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Social Cognitive and Affective Neuroscience, 2020, 1–11
doi: 10.1093/scan/nsaa042
Original Manuscript
Neural mechanisms of self-affirmation’s stress
buffering effects
Janine M. Dutcher, 1Naomi I. Eisenberger,2Hayoung Woo,3
William M. P. Klein,4Peter R. Harris, 5John M. Levine,6
and John David Creswell1
1Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213, USA, 2Department of
Psychology, University of California, Los Angeles, CA 90095, USA, 3Department of Psychology, New York
University, New York, NY 10003, USA, 4Behavioral Research Program, National Cancer Institute, Rockville, MD
20852, USA, 5School of Psychology, University of Sussex, Brighton BN1 9RH, UK and 6Department of
Psychology, University of Pittsburgh, Pittsburgh, PA 15260, USA
Correspondence should be addressed to Janine M. Dutcher, 5000 Forbes Ave, Pittsburgh, PA 15213, USA. E-mail: jdutcher@andrew.cmu.edu.
Abstract
Self-affirmation can buffer stress responses across different contexts, yet the neural mechanisms for these effects are
unknown. Self-affirmation has been shown to increase activity in reward-related neural regions, including the ventral
striatum and ventromedial prefrontal cortex (VMPFC). Given that reward-related prefrontal cortical regions such as the
VMPFC are involved in reducing neurobiological and behavioral responses to stress, we hypothesized that self-affirmation
would activate VMPFC and also reduce neural responses to stress in key neural threat system regions such as the dorsal
anterior cingulate cortex (dACC) and anterior insula (AI). We explored this hypothesis using self-affirmation and evaluative
stress tasks following a within-subjects design in the fMRI scanner. Consistent with prior work, self-affirmation blocks led
to lower self-reported stress and improved performance. With respect to neural activity, compared to control blocks,
self-affirmation blocks led to greater VMPFC activity, and subsequently less left AI (but not dACC) activity during stress task
blocks. Functional connectivity analyses revealed greater connectivity between the VMPFC and left and right AI during
self-affirmation compared to control. These findings begin to articulate the neural circuits involved in self-affirmation’s
effects during exposure to stressors, and more broadly specify neural reward-based responses to stressful situations.
Key words: health neuroscience; stress; interventions; self-affirmation
Introduction
Self-affirmation—the process of reflecting on important per-
sonal values or attributes—has a host of benefits from reducing
stress, to improving performance, to enhancing well-being
(Cohen and Sherman, 2014). Across a number of experimental
Received: 4 August 2019; Revised: 16 March 2020; Accepted: 24 March 2020
© The Author(s) 2020. Published by Oxford University Press.
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studies, self-affirmation has been shown to lead to reduced
physiological responses to stress (Creswell et al., 2005), lower
levels of a marker of damage to blood vessels (endothelial
cell-derived microparticles) following a stress induction (Spicer
et al., 2016) and improved performance, especially for those with
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2Social Cognitive and Affective Neuroscience, 2020, Vol. 00, No. 00
higher levels of chronic stress (Creswell et al., 2013, p.20). Outside
of the lab, there is wide-ranging evidence that a self-affirmation
intervention can lead to reduced physiological responses to
real-world stressors (Sherman et al., 2009) and foster enduring
improvements in academic performance outcomes among
vulnerable students (Cohen et al.,2006,2009;Sherman et al., 2013;
Brady et al.,2016;Goyer et al., 2017). Furthermore, self-affirmation
has been shown to reduce defensive responses to threatening
health information (for a review: Cohen and Sherman, 2014),
and to facilitate health behavior change (for reviews: Epton
et al., 2015;Ferrer and Cohen, 2019). These findings converge
to suggest that self-affirmation reduces stress and mitigates the
consequences of stress. While there has been some research
into the psychological mechanisms for self-affirmation effects
[focus on others (Crocker et al., 2008), attentional processes
(Klein and Harris, 2009)], the neural mechanisms underlying
the effects of self-affirmation on stress responses are not well
understood.
It is not known whether self-affirmation can affect neural
threat responses to a stressful experience, and how that effect
occurs. One plausible mechanism for these stress resilience
effects is via neural reward pathways. Previous studies have
found that engaging in a self-affirmation task, compared to
a control task, elicits greater neural activity in reward-related
regions including the ventromedial prefrontal cortex (VMPFC)
and ventral striatum (VS) (Cascio et al., 2016;Dutcher et al.,
2016;Kang et al., 2018). Moreover, across human and animal
studies, reward system activation can lead to reductions in stress
responding, including changes in behavior and physiology (for a
review: Dutcher and Creswell, 2018b). Thus, it is plausible that
self-affirmation’s stress buffering benefits occur via increased
reward-related neural activity, which, in turn, attenuates neural
responses to stressful experiences.
