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

J Camosun Psyc Res. (2021). Vol. 3(1), pp. 1-15.
Submitted Fall 2020; accepted July 2021.

The Effects of Anxiety on the Brain and Body.
Authors: Victoria Merrick*, Elena Muratova, Kathryn Rebalkin, and Anjandeep Kaur
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: torimerrick@outlook.com

ABSTRACT
In this paper, we want to examine the effect of increased levels of anxiety on the brain and
body in order to understand its adverse consequences for mental and physical functioning and
well-being. Previous research indicated that anxiety causes a left-hemispheric bias of the brain,
decreases amygdala activity, increases hippocampal-insula activity, and elevates blood pressure.
In our first (correlational) study, we tested the strength of these relationships by examining
naturalistic daily changes in their variables longitudinally over a two-week period. We rated the
level of anxiety on a scale of 0 to 100, measured left-hemispheric bias by the Global Local task,
determined amygdala activity by increases in heart rate after watching scary videos, measured
hippocampal-insula activity by the number of memory-induced gut feelings experienced each
day, and measured blood pressure by diastolic readings. Data pooled across participants in our
correlational study showed a significant correlation of anxiety level with hippocampal-insula
activity and blood pressure in half of the participants but not with amygdala activity and
hemispheric bias. Based on the strength of correlation found between anxiety level and
hippocampal-insula activity in our correlational study, we then conducted a second
(experimental) study to test for specifically a causal relationship between these two variables.
Over a twelve-day period, we randomly assigned participants each day to either a guided
meditation condition or a social media condition and measured the effect this had upon anxiety
level each day. The results of our experimental study failed to establish a causal role of anxiety
level upon hippocampal-insula activity in most of the participants, with only one participant
showing a significant effect. A possible practical application of these findings could be that
increased memory-induced gut feelings might indicate higher than usual anxiety in some people.
1. Introduction

1.1 Research Problem
Anxiety is an emotion many people
struggle with and can reduce the quality of
daily life. We would like to examine the
biological mechanisms of anxiety, and how
the disorder can impact one physically.

Also, we want to explore how anxiety
affects the amygdala and response time
under the threat condition. In addition, we
will also look at how anxiety affects the
connection between emotion and memory.
Along with this, the elevation of blood
pressure due to changes in levels of anxiety
will also be examined. By researching this
issue, we can learn how to lessen the
physical and mental effects of anxiety and
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hopefully improve mental and biological
well-being.
1.2 Literature Review
Anxiety has been found to cause different
patterns in brain activity, specifically
causing hemispheric bias in the brain. Keller
et al. (2000) conducted a study to determine
which hemisphere of the brain was more
highly affected by anxiety. They examined
80 undergraduate students, all of which were
right-handed, who completed two
questionnaires, the Mood and Anxiety
Symptom Questionnaire (MASQ) and Penn
State Worry Questionnaire (PSWQ), to selfreport different levels of anxiety. Afterward,
the participants completed a chimeric face
task (M = - 0.540, SD = 0.458), while an
electroencephalogram (EEG) reported their
brain wave patterns. The chimeric face task
to test hemispheric bias used a book of pairs
of human faces, with one-half of the face
smiling and the other half being neutral.
Participants were asked to choose which
face they believed appeared to be happier. If
a participant “consistently chose faces with
smiles on one side of the visual field”
(Keller et al., 2000), it demonstrated a level
of hemispheric bias. It was found that those
with higher levels of self-reported anxiety
had a larger left-hemispheric bias. This
study demonstrates the effects anxiety has
on biological mechanisms and that anxiety
has physical implications on the patterns of
activity in the brain.
It was also previously found that threat
monitoring affects the functioning of the
frontal-parietal cortex and amygdala. Fortyone people (22 female and 19 male)
participated in the experiment by Choi et al.
(2012). Participants needed to perform a
response-conflict task after being either
under a threat condition of mild electric
shock or safe condition for 1.75-5.75

