Full Terms & Conditions of access and use can be found athttp://www.tandfonline.com/action/journalInformation?journalCode=hrhd20

Download by: [MPI Max-Planck-Institute Fur Bildungsforschung] Date: 02 September 2017, At: 05:52

Research in Human Development

ISSN: 1542-7609 (Print) 1542-7617 (Online) Journal homepage: http://www.tandfonline.com/loi/hrhd20

Variability in Children’s Working MemoryIs Coupled With Perceived Disturbance: AnAmbulatory Assessment Study in the School andOut-of-School Context

Judith Dirk & Florian Schmiedek

To cite this article: Judith Dirk & Florian Schmiedek (2017) Variability in Children’s WorkingMemory Is Coupled With Perceived Disturbance: An Ambulatory Assessment Study in theSchool and Out-of-School Context, Research in Human Development, 14:3, 200-218, DOI:10.1080/15427609.2017.1340051

To link to this article: http://dx.doi.org/10.1080/15427609.2017.1340051

Published online: 10 Aug 2017.

Submit your article to this journal

Article views: 10

View related articles

View Crossmark data

Citing articles: 1 View citing articles

Variability in Children’s Working Memory Is Coupled WithPerceived Disturbance: An Ambulatory Assessment Study

in the School and Out-of-School Context

Judith Dirk

German Institute for International Educational Research (DIPF)

Florian Schmiedek

German Institute for International Educational Research (DIPF), IDeA (Individual Developmentand Adaptive Education of Risk) Center, and Goethe University

The detrimental effect of noise on cognitive performance particularly for younger children has beenrepeatedly demonstrated in numerous experimental and few field studies. We examined whetherchildren’s daily working memory (WM) performance is affected by daily perceived disturbance in theschool and out-of-school context. In an ambulatory assessment study, 110 third and fourth grade studentscompletedWM tasks and reported on their perceived disturbance on smartphones three times daily in andout of school for four weeks. Disturbance varied systematically within children and increased levels ofdisturbance were associated with decreased WM performance, independent of context.

In their everyday life, children are often confronted with noisy environments. In particularteaching and learning often take place in noisy settings. In settings like the classroom but alsoat home, children need to perform cognitive tasks under the influence of noise, which for manychildren results in feeling disturbed and not being able to perform at their highest possible level.Numerous studies have shown the detrimental effects of noise on children’s cognitive perfor-mance as well as on learning and school achievement (e.g., Elliott, 2002; Klatte, Bergström, &Lachmann, 2013; Klatte et al., 2016; Shield & Dockrell, 2008). Especially children in elemen-tary school seem to be affected by noise because their attentional control is limited and thus theircognitive performance is more prone to disturbances than those of older children or adults(Elliott, 2002; Klatte et al., 2013; but see Shield & Dockrell, 2008 who found negative effects ofexternal noise to be greater for older children in an age range of 7–11 years). These negativeeffects have been shown particularly for those cognitive activities that are based on workingmemory (WM; e.g., Elliott, 2002). Although the effects of noise on cognitive performance havebeen well studied, the majority of studies has relied on findings with adults (cf. Szalma &Hancock, 2011), experimental settings in the laboratory (e.g., Elliott, 2002), or effects of noise in

Address correspondence to Judith Dirk, German Institute for International Educational Research, Schloßstraße 29,Frankfurt am Main D-60486, Germany. E-mail: dirk@dipf.de

Research in Human Development, 14: 200–218, 2017Copyright © Taylor & Francis Group, LLCISSN: 1542-7609 print / 1542-7617 onlineDOI: https://doi.org/10.1080/15427609.2017.1340051

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

natural settings as indicated by objective sound characteristics (e.g., intensity, reverberation time;Klatte et al., 2013). Studies investigating the effects of children’s perceived disturbance due toclassroom noise on their actual cognitive performance in the school context, however, are scarce(Klatte, Hellbrück, Seidel, & Leistner, 2010; Shield & Dockrell, 2008). Similarly, studiesinvestigating noise effects in children’s out-of-school learning environments like their homeshave rarely been conducted (Dockrell & Shield, 2004; Klatte et al., 2016). For example,Dockrell and Shield (2004) studied elementary children’s ability to distinguish different sourcesof noise in school and at home as well as the degree of annoyance due to noise using a one-timeassessment via a questionnaire. They demonstrated that children could identify different sourcesof noise in the school and home learning environment and that they were also subjectivelydisturbed by these sounds. Similarly, Klatte and colleagues (2016) showed that increased levelsof exposure to noise in children between age 7 and 10 years are not only related to lower readingperformance but also to increased levels of perceived disturbance. Thus, both studies demon-strated the relationship between objective noise in children’s learning environments and theirperceived disturbance. However, these and most other studies on the effects of noise andperceived disturbance on cognitive performance in children’s natural learning environmentsapplied one-time assessments and focused on between-person differences. The focus onbetween-person differences allows answering, for example, whether children who are highlydisturbed by noise tend to show lower cognitive performance than children who are lessdisturbed. However, these between-person findings do not need to inform about the within-person relationship (i.e., coupling) between perceived disturbance and cognitive performance(i.e., whether children’s cognitive performance varies as a function of variation in perceiveddisturbance). Moreover, one-time assessments do not allow testing whether the level of disturb-ing sounds varies during the school day depending on external influences (e.g., traffic noise) aswell as internal sources of noise (e.g., other persons around talking). This varying disturbancemight be an important source of children’s cognitive performance fluctuations that are negativelyrelated to learning outcomes (Dirk & Schmiedek, 2016).This study therefore aimed at studyingvariation in perceived disturbance and testing the within-person coupling between children’sperceived disturbance and their cognitive performance as measured by WM tasks in the schooland out-of-school context.

Variability in WM Performance

We conceive of WM as the ability to maintain and process information simultaneously in acontrolled manner (Baddeley & Hitch, 1994). We focused on WM to study the effects ofperceived disturbance on children’s cognitive performance in the school context for threereasons: First, WM constitutes the basis for school achievement (cf. Swanson & Alloway,2012). Second, WM research has a long tradition in studying the effects of irrelevant soundsor irrelevant speech on cognitive performance (cf. Elliott, 2002). Third, recent research hasdemonstrated that WM varies on a daily basis in adults (Schmiedek, Lövdén, & Lindenberger,2013), adolescents (Riediger, Wrzus, Schmiedek, Wagner, & Lindenberger, 2011), and children(Dirk & Schmiedek, 2016). WM forms the basis for acquiring new capacities (e.g., Hitch,Towse, & Hutton, 2001). Better performance on WM tasks is related to higher achievement inmathematics (e.g., Friso-van den Bos, van der Ven, Kroesbergen, & van Luit, 2013) and reading(e.g., Loosli, Buschkuehl, Perrig, & Jaeggi, 2012). Given the evidence for substantial daily

VARIABILITY IN WORKING MEMORY AND PERCEIVED DISTURBANCE 201

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

fluctuations in WM, this important resource for learning and children’s achievement in school isnot available to the same degree in all learning situations (Dirk & Schmiedek, 2016). One sourceof these fluctuations might be irrelevant sounds. The so-called irrelevant-sound-effect states thatirrelevant auditory information competes with relevant to be remembered information forrepresentation in WM and thus causes confusion and decreases memory performance.

