A global test of brief reappraisal interventions on emotions during the COVID-19 pandemic

Abstract

The COVID-19 pandemic is increasing negative emotions and decreasing positive emotions globally. Left unchecked, these emotional changes may have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we will examine the impact of reappraisal, a widely studied and highly effective form of emotion regulation. Participants from 55 countries (expected N = 25,448) will be randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing), an active control condition, or a passive control condition. We predict that both reappraisal interventions will reduce negative emotions and increase positive emotions relative to the control conditions. We further predict that reconstrual will decrease negative emotions more than repurposing, and that repurposing will increase positive emotions more than reconstrual. We hope to inform efforts to create a scalable intervention for use around the world to build resilience during the pandemic and beyond.

Full text

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Stage 1 Registered Report
Nature Human Behaviour
A global test of brief reappraisal interventions on
emotions during the COVID-19 pandemic
Ke Wang1, Amit Goldenberg2, Charles A. Dorison1, Jeremy K. Miller3*, Jennifer S. Lerner1,4, James J.
Gross5, & Psychological Science Accelerator*
1 Harvard Kennedy School, Harvard University, Cambridge, US
2 Harvard Business School, Harvard University, Boston, US
3 Department of Psychology, Willamette University, Salem, US
4 Department of Psychology, Harvard University, Cambridge, US
5 Department of Psychology, Stanford University, Stanford, US
*Corresponding author: Jeremy K. Miller (millerj@willamette.edu)
*A list of authors and their affiliations appears at the end of the paper.

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Abstract
The COVID-19 pandemic is increasing negative emotions and decreasing positive emotions globally. Left
unchecked, these emotional changes may have a wide array of adverse impacts. To reduce negative
emotions and increase positive emotions, we will examine the impact of reappraisal, a widely studied and
highly effective form of emotion regulation. Participants from 55 countries (expected N = 25,448) will be
randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing), an active
control condition, or a passive control condition. We predict that both reappraisal interventions will
reduce negative emotions and increase positive emotions relative to the control conditions. We further
predict that reconstrual will decrease negative emotions more than repurposing, and that repurposing will
increase positive emotions more than reconstrual. We hope to inform efforts to create a scalable
intervention for use around the world to build resilience during the pandemic and beyond.

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Introduction
The COVID-19 pandemic is increasing negative emotions and decreasing positive emotions
around the globe1-10. Concurrently, individuals are reporting that COVID-19 is having a negative impact
on their psychological functioning and mental health4, 11, 12. For example, individuals report sleeping less,
consuming more alcohol or other drugs/substances, having trouble concentrating because their mind is
occupied by COVID-19, and having more fights with their partner or loved ones, some escalating to
domestic violence1, 9, 13.
These disturbing trends are partly caused by heightened levels of negative emotion and
diminished levels of positive emotion, which have been found to contribute to a number of negative
psychological, behavioural, and health consequences. These include increased risk for anxiety and
depressive disorders, as well as other forms of psychopathology14; impaired social connections15;
increased substance use16, 17, 18; compromised immune system functioning19, 20, 21; disturbed sleep22;
increased maladaptive eating23, 24; increased aggressive behaviour25, 26; impaired learning27; worse job
performance28, 29; and impaired economic decision-making30, 31.
As the COVID-19 pandemic unfolds around the world, we believe it is crucial to mitigate
expected adverse outcomes by reducing negative emotions and increasing positive emotions. Such a
change in emotions is central to increasing psychological resilience, a multifaceted concept that involves
adaptive emotional responses in the face of adversity32, 33, 34. Reappraisal, an emotion regulation strategy
that involves changing how one thinks about a situation with the goal of influencing one’s emotional
response35, is a promising candidate as an intervention to increase psychological resilience, due to its
adaptability, simplicity, and efficiency34, 36, 37, 38. In contrast to less effective emotion regulation strategies
such as suppression, reappraisal generally leads to more successful regulation (d = 0.45, 95% Confidence
Interval (CI) = [0.35, 0.56] in changing emotion experience in a meta-analysis39; see caveats about
interpreting effect sizes in past research in the “Sampling plan” section below). In particular, over the
short term, reappraisal leads to decreased reports of negative emotion and increased reports of positive

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emotion40, 41, 42, as well as corresponding changes both in peripheral physiological responses43, 44, 45 and
central physiological responses46-53. Over the longer term, reappraisal is associated with stronger social
connections54; higher academic achievement55, 56; enhanced psychological well-being57; fewer
psychopathological symptoms58, 59; better cardiovascular health60, 61, and greater resilience during the
COVID-19 pandemic62.
Despite these shorter-term and longer-term benefits, most people do not reappraise consistently63,
64, which has motivated efforts to teach people to use reappraisal (see reviews65, 66). For example, in the
context of anxiety, reappraisal training led to reduced intrusive memories67 and increased emotion
regulation self-efficacy68, 69. Reappraisal training also led to long-lasting changes in the neural
representation of unpleasant events70.
Although demand characteristics are always a concern when examining the effects of reappraisal
(given that one is teaching people to change their thinking in order to change how they’re feeling, and
then asking them how they feel)71, the wide array of self-report and non-self-report outcomes that show
reappraisal effects across studies increases confidence that these effects are real. It is also encouraging to
note that reappraisal generally out-performs other types of emotion regulation such as suppression, even
though demand characteristics appear comparable across regulation conditions39. In addition, evidence
indicates that reappraisal interventions can influence emotional outcomes even in intensely challenging
contexts in which people are often unmotivated to regulate their emotions72. For example, a brief
reappraisal training conducted in the context of the Israeli-Palestinian conflict, and replicated in the
context of the Colombian conflict73, has been found to contribute to reduced intergroup anger and
increased support for conciliatory political policies74.
As part of the Psychological Science Accelerator (PSA) attempt to address pressing questions
related to the psychological impact of COVID-19, the current study aims to use reappraisal interventions
to enhance psychological resilience in response to the pandemic. To maximize the impact of these
interventions, this project has a global reach of large, diverse samples via the PSA’s network75, and
employs highly scalable methods that are translated for use around the world. In order to make stronger

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and clearer inferences, our design includes two reappraisal interventions that are compared to two control
conditions, an active control and a passive control.
For our reappraisal interventions, we examine two theoretically defined forms of reappraisal76 --
namely reconstrual and repurposing. Reconstrual involves changing how a situation is construed or
mentally represented in a way that changes the emotional responses related to the situation. Examples of
reconstrual in response to COVID-19 are: “Washing hands, avoiding touching my face, keeping a safe
distance…There are simple and effective things I can do to protect myself and my loved ones from getting
sick and to stop the spread of the virus” and “I know from world history that keeping calm and carrying
on gets us through tough times.” Repurposing involves focusing on a potentially positive outcome that
could come from the current situation in a way that changes the emotional response to it. Examples of
repurposing in response to COVID-19 are: “This situation is helping us realize the importance of
meaningful social connections, and helping us understand who the most important people in our lives
are” and “Medical systems are now learning to deal with amazing challenges, which will make them
much more resilient in the future.” For our active control condition, we will ask participants to reflect on
their thoughts and feelings as they unfold. Reflecting on one’s thoughts and feelings has been found to
have small but reliable salutary effects (d = 0.07, 95% CI = [0.05, 0.17] in improving psychological
health that includes emotional responses in a meta-analysis77, 78). Examples of reflecting in response to
COVID-19 are: “I really wish we could find a vaccine soon” and “This situation is changing so fast, and
I don’t know how the future will develop.” By asking participants in this condition to actively use a
strategy that is likely to have a positive effect, we sought to match expectancy and demand across
reappraisal and active control conditions. For our passive control condition, we will ask participants to
respond as they naturally do, which is a commonly used passive control condition in prior research on
emotion regulation (for a meta-analysis39).
In comparing conditions, we chose to distinguish between negative and positive emotional
responses, as previous evidence suggests that the two are clearly separable79, 80. Specifically, we
hypothesized that our reappraisal interventions would lead to reduced negative emotional responses

