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Good self-control has been linked to adaptive outcomes such as better health, cohesive personal relationships, success in the workplace and at school, and less susceptibility to crime and addictions. In contrast, self-control failure is linked to maladaptive outcomes. Understanding the mechanisms by which self-control predicts behavior may assist in promoting better regulation and outcomes. A popular approach to understanding self-control is the strength or resource depletion model. Self-control is conceptualized as a limited resource that becomes depleted after a period of exertion resulting in self-control failure. The model has typically been tested using a sequential-task experimental paradigm, in which people completing an initial self-control task have reduced self-control capacity and poorer performance on a subsequent task, a state known as ego depletion. Although a meta-analysis of ego-depletion experiments found a medium-sized effect, subsequent meta-analyses have questioned the size and existence of the effect and identified instances of possible bias. The analyses served as a catalyst for the current Registered Replication Report of the ego-depletion effect. Multiple laboratories (k = 23, total N = 2,141) conducted replications of a standardized ego-depletion protocol based on a sequential-task paradigm by Sripada et al. Meta-analysis of the studies revealed that the size of the ego-depletion effect was small with 95% confidence intervals (CIs) that encompassed zero (d = 0.04, 95% CI [−0.07, 0.15]. We discuss implications of the findings for the ego-depletion effect and the resource depletion model of self-control.
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A Multi-Lab Pre-Registered Replication of the Ego-Depletion Effect
Proposing authors: Martin S. Hagger and Nikos L. D. Chatzisarantis
Contributing authors: Hugo Alberts, Calvin Octavianus Anggono, Cédric Batailler, Angela
Birt, Ralf Brand, Mark J. Brandt, Gene Brewer, Sabrina Bruyneel, Dustin P. Calvillo, W. Keith
Campbell, Peter R. Cannon, Marianna Carlucci, Nicholas Carruth, Tracy Cheung, Adrienne
Crowell, Denise T. D. De Ridder, Siegfried Dewitte, Malte Elson, Jaqueline R. Evans,
Benjamin A. Fay, Bob M. Fennis, Anna Finley, Zoë Francis, Elke Heise, Henrik Hoemann,
Michael Inzlicht, Sander L. Koole, Lina Koppel, Floor Kroese, Florian Lange, Kevin Lau,
Bridget P. Lynch, Carolien Martijn, Harald Merckelbach, Nicole V. Mills, Alexej Michirev,
Akira Miyake, Alexandra E. Mosser, Megan Muise, Dominique Muller, Milena Muzi, Dario
Nalis, Ratri Nurwanti, Henry Otgaar, Michael Philipp, Pierpaolo Primoceri, Katrin Rentzsch,
Lara Ringos, Caroline Schlinkert, Brandon J. Schmeichel, Sarah F. Schoch, Michel Schrama,
Astrid Schütz, Angelos Stamos, Gustav Tinghög, Johannes Ullrich, Michelle vanDellen, Supra
Wimbarti, Wanja Wolff, Cleoputri Yusainy, Oulmann Zerhouni, Maria Zwienenberg
Protocol vetted by: Chandra Sripada, Daniel Kessler, Roy Baumeister
Edited by: Alex O. Holcombe
Multi-lab direct replication of: Ego-depletion paradigm reported in Sripada, C., Kessler, D.,
& Jonides, J. (2014). Methylphenidate blocks effort-induced depletion of regulatory control in
healthy volunteers. Psychological Science, 25(6), 1227-1234. doi:10.1177/0956797614526415.
Data and registered protocols:
Citation: Hagger, M. S., Chatzisarantis, N. L. D., Alberts, H., Anggono, C. O., Batailler, C.,
Birt, A., … Zwienenberg, M. (2015). A multi-lab pre-registered replication of the ego-
depletion effect. Perspectives on Psychological Science, X, X-X.
Good self-control has been linked to adaptive outcomes such as better health, cohesive
personal relationships, success in the workplace and at school, and less susceptibility to crime
and addictions. In contrast, self-control failure is linked to maladaptive outcomes.
Understanding the mechanisms by which self-control predicts behavior may assist in
promoting better regulation and outcomes. A popular approach to understanding self-control is
the strength or ‘resource depletion’ model. Self-control is conceptualized as a limited resource
which becomes depleted after a period of exertion resulting self-control failure. The model has
typically been tested using a ‘sequential-task’ experimental paradigm in which people
completing an initial self-control task have reduced self-control capacity and poorer
performance on a subsequent task, a state known as ‘ego depletion’. Although a meta-analysis
of ego-depletion experiments found a medium-sized effect, subsequent meta-analyses have
questioned the size and existence of the effect and identified instances of possible bias. The
analyses served as a catalyst for the current registered replication report of the ego-depletion
effect. Multiple laboratories (k = 23, total N = 2141) conducted replications of a standardized
ego-depletion protocol based on a sequential-task paradigm by Sripada et al. Meta-analysis of
the studies revealed that the size of the ego-depletion effect was small with 95% confidence
intervals that encompassed zero. We discuss implications of the findings for the ego-depletion
effect and the resource depletion model of self-control.
Key words: strength model; energy model; resource depletion; self-regulation; meta-
A Multi-Lab Pre-Registered Replication of the Ego-Depletion Effect
Good self-control is important for optimal human functioning. Self-control has been
regarded as an individual’s capacity to actively override or inhibit impulses, suppress urges,
resist temptations, and break ingrained, well-learned behaviors, or habits. Self-control therefore
reflects the extent to which an individual can override a dominant response in favor of an
alternative, more effortful course of action. Good self-control has been linked to adaptive
outcomes in multiple domains including school, the workplace, social relationships, and health
(de Ridder, Lensvelt-Mulders, Finkenauer, Stok, & Baumeister, 2012; Dvorak & Simons,
2009; Hagger, Wood, Stiff, & Chatzisarantis, 2010b; Tangney, Baumeister, & Boone, 2004).
Analogously, poor self-control is associated with many maladaptive outcomes including poor
health, financial instability, dysfunctional social relationships, and susceptibility to drug abuse
and crime (Baumeister & Alquist, 2009; Baumeister, Heatherton, & Tice, 1994; Wills, Isasi,
Mendoza, & Ainette, 2007). Accordingly, it is vital to understand why people may succeed or
fail at self-control.
The conceptualization that self-control capacity depends on a finite resource has gained
considerable attention in the literature. In two key research articles, Baumeister and colleagues
(Baumeister, Bratslavsky, Muraven, & Tice, 1998; Muraven, Tice, & Baumeister, 1998)
proposed and tested a ‘limited resource’ or ‘strength’ model of self-control. According to their
model, performance on tasks requiring self-control is governed by a generalized, unitary, and
finite ‘internal’ resource. They proposed that engaging in tasks requiring self-control would
lead to the depletion of the resource and reduced performance on subsequent self-control tasks.
The state of reduced self-control capacity was termed ‘ego depletion’. Baumeister and
colleagues tested their model using a ‘sequential-task’ experimental paradigm in which
participants engaged in two consecutive tasks. For participants randomly allocated to the
experimental (ego depletion) group, both tasks both required self-control. For participants
allocated to the control (no depletion) group only the second task required self-control while
the first task did not require any, or very little, self-control. The self-control tasks used required
participants to alter or modify an instinctive, well-learned response, akin to resisting an
impulse or temptation (Baumeister, Vohs, & Tice, 2007).
Consistent with the predictions of the resource depletion model, participants in the
experimental group performed worse on the second task relative to participants in the control
group. Critically, the tasks used in the experiments were from different ‘domains’ of self-
control providing evidence to suggest that the resource was ‘domain-general’ and common to
all tasks that required self-control. The limited resource account has received considerable
support with numerous conceptual replications of the original findings using the sequential-
task paradigm. An initial meta-analysis revealed a medium effect size (d = 0.62) across 198
tests of the ego-depletion effect (Hagger, Wood, Stiff, & Chatzisarantis, 2010a).
