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\begin{document}

\title{Executive Functioning and Acute Stress Responses: The Role of Cognitive
Appraisals, Repetitive Thinking, and Emotion Regulation}



\author[1]{Elise Grimm}%
\author[1]{Stefan Agrigoroaei}%
\affil[1]{UCLouvain}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Exposure to stress can have significant, negative consequences on
long-term health. Among potentially modifiable targets for promoting
more adaptive stress responses, executive functioning has emerged as a
promising candidate. These functions may be involved in limiting
excessive stress reactivity when exposed to an acute stressor, while
also promoting faster recovery (i.e., return to baseline). Fewer studies
have been conducted to date on the role of executive functioning in
acute stress -- especially with a focus on its distinct facets (e.g.,
inhibition, flexibility, working memory, and updating). Moreover, there
is a need for research focused on potential cognitive and behavioral
mediators explaining the relationship between executive functioning and
stress responses. Thus, the current study sought to examine the extent
to which executive functioning facets may play a beneficial role in
acute stress reactivity and recovery. Furthermore, we will examine the
mediational role of cognitive appraisals, repetitive negative thinking,
and emotion regulation. The findings may shed light on existing
cognition-stress pathways that promote more adaptive acute stress
responses.%
\end{abstract}%



\sloppy


Executive Functioning and Acute Stress Responses: The Role of Cognitive
Appraisals, Repetitive Thinking, and Emotion Regulation

\textbf{Author Note}

All data sets and scripts will be made available on the Open Science
Framework. The authors have no conflicts of interest to report. This
research did not receive any specific grant from funding agencies in the
public, commercial, or not-for-profit sectors.

Abstract

Exposure to stress can have significant, negative consequences on
long-term health. Among potentially modifiable targets for promoting
more adaptive stress responses, executive functioning has emerged as a
promising candidate. These functions may be involved in limiting
excessive stress reactivity when exposed to an acute stressor, while
also promoting faster recovery (i.e., return to baseline). Fewer studies
have been conducted to date on the role of executive functioning in
acute stress -- especially with a focus on its distinct facets (e.g.,
inhibition, flexibility, working memory, and updating). Moreover, there
is a need for research focused on potential cognitive and behavioral
mediators explaining the relationship between executive functioning and
stress responses. Thus, the current study sought to examine the extent
to which executive functioning facets may play a beneficial role in
acute stress reactivity and recovery. Furthermore, we will examine the
mediational role of cognitive appraisals, repetitive negative thinking,
and emotion regulation. The findings may shed light on existing
cognition-stress pathways that promote more adaptive acute stress
responses.

\emph{Keywords} : Inhibition, Flexibility, Working Memory, Updating,
Stress, Cognitive Appraisals, Repetitive Negative Thinking, Emotion
Regulation

Stress is a natural phenomenon in our lives. When it is repeated and
sustained, however, it can lead to adverse consequences. To prevent
excessive stress and stress-related disorders, research has converged on
the need for identifying personal, modifiable factors that could be
targeted by prevention programs. Among these factors, executive
functioning represents a potential resource for promoting more adaptive
stress responses.

Executive functions are higher-order neurocognitive processes that
enable individuals to organize and carry out goal-related actions and
behaviors (Rodr\selectlanguage{ngerman}íguez-Nieto et al., 2022). They are an umbrella term that
can be conceptualized as a global index while distinguishing several
subdomains, including inhibition, flexibility, and working memory
updating (Eslinger \& Grattan, 1993; Miyake \& Friedman, 2012; Packwood
et al., 2011). Inhibition involves preventing previously relevant
responses, thoughts, and emotions that may be no-longer relevant or
inappropriate for the task at hand. Flexibility allows us to shift from
one goal, thought, idea, or perspective to another, as well as to
achieve optimal choice in a task, or to optimize the fluidity of our
thoughts (Diamond, 2013; Hofmann et al., 2012). Working memory updating
facilitates the monitoring of incoming information in working memory and
the deletion of content that is no longer relevant to the current task
(Miyake \& Friedman, 2012).

Given the unity and diversity of executive functions (Miyake \&
Friedman, 2012; Rodríguez-Nieto et al., 2022), it is interesting to
examine how they contribute both independently and collaboratively to
the orchestration of cognition and behavior. While previous research has
focused primarily on the cognitive deficits caused by acute stress
(e.g., Kan et al., 2021; Shields et al., 2016), our recent systematic
literature review and meta-analysis (Grimm et al., 2021) revealed that
executive functions may reduce subjective and physiological stress
responses to acute stressors induced in the laboratory.

\subsection*{Stressor Reactivity and
Recovery}

{\label{stressor-reactivity-and-recovery}}

An adaptive response to a stressful event is denoted by both the
reactivity of the physiological systems during the stressor (al'Absi,
2018) and their recovery, namely the prompt return to baseline following
stressor discontinuation (Panaite et al., 2015). Stress reactivity, on
the one hand, is defined as the change in physiological and cognitive
responses when an individual is exposed to a stressful situation. The
recovery phase, on the other hand, corresponds to the period following
the stressor offset, during which the body attempts to achieve
homeostasis and modify physiological activity for a return to baseline.

Two major systems are involved in the initial reactivity phase: the
immediate activation of the sympathetic-adrenaline-medullary axis and
the more delayed hypothalamic-pituitary-adrenal axis release of cortisol
(i.e., the primary glucocorticoid stress hormone). The sympathetic
system releases epinephrine, norepinephrine, and alpha amylase (Nater \&
Rohleder, 2009). With both the sweat glands and skin blood vessels being
innervated by the sympathetic nervous system, the galvanic skin
conductance and blood pressure are also indexes of sympathetic
activation and autonomous emotional regulation (Paszkiel et al., 2020;
Setz et al., 2010).

