Available online at:
https://cc.arcabc.ca/islandora/object/cc%3Apsycjournal

J Camosun Psyc Res. (2022). Vol. 4(1), pp. 164-190.
Submitted Fall 2021; accepted June 2022.

Physiological Effects of Negative Emotional States.
Authors: Glynis Miller*, Micah Morris, Nicholas Gavas, and Amye St Arnault
Supervising Instructor: Michael Pollock, Psyc 215 (“Biological Psychology”)
Department of Psychology, Camosun College, 3100 Foul Bay Road, Victoria, BC, Canada V8P 5J2
*Corresponding author email: mulligan.gmc@gmail.com

ABSTRACT
In this paper, we sought to understand why some negative emotions can have a physiological
effect on the body, so that we could learn to better manage, and normalize, our reactions in
emotionally stimulating situations. Previous research has predicted that these physiological
effects can include variables such as impaired immune system function, insula activity,
inflammation, and decreased prefrontal cortical activity. In our first (correlational) study, we
tested the strength of these relationships by examining naturalistic daily changes in their
variables longitudinally over a one week period. We measured emotional trauma such as stress
by using the Profile of Mood States (POMS) scale to track negative emotional states, and
immune function with a daily body temperature measurement with a thermometer. Social
rejection and anger levels were measured through the use of a negative emotions test called the
Social Rejection Lab, insula activity was measured by the amount of times gut sensation were
felt and prefrontal cortical activity with the Berg’s Card Sorting Task. Fatigue levels were
measured with a self-report Likert-style questionnaire, and inflammation measured by
participants’ bicep circumference. Based on the strength of correlation found between anger and
prefrontal cortex activity in our correlational study, we then conducted a second (experimental)
study to test for a causal relationship between these two variables. For the duration of the seven
day study, participants alternated between the experimental meditation condition (4-days total)
and the controlled non-meditation condition (3-days total) and measured the effect this
manipulation had upon prefrontal cortex activity and subjective anger levels. Data pooled across
participants in our correlational study showed the strongest correlation was between anger and
cortical activity, although these results were not statistically significant. Data pooled across
participants in our experimental study further showed that anger and cortical activity are not
related in that inducing a state of calm does not necessarily enhance an individual's cognitive
functioning. This research suggests that stress-reduction techniques do not lead to any large
improvements in prefrontal functioning.
1. Introduction
1.1 Research Problem
Emotions cause a variety of physiological
effects. Intense states of emotional stress can

affect immune system function. Emotions
such as guilt or shame can elicit a physical
response, for example, stomach aches or
other bodily discomfort. During depressive
episodes, feelings of fatigue and lethargy
tend to be overwhelming. Chronic
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resentment can cause behavior and
relationship problems. We wish to explain
the physiological effects of “negative”
emotions such as fear and sadness.
Understanding such effects will foster selfawareness, allowing us to develop tools to
better manage our emotional states.
1.2 Literature Review
One previously found physiological
effect of stress/trauma is upon immune
function. For example, in a nonexperimental study by Shirtcliff et al.
(2009), researchers hoped to find a link
between impaired immune system function
and emotionally traumatized children by
using results from testing HSV-1 (Herpes
Simplex Virus -1) antibodies from
emotionally traumatized adolescents. The
researchers studied a combined group of 155
children who had been documented as
targets of childhood trauma. Half of the
children lived in homes with reported
trauma and the other half experienced postinstitutional trauma (adopted from
institutions, orphanages, or hospitals). Two
separate tests were conducted with the
children. The first test was the
“Measurement of Immune Competence”,
calculated from the adolescent’s saliva
samples that were collected six times a day
for four days. The laboratory tested the
saliva samples for the specific, “type sigA
Assays”, (a specific type of immunoglobulin
found in the body's mucous membrane) for
the antibody that fights the HSV. The
amount of antibodies was determined by
ELISA (Enzyme-Linked Immunosorbent
Assay), an analyzing tool. The second test
was the Episodic Life Stress Interview (used
to assess chronic and episodic stress), which
was conducted with the adolescents and
their parents separately to ascertain any
existing or ongoing levels of stress. The

