A Multisite Preregistered Paradigmatic Test of the Ego-Depletion Effect

Abstract

We conducted a preregistered multi-laboratory project (k = 36; N = 3531) to assess the size and robustness of ego depletion effects using a novel replication method, termed the paradigmatic replication approach. Laboratories implemented one of two procedures that intended to manipulate self control and tested performance on a subsequent measure of self control. Confirmatory tests found a non-significant result, d = 0.06. Confirmatory Bayesian meta-analyses using an informed prior hypothesis (δ = 0.30; SD = 0.15) found the data were four times more likely under the null than the alternative hypothesis. Hence, preregistered analyses did not find evidence for a depletion effect. Exploratory analyses on the full sample (i.e., ignoring preregistered exclusion criteria see supplemental online materials found a statistically significant effect (d = 0.08), with data about equally likely under the null and informed prior hypotheses. Exploratory moderator tests suggested that the depletion effect was larger for participants reporting more fatigue but was not moderated by trait self control, willpower beliefs, or action orientation.

Full text

A Multi-Site Preregistered Paradigmatic Test of the Ego Depletion Effect
Kathleen D. Vohs, University of Minnesota
Brandon J. Schmeichel, Texas A&M University
Sophie Lohmann, Max Planck Institute for Demographic Research and University of
Illinois at Urbana-Champaign
Quentin F. Gronau, University of Amsterdam
Anna Finley, University of Wisconsin-Madison
…
E.-J. Wagenmakers, University of Amsterdam
Dolores Albarracín, University of Illinois at Urbana-Champaign
(Appendix A contains the entire author list.)
Author note: We thank Roy Baumeister, Eddie Harmon-Jones, Rebecca Schlegel, Uri
Simonsohn, Jake Westfall, and Wendy Wood for assistance. This research was
supported by a Netherlands Organisation for Scientific Research (NWO) grant to QFG
(406.16.528) and to EJW (016.Vici.170.083), as well as an Advanced ERC grant to
EJW (743086 UNIFY). Address correspondence to Kathleen Vohs, Carlson School of
Management, University of Minnesota, Minneapolis, MN, 55455. kvohs@umn.edu.
Citation: Vohs, K. D., Schmeichel, B. J., Lohmann, S., Gronau, Q., Finley, A. J….
Wagenmakers, E.-J., & Albarracín, D. (in press). A multi-site preregistered paradigmatic
test of the ego depletion effect. Psychological Science.

Paradigmatic Depletion Replication 2
Abstract
We conducted a preregistered, multi-laboratory project (k = 36; N = 3531) to assess the
size and robustness of ego depletion effects using a novel replication method, termed
the paradigmatic replication approach. Laboratories implemented one of two procedures
that intended to manipulate self-control and tested performance on a subsequent
measure of self-control. Confirmatory tests found a non-significant result, d = 0.06.
Confirmatory Bayesian meta-analyses using an informed prior hypothesis (δ = 0.30; SD
= 0.15) found the data were four times more likely under the null than the alternative
hypothesis. Hence, preregistered analyses did not find evidence for a depletion effect.
Exploratory analyses on the full sample (i.e., ignoring preregistered exclusion criteria;
see supplemental online materials) found a statistically significant effect (d = 0.08), with
data about equally likely under the null and informed prior hypotheses. Exploratory
moderator tests suggested that the depletion effect was larger for participants reporting
more fatigue but was not moderated by trait self-control, willpower beliefs, or action
orientation.

Paradigmatic Depletion Replication 3
A Multi-Site Preregistered Paradigmatic Test of the Ego Depletion Effect
The theory of ego depletion was introduced in 1998 and quickly gained interest
from scholars and lay audiences alike. Ego depletion is a theory of how self-control
operates, with self-control defined as the capacity to alter a predominant response
tendency, control impulses, and engage in volitional behavior. The central notion is that
self-control operates like a limited resource, such that using self-control on an initial task
renders subsequent self-control less successful than if not deployed earlier
(Baumeister, Bratslavsky, Muraven, & Tice, 1998; Muraven, Tice, & Baumeister, 1998).
The concept of ego depletion has been widely influential. The seminal article
(Baumeister et al., 1998) has had “transformational” impact (Nosek et al., 2010,
Supplement). In addition to a multitude of empirical articles, the theory inspired multiple
new theories as well (e.g., Evans, Boggero, & Segerstrom, 2015; Inzlicht & Schmeichel,
2012; Job, Dweck, & Walton, 2010; see Baumeister & Vohs, 2016a, for a review). In
short, the theory has been highly generative, both empirically and theoretically.
In recent years the evidentiary basis of ego depletion has been challenged and,
in response, we embarked on a multi-site, preregistered test of the phenomenon.
Challenges to ego depletion have come in two main forms: Meta-analytic analyses
(Carter et al., 2015) and a multi-site registered replication study (Hagger et al., 2016).
Those investigations cast doubt on ego depletion theory but have been criticized on
methodological and analytical grounds (Baumeister & Vohs, 2016b; Garrison, Finley, &
Schmeichel, 2019; Friese et al., 2019; Inzlicht, Gervais, & Berkman, 2015). Germane to
the current study is that the previous replication study used methods uncommon to ego
depletion studies (see Hagger & Chatzisarantis, 2016, for a rebuttal). As a result, we

Paradigmatic Depletion Replication 4
conducted a multi-site, preregistered study with methods more common to the literature
and more paradigmatic of the construct.
Paradigmatic Replication Approach
The current approach tested a hypothesis derived from the theory of ego
depletion and aimed to create a new model for replication studies. Termed the
paradigmatic replication approach, it made multiple changes to existing models (see
Spellman & Kahneman, 2018, for how the current project differs from others). Chiefly
and briefly, the procedures did not draw from any one published study. Instead,
candidate procedures were selected for how well they represent the phenomenon
— hence the paradigmatic moniker. Table 1 outlines key elements of the Paradigmatic
Replication Approach.
Additionally, the paradigmatic approach involved crowdsourcing with experts in
depletion research, scholars who sought to participate in data collection, and statistical
advisors. Experts generated possible tasks for the study’s procedures, focusing on their
paradigmatic fit with the construct. Labs then vetted those tasks for whether they would
provide good tests of the hypothesis and could be executed in their laboratories.
We recruited a group of scholars with little or no prior connection to ego depletion
research to serve as an Advisory Board. They made recommendations on data analytic
models, data analysis procedures, and study preregistrations. Prior to data collection,
the lead author (KV) created instructional videos for participating laboratories depicting
mock experimental sessions and held virtual meetings with experimenters to answer
questions. After completing data collection, laboratories sent their data to a handler who
created a master dataset and blinded the data, which was then sent to the data analysis

Paradigmatic Depletion Replication 5
team. The analysis team conducted preregistered analyses before sharing results with
the lead authors (KV and BS), who then generated recommendations for exploratory
analyses. Lead authors had access to the data only after the analysts had done their
work (Table 1).
Experimental Protocols
Laboratories used one of two protocols. (The term protocol refers to each
combination of independent and dependent variables.) The E-task protocol used a
manipulation that varied instructions to cross out the letter “e” within printed text and
measured subsequent self-control by persistence on unsolvable geometric puzzles.
Both tasks are common in the published depletion literature (e.g., Baumeister et al.,
1998; DeWall et al., 2007; Vohs et al., 2008). The writing task protocol used a
manipulation that had people write a story with or without difficult instructions and a self-
control outcome measure involving answering questions that benefited from controlled
cognitive processing. The Cognitive Estimation Test (CET; Bullard et al., 2004; Fein et
al., 1998) is thought to require self-control because answers cannot be determined
algorithmically or with declarative knowledge. These tasks also have been used in the
depletion literature (e.g., Mead et al., 2009; Schmeichel, 2007; Schmeichel, Vohs, &
Baumeister, 2003).
The primary hypothesis concerned ego depletion. In line with the theory, we
expected that people randomly assigned to use self-control during an initial task would
show worse self-control subsequently, compared to people who did not use self-control
initially. We expected the magnitude of the effect to be equivalent across protocols (see

Paradigmatic Depletion Replication 6
preregistration,
https://osf.io/952mv/?view_only=a81b3b1fd3e64898832cf19648b8c1dd).
We chose manipulation checks common to the depletion literature, namely
participants’ reports of the difficulty of the initial task, degree of effort required for it, and
feelings of frustration from it (Hagger, Wood, Stiff, & Chatzisarantis, 2010). Other self-
report measures included reports of being tired or fatigued. We predicted that compared
to the non-depletion condition, people in the depletion condition would report that the
initial task was more effortful and difficult—this was the primary manipulation check. We
also expected the manipulation to make them feel more tired, fatigued, and frustrated.
Additionally, Inzlicht and Schmeichel (2012) proposed that depletion hampers
motivation, which we tested with self-reports of being motivated and wanting to do well
on the outcome task. Inzlicht and Schmeichel’s theory would predict lower motivation
among people in the depletion, compared to non-depletion, condition. The original ego
depletion model does not make this prediction and thus anticipates no differences in
motivation.
We tested potential moderator variables, both by states thought to arise from the
manipulations and trait measures. On the former, we tested moderation by manipulation
check responses, predicting that being in the depletion condition and reporting higher
scores on those items would result in larger depletion effects.1 The more effortful,
fatiguing, or frustrating the initial task, the more it should undermine subsequent self-
control performance (e.g., Clarkson et al., 2010; Dang, 2016).
1 The term depletion effect refers to lower performance on outcome tasks among participants who had
previously exerted self-control. The term depletion condition refers to an initial task designed to require
self-control whereas non-depletion condition refers to a task designed to require relatively less self-
control.

Paradigmatic Depletion Replication 7
We tested potential moderation by individual differences as well. We measured beliefs
about willpower (Job et al., 2010), decision-related action orientation (Kuhl, 1994), and trait self-
control (Tangney, Baumeister, & Boone, 2004). Each has been found to moderate depletion
effects in prior research. We predicted that people who believe that willpower is a limited
resource (Job et al., 2010) or are less inclined toward action orientation (Jostmann & Koole,
2007) would show stronger depletion effects. Findings on trait self-control are mixed, with
stronger depletion effects found among people possessing higher (e.g., Dvorak & Simons,
2009) and lower trait self-control (e.g., DeWall et al., 2007), therefore we registered a research
question with no firm predictions regarding trait self-control.
Other project features aimed to track potential moderation variables. To assess
differences in study execution, laboratories provided videos of experimenters, which
were subjected to independent ratings. Other potential moderators included the number
of publications by laboratories’ principal investigators (PIs), number of depletion studies
published by the PI, and laboratory location (see Supplementary Online Materials
[SOM]).
The study also collected demographic information. Demographic variables
included gender identification (response options: female, male, other), age, and
language spoken at home.
Methods
Participants
Thirty-six laboratories (see Appendix B) tested 3531 people (2375 women, 1130
men, 11 listed “other,” and 15 did not report gender: M age = 20.92, SD = 5.19). Most of
the laboratories were located in the U.S. (k = 23), plus five labs in Germany, three in
Canada, two in the Netherlands, two in Australia, and one in Italy. Sixteen laboratories