There are a set of neural regions that play a central role
in detecting a threat, appraising the threat and resources
available for managing it, and deploying the physiological stress
response. These regions include the dorsal anterior cingulate
cortex (dACC), amygdala and anterior insula (AI) (Eisenberger
and Cole, 2012;Muscatell and Eisenberger, 2012;Gianaros and
Wager, 2015). Research has found that activity in these regions
is associated with stress physiology, including autonomic,
cardiovascular, neuroendocrine and immune responses, as
well as psychological stress (Gianaros and Wager, 2015;Cohen
et al., 2016). However, it is also critical for an individual to be
able to regulate and modulate this stress response. There are
important structural connections between reward regions and
these threat-related regions (Saper, 1982;Chiba et al., 2001;
Cloutman et al., 2012;Dutcher and Creswell, 2018b). For example,
neuroanatomical work suggests that connections between
medial prefrontal regions and the insula might lead to inte-
gration of autonomic systems with behavior and affect (Saper,
1982). Human neuroimaging has also found that VMPFC and VS
activity to rewarding stimuli leads to corresponding decreases
in dACC and AI activity to pain stimulation (Younger et al., 2010;
Eisenberger et al., 2011). Taken together, the extant literature
suggests that the VMPFC and VS are key regions modulating
dACC and AI responses to threatening or stressful stimuli.
Psychophysiological interaction (PPI) analysis is a method for
understanding the functional connectivity between regions
during a task, and helps to determine if there is transmission
of information between circuits in the brain. Thus, we addi-
tionally explored whether VMPFC, and VS, activity during self-
affirmation leads to greater functional connectivity with dACC
or AI, as a mechanism for self-affirmation’s stress buffering
effects.
The present study had three basic goals: (i) to replicate pre-
vious work demonstrating that self-affirmation leads to greater
reward-related neural activity (Cascio et al., 2016;Dutcher et al.,
2016;Kang et al., 2018); (ii) to determine if self-affirmation leads
to reduced neural responses to a stress manipulation and (iii)
to explore VMPFC functional connectivity differences during
self-affirmation compared to control.
Importantly, to understand how self-aff irmation affects neu-
ral and behavioral responses to stress in an fMRI paradigm, both
self-affirmation and stress needed to be manipulated within-
subjects. While self-affirmation research typically manipulates
self-affirmation between subjects, the fMRI environment
necessitates within-subject comparisons, so a secondary goal
of this study was to explore whether self-affirmation has stress
buffering effects in a within-subjects design. To explore self-
affirmation’s effects on neural and behavioral responses to
stress, we manipulated self-affirmation using a scanner-adapted
version of a self-affirmation task, which includes both self-
affirmation and non-affirmation control conditions (Dutcher
et al., 2016). To manipulate stress levels, participants completed
a stressful math task with a socially evaluative component
(Dedovic et al., 2005;Inagaki et al., 2016), which allowed us to
assess, within subjects, neural and self-reported stress, as well
as performance under stress. This task has been shown to lead
to increased activity in the dACC and bilateral AI (Wang et al.,
2005;Dedovic et al., 2009;Inagaki et al., 2016).
Based on previous behavioral work (Creswell et al., 2005,
2013), we hypothesized that self-aff irmation would lead to lower
self-reported stress and better performance compared to non-
affirmation control. Based on previous neuroimaging work (Cas-
cio et al., 2016;Dutcher et al., 2016;Kang et al., 2018), we predicted
that self-affirmation would lead to greater reward-related neural
activity compared to non-affirmation control. Furthermore, we
predicted there would be less threat-related neural activity dur-
ing the stressful math problems that followed self-affirmation
compared to those that followed the non-affirmation control.
Finally, we predicted that, during self-affirmation compared to
non-affirmation control, there would be greater connectivity
between reward-related regions (VMPFC and VS) and threat sys-
tem regions, suggesting a linkage between the neural effects
occurring during self-affirmation and the effects occurring in the
brain after the affirmation process.
Methods
Participants
Twenty-seven university students (18 female; mean age =
19.3 years, s.d. = 1.35) completed study procedures. All partici-
pants met eligibility criteria for fMRI studies (right-handed, not
claustrophobic, no metal). To ensure that students believed the
performance feedback on the math stress task (that they were
underperforming relative to their peers), only students in the
arts, social sciences and humanities were recruited (Carnegie
Mellon has high performing engineering and computer science
programs). Twenty-five participants had usable neuroimaging
data and were included in neuroimaging analyses (one was
excluded for excessive motion, one participant’s data were lost
by the scanner). The Carnegie Mellon University Institutional
Review Board approved all procedures and all participants gave
informed consent.
