(9, 318)� 6.36, p < .01; distress (R �.62), F(9, 318) �22.38, p < .01; and worry (R �.55), F(10, 317) �14.00, p < .01. The variance inflation factor (VIF)statistic for collinearity diagnosis was calculated foreach predictor in these equations. All VIFs were < 2.0,suggesting that collinearity was not a problem in these

Table 7Correlates of DSSQ Stress State Factors, Assessed Pre- (Pre) and Posttask (Post), in anOccupational Sample (n = 328)

Variable M SD

Taskengagement Distress Worry

Pre Post Pre Post Pre Post

WorkloadNASA-TLXa 5.72 1.32 .27** .19** .16** .49** .17** .36**

Job perceptionsJob rewards 0.00 1.00 .14* .00 −.18** −.09 −.08 −.01External support 0.00 1.00 .31** .21** −.19** −.00 −.04 .11*

CopingTask-focus 22.11 4.33 .19** .08 −.14* −.15** .00 .01Emotion-focus 18.65 7.59 −.10 −.07 .43** .42** .48** .39**Avoidance 13.68 5.60 .15** .06 .05 .19** .21** .32**

PersonalityExtraversion 8.66 3.28 .23** .10 −.28** −.10 −.06 −.01Neuroticism 4.73 3.30 −.24** −.08 .42** .29** .32** .15**

Note. DSSQ � Dundee Stress State Questionnaire (Matthews et al., 1999); NASA-TLX � NASA-Task Load Index (Hart & Staveland, 1988).a Modified index, excluding frustration item.* p < .05. **p < .01.

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regressions. Table 8 provides the standardized regres-sion coefficients (�s) for each predictor in the finalequations. For task engagement, only the cognitivevariables added significantly to the variance explainedby task type (�R2 � .034, �F(5, 320) � 2.56, p <.05). Individuals perceiving higher external support atwork were higher in task engagement. For distress,there were significant contributions to the equationfrom workload (�R2 � .208), �F(1, 325) � 88.53,p < .01; cognitive variables (�R2 � .135), �F(5, 320)� 13.82, p < .01; and personality (�R2 � .015),�F(2, 318) � 3.88, p < .05. Distress related to mul-tiple, independent factors: higher workload, higheremotion-focus, lower task-focus, and neurotic person-ality. For worry (with distress controlled), the contri-butions of workload (�R2 � .017), �F(1, 324) �7.03, p < .01, and cognitive variables (�R2 � .075),�F(5, 319) � 6.90, p < .01, were significant. Table 8shows that workload, emotion-focus, and avoidancewere significant as individual predictors in the finalequation.

Discussion

The two task types used here elicited profiles ofstate change similar to those for the visual WM tasksused in Study 2. The decline in worry was smaller forcustomer service than for auditory WM, a finding thatmay reflect greater personal relevance of customerservice, and a greater potential for self-reflection incase of suboptimal performance. Correlates of subjec-tive state were generally as expected. The impact of

workload on distress confirms the findings of Study 2.Perceptions of the work environment were also pre-dictive of stress state, with external support appearingas the strongest single predictor of task engagement.Job perceptions were more predictive of pretask thanposttask state, for which there are two possible expla-nations. First, appraisals of the work environment pre-sumably influence the state the person experiences atwork, before the study. This state may transfer to theinitial pretask phase of the study (people arrive indiffering moods) but fail to generalize to the artificialtask. Second, pretask correlations between job percep-tions and state may reflect environmental selection.For example, individuals with more positive job per-ceptions may be those who are assigned to lessdemanding work activities, or those who have suc-cessfully sought out such activities, generating corre-lations between job perceptions and pretask state withno causal link.

Emotion-focused coping was one of the strongestpredictors of both distress and worry, consistent withthe view that self-criticism is generally maladaptive,especially in situations requiring action in the externalworld (Matthews & Wells, 1996). Other emotion-focused strategies, such as positive reappraisal, maybe more benign. Task-focus appeared to promote taskengagement (pretask only) and reduce distress. As inprevious work (see Zeidner & Saklofske, 1996),avoidance was equivocal in its implications, relatingto higher pretask engagement, but also to higher dis-tress and worry. N appeared as a generalized predictorof state disturbance, especially distress, as in previouswork (Costa et al., 1996).