outcomes from the post-institutionalized
minors were higher in number than the
minors who experienced trauma in the
home, however both sets of results were
higher than the unaffected control group
who did not experience childhood trauma.
The study did not find significant
differences between the group’s herpes virus
levels, but only in the increased amounts of
antibodies for the herpes simplex-1 virus
with the traumatized children. The results of
the experiment clearly depict more immune
system response which was evident by the
unusually high levels of HSV-sigA
antibodies. Based on these results, the
researchers suggested that deprived children
who suffer from childhood trauma not only
have emotional wounds but have negative
physiological symptoms such as impaired
immune function.
A second previously found physiological
effect of negative emotions is an increase in
insula activity. For example, in an
experimental study by Kross et al. (2011),
researchers used fMRI technology to
demonstrate the connection of a somatic
response of rejection which encompasses
many emotions that are described as social
pain and compared to a physical pain
response. Forty individuals who had
experienced an unwanted breakup within a
6-month period were recruited to participate
in the study. Participants in a social rejection
task were presented with images of their expartner to whom they felt rejected, and a
same-gendered friend that they, within a
similar time frame, experienced a positive
experience. Participants were then given a
cue-phrase to associate to a specific
experience and asked to visualize and focus
on those moments. A physical pain task
stimulus was delivered to participants' left
forearm with a warm or hot thermal to
stimulate distress during fMRI scanning.
One physiological effect was an increase in
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insula activity. Participants then rated how
they felt on a five-point scale (lower
numbers meaning more distress).
Participants reported experiencing greater
distress in the ex-partner trial than the friend
trial. Negative emotions were associated
with an increase in insula activity.
A third previously found physiological
effect of negative emotions is inflammation.
For example, in a correlational study by
Sharpley et al. (2019), researchers recruited
individuals from rural Australia to measure
the correlation between self-reports of
fatigue-related depression, and the ratio of
cortisol to C-reactive protein (CRP).
Depression symptoms were measured using
a 20-item Zung Self-rating Depression
Scale. Specifically accounting for fatigue,
item 10 stated “I get tired for now reason”;
rated from 1 (none or a little of the time), to
4 (most or all of the time). They were then
asked to provide saliva and blood samples to
measure levels of cortisol and CRP
respectively. The researchers found that
elevated levels of CRP (which is produced
when the body is experiencing higher levels
of inflammation), rather than cortisol, had a
higher correlation with those experiencing
fatigue-related depression. Based on these
results, the researchers suggested that those
suffering from fatigue as a predominant
symptom of their depression may be
experiencing higher levels of inflammation,
which would cause the influx of CRP within
the bloodstream.
The literature has found that anger is
related to prefrontal cortical activity. For
example, in an experimental study by
Ricciardi et al. (2013), researchers used
fMRI technology to explore brain activity
during forgiveness and unforgiveness. While
in an fMRI scanner, ten healthy volunteers
were verbally presented with a series of
personalized narratives which described a
neutral scenario (pre-hurtful condition) and a