Paradigmatic Depletion Replication 8
chose to use the writing task protocol (n = 1679) and 20 laboratories chose the E-task
protocol (n = 1852). Among all participants, 1762 were randomly assigned to the
depletion condition and 1769 were randomly assigned to the non-depletion condition.
Based on preregistered criteria, we excluded 30.25% (n = 1068) of all participants in
confirmatory data analyses, most often because of excessive errors on the E-task, not
being a native speaker of the laboratory’s language, or failing to comply with instructions
to not use their phone (for more information on exclusions and how this rate compares
to other multi-site replications, see Table 2 and SOM). The exclusion rate exceeded our
informal expectations and prompted exploratory analyses on the full sample of
participants (i.e., with no exclusions), which are reported in the SOM.
Protocol Generation and Creation
Two months prior to the start of data collection, a list of possible
operationalizations of the independent and dependent variables was generated by
experts in depletion research and sent to scholars who had indicated interest in
participating in this project. Those scholars provided feedback on each of the
operationalizations as to how effective they believed the tasks would be for testing ego
depletion and how feasible they would be to conduct.
For potential manipulation tasks, effectiveness was defined as the extent to
which the task would be depleting for their participants. For potential outcome tasks, the
effectiveness item asked the extent to which the task would yield enough variance
within their sample so that a depletion effect could be detected.
Analyses identified the top-rated procedures, leading to three protocols.
Participating labs then ranked their preferences as to which protocol to execute. As it

Paradigmatic Depletion Replication 9
turned out, all laboratories save for two chose either the E-task protocol or writing task
protocol; we assigned those laboratories to their second choice. The two tasks used as
manipulations and the two tasks used as outcome measures received the top combined
ratings of effectiveness and feasibility.
Prior to data collection, laboratories received training on how to execute each
protocol via video tutorials and virtual meetings. Methods, predictions, exclusion criteria,
and analytical specifications were preregistered prior to data analysis
(https://osf.io/952mv/?view_only=a81b3b1fd3e64898832cf19648b8c1dd). The SOM
contains additional methods details.
Experimental Procedures
Overview. Both protocols followed the same basic procedure, with the only
difference being the operationalization of the independent and dependent variables.
Participants were told that the study examined different types of cognitive
processes and specifically people’s responses to tasks that tap into different cognitive
processes. They completed the independent and dependent variable tasks, which
varied by protocol. Next, they completed manipulation checks, motivation reports,
individual differences scales, demographic questions, and a post-experimental
questionnaire (https://osf.io/952mv/?view_only=a81b3b1fd3e64898832cf19648b8c1dd).
E-task protocol. First, participants completed a task that involved crossing off all
instances of the letter E on a sheet of text, after which everyone received a new page of
text. Depending on experimental condition, participants either followed the same rules
as before and crossed out all instances of the Es (non-depletion condition) or were
given new rules requiring them to selectively cross out Es as a function of whether there

Paradigmatic Depletion Replication 10
was a vowel before or after the letter (depletion condition). The task had time limits: 7
minutes for the first page and 8 minutes for the second.
The experimenter then introduced the dependent measure—a figure tracing task,
which was described as a spatial abilities task. The task involved using a highlighter
marker to trace each figure in its entirety without picking up the highlighter or crossing
over the same line segment twice. Once assured that participants understood,
experimenters laid down stacks of the three test images, telling participants they could
quit the task anytime by ringing a bell on their desk. Unbeknownst to participants, two of
the three figures could not be traced as instructed (i.e., they were unsolvable).
Experimenters started timing after leaving the room and stopped timing when
participants indicated they were done with the task (or after 20 minutes).
Time spent on the task (i.e., duration) and number of sheets attempted formed
the dependent measure of self-control. Number of figure tracing sheets used
(representing attempts) and duration of the task were standardized separately and
added to create an overall figure tracing score (r = 0.39, 95% CI [0.35, 0.43]).
Writing task protocol. Participants’ first task was to write a story about a recent
trip. Participants in the non-depletion condition received no additional instructions.
Participants in the depletion condition were further instructed not to use words
containing the letters A or N in their story. Both conditions wrote for 5 minutes. After the
writing task, the experimenter introduced the dependent measure, the Cognitive
Estimation Test (i.e., CET; sample item: “How many seeds are there in a
watermelon?”). Participants were told that they should give their best guess on each
item. There was no time limit on the CET.

Paradigmatic Depletion Replication 11
CET responses were awarded points for degree of accuracy (0-2) in accordance
with published standards (Bullard et al., 2004; Fein et al., 1998). After determining the
number of valid responses given by each participant (SOM), points were averaged to
form a final CET score, which then was standardized.
Manipulation checks. After the dependent measure, participants in both
protocols completed manipulation check items and other task-related reports. They
reported the difficulty and effort required for the manipulation task, which were the key
manipulation check items. Participants also reported how much the manipulation task
made them feel frustrated, fatigued, and tired. Two additional items assessed
participants’ motivation for the dependent measure. They reported how motivated they
felt during the task and how much they wanted to do well on it. All items were rated on
Likert scales from 1 = not at all to 7 = very.
Individual differences. Trait measures were administered last. Items were
averaged to create composite scores.
Participants completed the 12-item Decision-Related Action Orientation subscale
of the HAKEMP (Kuhl, 1994), which measures whether people take action to work on
tasks or tend to put them off (M = 5.78; SD= 2.85;  = 0.71). Sample item: “When I
know I must finish something soon: A) I have to push myself to get started, or B) I find it
easy to get it done and over with” (participants receive 1 point for each action-orientated
option they chose). Next, they completed the 13-item Trait Self-Control Scale (Tangney
et al., 2004), which measures dispositional self-control tendencies (M = 3.23; SD = 0.63;
 = 0.81). Sample item: “I am good at resisting temptation” (1 = not at all like me; 5 =
very much like me). Last, participants completed the 6-item Strenuous Mental Activity

Paradigmatic Depletion Replication 12
subscale of the Implicit Theories about Willpower Scale2 (Job et al., 2010), which
measures whether people think that self-control is a limited resource (M = 4.18, SD =
0.90;  = 0.84; n = 2452). Sample item: “After a strenuous mental activity, your energy
is depleted and you must rest to get it refueled again” (1 = strongly agree; 6 = strongly
disagree; scores were reversed such that higher numbers indicated stronger beliefs that
self-control is a limited resource).
Data and Analytic Procedures
Advisory board. We formed a methodological and statistical Advisory Board.
Members were selected for being experts in open data, replications, or statistical
techniques (i.e., frequentist and Bayesian meta-analyses).3 Advisory Board members
provided invaluable help in formulating hypotheses, suggesting analytical models,
analyzing data, and preregistering the project.
Dataset procedures. After labs completed data collection, they sent a dataset to
a member of the organizing team who previously had been uninvolved in depletion
research. This scholar’s role was to receive, merge, and otherwise handle the data,
thereby ensuring that the lead authors (KV and BS) would not have access to the data
until after the analysts4 from the Advisory Board performed analyses.
Two steps were taken to ensure data integrity. One involved blinding the data
prior to analyses. The data handler switched the names of the columns containing the
main dependent measures with another column before passing the dataset off to the
2 Due to formatting errors, some laboratories omitted the Implicit Theories of Willpower Scale, resulting in
different sample sizes.
3 Advisory Board members were Dolores Albarracín, Will Gervais, Quentin Gronau, Sophie Lohmann, EJ
Wagenmakers, Jake Westfall, and Wendy Wood.
4 Dolores Albarracín, Quentin Gronau, Sophie Lohmann, and EJ Wagenmakers

Paradigmatic Depletion Replication 13
analysts. Thus, lead authors did not have access to the data until after the analysts did,
nor did they conduct analyses. After initial analyses were conducted, the dataset was
unblinded. As a second step the analysts conducted all of the hypothesis tests and
populated the data displays.
Frequentist statistics. Prior to excluding participants according to preregistered
criteria, we standardized all outcome variables and centered all continuous moderators
for ease of interpretation. For the frequentist approach, we conducted random-effects
(RE) meta-analyses on each laboratory’s Cohen’s d effect size, representing the
difference between the non-depletion and depletion conditions. (Fixed-effects [FE]
analyses are reported in parentheses.) Larger effect sizes indicate a stronger ego
depletion effect (i.e., lower scores on the dependent measures of self-control). Analyses
were conducted in R (R Core Team, 2019). Moderators were tested using multi-level
linear models in the individual-level analyses (Bates et al., 2015) and using random-
effects meta-regression for meta-analytic analyses at the lab level (Viechtbauer, 2010).
Bayesian statistics. Bayes factors addressed the evidentiary basis of the
depletion effect. To address the question, “Does the effect exist?” we pitted a point-null
hypothesis, which states that the effect is absent, against an informed one-sided
alternative hypothesis centered on depletion effect of δ = 0.30 with a standard deviation
of 0.15. The preregistered alternative hypothesis estimate was based on effect sizes
from two prior large-scale depletion investigations: Hagger et al.’s (2010) meta-analysis,
which reported an overall effect size of d = 0.62, and Hagger et al.’s (2016) registered
replication report, which reported an overall effect size of d = 0.04. We split the
difference and arrived at δ = 0.30 (SD = 0.15). In line with the one-sided nature of the

Paradigmatic Depletion Replication 14
depletion hypothesis, the prior was truncated at zero to allow only positive effect size
values. We computed Bayes factors (e.g., Jeffreys, 1939) to quantify the relative
support for the informed ego depletion versus the point-null hypothesis.
Subsequent analyses provided information on the size of the ego depletion effect
after having seen the data. Posterior distributions for the effect size addressed the
question “Assuming that there is an effect, how large is it?”
We conducted a Bayesian meta-analysis on the t-test of the depletion effect from
each laboratory. In contrast to the classical approach, this approach used Bayesian
model averaging, which combines the results of fixed- and random-effect models
according to their plausibility given the data (Gronau et al., 2017; Scheibehenne,
Gronau, Jamil, & Wagenmakers, 2017). We quantified the model-averaged evidence for
an effect and identified a model-averaged posterior distribution for the meta-analytic
effect size. For this meta-analysis, we specified the informed prior for effect size and a
prior distribution for between-study heterogeneity. We used a preregistered informed
Beta (1,2) distribution for the between-study standard deviation (van Erp, Verhagen,
Grasman, & Wagenmakers, 2017).
Results
The results section reports preregistered and thus confirmatory analyses on the
reduced sample (i.e., after excluding participants on the basis of preregistered criteria;
Table 2). First, we report results on the manipulation check items using both frequentist
and Bayesian approaches. Next are tests of whether the depletion manipulations
affected subsequent self-control using both frequentist and Bayesian approaches. This

Paradigmatic Depletion Replication 15
section is followed by frequentist statistical tests of proposed moderator variables.
(Bayesian analyses were not available for moderator tests.)
Results are presented such that higher numbers indicate results in line with
hypotheses. That is, for the manipulation checks, higher numbers indicate that depletion
condition participants reported stronger feelings than did non-depletion participants. For
the main hypothesis-testing results, higher numbers indicate worse performance on the
outcome task in the depletion (versus non-depletion) condition, which is taken as
evidence of a depletion effect.
Exploratory tests can be found in the SOM. They include manipulation checks,
hypothesis tests using both Bayesian and frequentist approaches, and moderation
analyses. Most of the exploratory analyses are on the full sample (that is, without
excluding any participants).
Manipulation Checks
Frequentist analyses. Meta-analyses were conducted to check the
effectiveness of the depletion task (Table 3). Ratings of how much effort the
manipulation task required and its difficulty formed an internally consistent scale
(Spearman-Brown coefficient = .79) and therefore were averaged into a single index of
effort; we preregistered the effort index as the primary manipulation check. As
predicted, participants in the depletion condition reported that the manipulation task was
more difficult and effortful than did participants in the non-depletion condition. Although
scores on the effort index showed substantial heterogeneity across laboratories, with
effect sizes ranging from d = 0.08, 95% CI [-0.65, 0.81] to d = 4.57, 95% CI [3.21, 5.94],
there was evidence that the manipulation worked as intended.