However, recent conceptual and empirical analyses have challenged the resource
depletion explanation for the self-regulatory failures observed in ego-depletion experiments
and questioned the strength of the ego-depletion effect or whether it exists at all. Recent
analyses have suggested that original meta-analytic effect size for ego depletion may be
inflated. Re-analyses of Hagger et al.’s meta-analytic findings (Carter & McCullough, 2013b;
Carter & McCullough, 2014) and a new meta-analysis of tests of the ego-depletion effect that
included unpublished data (Carter, Kofler, Forster, & McCullough, 2015) applied regression
techniques based on funnel plots of the estimated effect size in each study against study
precision (i.e., the reciprocal of the sample size). These regression techniques have been
proposed as means to detect bias in sets of studies included in meta-analyses, known as ‘small
study’ bias. Small study bias refers to increased likelihood of improbably high effect sizes
relative to study precision in a sample of studies included in a meta-analyses. The bias may be
indicative of publication bias, that is, the propensity of journal editors to favour publication of
studies that achieve statistical significance and tend to have larger effect sizes relative to their
sample size (Sterne, Egger, & Davey Smith, 2001).
Carter et al.’s analyses revealed substantial ‘small study’ bias in the effect size reported
in Hagger et al.’s (2010a) original meta-analysis and indicated that many published studies
included in the original analysis, and in their updated meta-analysis, were substantially
underpowered suggesting that the likelihood of finding of so many large, statistically
significant effects was improbable. In both their re-analysis and updated meta-analysis, Carter
et al. (2015) suggested that, based on their regression analyses, a probable value for the ego-
depletion effect was zero and concluded that “the meta-analytic evidence does not support the
proposition (and popular belief) that self-control functions as if it relies on a limited resource,
at least when measured as it typically is in the laboratory” (p. 18). Consistent with these
findings, there have also been studies that have failed to replicate the ego-depletion effect (e.g.,
Xu et al., 2014), found it to be substantially smaller in size than reported in meta-analytic
syntheses (e.g., Tuk, Zhang, & Sweldens, 2015), or indicated that a facilitation effect may
occur in which task performance improves with prior self-control in multi-task experiments
(e.g., Converse & DeShon, 2009; Dewitte, Bruyneel, & Geyskens, 2009; Tuk et al., 2015).
Overall, these data, together with the data from the recent meta-analyses, cast doubt on the
existence of a large or even moderately-sized ego-depletion effect.
It is important, however, to note that the interpretation of the regression analyses
conducted by Carter et al. has been questioned. Hagger and Chatzisarantis (2014) indicated that
the interpretation of the regression techniques was misleading in the presence of substantial
heterogeneity in the effect size. This might be the case if, for example, the true effect is larger
in smaller studies (Sterne et al., 2001). Furthermore, the regression techniques are based on the
assumption that the relationship between sample size and effect size is zero, but Simonsohn
and colleagues (2009) point to instances where this may not be the case (e.g., where there is
considerable unexplained heterogeneity in the effect size or the sample may have been selected
based on a characteristic making them more prone to the depletion manipulation). Importantly,
while the regression techniques may indicate the existence of bias in meta-analytically derived
effect sizes attributable to small study effects, they cannot definitively identify the source of
the bias (Simonsohn, 2009).
Issues of interpretation notwithstanding, the existence of substantial bias across studies
testing the ego-depletion effect is important and the size of the effect is still uncertain given
competing interpretations of tests of bias of the meta-analytic findings. The literature on the
ego-depletion effect is a reflection of broader current debates over the reproducibility of effects
in psychological experiments (Pashler & Harris, 2012) and the need for high-powered
replications of prominent effects in the discipline (Open Science Collaboration, 2012, 2015).
We proposed a set of independent replications of the ego-depletion effect using the sequential-
task paradigm, as advocated by Carter and McCullough (2013b; 2014) and Hagger and
Chatzisarantis (2014).
Protocol Development
While the sequential-task paradigm has become the primary means by which to test the
ego-depletion effect, there is considerable variation in the tasks used in the literature due to
researchers’ desire to demonstrate the domain generality of the self-control ‘resource’. For
example, ‘exerting’ self-control on a task in one domain (e.g., impulse control) was expected to
lead to observed decrements in performance on tasks from another (e.g., thought or emotion
suppression). A consequence of this variability in tasks used is that there is no single agreed
standardized set of tasks for use in sequential-task paradigm tests of the ego-depletion effect.
A further issue in developing the protocol was the need for tasks to be sufficiently
standardized to rule out, wherever possible, idiosyncratic lab-specific differences in the
presentation of tasks or other variations that may reduce the consistency of the protocol
implementation across labs. Whereas typical practice in registered replications of
psychological research has tended to prioritize the replication of the original experiment (e.g.,
Alogna et al., 2014; Eerland et al., 2016), the tasks used in the original experiments were
deemed too elaborate or complex to be appropriate for a multi-lab replication. For example,
one of the tasks used to deplete self-control resources in the original tests of the ego-depletion
effect required participants to taste radishes and resist cookies (Baumeister et al., 1998, Study
1). This task would require extensive experimenter involvement in its delivery which may
increase variability across labs. Similarly, persistence on unsolvable anagrams (Baumeister et
al., 1998, Study 3) is likely to be too culture specific, and it would be difficult to develop
equivalence in the anagrams across labs from different countries. Furthermore, we also
considered it appropriate to adopt ‘behavioral’ tasks after Carter and colleagues’ (2015) plea
for researchers to do so in their meta-analysis. We therefore sought to identify a sequential-task
procedure that adopted standardized ‘behavioral’ tasks requiring little adaptation across labs
and minimal interpersonal involvement by the experimenter.
Given these concerns, we sought to identify a previously-published procedure that was
in keeping with original sequential-task tests of the ego-depletion effect, but could be
standardized for a multi-lab replication so as to minimize experimenter input and
methodological variability across laboratories. The ego-depletion paradigm adopted by
Sripada, Kessler, and Jonides (2014) was identified as one that fit well with our requirements:
the tasks used are similar to those used in the original depletion experiments (e.g., Baumeister
et al., 1998; Muraven et al., 1998), but computer-administered, a design feature that minimizes
variability across labs. The decision to use these tasks was based on the recommendation of
Roy Baumeister. The protocol was developed in close consultation with Chandra Sripada and
Daniel Kessler, co-authors of the original experiment, who made the tasks and procedure used
in the original study available for the replication project. It is important to note that Sripada et
al.’s original study also examined the effects of the ‘study drug’ Ritalin (methylphenidate) on
ego-depletion in a 2 x 2 placebo-controlled experimental design. So the procedure adopted in
the current replication is not a direct replication of Sripada et al.’s study but instead a test of the
ego-depletion effect in the context of their depletion paradigm. These authors found a
statistically significant effect for ego-depletion (d = 0.69).
Once the protocol was finalized, a public announcement of the replication and a call for
participating labs was posted by Perspectives on Psychological Science on October 28, 2014.
A deadline for applications to participate was set for January 9, 2015 and by that time 30 labs’
applications had been approved by the editor to conduct a replication. Six laboratories had to
abort data collection due to technical difficulties or insufficient resources (e.g., access to
participants or research assistants) leaving 24 labs contributing to the project. Participating labs
pre-registered their implementation plan on the Open Science Framework and conducted
independent replications. Each implementation plan was vetted by the registered replication
reports editor (Alex Holcombe) for consistency with the protocol prior to data collection.
Participating labs were in Australia, Belgium, Canada, France, Germany, Indonesia, the
Netherlands, New Zealand, Sweden, Switzerland, and the United States. Co-ordinated and
systematic translation efforts were undertaken to prepare study materials in labs recruiting
participants whose native language was not English. The investigators of each participating lab
had expertise in social psychology, social cognition, self-regulation and self-control, or
experimental design and are listed as co-authors on this manuscript. Some labs had no previous
experience in conducting studies on self-control but had expertise in conducting psychology
Protocol Requirements
In this section we provide details of the replication protocol. From the general protocol,
participating labs were required to create an entry on the Open Science Framework (OSF)
linked from the main ego depletion Sripada et al. registered replication report (RRR) webpage
( and post their implementation plan, registration documents, materials,
raw data, and analyses. The study protocol was required to be approved by labs’ institutional
review board (IRB) or the equivalent institutional committee responsible for research ethics in
advance of data collection.
Participants were undergraduate students who participated in return for course credit or
payment. Participants were recruited from institution-managed participant pools or in response
to study advertisements. Based on a statistical power analysis with alpha at 0.01 and beta at
0.95, we computed that a sample size of 168 participants, with 84 in each of the depletion and
non-depletion conditions, was required to detect the medium effect size (Hagger et al., 2010a).