In contrast, heart rate is influenced by both the sympathetic and
parasympathetic nervous systems (Malik, 1996; Reyes del Paso et al.,
2013; Thayer \& Lane, 2000). It is caused by both direct neural
innervation and the epinephrine and norepinephrine neuroendocrine
activity (Everly \& Lating, 2002). Heart rate variability (HRV), a
measure of the interval between consecutive heartbeats, represents the
ability to respond and adapt to fluctuating stimuli, with lower
variability indicating higher stress reactivity (Dimitriev \& Saperova,
2012; Kim et al., 2018).

In parallel to these physiological responses, stressful events can also
impact the subjective experience of stress (Calvo \& Gutierrez-Garcia,
2016). Subjective stress, defined as the psychological response to a
stressor, is often measured through self-reported questionnaires. Items
include self-related anxiety and stress, as well as feelings of
nervousness or tension.

In the laboratory, acute stress has been induced using validated,
standardized stress tasks. These include the Trier Social Stress Test
(TSST, social-evaluative stressor, Kirschbaum et al., 1993), the Cold
Pressor Test (CPT, physiological stressor with social elements, Becker
et al., 2019; Schwabe \& Schächinger, 2018), the Paced Auditory Serial
Addition Test (cognitive stressor, Gronwall, 1977), and the Maastricht
Acute Stress Test (MAST, social and physiological stressor, Smeets et
al., 2012). The latter is particularly interesting, as it is intended to
be rapid, non-invasive, and less resource-intensive, while combining the
social-evaluative and pain elements of the TSST and CPT, respectively.
The advantage of these induction methods is that they allow the
observation of different stages of the acute stress response, namely
reactivity and recovery, which would be otherwise difficult to capture
using less controlled paradigms.

\subsection*{Executive Functioning and Acute
Stress}

{\label{executive-functioning-and-acute-stress}}

Crum and colleagues (2020) recently proposed a stress optimization model
that outlines the importance of shifting one's attentional focus away
from negative-related stimuli and towards goal-specific information to
adjust stress responses. In line with this idea, our systematic review
found poorer executive functioning to correlate significantly with
subsequent higher reactivity to an acute stressor, as well as poorer
physiologically and subjectively return to baseline levels (Grimm et
al., 2021). In the meta-analysis, better working memory was associated
with lower cortisol reactivity during an acute stressor (Grimm et al.,
2021). In specific studies of the review, preconscious biases in
attentional control towards threat-related cues tended to enhance
cortisol reactivity to an acute stressor (Fox et al., 2010), while the
more voluntary control of attention away from negative information
towards positive material led to more adaptive stress responses
(Schwerdtfeger \& Derakshan, 2010). These findings imply that facets of
executive functioning may restrict excessive physiological stress
activation and promote a faster recovery.

This premise opens a new field of research that motivated our interest
in investigating the specific impact of multiple executive functions for
these stress phases. Indeed, the primary limitation in the field,
currently, lies in inconsistencies revealed in existing associations
between executive functioning and acute stress (e.g., Grimm et al.,
2021), the need for replication, and the lack of simultaneous
consideration of multiple executive subdomains in statistical models for
differentiating facet effects. Furthermore, cognitive and behavioral
factors such as primary and secondary cognitive appraisals, repetitive
negative thinking, and emotion regulation strategies (Grimm et al.,
2021) may also be implicated as mediators of the executive
functioning-stress relationship.

\subsection*{Cognitive Appraisals, Repetitive Negative Thinking, and
Emotion
Regulation}

{\label{cognitive-appraisals-repetitive-negative-thinking-and-emotion-regulation}}

\subsubsection*{Cognitive Appraisals}

{\label{cognitive-appraisals}}

According to Lazarus and Folkman's (1984) Transactional Model of Stress
and Coping, when confronted with a potential stressor, individuals
engage in two types of appraisals. Primary appraisals correspond to the
preliminary assessment of whether ''the encounter with the environment
is relevant to his or her well-being'' (e.g., centrality, Folkman et
al., 1986) and whether the event is perceived as neutral, positive, or
negative (Lazarus \& Folkman, 1984). If an individual rates the event
negatively, the event is further classified based on whether it is
depicted as harmful (i.e., if damage or loss has already occurred),
threatening (i.e., expecting harm or loss as a result of the upcoming
event), or challenging (i.e., a threat that may be overcome) (Carpenter,
2016). Secondary appraisals are evaluations of the range of coping
options, taking into account the demands of the situation, available
personal resources, and realistic control possibilities (Gaab et al.,
2005; Lazarus \& Folkman, 1984). The imbalance between the two appraisal
types, in turn, will influence the selection of emotional regulation and
coping strategies, as well as the degree of stress reactivity and
recovery.

Preliminary associations have been reported regarding the role of
executive functioning for cognitive appraisal. Better inhibition, for
example, has been associated with lower threat appraisal and stress
uncontrollability (Panganiban \& Matthews, 2014), whereas working memory
has also been negatively associated with threat appraisal (Zandara et
al., 2016). Similarly, Xin and colleagues (2020) discovered that lower
-- but not higher -- inhibition abilities moderated the relationship
between exposure to recent stressful events and smaller change in HRV
markers (Root Mean Square Successive Difference, RMSSD, and
High-Frequency HRV) during the TSST. The authors interpreted the lack of
findings for higher executive functioning as the result of these
individuals' more flexible and adaptive appraisal of stressful life
events, given a better performance of the prefrontal cortex.