The multiple regressions establish that styles ofcoping predict stress states in the controlled environ-ment of task performance, even with confounds con-trolled. Use of emotion-focus, for example, may gen-eralize from the work environment to the tasksituation, elevating distress and worry. As predictedfrom a self-regulative analysis, both distress andworry appear to reflect an integration of different as-pects of cognition that may be functionally linked tothe same self-regulative goal. Distress and worry weredifferentiated by the contribution of low task-focusedcoping to distress, and avoidance to worry, as well asthe stronger association between distress and work-load. The use of measures of general style of cogni-tion underestimates the role of situation-specific cog-nitions in shaping stress state: matched dispositionaland situational coping measures are typically onlymoderately correlated (Endler & Parker, 1994). Post-task engagement was rather weakly related to the cog-

Table 8Beta Weights for Multiple Regressions of Three DSSQState Factors on Various Predictors, in an OccupationalSample (n = 328)

Predictor

Taskengagement(R2 � .15)

Distress(R2 � .39)

Worry(R2 � .31)

Task type −.30** −.11* .18**Distress — — .19**Workload .10 .41** .13*Job rewards −.03 .04 .05External support .16** −.02 .09Task-focus .08 −.14** −.01Emotion-focus −.12 .27** .21**Avoidance −.03 −.02 .11*Extraversion .02 −.01 −.01Neuroticism .10 .16** .08

Note. �s are those for the final regression equations. DSSQ �Dundee Stress State Questionnaire (Matthews et al., 1999); dashesrepresent data that were not obtained or reported.* p < .05. **p < .01.

FUNDAMENTAL DIMENSIONS OF STATE 333

nitive measures, but stronger correlations may be ex-pected with, for example, a situational task-focusscale. In addition, use of the overall workload indexunderestimates the role of the specific workload com-ponents, such as effort, which were significantly cor-related with posttask engagement, as in Study 2.8

Finally, correlations between indexes of cognitionand state are not just artifacts of confounding by per-sonality traits representing PA and NA. In the post-task data, associations between N and posttask stateappeared to be partially mediated by the cognitivemeasures. N was independently predictive of distressbut not worry, which may reflect the mediating role of(a) consciously accessible cognitive stress processesnot indexed by the present measures, (b) unconsciousbiases in processing that could not be assessed byself-report, or (c) biologically based state biases withno cognitive counterpart (see Matthews et al., 2000).The attenuation of correlations between personalityand state in the posttask data may result from the roleof situational choice in mediating personality–statecorrelations; more neurotic individuals may be lessadept than emotionally stable persons in selecting oractively shaping environments to be nonstressful(Matthews, 1999). Future research could develop andtest path models to distinguish such hypotheses.

General Discussion

We demonstrated three higher order state dimen-sions by converging evidence from factor analysis,experimental studies of task-induced stress, and cor-relations between states and appraisal and coping di-mensions. Task engagement and distress cross the tra-ditional domain boundaries and integrate affective,cognitive, and motivational domains of state, whereasthe third dimension of worry is purely cognitive.Much of the reliable variance in state appears to beattributable to the 3 second-order factors, althoughanalysis of the first-order, domain-specific dimen-sions may sometimes add extra information on state(Matthews et al., 1999). There are two grounds forsupposing that these factors are fundamental. First,each factor incorporates dimensions fundamentalwithin their respective domains: the two basic mooddimensions of energy and tension (Thayer, 1989), andthe cognitive interference dimensions that are centralto changes in thinking under pressure (Sarason et al.,1986; Wells & Matthews, 1994). Second, self-regulative analyses of the performance setting haveidentified only a small number of basic challenges tothe person that may support state response: overload,

fatigue, and evaluation of self-relevance (Hockey,1997; Kluger & DeNisi, 1996). In this concludingsection, we first discuss the conceptualization ofstates as representations of different modes of self-regulation, and, second, the nature of task disengage-ment, distress, and worry as fundamental state syn-dromes in performance environments. We alsodiscuss some limitations of the research.

States as Indexes of Self-Regulation

Initially, we distinguished separate-systems fromcommon-systems approaches to interrelating affec-tive, motivational, and cognitive domains. A separate-systems approach suggests that state dimensions maybe organized around the three domains as separateentities with their own subjectivities (Mayer et al.,1997). In fact, this position seems oversimplified.First-order DSSQ dimensions of subjective state doseem to exclusively reflect mood, motivation, or cog-nition (Matthews et al., 1999). However, the higherorder structuring of awareness binds together func-tionally related aspects of affect, motivation, and sub-jective cognition. These complexes of state dimen-sions may be expressions of common self-regulativeoperations that require the integrated functioning ofaffect, motivation, and cognition. Self-regulation issupported by a multilevel cognitive architecture (Mat-thews & Wells, 1999; Wells & Matthews, 1994) thatdelineates a “lower” level of processing, comprisingmultiple distributed modules for various psychologi-cal functions that is overlaid by an “upper level” su-pervisory executive. Cognitive processes that are an-tecedent to emotion, such as appraisals, appear tooperate in parallel at different levels of this architec-ture (Clore & Ortony, 2000; van Reekum & Scherer,1997). The multiplicity of primary, domain-specificstate dimensions, such as energetic and tense arousal,may correspond to the multiplicity of specific lowerlevel systems. Energy and tension, for example, mayrepresent biocognitive systems supporting prepared-ness for vigorous action and handling threatening en-counters, respectively (Thayer, 1989). It may be adap-tive for there to be several independent, butintercommunicating, systems for handling key chal-