While we strongly recommended that participating laboratories’ replications met this sample
size, replications with sample sizes of 100 participants was considered the guideline minimum
( 50 participants in each condition). Most labs were able to achieve this target in their
recruitment, but due to the rigorous exclusion criteria for the tasks used in the sequential-task
paradigm, the targeted sample size was not achieved in some cases. Given evidence suggesting
that older participants show a weaker ego-depletion effect (Dahm et al., 2011), participants
were required to be between 18 and 30 years old. As study materials were language specific,
participants were required to be native speakers of the language in which the replication was
conducted. Participants from labs in English-speaking countries (Australia, Canada, New
Zealand, and the United States) were excluded if they did not report English as their first and
primary language. Labs in non-English speaking countries conducted the replication using
study materials translated into the primary language of the participants and non-native speakers
were excluded. One lab conducted the replication in a sample of English-speaking students in
Sweden (Tinghög & Koppel). While the participants from this lab were fluent English
speakers, their results were omitted from the final analysis because they deviated from the
native language inclusion criterion leaving 23 labs included in the final analysis1.
Testing Location
Participants were tested individually in laboratory conditions and were alone in the
room when completing the tasks. Participants were provided with written instructions and were
guided orally by the experimenter, who followed a script.
Researchers were postgraduate psychology students, research assistants, postdoctoral
researchers, or faculty researchers with experience in collecting psychology experimental data
and interacting with participants. Experimenters did not need to have specific domain
knowledge or prior familiarity with the paradigm. Experimenters were required to familiarize
themselves with the experimental step-by-step procedure available on the OSF site
( and practice it prior to data collection. The protocol recommended that
experimenters be naïve to the experimental hypothesis and condition assignment, but this was
not always feasible (whether it was attempted is noted on each lab’s OSF page).
Data Collection
The one-way experimental design reflected Sripada and colleagues’ (2014) ego-
depletion paradigm. Participants were allocated to experimental (ego depletion) or control (no
depletion) groups pseudo-randomly. In order to achieve approximately equal numbers of
participants across conditions and achieve the minimum numbers required, it was
recommended that labs randomized participants in blocks of 10 to ensure both conditions met
the minimum required sample size. As a result, one condition may have had more participants
than the other due to different rates of exclusion, but both would meet the required minimum.
1Supplementary analyses that include data from the Tinghög and Koppel lab can be found on the replication OSF
The experiment was presented as an experiment on “word and number recognition and
reaction time” to mask the study hypothesis. The detailed procedure is posted at Participants were welcomed by the experimenter, shown into the lab, and
asked to sit at a desk with a computer. They were informed that they would be required to
engage in two computer-administered tasks, presented consecutively, after a period of practice
on each task. Participants then completed practice versions of the two tasks. The practice
versions of both tasks were conducted prior to the main trials in order to minimize transition
time between the initial and second tasks in the depletion paradigm. The first task was the letter
‘e’ task and the second task was the modified multi-source interference task (MSIT, detailed
below in the “Materials” section). Both tasks were presented on a computer screen controlled
by E-Prime experimental software.
After the practice sessions, participants proceeded to the main trials of each task. After
completing the first task, participants completed self-report items measuring effort, fatigue,
difficulty, and frustration on the first task, which were used as manipulation checks for the ego-
depletion manipulation. Participants then completed the second task. In an exit questionnaire,
participants’ thoughts on the purpose of the experiment were probed. They were then thanked
and informed that the experiment had concluded. Some of the participating labs’ IRBs required
experimenters to provide participants with a debrief. Some labs were able to delay the debrief
until completion of the experiment and all participants’ data had been collected to minimize
potential for the study hypothesis being shared with others in the participant pool. Others
provided a debrief immediately after the experiment but asked participants not to share details
with fellow students.
Overall, there were two differences between the current replication protocol and the
original protocol by Sripada et al. (2014): (1) we did not administer a capsule prior to the task
protocol, where participants expected it to contain either placebo or Ritalin, and (2) we
administered self-report measures of task effort, fatigue, difficulty, and frustration after the first
task, while no measures were administered in the original study. The self-report measures were
included to check whether the initial task was subjectively arduous and depleting for
participants assigned to the ego-depletion group relative to the control group. Similar measures
such as these have been administered in many ego-depletion experiments, including the
original ego-depletion experiments (Baumeister et al., 1998; Muraven et al., 1998).
Letter ‘e’ task. The first task was a modified version of the letter ‘e’ task (Baumeister et
al., 1998, Study 4) with on-screen instructions administered using E-Prime (available at Two versions were used: depletion and no depletion. In the depletion
version, participants were presented with a series of words on a video screen and required to
press a button when a word with the letter ‘e’ was displayed and withhold the response if the
‘e’ was next to or one letter away from a vowel. The no depletion version was matched in all
respects with the exception that participants were required to press a button whenever a word
with the letter ‘e’ was displayed, with no stipulation to ever withhold their response to an ‘e’.
Participants were asked to respond as quickly and accurately as possible. Participants
completed 20 practice trials before the commencement of the experiment. The main session
comprised 150 trials and lasted 7 minutes and 30 seconds. Participants’ reaction times and
errors for the letter ‘e’ task were recorded. The depletion version of the letter ‘e’ task was
considered to be more demanding, and require greater self-control, than the no depletion
version because participants had to inhibit the tendency to respond to any ‘e’ and instead apply
the more restrictive rules.
Multi-source interference task (MSIT). The MSIT is a task requiring response inhibition
(Bush, Shin, Holmes, Rosen, & Vogt, 2003) and was administered by E-Prime (available at
Running head: EGO DEPLETION REGISTERED REPLICATION REPORT 13 Numeric stimuli were presented on the computer screen with participants
making responses using the keyboard. The stimuli were sets of three digits comprising
combinations of the numerals 1, 2, 3 or 0. Participants were asked to place their index, middle
and ring fingers of the right hand on three keys on the keyboard. Participants were told that
they would be presented with sets of three digits in the center of a video screen every few
seconds, and that one digit (the target digit) would always be different from the other two
(matching distractor) digits. Participants were told that they needed to indicate the identity of
the target digit, not its position in the set of digits. Participants were required to press the key
corresponding to the digit that differed from the other two. In ‘control’ or ‘congruent’ sets, the
target digit (1, 2 or 3) always matched its position on the response keys, such as the number ‘1’
appearing in the first (leftmost) position. For example, sets 100, 020 or 113 are examples of
congruent sets. In ‘interference’ or ‘incongruent’ number sets, the target number (1, 2 or 3)
never matched its position, and the distractors were themselves potential targets. For example,
for the number set 233 the correct response would be ‘2’. The task creates interference in that
the identity of the target number and its position relative to other numbers on the string
differed. Interference was also caused by varying the size of the digits in the set. In the
congruent version variation in the digit size was always consistent with the target digit, for
example the target digit was always the larger or smaller digit relative to the other digits in the
set. In the incongruent version, variation in digit size was not always consistent with the target
digit, requiring the participant to inhibit both the position and size of the target digit in favor of
its identity. Participants were asked to respond as quickly and accurately as possible.
Participants completed 20 practice MSIT trials before the commencement of the experiment.
The main task lasted approximately 10 minutes and comprised 200 trials (100 control
(congruent) and 100 interference (incongruent) trials) presented in an interspersed,
pseudorandom order. Reaction time and error data were recorded by the E-Prime program.
Performance on the MSIT comprised the dependent measure of self-control. The MSIT
provides two measures of performance: mean reaction time (RT) on incongruent trials and
reaction time variability (RTV) on incongruent trials, defined as the sum of the sigma and tau
variability parameters using ex-Gaussian modeling (Dawson, 1988; Sripada et al., 2014). RTV
on the MSIT was the primary dependent variable in Sripada et al.’s (2014) original study and
in the current protocol. RTV is considered an analog of attentional control. Participants with
good attentional control are effective in maintaining task-directed focus and supressing task-
irrelevant spontaneous thoughts. Reduction in attentional control induced by depletion is likely
to lead to more lapses in attention, manifesting as increased variability in response latencies
across incongruent trials on the MSIT (Weissman, Roberts, Visscher, & Woldorff, 2006).
While this should also inflate mean RT, RTV is a more sensitive measure. We also conducted
analyses on mean RT on MSIT incongruent items as a secondary dependent variable as this is
the typical criterion variable in other commonly-used interference tasks such as the Stroop
color-naming task.