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(hypothesis 1; The Design Table provides further detail about this and each subsequent hypothesis) and
increased positive emotional responses (hypothesis 2) compared to both control conditions combined.
While both reconstrual and repurposing strategies involve changing thinking, we hypothesized that
reconstrual would lead to greater decreases in negative emotional responses than repurposing (hypothesis
3) and that repurposing would lead to greater increases in positive emotional responses than reconstrual
(hypothesis 4). We theorized that reconstruing one’s situation should primarily decrease negative
emotions because it typically focuses on ameliorating the problem at hand. Reconstrual is most similar to
a previously studied subtype of reappraisal called reappraising emotional stimulus, which has been mainly
investigated on negative emotions and has a d = 0.38, 95% CI = [0.21, 0.55] in changing emotion
experience39. Repurposing one’s situation, by contrast, should primarily increase positive emotions
because it usually calls to mind positive experiences. Repurposing is similar to a few previously examined
types of reappraisals such as benefit finding and positive reappraisal, both of which are primarily
associated with positive outcomes81, 82 (the “Sampling plan” section below provides further detail).
In testing these hypotheses, we plan to employ orthogonal contrasts that make two primary
comparisons, while keeping all other comparisons exploratory (Table 1 provides further detail). The first
comparison will contrast both the reappraisal conditions combined with both the active control condition
and the passive control condition combined for negative (hypothesis 1) and positive (hypothesis 2)
emotions. The second comparison will contrast the reconstrual and repurposing interventions for negative
(hypothesis 3) and positive (hypothesis 4) emotions. One attractive feature of comparisons between
reappraisal conditions is that there is no reason to assume that demand or expectancies would differ across
these reappraisal conditions.
One potential concern about the current design is that the emotion regulation interventions may
reduce preventive health behaviours (e.g., keeping social distance and washing hands) that could
potentially be motivated by negative emotions. However, research on the connection between emotions
and health behaviour suggests that increased negative emotions such as fear do not seem to be a strong
motivator to change one’s health behaviour83. Furthermore, positive emotions augmented by the

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reappraisal interventions may contribute to a greater tendency to undertake health behaviours84, 85. For
example, positive emotions can lead to higher medication adherence86. To ensure that our interventions do
not adversely impact any relevant health behaviours, we will take two steps. First, during the instructions,
we will clarify that in some cases negative emotions such as fear and sadness may be helpful, and that it is
up to each person to determine when an emotion is unhelpful or not and to downregulate only those
emotions that are unhelpful. Second, in order to make sure that our training does not lead to reduced
vigilance, we will specifically measure and examine intentions for following stay-at-home orders and
washing hands in exploratory analyses.
In addition, we will conduct other exploratory analyses. Among other things, these analyses will
test the impact of our reappraisal interventions on negative and positive anticipated emotions and
intentions to enact potentially harmful versus beneficial behaviours associated with these emotions
(details described in the measure section below), and assess whether the effects of our reappraisal
interventions, if any, are moderated by motivation to use the given strategy71, belief in the effectiveness of
the given strategy87, demographics (gender39; socioeconomic status88, 89; or country90 (particularly in light
of the differing levels of impact of COVID-19 in any given country at any given point in time).
Methods
Ethics information and participants
This study is one of three studies in the PSA COVID-19 Rapid Project. The other two studies
investigate the effects of loss and gain message framing and self determination theory-guided message
framing, respectively. The other two studies will be reported elsewhere. The study will be conducted
online, and participants will click a single data collection link that will lead to either the current study or
the other two studies in the COVID-19 Rapid Project. A comprehensive summary of the PSA COVID-19

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Rapid Project—including descriptions of the study selection procedure, the other selected studies, the
internal peer review process, and implementation plans—can be found at https://psyarxiv.com/x976j/.
Participants will be recruited by the PSA network. At the time of Stage 1 submission, 194
research groups from 55 countries speaking 42 languages have signed up to recruit 25,448 participants to
complete the current study (not counting participants for the other two studies in the PSA COVID-19
Rapid Project), and 4,050 of them will be recruited through semi-representative paneling (based on sex,
age, and sometimes ethnicity) from the following countries: Egypt, Kenya, Nigeria, South Africa,
Mexico, United States, Austria, Romania, Russia, Sweden, Switzerland, United Kingdom, China, Japan,
and South Korea (270 participants per country). The remaining participants will be recruited through the
research groups by convenience sampling. Each research group will obtain approval from their local
Ethics Committee or IRB to conduct the study, will explicitly indicate that their institution does not
require approval for the researchers to conduct this type of task, or will explicitly indicate that the current
study is covered by a pre-existing approval. Although the specifics of the consent procedure will differ
across research groups, all participants will provide informed consent. At the time of Stage 1 submission,
135 research groups from 41 countries have ethics approval to collect data from 15,997 participants.
Other research groups are currently seeking ethics approval. The style and the amount of compensation
vary with local conventions (a common practice in PSA). More information regarding participant
compensation and final sample size will be updated via the pre-print (https://psyarxiv.com/x976j/).

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Procedure
Figure 1. Overview of the experiment. *Participants in the passive control condition will not have the
fourth step in the practice trials.
An overview of the experiment is depicted in Figure 1.
Pre-measure. Before reading the instructions, participants will report emotions they feel in the
moment (details for all study measures are described in the next section). These ratings will constitute a
baseline emotional measure.
Randomization to condition. Following the pre-measure, participants will be randomly assigned
to one of four between-subjects experimental conditions: two reappraisal intervention conditions
(reconstrual and repurposing), one active control condition, and one passive control condition. Because

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the study will be conducted online, data collection will be performed blind to the conditions of the
participants. The content of the instructions in each condition will differ, but the lengths will be matched
except for the passive control condition, which has a shorter set of instructions.
Participants in the two reappraisal intervention conditions (reconstrual and repurposing) and the
active control condition will receive the following instructions: “In this study, we will show you
photographs related to COVID-19 from various news sources. Our goal is to better understand how
people respond to such photos, which may include feelings of fear, anger, and sadness. Sometimes
emotions like these are helpful. At other times, however, these emotions can be unhelpful to us.
Researchers have found that when people think their emotions are unhelpful, they can take steps to
influence their emotions.”
In the reconstrual condition, participants will be told that (emphasis in original) “One strategy
that some people find helpful for influencing their emotions is rethinking. This strategy involves changing
one’s thinking in order to change one’s emotions. This strategy is based on the insight that different ways
of interpreting or thinking about any situation can lead to different emotions. This means that
finding new ways of thinking about a situation can change how you feel about the situation. For example,
consider someone who stays at home under lockdown due to COVID-19 and is feeling anxious, sad, or
angry. In this case, rethinking might involve realizing that the situation is only temporary because
dedicated people across the world are working hard to find a vaccine.” Participants will then be given four
examples of how rethinking might be employed for the COVID-19 situation (Example 1: “I know from
world history that keeping calm and carrying on gets us through tough times.” Example 2: “Scientists
across the world are working hard to find treatment and vaccines. Throughout history, humans have been
resourceful in finding solutions to new challenges.” Example 3: “Washing hands, avoiding touching my
face, keeping a safe distance…There are simple and effective things I can do to protect myself and my
loved ones from getting sick and to stop the spread of the virus.” Example 4: “In the past, people have
overcome many challenges that seemed overwhelming at the time, and we will overcome COVID-19
related challenges too.”).

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In the repurposing condition, participants will be told that (emphasis in original) “One strategy
that some people find helpful for influencing their emotions is refocusing. This strategy involves
changing one’s thinking in order to change one’s emotions. This strategy is based on the insight that
finding something good in even the most challenging situations can lead to different emotional
responses. This means that refocusing on whatever good aspects may be found in a situation can change
how you feel about the situation. For example, consider someone who stays at home under lockdown due
to COVID-19 and is feeling anxious, sad, or angry. In this case, refocusing might involve realizing that
staying at home gives them time to do things that they may not have been able to do before, like reading,
painting, and spending time with family.” Participants will then be given four examples of how
refocusing might be employed for the COVID-19 situation (Example 1: “This situation is helping us
realize the importance of meaningful social connections, and helping us understand who the most
important people in our lives are.” Example 2: “Medical systems are now learning to deal with amazing
challenges, which will make them much more resilient in the future.” Example 3: “Even though we are
physically apart, we are finding creative ways to stay connected and our hearts are more connected than
ever.” Example 4: “I have been inspired by the way that frontline health care workers have responded
with resilience, generosity, determination, and deep commitment.”).
In the active control condition, participants will be asked to reflect on their emotions as they
unfold. This condition is inspired by the literature on expressive writing and experimental disclosure,
which shows that asking people to reflect about their very deepest thoughts and feelings can improve
psychological health77, 78. By having an active control condition, which is likely to lead to some benefit to
participants, we can make stronger inferences regarding the impact of reappraisal interventions relative to
a potentially useful strategy designed to equate demand characteristics and expectancies. In the
instructions, participants will be told that (emphasis in original) “One strategy that some people find
helpful for influencing their emotions is reflecting. This strategy involves allowing oneself to freely
experience and reflect on one’s thoughts and feelings. This strategy is based on the insight that reflecting
on your thoughts and feelings about any situation can lead to different emotional responses. This