Paradigmatic Depletion Replication 16
We tested whether scores on the effort index differed by protocol, coded such
that the intercept (d = 1.76, 95% [1.66, 1.86], I2 = 0%) represents the average effect
across both protocols (-.5 = E-task; .5 = Writing task). We did not expect protocol to
moderate scores on the effort index but preregistered that we would test each protocol
separately if protocol were a significant moderator—which it was (b = 2.61, 95% CI
[2.41, 2.81]). Therefore, we calculated planned contrasts to examine the effect
separately for each protocol. The depletion task was rated as more difficult and effortful
than the non-depletion task in both protocols, but the difference was larger in the writing
task protocol (d = 3.09, 95% CI [2.87, 3.30], I2 = 39.29%) than in the E-task protocol (d
= 0.46, 95% CI [0.34, 0.57], I2 = 0%). These results suggest that the depletion
manipulation was more effortful than the non-depletion manipulation, as intended, and
that one protocol was more effortful than the other.
Reports of how tired and fatigued participants felt after performing the
manipulation task were internally consistent (Spearman-Brown = .90) and, as
preregistered, were averaged to form an index of fatigue. As predicted, the main effect
of depletion condition was significant, such that participants reported more fatigue in the
depletion than the non-depletion condition. Also as expected, participants in the
depletion condition reported feeling more frustrated than did participants in the non-
depletion condition (Table 3; also Tables S4 and S5).
Reports of motivation and wanting to do well on the dependent measure formed
an internally consistent scale (Spearman-Brown = .74). The two items were
standardized and averaged to form a motivation index. We preregistered competing
predictions: (a) that there will be no depletion condition effect (in line with the ego

Paradigmatic Depletion Replication 17
depletion theory) or (b) that depletion condition participants would report lower
motivation than non-depletion participants (in line with Inzlicht & Schmeichel, 2012).
Consistent with (a), there was no difference in self-reported motivation (Table 3; Tables
S4 and S5).
Bayesian analyses. To quantify the predictions under H1, a model-averaged
Bayesian meta-analysis using a one-sided Cauchy prior on effect size μ with mode 0
and scale 0.707 was conducted. Given that the preregistration plans for the primary
outcome variable specified using a Beta(1,2) prior distribution for the between-study
heterogeneity τ, we adopted that approach here. However, in work succeeding the
preregistration we consistently have used an inverse-Gamma prior with shape 1 and
scale 0.15 (e.g., Gronau et al., 2017; van Erp et al., 2017), which we used here as well.
Hence below we report the results both for the Beta prior and for the inverse-Gamma
prior. Noticeable differences between these priors are due to the fact that the Beta prior
does not allow values for τ higher than 1, contrary to what the data suggested.
For the effort index, BF(Beta prior) > 1.797693e+308 and BF(inverse-Gamma
prior) = 1,123,563; for feelings of frustration, BF(Beta prior) = 2,727,844,064 and
BF(inverse-Gamma prior) = 85,152; and for the fatigued index, BF(Beta prior) = 5.68
and BF(inverse-Gamma prior) = 6.13. For motivation, both priors yielded the same
Bayes factor in favor of the null hypothesis, BF+0 = 0.029 (in other words, BF = 34.48 in
favor of the null). These results provide clear evidence that, overall, the depletion
manipulations increased feelings of effort and frustration and moderate evidence that
depletion increased feelings of fatigue. As for self-reported motivation, we found that the
depletion manipulations did not affect it.

Paradigmatic Depletion Replication 18
Performance on the Outcome Tasks: Hypothesis Test Analyses
Frequentist analyses. Contrary to predictions, meta-analytic results showed that
the standardized mean performance difference between the depletion and the non-
depletion conditions was not statistically significant, d = 0.06 [-.02, 0.14] (see Table 4;
Figure 1).
Bayesian analyses. The presence of a depletion effect was then tested using
Bayesian analyses. In these analyses, a Bayes factor of BF+0 = 10 would indicate that
the data are 10 times more likely under the informed alternative hypothesis, which is
centered on δ = 0.30, than under the point-null hypothesis. Correspondingly, a BF+0 =
1/10 would indicate that the data are 10 times more likely under the point-null
hypothesis than under the informed alternative hypothesis.
The meta-analytic Bayes factors quantify the overall evidence in favor of either
the informed alternative hypothesis or the point-null hypothesis across all laboratories
simultaneously. The meta-analytic Bayes factor of focal interest is the model-averaged
one (Figure 2). For comparison, we displayed the meta-analytic Bayes factor for the
fixed- and random-effect models separately. All three meta-analytic Bayes factors
showed close agreement and favored the point-null hypothesis to approximately the
same degree (Figure 2). The model-averaged Bayes factor indicated that the data are
4.4 times more likely under the point-null hypothesis (which states that the effect is
absent) than under the one-sided informed alternative hypothesis of a depletion effect

Paradigmatic Depletion Replication 19
(Figure 3).5 This Bayes factor value indicates moderate evidence in favor of the point-
null hypothesis according to the classification scheme proposed by Jeffreys (1939).
Posterior distributions. All posterior distributions supported only positive effect
size values, which follows from an a priori decision to use an informed prior that does
not allow negative effect size values. When examining individual laboratories' data,
many showed a shift toward updating the effect size toward zero, indicating that even if
the effect was not zero, it was likely smaller than the expected d = 0.30.
Assuming a nonzero effect, an inspection of the data across the individual
laboratories did not permit strong conclusions about the size of the effect because of the
large uncertainty associated with individual laboratories’ effect sizes. To account for
findings from all laboratories simultaneously, we considered the results of the model-
averaged meta-analysis. We concluded that the data have shifted our beliefs about the
effect size of ego depletion from one centered around δ = 0.30 toward zero. The
posterior median was 0.08, 95% CI [0.01, 0.16] (Figure 3).
Potential Moderators
Protocol type. We first checked whether outcomes varied by protocol (the
specific combination of manipulation and dependent measures). The dependent
measure was performance, and protocol type was contrast-coded (-0.5: E-task, 0.5:
Writing task) so that the intercept represented the average effect across both protocols.
A meta-analytic test (main effect random-effects [RE] model: d = 0.06, 95% CI [-0.02,
0.14], moderator b = -0.10, 95% CI [-0.26, 0.06]) indicated that protocol type was not a
5 Our recent work uses an inverse-Gamma distribution, which we applied to the confirmatory depletion
hypothesis test. Results did not appreciably change compared to those using the Beta distribution (Figure
4). Using an inverse-Gamma prior for between-study heterogeneity tau, the model-averaged meta-
analytic BF+0 = 0.228 or, expressed in favor of the null, BF0+ = 4.39.

Paradigmatic Depletion Replication 20
significant moderator, suggesting that the magnitude of the effect did not differ across
protocols.
The total score on the figure tracing task was the combination of the number of
puzzle sheets participants used (as an indicator of attempts) and time spent on the task.
For the combined measure of figure tracing duration and attempts in the E-task
protocol, we found a non-significant effect of condition (Table 4).
We preregistered our intention to examine separately the two components of the
E-task protocol’s performance outcome (i.e., the figure tracing task). In prior work, the
two components correlated highly and showed parallel effects (e.g., Fennis, Janssen, &
Vohs, 2009; Vohs et al., 2008). In the current data, however, the two figure tracing
components exhibited only a moderate correlation. r = .39, 95% CI [0.35, 0.43].
Examining the two components separately, the effect of condition on number of
attempts was not statistically significant (unstandardized descriptives: non-depletion
condition M = 19.87, SD = 9.92; depletion condition M = 19.36, SD = 10.41; Table 4).
In contrast, there was a significant effect on duration (RE: d = 0.15, 95% CI [0.02,
0.29]; fixed-effects [FE] model: d = 0.13, 95% CI [0.02, 0.25]; Table 4). Participants in
the depletion condition gave up about 27s sooner on the figure tracing task than
participants in the non-depletion condition (unstandardized descriptives: non-depletion
condition M = 1012.20s, SD 266.30; depletion condition M = 985.10s, SD = 283.52).
We preregistered additional moderation tests of manipulation check ratings and
individual differences. We did not, however, specify the statistical approach we would
use, so we refer to them as exploratory analyses and report them in the SOM. The
results showed that the only variable to act as a significant moderator was the self-

Paradigmatic Depletion Replication 21
reported index of fatigue. Performance was worse in the depletion (compared to non-
depletion) condition among participants who reported being more fatigued by the
manipulation task (Table S2, Figure S4).
Discussion
We tested an ego depletion hypothesis on more than 3500 participants in 36
independent laboratories, which used one of two experimental protocols. The results
lead us to conclude that depletion is not as reliable or robust as previously assumed.6
Confirmatory frequentist analyses indicated that the two conditions did not differ,
although outcome performance was directionally worse in the depletion condition
compared to the non-depletion condition (d = 0.06; Table 4). Confirmatory Bayesian
tests found more evidence for the absence than presence of an ego depletion effect
(Figure 3). Hence, preregistered analyses did not show a depletion effect.
Our preregistered exclusion criteria led us to exclude data from nearly a third of
the overall sample, which exceeded expectations. Frequentist exploratory analyses
using the full sample of participants (without exclusions) found a statistically significant
but small (d = 0.08; Table S1, Figure S1) depletion effect. Comparable Bayesian
analyses showed no clear evidence to support or refute the informed alternative
hypothesis in support of a depletion effect (Figure S2, Figure S3).
Moving back to frequentist tests, the findings suggested that self-reported fatigue
acted as a moderator. The more that depletion condition participants felt fatigued, the
worse their subsequent self-control (Table S2, Figure S4). This pattern is congruent with
prior evidence regarding the role of subjective fatigue in the ego depletion effect (e.g.,
6 This language reflects our preregistered conclusion if analyses showed a non-significant result.

Paradigmatic Depletion Replication 22
Clarkson et al., 2010). There was no statistically significant moderation by self-reported
effort, frustration, or motivation. We also tested a host of plausible trait moderators that
evinced little predictive value.
Interpretations, Implications, and Integrations
How do these findings inform an understanding of ego depletion? We see
several potential interpretations of these findings. One is that there is no depletion
effect. The preregistered analyses support this interpretation (Table 4, Figures 1 and 2).
A second perspective is that the reliability of the effect is still unknown, supported
by the inconclusive exploratory Bayesian results on the full sample. Both the null
hypothesis and informed alternative hypothesis, specifying a 70% probability that the
effect size falls between δ = 0.15 and δ = 0.45, fit the full-sample data about equally well
(Figures S2 and S3).
A third perspective is that there may be a reliable, but small, depletion effect. The
exploratory frequentist analyses on the full sample support this interpretation (Table S1,
Figure S1). Exploratory analyses showing significant moderation by self-reported fatigue
further suggest that depletion effects may be conditional (Table S2, Figure S4).
There are several implications of these views for future research. First, some
analyses hinted at a small depletion effect, but those were exploratory analyses and
hence confidence in them should be low until they are replicated. Second, large
participant samples will be needed to reliably detect a depletion effect. To be sure,
manipulations vary in strength and dependent measures in sensitivity (which, in part, is
why we used a paradigmatic approach). As seen here, descriptively, the E-task protocol

Paradigmatic Depletion Replication 23
showed a bigger depletion effect than the writing-task protocol.7 Regardless, neither
protocol yielded large effects.
Further, researchers may consider the role of self-reported fatigue. The current
project found larger depletion effects among participants reporting more fatigue after the
manipulation task, similar to earlier findings (e.g., Clarkson et al., 2010). Measuring
fatigue, using manipulations that feel fatiguing, or applying manipulations known to
decrease fatigue (Sripada, Kessler, & Jonides, 2014) may be worthwhile.
The current project was inspired by a previous multi-lab study of ego depletion,
which reported an effect size of d = 0.04 (Hagger et al., 2016). A recent multi-lab test
reported an effect of d = 0.10. That study tested the same individual differences as did
we, finding little in the way of moderation (Dang et al., in press).
All told, the results from two multi-lab investigations compare similarly to the
current results. The general conclusion is that the depletion effect is likely small
(including zero) and not substantially moderated by theoretically-relevant dispositional
differences.
Paradigmatic Replication Approach Revisited
We introduced a number of changes to the way that multi-lab replication projects
typically are run, innovations aimed at increasing the knowledge gained from the
project. The project used two protocols (sets of independent and dependent variables)
that were not drawn from any specific study. Rather, the aim was to use permutations
that befit the essence of depletion theory (that is, were paradigmatic) while allowing for
7 By referring to protocols by their manipulation tasks, we do not mean to imply those tasks necessarily
made the difference. The dependent measures may have been differentially sensitive or other factors
were at work.