Translation for non-English speaking labs. Labs collecting data from non-English
speaking countries were required to translate all study materials into their native language by a
fluent bilingual translator followed by back-translation by an independent fluent bilingual
translator. The translated versions were also independently reviewed by the replication
proposer (Martin Hagger) and registered replication reports editor (Alex Holcombe). The
specific translation procedures of each non-English speaking lab are documented on their
respective OSF webpages. Assistance in developing the non-English word stimuli and
instruction slides for the letter ‘e’ task and embedding them into the E-Prime program was
provided by Daniel Kessler, who developed the original tasks in the Sripada et al. (2014) study.
The analysis plan was to conduct one meta-analysis of the data from all the participating labs,
plus separate meta-analyses for English and non-English-speaking labs.
Data Stopping Rules and Exclusions
Each lab pre-registered their stopping rules for data collection, how they planned to
meet the demographic requirements of the participants, how they would assess the first and
primary language of participants, how participants would be assigned to conditions, and rules
for exclusion of participants’ data from the analysis. The editor reviewed these procedures to
verify that participating labs met protocol requirements. Participant exclusion criteria were
specified prior to data collection. The criteria were: the participant reported that their native
language was one other than the language in which the experiment was conducted, they fell
outside the stipulated 18 to 30 years of age, they did not complete the study, they did not
follow, or failed to understand, instructions, or their responses fell below the 80% correct
response criteria for the letter ‘e’ or MSIT tasks. Participants were also excluded due to
equipment or software failure or experimenter error. Raw data files that include data excluded
from the analysis are provided on participating labs’ OSF webpages (
Critical comparisons
By convention in sequential-task paradigm studies examining the ego-depletion effect,
the critical analysis is a one-way test of difference on task performance across the depletion
and no-depletion groups. In the current replication, the primary dependent variable was RTV
for incongruent trials of the MSIT and the critical test was whether RTV was higher for
participants assigned to the depletion condition relative to those assigned to no depletion
condition. This is identical to the critical test conducted in the replicated experiment (Sripada et
al., 2014). It is also consistent with the critical tests in the original ego-depletion experiments
(Baumeister et al., 1998; Muraven et al., 1998) and those in the extant literature. In terms of
predictions, most labs predicted a non-trivial effect size. Some labs (k = 12) predicted that the
replicated effect would be similar in size to that reported in previous meta-analyses (Hagger et
al., 2010) or the original study (Sripada et al., 2014), and some (k = 10) indicated it would be
smaller than reported in previous analyses, but greater than d = 0.15. One lab predicted a null
Additional analyses were planned on the secondary dependent variable and the control
(manipulation check) variables: mean RT for incongruent items on the MSIT, mean RT for the
letter ‘e’ task, and self-report measures of effort, difficulty, fatigue, and frustration. Larger RTs
among participants assigned to the depletion group relative to participants assigned to the
control group would be indicative of an ego-depletion effect. It is important to note that
Sripada et al. found no effect on RT and considered the RTV a better indicator of self-control
failure as it was hypothesized to closely reflect levels of attentional control. Poorer accuracy
and greater levels of effort, difficulty, fatigue and frustration in the depletion version of the
letter ‘e’ task condition relative to the no depletion version would indicate that participants
found the depletion version more arduous and effortful.
Lab Demographics and Preliminary Analyses
Sample demographics and results for each of the participating labs (k = 23, total N =
2141) are provided in Table 1 for the depletion and non-depletion conditions alongside the
ego-depletion data from the replicated study for comparison (Sripada et al., 2014). The table
provides sample sizes, details of exclusions and reasons, and the means and standard deviations
of the mean RTV and mean RT dependent variables in each condition. Demographic details of
participants and reasons for exclusion, experimenters’ details, and deviations from
preregistered protocol for all participating labs can be found in Appendix A. Analysis of rates
of exclusions for inaccuracy on letter ‘e’ and MSIT tasks revealed significant differences in the
proportion of participants excluded for low accuracy (<80% accuracy on tasks) relative to
2Full details of the expectations and experience of all participating labs can be found on the replication OSF site:
inclusions across depletion and no-depletion groups in five of the 23 laboratories. These data
suggest that rates of exclusion due to accuracy were largely independent of condition
allocation. Details of these supplementary analyses are provided in Appendix B.
Data Analyses: Original and Present
In Sripada et al.’s original study, a two-way analysis was conducted examining the
interactive effect of the depletion manipulation and methylphenidate administration conditions
on the dependent variables. In the current analysis, consistent with convention in ego-depletion
experiments, our critical comparison was a test of difference (independent samples t-test) for
the primary and secondary dependent variables, mean RTV and RT for incongruent items on
the MSIT task, respectively, across the depletion conditions. We supplemented this with
identical analyses of overall accuracy on the letter ‘e’ task and participants’ self-reports of
effort, fatigue, difficulty, and frustration to establish the extent to which the initial task likely
involved effortful self-control. Each lab conducted these analyses independently and results are
reported on their OSF project webpages (
Effect Size Measurements
Differences in the dependent and control variables across conditions in pooled data from
the labs were tested using separate meta-analyses. We used a random effects model to weight
each effect by its sample size and report the effect size in standard deviation units (Cohen’s d)
and its confidence intervals. Heterogeneity in the effect sizes was evaluated using the Cochrane
Q and I2 statistics, with a statistically significant value for Q and an I2 value greater than 25%
indicative of substantial heterogeneity in the effect size across studies. Forest plots showing the
means of the target dependent variables (mean RTV and RT for the MSIT) in both conditions
for each lab, the effect size measured in each lab with 95% confidence intervals, and the
sample-weighted meta-analytic effect size for the dependent variables of interest are provided
in Figures 1 (RTV) and 2 (RT) alongside effect-size data for the placebo condition of the
Sripada et al. study for comparison. Positive effect sizes for RT and RTV represent the extent
of a relative deficit in performance on the second task in the depletion group and thus an ego-
depletion effect while negative numbers go against the effect. Plots and effect size data for
each lab for the letter ‘e’ task accuracy and participants’ scores on effort, fatigue, difficulty,
and frustration scales and results are presented in Appendix C. Summary statistics from the
meta-analyses for all dependent variables are presented in Table 23.
Averaged sample-weighted effect sizes for the mean RTV (d = 0.04, 95% confidence
interval: -0.07 to 0.15) and RT (d = 0.04, 95% confidence interval: -0.07 to 0.14) variables
were small and confidence intervals included the value of zero. In terms of individual labs’
data, only three of the 23 replications did not have 95% confidence intervals for the ego-
depletion effect size that included zero for RTV, and one of those was negative (i.e., in the
opposite direction to the hypothesized ego-depletion effect). Similarly, only three labs found
mean RT values with confidence intervals that did not include the value of zero, two of which
were negative. We also found moderate levels of heterogeneity in the effect sizes for mean
RTV (I2 = 36.08%, Q22 = 33.42, p = .045) and RT (I2 = 34.13%, Q22 = 33.40, p = .056)
indicating substantial variability in the effect across labs after correction for methodological
artifacts (i.e., sampling error). This finding suggests the presence of other extraneous variables
that may moderate the effect size across laboratories, despite all labs running the experiment
with strict inclusion criteria and an identical study protocol. Given that every laboratory
observed only very small effect sizes for both dependent variables, it is unlikely that a
moderator analysis would return a substantive or statistically significant effect size, but it may
serve to resolve the heterogeneity.
3The stringent inclusion criteria based on accuracy rates on the letter ‘e’ and MSIT tasks resulted in relatively high
rates of participant exclusion across labs. A possible concern with the high exclusion rates is that participants with low
accuracy on tasks were more vulnerable to depletion, which may have masked the effect. Given the potential for the exclusion
rates to affect the outcome, we conducted post hoc analyses identical to the planned analyses in which participants previously
excluded for accuracy were included. The analyses revealed very similar results to the analyses including the exclusions with
small close-to-zero effects for RTV and RT. Full analyses are reported in Appendix B and the data and analysis files are
provided on the OSF website under supplementary analyses:
A candidate moderator identified a priori was the language of the participants. As
planned we conducted separate meta-analyses on the data from English speaking and non-
English speaking labs. This moderator analysis tested the hypothesis that the use of translated
versions of the letter ‘e’ task introduced method variance to the ego-depletion effect. Results of
the separate meta-analyses for the English and non-English speaking labs are provided in Table
2. While there were only very small observed differences in effect sizes in the English speaking
labs’ data for the mean RTV (d = 0.14, 95% confidence interval: -0.02 to 0.30) and RT (d =
0.08, 95% confidence interval: -0.09 to 0.24) dependent variables relative to the non-English
speaking labs (RTV: d = -0.04, 95% confidence interval: -0.18 to 0.10; RT: d = 0.002, 95%
confidence interval: -0.14 to 0.15), the moderator analysis served to produce homogenous
cases in both the English speaking (I2 = 30.45%, Q10 = 14.38, p = .156) and non-English
speaking (I2 = 34.82%, Q11 = 16.88, p = .112) labs for mean RTV. The analysis also produced
a homogenous case for the non-English speaking labs (I2 = 20.38%, Q11 = 13.82, p = .243), but
not the English speaking labs (I2 = 47.84%, Q10 = 19.17, p = .038), for RT. The homogenous
effect sizes were based on the Q-statistic suggesting that the variability in the effect sizes
attributable to methodological artifacts (i.e., sampling error) was no different to the overall
variability in the effect size across samples. However, it is important to note that the I2 statistic,
often considered a better indicator of heterogeniety (Higgins & Thompson, 2002), indicated
moderate heterogeniety in the effect sizes. Importantly, there was substantial overlap in the
confidence intervals of each moderator group and all encompassed zero as a possible value.