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means that exploring your thoughts and emotions can change how you feel about the situation. For
example, consider someone who stays at home under lockdown due to COVID-19 and is feeling anxious,
sad, or angry. In this case, reflecting might involve allowing oneself to experience these feelings and be
fully immersed in the lockdown experience, reflecting on the meaning this situation has for the person
and their loved ones.” Participants will then be given four examples of how reflecting might be employed
for the COVID-19 situation (Example 1: “This situation is changing so fast, and I don’t know how the
future will develop.” Example 2: “People are struggling to cope with these unprecedented and
overwhelming challenges.” Example 3: “Someone I love might get sick and there might not even be
ventilators to help them.” Example 4: “I really wish we could find a vaccine soon.”).
To reinforce what they have learned, participants in the two reappraisal conditions and the active
control condition will then be asked to summarize, in 1-2 sentences, the strategy they have just learned.
This text response is collected only for exploratory purposes and will not be used in confirmatory
analysis.
In the passive control condition, participants will receive the following instructions: “In this
study, we will show you photographs related to COVID-19 from various news sources. Our goal is to
better understand how people respond to such photos, which may include feelings of fear, anger, and
sadness. As you view these photographs, please respond as you naturally would.” Having a passive
control condition will allow us to have clear interpretations in the case that we find no significant
difference in our contrast between both the reappraisal conditions combined and both the control
conditions combined. If this is the case, we will compare each reappraisal condition against the passive
control condition and compare the active control condition against the passive control condition in the
exploratory analysis to determine whether each strategy has a non-zero impact relative to individuals'
natural responses.
Practice trials. After receiving instructions by condition, participants will be asked to practice
the strategy in two trials designed to facilitate their understanding of the strategy. The practice trials will
include providing ratings and written responses to two photographs (per prior research91). The

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photographs in this study were selected by our research team from major media news sources (CNN, New
York Times, The Guardian, and Reuters) and present situations in Asia, Europe, and North America.
They were rated by our team to evoke either sadness or anxiety above the midpoint on a 7-point scale
ranging from “not at all” to “very” and to score close to or above the midpoint on a 7-point scale ranging
from “not at all” to “very” on the question “How much do you recommend using this picture?”
(photographs available at https://osf.io/8bjnz/). In each practice trial, participants will see a negative photo
related to the COVID-19 situation (e.g., an exhausted doctor, medical workers in hazmat suits) and a
reminder above the photo to use the strategy that was presented to them. In the reconstrual condition, the
reminder is “As you view the photo, draw on the examples we gave you earlier in order to interpret the
situation in a new way.” In the repurposing condition, the reminder is “As you view the photo, draw on
the examples we gave you earlier in order to focus on any good you can find in the situation.” In the
active control condition, the reminder is “As you view the photo, draw on the examples we gave you
earlier in order to reflect on your thoughts and feelings.” In the passive control condition, the reminder
is “As you view the photo, respond as you naturally would.” After ten seconds, participants will be
asked to rate their emotions in response to the photo using two corresponding unipolar 5-point Likert
scales, one for negative emotion and one for positive emotion. These ratings are designed to familiarize
participants with the task, and will not be used in the confirmatory analyses. After each photo,
participants in the two reappraisal conditions and the active control condition will be asked to write (in
text) how they applied the strategy while observing the photo. Participants in the passive control
condition will be asked to write (in text) anything that comes naturally to their mind about the photo. The
text response is also collected only for exploratory purposes and will not be used in the confirmatory
analysis. Participants in the two reappraisal conditions and the active control condition will then be given
one example of how the photo might be viewed (examples vary by condition). Note that the two
reappraisal conditions and the active control condition are designed to be matched for demand
characteristics and expectancy.

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Experimental trials. Following the two practice trials, participants will view additional photos
related to the COVID-19 situation in ten experimental trials. Participants in the two reappraisal conditions
and the active control condition will be asked to use the strategy that they practiced, and participants in
the passive control condition will be asked to respond naturally. All participants will see exactly the same
ten photos, but the order of the presentation will be randomized across the ten experimental trials. Each
photo will be presented to participants with the same reminder used in the practice trials. After observing
each photo for ten seconds, participants will be asked to rate both their negative and positive emotions in
response to the photo using the same 5-point Likert scales from the practice trials.
Post-measures. In the final section of the study, participants will complete several measures,
including (1) negative and positive state emotions, (2) negative and positive emotions about the COVID-
19 situation, (3) negative and positive anticipated emotions, (4) behavioural intentions, (5)
motivation/beliefs, (6) manipulation check, and (7) demographic questions.
Measures
Baseline emotions. To assess baseline emotion, we will ask participants how they are feeling
right now at the beginning of the session on a 5-point scale ranging from 1 (not at all) to 5 (extremely)
(All response options will be labelled and numbers will not be displayed to participants for clarity). For
negative baseline emotions, we will measure five items on fear, anger, sadness, distrust, and stress from
the modified Differential Emotions Scale92. For positive baseline emotions, we will measure five items on
hope, gratitude, love, inspiration, and serenity from the modified Differential Emotions Scale92 (details for
all scoring rules described in the Analysis Plan section). We will also measure three items on loneliness93
and three items on social connectedness94. These six items also will be included in the assessment of post-
photo state emotions and in the assessment of anticipated emotions (at each assessment point, these six
items will be used in exploratory analyses).

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Negative emotional responses. In order to capture descriptively rich, nuanced data, we will
measure negative emotional responses in four ways. The first way is to measure negative emotions in
response to the photos. For each photo, we will ask participants how negative the photo made them feel
using a unipolar scale ranging from 1 (not at all) to 5 (extremely). The second way is to measure negative
state emotions after viewing all ten photos. We will ask participants “how you are feeling right now” with
the same set of items used to measure baseline emotions, which include five negative state emotions of
fear, anger, sadness, distrust, and stress. The third way is to measure negative emotions about the
COVID-19 situation. We will ask participants how negative/hopeless they are feeling about the COVID-
19 situation right now on a unipolar scale ranging from 1 (not at all) to 5 (extremely). The fourth way is to
measure negative anticipated emotions, which will be an exploratory outcome. We will ask participants
“In the next week, to what extent, if at all, do you think you will feel each of the following?” with the
same set of items used to measure baseline emotions, which include five negative anticipated emotions of
fear, anger, sadness, distrust, and stress.
Positive emotional responses. Following a parallel procedure, we will measure positive
emotional responses in four ways. The first way is to measure positive emotions in response to the photos.
For each photo, we will ask participants how positive the photo made them feel using a unipolar scale
ranging from 1 (not at all) to 5 (extremely). The second way is to measure positive state emotions after
viewing all ten photos. We will ask participants “how you are feeling right now” with the same set of
items used to measure baseline emotions, which include five positive state emotions of hope, gratitude,
love, inspiration, and serenity. The third way is to measure positive emotions about the COVID-19
situation. We will ask participants how positive/hopeful they are feeling about the COVID-19 situation
right now on a unipolar scale ranging from 1 (not at all) to 5 (extremely). The fourth way is to measure
positive anticipated emotions, which will be an exploratory outcome. We will ask participants “In the
next week, to what extent, if at all, do you think you will feel each of the following?” with the same set of
items used to measure baseline emotions, which include five positive anticipated emotions of hope,
gratitude, love, inspiration, and serenity.

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Behavioural intentions. In addition to the emotional responses that are central to our four
confirmatory hypotheses in this study, we will also examine exploratory outcomes concerning
behavioural intentions. Such intentions matter because they have been shown to predict actual
behaviours95, 96. Following protocols from Fishbein and Ajzen97, we will ask participants to indicate on a
7-point scale ranging from 1 (extremely unlikely) to 7 (extremely likely) their intentions to engage in each
of 10 different behaviours within the next week. Five of the items concern potentially harmful behaviour,
which we chose based on documented links between negative emotions and substance use, aggressive
behaviour, and excessive information seeking17, 25, 98. Items include: drinking too much alcohol, using too
much tobacco (e.g., smoking/vaping) or other recreational drugs, yelling at someone, taking anger out
online, and spending too much time on media. The other five items concern beneficial behaviour, which
we chose based on evidence that positive emotions contribute to more health behaviours84, 85. Items
include: eating healthy food, getting enough physical activity, practicing healthy sleep habits (for
example, going to bed and waking at regular hours), washing hands regularly for at least 20 seconds, and
following a stay-at-home order stringently (if there isn't an order in your region now, assume that one is
imposed).
Motivation/beliefs. We will measure both the motivation to use the emotion regulatory strategy
and the belief in the effectiveness of the emotion regulatory strategy as exploratory moderators71, 87. We
will ask “Recall the instructions we gave you for viewing the photos. To what extent, if at all, do you
agree or disagree with the following statements?” Motivation to use the emotion regulatory strategy will
be measured with the item: “I tried my hardest to follow the instructions I was given while viewing the
photos.” Belief in the effectiveness of the emotion regulatory strategy employed by participants will be
measured with the item “I believed that following the instructions would influence my emotions.”
Participants will rate their answers using a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly
agree).
Manipulation check. We plan to evaluate participants’ attention to our instructions and photos
using two multiple-choice questions. The first question will ask participants to choose the instructions