Perceived Disturbance and WM Variability

It is well known that noise has detrimental effects on cognitive performance (Szalma & Hancock,2011) and negatively affects children’s learning (cf. Klatte et al., 2013). Especially WM is impairedby acute noise as studies addressing the so-called irrelevant sound effect have shown also forchildren (Elliott, 2002). In these studies, serial recall of visually presented items is impaired byirrelevant sounds. This effect is evoked by speech or nonspeech sounds of varying characteristics(e.g., different syllables or tones) and occurs not or only to a minor degree when steady-state noise(e.g., bla bla or single tones) is presented. Different theories explaining the irrelevant sound effectexist (cf. Klatte et al., 2013), most of which agree that attentional capture and interference of theirrelevant sounds with to be remembered information inWM form the basic mechanisms behind thiseffect. As summarized by Klatte and colleagues (2013), particularly sounds that are salient (e.g.,one’s name called by a classmate), unexpected (e.g., suddenly starting thunderstorm) or deviant fromthe auditory context (e.g., change in pitch of the voice of the teacher in a noisy classroom) potentiallycapture attention. Children are especially impaired by these sounds because of their less developedcontrol of attention (Elliott, 2002). These effects have been also addressed in field studies, in whichthe negative effects of internal noise (e.g., reverberation time of classrooms, Klatte et al., 2010) andexternal noise (e.g., aircraft noise; Klatte et al., 2016) on school-related cognitive tasks were shown.However, most studies in natural settings have focused on objective sound characteristics andignored children’s perception of noise as disturbing (but see Klatte et al., 2016). Rarely field studiesalso considered noise-induced subjective burden, annoyance, and effects on well-being and qualityof life (Klatte et al., 2010, 2016; Lundquist, Kjellberg, & Holmberg, 2002). For example, Klatte andcolleagues (2010) showed that children from objectively noisier classrooms perceived higher burdendue to noise than children from less noisy classrooms. Perceiving noise as disturbing and annoyingand dealing with the feeling of not being able to concentrate might also capture attention and therebydeteriorate children’s WM performance. Thus, perceived disturbance might also directly influencechildren’s WM performance in the daily school context. Disturbance resulting from various sourcesof noise—internal and external—might vary in intensity and degree of annoyance in school and alsoin learning contexts out-of-school (e.g., home context; Pujol et al., 2014). Even under relativelystable levels of objective noise, the degree to which children feel disturbed by that noise might vary.This variation might be explained by between-person differences in trait variables such as person-ality and noise sensitivity (Benfield et al., 2014). For example, individuals who are rather extraverted(i.e., seeking out social stimulation) prefer noisier environments or are at least less distracted byextraneous noise (see Benfield et al., 2014). Moreover, individuals who are rather anxiousand emotionally instable tend to be disturbed by noisy environments and show lower cognitiveperformance under noisy circumstances than individuals who score low on neuroticism (see, e.g.,Nurmi & vonWright, 1983). Research has also shown that older children or children in higher gradesmight be less affected by disturbing noise (e.g., Klatte et al., 2013). Moreover, one might speculatethat children who are less disturbed in their cognitive performance by external noise also show better

202 DIRK AND SCHMIEDEK

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

learning outcomes. In sum, we consider it important to study not only objective noise but alsovariability in children’s perceived disturbance resulting from noise to understand one potentialmechanism by which noise affects WM performance. Thereby this study extends previous researchin that it focuses on the within-person processes relating perceived disturbance to WM performancein children’s everyday learning environments in and out of school learning environments.

Ambulatory Assessment as a Chance for Increasing Ecological Validity in Studies in theSchool Context

Given the importance of WM performance for school achievement, it is crucial to assess WM inthe context in which it is highly needed for successful achievement outcomes. Assessing WM inthe school context has been much facilitated by technological advancements during the lastdecades. Assessment in naturalistic settings is possible via mobile devices thereby increasingecological validity. Ecological validity can be defined as the degree to which a study “accuratelyrepresents the conditions under which an effect occurs in the real world” (Reis, 2012, p. 6).Following this definition, we assessed children’s WM performance via smartphones in schooland after school in the contexts in which it is needed for achievement and learning. Thereby, weattempted to accurately represent the typical setting in which children learn and to increase thevalidity of findings pertaining to the impact of perceived noise on WM performance. Moreover,ambulatory assessment (AA) via smartphones facilitated the measurement-intensive microlongi-tudinal study over 4 weeks that offered the basis to study within-person processes. This allowed“testing hypotheses regarding associations among cognitive processes as they transpire withinindividuals” (Sliwinski, Smyth, Hofer, & Stawski, 2006, p. 545). Within-person findings mightreveal, whether children’s cognitive performance varies in the school context as a function ofperceived disturbance due to noisy classrooms or external learning environments. In contrast,between-person findings do not inform us about the processes that evolve within persons overtime (Molenaar, 2004). For example, between-person findings could demonstrate that in noisierclassrooms children generally tend to show lower cognitive performance. In contrast, within-person findings might reveal, whether children’s daily cognitive performance varies in theschool context as a function of perceived disturbance. Measurement-intensive microlongitudinalstudies can assess the nature and degree of naturally occurring variation in perceived disturbanceand cognitive performance and thus provide important insights about children’s everydaylearning conditions. Moreover, such studies can inform about potential differences betweenchildren in the strength of within-person associations (further also called couplings). However,such studies are lacking in the literature on environmental noise effects on children’s cognitiveperformance. We therefore aimed to add to the existing literature by studying whether perceiveddisturbance varies in the school and out-of-school context and to which degree variation inperceived disturbance is coupled with children’s WM performance.

SUMMARY OF THE CURRENT STUDY

This study investigated whether children’s variability in WM performance was associated withvariability in experiencing disturbance by noise or other persons, both, in the school and out-of-school context. We studied (1) whether children’s perceived disturbance varies

VARIABILITY IN WORKING MEMORY AND PERCEIVED DISTURBANCE 203

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

systematically in the school and out-of-school context and (2) whether perceived disturbanceis coupled with children’s WM performance in these contexts. Based on previous researchdemonstrating the detrimental effects of noise on cognitive performance (Elliott, 2002), weexpected decreased WM performance on occasions with increased levels of disturbance. Thatis, we expected to find within-person couplings of WM performance and disturbance (i.e.,systematic relations of the fluctuations in both variables relative to each individual’s mean onboth variables). Moreover, we expected to find individual differences in these couplings andaimed at exploring potential trait variables related to learning (e.g., school achievement, fluidintelligence), personality traits (e.g., neuroticism, extraversion) and person characteristics (i.e.,age, grade) that might explain these differences. Given that the effects of perceived disturbanceon children’s cognitive performance have rarely been studied in natural settings, we refrainedfrom specifying precise hypotheses about differences in the effects between the school andout-of-school context.

METHOD

This study was part of the Assessment of Cognitive Performance FLUctuations in the SchoolConteXt (FLUX) project, which aims at quantifying daily fluctuations in elementary school-children’s cognitive performance and identifying their antecedents and consequences in theschool context. The study followed a measurement-intensive longitudinal design with fourdaily assessments across 4 weeks as well as an extensive pre- and posttest protocol. In thestudy, cognitive performance, affect, disturbance during daily assessments, and several othervariables were assessed on a daily basis via smartphones. This article focuses on the influence ofperceived disturbance on WM performance, which was assessed three times daily.