Procedure
A week before the fMRI session, participants completed a sur-
vey in which they rated a list of personal values in order of
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J. M. Dutcher et al. 3
Fig. 1. Example trial displays for self-affirmation and non-affirmation control blocks.
importance to them. We then used these rankings to build
a self-affirmation task for each individual participant (details
below). When participants arrived for the fMRI scan session, they
provided informed consent, were screened for scanner eligibility,
and received instructions about the tasks they would be com-
pleting. Then participants underwent an fMRI scan in which they
completed a high-resolution structural scan (MPRAGE), two runs
of the self-affirmation and stressful math combined task, one
run of a self-affirmation and threat reactivity task, and one run
of a values statements task (always presented in this order). The
latter two tasks are not reported here as they were designed
to test different hypotheses from the task reported on in the
present analyses. During the scan, participants viewed trials on
a projector screen and were asked to make responses using a
button box. After the scan, participants completed questionnaire
measures, were debriefed and then dismissed.
Self-affirmation task. To manipulate self-affirmation, partic-
ipants completed a task that had participants affirm their
important values (self-affirmation) or a control condition
(adapted from: Dutcher et al., 2016). This task uses a standard
self-affirmation decision making paradigm (Steele and Liu, 1983;
Steele, 1988), in which participants were given a series of paired
personal-value statements and were asked to indicate their
relative preference (adapted from Vernon and Allport, 1931).
The self-affirmation blocks were based on the online survey
participants completed before their scheduled session. In these
self-affirmation blocks, they saw a screen that said,‘Think about
these values and indicate which value is more important to
you.’ The next screen displayed two values (see Figure 1), and
participants indicated which value they preferred, on a scale
from 1 to 4 [1 = strongly prefer (the value on the left), 2 = slightly
prefer (the value on the left), 3= slightly prefer (the value on the
right), 4= strongly prefer (the value on the right)]. Participants
would see their top value paired against each other value, and
which side the values appeared on screen was counterbalanced.
As a non-affirmation control condition, participants completed
alphabetizing trials that controlled for visual content (similar
images were displayed). They were shown a screen that said,
‘Which label is alphabetized?’ The next screen displayed images
with scrambled letter sequences below them (see Figure 1), and
participants pressed 1 if the letters in the sequence on the left
were shown in alphabetical order and 4 if the letters in the
sequence on the right were shown in alphabetical order. Both
trial types were shown on screen for 6 s, in blocks of four trials
(24 s per block). There were eight self-affirmation blocks and
eight non-affirmation blocks across two runs.
Stress task. To manipulate stress levels, we had participants
complete a math task with both stressful condition and non-
stressful control conditions. In the stressful ‘test’ condition,
participants completed math problems of varying difficulty and
were told their performance would be compared to their peers
at the university (based on: Inagaki et al., 2016). In the control
condition, participants completed ‘practice’ blocks that included
trials with easy math problems (e.g. 42+1=)whichtheywere
instructed to calculate in their heads. Once they arrived at an
answer, they pressed a button, and then saw a brief fixation fol-
lowed by the next trial.No feedback was provided in the practice
blocks. During the test blocks, participants were shown a more
difficult math problem (e.g. 62/9 =) and, under time pressure,
were asked to select an answer from a set of possible answers
shown on screen (see Figure 2). Participants then saw accuracy
feedback: red text stating ‘Incorrect. Please try harder.’ if they
were incorrect, blue text stating ‘Correct!’ if they were correct,
and black text stating ‘No response provided’ if they did not
respond in time. They then saw evaluative feedback regarding
how their performance compared to the average of their peers
at that point. Participants saw two horizontal bars on screen,
one indicating their performance and one indicating their peers’
performance. They were told their performance rating was based
both on how quickly they responded, as well as how accurate
they were. The feedback was designed so their performance
bar started to fall behind the peer bar, such that they were
performing worse over time relative to the average peer, which
made these blocks both socially evaluative and stressful. At the
end of each math block (both practice and test), participants
were asked to rate how stressful that block was on a 1 (not at
all) to 4 (a lot) scale. Blocks were 56-s long: eight math trials
presented for 5 s each. In the practice blocks, trials were followed
by 2 s of rest, while in the test blocks, trials were followed by 1 s
accuracy feedback, and 1 s of evaluative feedback (see Figure 2).
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4Social Cognitive and Affective Neuroscience, 2020, Vol. 00, No. 00
Fig. 2. (A) The images shown during the math test blocks and (B) math practice blocks.
Participants completed eight blocks of each condition across
two runs.
Run design. In order to explore the effect of self-affirmation on
stress responding, we crossed affirmation condition and stress
level in a 2 ×2 design. Hence, the tasks were interspersed
such that either a self-affirmation or a non-affirmation block
preceded each type of math block. To obtain four blocks of every
combination of stress and values blocks, this task was con-
ducted across two runs of the scanner. This 2 (self-aff irmation)
×2 (stress) design resulted in four conditions of interest: self-
affirmation practice, non-affirmation practice, self-affirmation
test and non-affirmation test. While the order of presentation of
math blocks was always a practice block followed by a test block,
the exact math problems and the presentation order of self-
affirmation and non-affirmation blocks was counterbalanced
based on four pseudorandom scripted presentation orders.