8 Posttask engagement was significantly correlated (p <.01), with higher ratings of workload attributed to mentaldemands (r � .22), temporal demands (r � .21), and effort(r � .33). Task engagement also correlated with lower rat-ings of workload associated with poor performance (r �−.22, p < .01) and with frustration (r � −.14, p < .01).

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lenges such as threat: If one system fails, another maystill signal the possibility of danger.

Subjective experience also reflects the operation ofa self-regulative executive system that receives infor-mation from lower level systems and outputs mentaland behavioral coping responses (Wells & Matthews,1994). Affect has previously been linked to the goalstatus of the executive system (Carver & Scheier,1990). However, executive processing of goal statusmay influence not just affect but also motivational andcognitive aspects of state. For example, computationof an “overload situation,” or threat to achievement oftask goals, may cause the executive system to outputtension, unhappiness, and low confidence concur-rently (or to bias lower level modules controllingthese states), as suggested by the correlated changesin these dimensions seen in high task-load conditions.

Study 3 indicated how specific coping and ap-praisal processes supporting self-regulation may feedinto subjective experience. However, states are non-isomorphic with specific processes, in that each di-mension relates to multiple processes, and a singleprocess may contribute to several state dimensions.States relate to patterns of processes, which may re-flect modes of self-regulation.9 These self-regulativemodes resemble Lazarus’s (1991, 1999) concept ofcore relational themes, in being abstracted represen-tations of the ecological relationship between personand environment. However, unlike Lazarus (1999),we do not see the state factors as relating directly tothe standard basic emotions (cf. Plutchik, 1984). Per-haps the three fundamental state dimensions representdifferent modes of adaptation to environmental or taskdemands, driven by different self-regulative goals andsupported by different configurations of appraisal andcoping. Of course, self-regulation may also have un-conscious aspects; it is an open question how closelythe subjective state indexes the functioning of the la-tent system.

Three Fundamental Aspects of Subjective State

Next, we look at how each of the three dimensionscaptures and develops some essential features of ex-isting conceptions of state. We also discuss their pos-sible bases in modes of self-regulation. In the Wellsand Matthews (1994) model, external demands pro-voke executive-level appraisal and coping processes,which (in interaction with lower level, stimulus-driven processes) typically appear as patterns or con-figurations of processing that support some centraladaptive purpose. For each subjective state response,we attempted to identify (a) some external demand

factors that might create a need for self-regulation, (b)patterns of processing that might implement self-regulation, and (c) an underlying self-regulativetheme.

Task engagement, as a complex of energy, motiva-tion, and concentration, develops the energetic arousal(Thayer, 1989) and PA (Watson & Tellegen, 1985)constructs. Energy, motivation, and concentrationwere distinct as primary factors, and the intercorrela-tions of these states were moderate (typically .3–.4).However, the three primary scales were interrelated atthe secondary factor level, and they showed broadlysimilar responses to stressors characterized by mo-notony (vigilance performance), suggesting an inte-grated task disengagement response. Task disengage-ment corresponds well to Hockey’s (1997) fatiguedmode of cognitive control, in linking tiredness to re-luctance to apply effort and difficulties in concentrat-ing on the task at hand. Task disengagement scalesalso correlated substantially with aspects of physicalfatigue such as muscular and visual fatigue (Matthews& Desmond, 1998). The motivational and cognitiveattributes of task engagement also match Thayer’s(1989) conception of energetic arousal as representingmobilization for vigorous action.

The present studies link task engagement to self-regulation. The WM tasks, requiring high short-termeffort, enhanced task engagement, whereas longer du-ration, monotonous tasks reduced task engagement.At the process level, task engagement was associatedwith some of the workload appraisals, a supportivework environment, and, in the pretask data of Study 3,with task-focused coping. The common theme heremay be commitment to effort: Task engagement sig-nals a patterning of appraisal and coping that pro-motes effortful striving directed toward task goals.Conversely, task disengagement represents effort re-duction (Hockey, 1997). Both task demands, such asduration and monotony, and personal characteristics,such as conscientiousness (Matthews et al., 1999),may influence this pattern of processing. Further workmay use concurrent measures of constructs such aschallenge appraisal and task-focused coping to pro-

9 There is no suggestion in the data that “modes” of self-regulation are incompatible or discontinuous with one an-other; a person may be task-engaged, distressed, and wor-ried simultaneously. We leave open the question of whetherexecutive function readily blends different transactionalthemes in continuous fashion or whether it “time-shares”processing of different challenges.