Paradigmatic Depletion Replication 24
the possibility that the protocols may evince different outcomes and thus inform future
work.
We used crowdsourcing — among topic experts and the laboratories that would
be enacting them — for which manipulation and outcome tasks to use. Topic experts
initially created lists of possible tasks. Subsequently, laboratories indicated whether they
could execute the tasks and whether they would provide good tests of the hypothesis,
which formed the basis of the tasks used.
Crowdsourcing in this way has advantages. Proponents of an effect can help
identify tasks that are road-tested and reflect the theory — and ideally cut down on
concerns about the methods after results are known. For replication attempts to move
the field forward, it will be helpful if proponents see them as credible. Further,
replications that are not direct copies of existing studies may benefit from evaluations by
participating scholars to determine the tasks likely to provide good tests of the
hypothesis.
Video recordings of experimenters were another novel aspect. Potential
variability in execution can be a concern for multi-site projects, but also an opportunity
for insights into what contributes to replication outcomes.
We followed open science practices and introduced a few of our own. The project
was preregistered and data blinded for analysts’ initial hypothesis tests. Outside experts
provided methodological and statistical advice, another use of crowdsourcing. We put
multiple layers between the project organizers and the data. Laboratories sent their data
to an independent scholar who then sent them to analysts. Project organizers received
the data only after initial analyses were done (Table 1).

Paradigmatic Depletion Replication 25
The goals of these implementations were two-fold. One was to conduct the
project in a high-quality, high-integrity manner. The other was to inspire future
replication projects. If replication studies are going to be a mainstay of the field, then
having more replication models can enable more suitable, relevant, and informative
tests.
All studies have their limitations, and this one is no exception. We undertook a
challenge by aiming to retain a large sample while introducing a new approach to
replication studies to test a controversial hypothesis, the results of which were likely to
have implications for the field (and for some of the authors).
One part of the project that incurred many hiccups was the preregistration,
namely in terms of preregistered analyses versus analyses that were most suitable for
testing the hypotheses. For instance, we did not preregister analyses at the participant
level for participant-level effects (e.g., moderation by psychological states and individual
differences) but should have. We could have made better preregistration choices.
The criteria for excluding participants’ data also deserves mention. They were
chosen with the aim of ensuring that the manipulations would elicit the intended
psychological states, but we did not anticipate that they would lead to excluding nearly a
third of the sample. In hindsight, perhaps we should have preregistered that we would
relax some exclusion criteria if the exclusion rate exceeded a certain percentage of the
total sample (e.g., 20%). More extensive pilot testing also may have helped to identify
issues with the exclusion criteria prior to data collection. Additional development and
validation of exclusion criteria in ego depletion research (and beyond) is sorely needed.

Paradigmatic Depletion Replication 26
A last consideration is the possibility that different procedures would have yielded
stronger evidence of ego depletion. Many different tasks have been used to
operationalize both the independent and dependent variables in depletion studies,
among which we used only four. At present, theoretical accounts generally do not
indicate whether or how depletion depends on the specific manipulation or outcome
tasks, but proponents of such an idea may consider high-powered, preregistered tests
of that hypothesis.
Conclusion
Ego depletion is one of the most storied and, of late, questioned effects in
psychological science. We embarked on a large-scale replication using two methods to
manipulate self-control usage and subsequently measure it, thereby establishing the
paradigmatic replication approach, a new way of testing the robustness of theoretical
phenomena.
In terms of results, both the frequentist and Bayesian preregistered analyses
showed no depletion effect. Exploratory Bayesian tests were inconclusive. Exploratory
frequentist analyses on the full sample (without exclusions) showed a small depletion
effect as well as moderation by fatigue, with a larger effect observed among depletion
condition participants who reported greater fatigue. Those doubtful of the theory may
see the findings as damning for the ego depletion hypothesis. Those inclined toward the
theory may retort that some exploratory results suggest that there may be an effect,
especially under certain conditions, although this conclusion must remain tentative.
Whether a depletion effect matters is a related but different issue. Funder and
Ozer (2019) proposed that small effects (in terms of effect size) are probably more

Paradigmatic Depletion Replication 27
realistic than large effects, and that their value should be judged in light of the
importance of the phenomenon. On that score, understanding how self-control operates
seems worthy indeed.

Paradigmatic Depletion Replication 28
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Paradigmatic Depletion Replication 34
Table 1. Paradigmatic Replication Approach: Goals, Strategies, and Rationales
Goals
Strategies
Rationale(s)
Formulation stage
Identify
representative
tasks
Crowdsource with area experts
•
Create list of possible IV and DV tasks
deemed paradigmatic for testing the
hypothesis
Collect diversity of possible methods
Get help from experienced researchers in
topic area
Select sound
methods
Prioritize the operationalization of
psychological states, not whether a
specific study replicates
Tasks need not mimic a published study
Not tied to other scholars’ choices and
methods
Can adjust for project goals, labs, participant
characteristics
Boost commitment
from participating
laboratories
Crowdsource with participating labs
•
Assess whether tasks are deemed to be
executable and effective
•
Gather preferences for possible tasks
Winnow down the set of possible tasks with
scholars who will be executing the study
Enable scholars who will be executing the
study to have some say in the methods
Ensure rigorous
design and
analysis choices
Assemble methods and statistics Advisory
Board
•
Understand implications of
methodological and statistical options
before preregistration
•
Perform main hypothesis-testing
analyses
•
Consider using both frequentist and
Bayesian approaches
Open science practices
Expand skill set beyond what project leaders
bring
Increase information value of results
Study preparation stage
Public statement(s)
of intent
Preregistration of hypotheses, methods,
participant exclusion criteria, and specify
conclusions given different possible
results
Open science practices
Reduce researcher degrees of freedom
Methods testing
and practice
Video recordings of how to conduct the
study
Write and review scripts for experimenters
to follow
Reduce variation in procedural execution
Team building
Virtual meetings with all members of
participating labs
Address questions, reinforce procedural
details, and bridge gap between project
leaders and data collection labs

Paradigmatic Depletion Replication 35
Post-data collection stage
Ensure data
integrity
Labs send data to independent handler.
Data handler:
•
Merges data files
•
Blinds outcome measures
•
Sends master dataset to Advisory
Board
Project managers do not receive data until
initial analyses are done
Ensure data integrity and increase
confidence in the results
Increase
information value
of data
After designated data analysts conduct
confirmatory tests, lead authors can
suggest exploratory analyses
Follow up on relevant hypothesis tests
Perform tests that were unanticipated or
underspecified in preregistration
Note: IV stands for independent variable. DV stands for dependent variable.

Paradigmatic Depletion Replication 36
Table 2. Exclusion Counts for Each Preregistered Criteria by Protocol
Criteria
E-Task Protocol
Writing Task Protocol
Errors on last completed E-task paragraph
(Page 1)
159
NA
Errors on last completed E-task paragraph
(Page 2)
133
NA
Knew puzzles were unsolvable
42
NA
Used few words in story
NA
7
Used forbidden letters in story
NA
83
Invalid responses on CET
NA
0
Non-native speakers
95
223
First three participants
111
96
Used phone during study
79
63
Belligerent
2
3
Distressed/distraught
9
7
Disruption or other unanticipated deviation
19
11
Other exclusions
174
34
TOTALS
823
527
Note: NA stands for not applicable. In total n = 1068 were excluded in accordance with
preregistered exclusion criteria. Some participants (n = 237) failed multiple exclusion
criteria. See SOM for additional details.

Paradigmatic Depletion Replication 37
Table 3. Manipulation Checks: Descriptive Statistics and Frequentist Meta-Analytic
Tests of Experimental Conditions
M (SD)
Variable
Depletion
Non-depletion
FE Average
CI
RE
Average
CI
I2
Effort index
4.52 (1.74)
2.59 (1.11)
1.31***
[1.22,1.40]
1.64***
[1.18, 2.09]
95.65%
Frustration
3.81 (2.01)
2.04 (1.39)
0.99***
[0.90, 1.08]
1.14***
[0.77, 1.50]
93.95%
Fatigue index
3.29 (1.53)
2.89 (1.53)
0.25***
[0.17, 0.33]
0.24**
[0.07, 0.41]
76.60%
Motivation index
5.25 (1.20)
5.14 (1.27)
0.05
[-0.03, 0.13]
0.04
[-0.06, 0.13]
30.33%
Note: N = 2463 (k = 36), with the exception that frustration ratings were missing for two
participants. Sample size varies from total sample size due to missing data. Condition
coded such that 0 = non-depletion, 1 = depletion condition. Higher numbers indicate
that participants in the depletion condition reported stronger feelings than participants in
the non-depletion condition. All tests were confirmatory (preregistered). Means and SD
are from unstandardized scales; range 1 (not at all) to 7 (very). FE indicates fixed-
effects models; RE indicates random-effects models. CI indicates 95% confidence
intervals. ** p < .01; *** p < .001

Paradigmatic Depletion Replication 38
Table 4. Depletion Effect: Frequentist Meta-Analyses
Random-effects
meta-analysis
Fixed-effects
meta-analysis
DV
N
d
CI
I2 %
d
CI
Overall depletion effect
2461
0.06
[-0.02, 0.14]
2.54
0.06
[-0.02, 0.14]
Overall figure tracing performance
1216
0.12
[-0.01, 0.24]
15.16
0.11
[-0.00, 0.22]
Figure tracing duration
1216
0.15 *
[0.02, 0.29]
28.46
0.13 *
[0.02, 0.25]
Figure tracing attempts
1217
0.05
[-0.06, 0.17]
0
0.05
[-0.06, 0.17]
Cognitive Estimation Test
1245
0.01
[-0.10, 0.12]
0
0.01
[-0.10, 0.12]
Note: Sample sizes vary due to missing data. For overall depletion effect analyses, k = 36; figure tracing
analyses, k = 20; Cognitive Estimation Test analyses, k = 16. Condition coded such that 0 = non-
depletion, 1 = depletion condition. Higher numbers indicate evidence of a depletion effect (i.e., self-
control was worse in the depletion condition). DV stands for dependent variable. FE indicates fixed-
effects models; RE indicates random-effects models. CI indicates 95% confidence intervals. * p < .05

Paradigmatic Depletion Replication 39
Figure 1. Forest Plot of Performance Outcome by Laboratory. The box plots and
numerical values illustrate the same effect size estimates. For the plots, the size of the
box represents its weighted contribution to the overall effect and its whiskers display
95% CIs. The dotted line represents a zero effect size. Numerical values show
standardized mean differences between depletion and non-depletion conditions
expressed in Cohen’s d (with 95% CIs). The diamond is the overall meta-analytic effect
derived from a random-effects model.

Paradigmatic Depletion Replication 40
Figure 2. Bayesian Forest Plot of Performance Outcome by Laboratory. The values
listed under BF+0 indicate relative support for the depletion hypothesis versus a
hypothesis that there is no effect. Diamonds indicate overall effect sizes from meta-
analytic models using fixed-effects, random-effects, and one that combined both
approaches.

Paradigmatic Depletion Replication 41
Figure 3. Tests of the Model-Averaged Meta-Analytic Effect Size Posterior and Bayes
Factor. The dotted line indicates the informed prior effect size distribution and the solid
line indicates the model-averaged meta-analytic posterior effect size distribution.
Roughly-speaking, the peak of the shape indicates the likelihood of the effect size and
its width indicates variance.