Participants

One hundred and ten German students (45 girls) participated in the study. All of them attended thesame elementary school in three third-grade classrooms (50 students) and four fourth-grade class-rooms (60 students). Their age ranged from 8 to 11 years (M = 9.88, SD = .61). For 101 children,information regarding their social background could be obtained from their parents. Most childrenwere born in Germany (98%), and German was the native language of 77% of the children, which iscommon for a German city. Class teachers confirmed that all children attended classes regularly,were fluent in German, and could understand the instructions without problems.

Procedure

This study focuses on the three daily assessments of the intensive longitudinal study phase inwhich cognitive performance as well as perceived disturbance were assessed and additionallyrelies on background measures obtained from children at pretest as well as from parents. Thepretest and intensive instruction and practice of the daily assessment battery were administeredin six lessons (4.5 hours total, including about 3 hours for pretests) distributed over 2 weeks inthe second term of the school year. All pretest assessments took place in the classroom in groupsof up to 20 students. The pretest protocol included, among others, a baseline session of a daily

204 DIRK AND SCHMIEDEK

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

assessment session on smartphones, including measures of cognitive performance. Intensivetraining by qualified research assistants assured that students knew how to operate the smart-phone, how to work on the cognitive tasks, and how to answer to the self-report questions.Smartphones (Dell Streak 5, with Android 2.2 operation system) were specially prepared toavoid unwanted usage (e.g., access to the Internet, GPS), equipped with an application pro-grammed for this project, and were handed to the students for the duration of the study. For 31consecutive days, students participated in daily sessions at the beginning (8:50 am, Occasion 1)and the end of school (11:25 am, Occasion 2) as well as in the afternoon (around 3:00 pm,Occasion 3). School sessions took place during class. Sessions were available up to 60 min andlasted about 10 to 15 minutes. Although school sessions were scheduled to fixed times for allchildren, afternoon sessions on school days and other out-of-school sessions (i.e., on weekendsand school and public holidays) could be scheduled individually within a time window of±2 hours. All sessions were carried out daily, including weekend days and public or schoolholidays. Teachers and parents kept minutes of children’s participation. As to be expected withintensive longitudinal protocols, missing data resulted, for example, from illness, exams, otherobligations during testing times as well as from technical problems such as an empty battery, orsmartphones left at home during school hours. For the WM tasks, on average (i.e., across tasksconditions and grades) 65% of the maximum possible data were available. For self-report dataon perceived disturbance during the daily assessments 57% of the maximum possible data wereavailable. The children received money or a gift certificate for their participation. In addition,children collected points with their performance and received feedback about the points col-lected at the end of each session. Students’ participation was voluntary and could be canceledanytime without giving reasons. Informed consent for participation was obtained from thestudents and their parents; 71% of the target students participated. Only four children interruptedthe study ahead of time indicating overall good compliance. In addition to the daily assessmentswith students, parents reported on their children’s demographic and social background as well ason their personality in a paper-based questionnaire that they sent back to the research team viamail. The return rate of this questionnaire was 92%. The study was approved by a local ethicscommittee.

Daily Measures

Working Memory Tasks

We presented two WM updating tasks with numerical and spatial content and twomemory load conditions (Load 2 and Load 3) each. In the numerical task, children had tomemorize and update numbers, and in the spatial task they had to memorize and updatepositions of differently colored and shaped cartoon creatures presented in a 4 x 4 grid. In theLoad 2 condition, three updating operations (i.e., additions and subtractions in the numericaltask, and spatial shifts in the spatial task) were presented sequentially. The Load 3 conditioncontained four updating operations. In each of the three daily occasions, four blocks of theLoad 2 and the Load 3 condition of each task were included. The mean accuracy of all tasksand load conditions served as a performance score in the analyses. The tasks are described indetail in Dirk and Schmiedek (2016). The reliability of the WM measures is reported in theResults section.

VARIABILITY IN WORKING MEMORY AND PERCEIVED DISTURBANCE 205

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

Disturbance

The children reported their current perceived disturbance in each daily session in which alsothe WM tasks were assessed. Due to the close temporal proximity, we did not expect memorybiases. All daily disturbance items (Table 1) were answered after WM was tested. Our itemswere developed by the FLUX team and were pretested in a study with 75 elementary schoolchildren (five occasions) and had to be brief and easy to understand for children. All items wereexplained in detail and practiced with a trained research assistant until children were familiarwith answering the items. Based on the structural model of the disturbance items (see Figure 1),we used the mean disturbance score in the analyses. The reliability of the disturbance measure isreported in the Results section.

Background Measures

School Achievement

Two subtests of a German mathematical achievement test for third and fourth graders,respectively, were used to assess mathematical skills (Deutscher Mathematiktest [DEMAT] 3+,Roick, Gölitz, & Hasselhorn, 2004; DEMAT 4, Gölitz, Roick, & Hasselhorn, 2006). These twotests include computation problems (subtest on arithmetic) and word problems (subtest onwritten math problems). One-hundred six children took the mathematics test (Cronbach’sα = .81 for both tests). Similarly, a standardized German test of reading for elementary schoolstudents, including three subtests on word, sentence, and text comprehension, was administeredto assess reading skills (Lenhard & Schneider, 2005). Ninety-six children took the reading test(Cronbach’s α = .96).

Fluid Intelligence

Fluid intelligence was assessed with the the Culture Fair Intelligence Test (CFT-20-R; Weiss,2006). One-hundred seven children took the test (Cronbach’s α = .72).

Personality

Parents reported on their children’s personality in a shortened version of the German FiveFactor Questionnaire for Children (FFFK; Asendorpf, 1998). This questionnaire assesses chil-dren’s neuroticism, agreeableness, extraversion, openness, and conscientiousness. Parents wereasked to indicate which of two opposite characteristics (e.g., talkative vs. quiet) describes theirchildren better in general on a bipolar five-point scale (e.g., 1 = ‘very talkative, 3 = ‘neithertalkative nor quiet, 5 = ‘very quiet). Each personality trait was assessed by four items (e.g.,neuroticism: nervous vs. relaxed, agreeableness: peaceful vs. quarrelsome, extraversion: out-going vs. withdrawn, openness: has no interest vs. has many interests, conscientiousness: tidyvs. untidy) and averaged to the respective personality trait score. The sample Cronbach’s alphacoefficients were neuroticism (α = .69), agreeableness (α = .63), extraversion (α = .79), openness(α = .74), and conscientiousness (α = .76).