Image acquisition. Data were acquired on a Siemens Verio 3-
T MRI scanner with foam padding surrounding each partici-
pant’s head to reduce head movement. For each participant, we
acquired a structural T2-weighted echo-planar imaging volume
(MPRAGE)—spin-echo, repetition time (TR) = 2300 ms, echo time
(TE) 1.97 ms, matrix size= 256 ×256, 1.0-mm isovoxel, field
of view (FOV)= 256 mm, 176 slices, 1-mm thick, f lip angle 9◦,
bandwidth = 240 Hz/Px (coplanar with the functional scans).Both
runs of the values and math stress task lasted 706 s (11 min,
46 s)—gradient-echo, TR= 2000 ms, TE= 25 ms, multiband fac-
tor 3, matrix size 70 ×70, 3.0-mm isovoxel, FOV = 210 mm, 51
axial slices, 3-mm thick, f lip angle 79◦, bandwidth = 1930 Hz/Px,
collected at a 38◦slice angle.
Data analysis. Imaging data were analyzed using Statisti-
cal Parametric Mapping (SPM) software (SPM8; Wellcome
Department of Cognitive Neurology, Institute of Neurology,
London, England). Prior to preprocessing, images were manually
reoriented to maximize preprocessing alignment quality.
For preprocessing, functional and anatomical images were
realigned, co-registered to the structural scan and normalized
using the DARTEL procedure in SPM8. For the self-affirmation
task, the 24-s values blocks were modeled as the self-affirmation
condition and the 24-s alphabetize blocks were modeled as the
non-affirmation condition. For the stress task, the practice math
trials that came after a self-affirmation block were modeled as
the self-affirmation practice condition, and the practice math
trials that came after a non-affirmation block were modeled as
the non-affirmation practice condition. Similarly, the test math
trials that came after a self-affirmation block were modeled
as the self-affirmation test condition, and the test math trials
that came after a non-affirmation block were modeled as
the non-affirmation test condition. The self-reported stress
rating block was modeled separately from the math blocks.
Implicit baseline consisted of the rest periods (viewing a fixation
cross). Conditions were replicated across both runs of the task.
Activation during each block was convolved with a canonical
hemodynamic response function. Six motion parameters were
included as nuisance predictors plus a predictor for each
timepoint that the global signal change (GSC) exceeded 2.5 s.d.
of the mean GSC or where estimated motion exceeds 1.5 mm of
translation or 1.5◦of rotation. We used a 128 Hz high-pass f ilter,
and modeled serial autocorrelation as an AR (1) process.
We computed linear contrasts for each participant compar-
ing BOLD signal for one main contrast of interest from the self-
affirmation task: self-affirmation vs non-affirmation control.
Moreover, we computed linear contrasts for each participant
comparing BOLD signal for two main contrasts of interest from
the stress task: non-affirmation test vs non-affirmation practice,
and self-affirmation test vs non-affirmation test. These indi-
vidual contrast images were then used in group-level analyses.
We conducted whole brain analyses to gain a comprehensive
picture of neural activity during these tasks. Due to this being
the first study exploring the neural stress buffering effects of
self-affirmation, whole brain analyses were calculated at an a
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J. M. Dutcher et al. 5
priori voxel extent threshold of 20 voxels, P<0.001 to balance
Type I and Type II errors (Lieberman and Cunningham, 2009),
and the tables note which clusters are significant at P<0.05
FDR-corrected.
To examine differences in functional connectivity between
self-affirmation and non-affirmation control, we also conducted
two PPI analyses. PPI analyses measure the functional con-
nectivity between the time series of a chosen seed and the
time series of the remaining voxels in the brain. Due to our
a priori interest in the effect of prefrontal regions’ influence
on threat system activity, we seeded the first analysis with a
VMPFC ROI. We built the ROI by taking the prefrontal cluster
seen in the whole brain analysis of self-affirmation compared
to the non-affirmation control and restricting it to the ventro-
medial portion (from −14 <x<14, anterior to y= 34, and ventral
to the corpus callosum, −20 <z<−4, see Figure 4B). We ran a
second PPI analysis using a VS ROI. We built the ROI by tak-
ing the prefrontal cluster seen in the whole brain analysis of
self-affirmation compared to the non-affirmation control and
restricting it to the portion that overlapped with the VS (from
−24 <x<24, 4 <y<18, and −12 <z<0). Using the generalized
psychophysiological interaction toolbox in SPM (McLaren et al.,
2012), we first calculated PPI measures at the subject level for the
self-affirmation compared to non-affirmation contrast. These
first-level contrast estimates were then compared as a t-test at
the group level of analysis to examine which regions in the brain
demonstrated greater functional connectivity with VMPFC and
VS during self-affirmation compared to non-affirmation control,
using our a priori threshold.
Results
Preliminary analyses
There were no significant differences in stress ratings or num-
ber correct for any of the conditions based on the order trials
were presented (all P’s >0.13), so all analyses presented will be
collapsed across counterbalanced order.