Paradigmatic Depletion Replication 42
A Multi-Site Preregistered, Paradigmatic Test of the Ego Depletion Effect
SUPPLEMENTARY ONLINE MATERIALS
Contents
Exploratory Analyses
Depletion effect
Frequentist analyses
Figure S1
Table S1
Bayesian analyses
Figure S2
Figure S3
Moderators of the depletion effect
Protocol type
Table S1
States and traits
Table S2
Figure S4
Secondary moderator analyses
Table S3
Full sample manipulation checks
Table S4
Table S5
Additional sample and methodological details
Recruitment
Materials and procedures
E-task protocol
Story-writing protocol
Videos of experimenters
Exclusions
E-task protocol
Story-writing protocol
Both protocols
References

Paradigmatic Depletion Replication 43
Exploratory Analyses
Depletion effect
Frequentist analyses. Analyses based on the full dataset were not
preregistered, but the rate of exclusions far exceeded expectations. We therefore
decide to conduct exploratory analyses using the full dataset.
Meta-analyses of the full dataset revealed a small significant effect in line with
predictions (RE: d = 0.08, 95% CI [0.01, 0.15]; FE: d = 0.07, 95% CI [0.01, 0.14]; I² =
11.69%; Figure S1). This effect was observed for both random- and fixed-effects
models. Experimental protocol did not appear to moderate the depletion effect, RE:
intercept d = 0.08 [0.00, 0.15], moderator b = -0.07 95% CI [-0.22, 0.07], I² = 13.90%.
We also tested whether there was evidence of an overall depletion effect using
multilevel regression approaches that nested the individual-level data within laboratories
in random-intercept mixed models. In the reduced sample (excluding 1068 participants,
following preregistered rules), task performance did not differ by depletion condition, b =
0.09 CI [-0.01, 0.19]. In the full sample (when participants marked for exclusion were
included), the effect of depletion condition was statistically significant but small (Table
S1).

Paradigmatic Depletion Replication 44
Figure S1. Forest Plot of Performance Outcome by Laboratory: Full Sample. The box
plots and numerical values illustrate the same effect size estimates. For the plots, the
size of the box represents its weighted contribution to the overall effect and its whiskers
display 95% CIs. The dotted line represents a zero effect size. Numerical values show
standardized mean differences between depletion and non-depletion conditions
expressed in Cohen’s d (with 95% CIs). The diamond is the overall meta-analytic effect
derived from a random-effects model.

Paradigmatic Depletion Replication 45
Table S1. Depletion Effect: Exploratory Frequentist Meta-Analyses and Multi-Level
Models
Random-effects
meta-analysis
Fixed-effects
meta-analysis
Multi-level regression
DV
N
d
CI
I2 %
d
CI
b
CI
Overall depletion effect
3524
0.08 *
[0.01, 0.15]
11.69
0.07 *
[0.01, 0.14]
0.11 *
[0.02, 0.20]
Overall figure tracing
performance
1847
0.12 *
[0.01, 0.23]
27.23
0.10 *
[0.01, 0.20]
0.18 *
[0.03, 0.32]
Figure tracing duration
1847
0.14 *
[0.01, 0.27]
46.83
0.12 *
[0.03, 0.21]
0.11 *
[0.02, 0.20]
Figure tracing attempts
1848
0.06
[-0.04, 0.15]
0
0.06
[-0.04, 0.15]
0.07
[-0.02, 0.15]
Cognitive Estimation Test
1677
0.04
[-0.06, 0.13]
0
0.04
[-0.06, 0.13]
0.04
[-0.06, 0.13]
Note: Results pertain to the entire sample. Sample sizes vary due to missing data. For
overall depletion effect analyses, k = 36; figure tracing analyses, k = 20; Cognitive
Estimation Test analyses, k = 16. Condition coded such that 0 = non-depletion, 1 =
depletion condition. Higher numbers indicate evidence of a depletion effect (i.e., self-
control was worse in the depletion condition). DV stands for dependent variable. FE
indicates fixed-effects models; RE indicates random-effects models. CI indicates 95%
confidence intervals. Multi-level models nested participants’ data within labs and used a
random intercept for labs. * p < .05

Paradigmatic Depletion Replication 46
Bayesian analyses. We next turn to the model-averaged meta-analytic Bayes
factor (which corresponds closely to the fixed- and random-effects Bayes factors; Figure
S2). The results indicated that the data are 1.33 times more likely under the point-null
hypothesis (which states that the effect is absent) than under the one-sided informed
alternative hypothesis (which states that the effect is present), suggesting that two
models predict the data almost equally well. Although the full sample data provided no
basis for shifting beliefs towards or away from either hypothesis, the posterior
distribution addressed the magnitude of the effect if it is present.
To take into account the findings from all laboratories simultaneously, we
considered the results of the model-averaged meta-analysis. Figure S3 displays the
model-averaged meta-analytic posterior for effect size as a solid line; the dotted line
indicates the informed prior distribution. As shown, the data have shifted our beliefs
about the effect size of ego depletion toward zero. Specifically, the posterior median
was 0.087 with a central 95% credible interval ranging from 0.023 to 0.152 (Figure S3).

Paradigmatic Depletion Replication 47
Figure S2. Bayesian Forest Plot of Performance Outcome by Laboratory: Full Sample.
The values listed under BF+0 indicate relative support for the depletion hypothesis
versus a hypothesis that there is no effect. Diamonds indicate overall effect sizes from
meta-analytic models using fixed-effects, random-effects, and one that combined both
approaches.

Paradigmatic Depletion Replication 48
Figure S3. Exploratory Tests of Model-Averaged Meta-Analytic Effect Size Posterior
and Bayes Factor: Full Sample. The dotted line indicates the informed prior effect size
distribution and the solid line indicates the model-averaged meta-analytic posterior
effect size distribution. Roughly-speaking, the peak of the shape indicates the likelihood
of the effect size and its width indicates variance.

Paradigmatic Depletion Replication 49
Moderators of the Depletion Effect
Protocol type. The main article reports confirmatory meta-analytical tests on the
reduced sample (after preregistered exclusions; see Table 4). Here, we supplant those
with parallel, exploratory results on the full sample and multi-level regressions on both
samples.
Full sample: We examined the two components of the figure tracing task
separately, the number of sheets participants used (as an indicator of attempts) and
time spent working on the task (in seconds). Examining the two components separately,
the effect of depletion condition on number of attempts was not statistically significant
(Table S1; unstandardized descriptives: non-depletion condition M = 19.71, SD = 10.05;
depletion condition M = 19.09, SD = 10.21).
There was a significant effect of depletion condition on duration in the full sample
(unstandardized descriptives: non-depletion condition M = 988.87s, SD = 283.95;
depletion condition M = 960.03s, SD = 298.52). These exploratory analyses showed
that participants in the depletion condition gave up on the figure tracing task around 28s
sooner than participants in the non-depletion condition (Table S1).
For the combined measure of figure tracing duration and attempts in the E-task
protocol, there was a statistically significant effect in the full sample, as judged by both
the meta-analytic and multi-level regression approaches. Participants in the depletion
condition had lower figure tracing scores than did participants in the non-depletion
condition (Table S1).
We conducted meta-analytic and multi-level analyses within the writing task
protocol, which used the Cognitive Estimation Test (CET) as the performance measure.

Paradigmatic Depletion Replication 50
The results were non-significant (unstandardized descriptives: non-depletion condition
M = 1.32, SD = 0.23; depletion condition M = 1.31, SD = 0.24; Table S1).
Reduced sample. Multi-level regression models analyzed the reduced sample’s
performance within each protocol. For overall figure tracing scores, the effect of
condition was not significant, b = 0.17, 95% CI [-0.01, 0.34].
As mentioned, that score has two elements. Breaking them down, the effect of
condition on number of attempts was not statistically significant (b = 0.06, 95% CI [-.05,
0.17]; unstandardized descriptives: non-depletion condition M = 19.87, SD = 9.92;
depletion condition M = 19.36, SD = 10.41).
As in the full sample, the effect of depletion condition on duration was significant
in the reduced sample (b = 0.11, 95% CI [.01, 0.21]; unstandardized descriptives: non-
depletion condition M = 1012.20s, SD 266.30; depletion condition M = 985.10s, SD =
283.52).
A last set of exploratory analyses regarded the depletion manipulation’s effect on
CET performance. As in the full sample, the effect of depletion condition was non-
significant in the reduced sample (b = 0.01, 95% CI [-.09, 0.12]; unstandardized
descriptives: non-depletion condition M = 1.34, SD = .23; depletion condition M = 1.34,
SD = .23).

Paradigmatic Depletion Replication 51
States and traits. We also examined whether self-reported states captured by
the manipulation check items (e.g., fatigue, effort) and individual difference measures
(i.e., trait self-control; willpower beliefs; action orientation) acted as moderators of the
depletion effect. Because self-reported traits and states are best modeled as individual-
level data, multilevel regressions were used as opposed to meta-analytic analyses
(Table S2).
The only significant moderator was the fatigue index, which was evident in both
the reduced and full samples. The depletion effect was larger for participants who
reported being more fatigued by the manipulation task.
For the reduced sample (after exclusions), simple-slope analyses revealed that
within the range of the data, the depletion effect was significant in a region from a
standardized score of 0.15 on the fatigue index to the sample maximum of 2.37 (Figure
S4). The magnitude of the depletion effect was b = 0.23, SE = 0.07, p = .001, at the 75th
percentile of fatigue (0.84). For the full sample, the magnitude of the depletion effect at
the 75th percentile of fatigue (0.84) was b = 0.21, SE = 0.06, p < .001.

Paradigmatic Depletion Replication 52
Table S2. Potential Moderators of the Depletion Effect: Frequentist Multi-Level Models
Intercept
Depletion
manipulation
Moderator
Interaction
Moderator
variable
Moderato
r type
Sample
N
b
CI
b
CI
b
CI
b
CI
Protocola
Study
design
Reduced
2461
0.08
[-0.07,
0.23]
0.09
[-0.01,
0.19]
0.02
[-0.28,
0.32]
-0.16
[-0.36,
0.05]
Full
3524
-0.04
[-0.19,
0.10]
0.11*
[0.02,
0.20]
0.06
[-0.22,
0.34]
-0.14
[-0.32,
0.04]
Effort index
Manipulati
on check
Reduced
2461
0.09
[-0.07,
0.24]
0.03
[-0.09,
0.16]
-0.02
[-0.11,
0.07]
-0.07
[-0.22,
0.09]
Full
3523
-0.03
[-0.17,
0.11]
0.04
[-0.07,
0.15]
-0.03
[-0.11,
0.05]
-0.07
[-0.21,
0.06]
Fatigue
index
Manipulati
on check
Reduced
2461
0.10
[-0.05,
0.24]
0.08
[-0.02,
0.18]
-
0.15*
**
[-0.23, -
0.07]
0.18**
[0.07,
0.29]
Full
3523
-0.03
[-0.16,
0.11]
0.09
[-0.00,
0.18]
-
0.15*
**
[-0.21, -
0.08]
0.14**
[0.05,
0.24]
Frustration
Manipulati
on check
Reduced
2459
0.12
[-0.03,
0.27]
0.02
[-0.13,
0.10]
-
0.11*
*
[-0.18, -
0.03]
0.06
[-0.07,
0.19]
Full
3521
-0.00
[-0.14,
0.14]
0.03
[-0.07,
0.13]
-
0.10*
*
[-0.17, -
0.04]
0.03
[-0.08,
0.14]
Action
Orientation
Individual
difference
Reduced
2356
0.08
[-0.07,
0.24]
0.08
[-0.02,
0.19]
-0.12
[-0.43,
0.20]
-0.07
[-0.51,
0.37]
Full
3395
-0.04
[-0.18,
0.11]
0.11*
[0.02,
0.20]
-0.17
[-0.44,
0.10]
-0.03
[-0.42,
0.35]
Implicit
Willpower
Theory
Individual
difference
Reduced
2341
0.05
[-0.10,
0.20]
0.09
[-0.01,
0.19]
0.01
[-0.07,
0.10]
0.02
[-0.09,
0.14]
Full
3315
-0.05
[-0.19,
0.10]
0.09
[-0.00,
0.18]
0.02
[-0.06,
0.09]
0.04
[-0.06,
0.14]
Trait Self-
Control
Individual
difference
Reduced
2444
0.07
[-0.08,
0.22]
0.10
[-0.00,
0.20]
0.00
[-0.12,
0.12]
0.01
[-0.15,
0.17]
Full
3490
-0.05
[-0.19,
0.09]
0.12**
[0.03,
0.21]
-0.01
[-0.11,
0.10]
0.03
[-0.11,
0.17]
Note: These tests are exploratory. Sample sizes vary due to missing data. Condition
coded such that 0 = non-depletion, 1 = depletion condition. Results are raw beta
weights (b) from random-effects multi-level mixed models; CI indicates 95% confidence
intervals. Participants’ data were nested within lab with random intercepts for labs and
separate regression models were used for each moderator. Individual differences
scores were mean-centered. a Contrast-coded, -.5 = E-task, .5 = Writing task. * p < .05