hurtful scenario (hurtful condition).
Following each narrative, subjects were then
instructed to either forgive the fictional
offender or to harbour resentment and
contemplate revenge (forgiving/unforgiving
response condition). Using a five-point
agreement scale ranging from 1 (not at all)
to 5 (extremely), subjects gave selfassessments on their imaginative skills,
anger levels, and ability to forgive/feel
resentment while imagining hurtful events,
as well as the degree of relief following
forgiveness. Participants rated anger and
frustration levels as significantly higher after
imagining unforgiveness and contemplating
revenge. Consistent with the hypothesis of
Ricciardi et al. (2013), brain scans showed
that forgiveness engages activation in the
prefrontal cortex, part of the brain associated
with information processing and emotion
regulation. Subsequently during
unforgiveness, significantly lower levels of
activity in the prefrontal cortex were found.
Based on these results, the researchers
suggested that low prefrontal cortical
activity is associated with resentment and
unforgiveness.
1.3 Hypotheses
Based on the above literature review, we
predicted the following hypotheses:
Hypothesis #1: If emotional trauma
increases, immune system function will
increase.
Hypothesis #2: If emotions elicited from
social rejection increase then insula activity
will increase.
Hypothesis #3: If fatigue increases then
levels of inflammation will increase.
Hypothesis #4: If anger levels increase then
prefrontal cortical activity will decrease.
2. Methods
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2.1 Participants
The four authors of this paper served as
the participants in its studies. The
participants ranged in age from 22 to 57
years old, with an average age of 35 years,
and included one cisgender man and three
cisgender women. The participants were all
undergraduate students at Camosun College
who completed the current studies as an
assignment for Psyc 215 (“Biological
Psychology”) and were grouped together
due to their mutual interest in the
physiological effects of emotions. The
majority of participants had a history of
mental health issues and/or childhood
trauma.
2.2 Correlational Study Methods
We first performed a correlational study
to test concurrently all four of our
hypotheses by examining naturalistic daily
changes in their variables longitudinally.
Each participant kept a study journal with
them at all times over this study’s one-week
period in order to record self-observations of
the four predictor variables (stress/trauma,
social rejection, anger, and fatigue) and the
four outcome variables (immune system
activity, insula activity, inflammation, and
prefrontal cortical activity).
2.2.1 Immune System Activity
To measure immune system activity,
participants measured and documented
fluctuations (increases or decreases) in their
body temperature using an (oral or forehead)
thermometer, at 5pm daily for seven days.
The participants kept a daily journal of their
temperatures (in Celsius degrees), where
higher temperatures indicated greater
immune system activity.
2.2.2 Social Rejection and Anger
To measure social rejection and anger
levels, each participant completed a negative
emotions test called the Social Rejection

Lab (see Appendix B) at 5pm each day for a
period of one week and recorded the results
in their journals. The test described 9
situations with 2 questions per scenario for a
total of 18 questions. For each scenario,
participants first rated their concern level on
a 1 to 6 scale with the following response
anchors: 1 = very unconcerned, 6 = very
concerned. Second, participants rated on a 1
to 6 scale, (1 = very unlikely, 6 = very
likely) how likely they would expect the
described response from the person in the
scenario. We then totaled the individual item
scores to create an overall score ranging
from 18 to 108, with higher scores
representing greater social rejection/anger
levels.
2.2.3 Fatigue
To measure fatigue, participants were
instructed to fill out a Likert-style
questionnaire consisting of a single selfrated question. This questionnaire was to be
filled out at the same time, 5pm, every day,
for seven days. The question asked
participants to rate their levels of fatigue
between 0 and 10 (where 0 was not tired,
and 10 was extremely tired), which they
were instructed to record in their journals.
2.2.4 Trauma
To measure trauma, each participant
evaluated their negative emotions such as
anger, confusion, guilt, and worthlessness all
taken from the POMS scale to measure their
daily intensity on the scale (see Appendix
A). The list of negative emotions were rated
by each participant with a number scale, (1)
being “not at all” and (5) being “extremely.
Each participant recorded their daily scores,
which were then tallied once the test was
completed. The possible range of scores was
between 18 to 90, with higher scores
meaning more emotional trauma.
2.2.5 Insula Activity
To measure insula activity, each group
member was instructed to keep track of the
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number of times they experienced gut
sensation throughout the day for seven days.
‘Gut’ sensation can be defined by stomach
pain, “gut feeling”, “butterflies”, cramps, or
stomach tension. Once members have
tracked the number of times throughout the
day they experienced ‘gut’ sensations , this
number was then tallied for each day.
2.2.6 Inflammation
To measure levels of inflammation,
participants were instructed to measure the
diameter of their right bicep, at the same
time, 5pm, every day, for seven days.
Participants were told to let their right arm
hang relaxed by their side. They were then
instructed to gently wrap a piece of string
around their bicep, ensuring the string was
tight enough to always be in contact with
their skin, but not too tight as to make any
depression in the skin. Participants were
then told to measure the piece of string with
a ruler and record the length, in cm, in their
journals. Larger bicep diameters indicated
greater inflammation.
2.2.7 Prefrontal Cortical Activity
To measure prefrontal cortical activity,
participants completed the Berg’s Card
Sorting Task through the Psych Lab 101 app
at 5pm each day and recorded the results in
their journals. Participants downloaded the
app and used their cellphones to complete
the task for the duration of the study.
Participants sorted a series of cards and
matched them to one of the four pile options.
Each card had some number or coloured
shapes and could be matched based on the
number of shapes on the card, the color of
the shapes, or the shape type. Participants
were not told how to sort the cards but rather
had to use their judgement to determine
which feature of the card was most
important. Based on these data, the daily
average of prefrontal cortical activity was
calculated for each participant by
determining the degree of accuracy with