206 DIRK AND SCHMIEDEK

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

Data Analyses

The study’s design resulted in daily WM and disturbance data that were hierarchically structuredwith repeated measures (Level 1) nested within individuals (Level 2). To establish the presenceand relevance of daily fluctuations in perceived disturbance, we conducted two-level confirma-tory factor analyses to determine whether children’s fluctuations in perceived disturbance aresystematic from occasion to occasion (i.e., not explainable in terms of fluctuations at fastertimescales and/or measurement error). For that, we used robust maximum likelihood estimation(MLR) in Mplus 7. In a second step, we fitted multilevel models in which the lower level(Level 1) represented occasions, and the upper level (Level 2) represented individuals. This wasaccomplished using maximum likelihood estimation with SAS PROC MIXED (SAS 9.3). Wetested the fixed (i.e., average) effect of disturbance on WM performance as well as the associatedrandom effect (i.e., between-person differences in the average effect indicating differences insize or direction of the average effect). Context (0 = school, 1 = out-of-school) was entered as acovariate to the models (i.e., as a fixed and a random effect) to test whether performance variesby context (main effect of context) and whether the effect of perceived disturbance on perfor-mance varies by context (interaction of context and disturbance). Perceived disturbance as a

TABLE 1Descriptive Statistics of All Daily Measures

Variable Scale Context M (SD) ICC M ISD (SD)

DisturbanceI could answer to the tasks and 1–5 (five point) School 2.15 (1.43) .26 1.15 (0.44)questions calmly.a Out-of-school 2.07 (1.42) .22 1.17 (0.48)

Overall 2.11 (1.43) .22 1.22 (0.36)

I felt disturbed by other persons 1–5 (five point) School 2.32 (1.50) .22 1.28 (0.47)while answering to the tasks and Out-of-school 2.12 (1.47) .21 1.27 (0.44)questions. Overall 2.21 (1.49) .19 1.32 (0.42)

I felt disturbed by noise while 1–5 (five point) School 2.26 (1.47) .23 1.21 (0.50)answering to the tasks and Out-of-school 2.12 (1.45) .25 1.19 (0.51)questions. Overall 2.18 (1.46) .22 1.26 (0.44)

I felt overall disturbed while 1–5 (five point) School 2.27 (1.47) .24 1.20 (0.50)answering to the tasks and Out-of-school 2.17 (1.47) .25 1.18 (0.47)questions. Overall 2.22 (1.47) .23 1.23 (0.43)

Mean disturbance School 2.26 (1.25) .28 0.99 (0.39)Out-of-school 2.12 (1.24) .27 0.98 (0.37)

Overall 2.18 (1.24) .25 1.01 (0.34)Working memoryMean working memory 0–1 (accuracy) School .67 (.27) .55 .18 (.07)

Out-of-school .68 (.27) .52 .18 (.08)Overall .68 (.27) .51 .18 (.07)

Note. ICC = intraclass correlation (the proportion of between variance to total variance), ISD = intraindividualstandard deviation. The items and tasks were presented in German. Descriptive information in this table refers to thereverse coded item. aThe item was reverse coded in all analyses.

VARIABILITY IN WORKING MEMORY AND PERCEIVED DISTURBANCE 207

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

Level-1 predictor was centered at the person mean to ease interpretation. To explore between-person variables that might explain children’s variation in the coupling between disturbance andWM performance (e.g., mean level of performance) we added cross-level interaction terms (e.g.,Disturbance x Math Achievement) as well as the respective main effects (e.g., MathAchievement) to the models. All models included individual daily trends (fixed and random

FIGURE 1 Mean within- and between-person structure of distraction inthe school and out-of-school context. DIST/Dist. = distraction. Factorloadings are standardized; grey numbers represent factor loading for theout-of-school model. Squares represent observed variables and circlesrepresent latent variables. All factor loadings were significant at p < .05.The fit of the school model to the data was good, χ2(4) = 5.156, p = .272;Comparative Fit Index (CFI) = .999; root mean square error ofapproximation (RMSEA) = .011; standardized root men square residual(SRMR) within = .007; SRMR between = .007. Similarly, the fit of theout-of-school model to the data was good, χ2(5) = 3.813, p = .577;CFI = 1.000; RMSEA = .000; SRMR within = .005; SRMRbetween = .005. Note that the residual variance of the item dist2 wasfixed to zero to improve model fit in the out-of-school-model.

208 DIRK AND SCHMIEDEK

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

effects) to control for expectable long-term effects within children (e.g., practice, loss ofmotivation). This was realized by including a (fixed and random) linear trend of the variablethat counts the daily sessions over the course of the study (i.e., from one to 91). In addition, allcovariances among random effects were estimated, and an autoregressive structure with lag 1was assumed for the residuals. The following full model was estimated, in which subscripts idenote individuals and t denote time (i.e., sessions). In addition, rit represents a residual term,and γ represents fixed and u random effects in the Level-2 models.

Level1 : WMit ¼ β0i þ β1i sessionitð Þ þ β2i disturbanceitð Þ þ β3i contextitð Þþ β4i contextit � disturbanceitð Þ þ rit

Level2 : β0i ¼ γ00 þ u0iβ1i ¼ γ10 þ u1iβ2i ¼ γ20 þ u2iβ3i ¼ γ30 þ u3iβ4i ¼ γ40 þ u4i

Summarizing the models, we tested whether performance of individual i in session t can bepredicted by individual i’s average level of performance (β0), an individual’s change over studytime (β1), concurrent perceived disturbance (β2), the effect of the context (β3) in which theindividual was during performance, and a potential moderator effect of context (β4). Althoughwe considered WM performance as the outcome and disturbance as the predictor, we are awareof the correlational nature of this study. As a consequence all findings may also indicate reverse,bidirectional, or spurious effects produced by unmeasured third variables.

RESULTS

Average Variability and Reliability of Daily Variability

Table 1 shows the descriptive statistics for all daily disturbance items and for mean disturbanceand mean accuracy in the school and out-of-school context as well as overall across contexts.Across single items, as well as for the mean over all items, disturbance was somewhat higher inschool than out of school (di1r: b = −0.11, SE = .04; di2: b = −0.21, SE = .04; di3: b = −0.14,SE = .04; di4: b = −0.13, SE = .04, mean: b = −0.15, SE = .03). The mean WM performance wasslightly higher out of school than in school (b = 0.012, SE = .005); however, taking individualdifferences in the context effect into account showed that children did not generally show highercognitive performance out of school but that this effect differed in size and direction betweenchildren (b = 0.011, SE = .007). The intraclass correlation (ICC; i.e., the portion of between-person variance over total variance) ranged from .52 to .55 for WM, and from .25 to .28 formean disturbance, indicating that independent of context the overall variance was dominated bywithin-person fluctuations. WM as well as disturbance showed a substantial average intraindi-vidual SD. For WM, the magnitude of the average within-person occasion-to-occasion varia-bility was found to average 67% of the between-person variability. For disturbance, the within-person variability averaged 79% (81% overall not considering contexts) of the between-personvariability.

VARIABILITY IN WORKING MEMORY AND PERCEIVED DISTURBANCE 209

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

Following suggestions by different authors who recently argued that reliability should beconsidered separately for within- and between-person measures (e.g., Shrout & Lane, 2012), weestimated reliability of daily WM and disturbance at the latent construct level by testing two-level confirmatory factor models. Based on these models, we assessed reliability for the within-and between-person level following an approach suggested by Wilhelm and Schoebi (2007) thatrelates the proportion of latent variation to total variation on each level. The reliability of WMfluctuations is reported in Dirk and Schmiedek (2016): we found reliabilities of WM of .78/.97on the within- and between-person level, respectively. Considering the reliability of WMfluctuations separately in the school and out-of-school context reliability of WM amounted to.79/.97 (within/between) for the school context and to .78/.96 (within/between) for the out-of-school context. For perceived disturbance we found reliability of .60/.91 on the within- andbetween- person level in the school context, and of .67/.90 (within/between) in the out-of-schoolcontext. The overall higher reliabilities at the between-person level naturally result from aver-aging over up to 91 measurement occasions. Overall, the pattern suggested that measurementerror in WM performance and disturbance related to the AA setting in daily life was most likelynot specifically related to one of the contexts.