Paradigmatic Depletion Replication 53
Figure S4. Exploratory Test of Moderation of Task Performance by Depletion Condition
and Self-Reported Fatigue: Reduced Sample. The figure represents the interaction of
depletion condition x fatigue scores on task performance with 95% confidence bands.
Task performance was standardized and ranged from -5.54 to 7.05 (only the region
from -1 to 1 is displayed). The fatigue index is an average of standardized ratings of
fatigue and tiredness.

Paradigmatic Depletion Replication 54
Secondary moderator analyses. We tested whether the depletion effect was
moderated by: the number of depletion studies published by the principal investigator
(PI) through 2016 (as counted independently by KV and BS), the number of total
publications by the PI through 2016 (as counted by KV and BS), experimenter behavior
(as rated by two independent coders of the videos submitted by each laboratory), and
laboratory location (North American countries versus other countries). The latter
moderator was chosen because many published depletion studies were conducted in
North America so it was plausible that location might make a difference in the outcome.
The only significant moderator in these analyses was the role of experimenter behavior
in the full sample (Table S3). Experimenter behavior was not a significant moderator in
the meta-analytic results in the full sample or in the meta-analytic or multi-level
regression results in the reduced sample.
Exploratory multi-level regression analyses using the full sample showed an
additional interaction of depletion condition and codings of experimenter behavior on
task performance, b = -0.25, 95% CI [-0.45, -0.05]. The main effect of condition was
significant in this model, b = 0.11, 95% CI [0.02, 0.20], and so was the main effect of
experimenter behavior scores, b = 0.22, 95% CI [0.00, 0.43]. Simple slopes analyses of
the interaction showed that experimenter behavior had no effect on performance in the
non-depletion condition, b = -0.03, SE = 0.11, p = .776, but in the depletion condition,
performance was worse when experimenters’ behavior was rated lower, b = 0.22, SE =
0.11, p = .046. For experimenter behavior scores at the sample median (0.16) or above
(that is, experimenters who were at least moderately professional, at ease, and stuck to
the script), there was no depletion effect, b = 0.06, SE = 0.05, p = .183. Below-average

Paradigmatic Depletion Replication 55
experimenter behavior scores were however related to the magnitude of the depletion
effect, b = 0.18, SE = 0.06, p = .001, at the 25th percentile of experimenter behavior
scores (-0.29).
We preregistered our intention to test moderation of the depletion effect by
experimenters’ awareness of the depletion hypothesis or whether investigators had a
Ph.D. We did not conduct these analyses because we did not solicit experimenters’
knowledge of the depletion hypothesis prior to some laboratories initiating data
collection and because there was very little variance in highest degree obtained. We
also preregistered that we would test whether exclusion of participants based on the
dependent measure differed as a function of depletion condition. The test, however,
turned out to be inapplicable because the exclusion criteria were not set up to enable it.

Paradigmatic Depletion Replication 56
Table S3. Potential Depletion Effect Moderators: Exploratory Frequentist Multi-Level
Models
Intercept
Depletion
manipulation
Moderator
Interaction
Moderator
variable
Sample
N
b
CI
b
CI
b
CI
b
CI
Experimenter
behavior
Reduced
2396
0.08
[-0.07,
0.23]
0.09
[-0.01,
0.19]
0.24*
[0.00,
0.48]
-0.19
[-0.42,
0.04]
Full
3441
-0.04
[-0.18,
0.10]
0.11*
[0.02,
0.20]
0.22*
[0.00,
0.43]
-0.25*
[-0.45, -
0.05]
Depletion
studies count
Reduced
2461
0.08
[-0.09,
0.25]
0.13*
[0.01,
0.24]
-0.00
[-0.02,
0.01]
-0.01
[-0.01,
0.00]
Full
3524
-0.07
[-0.23,
0.09]
0.15*
[0.05,
0.25]
0.00
[-0.01,
0.02]
-0.01
[-0.01,
0.00]
Publication
count
Reduced
2461
0.08
[-0.07,
0.22]
0.09
[-0.01,
0.19]
0.00
[-0.00,
0.01]
-0.00*
[-0.01, -
0.00]
Full
3524
-0.05
[-0.19,
0.09]
0.11*
[0.02,
0.20]
0.00
[-0.00,
0.00]
-0.00
[-0.00,
0.00]
Lab locationa
Reduced
2461
0.12
[-0.16,
0.40]
0.06
[-0.14,
0.25]
-0.06
[-0.39,
0.27]
0.05
[-0.18,
0.27]
Full
3524
-0.05
[-0.31,
0.22]
0.08
[-0.08,
0.25]
0.00
[-0.31,
0.31]
0.04
[-0.16,
0.24]
Note: These tests are exploratory. All moderators are at the level of the lab. Sample
sizes depart slightly from total sample sizes due to missing data. Condition coded such
that 0 = non-depletion, 1 = depletion condition. Participants’ data were nested within lab
with random intercepts for labs and separate regression models were used for each
moderator. Experimenter behavior scores were mean-centered. Depletion studies count
was the number of published depletion studies by each Primary Investigator. Results
are raw beta weights (b) from random-effects multi-level mixed models; CI indicates
95% confidence intervals. a Dummy-coded, 0 = Outside North America, 1 = North
America. * p<.05

Paradigmatic Depletion Replication 57
Full sample manipulation checks
We conducted the same meta-analytic tests reported in the main text on the full
sample of participants (i.e., no exclusions). Using the index of effort and difficulty
ratings, the manipulation worked as intended (Table S4). We tested whether effort
ratings differed by protocol, coded such that the intercept (d = 1.69, 95% [1.59, 1.79], I2
= 36.09%) represents the average effect across both protocols (-.5 = E-task; .5 =
Writing task). As in the confirmatory reduced sample tests, protocol was an unexpected
moderator of manipulation check scores, b = 2.46, 95% CI [2.26, 2.67]. Although the
depletion task was more difficult and effortful than the non-depletion task in both
protocols, the difference was substantially larger in the writing task protocol compared
to the E-task protocol.
We analyzed other task self-reports in a similar manner. The fatigue index
revealed higher scores in the depletion condition than in the non-depletion condition.
Similarly, reports of frustration were higher among depletion compared to non-depletion
participants. Scores on the motivation index again did not differ by condition (Tables S4
and S5).
Exploratory tests of whether the manipulation check reports were moderated by
protocol revealed some unanticipated patterns (Table S5). Reports on the effort index
were moderated by protocol for both samples. For the reduced sample, that test was
preregistered as it comprised the primary check of the manipulation (Table 3 in the main
article). Protocol moderated scores on the fatigue index, such that in the writing task
protocol, participants in the depletion condition reported being more fatigued than
participants in the non-depletion condition, whereas in the E-task protocol, participants

Paradigmatic Depletion Replication 58
in the non-depletion condition reported being more fatigued than participants in the
depletion condition. The latter pattern runs contrary to expectations and the published
literature (e.g., Baumeister, Bratslavsky, Muraven, & Tice, 1998; Legault, Green-
Demers, & Eadie, 2009). Scores on the motivation index also were moderated by
protocol. In the writing task protocol, participants in the depletion condition reported
being more motivated than did participants in the non-depletion condition, which is
another unexpected pattern. Motivation reports did not differ by condition in the E-task
protocol. Frustration reports were not moderated by protocol.
We hesitate to speculate about the unexpected patterns for the fatigue and
motivation indices, but there may be a few implications. An examination of the
conditional means on the fatigue index suggests that the non-depletion task in the E-
task protocol was not the clean, neutral exercise we assumed it would be. The
motivation index difference, with participants in the controlled writing (versus free
writing) condition reporting more motivation, is not consistent with any existing models
of the ego depletion effect. The unexpected results from exploratory analyses of the
manipulation checks would need to be replicated in future research to bolster
confidence in them.

Paradigmatic Depletion Replication 59
Table S4. Manipulation Checks: Descriptive Statistics and Exploratory Frequentist
Meta-Analytic Tests of Experimental Condition, Full Sample
Variable
M (SD)
FE Average
CI
RE Average
CI
I2
Effort index
3.56 (1.74)
1.21**
[1.14,1.29]
1.59**
[1.13, 2.03]
96.88%
Frustration
2.98 (1.94)
0.88**
[0.80, 0.95]
1.01**
[0.70, 1.33]
94.60%
Fatigue index
3.12 (1.56)
0.26*
[0.20, 0.33]
0.27*
[0.12, 0.42]
80.17%
Motivation index
5.23 (1.25)
0.04
[-0.03, 0.11]
0.04
[-0.04, 0.11]
20.84%
Note: N = 3528, with the exception that frustration ratings were missing for two
participants. Sample size departs slightly from total sample size due to missing data.
Condition coded such that 0 = non-depletion, 1 = depletion condition. Higher numbers
indicate that participants in the depletion condition reported stronger feelings than
participants in the non-depletion condition. All tests were exploratory. Ms and SDs are
from unstandardized scales ranging from 1 (not at all) to 7 (very). FE indicates fixed-
effects models; RE indicates random-effects models. CI indicates 95% confidence
intervals. * p<.05; ** p<.01