which the card sorting task was completed.
To do this, we divided the number of correct
sorts by the total number of trials for each
day of the study.
2.3 Correlational Study Planned Analyses
To assess the strength and statistical
significance of associations between
variables predicted by our four hypotheses,
we performed Pearson product moment
correlations of their predictor variables
(stress/trauma, social rejection/anger, and
fatigue) with their outcome variables
(immune system activity, insula activity,
inflammation, and prefrontal cortical
activity). For testing Hypothesis #1, we
correlated each participants' daily levels of
stress/trauma with measurements of body
temperature (which was an indicator of
immune system function). For testing
Hypothesis #2, we correlated each
participant’s daily social rejection/anger
score with that participant’s average selfreported gut sensation (which was an
indicator of insula activity). For testing
Hypothesis #3, we correlated self-reported
levels of fatigue with bicep circumference
(which was an indicator of inflammation).
For testing Hypothesis #4, we correlated
each participant's daily social rejection/anger
score with that participant’s average Berg’s
Card Sorting Task score (which was an
indicator of prefrontal cortical activity). We
performed all of the above correlations
separately for each participant as well as
using data pooled across all of the
participants. For the correlations using
pooled data, in addition to using the raw
data, we also performed correlations after
we had first transformed the data from each
participant into z-scores in order to
standardize differences in averages and
variability seen between the participants in
their data and thus make them more
comparable. A correlation coefficient was
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considered statistically significant if the
probability of its random occurrence (p) was
< .05 (i.e., less than 5% of the time expected
by chance alone).
2.4 Experimental Study Methods
Based on the strength of the correlation
between anger and prefrontal cortex activity
found in our correlational study, we then
chose to conduct an experimental study to
test for a causal relationship between these
two variables from Hypothesis #4: If anger
levels increase then prefrontal cortical
activity will decrease.
We manipulated the independent
variable, anger, over a one-week period by
having participants alternate conditions
(experimental “meditation” condition and
control “no-meditation” condition using an
ABAB design) across seven days. On
experimental days, participants performed a
five-minute meditation, laying on their
backs on the floor in ‘corpse pose’ (arms by
the side, with palms up) in an undisturbed
room. Participants set a five-minute timer on
their phones and listened to a healing
frequency audio track while also following a
basic breathing technique of 4-4-8 (four
seconds of inhale, four seconds holding their
breath in, and eight seconds of exhale) in
order to induce a state of calm. Afterwards,
they rated their levels of anger using the
Social Rejection Lab test, with scores
possibly ranging from 18 - 108 and higher
scores indicating higher levels of anger.
Participants then completed the Berg’s Card
Sorting Task, a type of neuropsychological
test, to measure levels of prefrontal cortex
functioning. Participants were left with a
percentage score, with higher percent scores
indicating higher levels of prefrontal cortex
activity. Both levels of anger and prefrontal
cortex activity were recorded. On control
days, no meditation was performed but the