As a manipulation check, we ran a 2 (self-affirmation
condition) ×2 (stress level) ANOVA to ensure the stress task
manipulated stress, as well as that the self-affirmation task
effectively buffered stress responding. As predicted, there was a
main effect of stress level such that participants rated the test
math problems as more stressful (M= 2.597, s.d. = 0.468) than the
practice math problems (M= 1.281, s.d. = 0.426), F(1,26) = 98.34,
P<0.001. Additionally, there was a significant main effect of
self-affirmation condition, such that participants rated math
test blocks as less stressful following the self-affirmation blocks
(M= 1.853, s.d. = 0.577), than following the non-aff irmation blocks
(M= 2.062, s.d. = 0.546), F(1,26) = 9.46, P= 0.005. There was also a
significant interaction between self-affirmation and stress on
stress ratings (see Figure 3A), F(1, 26) = 5.12, P= 0.032. Consistent
with previous research (Creswell et al., 2005;Sherman et al.,
2009), paired samples t-tests revealed a significant effect such
that individuals reported less stress to the math test blocks
following self-affirmation (M= 2.426, s.d. = 0.897), compared
to the math test blocks following non-affirmation (M= 2.769,
s.d. = 0.778), t(26) = −3.225, P= 0.003. This effect was not observed
for the practice math blocks following self-affirmation compared
to non-affirmation, t(26) = −1.055, P= 0.301. Finally, we ran a
paired samples t-test comparing the number of problems
correct in each test condition, as only the test math problems
required an answer response. Consistent with previous research
(Creswell et al., 2013), self-affirmation led to significantly
more correct answers on the test math problems (M=14.15,
s.d. = 3.371) compared to non-aff irmation (M= 12.33, s.d. = 3.679),
t(26) = 3.399, P= 0.002 (see Figure 3B). These results suggest that
the stress task manipulated stress and the self-affirmation
task effectively buffered stress responding and enhanced
performance.
Neuroimaging analyses
First, we examined the difference in neural activity for the
self-affirmation condition compared to the non-affirmation
control condition. Whole brain analyses focusing on clusters
with greater activity during self-affirmation compared to non-
affirmation revealed a number of regions (see Table 1 for full list).
Consistent with prior work (Dutcher et al., 2016), self-affirmation
led to more activity in a large cluster in medial and VMPFC that
extended into the caudate head of the VS (see Figure 4A)[MNI
coordinates: −9, 51, 39, t= 8.504, k(number of voxels) = 2105], as
well as clusters in the posterior cingulate cortex (−6, −51, 30;
t= 10.126, k= 980) and the angular gyrus (−42, −63, 27; t= 10.224,
k= 627).
Next, we ran a whole brain analysis focusing on the
stressful test trials that followed the non-affirmation condition
compared to the non-stressful practice trials that followed the
non-affirmation condition to explore the regions more active
during the stressful condition compared to control. The non-
affirmation test condition led to greater activity (compared to
non-affirmation practice) in a number of regions including left
and right AI and dACC (cluster: −12, −87, −9; t= 15.303, k= 13 966)
(for list, see Ta ble 2).
To test our hypothesis that self-affirmation would lead to
less threat-related neural activity compared to non-affirmation,
we conducted a whole brain analysis on the stressful trials
that followed the self-affirmation conditions compared to the
stressful trials that followed the non-affirmation condition. This
analysis produced one cluster of activity in the negative contrast,
a cluster in left AI (−18, 33, −3, t=−4.617, k= 29) (see Table 3 ), such
that the self-affirmation test condition led to less activation of
the left AI compared to the non-affirmation test condition (see
Figure 5). However, this cluster was not significant at the more
stringent FDR corrected P<0.05 value.
To examine whether there were differences in func-
tional connectivity during self-affirmation compared to non-
affirmation control, we conducted a PPI analysis using a VMPFC
seed (see Figure 4B). We then examined the whole brain to
determine which regions showed greater functional connectivity
with the VMPFC during self-affirmation compared to non-
affirmation. Relative to non-affirmation control, there was
greater functional connectivity between the VMPFC and left AI
(−36, 30, −6, t= 4.536, k= 44), right AI (27, 30, −6, t= 4.940, k= 23),
left dorsolateral prefrontal cortex (−39, 18, 30, t= 6.756, k= 495)
and supplementary motor area (−6, 15, 51, t= 6.627, k= 227) (for
full list, see Ta ble 4).
We also conducted a PPI analysis using a VS seed. We
then examined the whole brain to determine which regions
showed greater functional connectivity with the VS during
self-affirmation compared to non-affirmation. There were no
significant clusters in this analysis.