\subsubsection*{Repetitive Negative
Thinking}

{\label{repetitive-negative-thinking}}

Repetitive negative thinking is defined as recurring, passive and/or
uncontrollable thinking that focuses on negative content (Ehring \&
Watkins, 2008; Segerstrom et al., 2003). Depressive ruminations and
worry are two of the most emblematic examples of repetitive thoughts.
The former is a distress response in which the individual passively and
repeatedly focuses on his or her symptoms, as well as their causes and
consequences of the latter (Nolen-Hoeksema et al., 2008). Worry, on the
other hand, is characterized by a chain of negative thoughts and images
that are frequently perceived as uncontrollable (Borkovec et al., 1983;
Freeman et al., 2020). Despite a minor difference in temporal focus (one
oriented towards the past, the other towards the future), authors agree
that they form a larger, multidimensional, and transdiagnostic process
known as repetitive negative thinking (Gustavson et al., 2018;
McLaughlin \& Nolen-Hoeksema, 2011).

In the stress literature, this concept is also referred to as
perseverative cognitions (Brosschot et al., 2006). These are
representations of the stressor that are activated in a repeated and
chronic manner and maintained in working memory despite the stressor no
longer being objectively present. A systematic review and meta-analysis
on the relationship between perseverative cognitions and stress-related
physiological outcomes found that these thoughts exacerbate and prolong
physiological stress responses (Brosschot et al., 2006). They impact
cardiovascular, autonomic, and endocrine nervous system activity
(Ottaviani et al., 2016). Experimental studies have shown that the
presence of ruminations or worries after a stressor predicts a greater
increase in heart rate, blood pressure, and cortisol levels, as well as
a decrease in HRV (Maeda et al., 2017; Ottaviani et al., 2016; Zoccola
\& Dickerson, 2015). Even in non-clinical samples, negative repetitive
thinking induction also appears to reduce HRV (Rocha-Oliveira \&
Zibetti, 2022). In turn, prospective studies have established that
repetitive negative thinking and cognitive perseverations are involved
in the onset and/or maintenance of emotional difficulties and
psychopathology (e.g., Watkins \& Roberts, 2020), as well as increased
chronic stress, health risk behaviors, and health-related diseases over
time (Brosschot, 2010; Brosschot et al., 2006; Clancy et al., 2016;
Verkuil et al., 2010).

Interestingly, repetitive thoughts do not systematically have negative
implications. According to the processing-mode theory (Watkins et al.,
2008; Watkins, 2008), when an individual is faced with a stressful
event, he or she may engage in one of two modes of processing repetitive
thoughts: the abstract-analytical mode and the concrete-experiential
mode. The former corresponds to a generalized and decontextualized mode
of processing the event. It prompts the individual to ruminate on the
causes, meanings, and implications of an event (e.g., the ``why'' mode;
``why does this always happen to me?'' Watkins et al., 2008). Given that
this mode requires few cognitive resources, it is favored when
confronted with a stressor. This thinking mode is associated with
negative outcomes, such as mood deterioration, decreased participation
in pleasurable activities, persistence of psychological difficulties,
and poor therapeutic efficacy (Nolen-Hoeksema et al., 2008; Tanner et
al., 2013; Watkins et al., 2008).

In parallel, one can engage in concrete-experiential repetitive
thinking. The latter prompts individuals to focus on event elements that
are specific to the situation, on contextual and emotion-related details
and on solution options to deal with the stressor (e.g., the
``\emph{how} '' mode; ``how can I handle this situation?''; Moberly \&
Watkins, 2006). Unlike abstract thinking, this mode requires substantial
cognitive resources and is associated with improved emotional recovery
and problem solving (Bassanini et al., 2014; Harvey et al., 2004).

Given the effort required for switching towards concrete thinking,
executive functioning should play a role in the choice of thought
processing mode. In their H-EX-A-GO-N model, Watkins and Roberts (2020)
identify executive functioning as a causal contributor to the onset and
maintenance of ruminative thinking. Deficits in monitoring, shifting,
and updating working memory could cause excessive access to negative
information, inciting individuals to continue dwelling on this negative
material. In addition, deficits in executive functioning could also
prevent individuals to disregard habitual tendencies to ruminate when
faced with difficult situations. Similarly, in a review by Ottaviani
(2018), inhibition and flexibility were suggested as two essential
facets for stress regulation.

In support of these theories, correlational, experimental,
interventional, and longitudinal studies have supplied evidence for
executive functioning deficits linked to higher tendencies for
repetitive thinking. For example, deficits in flexibility (Cropley et
al., 2016; Davis \& Nolen-Hoeksema, 2000), updating (Joormann et al.,
2011; Meiran et al., 2011), or general executive functions (Whitmer \&
Gotlib, 2013) have been associated with greater rumination.
Meta-analytical efforts have shown significant negative associations
with ruminations for both inhibition and flexibility (Yang et al.,
2017). Experimental designs using cognitive control training have led to
improvements in ruminative tendencies (see Watkins \& Roberts, 2020 for
a review of existing literature for rumination). With respect to stress,
De Lissnyder and colleagues (2012) reported a moderating role for
executive functioning, where higher switch costs in the presence of
emotional material led to higher tendencies to ruminate in response to
daily stressful events. However, there has been few research into the
link between executive functioning and these modes of thought processing
in the context of acute stress.