same Social Rejection Lab test and Berg’s
Card Sort Task described above were taken.
Since participants were aware of on
which days they were receiving
experimental and control conditions, it was
not possible to control for expectancy
effects. Order effects were controlled for by
alternating experimental days and control
days (an ‘ABAB’ design). To account for
time of day and environmental confounds,
participants completed the
neuropsychological test, subjective test, and
meditation at the same time and place each
day.
2.5 Experimental Study Planned Analyses
To assess the statistical significance of
differences seen in prefrontal cortical
activity on meditation experimental days vs.
no-meditation control days, Student’s t-tests
were performed. We performed t-tests
separately for each participant as well as
using data pooled across all of the
participants. For the t-tests using pooled
data, in addition to using the raw data, we
also performed t-tests after we had first
transformed the data from each participant
into z-scores in order to standardize
differences in averages and variability seen
between the participants in their data and
thus make them more comparable. An
average difference between conditions was
considered statistically significant if, using a
one-tailed distribution (i.e., to determine if
there is a difference between groups in a
specific direction), the probability of its
random occurrence (p) was < .05 (i.e., less
than 5% of the time expected by chance
alone).
3. Results
3.1 Correlational Study Results

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Based on our correlation study results, it
was determined that none of our correlations
held any statistical significance (p < .05),
refer to Table 1. For Hypothesis #1, no
significant correlation was found between
emotional trauma and immune system
functioning using our pooled standardized
data (r = 0.15, p = 0.46; see Figure 2). For
Hypothesis #2, no significant correlation
was found between emotions elicited from
social rejection and insula activity using our
pooled standardized data (r = 0.07, p = 0.80;
see Figure 4). For Hypothesis #3, no
significant correlation was found between
fatigue and levels of inflammation using our
pooled standardized data (r = -0.10, p =
0.60; see Figure 6). For Hypothesis #4, no
significant correlation was found between
anger levels and prefrontal cortical activity
using our pooled standardized data (r = 0.22,
p = 0.26; see Figure 8). Across all of these
correlations using our pooled standardized
data, Hypothesis #4 (prefrontal cortex
activity and subjective states of anger) was
found to have the highest correlation, with
an r value of 0.22.
3.2 Experimental Study Results
There were some minor differences in
group means from the Berg’s Card Sorting
Task test scores as a function of the
meditation vs no meditation conditions
(control M = 74.33 vs experimental M =
76.38; see Table 2). However, after
performing the appropriate t-test using our
pooled raw data, this difference in prefrontal
cortex functioning did now show any
statistical significance (p = 0.13; see Figure
9)). There were also differences found in
group means of subjective anger as a
function of the meditation vs no meditation
conditions (control M = 33.08 vs
experimental M = 29.31; see Table 3). After
performing the appropriate t-test using our

