ArticlePDF Available

Abstract and Figures

Elementary schoolchildren’s working memory performance (WMP) fluctuates from moment to moment and day to day, yet the underlying mechanisms are not well understood. In the present study, affective states were investigated as predictors of these fluctuations. Interindividual differences in the intraindividual affect–WMP associations were expected, and their structure was explored. One hundred nine children (8–11 years) were investigated in an ambulatory assessment. Affective states (positive affect [PA], negative affect [NA], activation, deactivation) and WMP were assessed 3 times daily for up to 31 consecutive days. In the whole sample, WMP was lower at occasions with higher NA or deactivation, while there was no overall effect of PA or activation. Results of a mixture model analysis revealed meaningful heterogeneity in these effects: Approximately half of the children showed comparably weaker effects of affect on WMP, while the other three groups showed (1) comparably stronger negative effects of NA and deactivation; (2) a comparably stronger positive effect of activation; or (3) comparably stronger negative effects of NA and deactivation and stronger positive effects of PA and activation. Findings emphasize the importance of explicitly considering interindividual differences in intraindividual associations. They are discussed in the context of current frameworks of interindividual differences in environmental sensitivity.
Content may be subject to copyright.
Running Head: AFFECT WORKING MEMORY COUPLINGS 1
Momentary Working Memory Performance is Coupled with Different Dimensions of Affect
for Different Children: A Mixture Model Analysis of Ambulatory Assessment Data
Andreas B. Neubauer1,2, Judith Dirk1,2, & Florian Schmiedek1,2,3
1German Institute for International Educational Research (DIPF), Frankfurt am Main,
Germany
2Center for Research on Individual Development and Adaptive Education of Children at Risk
(IDeA), Frankfurt am Main, Germany
3Goethe University, Frankfurt am Main, Germany
Author Note
Andreas B. Neubauer, Department of Education and Human Development, German
Institute for International Educational Research (DIPF), Frankfurt am Main, Germany and
Center for Research on Individual Development and Adaptive Education of Children at Risk
(IDeA), Frankfurt am Main, Germany; Judith Dirk, Department of Education and Human
Development, German Institute for International Educational Research (DIPF), Frankfurt am
Main, Germany and Center for Research on Individual Development and Adaptive Education
of Children at Risk (IDeA), Frankfurt am Main, Germany; Florian Schmiedek, Department of
Education and Human Development, German Institute for International Educational Research
(DIPF), Frankfurt am Main, Germany, Center for Research on Individual Development and
Adaptive Education of Children at Risk (IDeA), Frankfurt am Main, Germany, and Goethe
University, Frankfurt am Main, Germany.
AFFECT WORKING MEMORY COUPLINGS 2
Correspondence concerning this article should be addressed to Andreas B. Neubauer,
Education and Human Development, German Institute for International Educational Research
(DIPF), Frankfurt am Main, Germany. E-Mail: neubauer.andreas@dipf.de
Acknowledgments
This research was part of the FLUX project at the IDeA-Center in Frankfurt am Main,
Germany, which was funded by the Hessian Initiative for the Development of Scientific and
Economic Excellence (LOEWE). We thank Heiko Rölke and the Technology Based
Assessment Group at the DIPF for developing and providing the software to assess children’s
performance and experiences via smartphones. We owe special thanks to Verena Diel, Tanja
Könen, Jan Kühnhausen, Anja Leonhardt, Philipp Wiesemann, and a team of highly
committed student assistants for their important roles in conducting the FLUX project. FS and
ABN further acknowledge support from the Jacobs Foundation (grant number 2016-1245-00).
© 2018, American Psychological Association. This paper is not the copy of record and may not
exactly replicate the final, authoritative version of the article. Please do not copy or cite without
authors' permission. The final article will be available, upon publication, via its DOI:
10.1037/dev0000668
AFFECT WORKING MEMORY COUPLINGS 3
Abstract
Elementary school children’s working memory performance (WMP) fluctuates from moment
to moment and day to day, yet the underlying mechanisms are not well understood. In the
present study, affective states were investigated as predictors of these fluctuations. Inter-
individual differences in the intra-individual affect-WMP associations were expected, and
their structure was explored. One hundred nine children (8-11 years) were investigated in an
ambulatory assessment. Affective states (positive affect, negative affect, activation,
deactivation) and WMP were assessed three times daily for up to 31 consecutive days. In the
whole sample, WMP was lower at occasions with higher negative affect or deactivation,
while there was no overall effect of positive affect or activation. Results of a mixture model
analysis revealed meaningful heterogeneity in these effects: Approximately half of the
children showed comparably weaker effects of affect on WMP, while the other three groups
showed (a) comparably stronger negative effects of negative affect and deactivation; (b) a
comparably stronger positive effect of activation; or (c) comparably stronger negative effects
of negative affect and deactivation and stronger positive effects of positive affect and
activation. Findings emphasize the importance of explicitly considering inter-individual
differences in intra-individual associations. They are discussed in the context of current
frameworks of inter-individual differences in environmental sensitivity.
Keywords: ambulatory assessment; within-person processes; working memory;
differential susceptibility; environmental sensitivity; intra-individual variability
AFFECT WORKING MEMORY COUPLINGS 4
Momentary Working Memory Performance is Coupled with Different Dimensions of
Affect for Different Children: A Mixture Model Analysis of Ambulatory Assessment
Data
Executive functions (EF) describe a set of cognitive processes involved in everyday tasks that
require top-down control (Diamond, 2013). Inter-individual differences in EF in childhood
have been related to positive adjustment throughout the human life span, with better EF
associated with better achievement in school (Gathercole, Pickering, Knight, & Stegmann,
2004) as well as various indicators of psychological adjustment up to 20 years later (e.g.,
physical health and financial situation; Moffitt et al., 2011). Importantly, these studies
conceptualized EF as a more or less stable, person-level (trait) variable, while intra-individual
(day-to-day or moment-to-moment) fluctuations of EF components have only recently
received some attention in research with children and adolescents (Dirk & Schmiedek, 2016;
Riediger, Wrzus, Schmiedek, Wagner, & Lindenberger, 2011). In the present work, we
examine momentary affective experiences as predictors of intra-individual fluctuations in
working memory performance (WMP). WMP requires simultaneous storage and processing
of several elements and thereby requires and encompasses central components of EF (Miyake
et al., 2000). Investigating the antecedents of ups-and-downs in cognitive functioning in
children’s daily lives allows for better understanding the processes that lead to better vs.
worse cognitive functioning in everyday contexts, and thereby potentially opens up
possibilities to improve WMP via interventions targeting these predictors.
Prior research has shown that intra-individual fluctuations in sleep (Könen, Dirk, &
Schmiedek, 2015) and perceived disturbance (Dirk & Schmiedek, 2017) are systematically
“coupled” with intra-individual fluctuations in WMP in elementary school children. In the
present work, we focus on current affective states as potential predictors of WMP.
Specifically, our focus lies on inter-individual differences in intra-individual couplings of
AFFECT WORKING MEMORY COUPLINGS 5
affective experiences and WMP in elementary school children. That is, we investigate if
children differ in the degree to which their WMP is associated with their current affective
state. The next sections will be organized as following: First, we will summarize prior
research that investigated the association between affective states and EF / WMP. We will
primarily focus either on research with children or on research that has specifically
investigated intra-individual associations of these two constructs. Next, we will introduce
environmental sensitivity (Pluess, 2015) as a framework to investigate the structure of inter-
individual differences in these intra-individual couplings. We then present findings from an
ambulatory assessment study with 109 elementary school children.
Associations Between Affect and WMP
Associations between affective states and cognitive functioning have received
substantial attention in the literature. Due to the scarcity of studies specifically targeting
WMP (especially in children) we provide a non-exhaustive overview of studies investigating
either WMP or EF components. Negative affect (NA) has often been negatively associated
with cognitive performance (Ellis, Moore, Varner, A. Ottaway, & S. Becker, 1997), but the
processes underlying this association remain largely unknown. One explanation proposed is
that NA interferes with resources available to work on cognitive tasks, and is therefore related
to poorer EF (Ashcraft & Kirk, 2001; Ellis & Ashbrook, 1988; Ilkowska & Engle, 2010).
Other mediating mechanisms have been proposed as well, such as decreases in serotonin
(Mitchell & Phillips, 2007) or motivational decrements (Brose, Schmiedek, Lövdén, &
Lindenberger, 2012). Furthermore, the hypothalamic–pituitaryadrenal (HPA) axis might also
play an important role in this association, since previous research found that its end product
(cortisol) can have detrimental effects on cognitive performance (Lupien, Gillin, & Hauger,
1999). While the proposed mechanisms for the effect of NA on cognitive performance concur
in predicting a negative association, the role of positive affect (PA) for EF is less clear. Some
AFFECT WORKING MEMORY COUPLINGS 6
(Ashby, Isen, & Turken, 1999) suggest that mild levels of PA should be associated with
slightly elevated dopamine levels andas a consequencewith better EF. Others argue that
elevated PA occupies cognitive resources as well and should therefore be associated with
diminished performance in EF tasks (Mitchell & Phillips, 2007).
Previous research points toward a deteriorating effect of NA on EF (Patel et al., 2016),
while findings regarding the effects of PA are more inconsistent with some reporting a
positive effect on EF (Palmiero, Nori, Rogolino, D'Amico, & Piccardi, 2015; Yang, Yang, &
Isen, 2013), and others reporting a negative effect (Martin & Kerns, 2011). Most of previous
research has, however, targeted these associations in adult samples and research in children is
comparatively sparse. Investigating the dynamics of EF / WMP in this population would,
however, be highly important because the development and maturation of EF and WMP is
ongoing from birth well into the teen years (Diamond, 2013; Gathercole et al., 2004), and a
better understanding of the processes involved in this developmental phase could hint towards
promising interventions and policy implications.
Two experimental studies revealed similar findings in children as studies among
adults. In one study on children between 9 and 12 years (Fartoukh, Chanquoy, & Piolat,
2014), a negative mood induction lead to decreasing performance in a letter–number
sequencing task (an indicator of WMP), while no effect of a positive mood induction could be
observed (relative to a neutral control condition). Similarly, Pnevmatikos and Trikkaliotis
(2013) showed that an anxiety induction led to poorer inhibitory control (a facet of EF) in
children between 8 and 12 years. That is, these studies suggest that negative affect is
associated with poorer EF in children, while no effect for PA could be observed.
The vast amount of the aforementioned studies has exclusively focused on between-
person associations of affect and EF / WMP. While this approach is certainly informative
when the core focus lies on determining inter-individual differences, it cannot uncover intra-
AFFECT WORKING MEMORY COUPLINGS 7
individual processes, unless unrealistic assumptions are being made (Molenaar, 2004).
Crucially, models explaining the associations between affect and EF such as the resource
allocation model (Ellis & Ashbrook, 1988) target effects that unfold within individuals over
time. Taking a within-person perspective with regard to both study design and data analysis is
paramount to investigate these intra-individual processes (Hamaker, 2012). A few studies
have targeted intra-individual effects of affective states on WMP, and all of these have been
conducted with adults or adolescents. With regard to NA, some studies revealed a negative
intra-individual effect on WMP (Brose et al., 2012; Riediger et al., 2011), while others
(Sliwinski, Smyth, Hofer, & Stawski, 2006; Stumm, 2016) did not find significant intra-
individual associations. Previous findings regarding the intra-individual effect of PA on WMP
are also mixed with some (Brose, Lövdén, & Schmiedek, 2014) reporting a positive
association, others a quadratic association, indicating elevated performance at medium levels
of PA (Riediger et al., 2011), or no association (Stumm, 2016).