\subsubsection*{Emotion Regulation}

{\label{emotion-regulation}}

Emotion regulation is the process of modulating one's cognitive,
physiological, behavioral, and emotional responses to meet
context-specific goals (Gross, 2014; McRae \& Gross, 2020). Gross (1998,
2014) proposes five core elements of emotion regulation after the
activation of an emotion-related goal (e.g., reducing negative affect):
situation selection, situation modification, attention deployment,
cognitive change, and response modulation. Numerous strategies have been
studied (see Gross, 2014 for an overview), with considerable research
devoted to reappraisal (i.e., cognitive change example) and expressive
suppression (i.e., response modulation example). The former consists of
modifying cognitive appraisals of the situation, as such to increase
positive emotions and/or decrease expressions of subjective negative
emotions (Gross, 2014). It is proposed as one of the most effective
strategies for emotion regulation, with small-to-medium sized effects on
emotions (Webb et al., 2012). The latter refers to the active attempt at
controlling the behavioral component of the emotion (i.e., hiding the
way they are feeling, Webb et al., 2012). Experimental studies have
reported that suppression reduces positive emotions but fails to impact
negative emotions (Gross, 2014).

Emotion regulation is increasingly being recognized as an important
factor for \emph{stress} management. The Dynamic Fit Model of Stress and
Emotion Regulation (Myruski et al., 2018), for example, has highlighted
the concept of ``good'' and ``bad'' fit between emotion regulation
processes and subsequent stress responses. According to their theory,
adaptive strategies can be identified by their successful reduction of
``the negative impact of stress'' and promoting ``emotional well-being
and resilience to adversity''. When systematically applied in the
context of chronic stress, specific emotion regulation strategies may
become maladaptive, generating poor mental-health consequences (for
example, higher depression in the context of rumination).

Given the overlap in brain regions solicited by executive functioning
and emotion regulation, the two are intrinsically linked. According to
Zelazo and Cunningham (2007), successful executive function is involved
in emotion regulation during (1) the assessment of one's own emotions,
(2) the assessment of one's goal state, (3) the generation of strategies
to reduce the discrepancy between the two states, (4) the evaluation of
strategy efficacy after its implementation, and (5) the learning to
switch deliberately and flexibly to other strategies if this one fails
to meet expectations. Furthermore, Pruessner and colleagues (2020)
propose an overall cognitive control framework, explaining the role of
executive functioning for strategy stopping and switching, strategy
maintenance, and monitoring. Schmeichel and Tang (2015) posit that
inhibition is important for suppressing negative emotions in response to
an event, flexibility is required to shift attention away from negative
material, and updating is involved in modifying initial emotional
appraisals within working memory.

In the context of stress regulation, Crum and colleagues (2020)
highlighted the role of executive functioning in appropriate situation
selection (i.e., choosing to expose oneself to goal-relevant situations
that may generate stress), attentional deployment towards goal-specific
material, and the reappraisal of the situation and emotions. Johnson
(2009) had previously reported that people who were better at switching
from emotional to neutral set were less frustrated during a stressful
anagram task and, moreover, persisted longer on the task. Similarly,
Hendricks and Buchanan (2016) found that working memory updating
predicted greater negative affect reduction during both reappraisal and
expressive suppression. However, research has yet to focus on the role
of different facets of executive functioning for emotion regulation
during acute stress, a limitation we address with the current study.

\subsection*{Current Study}

{\label{current-study}}

The goal of this study is to examine the relationship between executive
functioning facets and acute stress reactivity and recovery in a
controlled laboratory setting (Axis 1). We will also investigate the
role of cognitive appraisals (Axis 2), repetitive negative thinking
(Axis 3), and emotion regulation (Axis 4) in the relationship through
three additional research axes.

\subsubsection*{Axis 1: Executive Functioning and Acute
Stress}

{\label{axis-1-executive-functioning-and-acute-stress}}

This study will explore physiological and subjective reactivity and
recovery to a stress-inducing laboratory task using the Maastricht Acute
Stress Test (Smeets et al., 2012). Based on our previous findings (Grimm
et al., 2021), we expect the four facets (inhibition, flexibility,
working memory, and updating) to be negatively associated with
reactivity (i.e., lower stress reactivity), and positively with recovery
indicators (i.e., faster return to baseline; \textbf{Hypothesis 1} ).

\subsubsection*{Axis 2: Cognitive
Appraisals}

{\label{axis-2-cognitive-appraisals}}

Axis 2 focuses on the role of primary and secondary appraisals in the
executive functioning-stress relationship. Based on the literature
reviewed (Panganiban \& Matthews, 2014; Zandara et al., 2016), it could
be postulated that better inhibition and working memory should play a
role in keeping more adaptive appraisals in mind in anticipation of the
task (e.g., higher perceptions of challenge and control over the
stressor; \textbf{Hypothesis 2a} ). Consequently, this should reduce
physiological and subjective activations during the reactivity phase,
while predicting a faster return to baseline levels during recovery
(\textbf{Hypothesis 2b} ).

\subsubsection*{Axis 3: Negative Repetitive
Thinking}

{\label{axis-3-negative-repetitive-thinking}}

The objective of Axis 3 is twofold: first, to evaluate whether a
mediating role of repetitive negative thinking linking executive
functioning facets with stress reactivity and recovery can be
replicated. Second, our goal is to differentiate the roles of abstract
and concrete modes of processing. We expect the executive functions to
be associated with higher concrete-experiential thoughts and lower
abstract-analytical repetitive thoughts throughout the stressor
(\textbf{Hypothesis 3a).} Based on past research (Ottaviani, 2018;
Watkins \& Roberts, 2020), inhibition and flexibility should be the most
relevant facets. In turn, we expect to observe lower stress reactivity,
as well as a more rapid return to baseline levels after the stress
offset \textbf{(Hypothesis 3b} ).