pooled standardized data, this difference in
anger did show statistical significance (p =
0.02).
4. Discussion
4.1 Summary of Results
At the beginning of our research project,
we sought to understand if negative
emotional states (trauma, social rejection,
fatigue, and anger) would affect
physiological variables (immune system
functioning, insula activity, inflammation,
and prefrontal cortical activity). After testing
it was concluded that none of our results
were statistically significant.
4.2 Relation of Results to Past Research
Our current correlational study failed to
find a relationship between emotional
trauma and immune functioning as
previously researched by Shirtcliff et al.
(2008). Our study methods varied in the
following ways. We tested immune response
by monitoring fluctuations in body
temperature with a thermometer for seven
days, whereas the previous study by
Shirtcliff et al. (2008) tested the subject’s
saliva for increased numbers of HSV-1
antibodies. Emotional trauma was measured
as each participant evaluated their daily
negative emotions with the POMS scale for
seven days. It may be that these differences
of measurement methods may have been
responsible for the differences in results
seen. Our study also differed from the
original study with our participants being
adults, where the previous study involved
children from emotionally traumatized
backgrounds and the number of the children
studied was roughly 155, and our study
involved four adults. These speculations
could be tested in the future if the tests were
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closer to the original testing with saliva
samples, along with a larger number of
participants, with at least one half to one
third of the subjects being survivors of
childhood trauma.
Our current correlational study was not
effective in predicting if insula activity
increased when emotions elicited from
social rejection increased as was previously
found by Kross et al. (2011). In our methods
we used gut sensation to calculate insula
activity which varied considerably for each
participant. In contrast, the Kross et al.
(2011) study used physical pain using warm
and hot pads to elicit insula and other brain
activity measured using fMRI, which
provided a much more accurate
measurement and much more controlled
environment as counting gut sensation in a
day may be interpreted differently among
group members and at different levels of
sensation. This section was a challenge
because there are no ways to ethically test
(inflict pain) or gut sensation and measure
the sensation response ethically in a class
environment. In the future the insula activity
(outcome variable) could have been better
measured by measuring gut sensation on a
scale from 1 to 10 as a way to see how
strong the sensation was directly after the
social rejection task. This way there would
be less external variables involved and the
measurement might be more accurate.
Our current correlational study was not
effective in predicting levels of
inflammation as a function of fatigue, as did
previous research by Sharpley et al. (2019).
In our methods, we used a measuring tape in
order to calculate levels of inflammation in a
specific part of the body, the arm (bicep), as
opposed to Sharpley et al. (2019), who used
blood samples to measure levels of CRP in
the bloodstream; providing a more global
measure of inflammation. Our method may
have proven to be too localized, failing to

account for inflammation in other parts of
the body. The measure of inflammation
itself was also subject to several
confounding variables, such as the tightness
of string on the participant’s arm, level of
relaxation the subject could achieve while
measuring their arm, and level of exercise
the subject engaged in during any given day.
Fatigue was also prone to several external
factors. Although we used the same survey
style questionnaire as previous research, the
duration of participants' sleep was not
controlled, leaving room for fatigue as a
result of sleep deprivation, rather than
inflammation. Other confounding variables
included diet, amount of socialization, and
other external emotional stressors that could
have skewed the results. In the future, it
would be effective to account for variables
such as the participant’s hours of sleep per
night, a more global measure of
inflammation (blood or saliva samples), and
other external emotional stressors.
Our correlational and experimental
studies failed to find the relationship
between prefrontal cortical activity and
anger reported by previous research.
Ricciard et al. (2013) found that
unforgiveness (resentment/anger) of an
imagined offender was associated with
decreased prefrontal cortical activity
(specifically in the dorsolateral prefrontal
cortex). Conversely, all but one of our
participants found that increased anger
levels did not result in decreased prefrontal
cortical activity, with anger scores resulting
in much wider variations. The discrepancies
in our findings may be the result of two
methodological differences in our
correlational study as compared to the
Riccardi et al. (2013) study. First, the
findings could have been affected by the
different procedures used to measure cortical
activity. While our experiment used the
Berg’s Card Sorting Task (BCST) to
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measure cortical activity, measurements by
Riccardi et al. (2013) were taken using fMRI
technology. Second, the Riccardi et al.
(2013) study used tailored scenarios that
were specific to each participant, while our
study relied on a non-personalized Likert
scale. Consequently, our experimental study
did not find any causal relationship between
anger and prefrontal cortex activity. There
are multiple confounds, similar to the
correlational study, that could have skewed
these results. Each participant performed the
Berg’s Card Sorting Task every day which
could have led to test-retest biases, as well
as expectancy biases. Participants were also
not controlled for hours slept, daily caloric
intake, total hours of exercise, or time spent
with friends and family, all which could
have an effect on anger levels.
4.3 Implications of Results
Our study indicates that anger-inducing
situations are not enough to affect prefrontal
cortex functioning. For some participants,
we found that our manipulation (meditation)
did significantly reduce anger, however, it
was not enough to find a causal effect on
prefrontal cortical activity.
In this paper, we sought to understand
why some negative emotions can have a
physiological effect on the body, so that we
could learn to better manage, and normalize,
our reactions in emotionally stimulating
situations. Through experimentation, we
found there was no causal relationship
between anger and prefrontal cortex activity,
despite showing that meditation was an
effective strategy in mitigating anger.
During both correlational and experimental
studies of anger and prefrontal cortex
activity, we did not find any significant
relationship between these two variables.
This may have implications on the nature of