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vide a more grained account of the basis for taskengagement. High task engagement may sometimesbe directly adaptive in performance settings. Severalstudies have shown that energy, one of the compo-nents of task engagement, is associated with superiorperformance on a variety of attentionally demandingtasks (Matthews & Davies, 1998; Matthews et al.,1990).

In terms of its affective components, distress re-sembles tense arousal (Thayer, 1989) and NA (Wat-son & Tellegen, 1985). It links two of the Matthews etal. (1990) mood dimensions, tension and unpleasantmood (low hedonic tone), to the cognitive state oflack of confidence and control. Low-perceived con-trol may be an especially powerful driver of stresssymptoms (Fisher, 1984). Distress corresponds toHockey’s (1997) overload mode of control, in whichthe person attempts to maintain performance underhigh task or environmental load at the cost of subjec-tive strain. Appraisals of impending or actual loss ofperformance may link loss of confidence to NA.Speculatively, distress may also relate to escape mo-tivations, although this is a difficult construct to op-erationalize in the constrained performance setting.

Distress response was approximately scaled tooverall task demands, as indexed by NASA-TLXworkload. Distress correlated with appraisals of highworkload and with use of emotion-focused and avoid-ance coping strategies likely to exacerbate perceptionsof overload. These cognitions may be amplified byneurotic personality, which relates to negative self-appraisals of personal competence in performance set-tings (Matthews, Schwean, Campbell, Saklofske, &Mohamed, 2000). Distress may relate to a theme ofoverload of processing capacity. A person may aimfor “damage limitation” rather than successful perfor-mance through doing as well as the situation allows(e.g., performance under time pressure), or throughsimply enduring suboptimal performance.

The emergence of worry as a distinct dimensionextends two-factor models of affect and supports theworry–emotion distinction made in anxiety research(Zeidner, 1998). The attributes of state worry here,comprising interfering thoughts, self-focus of atten-tion, and sensitivity to criticism from others (low self-esteem), match the characteristics of worry describedby other authors (see Wells & Matthews, 1994). Thefissure between worry and emotional distress is wideenough to define two separate state syndromes; thatis, there was no global state anxiety factor unifyingworry and distress. Although some factors tend toelevate worry and distress conjointly (such as neurotic

personality), other factors differentiated these aspectsof state. Specifically, demanding tasks requiring rapidinformation processing (e.g., WM) elevated distresswhile lowering worry. Loosely, such tasks may bedescribed as anxiety-inducing, but separation of dis-tress from worry provides a more accurate picture ofstate change.

Worry generally declined during performance, butthe magnitude of decline varied across tasks. The re-quirement to perform an unfamiliar task may tend togenerate anticipatory worry, which dissipates in con-trol conditions, as in Study 2, and in tasks requiringnear-continuous information processing (WM) thatmay suppress worry. Decline in worry was less fol-lowing the work simulations, which were presumablypersonally relevant. Worry related to both emotion-focus and avoidance, in line with the self-regulativefunctions of these styles of coping in directing atten-tion toward personal shortcomings (Matthews &Wells, 1996) and to neurotic personality, consistentwith previous research (Wells, 1994). Worry may beassociated with a transactional theme of self-evaluation, similar to Kluger and DeNisi’s (1996)concept of “meta-task” processes: some forms offeedback elicit mental withdrawal from the task andevaluation of the self-relevance of the task. Hence, tothe broad approach and avoidance tendencies signaledby task engagement and distress, we can perhaps adda third (and characteristically human) tendency to re-flect on personal significance before further action.

Limitations of the Research

Potentially, the most serious limitation of the pre-sent research may be its reliance on self-reports,which are open to bias by response styles such asacquiescence and social desirability. Certainly, thepossibility of bias could be investigated in future stud-ies. However, there are several reasons for supposingthat the structure of subjective state revealed here isnonartifactual. First, extensive work on mood mea-surement shows that biases are minor if response for-mats are chosen correctly (e.g., Lucas, Diener, & Suh,1996). In addition, social desirability measures havelittle shared variance with mood and other state mea-sures (Matthews et al., 1990, 1999). Second, the useof experimental methods in the present studies pro-vides some protection against artifact. The demonstra-tion of large-magnitude state changes over fairly shorttime intervals indicates that participants are not justresponding according to some preset style. Use ofchange scores removes any bias stable over thechange period (Schimmack & Grob, 2000). Study 1

MATTHEWS ET AL.336

showed that the factor structure for state changescores was similar to the structure for single-occasionscores, again suggesting any artifact was minor, atmost. Third, various studies using the UMACL (Mat-thews et al., 1990) and DSSQ (e.g., Matthews &Davies, 1998; Matthews et al., 2001) showed thatstate measures correlate with objective performancemeasures, a finding inconsistent with a response-biasinterpretation of scale scores.