Part of the inconsistency in previous research might be attributed to the existence of
meaningful inter-individual differences in the intra-individual coupling of affect and WMP:
While there may be some individuals who exhibit meaningful intra-individual couplings
between PA / NA and WMP, others might not. Aggregating across individuals could therefore
result in a small coupling which could not be detected in these studies because it was masked
by inter-individual differences. In the present study, we specifically target these inter-
individual differences in the intra-individual affect-WMP couplings in children. We now
introduce the theoretical framework of environmental sensitivity (ES), which we utilize to
better understand the structure of inter-individual differences in the intra-individual affect-
WMP couplings in elementary school children.
Environmental Sensitivity and Inter-Individual Differences in Affect-WMP Couplings
AFFECT WORKING MEMORY COUPLINGS 8
The environmental sensitivity framework (Pluess, 2015) considers inter-individual
differences in environmental sensitivity as the core determinant of inter-individual differences
in reactivity that is, the degree to which current experiences affect physiological,
psychological, and behavioral outcomes. According to this view, individuals differ in their
ability to register and process external stimuli (=environmental sensitivity), and these
differences are associated with inter-individual differences in the degree to which they are
affected by their environment (=reactivity). This framework is intended as an integration of
various related theoretical accounts. Among others, these include differential susceptibility
theory (Belsky & Pluess, 2009), vantage sensitivity (Pluess & Belsky, 2013), the diathesis-
stress model (Zuckerman, 1999), the biological sensitivity to context model (Boyce & Ellis,
2005), and the adaptive calibration model (Del Giudice, Ellis, & Shirtcliff, 2011). In a
nutshell, all of these accounts aim to explain inter-individual differences in the effects of
environmental inputs on physiological, psychological, and behavioral outcomes. They differ,
for example, in whether they focus on the effects of positive (VS) or negative environmental
inputs (DS), or both (DST; BSC; ACM), as well as in the origins of these differences (genetic
vs. environmental) and their postulated mechanisms (Ellis, Boyce, Belsky, Bakermans-
Kranenburg, & van IJzendoorn, 2011; Pluess, 2015). On the most general level, however,
they all converge on the prediction that individuals differ in the degree to which
physiological, psychological, and behavioral outcomes are affected by environmental
conditions.
Subsuming these prior accounts in the ES framework, Pluess (2015) identified four
(mutually exclusive) sensitivity types: low sensitivity individuals are characterized by
attenuated responses to environmental inputs. That is, these individuals are barely affected by
either positive or negative environmental conditions. A second group, referred to as
vulnerability group, is hypothesized to respond particularly strongly to negative
AFFECT WORKING MEMORY COUPLINGS 9
environments. Hence, these individuals can be characterized as vulnerable for the impact of
negative environmental effects (sensu a diathesis stress model; Zuckerman, 1999), while they
do not necessarily exhibit a pronounced reaction to positive environmental conditions. The
third group, which Pluess (2015) called vantage sensitivity group, shows the reversed pattern
with enhanced effects specifically of positive environments. Vantage sensitivity is defined as
“the general proclivity of an individual to benefit from positive and presumptively well-being-
and competence-promoting features of the environment” (Pluess & Belsky, 2013), and can
therefore be seen as a tendency to profit from positive environmental conditions. The fourth
group, referred to as general sensitivity, combines elements of the vulnerability group and the
vantage sensitivity group: These individuals are strongly affected by both positive and
negative environmental inputs. Hence, general sensitivity is close to the concept of
differential susceptibility which has been defined as the proclivity to be affected by both
adverse and supportive environmental conditions in a “for-better-and-for-worse” manner
(Belsky & Pluess, 2009).
There has been a vast amount of research investigating differences in the effects of
environmental conditions, most of which has targeted questions related to human
development, in particular development in childhood. Almost exclusively, this research has
measured environmental conditions more or less directly (e.g., as parenting quality, physical
abuse, socioeconomic status, experimentally induced stress) and has targeted a plethora of
outcomes (e.g., academic achievement, attachment style, inhibitory control; see (Belsky
& Pluess, 2009). In the present work, we do not measure positive or negative environmental
conditions and examine their differential effects on WMP or affect. Thus, we do not test
predictions directly derived from the ES framework. We rather utilize central concepts of this
account to examine inter-individual differences in the intra-individual coupling of affect and
WMP in children.
AFFECT WORKING MEMORY COUPLINGS 10
How can the framework of ES, which targets inter-individual differences in reactivity
to the environment, inform hypotheses about the structure of inter-individual differences in
the intra-individual affect-WMP couplings in children? Conceptually, environmental
influences on both affect and WMP can be expected. That is, we can understand both affect
and WMP as outcomes of a common cause: environmental input. Further, we can understand
both of these variables as outcomes in the ES framework. For example, children in the general
sensitivity group will be more strongly affected by environmental inputs (both positive and
negative) in their affect and their WMP compared to children in the low sensitivity group.
Assuming that differences between children in the effect of environment on affect are related
to the same cause (e.g., the same genetic predispositions) and of the same size as differences
in the effect of environment on WMP1, children in the general sensitivity group will show
larger affect-WMP couplings compared to children in the low sensitivity group. Hence, under
these conditions inter-individual differences in affect-WMP couplings can be interpreted as
indicators of differences in ES.
The Present Study
The aim of the present study was to investigate intra-individual couplings of current
affective states and WMP in elementary school children’s everyday lives using ambulatory
assessment (Trull & Ebner-Priemer, 2013). Previous research investigating intra-individual
fluctuations in affect among elementary school children showed that children of this age
report affect in a differentiated fashion. Specifically, Leonhardt, Könen, Dirk, and Schmiedek
(2016) demonstrated that a six-factor model describing the three affect dimensions valence,
energetic arousal, and tense arousal best described the affective experiences reported in a
sample from this population. As discussed by Leonhardt et al. (2016), this three-dimensional
1 We note that, according to Pluess (2015), so far “it remains to be determined whether an individual’s
environmental sensitivity varies across different domains of functioning (e.g., cognitive, emotional, social) or
whether differences in sensitivity affect all domains equally” (p.142). For the interpretation of our findings in
light of the ES framework, we assume the latter.
AFFECT WORKING MEMORY COUPLINGS 11
affect space concurs with the three-dimensional model of affect by Steyer, Schwenkmezger,
Notz, and Eid (1994) and can be related to alternative models of affect such as the two-factor
model (Watson & Tellegen, 1985) and the pleasure-arousal model (Russell & Barrett, 1999).
In the present study, we assessed two of these three dimensions of affective experience
valence and energetic arousalby two scales each: Positive affect (PA) and negative affect
(NA) represented the two poles of the valence dimension, activation and deactivation
represented the two poles of the energetic arousal dimension.
Based on previous studies investigating the within-person effects of NA with cognitive
functioning, we hypothesized that indicators of negative affectivity (NA; deactivation) would
show a negative within-person association with WMP. Predictions regarding the effects of
positive affectivity (PA; activation) were less clear, given the inconsistency in previous
research. However, we expected that everyday experiences in our sample wouldin many
casesnot exceed mild levels of elevated PA, yielding a positive association between
positive affective sates and WMP.
Given the somewhat heterogeneous findings in previous studies, we further expected
that there would be meaningful inter-individual differences in the intra-individual couplings
of all affect indicators with WMP. To better understand these differences, we utilized central
ideas from the ES framework. This framework allows deriving the expectation that these
differences might be structured into four distinct groups. Specifically, there should be one
group of children who are virtually unaffected by affective experiences in their cognitive
performance (low sensitivity group). A second group of children is expected to show strong
negative effects of negative affective experiences (vulnerability group), while a third group
should exhibit pronounced positive effects of positive affective experiences (vantage
sensitivity group). Finally, a fourth group is expected that is strongly affected by both positive
and negative affective experiences (general sensitivity group).
AFFECT WORKING MEMORY COUPLINGS 12
Method
The present study comprised an ambulatory assessment (AA) phase during which
children repeatedly worked on cognitive tasks and reported on potential covariates of their
cognitive performance. Before and after the AA, extensive pre- and post-test measures were
obtained in a classroom setting. The focus of the present work is on the AA phase.
Participants
The sample comprised 110 children that attended the third (n = 50) and fourth grade (n
= 60), respectively, of an elementary school. The present research is conducted on an already
existing data set (Dirk & Schmiedek, 2016). Hence, sample size was not planned with regard
to the specific aims of the present study. One child was discarded for the analyses because he
did not provide any concurrent measures of WMP and affective experiences at the same
measurement occasion. The analyses are therefore based on the remaining 109 children (45
girls; 8-11 years; Mage = 9.88, SDage = 0.61). We chose to include only children of Grades 3
and 4 (but not younger) because children participating in this study needed to possess
elementary reading and arithmetic skills in order to understand and perform the cognitive
tasks. Notably, a recent review on ecological momentary assessment (EMA) in children and
adolescents concluded that self-report EMA studies are feasible with children of seven years
or older, but it is unclear whether they can be used with younger children (Heron, Everhart,
McHale, & Smyth, 2017). Both children and their parents provided informed consent.
Participation was voluntary and could be terminated at any point during the study. Children
and parents received money or a gift certificate as remuneration.
Procedure
The study started with 4.5 hours of training and pretesting, distributed across six
school lessons. Among other variables, baseline measures of fluid intelligence, WMP, trait
affect, and school achievement were assessed during this phase. The testing took place as
AFFECT WORKING MEMORY COUPLINGS 13
group testing in the class rooms. In addition to these measurements, children were also trained
in operating the study smartphones for the AA phase. These touchscreen based smartphones
(Dell Streak 5 with Android 2.2 operating system) were programmed with an application that
was specifically developed for this study. All other functionalities of the smartphones (e.g.,
internet browser, cellular reception, microphone, application menu) were disabled; it was
further ascertained that children could not exit or terminate the study application. Hence, the
smartphones could only be used for the intended purposes of the present study. During the
training session, the smartphones were distributed and detailed instructions were given by
qualified research assistants. They instructed the children how to operate the smartphones and
demonstrated each of the tasks and questions of the daily assessments. After that,
smartphones were collected by the research assistants and redistributed at the beginning of the
AA phase which started one week after the training session.
The AA phase lasted for 31 consecutive days and was conducted during the middle of
the spring term (May / June 2012). During that time, four assessments per day were signaled
by the smartphone, of which two took place during school hours (Occasion 1 at 8:50 am;
Occasion 2 at 11:25 am). The third assessment started around 3:00 pm; the exact timing was
individually adapted, depending on each child’s schedule within a four hour window. The
fourth session started around 7:00 pm (again +/- 2 hours to allow for individual schedules).
This session is not relevant for the present study, since no WMP assessments were taken at
the fourth occasion. Each assessment lasted approximately between 10 and 15 minutes.