anger and cortical functioning, suggesting
that other negative emotions may play a
more critical role in cognitive functioning.
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Table 1
Correlations for Study Variables

Variables

Trauma & Immune
System Activity

Participant
#1

Participant
#2

Participant
#3

Participant
#4

Pooled raw
data

r

n

r

n

r

n

r

n

r

n

Pooled
standardized
data
r
n

0.37

7

-0.17

7

0.02

7

0.38

7

0.07

28

0.15

28

-0.29

7

0.46

7

0.05

7

0.39

21

0.07

21

Social Rejection &
Insula Activity
Fatigue &
Inflammation
Anger & Prefrontal
Cortical Activity

0.30

7

0.19

7

-0.47

7

-0.43

7

0.12

28

-0.10

28

0.01

7

0.03

7

0.1

7

0.76

7

0.07

28

0.22

28

* p < .05.

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Table 2
Descriptive Statistics for Card Sorting Task Across Different Meditation vs No Meditation
Conditions

Condition
Meditation

No Meditation

Statistic

Participant Participant Participant
#1
#2
#3

Pooled
Participant
standardized
#4
data

Pooled
raw
data

M

77.00

76.25

74.25

78.00

0.15

76.38

SD

2.45

2.22

5.12

4.08

0.89

3.58

n

4.00

4.00

4.00

4.00

16.00

16.00

M

76.67

77.33

68.00

75.33

-0.20

74.33

SD

4.73

2.52

6.08

2.52

1.01

5.31

n

3.00

3.00

3.00

3.00

12.00

12.00

Note. M, SD, and n, represent mean, standard deviation, and sample size, respectively (% scores).
* p < .05 for comparison of meditation condition with its respective no meditation condition.

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Table 3
Descriptive Statistics for Anger (Social Rejection Lab Test) Across Different Meditation
vs No Meditation Conditions
Participant Participant Participant
#1
#2
#3

Pooled
Participant
standardized
#4
data

Pooled
raw
data

Condition

Statistic

Meditation

M

29.00

28.50

26.50

33.25

-0.30

29.31*

SD

1.83

5.92

4.51

20.21

0.86

9.99

n

4.00

4.00

4.00

4.00

16.00

16.00

M

35.33

40.67

25.67

30.67

0.41

33.08

SD

2.89

1.53

8.33

12.01

0.92

8.63

n

3.00

3.00

3.00

3.00

12.00

12.00

No
Meditation

Note. M, SD, and n, represent mean, standard deviation, and sample size, respectively.
* p < .05 for comparison of meditation condition with its respective no meditation condition.

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Figure 1
Association Between Trauma and body temperature in Celsius Using Pooled Raw Data

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2,
and yellow = participant #3, green = participant #4. Some data might not be visible in the figure
due to overlapping markers.

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Figure 2
Association Between immune system activity and Mood scale 18-90, Higher score indicates a
higher trauma level Using Pooled Standardized Data

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2,
and yellow = participant #3, green = participant #4. Some data might not be visible in the figure
due to overlapping markers.

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Figure 3
Association Between social rejection scale and gut sensation Using Pooled Raw Data

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2,
and yellow = participant #3. Some data might not be visible in the figure due to overlapping
markers.

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Figure 4
Association Between social rejection scale and gut sensation Using Pooled Standardized Data

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2,
and yellow = participant #3. Some data might not be visible in the figure due to overlapping
markers.