Forest plots for the meta-analyses of participants’ accuracy on the letter ‘e’ task and
self-report ratings of effort, fatigue, difficulty, and frustration are presented in Appendix C (see
Figures C1-C5). We found large effects for the depletion condition on letter ‘e’ task accuracy
(d = -1.82, 95% confidence interval: -1.98 to -1.67), and scores on effort (d = 0.78, 95%
confidence interval: 0.63 to 0.94), difficulty (d = 1.91, 95% confidence interval: 1.70 to 2.12),
and frustration (d = 0.82, 95% confidence interval: 0.67 to 0.98), but a substantially smaller
effect for fatigue with confidence intervals that included zero (d = 0.09, 95% confidence
interval: -0.03 to 0.20). Overall, these findings provide some evidence that the depletion
version of the letter ‘e’ task was more effortful and aversive than the no depletion version.
The current report presents the first registered multi-lab replication of the ego-depletion
effect. Results across 23 (N = 2141) participating laboratories revealed small effect sizes for
the ego-depletion effect on the primary and secondary dependent variables, reaction time
variability (RTV) and mean reaction time for incongruent items on the MSIT. In addition, the
95% confidence intervals for the effect sizes for the majority of laboratories’ replications
included the value of zero. The effects are substantially smaller than the ego-depletion effect
size for RTV in the placebo condition of the Sripada et al.’s (2014) study (d = 0.69), that the
present protocol was based on. The present effects are also much smaller than the uncorrected
ego-depletion effect sizes reported in Hagger et al.’s (2010) meta-analysis (d = 0.62) and
Carter and colleagues’ (2015) revision of the Hagger et al. meta-analysis in which 41% of the
included studies were unpublished (g = 0.43), and bias-corrected meta-analytic estimates such
as Carter et al.’s trim-and-fill analysis (g = 0.24). However, the overall effect size of the
present replications closely mirrors the regression-based estimate using the precision effect
estimation with standard error technique reported by Carter et al. (g = 0.003). The results are
consistent with a null effect for ego depletion for the current paradigm. There was substantial
heterogeneity in the effect size across labs. A moderator analysis with laboratory language
(English-speaking labs vs. non-English-speaking labs) revealed small differences in the effect
across English-speaking and non-English-speaking labs, with the 95% confidence intervals for
the ego-depletion effect in both groups encompassing zero with substantial heterogeneity.
An important issue in depletion experiments using sequential-task paradigms, including
the present study, is whether the initial task is sufficiently demanding to evoke a depletion
effect. From the perspective of the limited resource theory that underpins the ego-depletion
effect, the issue is whether the initial task depletes self-control sufficiently to impair
performance on the second task. Indication of the extent of depletion after the first task is
typically inferred from measures that assess the extent to which participants invested effort on
the first task. In the current replication, performance on the letter ‘e’ task and self-report
measures indicated that the depletion version of the task was more demanding and evoked
greater perceptions of effort, difficulty, and frustration than the no depletion version. This
evidence provides some indication that the initial task was more demanding for participants
allocated to the depletion condition relative to controls.
Do the current results suggest that the ego-depletion effect does not exist after all?
Certainly the current evidence does raise considerable doubts given the close correspondence
of the protocol to the standard sequential-task paradigm typically used in the literature, and the
tightly-controlled tasks and protocol across multiple laboratories. Evidence from the current
replication effort suggests that effect sizes observed in many tests of the depletion effect in the
literature, including bias-uncorrected meta-analytic estimates, are inflated. In a recent
commentary, Inzlicht, Gervais, and Berkman (2015) suggested that a range of estimates of the
ego-depletion effect size derived from different meta-analytic estimation methods should be
considered including: (a) the regression-based estimates reported by Carter et al.; (b) the effect
sizes derived from the studies with the top ten largest sample sizes in the meta-analyses; and
(c) the effect size from Carter et al.’s (2015) meta-analysis that includes unpublished studies.
Considering the variation in the estimates from the different sources, a definitive indication of
the true ego-depletion effect remains elusive. However, adding the averaged effect size from
the current analysis as an additional data point in this portfolio would appear to indicate that, at
the very least, the bias-uncorrected effect size estimates derived from meta-analyses are likely
to be substantially inflated. Furthermore, given the rigor with which the current replication was
conducted, substantial weight should be attributed to its findings in such considerations.
A number of limitations that may affect interpretation of the effect size generated in
current analysis. While the tasks adopted in the current replication closely mirror those that
have been used in previous ego-depletion experiments, they are not direct adaptations. For
example, the depletion version of the letter ‘e’ task did not include an initial period where
individuals familiarize themselves with the no depletion version of the task used in the control
group prior to engaging the depletion version. The initial period is supposed to induce a
‘habitual’ response that participants would need to override when engaging in the more
demanding depletion version (e.g., Fennis, Janssen, & Vohs, 2009). It could therefore be
argued that the depletion version of the letter ‘e’ task was not sufficient in inducing a response
that had to be suppressed by participants, that is, suppressing the urge to respond to a letter ‘e’
in favor of applying the conditional rules. However, in addition to Sripada et al.’s study, a
number of sequential-task paradigm experiments in the literature reported using a letter ‘e’ task
without an initial ‘habit forming’ period and found depletion effects (e.g., Baumeister et al.,
1998; Wan & Sternthal, 2008) and there are also variations of this task (such as Carter and
McCullough’s (2013a) essay writing task without letters ‘a’ and ‘n’) with no initial habit-
formation period. Tasks such as the letter ‘e’ task with complex rules and time pressure that
requires a search for a letter and then making a rule-based decision on whether or not to
respond will require the suppression of a tendency to make an immediate response. The use of
a task without a ‘habit forming’ period is unlikely to have been a decisive factor in determining
whether or not ego-depletion was induced.
It is possible that the letter ‘e’ task was sufficiently arduous but not of sufficient duration
to deplete individuals’ self-control resources. This is consistent with some preliminary
evidence that task duration moderates the ego-depletion effect (Hagger et al., 2010a), although
there is also evidence that longer duration may enhance self-control (Dang, Dewitte, Mao,
Xiao, & Shi, 2013). In the current replication, the duration of the letter ‘e’ task was identical to
the task used by Sripada et al., who found it sufficient to induce depletion. Furthermore, the
initial task duration of less than 10 minutes used in the current replication is typical in
sequential-task experiments (Hagger et al., 2010a). Nevertheless, duration on the first task may
serve to moderate ego-depletion and is in keeping with the premise that individuals need to
engage in a sufficient period of effortful self-control to induce a depleted state. Future research
that systematically varies the duration of the initial task may be informative as to whether task
duration can account for variation in ego-depletion findings (Lee, Chatzisarantis, & Hagger,
The MSIT used as the dependent self-control task here, while fit for purpose as a
response inhibition task that has been used previously in sequential-task paradigm experiments,
also led to a high number of participant exclusions due to low accuracy. Although the
instructions focused on the importance of correct responses, participants were also told to “go
as quickly as you can”, so it may be that some participants may have attached high value to
rapid responses over correct answers when responding, resulting in a speed-accuracy trade-off.
However, the exclusion rate in the depletion group was not significantly greater than the rate in
the no-depletion group, allaying concerns of bias as a result of greater error rates in the
depletion group. Another concern is that participants excluded for low accuracy on the MSIT
task might have been more vulnerable to depletion. However, our overall results do not differ
when the participants with accuracy rates below criterion levels are included (see Appendix B).