Discussion
The present study explored the effect of self-affirmation on
neural and behavioral stress responding. Compared to non-
affirmation control, self-affirmation led to lower self-reported
feelings of stress and enhanced performance in response to
the stressful math problems. These findings are consistent with
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6Social Cognitive and Affective Neuroscience, 2020, Vol. 00, No. 00
Tab l e 1. Whole brain analysis of self-affirmation vs non-affirmation contrast (k>20, P<0.001)
Anatomical region Brodmann’s
area
Hemisphere MNI coordinates of peak voxel
xyzt(24) k
Self-affirmation >non-affirmation
CerebellumaRight 30 −87 −36 7.055 253
CerebellumaLeft −27 −87 −39 6.450 87
Temporal poleaRight 54 9 −18 6.922 456
Temporal poleaLeft −60 0 −18 9.179 925
MPFC/VMPFCa9/10/11 Left −9 51 39 8.504 2105
Hippocampus Right 27 −9−18 4.421 42
Hippocampus Left −27 −30 −15 4.618 63
Occipital lobeaLeft −9−96 24 6.628 552
Posterior cingulate cortexaLeft −6−51 30 10.126 980
Temporoparietal junctionaRight 54 −60 24 7.665 546
Angular gyrusa39 Left −42 −63 27 10.224 627
Non-affirmation >self-affirmation
Cerebellum Left −30 −66 −48 −5.008 26
Cerebellum 0−54 −30 −5.111 22
Cerebellum Left −6−75 −24 −5.375 27
Inferior occipitalaRight 27 −90 −3−8.372 361
Inferior occipitalaLeft −27 −93 −6−8.869 1057
Inferior frontal gyrusaRight 42 9 27 −6.542 543
Inferior frontal gyrusaLeft −33 −330−6.818 324
Intraparietal sulcusaRight 27 −51 42 −7.384 479
aRegions significant with FDR P<0.05 correction.
Fig. 3. (A) Interaction between condition and math difficulty on self-reported stress ratings. (B) The effect of condition on the number of math problems correctly
answered during test trials. Error bars depict standard errors of the means. ∗P<0.05.
prior behavioral work demonstrating self-affirmation to be an
effective stress-reduction technique (Creswell et al., 2005,2013;
Cohen et al., 2009;Sherman et al., 2009,2013;Brady et al., 2016;
Goyer et al., 2017) and show that this effect can also be observed
in a within-subjects design. This has important implications
for future laboratory-based studies, and, in particular, facilitates
further examination of brain responses to self-affirmation and
its subsequent effects.
The primary goal of this study was to characterize the neural
mechanisms for self-affirmation’s stress buffering effects. First,
we replicated findings that self-affirmation (compared to con-
trol) elicited greater reward-related neural activity (VMPFC, in
particular). We also showed that self-affirmation (vs control) led
to less AI activity in response to stressful, evaluative math blocks.
We posit that this finding is consistent with neuroscience and
behavioral research finding that rewarding stimuli or activities
can mitigate stress responding (Dutcher and Creswell, 2018b).
Critically, anatomical and structural investigations in the brain
find pathways by which key hubs in the reward and threat
systems in the brain are linked (Ulrich-Lai and Herman, 2009;
Cloutman et al., 2012;Dutcher and Creswell, 2018b), supporting
the plausibility of this mechanism. In addition, this f inding sug-
gests that self-affirmation may be changing the way the brain
responds to exposure to threatening or stressful information,
consistent with previous behavioral work demonstrating that
self-affirmation leads to changes in attentional bias towards
threatening information (Klein and Harris, 2009) and ERP indi-
cators of error responsiveness (Legault et al., 2012).
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J. M. Dutcher et al. 7
Tab l e 2. Whole brain analysis of non-affirmation test vs non-affirmation practice contrast (k>20, P<0.001)
Anatomical region Brodmann’s
area
Hemisphere MNI coordinates of peak voxel
xyzt(24) k
Non-affirmation test >non-affirmation practice
Bilateral insula/dACC/precuneus/
thalamusa
Left −12 −87 −9 15.303 13 966
Non-affirmation practice >non-affirmation test
Temporal pole Left −45 6 −30 −4.175 31
VMPFC Right 42 39 −9−4.367 32
DMPFC Left −18 39 45 −5.199 244
Dorsal posterior insulaaRight 39 −12 15 −6.740 175
Dorsal posterior insula Left −39 −15 21 −4.719 25
Angular gyrusaLeft −45 −72 42 −6.927 295
Posterior cingulate cortex Right 51 −57 30 −4.938 88
MPFC Right 15 57 24 −4.386 20
Posterior cingulate cortex Left −12 −45 36 −4.169 30
Supplementary motor area Right 9 −21 54 −4.172 62
DMPFC Right 18 30 54 −4.716 65
aRegions significant with FDR P<0.05 correction.
Tab l e 3. Whole brain analysis of self-affirmation test vs non-affirmation test contrast (k>20, P<0.001)
Anatomical region Brodmann’s
area
Hemisphere MNI coordinates of peak voxel
xyzt(24) k
Self-affirmation test >non-affirmation test
No clusters
Non-affirmation test >self-affirmation test
AI Left −18 33 −3−4.617 29
No regions significant with FDR P<0.05 correction.