Other measurement issues also require further re-search. The factor structure and sensitivity to taskdemands of the DSSQ generalize across British andNorth American samples, but it is unclear whetherstates in non-Western cultures are comparable. Theapplicability of the three-factor state model to con-texts other than performance remains to be explored.The transactional themes or modes of self-regulationassociated with the three broad state syndromes maybe sufficiently general to drive state in other contexts.Many social encounters also pose challenges associ-ated with commitment of personal effort, handlingoverload, and reevaluating the self. However, theremay be purely social states, such as dominance versussubmission (Sjoberg et al., 1979), which the currentstate model does not encompass.

Finally, the present study of cognitive stress pro-cesses aimed only to link the state factors to differentpatterns of appraisal and coping. More work is re-quired to develop detailed causal models, which mayalso incorporate feedback from states to process, asthe person appraises and regulates their own state. It isalso unclear whether associations between processand outcome generalize across different contexts,given that the adaptive success of particular copingstrategies varies across contexts, occasions, and indi-viduals (Zeidner & Saklofske, 1996). Given that statesare influenced by biological bases (Thayer, 1989), amore complete account would also integrate neuraland cognitive influences on state.

References

Bhagat, R. S., Allie, S. M., & Ford, D. L., Jr. (1995). Cop-ing with stressful life events: An empirical analysis. In R.Crandall & P. L. Perrewe (Eds.), Occupational stress: Ahandbook (pp. 93–112). Philadelphia: Taylor & Francis.

Carver, C. S., & Scheier, M. F. (1990). Origins and func-tions of positive and negative affect: A control-processview. Psychological Review, 97, 19–35.

Cattell, R. B. (1973). Personality and mood by question-naire. New York: Jossey-Bass.

Clore, G. L., & Ortony, A. (2000). Cognition in emotion:

Always, sometimes, or never? In R. D. Lane & L. Nadel(Eds.), Cognitive neuroscience of emotion (pp. 24–61).New York: Oxford University Press.

Cohen, J. (1988). Statistical power analysis for the behav-ioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

Costa, P. T., Jr., Somerfield, M. R., & McCrae, R. R.(1996). Personality and coping: A reconceptualization. InM. Zeidner & N. S. Endler (Eds.), Handbook of coping:Theory, research, applications (pp. 44–61). New York:Wiley.

Dweck, C. S. (2000). Self-theories: Their role in motivation,personality, and development. Philadelphia: PsychologyPress.

Endler, N. S., & Parker, J. D. (1990). Multidimensional as-sessment of coping: A critical review. Journal of Person-ality and Social Psychology, 58, 844–854.

Endler, N. S., & Parker, J. D. A. (1994). Assessment ofmultidimensional coping: Task, emotion, and avoidancestrategies. Psychological Assessment, 6, 50–60.

Everett, J. E. (1983). Factor comparability as a means ofdetermining the number of factors and their rotation. Mul-tivariate Behavioral Research, 18, 197–218.

Eysenck, S. B. G., Eysenck, H. J., & Barrett, P. (1985). Arevised version of the Psychoticism scale. Personalityand Individual Differences, 6, 21–29.

Fenigstein, A., Scheier, M. F., & Buss, A. H. (1975). Publicand private self-consciousness: Assessment and theory.Journal of Consulting and Clinical Psychology, 43, 522–527.

Fisher, S. (1984). Stress and the perception of control.Hillsdale, NJ: Erlbaum.

Geen, R. G. (1987). Test anxiety and behavioral avoidance.Journal of Research in Personality, 21, 481–488.

Guàrdia, J., & Adan, A. (1997). Confirmatory factor analy-sis applied to Matthews adjective checklist of self-reported activation: Effect of time of day. Quality andQuantity, 31, 95–106.

Harman, H. H. (1976). Modern factor analysis. Chicago:University of Chicago Press.

Hart, S. G., & Staveland, L. E. (1988). Development of amultidimensional workload rating scale: Results of em-pirical and theoretical research. In P. A. Hancock & N.Meshkati (Eds.), Human mental workload (pp. 139–183).Amsterdam: Elsevier.