\subsubsection*{Axis 4: Emotion Regulation
Strategies}

{\label{axis-4-emotion-regulation-strategies}}

The role of emotion regulation during an acute stressor is the final
project axis. We will explore the role of each facet for reappraisal and
suppression during reactivity. We expect inhibition, flexibility, and
updating to predict higher reappraisal and lower
suppression\textbf{(Hypothesis} \textbf{4a} ). In turn, these should
lead to lower stress reactivity and faster recovery following the MAST
(\textbf{Hypothesis 4b} ).

\section*{Method}

{\label{method}}

\subsection*{Study Design}

{\label{study-design}}

The study is divided into two consecutive laboratory sessions. In the
first session, we will administer the cognitive tasks and a series of
computer-based questionnaires. During the second session, participants
will complete the MAST (Smeets et al., 2012). The study has received
ethical approval from the UCLouvain-St Luc Biomedical Committee.

\subsection*{Participants}

{\label{participants}}

Participants will be recruited through social media (e.g., UCLouvain
student groups on Facebook), posters placed around Louvain-La-Neuve and
Brussels (Belgium), as well as emails sent out to the UCLouvain pool of
participants and students at the Faculty of Psychology and Educational
Sciences. The target sample size will be 85 in the MAST stressor
induction group. This was estimated using G*Power 3.1.9.7 for a
regression model with an estimated effect size of 0.15, an alpha of
0.05, power of 0.80, four predictors (each cognitive task) and four
covariates (age, gender, chronic stress, and baseline or reactivity
stress to calculate change indices). A further 43 individuals will be
recruited for the control group (placebo-MAST), and 22 more across both
groups for estimated participant dropout and equipment malfunction. We
will stop recruitment once we have reached 150 participants for the
current study.

Inclusion criteria will include being over 18 years of age, having
French as one's mother tongue language (or having acquired it before age
10), and presenting a Body Mass Index between 18 and 30 (Shapiro et al.,
1996). Participants will be excluded if they smoke, consume more than 25
units of alcohol per week, have a habit of using soft or hard drugs, or
present a current diagnosis or treatment for specific physical disorders
(i.e., cardiovascular, endocrine, diabetes; Jennings et al., 1981;
Narvaez Linares et al., 2020; Quintana et al., 2016; Shapiro et al.,
1996; Shilton et al., 2017). They will also be free of current
psychiatric diagnosis or treatment (e.g., for depression, anxiety;
Quintana et al., 2016; Stone et al., 2021; Vila et al., 2019) and will
be systematically excluded if they were diagnosed with stress- or
anxiety-related disorders (e.g., post-traumatic stress disorder, general
anxiety disorder) in their lifetime, if they are undergoing an unusually
stressful period (e.g., separation, divorce, examination, current
events), or if they have been previously exposed to the MAST (e.g.,
Gianferante et al., 2014; Stone et al., 2021).

\subsection*{Measures}

{\label{measures}}

\subsubsection*{Online Questionnaires}

{\label{online-questionnaires}}

\textbf{Sociodemographic and Physical Information} . We will include
items relating to one's age, gender, education, weight and height to
calculate body mass index, and handedness.

\textbf{Chronic Stress.} The Perceived Stress Scale (Bellinghausen et
al., 2009; Cohen et al., 1994) will evaluate perceived stress over the
last month. The scale is composed of 10 items, four of which require
reversing. All items are assessed on a Likert scale ranging from
0\selectlanguage{english} (never)\selectlanguage{english} to 4\selectlanguage{english} (very often). The total sum score represents an index of
chronic stress. The scale has shown good consistency in previous studies
(e.g., Cohen et al., 1994, Grimm \& Agrigoroaei, \emph{in prep.} ).

\subsubsection*{Executive Functioning}

{\label{executive-functioning}}

All cognitive tasks were originally programmed by Millisecond Software
and will run on Inquisit 6 (2021). The order of task presentation will
be counterbalanced across participants.

\textbf{Inhibition.} The Stop Signal Task script was modified to meet
recent recommendations (Verbruggen et al., 2019). On each trial, a
fixation circle will appear at the center of the computer screen,
following which an arrow pointing left or right will appear within a
circle. Participants will indicate the arrow direction (left or right)
using the ``D'' and ``K'' keys, respectively. Participants must respond
as quickly as possible without waiting for the stop signal to occur, as
that would otherwise delay stimuli presentation. On specific trials,
participants will hear a beep through the headphones, indicating that
they must omit from responding. The ratio for signal to no-signal trials
will be 1:3 and the target response inhibition probability fixed at 0.5.

The task is divided into four blocks of 64 trials, with arrows pointing
to the left and right half of the time. The arrow direction will be
randomized for each trial. The initial Stop Signal Delay will be set at
250ms and adjusted up or down by increments of 50s depending upon
successful (i.e., increases) or unsuccessful (i.e., decreases)
performance. The minimum and maximum delays will be 50ms and 1150ms,
respectively. Each trial (signal or no-signal) will last 2000ms,
preceded by a pre-trial 500ms pause. A 15-second pause between each
block will provide participants with prior block results and instruction
reminders. The test will be preceded by a 32-trial practice block, with
25\% signal trials (i.e., 8 signals).