Fig. 4. (A) The prefrontal cluster from whole brain analysis of the self-affirmation >non-affirmation control contrast. (B) This cluster restricted to just the VMPFC. This
seed was used in the PPI analysis.
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8Social Cognitive and Affective Neuroscience, 2020, Vol. 00, No. 00
Tab l e 4. PPI analysis comparing functional connectivity in the VMPFC between self-affirmation vs non-affirmation control (k>20, P<0.001)
Anatomical region Brodmann’s
area
Hemisphere MNI coordinates of peak voxel
xyzt(24) k
Self-affirmation >non-affirmation
Cerebellum Left −9−75 −30 5.420 172
Occipital lobeaRight 30 −93 −6 8.774 630
Occipital lobeaLeft −30 −93 −6 9.516 678
Midbrain Left −15 −18 −9 4.432 32
AI Right 27 30 −6 4.940 23
AI Left −36 30 −6 4.536 44
Thalamus Right 3 −6 3 5.123 49
Thalamus Right 15 −21 15 4.852 28
DLPFCaLeft −39 18 30 6.756 495
Posterior parietal cortexaLeft −27 −72 42 8.761 311
DLPFC Right 48 33 27 5.069 38
Inferior frontal gyrus Right 33 15 27 4.249 39
Posterior parietal cortexaRight 30 −66 45 6.037 290
Supplementary motor areaaLeft −6 15 51 6.627 227
Non-affirmation >self-affirmation
Occipital lobe Left −6−96 21 −4.810 20
Posterior cingulate cortex Right 3 −18 39 −4.470 39
aRegions significant with FDR P<0.05 correction.
Fig. 5. Parameter estimates from the left AI cluster found in whole brain analysis comparing self-affirmation test to non-affirmation test. ∗P<0.001.
Moreover, we explored the functional connectivity of reward-
related regions during self-affirmation compared to non-
affirmation control to test a pathway for stress buffering effects.
Results found greater connectivity between the VMPFC and both
left and right AI, suggesting that during self-affirmation, there
is greater communication between these regions, mirroring
structural AI connectivity (Saper, 1982;Chiba et al., 2001;
Cloutman et al., 2012). There were no significant differences
in functional connectivity for the VS during self-affirmation
compared to non-affirmation control. While PPI analysis cannot
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J. M. Dutcher et al. 9
characterize the nature or direction of communication, animal
tracing studies have identified projections from VMPFC to
insular cortex, supporting the plausibility of VMPFC inputs
on insula activity. Furthermore, in one study, activity in the
VMPFC was associated with less AI activity to a subsequent
threat (Eisenberger et al., 2011). In the present study, increased
connectivity occurred during the self-affirmation task, and the
subsequent response to stress was characterized by a relative
decrease in left AI activity following self-affirmation. However,
little work has explored functional connectivity between reward
and threat system regions and whether greater connectivity
indicates an enhanced ability to down-regulate neural stress
responses. Future work can examine whether this increased
functional connectivity leads to an inhibitory influence on
subsequent AI responses to threat and if it is related to changes
in physiological responses to stress.
While prior research has found that self-affirmation can
buffer the physiological stress response (Creswell et al., 2005;
Sherman et al., 2009;Spicer et al., 2016), this is the first study
to examine its effects on neural responses during stress
exposure. Activity in the AI has been linked to inflammatory
and physiological responses to stress (Gianaros et al., 2005;
Slavich et al.,2010;Muscatell and Eisenberger, 2012;Gianaros and
Wager, 2015), suggesting these effects present a possible neural
mechanism by which self-affirmation affects the physiological
responses to stress. Future research can continue to explore
this possibility by conducting studies that integrate fMRI with
stress physiology measures to determine if reductions in AI
activity to stress after a self-affirmation intervention are related
to alterations in stress physiology (e.g. blood pressure, heart rate
or cortisol).
We used a socially evaluative stress task to be consistent
with the extant literature on self-affirmation effects on labo-
ratory stress responding (Creswell et al., 2005,2013;Sherman
et al., 2009). Studies outside the laboratory often examine self-
affirmation’s effects on stressors that have social implications.
It is possible, then,that self-affirmation’s stress buffering effects
are limited to social stressors. However, self-affirmation has
been shown to mitigate the effect of ego depletion on later pain
tolerance (Schmeichel and Vohs, 2009) and self-enhancement
has been shown to lead to longer persistence in the ice bath
during a cold-pressor task (O’Mara and Gaertner, 2017), sug-
gesting that self-affirmation’s stress buffering effects are not
limited to social stressors. This work has not tested whether self-
affirmation has differential effects on different types of stres-
sors, thus future work should explore self-aff irmation’s stress
buffering effects in the context of a variety of different stressors.