Assessments during school lessons (Occasions 1 and 2 during school days) were supervised
by teachers and trained research assistants to ascertain that participating children were not
distracted by their fellow students who did not participate (since not all students of a class
participated in the study, non-participating students were provided with coloring books to
work on during the assessments). Across all days and children, at least one valid data point on
AFFECT WORKING MEMORY COUPLINGS 14
the five relevant study variables (see below) was present in 6,753 of the 10,137 occasions
(109 children x 31 days x 3 measurement occasions per day), yielding an overall compliance
rate of 66.6%. Split up into the three occasions, compliance was slightly higher at Occasion 1
(68.9%) and Occasion 2 (69.4%) than at Occasion 3 (61.6%). These compliance rates are
slightly below the average, yet in the typical range of, compliance rates in comparable AA
studies with children and adolescents reported in a recent review (Heron et al., 2017).2 The
study protocol for the project FLUX (Assessment of Cognitive Performance FLUctuations in
the School ConteXt) was approved by the ethics committee of the Faculty of Psychology and
Sport Sciences at Goethe University in Frankfurt am Main, Germany (protocol number 2011-
25 R2).
Measurements
Momentary Measures (Ambulatory Assessment).
Affect. Previous research in elementary school children has shown that the four scales
PA, NA, activation, and deactivation, although correlated, are psychometrically distinct and
should therefore be considered separately (Leonhardt et al., 2016). In the present study, each
scale was assessed with three items each. Specifically, at each measurement occasion children
were asked to indicate to what extent they felt each state right now (“Right now, I feel …”) on
a five-point Likert scale ranging from 1 (“not at all”) to 5 (“very much”). The items were
good, fantastic, content (PA), unhappy, miserable, afraid (NA), active, interested,
concentrated (activation), and exhausted, tired, faint (deactivation), respectively. The three
items belonging to the same factor were averaged, resulting in four affect variables used for
further analyses. A multilevel confirmatory factor analysis with four factors on the between-
person level and four factors on the within-person level revealed adequate model fit in the
2 We examined whether individual compliance rates were associated with mean levels in our study variables.
Results showed that compliance rates were unrelated to gender, age, or any of the four affect scales, |r| < .13, p >
.19 for all. There was a small but significant association with mean working memory performance, r = .21, p =
.03, indicating that children with worse average performance tended to comply less with the measurement
procedure.
AFFECT WORKING MEMORY COUPLINGS 15
present sample, comparative fit index (CFI) = .926, root mean square error of approximation
(RMSEA) = .032, standardized root mean square residual (SRMR) = .043 (within) / .073
(between). Estimated reliabilities of the four scales were obtained as multilevel ω (Geldhof,
Preacher, & Zyphur, 2014). The respective estimates suggested good internal consistency on
the between-person level, ω = .85 (PA), ω = .99 (NA), ω = .90 (activation), and ω = .97
(deactivation), respectively, and adequate internal consistency on the within-person level, ω =
.70 (PA), ω = .75 (NA), ω = .65 (activation), and ω = .70 (deactivation), respectively.
Working memory performance. At each measurement occasion, children worked on
two memory updating tasks of working memory. In the numerical updating task, children
were presented two (Load 2 task) or three (Load 3 task) digits (0-9) on the screen of the
smartphone. After that, three (Load 2 task) or four (Load 3 task) updating operations were
presented in the same place where the digits had appeared. These operations were subtractions
and additions ranging from -2 to +2. Children had to apply these operations to the digits that
were presented in the corresponding location. After all operations had been presented, the two
(Load 2 task) or three (Load 3 task) end results had to be entered within 20000ms. During
each measurement occasion (i.e., three times per day) four Load 2 tasks and four Load 3 tasks
were administered. Each of the total 20 responses (4 x 2 results in the Load 2 tasks and 4 x 3
results in the Load 3 tasks) was coded as 1 if the correct response was given within the
20000ms response window (0 otherwise). Mean accuracy of the 8 (Load 2) and 12 (Load 3)
responses per measurement occasion was computed.
In the spatial memory updating task, children were presented a 4x4 grid on the
smartphone screen. Two (Load 2 task) or three (Load 3 task) cartoon creatures were
simultaneously presented, each in one of the 16 squares. Three (Load 2 task) or four (Load 3
task) updating operations were sequentially presented. These operations were represented as
colored arrows positioned at the center of the grid. These arrows indicated movements of the
AFFECT WORKING MEMORY COUPLINGS 16
cartoon creature with the corresponding color (e.g., if a red arrow facing downward appeared
this indicated that the red creature had moved one square downward). After all updating
operations had been shown, children had to indicate the position of the two or three creatures
by tapping the respective square within 30000ms. Four blocks of the Load 2 task and four
blocks of the Load 3 task were presented and the average accuracy across the 8 (Load 2) and
12 (Load 3) responses per measurement occasion was computed.
Previous research using the same data3 as the present study (Dirk & Schmiedek, 2016)
showed that the four performance indicators (numerical Load 2, numerical Load 3, spatial
Load 2, and spatial Load 3) load on one common factor on both the between-person and the
within-person level. We therefore aggregated the four indicators into one accuracy score per
measurement occasion. For detailed information on the tasks and psychometric properties of
this assessment see Dirk and Schmiedek (2016) and Galeano Weber, Dirk, and Schmiedek
(2018).
Data Analysis
In a first step, four multilevel models were run in which WMP was predicted by one
affect scale (PA, activation, NA, deactivation) each. To control for retest effects, a linear
effect of measurement occasion was added in the model. Random effects were estimated for
the intercept, the focal predictor (affect) and time. That is, child i’s WMP at measurement
occasion t (WMPti) was predicted by this child’s affect at the same measurement occasion
(affti) and time (timeti):
Level 1:
 =
β0 +β1 time +β2 aff +ε
(1)
Level 2:
β0 =
γ00 +υ0
(2)
3 We note that we excluded one child from the present analyses that had been included in the previous
publication.
AFFECT WORKING MEMORY COUPLINGS 17
β1 =
γ10 +υ1
(3)
β2 =
γ20 +υ2
(4)
Affect was centered on the respective person mean to obtain an unbiased estimate of
the intra-individual effect (Wang & Maxwell, 2015); time was centered on the 50th
measurement occasion and raw scores were divided by ten. Correlations between random
effects were estimated. In a next step, intra-individual couplings were extracted as estimates
(specifically, best linear unbiased predictors, or BLUPs) of person specific regression
coefficients (β2 ). Couplings between NA and WMP and between deactivation and WMP,
respectively, were multiplied by -1 in order to facilitate interpretation: Positive coupling
estimates represent an association between affect and WMP in the expected direction (i.e.,
positive association of WMP with PA and activation, negative association with NA and
deactivation). Person means of the five variables of interest (four affect scales and WMP)
were computed as each child’s arithmetic mean across all observations. These mean scores
and the four coupling estimates were then subjected to a mixture model analysis (MMA).
Since the variances of these nine variables were substantially heterogeneous and unequal
variances affect the solution of the MMA (Steinley & Brusco, 2011), we transformed them
prior to the analyses. Specifically, we standardized the variables via a variance-to-range
weighting procedure which has been shown to be superior to traditional transformations such
as the z-standardization (Steinley & Brusco, 2008). Within-class variances of the nine
indicators were constrained to equality across classes. Correlations between the couplings of
WMP with PA and activation, the couplings of WMP with NA and deactivation, mean PA
and activation, and mean NA and deactivation, respectively, were estimated within each class.
This MMA allows for examining whether the multivariate distribution of the study variables
might represent a mixture of different distributions arising from distinct subgroups in the
population. That is, in these models we examined whether the total sample might consist of
AFFECT WORKING MEMORY COUPLINGS 18
various subsamples (classes) that differ in their distributions of the nine study variables (five
person means and four couplings). Utilizing concepts derived from the theoretical account of
ES, it can be expected that four classes can be identified that differ in the extent to which their
members’ affect is coupled with WMP.
The MMA was conducted in Mplus version 8 (Muthén & Muthén, 2017), all other
analyses using R version 3.4.0 for Windows (R core team, 2017). Multilevel models were
estimated using the lme4 package (Bates, Mächler, Bolker, & Walker, 2015); 95% bootstrap
confidence intervals for multilevel model parameters were obtained via the confint.merMod
function of the lme4 package. A conventional α-level of .05 was set for all analyses.
Results
Table 1 depicts descriptive statistics on the person level. PA correlated positively with
activation, r = .79, p < .001, and negatively with NA, r = -.30, p = .002, and deactivation, r = -
.23, p = .016, respectively. Activation was not significantly related to both NA, r = -.17, p =
.077, and deactivation, r = -.18, p = .061. Deactivation and NA were strongly positively
correlated on the person level, r = .90, p < 001. Average WMP was negatively associated with
average NA, r = -.33, p < .001, and deactivation, r = -.29, p = .002, and positively associated
with average PA, r = .27, p = .005.
Multilevel Models
We estimated separate models for each of the four predictors of interest. Table 2
depicts the results. Fixed effects were statistically significant for NA and deactivation, but not
PA and activation. Importantly, random effects for the focal predictors were statistically
significant for all predictors. That is, although the effect of PA (activation) on WMP was not
statistically significant for the average sample, there were statistically meaningful inter-
individual differences in the strength of the intra-individual association of PA (activation) and
WMP. This was evident by both significant likelihood ratio tests (comparing models with vs.
AFFECT WORKING MEMORY COUPLINGS 19
without random effects for the predictors), χ²(3) > 22.77, p < .001 for all, and lower bounds of
the confidence intervals for the random effect standard deviations markedly above 0 (see
Table 2).4
Structure of Within-Person Couplings
From the multilevel models shown in Table 2, we extracted each child’s estimated
couplings (see Data Analysis for details). Table 3 shows the correlations of these person-
specific within-person couplings. All correlations were positive and (with the exception of the
coupling of activation and deactivation) statistically significant. Correlations for the couplings
of PA and activation, r = .67, and NA and deactivation, r = .69, were substantial. The upper
bounds of the 95% bootstrap confidence intervals for these two correlations were, however,
markedly smaller than 1, suggesting that these couplings are distinct from one another.
We then subjected these four variables, as well as the person means of the four
predictors (PA, activation, NA, deactivation) and the person mean of WMP across all
observations to a MMA. Models with two to six latent classes were estimated; only models
with two, three, and four classes successfully converged and only these were examined in
detail. Model comparisons are depicted in Table 4. Based on these results, a four-class model
provided the best description of the data. The four classes were made up of 16, 51, 19, and 23
children, respectively. Average latent class probabilities for the four classes were .97, .95, .97,
and .99, respectively, suggesting good classification of individuals into the four latent classes.
Figure 1 depicts the means of the nine variables (i.e., the four couplings and the person
means of WMP and the four affect scales) separately for the four latent classes (note that for
better interpretability, Figure 1 depicts z-standardized values). The largest class (Class 2) was
4 In post-hoc analyses, we investigated whether including a quadratic time trend (as fixed and random effect)
would alter the conclusions. Results revealed a significant positive quadratic trend in all four models (indicating
that the change in working memory performance was decelerated towards the end of the study period), b >
0.001, p < .025 for all. When including the quadratic time trend, the fixed effect for PA was significant, b =
0.009, p = .036. All other results remained unaltered (this also applies to the results reported later in the
manuscript).
AFFECT WORKING MEMORY COUPLINGS 20
characterized by above-average WMP and PA, as well as below-average NA and
deactivation. Couplings in this group were all around or slightly below the sample average.