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they had at the beginning of the survey from among four options. The second question will ask
participants to choose the photo that was not shown to them in the survey from among three options.
For exploratory purposes, we will also ask how often participants actually used each approach
when viewing the photographs and their global change of emotions compared to the beginning of the
study. Participants will be asked, “When viewing the ten photographs related to COVID-19 earlier, how
often did you use each of the following approaches?” and rate four approaches: “responding as I naturally
would,” “reflecting on my thoughts and feelings,” “interpreting the situation in a new way,” and
“focusing on any good I could find in the situation.” Participants will rate their answers using a 5-point
scale ranging from 1 (never) to 5 (always). To measure global change of emotion, participants will be
asked, “Overall, compared to the beginning of this study, how negative do you feel right now?” using a 5-
point scale ranging from 1 (much more negative) to 5 (much less negative) and “Overall, compared to the
beginning of this study, how positive do you feel right now?” using a 5-point scale ranging from 1 (much
more positive) to 5 (much less positive).
Demographics. At the end of the study, participants will complete a general survey that includes
demographic questions and some questions related to COVID-19 shared by all three studies in the PSA
COVID-19 Rapid Project. Details about the general survey can be found at https://osf.io/7axc4/.
Order of items. For measures above, items belonging to the negative category (i.e., negative
emotional responses and intentions for harmful behaviour) and to the positive category (i.e., positive
emotional responses and intentions for beneficial behaviour) will be presented in a counterbalanced order
within each measure across participants. In other words, half of the participants will always rate an item
from the negative category first and then an item from the positive category, whereas the other half will
always rate an item from the positive category first and then an item from the negative category. For
measures that have multiple items, items belonging to the negative category will be randomized within
the negative category, and items belonging to the positive category will be randomized within the positive
category. When the same set of items used to measure baseline emotions is repeated, the set will have the
same order for every given participant.

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Analysis plan
Pre-processing
Exclusion. We plan to exclude (1) participants who answer both multiple choice manipulation
check questions incorrectly, and (2) participants who complete fewer than 50% of the questions in the
study.
Reliability of measures. For items from the modified Differential Emotions Scale92, we plan to
create overall negative emotion scores at each time point by averaging the five negative emotions (fear,
anger, sadness, distrust, and stress) and overall positive emotion scores at each time point by averaging
the five positive emotions (hope, gratitude, love, inspiration, and serenity) if the average inter-item
correlation is above .40 for negative emotions and for positive emotions, respectively. If the average inter-
item correlation is below .40, we will conduct an exploratory factor analysis with oblique rotation and
maintain factors with an eigenvalue above 1.00. If no factors have an eigenvalue above 1, we will report
results by item rather than as a composite.
Missing data. We will drop incomplete cases on an analysis-by-analysis basis. Given our
sampling plan described below, we should have power of 0.95 or above.
Outliers. In order to be maximally conservative, we will not define or identify outliers.
Analytic plan for hypotheses
Since negative emotional responses and positive emotional responses are separable79, 80, we will
examine negative emotional responses and positive emotional responses separately. To control family-
wise error rates in multiple comparisons, we will use the Holm-Bonferroni method within each of the four
hypotheses separately. For all analyses testing negative emotional responses (hypothesis 1 and hypothesis
3), we plan to control for the participants' negative baseline emotions. As originally intended by the
scale92, we plan to create an overall negative baseline emotion score by averaging the five negative

19
emotions (fear, anger, sadness, distrust, and stress). For all analyses testing positive emotional responses
(hypothesis 2 and hypothesis 4), we plan to control for the participants' positive baseline emotions. As
originally intended by the scale92, we plan to create an overall positive baseline emotion score by
averaging the five positive emotions (hope, gratitude, love, inspiration, and serenity). To account for the
nested structure in our data (e.g., participant nested by country), we will fit multilevel models with the
condition using the contrast in Table 1, random by-country slopes, and random by-country intercepts. If a
model fails to converge, we plan to explore other reasonable models99 and report results of all explored
models in an appendix.
Although we use the frequentist approach for confirmatory analyses, we will also report Bayes
factors (BF) for every result to gain information about the strength of evidence provided by the data
comparing the null and alternative hypotheses100. If we get non-significant results from the frequentist
approach, we will use BF to help us interpret non-significant results and differentiate between insensitive
results and those that reveal good enough evidence supporting the null-hypothesis. We will set these
evidence thresholds to BF10 to > 10 for H1 and < 1/10 for H0. If BFs do not cross the evidence thresholds,
we think our sample size is sufficiently large that inconclusive results at this sample size would be an
important message for the field. We will use informed priors for the alternative model: a one-tailed
Cauchy distribution with a mode of zero and a scale r = 0.18 (hypotheses 1 and 2), r = 0.17 (hypothesis
3), and r = 0.25 (hypothesis 4) on the standardized effect size using the BayesFactor package in R for the
analysis101. These priors are based on the lowest available estimates of effect sizes in past research (See
the “Sampling plan” section for more information). To model variance, the package will use a non-
informative prior, which should not influence the value of the BF due to being represented equally in H0
and H1. To probe the robustness of our conclusions, we will report Robustness Regions for each Bayes
factor, which can specify the range of expected effect sizes used when in the alternative model that would
support the same conclusion. Robustness Regions will be notated as RR[min, max], where min indicates
the smallest scaling factor and max indicates the largest scaling factor that would lead us to the same
conclusion as the originally chosen scaling factor102.

20
Tests for hypotheses 1 and 3
Overall, we expect that reappraisal interventions (vs. control) will reduce negative emotional
responses (hypothesis 1), and that reconstrual will lead to greater decreases in negative emotional
responses than repurposing (hypothesis 3). We will test hypothesis 1 and hypothesis 3 using two
orthogonal contrasts (Table 1). The first contrast is between both reappraisal conditions combined and
both control conditions combined for hypothesis 1. The second contrast is between the reconstrual
condition and the repurposing condition for hypothesis 3. Negative emotional responses are measured in
four ways (negative emotions in response to the photos, negative state emotions after viewing the photos,
negative emotions about the COVID-19 situation, and negative anticipated emotions). We have
confirmatory hypotheses regarding the first three outcomes and will examine negative anticipated
emotions in the exploratory analysis. Therefore, hypothesis 1 can be subdivided into hypotheses 1a to 1c,
and hypothesis 3 can be subdivided into hypotheses 3a to 3c. We will consider a hypothesis to be
supported if at least 1 of the 3 sub-hypotheses is significant after Holm-Bonferroni correction (controlling
for 3 comparisons within each hypothesis). If we find non-significant results for any sub-hypothesis, we
will compare each reappraisal condition against the passive control condition and compare the active
control condition against the passive control condition in the exploratory analysis to determine whether
each strategy has a non-zero impact relative to individuals' natural responses.
Testing effects on negative emotions in response to the photos: We expect that reappraisal
interventions (vs. control) will reduce negative emotions in response to the photos (hypothesis 1a), and
reconstrual will lead to greater decreases in negative emotional responses in response to the photos than
repurposing (hypothesis 3a). We will model ratings of negativity in response to each photo in the
experimental trials as a function of the fixed effects of condition using our contrast. We will include by-
participant random intercepts, by-country random intercepts, as well as by-country random slopes for
each contrast.

21
Testing effects on negative state emotions: We expect that reappraisal interventions (vs. control)
will reduce negative state emotions (hypothesis 1b), and reconstrual will lead to greater decreases in
negative state emotions than repurposing (hypothesis 3b). Similar to creating the overall negative baseline
emotion score, we plan to create an overall negative state emotion score by averaging the five negative
emotions (fear, anger, sadness, distrust, and stress). We will model the overall negative state emotion
score as a function of the fixed effects of condition using our contrast. We will include by-country
random intercepts, as well as by-country random slopes for each contrast.
Testing effects on negative emotions about the COVID-19 situation: We expect that reappraisal
interventions (vs. control) will reduce negative emotions about the COVID-19 situation (hypothesis 1c),
and reconstrual will lead to greater decreases in negative emotions about the COVID-19 situation than
repurposing (hypothesis 3c). We will model negative emotions about the COVID-19 situation as a
function of the fixed effects of condition using our contrast. We will include by-country random
intercepts, as well as by-country random slopes for each contrast.
Tests for hypotheses 2 and 4
Overall, we expect that reappraisal interventions (vs. control) will increase positive emotional
responses (hypothesis 2), and repurposing will lead to greater increases in positive emotional responses
than reconstrual (hypothesis 4). We will test hypothesis 2 and hypothesis 4 using two orthogonal contrasts
(Table 1). The first contrast is between both reappraisal conditions combined and both control conditions
combined for hypothesis 2. The second contrast is between the reconstrual condition and the repurposing
condition for hypothesis 4. Positive emotional responses are measured in four ways (positive emotions in
response to the photos, positive state emotions after viewing the photos, positive emotions about the
COVID-19 situation, and positive anticipated emotions). We have confirmatory hypotheses regarding the
first three outcomes and will examine positive anticipated emotions in an exploratory analysis. Therefore,
hypothesis 2 can be subdivided into hypotheses 2a to 2c, and hypothesis 4 can be subdivided into