Perceived Disturbance in the School and Out-of-School Context

To assess perceived disturbance in the school and out-of-school context, we tested for systematicwithin-person and between-person factors across the four disturbance items, separately for bothcontexts (see Figure 1). The factors were well defined with significant factor loadings at thewithin- and between-person level in both contexts, implying systematic common variance onboth levels. The somewhat smaller factor loading of the first item at the within- and between-person level might be explained by the negative wording of the item compared to the other threeitems. The factor loadings were comparable in size across contexts, indicating that the items didnot differ essentially in their contribution to the overall disturbance factor in school and out ofschool. The within-person factor demonstrated that on occasions when children reported to bemore disturbed by other persons, they were also more disturbed by noise and could not workcalmly. Found systematic between-person differences were also apparent indicating that childrenwho generally showed higher levels of perceived disturbance in one aspect (e.g., disturbance bynoise) reported also feeling more disturbed by other aspects (e.g., noise or not being able to workcalmly) across study occasions. The overall model fits were good (see Figure 1 for details).Thus, the perceived disturbance items presented in this study allow assessing both systematicvariability within-person and between-person differences in children’s perceived disturbance inthe school and out-of-school context.

Couplings of WM With Perceived Disturbance

For our main analyses we used the average of all WM tasks and load conditions as aperformance score. The perceived disturbance score was the aggregate across the four distur-bance items. Table 2 shows the couplings of WM with perceived disturbance. In the full model,we tested the effects of perceived disturbance on WM including all context effects. Perceiveddisturbance had a significant negative effect on WM performance indicating that on occasionswith higher perceived disturbance children’s WM performance was lowered. This fixed effect

210 DIRK AND SCHMIEDEK

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

was accompanied by a significant random effect indicating that the effect of perceived distur-bance on WM varied in size or direction between children. The fixed effect of context was notsignificant. However, we found a significant random effect of context indicating that the effectof context on WM might have been concealed by differences between children in size ordirection of the effect. The interaction of perceived disturbance and context was not significant.Thus, we excluded the interaction term from the final model. We also tried to identify between-person variables that were related to the random effect of disturbance. The children’s age inmonths, their grade, mean perceived disturbance, mean performance across study occasions,school achievement in mathematics and reading, fluid intelligence, and personality traits ofneuroticism, extraversion, and conscientiousness were not predictive (all ps > .05, see Table 3).However, openness to experience significantly accounted for random effect variance(b = −0.012, SE = .005), which indicates that the effect of disturbance on WM was particularlypronounced for children with higher levels of openness for experience. In addition, agreeable-ness significantly explained random effect variance (b = .012, SE = .005), which shows that theeffect of disturbance on WM was also particularly pronounced for children with lower levels ofagreeableness. All effects were supported by likelihood ratio tests (see Table 2). The fixed andrandom effect of perceived disturbance explained almost four percent of the within-personvariance in concurrent WM performance according to the pseudo R2 (i.e., the reduction inresidual variance when the predictor is added to the model). To further illustrate this effect: For a

TABLE 2Daily Couplings of Working Memory and Perceived Disturbance

Full Model Final Model

Estimate SE Estimate SE

Fixed effectsIntercept 0.626* 0.021 0.626* 0.021Trend −0.242* 0.027 −0.242* 0.027Perceived disturbance −0.026* 0.004 −0.028* 0.003Context 0.003 0.007 0.003 0.007Perceived Disturbance * Context −0.004 0.004Random effectsIntercept 0.044* 0.006 0.044* 0.006Trend 0.055* 0.010 0.055* 0.010Perceived disturbance < 0.001* < 0.001 < 0.001* < 0.001Context 0.003* 0.001 0.003* 0.001Residual 0.028* < 0.001 0.028* < 0.001Pseudo-R2 disturbance in % 3.8 3.8Pseudo-R2 context in % 1.9 1.9−2 log-likelihood −3577.4 −3576.7Deviance differences (df)Model w/o Perceived Disturbance * Context 0.7 (1)Model w/o context random 34.7 (4)Model w/o context fix + random 34.7 (5)Model w/o disturbance random 13.7 (4)Model w/o disturbance fix + random 160.0 (5)

Note. All random covariances and AR(1) residual estimated but not listed. *p < .05.

VARIABILITY IN WORKING MEMORY AND PERCEIVED DISTURBANCE 211

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

TABLE 3Daily Couplings of Working Memory and Perceived Disturbance: The Effects of Potential Moderator Variables

M1: Age M2: Grade M3: M Disturbance M4: M WM M5: CFT

Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE

Fixed effects

Intercept 0.297 0.293 0.150 0.117 0.830* 0.059 0.638* 0.004 0.640* 0.018

Trend −0.242* 0.027 −0.239* 0.027 −0.241* 0.027 −0.184* 0.023 −0.237* 0.027

P. Dist. −0.006 0.052 −0.013 0.024 −0.027* 0.013 −0.028* 0.003 −0.028* 0.003

Context 0.004 0.007 0.003 0.007 0.003 0.007 0.003 0.007 0.002 0.007

Moderator 0.003 0.002 0.134 0.032 −0.092* 0.025 0.995* 0.006 −0.015* 0.002

Mod. * P. Dist. < −0.001 < 0.001 −0.004 0.006 −0.001 0.005 0.013 0.017 < −0.001 < 0.001

Random effects

Intercept 0.043* 0.006 0.034* 0.005 0.039* 0.006 < 0.001 < 0.001 0.032* 0.005

Trend 0.056* 0.010 0.056* 0.010 0.056* 0.010 0.045* 0.008 0.054* 0.010

P. Dist < 0.001* < 0.001 < 0.001* < 0.001 < 0.001* < 0.001 < 0.001* < 0.001 < 0.001* < 0.001

Context 0.003* 0.001 0.003* 0.001 0.003* 0.001 0.002* 0.001 0.003* 0.001

Residual 0.028* < 0.001 0.028* < 0.001 0.028* < 0.001 0.028* 0.001 0.028* < 0.001

−2 log-likelihood −3564.5 −3564.5 −3589.8 −4083.7 −3525.8

M6: Math M7: Reading M8: Neuroticism M9: Extraversion M10: Conscientiousness

Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE

Fixed effects

Intercept 0.636* 0.019 0.638* 0.019 0.646* 0.021 0.636* 0.022 0.647* 0.021

Trend −0.238* 0.027 −0.238* 0.027 −0.238* 0.028 −0.242* 0.027 −0.239* 0.028

P. Dist. −0.028* 0.003 −0.028* 0.004 −0.028* 0.003 −0.028* 0.003 −0.027* 0.003

Context −0.001 0.007 0.006 0.008 0.003 0.008 0.005 0.008 0.004 0.008

Moderator 0.017* 0.003 0.004* 0.001 −0.043 0.025 0.002 0.026 0.070* 0.023

Mod. * P. Dist. < −0.001 0.001 < −0.001 < 0.001 −0.001 0.005 −0.004 0.004 0.006 0.004