We interpreted this class as representing the group of children with low sensitivity. Class 1
was characterized by below-average WMP, NA, and deactivation, as well as above-average
mean PA and activation. Children in this class exhibited above-average couplings of WMP
with PA, activation, and NA, as well as deactivation (descriptively). The above average
couplings for all four predictors in this class are consistent with the notion of a general
sensitivity group. Children in Class 3 were characterized by below-average WMP, mean NA,
and mean activation, but above-average activation-WMP couplings; this pattern is broadly in
line with a group of children high in vantage sensitivity. Finally, Class 4 also had below-
average mean levels in WMP and, additionally, below-average mean PA. Mean NA and
deactivation were markedly above the sample average. WMP couplings with PA and
activation were around average for children in this class, but couplings with NA and
deactivation were about .75 SDs above the sample average, as would be expected for a
vulnerability group.
Figure 2 depicts each child’s intra-individual couplings (separately for the four
predictors; please note again that couplings with NA and deactivation were multiplied by -1).
Children with couplings one or more standard errors away from zero are marked in black. The
mean couplings together with their associated 95% confidence interval are reported in Table
5. Figure 2 and Table 5 show that (a) couplings in Class 2 (low sensitivity group) were close
to zero, (b) children in Class 3 (vantage sensitivity group) showed overall a positive
association of activation (and to a lesser extent a negative association of NA) with WMP, (c)
in Class 4 (vulnerability group), the associations of NA and deactivation, respectively, with
WMP were negative, while no overall association between PA/activation and WMP could be
observed, and (d) Class 1 (general sensitivity) showed negative effects of NA and
AFFECT WORKING MEMORY COUPLINGS 21
deactivation, respectively, on WMP, and positive effects of PA and activation, respectively,
on WMP.
Discussion
WMP reliably fluctuates within children (Dirk & Schmiedek, 2016), yet little is known
about the predictors of these fluctuations. Why do children sometimes perform better or worse
on the same tasks? In the present study, we focused on one group of predictors: affective
experiences in everyday lives in a sample of elementary school children. Building on previous
research, we expected that momentary states characterized by positive affective experiences
(positive valence, high energetic arousal) would be associated with better WMP, while
negative affective experiences (negative valence, low energetic arousal) would be associated
with worse WMP. Regarding the average associations with indicators of negative affectivity
(NA, deactivation), our findings are consistent with the greatest portion of prior research that
has reported a negative effect of NA on cognitive performance (Brose et al., 2014; Fartoukh et
al., 2014; Patel et al., 2016; Pnevmatikos & Trikkaliotis, 2013): At occasions when children
reported higher NA or higher deactivation, their WMP was worse compared to occasions with
lower NA / deactivation. This finding is consistent with theoretical accounts such as the
resource allocation model (Ellis & Ashbrook, 1988) which proposes that the regulation of
negative affective states takes up resources that cannot be simultaneously engaged for
cognitive tasks requiring working memory resources. We note that, although the average
effects across the whole sample were statistically significant, there were meaningful inter-
individual differences in the intra-individual association between NA / deactivation and
WMP, indicating that children differed in the magnitude of this association. Further
investigating these heterogeneities via mixture models showed that there were two groups of
children (latent Classes 1 and 4) who exhibited particularly strong NA-/deactivation-WMP
couplings.
AFFECT WORKING MEMORY COUPLINGS 22
Positive affective states (PA / activation) were, on average, not significantly coupled
with WMP. However, our results showed that there were substantial inter-individual
differences in the intra-individual association of PA / activation with WMP. Mixture model
analyses suggested that in two groups of children (latent Classes 1 and 3), the (positive)
association of activation with WMP was statistically significant: That is, these children
showed better performance on the working memory tasks at occasions when they reported
higher perceived energetic arousal. Children in latent Class 1 further also exhibited
pronounced (positive) intra-individual PA-WMP couplings.
Taken together, our findings emphasize the importance of considering inter-individual
differences when investigating intra-individual affect-WMP couplings: Had we only
considered the average (fixed) effects, we would have concluded that there was a negative
association of NA/deactivation with WMP, and no association between PA/activation and
WMP. The picture seems to be a bit more nuanced, however: While the negative association
between NA and WMP primarily differed in magnitude between children (larger in Classes 1
and 4), differences in the PA/activation/deactivation associations with WMP were more
complex: For some children (latent Class 2), all these within-person associations were close to
zero. Other children (Classes 1 and 4) showed pronounced couplings between deactivation
and WMP. We note that these were the same children who exhibited particularly strong NA-
WMP couplings as well. In order to better understand the structure of these inter-individual
differences, we interpret these groups along the terminology proposed in the ES framework.
Inter-Individual Differences in Affect-WMP Couplings
Individuals differ in the degree to which they are affected by their environment. While
the processes accounting for these differences are largely unknown (see below for a brief
discussion), the assumption of the existence of such differences is shared among various
theoretical frameworks (Aron, Aron, & Jagiellowicz, 2012; Belsky & Pluess, 2009; Boyce
AFFECT WORKING MEMORY COUPLINGS 23
& Ellis, 2005; Del Giudice et al., 2011; Pluess & Belsky, 2013). According to the ES account
(Pluess, 2015), individuals differ in the effect of the environment on physiological,
psychological, and behavioral outcomes, and these differences can be structured in four
groups: low sensitivity, vulnerability, vantage sensitivity, and general sensitivity. These four
groups resemble the four latent classes identified in the present work: Class 1 consisted of
children with particularly strong couplings for all four affect variables with WMP, and
therefore paralleled the idea of a general sensitivity group. These children showed a strong
(positive) effect of PA and activation on WMP, but also a strong (negative) effect of NA and
deactivation on WMP. Hence, cognitive performance of these children is associated with
current affective states in a “for-better-and-for-worse” manner (Belsky & Pluess, 2009).
Children’s WMP in Class 2 was substantially less affected by their current affective state.
More specifically, couplings in this group were smaller than in Class 1 (NA) or close to zero
(PA, activation, deactivation) in line with the idea of a low sensitivity group. Notably, since
genetic predispositions towards heightened ES, such as the dopamine D4 receptor (DRD4)
gene 7-repeat allele (Bakermans-Kranenburg, van IJzendoorn, Pijlman, Mesman, & Juffer,
2008) or the serotonin transporter-linked polymorphic region (5-HTTLPR) short allele (van
Ijzendoorn, Belsky, & Bakermans-Kranenburg, 2012), are expected to be prevalent in a
minority of the population only (Wolf, van Doorn, & Weissing, 2008), it can be expected that
the largest proportion of children should not demonstrate pronounced responsiveness. This is
in line with our results which revealed that approximately half (47%) of the children in our
sample were categorized into the low sensitivity group (Class 2).
Latent Classes 3 and 4 further emphasize that inter-individual differences in
responsivity to positive and negative experiential influences, respectively, might not
necessarily be consequences of the same mechanism. Specifically, children in Class 3 on
average exhibited positive couplings between activation and WMP, coinciding with the
AFFECT WORKING MEMORY COUPLINGS 24
proposal of a vantage sensitivity group. Class 4, on the other hand, was characterized by
particularly strong (negative) couplings of WMP with NA and deactivation, paralleling the
pattern of a vulnerability group. These results, when interpreted in the ES framework, can
provide important information with regard to potential mechanisms accounting for inter-
individual differences in the effects of environmental influences.
Potential Mechanisms Accounting for Inter-Individual Differences in Reactivity
Previous theorizing on potential mechanisms accounting for inter-individual
differences in the effect of environmental inputs has often not differentiated between the
impacts of positive vs. negative environments. For example, the BSC (Boyce & Ellis, 2005)
and its later refinement as the ACM (Del Giudice et al., 2011) propose that differences in the
sensitivity to environmental influences are a consequence of differences in physiological
stress reactivity. According to this view, individuals differ in the sensitivity of various
subsystems of the stress response system (SRS; i.e., the sympathetic and parasympathetic
systems and the HPA axis) and these differences account for differences in the effects of
environmental inputs on behavioral outcomes. Evidence for this proposition comes from
research examining inter-individual differences in physiological stress reactivity. For
example, in a study by El-Sheikh, Keller, and Erath (2007) inter-individual differences in skin
conductance reactivity (a marker for reactivity of the sympathetic nervous system) moderated
the longitudinal association between parental marital conflict and internalizing problems
among girls (but not boys). This finding is in line with the assumption that differences in SRS
drive differences in environmental effects. So far, however, whether differences in
physiological stress reactivity can account for an “organism’s adjustment to both positive and
negative events”, as postulated by Del Giudice et al. (2011; p. 1572) remains an open
question. Our findings suggested a separation between vantage sensitive children and
vulnerable children, hence challenging the assumption that a single mechanism underlies
AFFECT WORKING MEMORY COUPLINGS 25
environmental sensitivity to both positive and negative input. Nevertheless, it could be that
differences in the responsivity of various subsystems of the SRS interact in giving rise to the
observed structure of inter-individual differences in the intra-individual couplings of affective
states and WMP. Based on our findings, a more fine-grained analysis of interactive effects of
these subsystems, as well as their interaction with serotonergic and dopaminergic pathways
seems to be a promising avenue for future research.
Another mechanism that has been discussed to underlie differences in ES is sensory
processing sensitivity (SPS)—the tendency to be “more or less responsive, reactive, flexible,
or sensitive to the environment(Aron et al., 2012). Recent advances in the measurement of
inter-individual differences in SPS in adults (Lionetti et al., 2018) and children (Pluess et al.,
2018) have shown that this construct can be further divided into three subscales, of which one
(aesthetic sensitivity) has been related to positive emotionality and vantage sensitivity, and
two (ease of excitation; low sensory threshold) have been more closely related to negative
emotionality and vulnerability (Pluess et al., 2018). These findings are concordant with the
observation of the present study that differences in sensitivity to positive contexts and
sensitivity to negative contexts seem to be not necessarily two sides of the same coin. An
interesting agenda for future research would be to examine differences in SPS between the
latent classes identified by intra-individual couplings in the present study: Building on prior
psychometric work, suggesting some discriminant validity among the three SPS subscales
(Lionetti et al., 2018; Pluess et al., 2018) it might be expected that children in the
vulnerability group (latent Class 4 in the present research) show elevated levels in SPS, ease
of excitation, and low sensory threshold, whereas children from the vantage sensitivity group
(latent Class 3 in the present research) show higher levels in SPS and aesthetic sensitivity than
less responsive children. Children in the general sensitivity group (Class 1) might exhibit
higher levels in SPS and all three subscales (aesthetic sensitivity, ease of excitation, and low
AFFECT WORKING MEMORY COUPLINGS 26
sensory threshold). Ultimately, investigating the mechanisms underlying inter-individual
differences in ES is beyond the scope of the present work, but our results point towards
potentially interesting directions for future research, emphasizing the importance of
accounting for mechanisms that can explain evidence of both convergence (general sensitivity
group) and divergence (vulnerability group; vantage sensitivity group) between responsivity
to positive and negative states, respectively.
Limitations
In interpreting the results, a number of limitations have to be considered. First,
although fairly large for an AA study in the school context, the number of participants in the
study has to be considered rather small for a mixture model analysis. The sample size might
have been too small to reveal a further differentiation into more latent classes. Specifically,
models with more than four latent classes failed to converge, and we therefore cannot
conclude that the reported four latent classes might not be divided further into smaller groups.