Paradigmatic Depletion Replication 60
Table S5. Manipulation Checks: Descriptive Statistics and Exploratory Frequentist
Meta-Analytic Tests of Experimental Condition by Protocol, Full Sample
M (SD)
RE Average
Variable
Sample
Task
Depletion
Non-Depletion
k
N
d
CI
I2 %
Effort Index
Reduced
Writing Task
5.81 (1.09)
2.48 (1.03)
16
1246
3.09***
[2.87, 3.30]
39.29
E-Task
3.27 (1.29)
2.71 (1.18)
20
1217
0.46***
[0.34, 0.57]
0
Full
Writing Task
5.77 (1.13)
2.52 (1.05)
16
1679
2.98***
[2.71, 3.25]
72.48
E-Task
3.33 (1.34)
2.74 (1.23)
20
1849
0.45***
[0.36, 0.55]
0
Frustration
Reduced
Writing Task
5.05 (1.65)
1.77 (1.24)
16
1246
2.26***
[2.07, 2.46]
45.04
E-Task
2.62 (1.55)
2.34 (1.48)
20
1215
0.19**
[0.06, 0.32]
22.08
Full
Writing Task
4.98 (1.74)
1.89 (1.34)
16
1679
2.01***
[1.84, 2.19]
51.81
E-Task
2.74 (1.62)
2.42 (1.52)
20
1847
0.19***
[0.10, 0.29]
0
Fatigue Index
Reduced
Writing Task
3.24 (1.59)
2.29 (1.31)
16
1246
0.67***
[0.52, 0.83]
43.08
E-Task
3.33 (1.47)
3.53 (1.50)
20
1217
-0.15*
[-0.29, -0.01]
30.61
Full
Writing Task
3.30 (1.61)
2.29 (1.33)
16
1679
0.70***
[0.59, 0.80]
14.61
E-Task
3.35 (1.51)
3.47 (1.49)
20
1849
-0.10
[-0.20, 0.00]
18.00
Motivation
Index
Reduced
Writing Task
4.87 (1.19)
4.62 (1.22)
16
1246
0.19**
[0.07, 0.31]
12.52
E-Task
5.61 (1.10)
5.70 (1.06)
20
1217
-0.10
[-0.22, 0.01]
1.85
Full
Writing Task
4.85 (1.22)
4.65 (1.22)
16
1679
0.14**
[0.04, 0.24]
8.10
E-Task
5.64 (1.11)
5.69 (1.11)
20
1849
-0.06
[-0.15, 0.04]
8.34
Note: Condition coded such that 0 = non-depletion, 1 = depletion condition. Higher
numbers indicate that participants in the depletion condition reported stronger feelings
than participants in the non-depletion condition. All tests were exploratory. Ms and SDs
are from unstandardized scales ranging from 1 (not at all) to 7 (very). RE indicates
random-effects models. CI indicates 95% confidence intervals.
* p<.05; ** p<.01; *** p<.001

Paradigmatic Depletion Replication 61
Additional Sample and Methodological Details
Recruitment
The lead author (KV) announced the intention to conduct this replication on
behavioral science listservs. She also sent personal emails to prominent scholars who
have published on ego depletion, including to scholars who have been publicly critical of
depletion. Forty laboratories indicated commitment to participating in the project. Six
dropped out before initiating or completing data collection and two additional
laboratories joined before the end of the data collection period.
Materials and procedures
Participating laboratories received a script for how to conduct the experiment,
complete with the wording they should use and the arrangement of the laboratory.
When necessary, members of non-English-speaking laboratories translated the script
and experimental materials into the language in which the study would be conducted.
Additionally, KV created video samples of how to conduct each protocol and shared
them with participating labs. Via Skype, KV or BS communicated with laboratories to
answer questions and provide additional information. Last, laboratories in both protocols
were instructed to have experimenters leave the room while participants performed the
study’s tasks (independent variable task, dependent variable task, manipulation check
ratings, individual difference measures, demographics, and post-experimental
questionnaires).
E-task protocol. The instructions for both pages of this task were in the
laboratory’s native language whereas the E-task text was in English for all participants
(even if the laboratory’s native language was not English).

Paradigmatic Depletion Replication 62
Participating laboratories reported the number of errors participants made on the
last full paragraph participants completed of the manipulation task used in the E-task
protocol. Crossing out an E that should have been skipped and skipping an E that
should have been crossed out both counted as errors.
A figure-tracing task served as the dependent measure in this protocol.
Experimenters surreptitiously recorded how long participants persisted at figure tracing
and counted the number of figure sheets participants attempted to solve.
Story-writing protocol. Laboratories reported uses of forbidden letters (i.e., a
and n) and simple omissions of forbidden letters (e.g., “the dog b_rked”) for each
participant in the depletion condition of the story-writing protocol. Only depletion
condition participants could have errors. Across both conditions, story word counts were
reported.
The CET served as the dependent measure in this protocol. Experimenters timed
the duration participants took to complete the CET.
We did not include one item from the published version of the CET, “How much
does a telephone weigh?” The published scoring metric (see Bullard et al., 2004; Fein et
al., 1998) does not correspond to the weight of contemporary telephones. Additionally,
some items on the CET ask for imperial measurements (e.g., “How many sticks of
spaghetti are there in a one pound package?”). For labs outside North America, those
items were revised to indicate the metric system.
Responses to each item were converted to a common metric before final scoring
of the CET. The CET was scored using published norms (Bullard et al., 2004; Fein et
al., 1998). Answers within 25-75% of the normative range (i.e., good estimates)

Paradigmatic Depletion Replication 63
received 2 points. Answers outside the 25-75% range but within the 5-95% normative
range received 1 point. Answers outside the normative range (i.e., extreme estimates)
received 0 points. Participants occasionally gave answers with a tilde (e.g., ~1), which
we treated as the numerical value (e.g., 1). Responses given as a range (e.g., 6 to 8)
were treated as the median of the two values (e.g., 7).
We considered some answers invalid. Some items did not specify a unit of
measurement (e.g., distance could be reported in inches, feet, miles, and so on), and
participants were instructed to provide the unit of their response. If they did not provide
a unit of measurement for a relevant item, the response was considered invalid. If
participants did not report a numerical answer (e.g., “infinite”) or provided a nonsensical
answer (e.g., “0.5 pounds” for an item asking for a number of spaghetti sticks), the
response was considered invalid. Last, if participants skipped an item, it counted as
invalid. The final CET score for each participant was an average calculated by summing
item scores and dividing by the number of valid responses.
Videos of experimenters. All but two labs submitted recordings of
experimenters conducting the study on a practice subject, although five lacked usable
audio or video. A total of 65 videos were coded by two independent coders using scales
from 1 (not at all) to 5 (very much) on professionalism (i.e., how competent, in charge,
like a leader, and professional in appearance the experimenter behaved), r = 0.70, 95%
CI [0.68, 0.73], κ = 0.63, M = 4.64, SD = 0.49), and ease/comfort (i.e., how warm,
natural, comfortable, and not stiff or robotic the experimenter behaved), r = 0.53, 95%
CI [0.50, 0.55], κ = 0.36, M = 4.56, SD = 0.56). For labs that conducted the study in
English, videos (n = 49) also were coded for adherence to the script (r = 0.72, 95% CI

Paradigmatic Depletion Replication 64
[0.69, 0.74], κ = 0.44, M = 4.61, SD = 0.65). The judges’ ratings were averaged together
and these average scores for professionalism, ease/comfort, and adherence to the
script were combined into a composite score of experimenter behavior. Descriptive
statistics for the video codings were based on the full sample of participants.
Exclusions
Following preregistered criteria, we excluded data from n = 1068 participants as
follows. (Some participants failed multiple exclusion criteria.) The overall number of
participants who were excluded was more than we expected, but by percentage of all
participants the exclusion rate aligns closely with another multi-site depletion replication
study. Hagger et al.’s (2016) multi-lab depletion replication paper reported an exclusion
rate of 30.9% (n = 958 out of 3099 total participants). By comparison, our exclusion rate
was 30.25% (1068 out of a total sample size of 3531).
The exclusion criteria can be broadly understood as belonging to four categories:
1) participants’ performance errors or mistakes on the tasks (e.g., errors on the E-task,
invalid responses on the CET), 2) participants’ behavior (e.g., being disturbed,
disruptive, or disrupted; using their phone in violation of instructions; knowing that the
puzzles were unsolvable in the E-task protocol), 3) participant characteristics (being a
non-native speaker of the language in which the study was run; being one of the
experimenters’ first three participants), and 4) other exclusions. Experimenters noted
irregularities that occurred during the course of the study, and three independent coders
determined whether each irregularity qualified as an exclusion. (For more information on
that process, see below under “Both protocols.”) Examples of issues determined to be
disqualifying included noise from construction during the study, a repeat participant,

Paradigmatic Depletion Replication 65
missing the timing cue to stop a task, and experimenters being acquainted with
participants. Counts of excluded participants based on each preregistered criterion are
reported in Table 2 in the main article.
E-task protocol. We excluded data from participants who made more than 2.5
MAD (median absolute deviation) errors on the last full paragraph they completed on
the E-crossing task (Leys, Ley, Klein, Bernard, & Licata, 2013). For page 1 of the task
(the habit-forming portion), MAD calculations were done at the lab level. For page 2 of
the task (the habit-breaking portion), MAD calculations were done within lab and
separately by condition. We also excluded data from participants who expressed
knowledge (prior to the debriefing) that the figures used in the figure-tracing task (the
dependent measure in this protocol) were unsolvable. Table 2 in the main text displays
exclusion counts.
Story writing protocol. We excluded data from participants who used 2.5 MAD
or fewer words than other participants in their lab and in the same experimental
condition, participants who used the restricted letters (a and n) more often than 2.5
MAD of the lab (this criterion applied only to the depletion condition), and participants
who scored beyond 2.5 MAD of the lab mean on invalid responses on the CET (Table
2).
Both protocols. As preregistered, we excluded participants who were non-
native speakers as indicated by matching the language(s) they reported speaking at
home against the language in which the study was run, who were among the first three
run by each experimenter, who reported using their phone during the study, and who
were reported by the experimenter to be belligerent, or distressed or distraught. Also as

Paradigmatic Depletion Replication 66
preregistered, we excluded data from participants who experienced a disruption during
the experiment session or otherwise experienced an unanticipated deviation from the
experimental procedures, as indicated by the experimenter (Table 2).
Further, we instructed experimenters to note other concerns that may warrant
excluding the participant. That information was culled and sent to KV, BS, and Rebecca
Schlegel, who independently coded whether the concerns merited exclusion of that
participant’s data. Coders were blind to all other data pertaining to the participant (e.g.,
condition, protocol, scores on the dependent measures). Exclusions occurred only when
all three coders agreed that a participant should be excluded (“Other exclusions;” Table
2). In cases when two of the three coders thought a participant should be excluded, all
coders conferred and came to a consensus.

Paradigmatic Depletion Replication 67
References
Baumeister, R. F., Bratslavsky, M., Muraven, M., & Tice, D. M. (1998). Ego depletion: Is
the active self a limited resource? Journal of Personality and Social Psychology,
74, 1252-1265.
Bullard, S. E., Fein, D., Gleeson, M. K., Tischer, N., Mapou, R. L., & Kaplan, E. (2004).
The Biber cognitive estimation test. Archives of Clinical Neuropsychology, 19,
835-846.
Fein, D., Gleeson, M. K., Bullard, S., Mapou, R., & Kaplan, E. (1998, February). The
Biber Cognitive Estimation Test. Poster presented at the annual meeting of the
International Neuropsychological Society, Honolulu, HI.
Hagger, M. S., Chatzisarantis, N. L. D., Alberts, H., Anggono, C. O., Batailler, C., Birt,
A., . . . Zwienenberg, M. (2016). A multilab preregistered replication of the ego-
depletion effect. Perspectives on Psychological Science, 11, 546-573.
Legault, L., Green-Demers, I., & Eadie, A. L. (2009). When internalization leads to
automatization: The role of self-determination in automatic stereotype
suppression and implicit prejudice regulation. Motivation and Emotion, 33, 10-24.
Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not
use standard deviation around the mean, use absolute deviation around the
median. Journal of Experimental Social Psychology, 49, 764 –766.
https://doi.org/10.1016/j.jesp.2013.03.013