22
hypotheses 4a to 4c. We will consider a hypothesis to be supported if at least 1 of the 3 sub-hypotheses is
significant after Holm-Bonferroni correction (controlling for 3 comparisons within each hypothesis). If
we find non-significant results for any sub-hypothesis, we will compare each reappraisal condition against
the passive control condition and compare the active control condition against the passive control
condition in the exploratory analysis to determine whether each strategy has a non-zero impact relative to
individuals' natural responses.
Testing effects on positive emotions in response to the photos: We expect that reappraisal
interventions (vs. control) will increase positive emotions in response to the photos (hypothesis 2a), and
repurposing will lead to greater increases in positive emotions in response to the photos than reconstrual
(hypothesis 4a). We will model ratings of positivity in response to each photo in the experimental trials as
a function of the fixed effects of condition using our contrast. We will include by-participant random
intercepts, by-country random intercepts, as well as by-country random slopes for each contrast.
Testing effects on positive state emotions: We expect that reappraisal interventions (vs. control)
will increase positive state emotions (hypothesis 2b), and repurposing will lead to greater increases in
positive state emotions in response to the photos than reconstrual (hypothesis 4b). Similar to creating the
overall positive baseline emotion score, we plan to create an overall positive state emotion score by
averaging the five positive emotions (hope, gratitude, love, inspiration, and serenity). We will model the
overall positive state emotion score as a function of the fixed effects of condition using our contrast. We
will include by-country random intercepts, as well as by-country random slopes for each contrast.
Testing effects on positive emotions about the COVID-19 situation: We expect that reappraisal
interventions (vs. control) will increase positive emotions about the COVID-19 situation (hypothesis 2c),
and repurposing will lead to greater increases in positive emotions about the COVID-19 situation than
reconstrual (hypothesis 4c). We will model positive emotions about the COVID-19 situation as a function
of the fixed effects of condition using our contrast. We will include by-country random intercepts, as well
as by-country random slopes for each contrast.

23
Exploratory analyses
We will conduct a series of exploratory analyses to address supplemental questions regarding our
hypotheses, including, but not limited to: (1) Are there any differences in other pairwise comparisons in
testing hypotheses 1 - 2? (2) Are there emotion-specific effects of reappraisal103? (3) Are the effects on
emotions subjectively detectable by participants104? Do the effects of strategy use vary by (4) motivation
to use the strategy71; (5) beliefs in the strategy’s effectiveness87; or (6) the participant’s country of
residence90?
We will investigate the impacts of strategy use on other outcomes, including, but not limited to:
(1) positive and negative anticipated emotions; (2) intentions to enact potentially harmful versus
beneficial behaviours; and (3) loneliness and social connectedness.
Sampling plan
Expected effect sizes. In order to compare effect sizes across studies, below we report Cohen’s
ds, which in some cases were transformed or calculated from the results reported in the original studies
(see the Supplementary Information for details). Several caveats are in order regarding the effect sizes
that follow. First, meta-analyses tend to overestimate effect sizes, although the size of overestimation
varies considerably across studies and sometimes shows no overestimation105. Second, most prior studies
were conducted in the lab, whereas the current study will be conducted online. Third, the current crisis is
likely to lead to strong emotional responses, especially for participants who are facing financial or health-
related setbacks, although strong negative emotions also motivate people to regulate emotions more64.
These caveats suggest uncertainty in effect sizes.
In general, reappraisal has an average effect size of d = 0.45, 95% CI = [0.35, 0.56] in changing
emotion experience relative to passive control conditions (i.e., no instruction, instructions to experience
naturally, instructions to not regulate in a certain manner, or instructions to enhance or maintain the focal
emotion) (meta-analysis39; It finds no evidence of publication bias). Experimental disclosure and

24
expressive writing, which inspired the instruction in the active control condition, have an average effect
size of d = 0.07, 95% CI = [0.05, 0.17] in improving psychological health (including emotional
responses), relative to engaging in non-treatment neutral activities (e.g., describing what they have done
in the past 24 hours) or no activities (meta-analysis77; It finds evidence of publication bias). These works
suggest the lowest available estimate of the effect size to be d = 0.18 (subtracting the upper bound of 95%
CI d = 0.17 for experimental disclosure and expressive writing from the lower bound of 95% CI of d =
0.35 for the reappraisal interventions) between our reappraisal interventions and the control conditions for
hypothesis 1 and hypothesis 2.
In relation to the comparison between reconstrual and repurposing, although prior research has
not used the same theoretical framework76 to empirically contrast reconstrual and repurposing as we do in
the current study, research on closely related constructs can provide estimates of effect sizes. Reconstrual
is most similar to a previously studied subtype of reappraisal called “reappraising emotional stimulus” in
Webb, Miles, & Sheeran’s meta-analysis39, which has a d = 0.38, 95% CI = [0.21, 0.55] in changing
emotion experience (this effect size is primarily for negative emotions, as all but one study examined
negative emotions). Repurposing is similar to the construct “benefit finding” (perceiving positive
consequences that resulted from a traumatic event), which is associated with positive well-being, d =
0.45, 95% CI = [0.37, 0.52], but not global distress, d = 0.00, 95% CI = [-0.04, 0.04] (meta-analysis81).
Repurposing is also similar to the subtype of reappraisal called “positive reappraisal,” which is more
effective in increasing positive thoughts than other types of reappraisals, d = 0.49, 95% CI = [0.25, 0.72]
relative to detached reappraisal106. These works suggest the lowest available estimate of the effect size to
be d = 0.17 (subtracting the upper bound of 95% CI d = 0.04 for the association between benefit finding
and global distress from the lower bound of 95% CI of d = 0.21 for “reappraising emotional stimulus” in
Webb, Miles, & Sheeran39) between reconstrual and repurposing in changing negative emotions for
hypothesis 3, and d = 0.25 (the lower bound of 95% CI of positive reappraisal in increasing positive
thoughts than detached reappraisal in Shiota & Levenson106) between reconstrual and repurposing in
changing positive emotions for hypothesis 4.

25
Sample size. For practical reasons, sample size was primarily decided based on the availability of
resources among members of the PSA. At the time of submission, the present project is expected to be
completed by approximately 25,448 participants in total, or 8,482 participants per condition.
Adjusted alpha levels. The tests of each hypothesis involve three comparisons, with α for the
smallest p-value = 0.017 (0.05/3), α for the second-smallest p-value = 0.025 (0.05/2), and α for the largest
p-value = 0.05 (Holm-Bonferroni corrections).
Power analysis. We conducted a simulation study to estimate power for a variety of potential
effect sizes (|d| = 0.05 to 0.29, separated by increments of 0.02), number of countries (Ncountry = 30, 35, 40,
45, 50, 55, 60), within-country sample sizes (N = 200, 400, 600, 800), by-country intercept variances
(σ2intercept = 0.05, 0.30, 0.55, 0.80), and by-country slope variances (σ2slope = 0.01, 0.02, 0.03, 0.04) at α
= .017. The lowest level of intercept variances in our simulation was chosen on the basis of an ongoing
multi-country project tracking rates of depression (σ2intercept = 0.04) and worries about the COVID-19
(σ2intercept = 0.06) across countries during the COVID-19 outbreak107 (See the Supplemental Information
for details). The lowest level of slope variances in our simulation was chosen on the basis of the average
slope variance (σ2slope < 0.01) in a large multi-site, multi-country project involving 28 psychological
manipulations108. The slope variances capture the variability of the effect of psychological manipulations,
and there is no apparent reason to expect that the effect of reappraisal interventions on emotions is more
variable than most other psychological manipulations in Klein et al.108. In fact, appraisal theories of
emotion argue that the relationship between appraisals and emotions is culturally universal109, suggesting
low variability. As one example to show that similar appraisals associate with similar emotional
experiences, we find the associations vary little across countries between perceived insufficient
government response and depression (σ2slope = 0.003) and between perceived insufficient government
response and worries (σ2slope = 0.003) during the COVID-19 pandemic107 (See the Supplemental
Information for details), consistent with the observation of low slope variances (σ2slope < 0.01) in Klein et
al.108. Despite expecting low variability from empirical findings and theories, we tested a variety of

26
intercept variances and slope variances in our power simulation, some of which were much higher than
those in the Klein et al.108 and Fetzer et al.107 to be maximally conservative. We conducted 1000
simulations for each set of simulation parameters using the simr package110 using computing power
harnessed through the Open Science Grid111, 112.
We show abbreviated results for our simulation study in Figure 2 and comprehensive results at
https://osf.io/mf5z4/. Across simulations, power did not change much across the intercept variances that
we tested. We estimate that 400 participants in each of 35 countries (total N = 14,000) would provide over
95% power to detect an effect size of |d| = 0.15 across the slope variances that we tested, even when the
slope variability is much higher than what we observed in the past evidence. As prior research suggests
effect sizes above |d| = 0.17 (see the “Expected effect sizes” section above) and we have ethics approvals
to collect data from 41 countries and 15,997 participants at the time of Stage 1 submission (with a target
to collect data from 55 countries and 25,448 participants), our sample size should provide over 95%
power for testing our hypotheses. We believe our study is highly likely to detect any useful effects caused
by our interventions.