An important consideration when evaluating the evidence for the ego-depletion effect is
that the effect has been tested in multiple experiments using an array of different initial and
dependent tasks in the sequential-task paradigm. This is consistent with the underlying
hypothesis that self-control performance is governed by a generalized resource that is domain
general. In other words, engaging in a task in one domain of self-control such as impulse
suppression will lead to impaired performance on a task in another domain such as thought or
emotional control. While the current replication of the effect using a standardized paradigm
and two impulse control tasks provides good evidence of a null ego-depletion effect, further
coordinated replication efforts adopting different tasks from multiple domains would provide
additional converging evidence that the depletion effect is null, a position that has been
advocated elsewhere (Carter et al., 2015; Hagger & Chatzisarantis, 2014).
Finally, we note the non-trivial, moderate levels of heterogeneity in the ego-depletion
effect size across laboratories that cannot be attributed to sampling error alone. This is
indicative of some instability in the effect size across labs. One possible cause of the
heterogeneity is the presence of moderators. For example, cultural differences of participants
from the different national groups may have influenced responses to the tasks, perhaps, for
example, influencing the amount of effort that participants invested in the tasks. It is also
possible that the implementation of the experimental procedure varied across the labs, the
stringent specification of the experimental protocol and methods notwithstanding. Previous
multi-lab registered replication reports also observed substantial heterogeneity in some, but not
all, of the meta-analyses of the replicated effects across labs (Eerland et al., 2016; Klein et al.,
2014). The presence of substantial heterogeneity in some effects may provide useful
information on the replicability of experimental results in psychological science. Pre-
registration and strict specification of procedures in replication projects is aimed at restricting
method variance across labs. If substantial unattributed variability in effects remain with this
level of stringency and control, then without such controls the variability may be more
substantive. Journal editors should, therefore, demand the highest levels of clarity of reporting
and precision in study descriptions, including making complete materials and data freely
available, in order to ensure that research findings can be judged appropriately in the context of
the methods used and that results can be replicated with the highest possible levels of
Results from the current multi-lab registered replication of the ego-depletion effect
provide evidence that, if there is any effect, it is close to zero. When looking at the converging
evidence from meta-analyses for the effect, including those that correct for bias, evidence
seems to suggest that estimates of the size of the depletion effect should, at the very least, be
revised downwards from the effect size reported in bias-uncorrected meta-analyses (Hagger et
al., 2010a). While the current analysis provides robust evidence that questions the strength of
the ego-depletion effect and its replicability, it may be premature to reject the ego-depletion
effect altogether based on these data alone. Of course, the current replication provides an
important source of data with regards to the effect given it is based on a pre-registered design
with data from multiple labs, but we recognize it is only one source. We have outlined possible
avenues as to how the research community can move the field forward in providing additional
data for the depletion effect and exploring the possibility of converging evidence from multiple
replication efforts across different depletion domains.
It is also important to note that the current replication speaks little to the underlying
mechanism for the ego-depletion effect. Numerous alternative explanations have been
proposed that challenge the ‘strength’ or ‘resource depletion’ model (Beedie & Lane, 2011;
Evans, Boggero, & Segerstrom, 2015; Giacomantonio, Jordan, Fennis, & Panno, 2014; Inzlicht
& Schmeichel, 2012; Inzlicht, Schmeichel, & Macrae, 2014; Kotabe & Hofmann, 2015) and
research identifying the underlying neural processes may shed light on the processes that
underpin ego-depletion (Heatherton & Wagner, 2011; Hedgcock, Vohs, & Rao, 2012; Inzlicht
& Gutsell, 2007; Kool, McGuire, Wang, & Botvinick, 2013; Loftus, Yalcin, Baughman,
Vanman, & Hagger, 2015; Schel, Ridderinkhof, & Crone, 2014). We are also aware of
competing literatures such as research on mental fatigue and vigilance (e.g., Gergelyfi, Jacob,
Olivier, & Zénon, 2015; Roy, Charbonnier, & Bonnet, 2014) which have yet to be formally
unified with the literature on ego-depletion. The literature on mental fatigue, for example,
suggests that self-regulatory failure is a real phenomenon, but may take longer to materialize.
This may tally with findings of the current replication which revealed a null meta-analytic
effect size of depletion condition on subjective measures of fatigue across studies. Although we
note that fatigue ratings were uncorrelated with the ego-depletion effect size for RT and RTV
across studies, a lack of an effect of depletion on fatigue may indicate that although the task
was sufficiently arduous, as indicated by difficulty, effort, and frustration ratings, it may not
have been of sufficient duration or intensity to result in sufficient fatigue, a candidate proxy
measure of depletion. We call for further co-ordinated research programmes and syntheses that
explore the possible mechanisms for the effect and, particularly, moderating variables and
parameters of the sequential task paradigm that may explain variability in depletion effect sizes
across studies (Lee et al., 2016), and analogs that may assist in mechanistic explanations for
the effect.
We thank Roy Baumeister, Chandra Sripada, and Daniel Kessler for their assistance in
the development of the protocol for the replication studies. We also thank Daniel Kessler for
his assistance with the development of the tasks and data analysis. Finally, we thank the
following people for their help in conducting the experiments: T-Jay Anderson, Dayna Bell,
and Kayla Douglas (Birt Lab); Koen Grootswagers, Femke Kortekaas, David Lacle, Geert
Telkamp, Danielle van Dijl, Joeri Wissink, and Joey Zagers (Brandt Lab); Patrick Alarcon,
Jessee Marriott, Derrick Ocampo, Briana Peralta, and Rachael Van Gundy (Calvillo Lab);
Jantine van Soolingen (Cheung Lab); Josh Cook, Anna Finley, Yvette Ibarra, and Laney Rowe
(Crowell Lab); Jessica Carvajal, Giuliana Kunzle, Julio Martin, and Orlando Olano (Evans
Lab); Jacqueline Conway and Clarence Kwong (Francis Lab); Felix Burgdorf and Veronika
Drößler (Lange Lab); Aza Maltai (Lau Lab); Sophia Huynh, Sarah Kirschbaum, Molly
Minnen, and Ana Moldoveanu (Lynch Lab); Camille Piollet (Muller Lab); Adam Burston,
Katie Knapp, Randi Nehls, Natalie Nikora, and Olivia Sievwright (Philipp Lab); Emily
Devaney, Kaitlin Cassidy, Miriam Mckiney, Caitlin Romano, and Theresa Tokar (Ringos Lab);
Isabel van Oorschot and Joyce van Brecht (Schlinkert Lab); Suzanne Bauwens, Tatjana
Dessers, Sientje Palmans, and Mitte Scheldeman (Stamos Lab); Marlon Fedke, Jessika Fuhr,
Lisa Häfker, Richard Heinrich, and Georg Hetland (Wolff Lab).
Declaration of Conflicting Interests
The authors declared no conflicts of interest with respect to the authorship or the
publication of this article.