Following previous recommendations (Verbruggen et al., 2008), the first
trial in each test block will not be analyzed. Inquisit 6 (2021) will
automatically calculate the Stop-Signal Reaction Time using the
integration model, as well as the response time for go responses on
unsuccessful stop signals. We will verify whether the race model is
violated (i.e., where the reaction time on Stop Signal trials is
numerically longer than reaction times on Go trials that are
successfully inhibited) and will ensure that all
p(respond\textbar{}signal) are above 0.25 and below 0.75, as recommended
(Congdon et al., 2012; Verbruggen et al., 2019). Participants will be
omitted from further analysis if this is not the case.

\textbf{Flexibility} . Cognitive flexibility will be operationalized
using the Letter-Number Task (Miyake et al., 2000). Letter-number pairs
(e.g., ``7G''), moving in clockwise fashion to the next box on each new
trial, will appear within a 2x2 quadrant. Participants will respond to
one of two categorization tasks depending on the position of the pair
onscreen. If it appears in the top two squares, participants will
indicate whether the letter is a consonant (``E'' key) or a vowel (``I''
key) (i.e., Letter Task). If the pair appears on the two lower
quadrants, participants will indicate whether the number is even (``E'')
or odd (``I'') (i.e., Number Task). The task will begin with two
practice blocks, during which the participants will complete 32 trials
of both tasks, separately. The order for task presentation (i.e., Letter
followed Number, or vice versa) is randomized across participants.
Participants will complete a combined practice block of 16 trials (i.e.,
the two tasks simultaneously). The test consists of 128 trials (64
switch trials, 64 non-switch trials). The stimuli will disappear 150ms
after the response if correct and after 1500ms if not. The outcome for
this task will be the latency shift cost, calculated as the difference
between average correct response times on switch trials and non-switch
trials in the combined test block. A positive value will designate
longer time to switch between the two tasks and, therefore, poorer
flexibility. We will compare accuracy and latency across switch and
non-switch trials to confirm task validity.

\textbf{Working Memory.} The Automated version of the Operation Span
Task will be administered (Conway et al., 2005), which alternates
between two tasks: a letter memory test and a mathematical problem.
Three- to seven spans of individual letters will be presented onscreen.
Participants will need to recall as many as possible in the correct
order using a letter matrix. Between letter trials, simple addition,
subtraction, multiplication, or division problems, as well as a possible
solution, will be presented. Participants will choose whether the
proposed solution is correct. The practice block comprises (a) four
trials of recalling letter spans (with set sizes of two to three in
ascending order), (b) 15 trials of the mathematical task, and (c) three
trials of the combined tasks (letter set size of two and one math
problem). The test block comprises 15 trials, in which five set sizes
will be repeated three times each. The presentation order of the set
sizes will be randomized. A math problem will precede each letter. The
traditional Ospan score -- the sum of all perfectly recalled sets --
will be used as a proxy for working memory. Following previous
recommendations (Unsworth et al., 2005), we will remove participants
with accuracy scores of less than 85\% on the mathematical problems.

\textbf{Working Memory Updating.} In the Letter Memory Task (Friedman et
al., 2008), participants will see a fixation cross for 1000ms, followed
by individual letters. For each new letter, they will name the last
three letters they have seen, which they will select on a letter matrix.
Each letter will appear for 2500ms, and each letter set will contain
five, seven, or nine letters. Three trials (one for each set size) will
be presented in random order during the practice block. The test block
will consist of 12 trials (four per set size), with the order randomized
but constrained so that each set size appears once every three trials.
The proportion of letters correctly recalled across all test trials will
be the dependent variable.

\subsubsection*{Measures throughout the
MAST}

{\label{measures-throughout-the-mast}}

\textbf{Subjective Stress.}

State Anxiety. State anxiety will be evaluated using the state scale of
the State Trait Anxiety Questionnaire (STAI; Spielberger, 1983, French
version: Gauthier \& Bouchard, 1993).

It includes 20 items concerning the current emotional intensity at that
moment, using a 4-point Likert scale ranging from 1 (not at all) to 4
(very much). A score is computed by summing the items. Scores range from
20 to 80, with higher values indicating greater anxiety. The scale shows
good internal consistency and test-retest reliability in different
samples (Barnes et al., 2002).

\emph{Stress Item.} Based on previous research (Becker et al., 2019;
Lesage et al., 2012; Luettgau et al., 2018; Paszkiel et al., 2020), the
perceived stress item (``Right now, how stressed do you feel?'') will be
measured along a visual analog scale ranging from 0 (``not stressed at
all'') to 10 (``extremely stressed'').

Stress Appraisal. We will administer the Primary and Secondary Appraisal
Scale (PASA, Gaab et al., 2005). The wording of the items will be
modified to match the context of the MAST (e.g., replacing ``jury'' with
``experimenter''). The scale is composed of 16 items on a scale from 1
(totally disagree) to 6 (totally agree). It measures threat (e.g.,
``This situation scares me''), challenge (e.g., This task challenges
me''), self-efficacy (e.g., ``In this situation I know what I can do''),
and control expectancy (e.g., ``It mainly depends on me whether the
experimenter judges me positively''). We will obtain primary and
secondary appraisal scores by combining the scores from the first two
and last two, respectively. The four factors have shown good internal
consistency (e.g., from .60 to .83; Wirtz et al., 2006).