We hypothesized that self-affirmation would lead to less AI
and dACC activity during stress compared to non-affirmation
control. It was therefore surprising that results only support
reduced AI activity during stress following self-affirmation, not
dACC activity. While we are cautious not to over-interpret a
null finding, the roles that dACC and AI play in threat detection
and processing are known to differ (Muscatell and Eisenberger,
2012). The dACC is believed to signal distress (Spunt et al., 2012),
whereas the AI is believed to be critically involved in awareness
of physiology and bodily states (Craig and Craig, 2009). It is pos-
sible that self-affirmation’s effects are stronger on AI responses
to stress than dACC responses to stress, if self-affirmation
has stronger effects on physiological stress responses than
distress responses. While there was a decrease in self-
reported stress to the stressful math problems that followed
self-affirmation compared to non-affirmation control, these
self-report findings did not correlate with VMPFC responses
to self-affirmation or AI responses during the stressful math
trials, and not significantly to VMPFC-left AI connectivity (see
Supplementary data for details on these analyses). Thus, self-
affirmation might affect other processes in the brain that
contribute to distress or stress appraisals that were not captured
here, such as affecting the individual’s perception of the
resources they have to manage a stressor. This could influence
retrospective self-reported stress at the end of the block while
not necessarily influencing immediate dACC responses to the
stress exposure. It will be important for future research to clarify
if this dACC null effect is reliable and its interpretation.
There is significant scientific interest in empirically sup-
ported stress-reduction interventions. Yet there is still work to
be done articulating the neural mechanisms for stress reduction
effects and their health consequences (for a review: Dutcher and
Creswell, 2018a). Indeed, few studies have explored the effects
of any behavioral intervention on neural responses to stressful
experience. One pilot study found that mindfulness training led
to less stress reactivity in the amygdala and anterior/middle
insula compared to control (Kober et al., 2017). Because this
is the first study to explore the effect of self-affirmation on
neural responses to stress, it also offers potential insight on
the effect of stress reduction interventions more generally on
neural responses to stress. It is possible that increased reward-
related activity and subsequent decreased threat-related activity
is a neural mechanism for other stress reduction interventions
as well.
The dearth of findings in this area highlights the need
for more health neuroscience research on stress-reduction
pathways. There are a few limitations to the present work.
First, the study design necessitated an acute laboratory stressor
and a within-subjects design, which limits the generalizability
to chronic stress conditions. However, this study serves as a
‘proof of concept’ for future studies aiming to explore the effect
of self-affirmation on chronic stress and the consequences
therein. Additionally, participants were between the ages of 18–
23, and much of the work on self-affirmation’s stress buffering
effects has been in young adults and adolescents. However,
studies of the neural correlates of self-affirmation have been
conducted across a range of ages, suggesting that the initial
reward-related activity associated with self-affirmation is not
limited to this age group (Cascio et al., 2016;Dutcher et al.,
2016;Kang et al., 2018).Some results were not significant at
more stringent FDR-corrected thresholds, including the reduced
left AI activity during self-affirmation test compared to non-
affirmation test. These effects may or may not be stronger in
a larger sample. Thus, the age group and the smaller sample
size in this study suggest that it will be important to replicate
these findings in a larger study,with broader range of participant
demographics. Finally, in order to ensure the believability of the
performance feedback, participants were given showing they
were underperforming compared to their peers, we recruited
humanities, social sciences and art students. While this may
have weakened the self-relevance of the task, participants
still found the task stressful, providing the opportunity to test
self-affirmation’s stress buffering effects.
The present results found that, consistent with previous
work, self-affirmation led to lower self-reported stress and
enhanced performance to a socially evaluative experience
compared to a non-affirmation control. Importantly, self-
affirmation also led to less AI activity to the stressful task
compared to control, suggesting that self-affirmation can
reduce both affective and neural responses to stress exposure,
and mitigate some of the performance consequences of
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10 Social Cognitive and Affective Neuroscience, 2020, Vol. 00, No. 00
stress. Furthermore, the AI is a key region for facilitating
the physiological stress response (Gianaros and Wager, 2015),
perhaps serving as a mechanism for self-affirmation’s effects
on stress physiology (Creswell et al., 2005;Sherman et al., 2009;
Spicer et al., 2016). While this and previous work have primarily
been conducted in laboratory studies, they underscore the
effects a self-affirmation intervention could have on stress-
related health outcomes. Considering the rising rates of chronic
stress (American Psychological Association, 2018), the negative
effects stress has on health (Cohen et al., 2007), and the cost-
effective and easy to implement nature of a self-affirmation
intervention, the present findings suggest self-affirmation
interventions for stress and stress-related health are worth
exploring.
Supplementary data
Supplementary data are available at SCAN online.
Acknowledgements
This work was conducted with the Health and Human Per-
formance Laboratory at Carnegie Mellon University.
Conflict of interest
The authors have no conflicts of interest to report with this work.
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