Funding for participant payments and E-Prime licenses was provided to individual labs
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Table 1
Sample Sizes, Exclusion Information, and Dependent Variable Data for Each Replication of the Ego-Depletion Effect
Lab Country and
language of
Depletion condition Non-depletion condition
d age
M (SD)
M (SD)
M (SD)
M (SD)
Sripada et al. (2014) (basis for
26 0 0 0 3 23 0.32 (0.07) 0.96 (0.16) 28 0 0 0 4 24 0.27 (0.05) 0.91 (0.13)
Birt & Muise Canada
55 3 3 0 22 31 0.31 (0.07) 0.98 (0.14) 55 2 3 0 24 28 0.29 (0.06) 0.94 (0.11)
Calvillo & Mills USA
74 1 7 0 30 36 0.35 (0.08) 1.02 (0.14) 72 0 5 0 28 39 0.32 (0.06) 0.96 (0.15)
Carruth & Miyake USA
92 0 5 0 32 55 0.32 (0.09) 0.97 (0.14) 93 0 2 0 20 71 0.33 (0.08) 0.97 (0.14)
Crowell, Finley, & Schmeichel USA
65 0 0 2 29 34 0.32 (0.07) 0.96 (0.14) 65 0 0 1 25 39 0.29 (0.06) 0.96 (0.13)
Evans & Fay USA
83 1 1 0 41 40 0.32 (0.08) 0.97 (0.15) 84 0 2 0 33 49 0.35 (0.09) 1.03 (0.15)
Francis & Inzlicht Canada
71 4 13 0 33 23 0.30 (0.08) 0.86 (0.13) 69 0 12 2 28 27 0.32 (0.09) 0.91 (0.15)
Hagger, Chatzisarantis &
71 5 11 0 14 46 0.32 (0.08) 0.93 (0.13) 73 2 8 0 9 55 0.32 (0.08) 0.95 (0.13)
Lau & Brewer USA
67 0 0 1 19 47 0.32 (0.08) 0.97 (0.14) 65 0 0 0 13 52 0.31 (0.08) 0.93 (0.13)
Lynch, vanDellen & Campbell USA
86 0 4 9 31 42 0.34 (0.09) 0.95 (0.15) 86 0 6 12 31 37 0.31 (0.07) 0.88 (0.14)
Philipp & Cannon New Zealand
43 0 0 0 5 38 0.31 (0.08) 0.95 (0.16) 43 0 0 0 6 37 0.31 (0.07) 0.97 (0.13)
Ringos & Carlucci USA
50 0 0 1 17 32 0.33 (0.06) 0.93 (0.05) 50 0 0 1 13 36 0.30 (0.06) 0.99 (0.01)
Brandt Netherlands
85 0 10 0 32 48 0.28 (0.07) 0.88 (0.13) 80 2 7 0 20 54 0.29 (0.07) 0.89 (0.13)
Cheung, Kroese, Fennis, & de Ridder Netherlands
102 0 0 1 12 89 0.31 (0.08) 0.96 (0.13) 102 0 0 0 10 92 0.31 (0.07) 0.94 (0.13)
Elson Germany
52 2 0 0 9 42 0.30 (0.07) 0.96 (0.14) 54 0 0 1 5 48 0.30 (0.07) 0.95 (0.15)
Lange, Heise & Hoemann Germany
60 0 0 0 6 54 0.28 (0.06) 0.88 (0.10) 60 0 0 0 8 52 0.30 (0.07) 0.90 (0.15)
Muller, Zerhouni & Batailler France
56 0 0 1 23 32 0.34 (0.07) 1.04 (0.13) 57 0 1 0 10 46 0.38 (0.08) 1.09 (0.14)
Otgaar, Martijn, Alberts,
Merckelbach, Michirev & Howe
50 0 0 2 23 25 0.28 (0.07) 0.86 (0.12) 50 0 0 0 6 44 0.31 (0.07) 0.93 (0.14)
Rentzsch, Nalis & Schütz Germany
62 0 0 0 11 51 0.28 (0.06) 0.88 (0.09) 60 0 1 1 6 52 0.29 (0.06) 0.88 (0.11)
Schlinkert, Schrama & Koole Netherlands
53 0 0 0 17 36 0.32 (0.08) 0.93 (0.14) 55 0 0 3 9 43 0.32 (0.08) 0.92 (0.12)
Stamos, Bruyneel & Dewitte Belgium
59 0 0 0 16 43 0.30 (0.07) 0.94 (0.15) 58 2 0 0 6 50 0.31 (0.07) 0.94 (0.14)
Ullrich & Primoceri Switzerland
59 0 0 2 7 50 0.29 (0.06) 0.90 (0.12) 62 0 3 1 5 53 0.29 (0.06) 0.89 (0.11)
Wolff, Muzi & Brand Germany
55 0 0 1 16 38 0.33 (0.07) 0.97 (0.12) 56 0 0 1 6 49 0.30 (0.06) 0.92 (0.12)
Yusainy, Wimbarti, Nurwanti &
100 0 0 1 17 82 0.29 (0.08) 0.91 (0.13) 100 0 0 1 25 74 0.27 (0.06) 0.89 (0.13)
Note. Labs are grouped by English-speaking and non-English-speaking labs and in alphabetical order by lead author. Exclusion columns are not
mutually exclusive (e.g., some participants were excluded because they failed to meet age and language criteria). RTV = Reaction time
variability on incongruent items of the multi-source interference task (MSIT), RT = Overall reaction time on incongruent items on the MSIT.
aConducted on fluent English-speaking students in Sweden.
Table 2
Results of Meta-Analysis of Replications of Ego-Depletion Effect
Dependent variable d CI95 SE
Q p I2
Full sample 0.04 -0.07 0.15 .06 34.42 .045 36.08
English-speaking labs 0.14 -0.02 0.30 .08 14.38 .156 30.45
Non-English speaking labs -0.04 -0.18 0.10 .08 16.88 .112 34.82
Full sample 0.04 -0.07 0.14 .05 33.40 .056 34.13
English-speaking labs 0.08 -0.09 0.24 .08 19.17 .038 47.84
Non-English speaking labs 0.00 -0.14 0.15 .07 13.82 .243 20.38
Letter ‘e’ accuracy -1.82 -1.98 -1.67 .08 50.65 .001 56.57
Self-report measures
Effort 0.78 0.63 0.94 .08 66.16 <.001 66.75
Fatigue 0.09 -0.03 0.20 .06 36.76 .025 40.15
Difficulty 1.91 1.70 2.12 .11 90.27 <.001 75.63
Frustration 0.82 0.67 0.98 .08 66.51 <.001 66.92
Note. In all cases number of studies was 23. RTV = Reaction time variability on incongruent
items of the multi-source interference task (MSIT), RT = Overall reaction time on
incongruent items on the MSIT. d = averaged corrected standardized mean difference across
ego-depletion and control groups; CI95 = 95% confidence intervals of d; LL = Lower limit of
confidence interval; UL = Upper limit of confidence interval; SE = Standard error of d; Q =
Cochran’s (1952) Q Statistic; I2 = Higgins and Thompson’s (2002) I2 statistic.
Figure 1. Forest plot of the effect of depletion condition on RTV (reaction time variability)
for the multi-source interference task with larger, positive effect sizes indicating greater
depletion. For each lab, the figure shows the standardized mean difference (Cohen’s d) across
depletion and control groups and a forest plot of the standardized mean difference scores with
95% confidence intervals. The calculation of the overall meta-analytic effect size does not
include data from Sripada et al.’s (2014) study.
Figure 2. Forest plot of the effect of depletion condition on reaction time (RT) for the multi-
source interference task with larger, positive effect sizes indicating greater depletion. For each
lab, the figure shows the standardized mean difference (Cohen’s d) across depletion and
control groups and a forest plot of the standardized mean difference scores with 95%
confidence intervals. The calculation of the overall meta-analytic effect size does not include
data from Sripada et al.’s (2014) study.
... In current literature, there is a debate regarding whether the ego depletion effect is real or not. Some researchers did not find this effect (e.g., Hagger et al., 2016) while others reported it (e.g., Garrison et al., 2019) in the lab setting. One reason for the inconsistent findings is that different tasks were used to manipulate ego depletion in previous experiments, yet some of which were too brief to generate the effect (Baumeister & Vohs, 2016). ...
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Despite the well-established findings regarding the negative effect of impostor phenomenon (IP) on individuals’ career development, we know little about its underlying mechanism. It is also unclear whether IP differentially affects the way men and women manage their careers. Drawn upon ego depletion theory, we explored the relationship among IP, gender, ego depletion, and career preparatory activities via an experiment and a two-wave survey conducted in China. The results showed that the negative relationship between IP and career preparatory activities was mediated by ego depletion. We further found that gender moderated the indirect effect of IP on career preparatory activities via ego depletion, and this effect was stronger for women than men. Based on these new findings, some theoretical and practical implications were discussed.
... The findings are consistent with our hypotheses that two-week self-control training improved individual state forgiveness and inhibited negative behavioral responses after being offended. The results are also consistent with studies related to the ego-strength model of self-control in which self-control can be enhanced by practice over time [7,38]. ...
... EEG methods may be a way to index cognitive effort simultaneously with a task or activity, the time course of depletion, and substrates underlying it (Compton et al., 2011) through more objective means. Such measures may address criticisms about the validity of the depletion effect (Dang, 2018;Hagger et al., 2016) as well as calls for measuring neurophysiological processes underlying self-regulatory control during motor task acquisition and retention to better self-regulatory limitations (Wolff et al., 2020). ...