Random effects

Intercept 0.034* 0.005 0.030* 0.005 0.039* 0.006 0.043* 0.007 0.037* 0.006

Trend 0.056* 0.010 0.047* 0.010 0.054* 0.010 0.055* 0.010 0.055* 0.011

P. Dist < 0.001* < 0.001 < 0.001* < 0.001 < 0.001* < 0.001 < 0.001* < 0.001 < 0.001* < 0.001

Context 0.002* 0.001 0.003* 0.001 0.003* 0.001 0.003* 0.001 0.003* 0.001

Residual 0.028* 0.001 0.029* 0.001 0.027* 0.001 0.027* 0.001 0.027* 0.001

−2 log-likelihood −3387.3 −2876.4 −3383.2 −3419.9 −3391.0

M11: Openness M12: Agreeableness

Estimate SE Estimate SE

Fixed effects

Intercept 0.649* 0.021 0.641* 0.022

Trend −0.238* 0.028 −0.241* 0.028

P. Dist. −0.028* 0.003 −0.028* 0.003

Context 0.003 0.008 0.005 0.008

Moderator 0.075* 0.027 −0.016 0.028

Mod. * P. Dist. −0.012* 0.005 0.012* 0.005

(Continued )

212 DIRK AND SCHMIEDEK

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

hypothetical child experiencing disturbance that is 1.01 scale points above average (1.01 is theaverage intraindividual standard deviation (SD) across study occasions, see Table 1) would beassociated with a decrease in accuracy of WM performance from .63 (fixed intercept) to .54 (b-weight of predictor * mean + 1 average intraindividual SD of disturbance) which corresponds toa relative reduction of 14.3%. This is about one half of the average amount of within-personvariation in WM performance (average intraindividual SD, see Table 1).

DISCUSSION

This study investigated whether children’s perceived disturbance varied systematically in theschool and out-of-school context, and whether these variations are coupled with children’s WMperformance in these contexts. The four items administered in this study to assess children’sperceived disturbance were reliable measures for detecting within-person fluctuations as well asbetween-person differences in perceived disturbance in the school and out-of-school context. Weidentified substantial within-person fluctuations in elementary school children’s daily perceiveddisturbance that amounted to about 80% of between-person differences. Perceived disturbancewas negatively related to daily WM performance. This effect holds independent of context onschool and out-of-school occasions. On occasions with perceived disturbance increased by oneaverage intraindividual SD, a hypothetical child’s WM performance decreased by 14%. Moregenerally, perceived disturbance could explain four percent of the variance in daily WMperformance. The personality traits of openness to experience and agreeableness moderatedthe coupling between perceived disturbance and WM.

Systematic Variation in Children’s Daily Perceived Disturbance

To the best of our knowledge, the present study is the first to investigate whether and howperceived disturbance due to noise varies within children in the school and out-of-schoolcontext. Over 4 weeks, on three daily occasions, in the morning in school as well as in the

TABLE 3(Continued)

M11: Openness M12: Agreeableness

Estimate SE Estimate SE

Random effects

Intercept 0.037* 0.006 0.041* 0.006

Trend 0.055* 0.011 0.055* 0.010

P. Dist < 0.001* < 0.001 < 0.001* < 0.001

Context 0.003* 0.001 0.003* 0.001

Residual 0.027* 0.001 0.027* 0.001

−2 log-likelihood −3395.2 −3419.5

Note. * p < .05. All random covariances and AR(1) residual estimated but not listed. P. Dist = perceived disturbance.Mod. = moderator. WM = Working memory. M Disturbance and M WM stand for the average level of disturbance andworking memory, respectively, across all occasions of the study.

VARIABILITY IN WORKING MEMORY AND PERCEIVED DISTURBANCE 213

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

afternoon and on free days (i.e., public and school holidays) out of school, intraindividualvariation in children’s perceived disturbance as measured by four items accounted for between74% and 79% of the overall variance as indicated by the ICC. Whereas variation betweenchildren indicates that perceived disturbance differs from one child to another, variation withinchildren indicates that the perception of feeling disturbed by noise varies within children overtime. The fact that variability in perceived disturbance correlated substantially within childrenacross the four items excludes the possibility that this within-person variation is mainly due tomeasurement error. Trying to quantify the size of within-person variation in perceived distur-bance, we also considered the ratio of variation between children to variation within children asdescribed by the between-person and within-person SDs (cf. Nesselroade & Salthouse, 2004).Following this approach, within-person fluctuations in children’s perceived disturbanceamounted to 79% of between-person differences. To compare, within-person in children’sWM amounted to 67% of between-person differences. This indicates that the magnitude ofaverage within-child variation in disturbance is more than three fourths that of between-persondifferences and is even larger than that previously found for cognitive performance (Dirk &Schmiedek, 2016). This first descriptive evidence for substantial variation in children’s per-ceived disturbance over time was confirmed by a two-level confirmatory factor analysis thatdemonstrated that the observed within-person variation is systematic. On occasions whenchildren reported to be more disturbed by other persons, they also tended to be more disturbedby noise and not to be able to work calmly. This partly confirms previous findings by Pujol andcolleagues (2014) who found indoor noise, objectively measured by sound-level meters andmicrophones, to vary substantially within and across days in children’s home environments. Todate, few studies have considered children’s perceived disturbance due to noise (Klatte et al.,2010, 2016), and the majority of studies have been conducted in laboratory settings. The presentfindings therefore extend existing research in important ways. We could demonstrate thatperceived disturbance due to noise in the school and out of school context is not stable butvaries within and across days, thereby likely reflecting variation in sources of internal (e.g.,speech level in the classroom) and external noise (e.g., traffic noise). Moreover, with the fouritems used in this study, we offer a tool to assess within-person variation and between-persondifferences in children’s perceived disturbance in the school and out-of-school context. Similarmeasures of noise related burden (Klatte et al., 2010), annoyance (Lundquist et al., 2002), ornoise sensitivity (Benfield et al., 2014) exist in the literature. However, all but one of thesemeasures have a larger number of items than our set, or they have not been tested with childrenin elementary school, and for all of them it is not clear whether they can reliably assess within-person variation in perceived disturbance.

Couplings of Children’s Daily Perceived Disturbance and WM Performance

In line with our hypotheses, we found a detrimental effect of perceived disturbance on children’sWM. We thereby extended previous findings on negative effects of objectively measured noiseon children’s cognitive performance (e.g., Klatte et al., 2010) to subjectively perceived distur-bance. The found average effect also showed between-person variation indicating that either notall children perceive noise in the school and out-of-school context as similarly disturbing or theimpact of feeling disturbed on WM performance varies in size between children. Surprisingly,we found openness to experience and agreeableness to explain some of the variance in these

214 DIRK AND SCHMIEDEK

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

individual differences. Children who tend to be more open to new experiences also seem to bemore disturbed by sounds and other persons around them when performing cognitive tasks.Potentially, these children are highly curious and lose track when interesting sounds or contentsfrom talks around them interfere with their actual task. We further found children who tend to beless agreeable to be more disturbed by sounds and other persons around them. However, with theexception of these two personality traits, we did not find trait variables that could explaindifferences between children. This might have been due the sample size that is comparativelysmall at the between-person level because the larger study focused on within-person processes.Future studies with larger sample sizes at the between-person level should aim at furtherinvestigating the effects of personality traits and abilities on the relationship between disturbanceand WM. Somewhat surprising, the effects of perceived disturbance did not differ between theschool and out-of-school context. Although only few studies have assessed noise effects oncognitive performance in the school and out-of-school context (e.g., Klatte et al., 2016), onemight have expected the effects to be weaker in the school context because during lessons thereshould be discipline and at least indoor noise levels resulting for example from other childrenspeaking should be relatively low. In comparison, assessments in out-of-school contextsoccurred at home but also in noisy environments like the school bus or in after-school carefacilities and should thus be noisier. One explanation for the lack of context effects might havebeen the instruction given to children to work on the daily assessments as focused as possibleand in a quiet environment.