27
Figure 2. Power curves across different levels of variance in the by-country slopes. Lines represent
the estimated power across 1000 simulated datasets; envelopes are Monte Carlo 95% confidence
intervals. Power curves represent results for 35 countries with 400 participants each, with σ2intercept = 0.05.
Data Availability
All data and materials will be made openly available on the Open Science Framework (OSF) website
(https://osf.io/4yf9d/).
Code Availability
All analysis code (completed in R) will be made openly available on the Open Science Framework (OSF)
website (https://osf.io/4yf9d/).
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Acknowledgements
This work was supported by a grant from the American Psychological Society (granted to the
Psychological Science Accelerator). Further financial support was provided by the Psychological Science
Accelerator and a special crowdfunding campaign initiated by the Psychological Science Accelerator. We
would like to thank Amazon Web Services for help with server needs. We also thank Leibniz Institute for
Psychology (ZPID) for help with data collection via the organization and implementation of semi-
representative panels, and Prolific for offering discounted recruitment. Finally, this research was
supported by resources provided by the Open Science Grid, which is supported by the National Science
Foundation award 1148698, and the U.S. Department of Energy's Office of Science. Beyond those roles
already acknowledged, the funders had no role in study design, data collection and analysis, decision to
publish or preparation of the manuscript.
Author contributions
K.W., A.G., C.A.D., J.S.L., and J.J.G. designed research. K.W. wrote the initial draft. K.W., A.G.,
C.A.D., J.S.L., J.J.G., J.K.M., and P.F. reviewed and edited the manuscript. J.K.M. coordinated the
implementation of the project with the PSA consortium. P.F. conducted the power analysis and managed
the OSF repository of the project. L.D., B.A., and B.P. contributed to the analysis plan. E.B. coded the
experimental tasks. The rest of the authors from PSA consortium provided comments on the manuscript.
All authors listed in the PSA consortium will contribute to either data collection and/or translation and
will review and approve the final manuscript.
Competing interests
The authors declare no competing interests.

38
Table 1. Contrast structure of testing hypotheses 1 - 4 (with unit-
weighting).
Active Control
Passive Control
Reconstrual
Repurposing
Contrast 1 (hypotheses
1-2)
1/2
1/2
-1/2
-1/2
Contrast 2 (hypotheses
3-4)
0
0
1/2
-1/2
Table 2. Design table
Question
Hypothesis
Sampling plan (e.g.
power analysis)
Analysis Planabc
Interpretation given
to different
outcomes
Will
reappraisal
interventions
(vs. control)
reduce
negative
emotions in
response to the
photos?
Reappraisal
interventions
(vs. control)
will reduce
negative
emotions in
response to the
photos
(hypothesis
1a).
Prior works suggest
the lowest available
estimate of the effect
size to be d = 0.18
between our
reappraisal
interventions and the
control conditions
for hypothesis 1 and
hypothesis 2. Our
power simulation
suggests that 400
participants in each
of 35 countries (total
N = 14,000) would
provide over 95%
power to detect an
effect size of d ≥
We will model
ratings of negativity
in response to each
photo in the
experimental trials
as a function of the
fixed effects of
condition using our
contrast 1 in Table
1 and control for
the participants'
negative baseline
emotions. We will
include by-
participant random
intercepts, by-
country random
intercepts, as well
If the ratings are
significantly lower
(higher) in the
reappraisal conditions
than the control
conditions, we will
conclude finding
evidence for (against)
hypothesis 1a.
If the difference
between reappraisal
conditions and
control conditions is
not significantly
different from 0, we
will compare each
reappraisal condition
against the passive

39
Question
Hypothesis
Sampling plan (e.g.
power analysis)
Analysis Planabc
Interpretation given
to different
outcomes
0.15 across the slope
variances that we
tested. For practical
reasons, sample size
was primarily
decided based on the
availability of
resources among
members of the PSA.
At the time of
submission, we
expect to recruit
approximately
25,448 participants
in total and have
ethics approval to
collect data from
15,997 participants
(other research
groups are currently
seeking ethics
approval).
as by-country
random slopes for
each contrast. If we
find non-significant
results, we will
compare each
reappraisal
condition against
the passive control
condition and
compare the active
control condition
against the passive
control condition in
the exploratory
analysis to
determine whether
each strategy has a
non-zero impact
relative to
individuals' natural
responses.
control condition and
compare the active
control condition
against the passive
control condition in
the exploratory
analysis to determine
whether each strategy
has a non-zero impact
relative to individuals'
natural responses. For
any non-significant
results, we will
interpret the Bayes
factor.
Will
reappraisal
interventions
(vs. control)
reduce
negative state
emotions?
Reappraisal
interventions
(vs. control)
will reduce
negative state
emotions
(hypothesis 1b)
Prior works suggest
the lowest available
estimate of the effect
size to be d = 0.18
between our
reappraisal
interventions and the
control conditions
for hypothesis 1 and
hypothesis 2. Our
power simulation
suggests that 400
participants in each
of 35 countries (total
N = 14,000) would
provide over 95%
power to detect an
We plan to create
an overall negative
state emotion score
by averaging the
five negative
emotions (fear,
anger, sadness,
distrust, and stress).
We will model the
overall negative
state emotion score
as a function of the
fixed effects of
condition using our
contrast 1 in Table
1 and control for
the participants'
If the score is
significantly lower
(higher) in the
reappraisal conditions
than the control
conditions, we will
conclude finding
evidence for (against)
hypothesis 1b.
If the difference
between reappraisal
conditions and
control conditions is
not significantly
different from 0, we
will compare each
reappraisal condition

40
Question
Hypothesis
Sampling plan (e.g.
power analysis)
Analysis Planabc
Interpretation given
to different
outcomes
effect size of d ≥
0.15 across the slope
variances that we
tested. For practical
reasons, sample size
was primarily
decided based on the
availability of
resources among
members of the PSA.
At the time of
submission, we
expect to recruit
approximately
25,448 participants
in total and have
ethics approval to
collect data from
15,997 participants
(other research
groups are currently
seeking ethics
approval).
negative baseline
emotions. We will
include by-country
random intercepts,
as well as by-
country random
slopes for each
contrast. If we find
non-significant
results, we will
compare each
reappraisal
condition against
the passive control
condition and
compare the active
control condition
against the passive
control condition in
the exploratory
analysis to
determine whether
each strategy has a
non-zero impact
relative to
individuals' natural
responses.
against the passive
control condition and
compare the active
control condition
against the passive
control condition in
the exploratory
analysis to determine
whether each strategy
has a non-zero impact
relative to individuals'
natural responses. For
any non-significant
results, we will
interpret the Bayes
factor.
Will
reappraisal
interventions
(vs. control)
reduce
negative
emotions
about the
COVID-19
situation?
Reappraisal
interventions
(vs. control)
will reduce
negative
emotions about
the COVID-19
situation
(hypothesis 1c)
Prior works suggest
the lowest available
estimate of the effect
size to be d = 0.18
between our
reappraisal
interventions and the
control conditions
for hypothesis 1 and
hypothesis 2. Our
power simulation
suggests that 400
We will model
negative emotions
about the COVID-
19 situation as a
function of the
fixed effects of
condition using our
contrast 1 in Table
1 and control for
the participants'
negative baseline
emotions. We will
If the ratings are
significantly lower
(higher) in the
reappraisal conditions
than the control
conditions, we will
conclude finding
evidence for (against)
hypothesis 1c.
If the difference
between reappraisal
conditions and