Purpose: The purpose of this study was to index cognitive resource usage for acquisition of initial targets of two common voice therapy techniques (resonant voice therapy [RVT] and conversation training therapy [CTT]) based on the theorized depletion effect (i.e., when an initial task requiring high cognitive load leads to poorer performance on a subsequent task). Method: Eleven vocally healthy participants, ages 23-41 years, read aloud the Rainbow Passage and produced consonant-vowel resonant targets (/mi, ma, mu/) followed by a baseline computerized Stroop task and a 15-min washout. Following this baseline period, participants watched and interacted with two videos instructing them in RVT or CTT initial targets. After viewing each video and practicing the associated vocal skills, participants rated the degree of mental effort required to engage in the target vocal technique on a modified Borg scale. Participants recorded their attempts at RVT on /mi, ma, mu/ and CTT on the Rainbow Passage, which were later rated by three voice-specialized speech-language pathologists as to how representative they were of each respective target technique. Changes in fundamental frequency and average auditory-perceptual ratings from baseline were examined to determine if participants adjusted their technique from RVT and CTT baseline to acquisition. Results: Performance on the Stroop task was, on average, worse post CTT than post RVT, but both post-CTT and post-RVT Stroop scores were poorer than baseline. These results suggest that both treatment techniques taxed cognitive resources but that CTT was more cognitively taxing than RVT. However, despite differences in raw averages, no statistically significant differences were found between the baseline, post-CTT, and post-RVT Stroop scores, likely due to the small sample size. Participant ratings of mental effort for CTT and RVT were statistically similar. Likewise, poorer post-RVT Stroop scores were associated with participants' greater perceived mental effort with RVT acquisition, but there was no significant association between mental effort ratings for CTT acquisition and post-CTT Stroop scores. Significantly higher fundamental frequency and perceived ratings of the accuracy of technique from baseline to acquisition for both CTT and RVT were found, providing evidence of vocal behavior changes as a result of each technique. Conclusions: Brief exposure to initial treatment tasks in CTT is more cognitively depleting than initial RVT tasks. Results also indicate that vocally healthy participants are able to make a voice change in response to a brief therapy prompt. Finally, participant-rated measures of mental effort and secondary measures of cognitive depletion do not always correlate.
... Implicit Bias (Errors) in Study 5 14 include a manipulation check of mental fatigue (Ma et al., 2013), and used a relatively brief cognitively taxing task (about 10 min of a response inhibition task) to deplete participants. In recent years, the replicability of studies relying on such brief cognitively taxing tasks has been seriously questioned (e.g., Etherton et al., 2018;Hagger et al., 2016;Lurquin et al., 2016;Xu et al., 2014). Therefore, additional evidence is needed to clearly document the effect of mental fatigue on implicit bias in the decision to shoot. ...
Twenty years after 9/11, the impact of terrorism on social and political attitudes remains unclear. Several large-scale surveys suggest that terrorism has no discernible effects on direct, self-report measures of prejudice toward Arab-Muslims. However, direct measures may lack the sensitivity to detect subtle underlying attitudes that are considered socially unacceptable to openly express. To tap these subtle reactions, we assessed more sensitive and implicit measures of the cognitive-affective aspects of prejudice. Building on the justification-suppression model of prejudice, we hypothesized that terrorist attacks increase implicit bias toward Arab-Muslims, especially among individuals who are unable to regulate automatic hostile reactions due to personality or situational variables. Study 1, using data from Project Implicit (N = 276,311), showed that terrorist attacks increased implicit bias but not expressed prejudice toward Arab-Muslims. Study 2, using data from Google Trends, showed that terrorist attacks increased anti-Islamic searches on the internet. Four studies that collected original data (total N = 851) showed that the effects of reminders of terrorism on anti-Islamic implicit bias are moderated by individual differences in prejudice and automaticity (Studies 3-4), by the strength of implicit Muslim-terrorist associations (Study 5), and by momentary self-control depletion (Study 6). Overall, the present research indicates that despite little evidence for elevated overt expression of prejudice against Arab-Muslims following terrorist attacks, terrorist attacks increase anti-Islamic implicit bias whenever individuals are unlikely to control automatic hostile reactions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Regular physical activity, healthy nutrition, and even learning sufficiently for a final exam are desirable behaviors that many individuals fail to implement in their lifestyle. In addition to motivation, volition plays a decisive role in the persistent implementation of target intentions. In this context, volition serves as a collective term for self-regulatory functions that enable the initiation and maintenance of a target intention, even when barriers to action arise. In this chapter, three of the central theories of volition will be presented and discussed: the “Rubicon model of action phases” (e.g., Heckhausen, 1989), the “theory of action control” (e.g., Kuhl, 1983; Kuhl and Beckmann, 1994), and the “strength model of self-control” (e.g., Baumeister et al., 1998). In addition, recommendations for action are derived from each theory in order to support the permanent implementation of target intentions.
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Abstract Background The negative effect of mental fatigue (MF) on physical performance has recently been questioned. One reason behind this could lie in the interindividual differences in MF-susceptibility and the individual features influencing them. However, the range of individual differences in mental fatigue-susceptibility is not known, and there is no clear consensus on which individual features could be responsible for these differences. Objective To give an overview of interindividual differences in the effects of MF on whole-body endurance performance, and individual features influencing this effect. Methods The review was registered on the PROSPERO database (CRD42022293242). PubMed, Web of Science, SPORTDiscus and PsycINFO were searched until the 16th of June 2022 for studies detailing the effect of MF on dynamic maximal whole-body endurance performance. Studies needed to include healthy participants, describe at least one individual feature in participant characteristics, and apply at least one manipulation check. The Cochrane crossover risk of bias tool was used to assess risk of bias. The meta-analysis and regression were conducted in R. Results Twenty-eight studies were included, with 23 added to the meta-analysis. Overall risk of bias of the included studies was high, with only three presenting an unclear or low rating. The meta-analysis shows the effect of MF on endurance performance was on average slightly negative (g = − 0.32, [95% CI − 0.46; − 0.18], p
Psychological science is in a crisis, and has been for quite some time.
Science reformers suggest direct replications as a solution to the replication crisis, but they might not be able to deal with the conceptual and measurement issues that underlie the crisis.
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Categorical moderators are often included in mixed‐effects meta‐analysis to explain heterogeneity in effect sizes. An assumption in tests of categorical moderator effects is that of a constant between‐study variance across all levels of the moderator. Although it rarely receives serious thought, there can be statistical ramifications to upholding this assumption. We propose that researchers should instead default to assuming unequal between‐study variances when analysing categorical moderators. To achieve this, we suggest using a mixed‐effects location‐scale model (MELSM) to allow group‐specific estimates for the between‐study variance. In two extensive simulation studies, we show that in terms of Type I error and statistical power, little is lost by using the MELSM for moderator tests, but there can be serious costs when an equal variance mixed‐effects model (MEM) is used. Most notably, in scenarios with balanced sample sizes or equal between‐study variance, the Type I error and power rates are nearly identical between the MEM and the MELSM. On the other hand, with imbalanced sample sizes and unequal variances, the Type I error rate under the MEM can be grossly inflated or overly conservative, whereas the MELSM does comparatively well in controlling the Type I error across the majority of cases. A notable exception where the MELSM did not clearly outperform the MEM was in the case of few studies (e.g., 5). With respect to power, the MELSM had similar or higher power than the MEM in conditions where the latter produced non‐inflated Type 1 error rates. Together, our results support the idea that assuming unequal between‐study variances is preferred as a default strategy when testing categorical moderators.
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Carter, Kofler, Forster, & McCullough (2015) conducted a bias-corrected meta-analysis of the so-called ego depletion effect to determine its real size and robustness. Their efforts have raised awareness of how badly meta-analyses can mislead when the articles that go into them are products of publication bias. Despite our genuine enthusiasm for their work, we worry that in their zeal to correct the record of publication bias, they have drawn too heavily on largely untested statistical techniques that can be insensitive and sometimes misleading. We tested a set of bias-correction techniques, including those favored by Carter and colleagues, by simulating 40,000 meta-analyses in a range of situations that approximate what is found in the ego depletion literature, most notably the presence of heterogeneous effects filtered by publication bias. Our simulations revealed that not one of the bias-correction techniques revealed itself superior in all conditions, with corrections performing adequately in some situations but inadequately in others. Such a result implies that meta-analysts ought to present a range of possible effect sizes and to consider them all as being possible. The problem with the ego depletion literature is that the bias-corrected estimates for the overall effect do not converge, with estimates ranging from g=0 to g=0.24 to g=0.26. Despite our admiration for this program of meta-research, we suggest that bias-corrected meta-analyses cannot yet resolve whether the overall ego depletion is different from zero or not.
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