Repetitive Thinking Processing Modes. The State Repetitive Thinking
Questionnaire (SRTQ; Philippot, 2019) will capture state negative
repetitive thinking. Individuals will rate the extent to which they are
having 10 specific thoughts in the preceding minutes, on a scale from 0
(``totally disagree'') to 4 (``totally agree''). We will focus on the
Concrete-Experiential (3 items, e.g., ``I am focused on what I am
concretely experiencing now'') and Abstract Evaluative (4 items, e.g.,
``I wonder why I react like this'') processing modes.

Emotion Regulation. The Emotion Regulation Questionnaire (ERQ; Gross \&
John, 2003, French version: Christophe et al., 2009) will measure
participants' tendencies for reappraisal (six items, e.g., ``When I was
faced with the stressful situation, I made myself think about it in a
way that helped me stay calm'') and suppression (four items, e.g., ``I
controlled my emotions by not expressing them''). The wording was
modified to evaluate the strategies implemented during the stressor,
ranging from 1 (not at all) to 7 (completely). Previous studies
demonstrated adequate internal consistency (reappraisal, \selectlanguage{greek}α \selectlanguage{english}= .76, and
suppression, \selectlanguage{greek}α \selectlanguage{english}= .72, Christophe et al., 2009).

\textbf{Physiological Stress.} The Mindware Mobile Recorder (Mindware
Technologies, LTD) will be used to measure heart rate, skin conductance,
and respiration at a sampling rate of 500 samples per second. Three
Electrocardiogram electrodes (45mm, Ag/AgCl, liquid gel, MedCat) will be
placed according to Mindware Technologies protocols
(see\emph{https://support.mindwaretech.com/training/}): one on the right
collarbone and two on the last two ribs on both sides. Respiration will
also be measured using a belt. In line with the Society for
Psychophysiological Research Ad Hoc Committee on Electrodermal Measures
(SPRAHCEM, (2012), electrodermal activity will be measured using
disposable finger electrode strips (Ag/AgCl, MedCat) placed on the
distal phalanges of the left major and index fingers and filled with
Isotonic Electrode Gel (0.05 molar NaCl paste, Biopac).

These physiological measures will be collected using the BioLab
Acquisition Software (V3.4.1). Systolic and diastolic blood pressure
will be event-based, measured using the oscillometer method with the
Omron M6 Comfort (HEM-7360-E, Omron Healthcare Co., Ltd; for more
information see Topouchian et al., 2014) in sitting position (Shapiro et
al., 1996). The M6 Comfort takes three consecutive 30-second readings.

\subsection*{Procedure}

{\label{procedure}}

\subsubsection*{General Procedure}

{\label{general-procedure}}

The study will be divided into three successive parts. It will begin
with the signing of an informed consent form and the completion of
online questionnaires before one's visit to the laboratory. These will
include sociodemographic items, as well as validated questionnaires
evaluating chronic stress (PSS-10; Cohen et al., 1983).

Then, participants will come into the lab for a two-hour visit composed
of cognitive testing followed by an acute stress induction. Participants
will take a 10-minute break between the two sessions to allow for
acclimatization (Shapiro et al., 1996). They will be able to hydrate,
use the restroom, and complete a HRV screening checklist (Laborde et
al., 2017). This questionnaire will help ensure that participants have
avoided alcohol and strenuous physical activity for the previous 24
hours, as well as caffeine, food, and smoking for the last two hours
(Jennings et al., 1981; Shapiro et al., 1996). They will provide their
sleep and wake times and confirm whether they adhered to their usual
sleep routine (Labuschagne et al., 2019).

Participants' weight and height will be measured before the stress
induction. The lab will be windowless, temperature-controlled, and
electrically shielded to minimize any influences on psychophysiology
measures (Shapiro et al., 1996, SPRAHCEM, 2012).

\subsubsection*{Acute Stress Induction}

{\label{acute-stress-induction}}

Acute stress will be induced using the validated Maastricht Acute Stress
Test (MAST; Smeets et al., 2012; see Figure 1 for the overall
procedure). Participants will first relax for five minutes, while heart
rate, skin conductance, and respiration are continuously measured. Blood
pressure will then be obtained, while participants complete the state
items of the STAI (Spielberger, 1983) and a perceived stress level item
(Becker et al., 2019).

The MAST procedure includes an anticipation, stress, and recovery phase.
During the anticipation phase, participants will read through the MAST
instructions using PowerPoint 2016 (provided by Tom Smeets, Smeets et
al., 2012) and ask any questions. Then, they will complete the stress
item, as well as questionnaires evaluating primary appraisal, emotion
regulation, and repetitive thinking mode. During the \emph{stress}phase,
participants will alternate immersing their right hand in ice-cold water
and performing a mental arithmetic task (serially subtracting 17 from
2043). Water and arithmetic trials will be exactly 90 and 45 seconds,
respectively. The experimenter will be dressed in a white lab coat and
will maintain a neutral attitude. A video camera will also be placed in
front of the participant to record their facial expressions.
Participants will be informed that their facial expressions throughout
the MAST will be analyzed by a research team. Following the initial 12
minutes of the MAST, participants will complete the questionnaires again
(see Figure 1), and blood pressure will be measured once more.

Upon completing the scales, participants will be informed that the task
is finished and will be given a 10-minute recovery phase to relax. They
will then complete the STAI and stress items, and we will measure their
blood pressure. Participants will be fully debriefed about the study and
financially compensated with 20 euros.