Second, the tense arousal dimension of affective experiences that has been shown to be
relevant in elementary school children’s everyday affective experiences (Leonhardt et al.,
2016) was not assessed in the present research. Negative tense arousal (such as stress) has
been related to intra-individual fluctuations in cognitive performance (Sliwinski et al., 2006)
and future research needs to determine if vulnerability to stress co-varies with vulnerabilities
to the other two negative affectivity dimensions assessed in the present study (NA,
deactivation). Third, relevant covariates and predictors of membership in the latent classes
remain unknown. As discussed above, physiological stress reactivity and sensory processing
sensitivity would be prime candidates for this purpose and should be explored in future
research. Fourth, our research primarily targeted inter-individual differences in the intra-
individual coupling of affective states and WMP and we utilized the ES framework to better
understand the structure of these inter-individual differences. In order to interpret our findings
AFFECT WORKING MEMORY COUPLINGS 27
in light of the ES framework, we need to make one of the following two assumptions: Either
(a) children’s momentary affective experiences are interpreted as proxy measures for positive
and negative environmental input, or (b) both affective states and WMP are considered as
outcomes of the common cause environmental input. With regard to the first possibility,
affective states are most likely only distal indicators for environmental inputs that are central
to ES. In fact, children might already differ in the extent to which they respond to positive /
negative environmental input (e.g., the occurrence of stressors or uplifting events) in terms of
PA and NA. However, as long as assumption (b) holds true, meaning that inter-individual
differences in PA / NA reactivity (i.e., difference in the degree to which PA / NA are affected
by positive / negative environmental input) are predicted by the same characteristics as are
inter-individual differences in WMP reactivity (i.e., difference in the degree to which WMP is
affected by positive / negative environmental input), this does not pose a threat to the
conclusions drawn in the present research. We note, however, that this assumption needs to be
examined in future research (Pluess, 2018). No concurrent assessments of such events and
WMP were available in the present study, precluding us from directly assessing the effect of
events on WMP. Future research should consider implementing both stressor events and
uplifting events together with concurrent WMP assessments to more directly assess the
impact of environmental input on cognitive performance and inter-individual differences
therein.
Conclusions
Intra-individual fluctuations in elementary school children’s WMP were associated
with concurrently measured fluctuations in affective states. Across the whole sample, higher
negative affect and higher deactivation were associated with poorer performance, while no
overall associations of WMP with positive affect and activation could be detected. Children
differed in the degree to which their performance was associated with their affective states: In
AFFECT WORKING MEMORY COUPLINGS 28
approximately half of the sample, WMP was more or less unrelated to current affect. One
group of children showed pronounced decrements in performance when negative affect or
deactivation were higher than usual, while another group exhibited elevated performance in
situations of high energetic arousal (activation). One further group showed positive
associations with positive affective experiences and negative associations with negative
affective experiences. Differences between the four groups might be a result of genetic by
early environment interactions that are mediated by differences in ES.
AFFECT WORKING MEMORY COUPLINGS 29
References
Aron, E. N., Aron, A., & Jagiellowicz, J. (2012). Sensory processing sensitivity: A review in
the light of the evolution of biological responsivity. Personality and Social Psychology
Review, 16, 262–282. https://doi.org/10.1177/1088868311434213
Ashby, F. G., Isen, A. M., & Turken, A. U. (1999). A neuropsychological theory of positive
affect and its influence on cognition. Psychological Review, 106, 529–550.
https://doi.org/10.1037//0033-295X.106.3.529
Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among working memory, math
anxiety, and performance. Journal of Experimental Psychology: General, 130, 224–237.
https://doi.org/10.1037//0096-3445.130.2.224
Bakermans-Kranenburg, M. J., van IJzendoorn, M. H., Pijlman, F. T. A., Mesman, J., &
Juffer, F. (2008). Experimental evidence for differential susceptibility: Dopamine D4
receptor polymorphism (DRD4 VNTR) moderates intervention effects on toddlers'
externalizing behavior in a randomized controlled trial. Developmental Psychology, 44,
293–300. https://doi.org/10.1037/0012-1649.44.1.293
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting lnear mixed-effects models
using lme4. Journal of Statistical Software, 67. https://doi.org/10.18637/jss.v067.i01
Belsky, J., & Pluess, M. (2009). Beyond diathesis stress: Differential susceptibility to
environmental influences. Psychological Bulletin, 135, 885–908.
https://doi.org/10.1037/a0017376
Boyce, W. T., & Ellis, B. J. (2005). Biological sensitivity to context: I. An evolutionary-
developmental theory of the origins and functions of stress reactivity. Development and
Psychopathology, 17, 271–301.
Brose, A., Lövdén, M., & Schmiedek, F. (2014). Daily fluctuations in positive affect
positively co-vary with working memory performance. Emotion, 14, 1–6.
https://doi.org/10.1037/a0035210
Brose, A., Schmiedek, F., Lövdén, M., & Lindenberger, U. (2012). Daily variability in
working memory is coupled with negative affect: The role of attention and motivation.
Emotion, 12, 605–617. https://doi.org/10.1037/a0024436
Del Giudice, M., Ellis, B. J., & Shirtcliff, E. A. (2011). The Adaptive Calibration Model of
stress responsivity. Neuroscience and Biobehavioral Reviews, 35, 1562–1592.
https://doi.org/10.1016/j.neubiorev.2010.11.007
Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135–168.
https://doi.org/10.1146/annurev-psych-113011-143750
Dirk, J., & Schmiedek, F. (2016). Fluctuations in elementary school children’s working
memory performance in the school context. Journal of Educational Psychology, 108, 722–
739. https://doi.org/10.1037/edu0000076
Dirk, J., & Schmiedek, F. (2017). Variability in children’s working memory is coupled with
perceived disturbance: An ambulatory assessment study in the school and out-of-school
context. Research in Human Development, 14, 200–218.
https://doi.org/10.1080/15427609.2017.1340051
Ellis, B. J., Boyce, W. T., Belsky, J., Bakermans-Kranenburg, M. J., & van IJzendoorn, M. H.
(2011). Differential susceptibility to the environment: An evolutionary--
neurodevelopmental theory. Development and Psychopathology, 23, 7–28.
https://doi.org/10.1017/S0954579410000611
Ellis, H. C., & Ashbrook, P. W. (1988). Resource allocation model of the effects of depressed
mood states on memory. In K. Fiedler & J. Forgas (Eds.), Affect, cognition and social
behavior: New evidence and integrative attempts (pp. 25–43). Toronto: Hogrefe.
Ellis, H. C., Moore, B. A., Varner, L. J., A. Ottaway, S., & S. Becker, A. (1997). Depressed
mood, task organization, cognitive interference, and memory: Irrelevant thoughts predict
recall performance. Journal of Social Behavior and Personality, 12.
El-Sheikh, M., Keller, P. S., & Erath, S. A. (2007). Marital conflict and risk for child
maladjustment over time: Skin conductance level reactivity as a vulnerability factor.
Journal of Abnormal Child Psychology, 35, 715–727. https://doi.org/10.1007/s10802-007-
9127-2
Fartoukh, M., Chanquoy, L., & Piolat, A. (2014). Mood induction in children: Effect of the
affective valence of a text on phonological working memory. Advances in Cognitive
Psychology, 10, 113–118. https://doi.org/10.5709/acp-0162-z
Galeano Weber, E., Dirk, J., & Schmiedek, F. (2018). Variability in the precision of
children’s spatial working memory. Journal of Intelligence, 6, 8.
https://doi.org/10.3390/jintelligence6010008
Gathercole, S. E., Pickering, S. J., Knight, C., & Stegmann, Z. (2004). Working memory skills
and educational attainment: Evidence from national curriculum assessments at 7 and 14
years of age. Applied Cognitive Psychology, 18, 1–16. https://doi.org/10.1002/acp.934
Geldhof, G. J., Preacher, K. J., & Zyphur, M. J. (2014). Reliability estimation in a multilevel
confirmatory factor analysis framework. Psychological Methods, 19, 72–91.
https://doi.org/10.1037/a0032138
Hamaker, E. L. (2012). Why researchers should think "within-person". A paradigmatic
rationale. In M. R. Mehl & T. S. Conner (Eds.), Handbook of research methods for
studying daily life (pp. 43–61). New York: Guilford Press.
Heron, K. E., Everhart, R. S., McHale, S. M., & Smyth, J. M. (2017). Using mobile-
technology-based ecological momentary assessment (EMA) methods with youth: A
systematic review and recommendations. Journal of Pediatric Psychology, 42, 1087–1107.
https://doi.org/10.1093/jpepsy/jsx078
Ilkowska, M., & Engle, R. W. (2010). Working memory capaciry and self-regulation. In R. H.
Hoyle (Ed.), Handbook of personality and self-regulation (365-290). Chichester: Wiley
Blackwell.
Könen, T., Dirk, J., & Schmiedek, F. (2015). Cognitive benefits of last night's sleep: Daily
variations in children's sleep behavior are related to working memory fluctuations. Journal
of Child Psychology and Psychiatry, and Allied Disciplines, 56, 171–182.
https://doi.org/10.1111/jcpp.12296
Leonhardt, A., Könen, T., Dirk, J., & Schmiedek, F. (2016). How differentiated do children
experience affect? An investigation of the within- and between-person structure of
children's affect. Psychological Assessment, 28, 575–585.
https://doi.org/10.1037/pas0000195
Lionetti, F., Aron, A., Aron, E. N., Burns, G. L., Jagiellowicz, J., & Pluess, M. (2018).
Dandelions, tulips and orchids: Evidence for the existence of low-sensitive, medium-
sensitive and high-sensitive individuals. Translational Psychiatry, 8, 24.
https://doi.org/10.1038/s41398-017-0090-6
Lupien, S. J., Gillin, C. J., & Hauger, R. L. (1999). Working memory is more sensitive than
declarative memory to the acute effects of corticosteroids: A dose–response study in
humans. Behavioral Neuroscience, 113, 420–430. https://doi.org/10.1037/0735-
7044.113.3.420
Martin, E. A., & Kerns, J. G. (2011). The influence of positive mood on different aspects of
cognitive control. Cognition & Emotion, 25, 265–279.
https://doi.org/10.1080/02699931.2010.491652
Mitchell, R. L. C., & Phillips, L. H. (2007). The psychological, neurochemical and functional
neuroanatomical mediators of the effects of positive and negative mood on executive
functions. Neuropsychologia, 45, 617–629.
https://doi.org/10.1016/j.neuropsychologia.2006.06.030
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D.
(2000). The unity and diversity of executive functions and their contributions to complex
"Frontal Lobe" tasks: A latent variable analysis. Cognitive Psychology, 41, 49–100.
https://doi.org/10.1006/cogp.1999.0734
Moffitt, T. E., Arseneault, L., Belsky, D., Dickson, N., Hancox, R. J., Harrington, H., . . .
Caspi, A. (2011). A gradient of childhood self-control predicts health, wealth, and public
safety. Proceedings of the National Academy of Sciences of the United States of America,
108, 2693–2698. https://doi.org/10.1073/pnas.1010076108
Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the
person back into scientific psychology, this time forever. Measurement: Interdisciplinary
Research & Perspective, 2, 201–218. https://doi.org/10.1207/s15366359mea0204_1
Muthén, L. K., & Muthén, B. O. (2017). Mplus User’s Guide. Eighth Edition. Los Angeles,
CA: Muthén & Muthén.