Heatherton, T. F., & Polivy, J. (1991). Development of ascale for measuring state self-esteem. Journal of Person-ality and Social Psychology, 60, 895–910.

Hilgard, E. R. (1980). The trilogy of mind: Cognition, af-fection, and conation. Journal of the History of the Be-havioral Sciences, 16, 107–117.

Hockey, G. R. J. (1997). Compensatory control in the regu-lation of human performance under stress and high work-

FUNDAMENTAL DIMENSIONS OF STATE 337

load: A cognitive–energetical framework. BiologicalPsychology, 45, 73–93.

Horn, J. L. (1965). A rationale and test for the number offactors in factor analysis. Psychometrika, 30, 179–185.

Humphreys, M. S., & Revelle, W. (1984). Personality, mo-tivation, and performance: A theory of the relationshipbetween individual differences and information process-ing. Psychological Review, 91, 153–184.

Kluger, A. N., & DeNisi, A. (1996). Effects of feedbackintervention on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory.Psychological Bulletin, 119, 254–284.

Lazarus, R. S. (1991). Emotion and adaptation. Oxford, En-gland: Oxford University Press.

Lazarus, R. S. (1999). Stress and emotion: A new synthesis.New York: Springer.

Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal andcoping. New York: Springer.

Lieberman, H. R., Wurtman, R. J., Emde, G. G., Roberts,C., & Coviella, I. L. G. (1987). The effects of caffeineand aspirin on mood and performance. Journal of Clini-cal Psychopharmacology, 7, 315–320.

Lucas, R. E., Diener, E., & Suh, E. (1996). Discriminantvalidity of well-being measures. Journal of Personalityand Social Psychology, 71, 616–628.

Matthews, G. (1992). Mood. In A. P. Smith & D. M. Jones(Eds.), Handbook of human performance. Vol. 3: Stateand trait (pp. 161–194). London: Academic Press.

Matthews, G. (1996). Individual differences in driver stressand performance. Proceedings of the Human Factors andErgonomics Society 40th Annual Meeting (pp. 579–583).Santa Monica, CA: Human Factors and Ergonomics So-ciety.

Matthews, G. (1999). Personality and skill: A cognitive–adaptive framework. In P. L. Ackerman, P. C. Kyllonen,& R. D. Roberts (Eds.), The future of learning and indi-vidual differences research: Processes, traits, and con-tent (pp. 251–270). Washington, DC: American Psycho-logical Association.

Matthews, G., & Campbell, S. E. (1999). Individual differ-ences in stress response and working memory. Proceed-ings of the Human Factors and Ergonomics Society 43rdAnnual Meeting (pp. 634–638). Santa Monica, CA: Hu-man Factors and Ergonomics Society.

Matthews, G., & Davies, D. R. (1998). Arousal and vigi-lance: The role of task factors. In R. B. Hoffman, M. F.Sherrick, & J. S. Warm (Eds.), Viewing psychology as awhole: The integrative science of William N. Dember (pp.113–144). Washington, DC: American Psychological As-sociation.

Matthews, G., Davies, D. R., & Holley, P. J. (1993). Cog-nitive predictors of vigilance. Human Factors, 35, 3–24.

Matthews, G., Davies, D. R., & Lees, J. L. (1990). Arousal,extraversion, and individual differences in resource avail-ability. Journal of Personality and Social Psychology, 59,150–168.

Matthews, G., Davies, D. R., Westerman, S. J., & Stam-mers, R. B. (2000). Human performance: Cognition,stress and individual differences. London: PsychologyPress.

Matthews, G., & Deary, I. (1998). Personality traits. Cam-bridge, England: Cambridge University Press.

Matthews, G., Derryberry, D., & Siegle, G. J. (2000). Per-sonality and emotion: Cognitive science perspectives. InS. E. Hampson (Ed.), Advances in personality psychology(Vol. 1, pp. 199–237). London: Routledge.

Matthews, G., & Desmond, P. A. (1998). Personality andmultiple dimensions of task-induced fatigue: A study ofsimulated driving. Personality and Individual Differ-ences, 25, 443–458.

Matthews, G., & Desmond, P. A. (2002). Task-induced fa-tigue states and simulated driving performance. Quar-terly Journal of Experimental Psychology: Human Ex-perimental Psychology, 55A, 659–686.

Matthews, G., Dorn, L., & Glendon, A. I. (1991). Person-ality correlates of driver stress. Personality and Indi-vidual Differences, 12, 535–549.

Matthews, G., & Falconer, S. (2000). Individual differencesin task-induced stress in customer service personnel. Pro-ceedings of the Human Factors and Ergonomics Society44th Annual Meeting (pp. 145–148). Santa Monica, CA:Human Factors and Ergonomics Society.