Ecological Validity and Generalizability

Regarding the generalizability and ecological validity, the present study extends existing research inimportant ways. It shows that detrimental effects of noise also hold for subjectively experienceddisturbance and extend from the controlled laboratory setting into elementary school children’severyday lives. This is no self-evident finding, as such an extension rests on the fulfillment of theassumption that the quality and quantity of disturbances in real life are sufficiently similar to thoseimplemented in experimental studies. Real-life contexts can be expected to show considerableheterogeneity across schools, classrooms, and teachers—because of physical attributes of theenvironment, social aspects of the class composition, and aspects of instructional quality, likeclassroommanagement. We acknowledge that such potential heterogeneity also renders our findingsbeing more of a proof of existence (i.e., that disturbance can harm cognitive performance in real life)than a quantitative estimate of the strength of disturbance effects that can be easily generalizedto other contexts. The finding that there were no significant differences between the school andout-of-school context, neither in the amount of the experienced disturbance nor in their effect onperformance, provides some first evidence regarding the generalizability of findings, however.

A further precondition for effects to be detectable at the within-person level that we focusedon in this study, is the presence of within-person variability in the occurence and/or quantity ofdisturbances. If individual children would continuously experience the same (high, medium, orlow) level of disturbance across all measurement occasions, the individual counterfactual effect(of different levels of disturbance) would not be observable. Our findings therefore not onlydemonstrate that disturbances potentially can have effects in real life, as experiments do too—they also demonstrate that variability in disturbances indeed can account for observed fluctua-tions in cognitive performance.

VARIABILITY IN WORKING MEMORY AND PERCEIVED DISTURBANCE 215

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

Importantly, the effects varied significantly in strength across different children. This addsanother dimension of generalizability (and its limits) of findings in real life: children differ fromeach other in whether and how much their cognitive performance is affected by noise or otherdisturbances in everyday contexts. This could be due to a number of trait-like person character-istics that we have little information on in this study, including personality factors, self-regulatory skills, and executive functions that support focusing attention.

Finally, this study demonstrates that the WM tasks administered allow for the reliableassessment of WM fluctuations in real-life learning situation in and out of school. This is notself-evident because for a long time WM has been considered stable and to demonstrateindividual differences (cf. Conway et al., 2005) rather than to show state-like variation withinindividuals over time and contexts. Only in the last decade, the number of studies demonstratingsubstantial within-person variability in adults’, adolescents’, and children’s WM performancehas been growing (e.g., Riediger et al., 2014; Schmiedek et al., 2013; Sliwinski et al., 2006).However, only few studies have considered WM performance in natural learning contexts (Dirk& Schmiedek, 2016; Gasimova et al., 2014; Könen, Dirk, & Schmiedek, 2015). For example,Könen and colleagues (2015) found WM performance to be higher in children age 8 to 11 yearson days when they reported having slept well and not substantially more or less than usual. Dirkand Schmiedek (2016) could show that children with lower school achievement showed largerWM fluctuations at different timescales (i.e., from day to day but also between different timeswithin the school day) in the school context indicating that WM fluctuations might serve as asensitive indicator of learning problems. Taken together, we know only little about the role ofWM fluctuations in real-life learning situations and future studies are needed that assess not onlyWM but also school-related tasks for which WM is needed (i.e., problem solving tasks, textcomprehension) repeatedly in the school context. This would be a first step to further increasethe ecological validity and generalizability of findings pertaining to the role of WM fluctuationsfor learning and achievement in real-life learning situations.

Limitations and Implications for Future Research

Future studies should further explore the found effects and focus on theory-guided testing.Contextual factors should be more systematically varied and directly assessed, and antecedentsof between- and within-person variability in the occurrence of disturbance should be identified.In addition, variables that moderate the strength of the coupling between disturbance andperformance should be investigated and the potential of this coupling to predict children’sschool achievement, motivation and well-being should be considered. In all these aspectspotential variation across schools, classes, teachers, and children should be considered. Tostrengthen the causal interpretation that disturbance negatively affects WM performance, futureAA studies might assess perceived disturbance before and after WM performance. This wouldallow comparing these effects and ruling out that children might indicate having been stronglydisturbed whenever they were unsatisfied with their own task performance to preserve theirpositive self-concept. As technological advancement makes possible the assessment not only ofcognitive tasks, but also of all kinds of other variables, we see a lot of opportunity for the much-needed steps in increasing the ecological validity and extending the applied relevance ofsupposedly important antecedents of cognitive performance.

216 DIRK AND SCHMIEDEK

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

ACKNOWLEDGMENTS

This research was part of the FLUX project at the Center for Individual Development and AdaptiveEducation of Children at Risk (IDeA) in Frankfurt, Germany, funded by the Hessian Initiative for theDevelopment of Scientific and Economic Excellence (LOEWE). We thank Heiko Rölke and theTechnology BasedAssessment Group at the German Institute for International Educational Researchfor developing and providing the software to assess children’s performance and experiences viasmartphones. We owe special thanks to Verena Diel, Tanja Könen, Jan Kühnhausen, AnjaLeonhardt, PhilippWiesemann, and a team of highly committed student assistants for their importantroles in conducting the FLUX project.

ORCID

Judith Dirk http://orcid.org/0000-0001-7224-650X

REFERENCES

Asendorpf, J. B. (1998). Fünf-Faktoren-Fragebogen für Kinder (FFFK) [Five-Factor Questionnaire for Children(FFFK)]. Berlin, Germany: Humboldt-Universität, Institut für Psychologie.