Palmiero, M., Nori, R., Rogolino, C., D'Amico, S., & Piccardi, L. (2015). Situated
navigational working memory: The role of positive mood. Cognitive Processing, 16 Suppl
1, 327–330. https://doi.org/10.1007/s10339-015-0670-4
Patel, N., Vytal, K., Pavletic, N., Stoodley, C., Pine, D. S., Grillon, C., & Ernst, M. (2016).
Interaction of threat and verbal working memory in adolescents. Psychophysiology, 53,
518–526. https://doi.org/10.1111/psyp.12582
Pluess, M. (2015). Individual differences in environmental sensitivity. Child Development
Perspectives, 9, 138–143. https://doi.org/10.1111/cdep.12120
Pluess, M., Assary, E., Lionetti, F., Lester, K. J., Krapohl, E., Aron, E. N., & Aron, A. (2018).
Environmental sensitivity in children: Development of the Highly Sensitive Child Scale
and identification of sensitivity groups. Developmental Psychology, 54, 51–70.
https://doi.org/10.1037/dev0000406
Pluess, M., & Belsky, J. (2013). Vantage sensitivity: Individual differences in response to
positive experiences. Psychological Bulletin, 139, 901–916.
https://doi.org/10.1037/a0030196
Pnevmatikos, D., & Trikkaliotis, I. (2013). Intraindividual differences in executive functions
during childhood: The role of emotions. Journal of Experimental Child Psychology, 115,
245–261. https://doi.org/10.1016/j.jecp.2013.01.010
R core team. (2017). R: A language and environment for statistical computing. R Foundation
for Statistical Computing. Vienna, Austria. Retrieved from https://www.R-project.org/
Riediger, M., Wrzus, C., Schmiedek, F., Wagner, G. G., & Lindenberger, U. (2011). Is
seeking bad mood cognitively demanding? Contra-hedonic orientation and working-
memory capacity in everyday life. Emotion, 11, 656–665.
https://doi.org/10.1037/a0022756
Russell, J. A., & Barrett, L. F. (1999). Core affect, prototypical emotional episodes, and other
things called emotion: Dissecting the elephant. Journal of Personality and Social
Psychology, 76, 805–819. https://doi.org/10.1037/0022-3514.76.5.805
Sliwinski, M. J., Smyth, J. M., Hofer, S. M., & Stawski, R. S. (2006). Intraindividual coupling
of daily stress and cognition. Psychology and Aging, 21, 545–557.
https://doi.org/10.1037/0882-7974.21.3.545
Steinley, D., & Brusco, M. J. (2008). A new variable weighting and selection procedure for
K-means cluster analysis. Multivariate Behavioral Research, 43, 77–108.
https://doi.org/10.1080/00273170701836695
Steinley, D., & Brusco, M. J. (2011). Evaluating mixture modeling for clustering:
Recommendations and cautions. Psychological Methods, 16, 63–79.
https://doi.org/10.1037/a0022673
Steyer, R., Schwenkmezger, P., Notz, P., & Eid, M. (1994). Testtheoretische Analysen des
Mehrdimensionalen Befindlichkeitsfragebogen (MDBF) [Theoretical analysis of a
multidimensional mood questionnaire (MDBF)]. Diagnostica, 40, 320–328.
Stumm, S. von. (2016). Is day-to-day variability in cognitive function coupled with day-to-
day variability in affect? Intelligence, 55, 1–6. https://doi.org/10.1016/j.intell.2015.12.006
Trull, T. J., & Ebner-Priemer, U. (2013). Ambulatory assessment. Annual Review of Clinical
Psychology, 9, 151–176. https://doi.org/10.1146/annurev-clinpsy-050212-185510
Van Ijzendoorn, M. H., Belsky, J., & Bakermans-Kranenburg, M. J. (2012). Serotonin
transporter genotype 5HTTLPR as a marker of differential susceptibility? A meta-analysis
of child and adolescent gene-by-environment studies. Translational Psychiatry, 2, e147.
https://doi.org/10.1038/tp.2012.73
Wang, L., & Maxwell, S. E. (2015). On disaggregating between-person and within-person
effects with longitudinal data using multilevel models. Psychological Methods, 20, 63–83.
https://doi.org/10.1037/met0000030
Watson, D., & Tellegen, A. (1985). Toward a consensual structure of mood. Psychological
Bulletin, 98, 219–235. https://doi.org/10.1037/0033-2909.98.2.219
Wolf, M., van Doorn, G. S., & Weissing, F. J. (2008). Evolutionary emergence of responsive
and unresponsive personalities. Proceedings of the National Academy of Sciences of the
United States of America, 105, 15825–15830. https://doi.org/10.1073/pnas.0805473105
Yang, H., Yang, S., & Isen, A. M. (2013). Positive affect improves working memory:
Implications for controlled cognitive processing. Cognition & Emotion, 27, 474–482.
https://doi.org/10.1080/02699931.2012.713325
Zuckerman, M. (1999). Vulnerability to psychopathology: A biosocial model (1. ed.).
Washington, DC: American Psychological Ass.
AFFECT WORKING MEMORY COUPLINGS 36
Table 1
Descriptive Statistics
(SD)
ICC
Range
2
3
4
5
6
7
1 Age
-
8.1-11.2
-.18
.02
.00
-.04
.00
.23*
2 Gendera
-
0-1
.04
-.06
-.02
.03
.08
3 Positive Affect
.435
1.76-5.00
.79*
-.30*
-.23*
.27*
4 Activation
.538
1.21-5.00
-.17
-.18
.09
5 Negative Affect
.367
1.00-3.53
.90*
-.33*
6 Deactivation
.371
1.00-3.55
-.29*
7 Working memory
.522
0.13-0.97
Note. Table depicts descriptive statistics on the person-level. ICC = intra-class correlation.
a0=male; 1= female. *p < .05. N = 107 (age); N = 108 (gender); N = 109 (other variables).
Table 2
Multilevel Models (Predicting Working Memory Performance)
Positive Affect
Activation
Negative Affect
Deactivation
Fixed Effects
Intercept
0.636*
[0.596, 0.674]
0.639*
[0.601, 0.677]
0.638*
[0.599, 0.675]
0.637*
[0.599, 0.676]
Time
-0.019*
[-0.023, -0.015]
-0.018*
[-0.022, -0.014]
-0.019*
[-0.023, -0.014]
-0.019*
[-0.023, -0.015]
Predictor
0.008
[-0.001, 0.018]
0.008
[-0.002, 0.017]
-0.023*
[-0.034, -0.013]
-0.011*
[-0.020, -0.002]
Random Effects
(Standard Deviations)
Intercept
0.199
[0.172, 0.227]
0.199
[0.171, 0.226]
0.199
[0.171, 0.226]
0.198
[0.171, 0.226]
Time
0.019
[0.016, 0.022]
0.018
[0.015, 0.021]
0.019
[0.016, 0.022]
0.019
[0.016, 0.023]
Predictor
0.032
[0.023, 0.041]
0.035
[0.025, 0.044]
0.034
[0.024, 0.044]
0.028
[0.017, 0.036]
Level-1 Residual Standard
Deviation 0.170
[0.167, 0.173]
0.169
[0.165, 0.172]
0.169
[0.165, 0.172]
0.170
[0.167, 0.173]
Pseudo-R² (Level 1)
21.5%
22.1%
22.2%
21.3%
Note. Table depicts point estimates (95% bootstrap confidence intervals in parentheses). Pseudo-R² was estimated as the proportion of residual
(Level 1) variance explained by the model (compared to a baseline model which includes no predictors). *p < .05
N = 109.
Table 3
Intercorrelations of Intra-Individual Couplings
2
3
4
1 Positive Affect
.67*
[.55, .76]
.31*
[.06, .52]
.35*
[.09, .57]
2 Activation
.25*
[.02, .45]
.23
[-.01, .45]
3 Negative Affect
.69*
[.52, .82]
4
Deactivation
Note. Couplings involving negative affect and deactivation have been multiplied by -1 to facilitate interpretation; high values on all estimates
correspond to a strong coupling in the hypothesized direction. 95% bootstrap confidence intervals are depicted in parentheses.
*p < .05. N = 109.
Table 4
Model Comparisons
Number of latent
classes
Log likelihood
Number of free
parameters
Scaling
correction
factor
BIC
Entropy
2
-1699.233
36
1.367
3567.355
0.858
3
-1655.427
50
1.233
3545.422
0.887
4
-1619.981
64
1.144
3540.209
0.923
Note. BIC = Bayesian information criterion.
Table 5
Person-Specific Couplings Separated by Latent Class
Class 1 (N = 16)
General Sensitivity
Class 2 (N = 51)
Low Sensitivity
Class 3 (N = 19)
Vantage Sensitivity
Class 4 (N = 23)
Vulnerability
Positive Affect
0.0215
[0.0069, 0.0362]
0.0050
[0.0014, 0.0086]
0.0075
[-0.0030, 0.0179]
0.0070
[-0.0066, 0.0205]
Activation
0.0268
[0.0133, 0.0402]
-0.0022
[-0.0059, 0.0015]
0.0248
[0.0138, 0.0357]
0.0035
[-0.0103, 0.0173]
Negative Affect
0.0386
[0.0290, 0.0483]
0.0128
[0.0097, 0.0159]
0.0138
[0.0057, 0.0218]
0.0427
[0.0303, 0.0552]
Deactivation
0.0179
[0.0094, 0.0264]
0.0041
[0.0012, 0.0071]
0.0068
[-0.0014, 0.0149]
0.0250
[0.0158, 0.0343]
Note. Table depicts means (95% confidence intervals in brackets). Bold values indicate means statistically significant larger than zero (p < .05).
Note that couplings with negative affect and deactivation have been multiplied by -1 to facilitate interpretation; high values on all estimates
correspond to a strong coupling in the hypothesized direction.
Figure 1. Figure depicts mean z-scores of the nine variables. Error bars indicate 95% bootstrap confidence intervals. Note that couplings with
negative affect and deactivation have been multiplied by -1 to facilitate interpretation; high values on all estimates correspond to a strong coupling
in the hypothesized direction.
Figure 2. Figure depicts person-specific intra-individual couplings (see β2 in Equation (4)); error bars indicate +/- 1 standard error. Each symbol
represents one child. Black symbols indicate that person specific intra-individual regression coefficients are at least one standard error larger /
smaller than zero. Shaded grey areas depict 95% confidence intervals around the class mean. Note that couplings with negative affect and
deactivation have been multiplied by -1 to facilitate interpretation; high values on all estimates correspond to a strong coupling in the
hypothesized direction.
... Studies on more enduring fluctuations in WM require intensive data collection, that is, many daily observations of each participant over several weeks (Sliwinski et al., 2018). Recently, ambulatory assessment has proven to be a fruitful approach to measure such day-to-day fluctuations in WM, and their relations to other variables, also in children (Dirk & Schmiedek, 2017;Könen et al., 2015;Kramer et al., 2021;Neubauer et al., 2019). These studies found that WM performance was enhanced on days when children reported higher sleep quality (Könen et al., 2015), lower perceived disturbance (Dirk & Schmiedek, 2017), or lower negative affect (Neubauer et al., 2019). ...
... Recently, ambulatory assessment has proven to be a fruitful approach to measure such day-to-day fluctuations in WM, and their relations to other variables, also in children (Dirk & Schmiedek, 2017;Könen et al., 2015;Kramer et al., 2021;Neubauer et al., 2019). These studies found that WM performance was enhanced on days when children reported higher sleep quality (Könen et al., 2015), lower perceived disturbance (Dirk & Schmiedek, 2017), or lower negative affect (Neubauer et al., 2019). Thus, examining within-person associations between WM and other time-varying processes may help to identify short-term regulatory effects on daily WM functioning. ...