Matthews, G., & Harley, T. A. (1996). Connectionist mod-els of emotional distress and attentional bias. Cognitionand Emotion, 10, 561–600.

Matthews, G., Jones, D. M., & Chamberlain, A. G. (1990).Refining the measurement of mood: The UWIST MoodAdjective Checklist. British Journal of Psychology, 81,17–42.

Matthews, G., Joyner, L., Gilliland, K., Campbell, S. E.,Falconer, S., & Huggins, J. (1999). Validation of a com-prehensive stress state questionnaire: Towards a state‘Big Three’? In I. Mervielde, I. J. Deary, F. De Fruyt, &F. Ostendorf (Eds.), Personality psychology in Europe(Vol. 7, pp. 335–350). Tilburg, the Netherlands: TilburgUniversity Press.

Matthews, G., Schwean, V. L., Campbell, S. E., Saklofske,D. H., & Mohamed, A. A. R. (2000). Personality, self-regulation and adaptation: A cognitive–social framework.In M. Boekarts, P. R. Pintrich, & M. Zeidner (Eds.),Handbook of self-regulation (pp. 171–207). New York:Academic Press.

Matthews, G., Warm, J. S., Dember, W. N., Mizoguchi, H.,& Smith, A. P. (2001). The common cold impairs visual

MATTHEWS ET AL.338

attention, psychomotor performance and task engage-ment. Proceedings of the Human Factors and Ergonom-ics Society 45th Annual Meeting (pp. 1377–1381). SantaMonica, CA: Human Factors and Ergonomics Society.

Matthews, G., & Wells, A. (1996). Attentional processes,coping strategies and clinical intervention. In M. Zeidner& N. S. Endler (Eds.), Handbook of coping: Theory, re-search, applications (pp. 573–601). New York: Wiley.

Matthews, G., & Wells, A. (1999). The cognitive science ofattention and emotion. In T. Dalgleish & M. Power(Eds.), Handbook of cognition and emotion (pp. 171–192). New York: Wiley.

Mayer, J. D., Frasier Chabot, H., & Carlsmith, K. M.(1997). Conation, affect, and cognition in personality. InG. Matthews (Ed.), Cognitive science perspectives onpersonality and emotion (pp. 31–63). Amsterdam:Elsevier.

Mohamed, A. A. R. (1996). Stress processes in British andoverseas students. Unpublished doctoral dissertation,University of Dundee, Scotland.

Plutchik, R. (1984). Emotions: A general psychoevolution-ary theory. In K. R. Scherer & P. Ekman (Eds.), Ap-proaches to emotion (pp. 197–219). Hillsdale, NJ:Erlbaum.

Reisenzein, R. (1994). Pleasure-arousal theory and the in-tensity of emotions. Journal of Personality and SocialPsychology, 67, 525–539.

Russell, J. A., & Feldman Barrett, L. (1999). Core affect,prototypical emotional episodes, and other things calledemotion: Dissecting the elephant. Journal of Personalityand Social Psychology, 76, 805–819.

Sarason, I. G., Sarason, B. R., Keefe, D. E., Hayes, B. E., &Shearin, E. N. (1986). Cognitive interference: Situationaldeterminants and traitlike characteristics. Journal of Per-sonality and Social Psychology, 51, 215–226.

Scerbo, M. W. (1998). What’s so boring about vigilance? InR. R. Hoffman, M. F. Sherrick, & J. S. Warm (Eds.),Viewing psychology as a whole: The integrative scienceof William N. Dember (pp. 145–166). Washington, DC:American Psychological Association.

Schimmack, U., & Grob, A. (2000). Dimensional models ofcore affect: A quantitative comparison by means of struc-tural equation modeling. European Journal of Personal-ity, 14, 325–345.

Sims, P. H., Szilagyi, A. D., & Keller, R. T. (1976). Themeasurement of job characteristics. Academy of Manage-ment Journal, 19, 547–559.

Sjoberg, L., Svensson, E., & Persson, L.-O. (1979). Themeasurement of mood. Scandinavian Journal of Psychol-ogy, 20, 1–18.

Temple, J. G., Warm, J. S., Dember, W. N., Jones, K. S.,LaGrange, C. M., & Matthews, G. (2000). The effects of

signal salience and caffeine on performance, workload,and stress in an abbreviated vigilance task. Human Fac-tors, 42, 183–194.

Thayer, R. E. (1989). The biopsychology of mood andarousal. New York: Oxford University Press.