Baddeley, A. D., & Hitch, G. J. (1994). Developments in the concept of working memory. Neuropsychology, 8, 485–493.doi:10.1037/0894-4105.8.4.485

Benfield, J. A., Nurse, G. A., Jakubowski, R., Gibson, A. W., Taff, B. D., Newmann, P., & Bell, P. A. (2014). Testingnoise in the field: A brief measure of individual noise sensitivity. Environment and Behavior, 46, 353–372.doi:10.1177/0013916512454430

Conway, A. R. A., Kane, M. J., Bunting, M. F., Hambrick, D. Z., Wilhelm, O., & Engle, R. W. (2005). Working memoryspan tasks: A methodological review and user’s guide. Psychonomic Bulletin & Review, 12, 769–786. doi:10.3758/BF03196772

Dirk, J., & Schmiedek, F. (2016). Fluctuations in elementary school children’s working memory performance in theschool context. Journal of Educational Psychology, 108, 722–739. doi:10.1037/edu0000076

Dockrell, J. E., & Shield, B. (2004). Children’s perceptions of their acoustic environment at school and at home. Journalof the Acoustical Society of America, 115, 2964–2973. doi:10.1121/1.1652610

Elliott, E. M. (2002). The irrelevant speech effect and children: Theoretical implications of developmental change.Memory and Cognition, 30, 478–487. doi:10.3758/BF03194948

Friso-van den Bos, I., van der Ven, S. H., Kroesbergen, E. H., & van Luit, J. E. (2013). Working memory andmathematics in primary school children: A meta-analysis. Educational Research Review, 10, 29–44. doi:10.1016/j.edurev.2013.05.003

Gasimova, F., Robitzsch, A., Wilhelm, O., Boker, S. M., Hu, Y., & Hülür, G. (2014). Dynamical systems analysis appliedto working memory data. Frontiers in Psychology, 5, 1–12. doi:10.3389/fpsyg.2014.00687

Gölitz, D., Roick, T., & Hasselhorn, M. (2006). Deutscher mathematiktest für vierte klassen (DEMAT 4) [Germanmathematics test for fourth grades (DEMAT 4)]. Göttingen, Germany: Beltz.

Hitch, G. J., Towse, J. N., & Hutton, U. (2001). What limits children’s working memory span? Theoretical accounts andapplications for scholastic development. Journal of Experimental Psychology: General, 130, 184–198. doi:10.1037/0096-3445.130.2.184

Klatte, M., Bergström, K., & Lachmann, T. (2013). Does noise affect learning? A short review of noise effects oncognitive performance in children. Frontiers in Developmental Psychology, 4, 1–6. doi:10.3389/fpsyg.2013.00578

Klatte, M., Hellbrück, J., Seidel, J., & Leistner, P. (2010). Effects of classroom acoustics on performance and well-beingin elementary school children: A field study. Environment & Behavior, 42, 659–692. doi:10.1177/0013916509336813

VARIABILITY IN WORKING MEMORY AND PERCEIVED DISTURBANCE 217

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017

Klatte, M., Spilski, J., Mayerl, J., Möhler, U., Lachmann, T., & Bergström, K. (2016). Effects of aircraft noise on readingand quality of life in primary school children in Germany: Results from the NORAH study. Environment & Behavior,49, 390–424. doi:10.1177/0013916516642580

Könen, T., Dirk, J., & Schmiedek, F. (2015). Cognitive benefits of last night’s sleep: Daily variations in children’s sleepbehavior are related to working memory fluctuations. Journal of Child Psychology and Psychiatry, 56, 171–182.doi:10.1111/jcpp.12296

Lenhard, W., & Schneider, W. (2005). ELFE 1-6: ein leseverständnistest für erst- bis sechstklässer [A readingcomprehension test for first to sixth graders]. Göttingen, Germany: Hogrefe.

Loosli, S. V., Buschkuehl, M., Perrig, W. J., & Jaeggi, S. M. (2012). Working memory training improves readingprocesses in typically developing children. Child Neuropsychology, 18, 62–78. doi:10.1080/09297049.2011.575772

Lundquist, P., Kjellberg, A., & Holmberg, K. (2002). Evaluating effects of the class room environment: Development ofan instrument for the measurement of self-reported mood among school children. Journal of EnvironmentalPsychology, 22, 289–293. doi:10.1006/jevp.2002.0238

Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientificpsychology, this time forever. Measurement, 2, 201–218.

Nesselroade, J. R., & Salthouse, T. A. (2004). Methodological and theoretical implications of intraindividual variabilityin perceptual-motor performance. Journal of Gerontology, 59, P49–P55. doi:10.1093/geronb/59.2.P49

Nurmi, J., & von Wright, J. (1983). Interactive effect of noise, neuroticism and state anxiety in the learning and recall of atextbook passage. Human Learning, 2, 119–125.

Pujol, S., Berthillier, M., Defrance, J., Lardies, J., Levain, J.-P., Petit, R., . . . Mauny, F. (2014). Indoor noise exposure athome: A field study in the family of urban schoolchildren. Indoor Air, 24, 511–520. doi:10.1111/ina.12094

Reis, H. T. (2012). Why researchers should think “real-world”: A conceptual rationale. In M. R. Mehl & T. S. Conner(Eds.), Handbook of research methods for studying daily life (pp. 3–21). New York, NY: Guilford Press.

Riediger, M., Wrzus, C., Schmiedek, F., Wagner, G. G., & Lindenberger, U. (2011). Is seeking bad mood cognitivelydemanding? Contra-hedonic orientation and working-memory capacity in everyday life. Emotion, 11, 656–665.

Riediger, M., Wrzus, C., Klipker, K., Müller, V., Schmiedek, F., & Wagner, G. G. (2014). Outside of the laboratory:Associations of working-memory performance with psychological and physiological arousal vary with age.Psychology and Aging, 29, 103–114. doi:10.1037/a0035766

Roick, T., Gölitz, D., & Hasselhorn, M. (2004). Deutscher mathematiktest für dritte klassen (DEMAT 3+) [Germanmathematics test for third grades (DEMAT 3+)]. Göttingen, Germany: Beltz.

Schmiedek, F., Lövdén, M., & Lindenberger, U. (2013). Keeping it steady: Older adults perform more consistently oncognitive tasks than younger adults. Psychological Science, 24, 1747–1754. doi:10.1177/0956797613479611

Shield, B. M., & Dockrell, J. E. (2008). The effects of environmental and classroom noise on the academic attainments ofprimary school children. Journal of the Acoustical Society of America, 123, 133–144. doi:10.1121/1.2812596

Shrout, P. E., & Lane, S. P. (2012). Psychometrics. In M. R. Mehl & T. S. Conner (Eds.), Handbook of research methodsfor studying daily life (pp. 302–320). New York, NY: Guilford.

Sliwinski, M. J., Smyth, J. M., Hofer, S. M., & Stawski, R. S. (2006). Intraindividual coupling of daily stress andcognition. Psychology and Aging, 21, 545–557. doi:10.1037/0882-7974.21.3.545

Swanson, H. L., & Alloway, T. P. (2012). Working memory, learning, and academic achievement. In K. R. Harris, S.Graham, T. Urdan, C. B. McCormick, G. M. Sinatra, & J. Sweller (Eds.), APA educational psychology handbook, Vol1: Theories, constructs, and critical issues (pp. 327–366). Washington, DC: American Psychological Association.

Szalma, J. L., & Hancock, P. A. (2011). Noise effects on human performance: A meta-analytic synthesis. PsychologicalBulletin, 137, 682–707. doi:10.1037/a0023987

Weiss, H. R. (2006). Grundintelligenztest Skala 2 - Revision (CFT 20-R) [Basic Intelligence Test Scale 2].Göttingen,Germany: Hogrefe.

Wilhelm, P., & Schoebi, D. (2007). Assessing mood in daily life. Structural validity, sensitivity to change, and reliabilityof a short scale to measure three basic dimensions of mood. European Journal of Psychological Assessment, 23, 258–267. doi:10.1027/1015-5759.23.4.258

218 DIRK AND SCHMIEDEK

Dow

nloa

ded

by [

MPI

Max

-Pla

nck-

Inst

itute

Fur

Bild

ungs

fors

chun

g] a

t 05:

52 0

2 Se

ptem

ber

2017