Article
Children often experience better and worse days when performing cognitive tasks. Whether such fluctuations are systematic and how they are linked to fluctuations at faster time scales within days is less clear. To fill these gaps, we probed N = 108 fifth graders on WM tasks twice daily in morning and afternoon sessions, and also assessed nightly sleep behavior, over a period of four weeks using ambulatory assessment. Children systematically fluctuated in their recall of visuospatial and numerical information in WM across multiple time scales. These fluctuations showed consistencies but also discrepancies among each other. Especially, fast variability of memory precision across moments was related to load and fluid intelligence. Daily WM accuracy was positively coupled to sleep quality, but only in a subset of children with larger daily WM fluctuations. We propose that short-term WM fluctuations and their couplings to other time-varying constructs could help to explain long-term cognitive development.
... Studies on more enduring fluctuations in WM require intensive data collection, that is, many daily observations of each participant over several weeks (Sliwinski et al., 2018). Recently, ambulatory assessment has proven to be a fruitful approach to measure such day-to-day fluctuations in WM, and their relations to other variables, also in children (Dirk & Schmiedek, 2017;Könen et al., 2015;Kramer et al., 2021;Neubauer et al., 2019). These studies found that WM performance was enhanced on days when children reported higher sleep quality (Könen et al., 2015), lower perceived disturbance (Dirk & Schmiedek, 2017), or lower negative affect (Neubauer et al., 2019). ...
... Recently, ambulatory assessment has proven to be a fruitful approach to measure such day-to-day fluctuations in WM, and their relations to other variables, also in children (Dirk & Schmiedek, 2017;Könen et al., 2015;Kramer et al., 2021;Neubauer et al., 2019). These studies found that WM performance was enhanced on days when children reported higher sleep quality (Könen et al., 2015), lower perceived disturbance (Dirk & Schmiedek, 2017), or lower negative affect (Neubauer et al., 2019). ...
... Toutefois, le fait que le RAM propose une variation de l'effet des émotions en fonction Le contrôle attentionnel fait référence à l'orientation et au maintien des processus attentionnels sur un 4 objet en particulier (LaBerge, 1998 Cuisinier et al., 2010 ;Neubauer et al., 2019 ;Scrimin et al., 2015). ...
... Or, si l'effet des émotions sur l'attention sélective a fait l'objet d'un grand nombre de recherches chez l'adulte (pour une revue voir Yiend, 2010), aucune étude n'a, à notre connaissance, questionné cet effet chez l'enfant. Néanmoins, la littérature rend compte de nombreux travaux concernant l'effet des émotions sur des processus cognitifs tels que le raisonnement ou la mémorisation (e.g., Blanchette & Richard, 2004 ;Neubauer et al., 2019). Leurs résultats soulèvent d'importantes controverses, rendant actuellement impossible toutes conclusions claires concernant la nature de l'effet des émotions sur la cognition. ...
Thesis
De par le lien étroit qu’elles entretiennent avec la cognition, les émotions influencent nos comportements, nos perceptions ainsi que nos performances lorsqu’il s’agit d’apprendre. Si l’existence de ce lien semble faire consensus au sein de la communauté scientifique, la nature de celui-ci fait aujourd’hui encore débat. Ainsi, pour certains, les émotions seraient une entrave aux fonctions cognitives (e.g., Hadwin, Brogan, & Stevenson, 2005). Selon le RAM (Ellis & Moore, 1999), toutes émotions mobiliseraient une partie des ressources attentionnelles au détriment de la tâche à réaliser. Cependant, à l’inverse, d’autres études font état d’un effet facilitateur des émotions (e.g., Burkitt & Barnett, 2006). Cette apparente opposition pourrait être liée à l’interaction entre l’émotion induite et l’état initial des participants. Selon le modèle de la congruence émotionnelle (Bower, 1981), une information véhiculant une émotion de même nature que celle ressentie par l’individu (congruence) serait plus rapidement traitée qu’une information véhiculant une émotion non similaire (incongruence). Or, rares sont les études prenant en considération l’état des participants avant la tâche. De plus, un grand nombre de travaux étudie l’effet des émotions sur des processus cognitifs de haut niveau. Cependant, ceux-ci sont sous-tendus par l’activation de différents processus tels que l’attention qui est impliquée dans toutes tâches d’apprentissage. Il est possible, d’une part, que les émotions n’aient pas le même effet sur l’ensemble des processus cognitifs et d’autre part, que cet effet soit variable au cours du développement de l’individu. A l’heure actuelle, peu de travaux ont été conduits chez l’enfant et encore moins en milieu scolaire. Aussi, ce travail de thèse a pour objectif d’étudier l’influence des émotions sur les processus de focalisation et d’orientation de l’attention sélective chez l’enfant d’école maternelle et primaire. Pour ce faire, cinq études expérimentales ont été réalisées.
... C. Ellis, Seibert, & Varner, 1995 ;H. C. Ellis et al., 1984 ;Oaksford et al., 1996 ;Seibert & Ellis, 1991 ;Vieillard & Bougeant, 2005) ou bien son temps d'exécution (Oaksford et al., 1996 ;Vieillard & Bougeant, 2005) que sur des indicateurs plus spécifiques de l'activité cérébrale (Kliegel et al., 2003 ;Meinhardt & Pekrun, 2003). De plus, des résultats en accord avec ce modèle sont également retrouvés chez l'enfant (Neubauer et al., 2019 ;Scrimin et al., 2014. L'ensemble de ces données constitue un argument supplémentaire en fa-39 1.3 Présentation du modèle RAM veur du modèle RAM, qui permet de tester et d'expliquer l'effet de l'émotion sur de nombreuses tâches plus ou moins complexes, et à différents niveaux. ...
... Ce même schéma est observé lorsque l'on s'intéresse à l'impact de l'émotion positive sur le fonctionnement exécutif chez l'adulte (E. A. Martin & Kerns, 2011 ;Phillips et al., 2002), mais aussi chez l'enfant (Neubauer et al., 2019). Dans le cas de la production écrite, là encore les rares études sur le sujet rendent compte de résultats contrastés quant aux effets de l'émotion positive, cette dernière pouvant être inhibitrice sur la longueur des textes et les performances orthographiques (Cuisinier et al., 2010 ;Fartoukh et al., 2012Fartoukh et al., , 2014, facilitatrice en tâche de grammaire (Tornare et al., 2017) ou bien sans effet par rapport à une induction neutre en tâche de dictée (Tornare et al., 2016). ...
Thesis
La question de l’impact de l’émotion sur la cognition constitue aujourd’hui un champ de recherche abondant, tant en neuro-sciences qu’en Sciences Humaines. Alors qu’il existe de nombreuses études traitant de cette question chez l’adulte, peu d’études ont été conduites chez l’enfant et encore moins dans le cadre plus écologique des apprentissages scolaires. Ce travail de thèse a pour objectif d’étudier l’influence des émotions sur la mise en œuvre des processus orthographiques chez l’enfant d’école primaire. En utilisant les spécificités de l’orthographe et la diversité des processus qu’elle mobilise, nous entendons tester l’hypothèse formulée par le modèle RAM (Ellis & Ashbrook, 1988 ; Ellis & Moore, 1999) selon laquelle l’émotion mobiliserait une partie des ressources attentionnelles de l’individu aux dépens du traitement de la tâche en cours. Pour ce faire, cinq études expérimentales ont été réalisées. L’effet d’une induction d’un état émotionnel, positif ou négatif, par la musique, a été testé sur les performances orthographiques dans différentes tâches (i.e., production écrite libre et contrôlée, tâche de copie et tâche de détection d’erreurs). Les résultats de nos études montrent que, de façon générale, l’induction d’un état émotionnel négatif provoque une altération des performances orthographiques chez le scripteur novice. En accord avec l’hypothèse de la privation attentionnelle générée par l’émotion, l’effet de cette dernière est différencié en fonction de la nature et du coût de la tâche, de la nature de l’item à traiter et du niveau d’expertise du scripteur. L’émotion négative est associée à des performances déficitaires principalement lorsque la situation est coûteuse et mobilise un contrôle attentionnel élevé. Cet effet s’observe sur différents indicateurs tels que la réussite à la tâche et son temps d’exécution. En montrant un effet différencié de l’émotion selon le coût attentionnel de la tâche, les résultats de ces études valident l’hypothèse selon laquelle l’émotion constitue une charge cognitive supplémentaire. Cependant, de nouvelles études doivent être conduites pour identifier la nature des mécanismes à l’origine de cette privation attentionnelle. De plus, contrairement aux prédictions formulées par le modèle RAM, ces études ne rendent pas compte d’un effet, positif ou négatif, de l’induction émotionnelle positive sur les performances. Ce résultat est discuté au regard des difficultés méthodologiques à induire et mesurer une émotion.
... Für alle verhaltensnahen Testanforderungen für Kinder gilt, dass es besonders wünschenswert wäre, nicht nur interindividuelle Unterschiede zu erfassen, sondern auch die intraindividuellen Schwankungen, die ebenfalls ein wichtiger Prädiktor für die weitere Entwicklung sein können (siehe Exkurs "Interindividuelle Unterschiede im intraindividuellen Verlauf"). Diese Herangehensweise ist recht innovativ und wird teilweise im Schulalter schon genutzt (Dirk & Schmiedek, 2016;Neubauer et al., 2019). Sie erfordert jedoch so genannte "measurement bursts", also die mehrfache Messung eines Konstruktes innerhalb kurzer Zeit (z. ...
... Other links between learning-related emotions and their antecedents and outcomes might, however, be more universal across individuals (Berweger et al., 2022). It is thus a milestone that heterogeneity vs. universality in momentto-moment processes can explicitly be tested rather than be just assumed (see also Neubauer et al., 2019). ...
Preprint
Motivation and emotions often change during learning processes and show much Motivation and emotions often change during learning processes and show much fluctuation across learning situations and contexts. That makes it necessary to assess and analyze their processes of change and the situation-specific, context-specific, and other sources of variation. A method to assess the situation- and context-specificity and fluctuation of emotions and motivation is the Experience Sampling Method (ESM). The ESM produces intensive longitudinal data with many measurement time points per person (see Figure 1). Typically, participants, such as students, are surveyed repeatedly during their day about their current emotions or motivational states with self-report surveys on portable devices, such as smartphones. This chapter serves as a resource for both beginners and advanced ESM researchers. It starts with an introduction to the Experience Sampling Method, referencing useful resources to researchers interested in applying the ESM in their own studies of emotion and motivation in education. Then, the contribution of the ESM to the recent and expected future theoretical shifts towards situated models of motivation and emotions are discussed. The unique insights that the ESM can provide to the research on situated and contextualized learning-related motivation and emotions are illustrated by discussing empirical examples. The chapter gives an overview of cutting-edge innovations in the research with the ESM and addresses current challenges in this field, including limitations to the replicability and generalizability of ESM studies across contexts. First solutions to these challenges are proposed, along with a description of further directions for future research. Keywords: Experience Sampling Method, Emotion, Motivation
... Some labbased experimental studies of EFs suggest that lab-induced affect, such as anxiety or pleasant mood, can lead to changes in EF performance, suggesting that such variation reflects fluctuations in EFs during a short period of time (Katzir et al., 2010;Lindström & Bohlin, 2012;Oaksford et al., 1996;Phillips et al., 2002). Studies using repeated assessments have also documented meaningful within-person fluctuations in EF in naturalistic settings (Brose et al., 2010(Brose et al., , 2012(Brose et al., , 2015Gamaldo et al., 2010;Könen et al., 2015;Kramer et al., 2020;Neubauer et al., 2019;Schmiedek et al., 2009;Yu et al., 2020Yu et al., , 2021. Identifying factors in the context or the person that coact with fluctuations in EF can provide information for optimization EF on a daily basis. ...