Thayer, R. E. (1996). The origin of everyday moods. NewYork: Oxford University Press.

van Reekum, C. M., & Scherer, K. R. (1997). Levels ofprocessing in emotion-antecedent appraisal. In G. Mat-thews (Ed.), Cognitive science perspectives on personal-ity and emotion (pp. 259–300). Amsterdam: Elsevier.

Warr, P., Cook, J., & Wall, T. (1979). Scales for the mea-surement of some work attitudes and aspects of psycho-logical well-being. Journal of Occupational Psychology,52, 129–148.

Watson, D., & Clark, L. A. (1984). Negative affectivity:The disposition to experience aversive emotional states.Psychological Bulletin, 96, 465–490.

Watson, D., & Clark, L. A. (1992). On traits and tempera-ment: General and specific factors of emotional experi-ence and their relation to the five-factor model. Journal ofPersonality, 60, 441–476.

Watson, D., & Tellegen, A. (1985). Toward a consensualstructure of mood. Psychological Bulletin, 98, 219–235.

Wells, A. (1994). A multi-dimensional measure of worry:Development and preliminary validation of the AnxiousThoughts Inventory. Anxiety, Stress and Coping, 6, 289–299.

Wells, A., & Matthews, G. (1994). Attention and emotion:A clinical perspective. Hove, England: Erlbaum.

Wickens, C. D., & Hollands, J. G. (1999). Engineering psy-chology and human performance (3rd ed.). Upper SaddleRiver, NJ: Prentice Hall.

Wundt, W. (1896). Grundrisse der Psychologie [Outlines ofpsychology]. Leipzig, Germany: Engelmann.

Yuill, N., Oakhill, J., & Parkin, N. (1989). Workingmemory, comprehension ability and the resolution of textanomaly. British Journal of Psychology, 80, 351–362.

Zeidner, M. (1998). Test anxiety: The state of the art. NewYork: Plenum.

Zeidner, M., & Saklofske, D. (1996). Adaptive and mal-adaptive coping. In M. Zeidner & N. S. Endler (Eds.),Handbook of coping: Theory, research, applications (pp.505–531). New York: Wiley.

Zuckerman, M. (1976). General and situation-specific traitsand states: New approaches to assessment of anxiety andother constructs. In M. Zuckerman & C. D. Spielberger(Eds.), Emotions and anxiety: New concepts, methodsand applications (pp. 133–174). Hillsdale, NJ: Erlbaum.

Zwick, W., & Velicer, W. (1986). Comparison of five rulesfor determining the number of components to retain. Psy-chological Bulletin, 99, 432–442.

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Appendix A

Means (and Standard Deviations) of DSSQ Secondary Factors Before (Pre) and After (Post)Performance, in Six Groups

Appendix B

Means (and Standard Deviations) of DSSQ Secondary Factors Before (Pre) and After (Post)Performance, in Four Groups

Received December 14, 2001Revision received May 7, 2002

Accepted May 7, 2002 �

Group Time Engagement Distress Worry

Cardsort Pre −.01 (.63) −.06 (.70) .31 (.83)Post .32 (.80) .00 (.94) −.75 (.86)

Reading Pre .04 (.79) −.37 (.87) .38 (1.14)Post .07 (.79) −.63 (.89) −.52 (1.11)

Auditory vigilance Pre .02 (.82) .07 (.92) −.27 (.89)Post −.84 (1.02) .35 (.99) −.50 (.85)

Visual vigilance Pre −.16 (.81) .13 (.89) −.12 (.92)Post −1.46 (.99) .59 (.94) −.25 (.91)

Working memory−UK Pre −.07 (.92) −.07 (.92) .21 (1.14)Post .39 (.80) 1.22 (1.11) −.62 (1.01)

Working memory−USA Pre .39 (.91) −.38 (.82) −.34 (1.03)Post .97 (1.00) .61 (1.16) −1.22 (.87)

Note. DSSQ � Dundee Stress State Questionnaire (Matthews et al., 1999); UK � British group; USA� American group.

Group Time Engagement Distress Worry

Auditory WM Pre .02 (.78) −.03 (1.05) .11 (.94)Post .37 (.86) .58 (1.19) −.71 (.84)

Auditory WM Pre .29 (.79) −.12 (.93) .07 (1.04)Post .47 (.90) .46 (.98) −.72 (.82)

Simulation Pre .92 (.75) .05 (.89) .09 (.80)Post .88 (.98) .78 (1.09) −.26 (.87)

Simulation Pre .79 (.67) .14 (1.04) .33 (1.04)Post 1.10 (.65) 1.05 (1.23) −.20 (.84)

Note. DSSQ � Dundee Stress State Questionnaire (Matthews et al., 1999); WM � working memory.

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