41
Question
Hypothesis
Sampling plan (e.g.
power analysis)
Analysis Planabc
Interpretation given
to different
outcomes
participants in each
of 35 countries (total
N = 14,000) would
provide over 95%
power to detect an
effect size of d ≥
0.15 across the slope
variances that we
tested. For practical
reasons, sample size
was primarily
decided based on the
availability of
resources among
members of the PSA.
At the time of
submission, we
expect to recruit
approximately
25,448 participants
in total and have
ethics approval to
collect data from
15,997 participants
(other research
groups are currently
seeking ethics
approval).
include by-country
random intercepts,
as well as by-
country random
slopes for each
contrast. If we find
non-significant
results, we will
compare each
reappraisal
condition against
the passive control
condition and
compare the active
control condition
against the passive
control condition in
the exploratory
analysis to
determine whether
each strategy has a
non-zero impact
relative to
individuals' natural
responses.
control conditions is
not significantly
different from 0, we
will compare each
reappraisal condition
against the passive
control condition and
compare the active
control condition
against the passive
control condition in
the exploratory
analysis to determine
whether each strategy
has a non-zero impact
relative to individuals'
natural responses. For
any non-significant
results, we will
interpret the Bayes
factor.
Will
reappraisal
interventions
(vs. control)
increase
positive
emotions in
response to the
photos?
Reappraisal
interventions
(vs. control)
will increase
positive
emotions in
response to the
photos
(hypothesis 2a)
Prior works suggest
the lowest available
estimate of the effect
size to be d = 0.18
between our
reappraisal
interventions and the
control conditions
for hypothesis 1 and
hypothesis 2. Our
power simulation
We will model
ratings of positivity
in response to each
photo in the
experimental trials
as a function of the
fixed effects of
condition using our
contrast 1 in Table
1 and control for
the participants'
If the ratings are
significantly higher
(lower) in the
reappraisal conditions
than the control
conditions, we will
conclude finding
evidence for (against)
hypothesis 2a.
If the difference
between reappraisal

42
Question
Hypothesis
Sampling plan (e.g.
power analysis)
Analysis Planabc
Interpretation given
to different
outcomes
suggests that 400
participants in each
of 35 countries (total
N = 14,000) would
provide over 95%
power to detect an
effect size of d ≥
0.15 across the slope
variances that we
tested. For practical
reasons, sample size
was primarily
decided based on the
availability of
resources among
members of the PSA.
At the time of
submission, we
expect to recruit
approximately
25,448 participants
in total and have
ethics approval to
collect data from
15,997 participants
(other research
groups are currently
seeking ethics
approval).
positive baseline
emotions. We will
include by-
participant random
intercepts, by-
country random
intercepts, as well
as by-country
random slopes for
each contrast. If we
find non-significant
results, we will
compare each
reappraisal
condition against
the passive control
condition and
compare the active
control condition
against the passive
control condition in
the exploratory
analysis to
determine whether
each strategy has a
non-zero impact
relative to
individuals' natural
responses.
conditions and
control conditions is
not significantly
different from 0, we
will compare each
reappraisal condition
against the passive
control condition and
compare the active
control condition
against the passive
control condition in
the exploratory
analysis to determine
whether each strategy
has a non-zero impact
relative to individuals'
natural responses. For
any non-significant
results, we will
interpret the Bayes
factor.
Will
reappraisal
interventions
(vs. control)
increase
positive state
emotions?
Reappraisal
interventions
(vs. control)
will increase
positive state
emotions
(hypothesis 2b)
Prior works suggest
the lowest available
estimate of the effect
size to be d = 0.18
between our
reappraisal
interventions and the
control conditions
for hypothesis 1 and
hypothesis 2. Our
We plan to create
an overall positive
state emotion score
by averaging the
five positive
emotions (hope,
gratitude, love,
inspiration, and
serenity). We will
model the overall
If the score is
significantly higher
(lower) in the
reappraisal conditions
than the control
conditions, we will
conclude finding
evidence for (against)
hypothesis 2b.

43
Question
Hypothesis
Sampling plan (e.g.
power analysis)
Analysis Planabc
Interpretation given
to different
outcomes
power simulation
suggests that 400
participants in each
of 35 countries (total
N = 14,000) would
provide over 95%
power to detect an
effect size of d ≥
0.15 across the slope
variances that we
tested. For practical
reasons, sample size
was primarily
decided based on the
availability of
resources among
members of the PSA.
At the time of
submission, we
expect to recruit
approximately
25,448 participants
in total and have
ethics approval to
collect data from
15,997 participants
(other research
groups are currently
seeking ethics
approval).
positive state
emotion score as a
function of the
fixed effects of
condition using our
contrast 1 in Table
1 and control for
the participants'
positive baseline
emotions. We will
include by-country
random intercepts,
as well as by-
country random
slopes for each
contrast. If we find
non-significant
results, we will
compare each
reappraisal
condition against
the passive control
condition and
compare the active
control condition
against the passive
control condition in
the exploratory
analysis to
determine whether
each strategy has a
non-zero impact
relative to
individuals' natural
responses.
If the difference
between reappraisal
conditions and
control conditions is
not significantly
different from 0, we
will compare each
reappraisal condition
against the passive
control condition and
compare the active
control condition
against the passive
control condition in
the exploratory
analysis to determine
whether each strategy
has a non-zero impact
relative to individuals'
natural responses. For
any non-significant
results, we will
interpret the Bayes
factor.

44
Question
Hypothesis
Sampling plan (e.g.
power analysis)
Analysis Planabc
Interpretation given
to different
outcomes
Will
reappraisal
interventions
(vs. control)
increase
positive
emotions
about the
COVID-19
situation?
Reappraisal
interventions
(vs. control)
will increase
positive
emotions about
the COVID-19
situation
(hypothesis 2c)
Prior works suggest
the lowest available
estimate of the effect
size to be d = 0.18
between our
reappraisal
interventions and the
control conditions
for hypothesis 1 and
hypothesis 2. Our
power simulation
suggests that 400
participants in each
of 35 countries (total
N = 14,000) would
provide over 95%
power to detect an
effect size of d ≥
0.15 across the slope
variances that we
tested. For practical
reasons, sample size
was primarily
decided based on the
availability of
resources among
members of the PSA.
At the time of
submission, we
expect to recruit
approximately
25,448 participants
in total and have
ethics approval to
collect data from
15,997 participants
(other research
groups are currently
We will model
positive emotions
about the COVID-
19 situation as a
function of the
fixed effects of
condition using our
contrast 1 in Table
1 and control for
the participants'
positive baseline
emotions. We will
include by-country
random intercepts,
as well as by-
country random
slopes for each
contrast. If we find
non-significant
results, we will
compare each
reappraisal
condition against
the passive control
condition and
compare the active
control condition
against the passive
control condition in
the exploratory
analysis to
determine whether
each strategy has a
non-zero impact
relative to
individuals' natural
responses.
If the ratings are
significantly higher
(lower) in the
reappraisal conditions
than the control
conditions, we will
conclude finding
evidence for (against)
hypothesis 2c.
If the difference
between reappraisal
conditions and
control conditions is
not significantly
different from 0, we
will compare each
reappraisal condition
against the passive
control condition and
compare the active
control condition
against the passive
control condition in
the exploratory
analysis to determine
whether each strategy
has a non-zero impact
relative to individuals'
natural responses. For
any non-significant
results, we will
interpret the Bayes
factor.

45
Question
Hypothesis
Sampling plan (e.g.
power analysis)
Analysis Planabc
Interpretation given
to different
outcomes
seeking ethics
approval).
Will
reconstrual
lead to greater
decreases in
negative
emotional
responses in
response to the
photos than
repurposing?
Reconstrual
will lead to
greater
decreases in
negative
emotional
responses in
response to the
photos than
repurposing
(hypothesis
3a).
Prior works suggest
the lowest available
estimate of the effect
size to be d = 0.17
between reconstrual
and repurposing in
changing negative
emotions for
hypothesis 3. Our
power simulation
suggests that 400
participants in each
of 35 countries (total
N = 14,000) would
provide over 95%
power to detect an
effect size of d ≥
0.15 across the slope
variances that we
tested. For practical
reasons, sample size
was primarily
decided based on the
availability of
resources among
members of the PSA.
At the time of
submission, we
expect to recruit
approximately
25,448 participants
in total and have
ethics approval to
collect data from
We will model
ratings of negativity
in response to each
photo in the
experimental trials
as a function of the
fixed effects of
condition using our
contrast 2 in Table
1 and control for
the participants'
negative baseline
emotions. We will
include by-
participant random
intercepts, by-
country random
intercepts, as well
as by-country
random slopes for
each contrast.
If the ratings are
significantly lower
(higher) in
the reconstrual
condition than the
repurposing
condition, we will
conclude finding
evidence for (against)
hypothesis 3a.
If the difference
between reconstrual
and repurposing is not
significantly different
from 0, we will
interpret the Bayes
factor.