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Figure 5
Association Between inflammation and fatigue Using Pooled Raw Data

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2,
yellow = participant #3, and green = participant #4. Some data might not be visible in the figure
due to overlapping markers.

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Figure 6
Association Between inflammation and fatigue Using Pooled Standardized Data

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2,
yellow = participant #3, and green = participant #4. Some data might not be visible in the figure
due to overlapping markers.

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Figure 7
Association Between Anger and Prefrontal Cortical Activity Using Pooled Standardized Data

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2,
and yellow = participant #3, green = participant #4. Some data might not be visible in the figure
due to overlapping markers.

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Figure 8
Association Between Anger and Prefrontal Cortical Activity Using Pooled Standardized Data

Notes. Marker colour differentiates participants: red = participant #1, orange = participant #2,
and yellow = participant #3. Some data might not be visible in the figure due to overlapping
markers.

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Figure 9
Average Score on Berg’s Card Sorting Task Across Different Meditation Conditions Using
Pooled Raw Data

Notes. Berg’s Card Sorting Task scores are shown for meditation and no-meditation conditions
using pooled raw data from all participants. Error bars show ± 95% confidence levels.
Overlapping scatterplot shows data from each participant. Marker colour differentiates
participants: red = participant #1, orange = participant #2, yellow = participant #3, and green =
participant #4.

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Figure 10
Average Score of Mood States Test Across Different Meditation Conditions Using Pooled Raw
Data

Notes. Mood States Test scores are shown for meditation and the no-meditation conditions using
pooled raw data from all participants. Errors bars show ± 95% confidence levels. Overlapping
scatterplot shows data from each participant. Marker colour differentiates participants: red =
participant #1, orange = participant #2, yellow = participant #3, and green = participant #4.

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Figure 11
Average Score of Berg’s Card Sorting Task Across Different Meditation Conditions Using
Pooled Standardized Data

Notes. Berg’s Card Sorting Task scores are shown for meditation and the no-meditation
conditions using pooled standardized data from all participants. Errors bars show ± 95%
confidence levels. Overlapping scatterplot shows data from each participant. Marker colour
differentiates participants: red = participant #1d, orange = participant #2, yellow = participant #3,
and green = participant #4.

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Figure 12
Average Score of Moods States Test Across Different Meditation Conditions Using Pooled
Standardized Data

Notes. Mood States Test scores are shown for meditation and the no-meditation conditions using
pooled standardized data from all participants. Errors bars show ± 95% confidence levels.
Overlapping scatterplot shows data from each participant. Marker colour differentiates
participants: red = participant #1, orange = participant #2, yellow = participant #3, and green =
participant #4.

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Appendix A
The Profile Of Mood States (POMS) Scale
Profile of Mood States
Subject’s Initials _______
Birth date _______
Date _______
Directions: Describe HOW YOU FEEL at 5pm daily for seven days, by checking one space after
each of the words listed below:

FEELING
Extremely
Tense

Not at all

A little

Moderate

Quite a bit

1

2

3

4

5

Angry
Worn Out

1
1

2
2

3
3

4
4

5
5

Confusion

1

2

3

4

5

Guilt

1

2

3

4

5

Panicky
Unworthy

1
1

2
2

3
3

4
4

5
5

Spiteful
Uneasy
5

1
1

2
2

3
3

4

5

Fatigued
5
Annoyed

1

2

3

1

2

3

4

5

Exhausted
Anxious

1
1

2
2

3
3

4
4

5
5

Ready to fight

1

2

3

4

5

Worthlessness

1

2

3

4

5

Desperate
Weary

1
1

2
2

3
3

4
4

5
5

Deceived

1

2

3

4

5

4
4

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Appendix B

Social Rejection Lab Test. Retrieved October 14, 2021, from
https://socialrelationslab.psychology.columbia.edu/content/measures.
When using the Social Relations scale, imagine the scenario personal to your own experiences
in order to get a more accurate rating when testing.

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190