For a manipulation check, a control group will take part in a modified
version of the MAST (Placebo-MAST, Smeets et al., 2012). Participants
will place their hand in lukewarm water and perform a simple counting
task at their own pace. All elements of evaluation (white lab coat,
camera, or negative feedback for errors in calculations) will be
removed.

\textbf{Data Analysis Plan}

Data Cleaning

\textbf{Cognitive Tasks.} Data cleaning for the executive functioning
tasks will follow individual task recommendations (e.g., in the
flexibility task, trials with response times below 200ms and trials
following errors will be eliminated; Friedman et al., 2008).

\textbf{Physiological Data.} Heart rate, HRV, and electrodermal activity
will be analyzed using Mindware Technologies HRV Analysis Software
(V3.2.11.). We will select the multi-events option to analyze heart rate
between for each phase (see Figure 1): Baseline (between baseline and T1
questionnaires), reactivity (between MAST onset and T3 questionnaires),
and recovery (between recovery onset and T4 questionnaires).

\emph{Heart Rate and HRV.} The Mindware Technologies HRV software allows
for the calculation of R-R intervals. The Very Low Frequency (VLF) Band
(Hz) will be set between 0.003 and 0.040, the Low Frequency (LF) Band
(Hz) between 0.04 and 0.12, and the High Frequency/RSA Band (Hz) between
0.12 and 0.40 (see Task Force recommendations, 1996). The minimum and
maximum heart rates will be set at 40 and 200bpms, respectively. Each of
the three phases will be divided into 60-second epochs, where any
segments that are less than 30 seconds will not be deemed fit for
analysis. In addition, any segment for which the respiration power (in
volts\textsuperscript{2}, as recorded through the respiration belt)
falls outside the recommended LF and HF Bands will not be considered for
analysis (Berntson et al., 1997). Each segment will be screened for peak
artifacts (e.g., incorrectly identified peaks, missing heartbeats, or
unusable peaks due to movement), which, on a case-by-case basis, will be
deleted. Depending on the within-software recommendations, a mid-beat
will be inserted to correct for these deleted artifacts. Following
sufficient agreement between the two raters, the first author will carry
out the remaining data visualization and modification. We will report
the average number of normal R peaks, as well as the mean number of
deleted segments. We will average heart rate and HRV values over each
respective segments for each phase.

\textbf{Skin Conductance Levels.} Electrodermal activity will be
analyzed using the Mindware Technologies EDA software, version 3.2.9.,
similarly to heart rate. The Event Related Skin Conductance Minimum
Response Latency will be set at one second and the maximum at three
seconds from the event (Levinson \& Edelberg, 1985). The threshold level
in \selectlanguage{greek}µ\selectlanguage{english}Siemens to detect a skin conductance response will be set at 0.05
(SPRAHCEM, 2012). A constant rolling filter of 500 data points per block
will be applied to smooth the EDA signal and we will perform a visual
inspection of the segments. Artifacts such as subject movement and
respiration will be identified and edited out (e.g., using the spline
function of the software). We will calculate mean skin conductance
levels for the three phases.

\subsubsection*{Data Analysis}

{\label{data-analysis}}

All data analyses will be conducted on RStudio and SPSS. Data and
scripts will be made available to readers upon request.

\textbf{Screening and Removal of Outliers.} We will check for normality
in distribution for all predictor and outcome variables. We will examine
the standardized residuals from the regression analyses using the Q-Q
plots. Any significant outliers will be removed from the analyses and
the models will be reanalyzed without them.

\textbf{Stress Induction Verification.} For all stress indicators
(physiological and subjective), we will use multilevel models with
condition (MAST vs. placebo-MAST) and time (baseline, instructions,
MAST, and recovery levels) as categorical variables, along with their
interaction term to verify the efficacy of the stress induction. The
potential influence of age and gender will be considered in the analyses
(e.g., Shapiro et al., 1996).

\textbf{Hypotheses Testing by Axis.}

Given that we do not expect to see relationships with stress in the
Placebo group, all following models will be applied on the experimental
group, only (\emph{N} [?] 100).

Hypothesis 1: Executive Functioning and Stress Phases. We will run
multilevel models for each stress outcome (STAI, heart rate, HRV,
electrodermal activity, and blood pressure), with executive functioning
facets, time (baseline, instructions, MAST, post-MAST) and the
interaction terms between executive functioning and time as predictors,
along with relevant covariates that correlate with the dependent and
independent variables. We will perform correlations between all
independent variables to optimize model parsimony.

\emph{Hypotheses 2, 3, and 4: Mediating roles of Cognitive Appraisals,
Negative Repetitive Thinking, and Emotion Regulation.} In an exploratory
manner, we will run partial correlations (Pearson or Spearman depending
on the normality of the distribution) between the executive functioning
facets and all mediators. We will run three types of multiple mediation
models using bootstrapping with Haye's PROCESS macro in SPSS Statistics
(IBM Corp., 2017), with cognitive appraisals (four mediators),
repetitive negative thinking (two mediators), and emotion regulation
(two mediators). For parsimony reasons, we will conduct these models
when we observe a significant correlation between (a) an executive
functioning facet and one potential mediator, and (b) one mediator and a
stress outcome. In addition, we will include baseline stress levels for
all reactivity models, and the reactivity stress levels as covariates
for the recovery models. This will capture change from baseline to
reactivity, and from reactivity to recovery. We will report \emph{p}
-values and consider results as significant when they fulfill the
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\textbf{Figure}\selectlanguage{english}
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\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\emph{Figure 1} . Visual Representation of the Lab Stress Induction
Session.

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