46
Question
Hypothesis
Sampling plan (e.g.
power analysis)
Analysis Planabc
Interpretation given
to different
outcomes
15,997 participants
(other research
groups are currently
seeking ethics
approval).
Will
reconstrual
lead to greater
decreases in
negative state
emotions than
repurposing?
Reconstrual
will lead to
greater
decreases in
negative state
emotions than
repurposing
(hypothesis 3b)
Prior works suggest
the lowest available
estimate of the effect
size to be d = 0.17
between reconstrual
and repurposing in
changing negative
emotions for
hypothesis 3. Our
power simulation
suggests that 400
participants in each
of 35 countries (total
N = 14,000) would
provide over 95%
power to detect an
effect size of d ≥
0.15 across the slope
variances that we
tested. For practical
reasons, sample size
was primarily
decided based on the
availability of
resources among
members of the PSA.
At the time of
submission, we
expect to recruit
approximately
25,448 participants
in total and have
ethics approval to
collect data from
We plan to create
an overall negative
state emotion score
by averaging the
five negative
emotions (fear,
anger, sadness,
distrust, and stress).
We will model the
overall negative
state emotion score
as a function of the
fixed effects of
condition using our
contrast 2 in Table
1 and control for
the participants'
negative baseline
emotions. We will
include by-country
random intercepts,
as well as by-
country random
slopes for each
contrast.
If the score is
significantly lower
(higher) in the
reconstrual condition
than the repurposing
condition, we will
conclude finding
evidence for (against)
hypothesis 3b.
If the difference
between reconstrual
and repurposing is not
significantly different
from 0, we will
interpret the Bayes
factor.

47
Question
Hypothesis
Sampling plan (e.g.
power analysis)
Analysis Planabc
Interpretation given
to different
outcomes
15,997 participants
(other research
groups are currently
seeking ethics
approval).
Will
reconstrual
lead to greater
decreases in
negative
emotions
about the
COVID-19
situation than
repurposing?
Reconstrual
will lead to
greater
decreases in
negative
emotions about
the COVID-19
situation than
repurposing
(hypothesis 3c)
Prior works suggest
the lowest available
estimate of the effect
size to be d = 0.17
between reconstrual
and repurposing in
changing negative
emotions for
hypothesis 3. Our
power simulation
suggests that 400
participants in each
of 35 countries (total
N = 14,000) would
provide over 95%
power to detect an
effect size of d ≥
0.15 across the slope
variances that we
tested. For practical
reasons, sample size
was primarily
decided based on the
availability of
resources among
members of the PSA.
At the time of
submission, we
expect to recruit
approximately
25,448 participants
in total and have
ethics approval to
collect data from
We will model
negative emotions
about the COVID-
19 situation as a
function of the
fixed effects of
condition using our
contrast 2 in Table
1 and control for
the participants'
negative baseline
emotions. We will
include by-country
random intercepts,
as well as by-
country random
slopes for each
contrast.
If the ratings are
significantly lower
(higher) in the
reconstrual condition
than the repurposing
condition, we will
conclude finding
evidence for (against)
hypothesis 3c.
If the difference
between reconstrual
and repurposing is not
significantly different
from 0, we will
interpret the Bayes
factor.

48
Question
Hypothesis
Sampling plan (e.g.
power analysis)
Analysis Planabc
Interpretation given
to different
outcomes
15,997 participants
(other research
groups are currently
seeking ethics
approval).
Will
repurposing
lead to greater
increases in
positive
emotions in
response to the
photos than
reconstrual?
Repurposing
will lead to
greater
increases in
positive
emotions in
response to the
photos than
reconstrual
(hypothesis 4a)
Prior works suggest
the lowest available
estimate of the effect
size to be d = 0.25
between repurposing
and reconstrual in
changing positive
emotions for
hypothesis 4. Our
power simulation
suggests that 400
participants in each
of 35 countries (total
N = 14,000) would
provide over 95%
power to detect an
effect size of d ≥
0.15 across the slope
variances that we
tested. For practical
reasons, sample size
was primarily
decided based on the
availability of
resources among
members of the PSA.
At the time of
submission, we
expect to recruit
approximately
25,448 participants
in total and have
ethics approval to
collect data from
We will model
ratings of positivity
in response to each
photo in the
experimental trials
as a function of the
fixed effects of
condition using our
contrast 2 in Table
1 and control for
the participants'
positive baseline
emotions. We will
include by-
participant random
intercepts, by-
country random
intercepts, as well
as by-country
random slopes for
each contrast.
If the ratings are
significantly higher
(lower) in the
repurposing condition
than the reconstrual
condition, we will
conclude finding
evidence for (against)
hypothesis 4a.
If the difference
between repurposing
and reconstrual is not
significantly different
from 0, we will
interpret the Bayes
factor.

49
Question
Hypothesis
Sampling plan (e.g.
power analysis)
Analysis Planabc
Interpretation given
to different
outcomes
15,997 participants
(other research
groups are currently
seeking ethics
approval).
Will
repurposing
lead to greater
increases in
positive state
emotions in
response to the
photos than
reconstrual?
Repurposing
will lead to
greater
increases in
positive state
emotions in
response to the
photos than
reconstrual
(hypothesis 4b)
Prior works suggest
the lowest available
estimate of the effect
size to be d = 0.25
between repurposing
and reconstrual in
changing positive
emotions for
hypothesis 4. Our
power simulation
suggests that 400
participants in each
of 35 countries (total
N = 14,000) would
provide over 95%
power to detect an
effect size of d ≥
0.15 across the slope
variances that we
tested. For practical
reasons, sample size
was primarily
decided based on the
availability of
resources among
members of the PSA.
At the time of
submission, we
expect to recruit
approximately
25,448 participants
in total and have
ethics approval to
collect data from
We plan to create
an overall positive
state emotion score
by averaging the
five positive
emotions (hope,
gratitude, love,
inspiration, and
serenity). We will
model the overall
positive state
emotion score as a
function of the
fixed effects of
condition using our
contrast 2 in Table
1 and control for
the participants'
positive baseline
emotions. We will
include by-country
random intercepts,
as well as by-
country random
slopes for each
contrast.
If the score is
significantly higher
(lower) in the
repurposing condition
than the reconstrual
condition, we will
conclude finding
evidence for (against)
hypothesis 4b.
If the difference
between repurposing
and reconstrual is not
significantly different
from 0, we will
interpret the Bayes
factor.

50
Question
Hypothesis
Sampling plan (e.g.
power analysis)
Analysis Planabc
Interpretation given
to different
outcomes
15,997 participants
(other research
groups are currently
seeking ethics
approval).
Will
repurposing
lead to greater
increases in
positive
emotions
about the
COVID-19
situation than
reconstrual?
Repurposing
will lead to
greater
increases in
positive
emotions about
the COVID-19
situation than
reconstrual
(hypothesis 4c)
Prior works suggest
the lowest available
estimate of the effect
size to be d = 0.25
between repurposing
and reconstrual in
changing positive
emotions for
hypothesis 4. Our
power simulation
suggests that 400
participants in each
of 35 countries (total
N = 14,000) would
provide over 95%
power to detect an
effect size of d ≥
0.15 across the slope
variances that we
tested. For practical
reasons, sample size
was primarily
decided based on the
availability of
resources among
members of the PSA.
At the time of
submission, we
expect to recruit
approximately
25,448 participants
in total and have
ethics approval to
collect data from
We will model
positive emotions
about the COVID-
19 situation as a
function of the
fixed effects of
condition using our
contrast 2 in Table
1 and control for
the participants'
positive baseline
emotions. We will
include by-country
random intercepts,
as well as by-
country random
slopes for each
contrast.
If the ratings are
significantly higher
(lower) in the
repurposing condition
than the reconstrual
condition, we will
conclude finding
evidence for (against)
hypothesis 4c.
If the difference
between repurposing
and reconstrual is not
significantly different
from 0, we will
interpret the Bayes
factor.

51
Question
Hypothesis
Sampling plan (e.g.
power analysis)
Analysis Planabc
Interpretation given
to different
outcomes
15,997 participants
(other research
groups are currently
seeking ethics
approval).
a Exclusion. We plan to exclude:
(1) Participants who answer both multiple choice manipulation check questions incorrectly.
(2) Participants who complete fewer than 50% of the questions in the study.
b Reliability of measures. For items from the modified Differential Emotions Scale92, we plan to create
overall negative emotion scores at each time point by averaging the five negative emotions (fear, anger,
sadness, distrust, and stress) and overall positive emotion scores at each time point by averaging the five
positive emotions (hope, gratitude, love, inspiration, and serenity) if the average inter-item correlation is
above .40 for negative emotions and for positive emotions, respectively. If the average inter-item
correlation is below .40, we will conduct an exploratory factor analysis with oblique rotation and maintain
factors with an eigenvalue above 1.00. If no factors have an eigenvalue above 1, we will report results by
item rather than as a composite.
c Missing data. We will drop incomplete cases on an analysis-by-analysis basis.

52
Psychological Science Accelerator
Patrick Forscher6, Balazs Aczel7, Bence Pálfi8, Lisa M. Debruine9, Erin Buchanan10
6Université Grenoble Alpes, Gières, France. 7Institute of Psychology, ELTE, Budapest, Hungary.
8University of Sussex, Brighton, UK. 9University of Glasgow, Scotland, UK. 10 Harrisburg University of
Science and Technology, Harrisburg, PA, USA. A full list of members and their affiliations appears in the
Supplementary Information.