Paradigmatic Depletion Replication 68
APPENDIX A
Full List of Authors
Vohs, Kathleen D., University of Minnesota
Schmeichel, Brandon J., Texas A&M University
Lohmann, Sophie, Max Planck Institute for Demographic Research and University of
Illinois at Urbana-Champaign
Gronau, Quentin, F., University of Amsterdam
Finley, Anna, University of Wisconsin-Madison
Ainsworth, Sarah E., Tallahassee Community College
Alquist, Jessica, L., Texas Tech University
Baker, Michael, D., East Carolina University
Brizi, Ambra, University “Sapienza” of Rome
Bunyi, Angelica, University of North Florida
Butschek, Grant, J., University of Georgia
Campbell, Collier, Texas Tech University
Capaldi, Jonathan, Carleton University
Cau, Chuting, University of Toronto
Chambers, Heather, Texas A&M University
Chatzisarantis, Nikos, L. D., Curtin University
Christensen, Weston, J., Brigham Young University-Idaho
Clay, Samuel L., Brigham Young University-Idaho
Curtis, Jessica, Arkansas State University
De Cristofaro, Valeria, University “Sapienza” of Rome
del Rosario, Kareena, University of California, San Francisco
Diel, Katharina, Ruhr University Bochum
Doğruol, Yasemin, Northwestern University
Doi, Megan, University of Minnesota
Donaldson, Tina L., University at Albany
Eder, Andreas B., University of Würzburg
Ersoff, Mia, Florida State University
Eyink, Julie, R., Indiana University
Falkenstein, Angelica, University of California, Riverside
Fennis, Bob M., University of Groningen, the Netherlands
Findley, Matthew, B., Austin College
Finkel, Eli, J., Northwestern University
Forgea, Victoria, Georgia Southern University
Friese, Malte, Saarland University
Fuglestad, Paul, University of North Florida
Garcia-Willingham, Natasha, E., University of Kentucky
Geraedts, Lea F., University of Würzburg
Gervais, Will, M., University of Kentucky
Giacomantonio, Mauro, University “Sapienza” of Rome
Gibson, Bryan, Central Michigan University
Gieseler, Karolin, Saarland University

Paradigmatic Depletion Replication 69
Gineikiene, Justina, ISM, University of Management and Economics, Vilnius, Lithuania
Gloger, Elana, M., University of Kentucky
Gobes, Carina, M., Florida State University
Grande, Maria, University of Cologne
Hagger, Martin S., University of California, Merced
Hartsell, Bethany, University of North Florida
Hermann, Anthony, D., Bradley University
Hidding, Jasper, J., University of Groningen, the Netherlands
Hirt, Edward R., Indiana University
Hodge, Josh, University of Melbourne
Hofmann, Wilhelm, Ruhr University Bochum
Howell, Jennifer L., University of California, Merced
Hutton, Robert, D., Bradley University
Inzlicht, Michael, University of Toronto
James, Lily, University of Melbourne
Johnson, Emily, Arkansas State University
Johnson, Hannah, L., Brigham Young University-Idaho
Joyce, Sarah, M., Florida State University
Joye, Yannick, ISM University of Management and Economics, Vilnius, Lithuania
Kaben, Jan Helge, Saarland University
Kammrath, Lara, K., Wake Forest University
Kelly, Caitlin, N., Florida State University
Kissell, Brian L., Central Michigan University
Koole, Sander, L., VU Amsterdam
Krishna, Anand, University of Würzburg
Lam, Christine, University of California, Riverside
Lee, Kelemen, T., Bradley University
Lee, Nick, Curtin University
Leighton, Dana, Texas A&M University, Texarkana
Loschelder, David D., Leuphana University Lüneburg
Maranges, Heather, M., Florida State University
Masicampo, E.J., Wake Forest University
Mazara, Jr., Kennedy, Austin College
McCarthy, Samantha, University at Albany
McGregor, Ian, University of Waterloo
Mead, Nicole L., Schulich School of Business, York University
Mendes, Wendy B., University of California, San Francisco
Meslot, Carine, Curtin University
Michalak, Nicholas, M., University of Michigan
Milyavskaya, Marina, Carleton University
Miyake, Akira, University of Colorado Boulder
Moeini-Jazani, Mehrad, University of Groningen, the Netherlands
Muraven, Mark, University at Albany
Nakahara, Erin, University of California, San Francisco
Patel, Krishna, University of Toronto
Petrocelli, John, V., Wake Forest University

Paradigmatic Depletion Replication 70
Pollak, Katja, M., Leuphana University Lüneburg
Price, Mindi, M., Texas Tech University
Ramsey, Haley, J., Western Kentucky University
Rath, Maximilian, Leuphana University Lüneburg
Robertson. Jacob A., University of Colorado Boulder
Rockwell, Rachael, Ohio University
Russ, Isabella F., University of Würzburg
Salvati, Marco, University “Sapienza” of Rome
Saunders, Blair, University of Dundee
Scherer, Anne, Wake Forest University
Schütz, Astrid, University of Bamberg
Schmitt, Kristin N., University of Colorado Boulder
Segerstrom, Suzanne C., University of Kentucky
Serenka, Benjamin, University at Albany
Sharpinskyi, Konstantyn, University of Waterloo
Shaw, Meaghan, Carleton University
Sherman, Janelle, Indiana University
Song, Yu, Wake Forest University
Sosa, Nicholas, Ohio University
Spillane, Kaitlyn, University of California, Riverside
Stapels, Julia, University of Cologne
Stinnett, Alec, J., Texas Tech University
Strawser, Hannah, R., Texas A&M University
Sweeny, Kate, University of California, Riverside
Theodore, Dominic, Ohio University
Tonnu, Karine, Texas Tech University
van Oldenbeuving, Yasmijn, VU Amsterdam
vanDellen, Michelle R., University of Georgia
Vergara, Raiza, C., Florida State University
Walker, Jasmine, S., East Carolina University
Waugh, Christian, E., Wake Forest University
Weise, Feline, VU Amsterdam
Werner, Kaitlyn, M., Carleton University
Wheeler, Craig, University of Waterloo
White, Rachel, A., East Carolina University
Wichman, Aaron L., Western Kentucky University
Wiggins, Bradford, J., Brigham Young University-Idaho
Wills, Julian A., New York University
Wilson, Janie H., Georgia Southern University
Wagenmakers, E.J., University of Amsterdam
Albarracín, Dolores, University of Illinois at Urbana-Champaign

Paradigmatic Depletion Replication 71
APPENDIX B
PIs and Laboratory Members
*Ainsworth, Sarah E., Tallahassee Community College
Bunyi, Angelica, University of North Florida
*Fuglestad, Paul, University of North Florida
Hartsell, Bethany, University of North Florida
*Alquist, Jessica, L., Texas Tech University
Campbell, Collier, Texas Tech University
Price, Mindi, M., Texas Tech University
Stinnett, Alec, J., Texas Tech University
Tonnu, Karine, Texas Tech University
*Baker, Michael, D., East Carolina University
Walker, Jasmine, S., East Carolina University
White, Rachel, A., East Carolina University
*Clay, Samuel L., Brigham Young University-Idaho
Christensen, Weston, J., Brigham Young University-Idaho
Johnson, Hannah, L., Brigham Young University-Idaho
*Wiggins, Brady, J., Brigham Young University-Idaho
*Curtis, Jessica, Arkansas State University
Johnson, Emily, Arkansas State University
*Hagger, Martin S., University of California, Merced
Chatzisarantis, Nikos, L. D., Curtin University
Lee, Nick, Curtin University
Meslot, Carine, Curtin University
*Hermann, Anthony, D., Bradley University
Hutton, Robert, D., Bradley University
Lee, Kelemen, T., Bradley University
*Hirt, Edward R., Indiana University
Eyink, Julie, R., Indiana University
Sherman, Janelle, Indiana University
*Howell, Jennifer L., University of California, Merced
Rockwell, Rachael, Ohio University
Sosa, Nicholas, Ohio University
Theodore, Dominic, Ohio University
*Fennis, Bob M., University of Groningen, the Netherlands
Gineikiene, Justina, ISM, University of Management and Economics, Vilnius, Lithuania

Paradigmatic Depletion Replication 72
Hidding, Jasper, J., University of Groningen, the Netherlands
Joye, Yannick, ISM University of Management and Economics, Vilnius, Lithuania
Moeini-Jazani, Mehrad, University of Groningen, the Netherlands
*Findley, Matthew, B., Austin College
Mazara, Jr., Kennedy, Austin College
*Finkel, Eli, J., Northwestern University
Doğruol, Yasemin, Northwestern University
*Friese, Malte, Saarland University
Kaben, Jan Helge, Saarland University
Gieseler, Karolin, Saarland University
*Giacomantonio, Mauro, University “Sapienza” of Rome
Brizi, Ambra, University “Sapienza” of Rome
De Cristofaro, Valeria, University “Sapienza” of Rome
Salvati, Marco, University “Sapienza” of Rome
*Hofmann, Wilhelm, Ruhr University Bochum
Diel, Katharina, Ruhr University Bochum
Grande, Maria, University of Cologne
Stapels, Julia, University of Cologne
*Inzlicht, Michael, University of Toronto
Cau, Chuting, University of Toronto
Patel, Krishna, University of Toronto
Saunders, Blair, University of Dundee
*Kammrath, Lara, K., Wake Forest University
*Masicampo, E.J., Wake Forest University
*Petrocelli, John, V., Wake Forest University
*Scherer, Anne, Wake Forest University
*Song, Yu, Wake Forest University
*Waugh, Christian, E., Wake Forest University
*Kissell, Brian L., Central Michigan University
Gibson, Bryan, Central Michigan University
*Koole, Sander, L., VU Amsterdam
van Oldenbeuving, Yasmijn, VU Amsterdam
Weise, Feline, VU Amsterdam
*Krishna, Anand, University of Würzburg
Eder, Andreas B., University of Würzburg
Geraedts, Lea F., University of Würzburg

Paradigmatic Depletion Replication 73
Russ, Isabella F., University of Würzburg
*Leighton, Dana, Texas A&M University, Texarkana
*Loschelder, David D., Leuphana University Lüneburg
Pollak, Katja, M., Leuphana University Lüneburg
Rath, Maximilian, Leuphana University Lüneburg
*Maranges, Heather, M., Florida State University
Ersoff, Mia, Florida State University
Gobes, Carina, M., Florida State University
Joyce, Sarah, M., Florida State University
Kelly, Caitlin, N., Florida State University
Vergara, Raiza, C., Florida State University
*McGregor, Ian, University of Waterloo
Sharpinskyi, Konstantyn, University of Waterloo
Wheeler, Craig, University of Waterloo
*Mead, Nicole L., Schulich School of Business, York University
Hodge, Josh, University of Melbourne
James, Lily, University of Melbourne
*Mendes, Wendy B., University of California, San Francisco
del Rosario, Kareena, University of California, San Francisco
Nakahara, Erin, University of California, San Francisco
*Milyavskaya, Marina, Carleton University
Capaldi, Jonathan, Carleton University
Werner, Kaitlyn, M., Carleton University
Shaw, Meaghan, Carleton University
*Miyake, Akira, University of Colorado Boulder
Robertson, Jacob A., University of Colorado Boulder
Schmitt, Kristin N., University of Colorado Boulder
*Muraven, Mark, University at Albany
Donaldson, Tina L., University at Albany
McCarthy, Samantha, University at Albany
Serenka, Benjamin, University at Albany
*Schmeichel, Brandon J., Texas A&M University
Chambers, Heather, Texas A&M University
Finley, Anna, University of Wisconsin-Madison
Strawser, Hannah, R., Texas A&M University

Paradigmatic Depletion Replication 74
*Schütz, Astrid, University of Bamberg
*Segerstrom, Suzanne C., University of Kentucky
Gloger, Elana, M., University of Kentucky
Garcia-Willingham, Natasha, E., University of Kentucky
*Sweeny, Kate, University of California, Riverside
Lam, Christine, University of California, Riverside
Spillane, Kaitlyn, University of California, Riverside
Falkenstein, Angelica, University of California, Riverside
*vanDellen, Michelle R., University of Georgia
Butschek, Grant, J., University of Georgia
*Wichman, Aaron L., Western Kentucky University
Ramsey, Haley, J., Western Kentucky University
*Wilson, Janie H., Georgia Southern University
Forgea, Victoria, Georgia Southern University
Note: Laboratories are listed under the name of the PI used in the tables and figures,
followed by additional members. For ease of presentation, tables and figures refer to
each laboratory using the name of a PI, although some groups had more than one PI.
The Wake Forest laboratory considered all members to be PIs and therefore is listed by
site.
* indicates laboratory PIs.