Article
Full-text available
Executive functioning (EF) is a series of fundamental goal-directed cognitive abilities that enable effective learning. Differences in daily sleep quality may covary with fluctuations in EF among youth. Most studies linking sleep to EF rely on between-person differences and average effects for the sample. This study employed an intensive longitudinal design and examined the within-person relations between self-reported prior night’s sleep quality and next day’s EF. Students from Grades 4 to 12 (M age= 14.60, SD = 2.53) completed three behavioral EF tasks repeatedly across approximately one semester. The final analytic sample included 2898 observations embedded in 73 participants. Although, on average, sleep did not significantly covary with EF, there was heterogeneity in within-person sleep-EF relations. Moreover, individuals’ average sleep quality moderated within-person effects. For individuals with low mean sleep quality, a better-than-usual sleep quality was linked to better EF performance. However, for individuals with high mean sleep quality, better-than-usual sleep quality was linked to worse EF performance. Understanding person-specific relations between sleep and EF can help educators optimize EF and learning on a daily basis and produce positive academic outcomes across longer time periods.
Article
The aim of this Special Issue was to bring together studies that help to uncover the situational impact in educational settings above and beyond the impact of individual learners’ characteristics. In this conclusion, we evaluate the degree to which the studies were successful in meeting the challenges accompanying this pursuit. Educational research has long focused on interindvidual differences while neglecting intraindividual processes and the temporal dynamics of important constructs like emotions and motivation as well as their antecedents and correlates. Studying intraindividual processes in education entails a) assessing variables repeatedly over time, and in doing so, sampling different learning situations, and b) dealing with complex data by applying advanced statistical methods to identify dynamic relationships between variables. This post-script takes a critical look at the efforts made by the studies to meet these requirements and summarizes lessons for future research.
Article
Full-text available
Individuals with high math anxiety demonstrated smaller working memory spans, especially when assessed with a computation-based span task. This reduced working memory capacity led to a pronounced increase in reaction time and errors when mental addition was performed concurrently with a memory load task. The effects of the reduction also generalized to a working memory-intensive transformation task. Overall, the results demonstrated that an individual difference variable, math anxiety, affects on-line performance in math-related tasks and that this effect is a transitory disruption of working memory. The authors consider a possible mechanism underlying this effect - disruption of central executive processes - and suggest that individual difference variables like math anxiety deserve greater empirical attention, especially on assessments of working memory capacity and functioning.
Article
Full-text available
Cognitive modeling studies in adults have established that visual working memory (WM) capacity depends on the representational precision, as well as its variability from moment to moment. By contrast, visuospatial WM performance in children has been typically indexed by response accuracy—a binary measure that provides less information about precision with which items are stored. Here, we aimed at identifying whether and how children’s WM performance depends on the spatial precision and its variability over time in real-world contexts. Using smartphones, 110 Grade 3 and Grade 4 students performed a spatial WM updating task three times a day in school and at home for four weeks. Measures of spatial precision (i.e., Euclidean distance between presented and reported location) were used for hierarchical modeling to estimate variability of spatial precision across different time scales. Results demonstrated considerable within-person variability in spatial precision across items within trials, from trial to trial and from occasion to occasion within days and from day to day. In particular, item-to-item variability was systematically increased with memory load and lowered with higher grade. Further, children with higher precision variability across items scored lower in measures of fluid intelligence. These findings emphasize the important role of transient changes in spatial precision for the development of WM.
Article
Full-text available
According to empirical studies and recent theories, people differ substantially in their reactivity or sensitivity to environmental influences with some being generally more affected than others. More sensitive individuals have been described as orchids and less-sensitive ones as dandelions. Applying a data-driven approach, we explored the existence of sensitivity groups in a sample of 906 adults who completed the highly sensitive person (HSP) scale. According to factor analyses, the HSP scale reflects a bifactor model with a general sensitivity factor. In contrast to prevailing theories, latent class analyses consistently suggested the existence of three rather than two groups. While we were able to identify a highly sensitive (orchids, 31%) and a low-sensitive group (dandelions, 29%), we also detected a third group (40%) characterised by medium sensitivity, which we refer to as tulips in keeping with the flower metaphor. Preliminary cut-off scores for all three groups are provided. In order to characterise the different sensitivity groups, we investigated group differences regarding the Big Five personality traits, as well as experimentally assessed emotional reactivity in an additional independent sample. According to these follow-up analyses, the three groups differed in neuroticism, extraversion and emotional reactivity to positive mood induction with orchids scoring significantly higher in neuroticism and emotional reactivity and lower in extraversion than the other two groups (dandelions also differed significantly from tulips). Findings suggest that environmental sensitivity is a continuous and normally distributed trait but that people fall into three distinct sensitive groups along a sensitivity continuum.
Article
Full-text available
A large number of studies document that children differ in the degree they are shaped by their developmental context with some being more sensitive to environmental influences than others. Multiple theories suggest that Environmental Sensitivity is a common trait predicting the response to negative as well as positive exposures. However, most research to date has relied on more or less proximal markers of Environmental Sensitivity. In this paper we introduce a new questionnaire-the Highly Sensitive Child (HSC) scale-as a promising self-report measure of Environmental Sensitivity. After describing the development of the short 12-item HSC scale for children and adolescents, we report on the psychometric properties of the scale, including confirmatory factor analysis and test-retest reliability. After considering bivariate and multivariate associations with well-established temperament and personality traits, we apply Latent Class Analysis to test for the existence of hypothesized sensitivity groups. Analyses are conducted across 5 studies featuring 4 different U.K.-based samples ranging in age from 8-19 years and with a total sample size of N = 3,581. Results suggest the 12-item HSC scale is a psychometrically robust measure that performs well in both children and adolescents. Besides being relatively independent from other common traits, the Latent Class Analysis suggests that there are 3 distinct groups with different levels of Environmental Sensitivity-low (approx. 25-35%), medium (approx. 41-47%), and high (20-35%). Finally, we provide exploratory cut-off scores for the categorization of children into these different groups which may be useful for both researchers and practitioners. (PsycINFO Database Record
Article
The detrimental effect of noise on cognitive performance particularly for younger children has been repeatedly demonstrated in numerous experimental and few field studies. We examined whether children’s daily working memory (WM) performance is affected by daily perceived disturbance in the school and out-of-school context. In an ambulatory assessment study, 110 third and fourth grade students completed WM tasks and reported on their perceived disturbance on smartphones three times daily in and out of school for four weeks. Disturbance varied systematically within children and increased levels of disturbance were associated with decreased WM performance, independent of context.
Article
Ecological momentary assessment (EMA) methods are increasingly used in social and health sciences, but the feasibility and best practices for using EMA with youth are not yet clear. We conducted a systematic review of studies that used self-report EMA methods with youth; the goal was to identify common approaches and challenges to implementation and develop recommendations for future research. We examined 54 peer-reviewed papers that reported on 24 unique studies. Papers were evaluated using a standardized, three-dimensional coding scheme focused on the following: (1) sample characteristics; (2) EMA data collection methods (sampling duration, frequency, hardware/software); (3) study implementation methods (technical/logistical challenges, training participants, compliance). Overall, the research suggests EMA can be successfully implemented with youth (age ∼ ≥7) from diverse backgrounds, but protocol adaptations may be necessary for younger children. Study design and implementation challenges and recommendations for research on youth are provided.
Article
Intra-individual differences in cognitive function that occur reliably across repeated assessment occasions are thought to correspond to contemporaneous fluctuations in affect. However, the empirical evidence for this hypothesis is to date inconclusive. Here, a sample of 98 participants was recruited to complete tests of short-term memory, processing speed, and working memory, as well as rating daily their positive and negative affect (PANAS), on each of five consecutive days. Cognitive tests' re-test correlations averaged at .72; for affect, test re-test correlations averaged .53. The within-person variability in cognitive tests was overall smaller (13.5% for both working memory and short-term memory, and 16% for processing speed) than in affect (24% for positive and 51.7% for negative affect). A series of linear mixed effects models showed that day-to-day-variability in cognitive function was not coupled with contemporaneous fluctuations in positive and negative affect (i.e. states; ns in all cases). Thus, affect and cognitive function fluctuate within individuals across days but they appear to do so independently of one another.
Article
Threat induces a state of sustained anxiety that can disrupt cognitive processing, and, reciprocally, cognitive processing can modulate an anxiety response to threat. These effects depend on the level of cognitive engagement, which itself varies as a function of task difficulty. In adults, we recently showed that induced anxiety impaired working memory accuracy at low and medium but not high load. Conversely, increasing the task load reduced the physiological correlates of anxiety (anxiety-potentiated startle). The present work examines such threat-cognition interactions as a function of age. We expected threat to more strongly impact working memory in younger individuals by virtue of putatively restricted cognitive resources and weaker emotion regulation. This was tested by examining the influence of age on the interaction of anxiety and working memory in 25 adolescents (10 to 17 years) and 25 adults (22 to 46 years). Working memory load was manipulated using a verbal n-back task. Anxiety was induced using the threat of an aversive loud scream and measured via eyeblink startle. Findings revealed that, in both age groups, accuracy was lower during threat than safe conditions at low and medium but not high load, and reaction times were faster during threat than safe conditions at high load but did not differ at other loads. Additionally, anxiety-potentiated startle was greater during low and medium than high load. Thus, the interactions of anxiety with working memory appear similar in adolescents and adults. Whether these similarities reflect common neural mechanisms would need to be assessed using functional neuroimaging.
Article
Children experience good and bad days in their performance. Although this phenomenon is well-known to teachers, parents, and students it has not been investigated empirically. We examined whether children's working memory performance varies systematically from day to day and to which extent fluctuations at faster timescales (i.e., occasions, moments) contribute to daily WM fluctuations in the school context. In an ambulatory assessment study, Grade 3 and Grade 4 students (8 to 11 years old; N = 110) completed WM tasks on smartphones 3 times a day in school and at home for 4 weeks. Results showed substantial within-person fluctuations in children's daily WM performance. Across task conditions, day-to-day, occasion-to-occasion, and moment-to-moment variability accounted for roughly the same extent of observed day-to-day variability with large individual differences in the amount of reliable fluctuations at the different timescales. Grade 3 students were more variable than were Grade 4 students at the faster timescales, more variable WM performance at all timescales was related to lower school achievement, and more day-to-day variability was associated with lower fluid intelligence. These findings build the foundation for research on the antecedents and consequences of children's fluctuating cognitive resources. Theories about cognitive development and learning should consider performance fluctuations across and within days to understand the processes underlying long-term changes. Educational practice may be informed by the substantial WM fluctuations at all timescales and adopt interventions that increase children's attentional focus and self-regulation. (PsycINFO Database Record