seconds. At the end of the anticipation,
period participants were presented with a
picture of a house or building overlaid with
five letters of string that could appear in one
of three variations: congruent, incongruent,
or neutral. The participants were asked to
respond by using their index finger for
building or middle finger for a house
regardless of the overlaid letters. The
experiment was performed with the use of
functional magnetic resonance imaging. The
neural processes as well as skin conductance
response and reaction time were recorded.
The stronger responses to the threat
condition were shown across the frontalparietal cortex, but the amygdala were
activated more by the safe condition. Skin
conductance response demonstrated that
responses were greater after the period of
shock monitoring (mean in long-transformed
unit .019) than after safe one (.003). Thus,
threat monitoring increased response in the
frontal-parietal cortex and decreased
response in the amygdala.
Trait anxiety also attributes to an aversion
of risk-taking behaviors through connections
with the hippocampus and right insula. 110
college students, 32 males, and 78 females
with an average age of 20.18 were
participants in a study by Huo et. al (2020).
This study looked at the connection between
anxiety and risk-taking, and the connection
to the hippocampus and right insula. The
participants all took a self-reported trait
anxiety report, as well as, Balloon Analogue
Risk Task (BART) where participants
received a reward as they pumped the
balloon. This helped to measure risk-taking
because if the participants’ balloon popped
they would lose the money. After a certain
point, it was risky for participants to
continue pumping the balloon. To examine
the connection to the hippocampus and
Insula, recordings of the participants resting
state and structure were taken using an fMRI
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for eight minutes. To test the functioning of
the connections in the brain a VBM analysis
was used to assess the specific regions of the
brain. As a result of this study, it was found
that anxiety was negatively correlated with
risk-taking behaviors. Also, an increased
response in the hippocampus and the insula
showed that people who have higher trait
anxiety use emotion and memory when
making decisions and avoid making risky
decisions (Huo et. al. 2020). Thus, anxiety
increases activity in the hippocampus and
insula, which is connected to emotion,
decision-making, and memory.
Anxiety is supposed to level up
hypertension (HT), which is the persistent
elevated blood pressure in the arterial
vessels. In total, 412 students who belonged
to several, health care profession courses
(which indicates high levels of anxiety)
participated in the study administered by
Mucci et al. (2016). 283 participants were
women (68.7%) and 129 were men (31.3%).
The average age ± standard deviation was
23.9 ± 7.5 years. DBP and SBP readings
were obtained by using the professional
aneroid sphygmomanometer on each
student’s right arm in a seated position,
maintained for at least 5 min at a constant
room temperature. The diastolic scores
ranged between 70 and 90 mmHg; the
systolic scores ranged between 100 and 170
mmHg. Life-style variables were also
investigated during the medical consultation.
As a result of this study, it was found that
hypertension and anxiety are correlated.
Anxiety will improve the sympathetic
response and more easily activate the
sympathetic nervous system. Activation of
the sympathetic nervous system not only
reduces renal blood flow, increases renal
water and sodium retention, but also elevates
blood pressure. Thus, anxiety levels up
hypertension (blood pressure).

1.3 Hypotheses
Based on the above literature review,
we predicted the following hypotheses:
●
Hypothesis #1: If anxiety increases
then the left-hemispheric bias of the brain
will increase.
●
Hypothesis #2: If anxiety increases
then amygdala activity will decrease.
●
Hypothesis #3: If anxiety increases
then hippocampal-insula activity will
increase.
● Hypothesis #4: If anxiety increases
then blood pressure will increase.
2. Methods
2.1 Participants
The four authors of this paper served as
the participants in its studies. The
participants ranged in age from 20 to 33
years old, with an average age of 23.5 years,
and included all female participants. 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 effects of anxiety on the brain and
body. All participants had high levels of
self-reported anxiety.
2.2 Materials and Procedure
2.2.1 Correlational Study Methods
We first performed a correlational study
to test concurrently all 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 two-week period in order to
record self-observations of the following
four variables: (1) levels of anxiety in
correlation with (2) the levels of activity in
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the left-hemispheric bias of the brain, (3) the
response of the amygdala, (4) the activity in
the hippocampal-insula, and (5) the blood
pressure.
To measure the amount of hemispheric
bias in the brain, participants used the
Navon task from the website
PsyToolKits.org (see Appendix A) to
measure their levels of right or left
hemisphere bias in the brain based on Global
and Local dominance. The participants
followed the link to the website and ran the
demo. All participants completed the Navon
task once daily by following the instructions
provided within the task, between the hours
6 pm and 8 pm. The task used an
interference experiment model, in which the
participants were shown large letters made
up of smaller letters. If the participants saw
the letter H or O, as either the large letter or
the smaller letters, they clicked the letter B
on their keyboards. If they did not see the
letter H or O in either form, they clicked the
letter N on their keyboards. The results of
the Navon task were recorded in each
participant’s research journal, based on the
amount of Global (large letter) errors and
Local (small letters) errors they made. Each
participant also recorded the level of anxiety
they felt overall throughout that day.
To determine the level of activity in the
amygdala, participants used the amount of
increase in heart rate after the scary video
compared to before it as an indirect measure
of amygdala activity. Heart rate was
measured by using the “Heart Rate Monitor”
App by Health & Fitness AI Lab installed on
a cell phone of each participant. Every day
the participants performed three
measurements. The first measurement was
taken between 6 pm and 8 pm. Then
participants started watching a 30 seconds
video assigned for that day (see Appendix
B) and did another measurement right away.
The third measurement was performed

immediately after the video stopped. The
amount of increase in heart rate was
calculated by subtracting the second heart
rate measurement from the third heart rate
measure. All measurements and results were
recorded in an individual research journal.
To determine how anxiety affected the
connection between the hippocampus and
the insula, participants measured memory
induced gut feelings. The participants were
asked to keep a journal to tally the amount
of memory-induced gut feelings they had
throughout their day that will measure the
connection between the hippocampus,
memory, and insula, emotional gut feelings.
The results of the procedure were recorded
in the participants' journals with their overall
rating of daily anxiety.
To determine the correlation between
anxiety and blood pressure, participants
measured blood pressure, using a cuff
machine around their arm and recorded their
diastolic readings. If the participants did not
have the machine in their homes, they went
to the nearest pharmacy and got their blood
pressure measured. The participants
performed measurements and recorded their
results once daily, during the evenings
between the hours 6 pm and 8 pm. The
results were recorded in the research journal
along with their level of anxiety they felt
throughout that day.
To measure the levels of anxiety
throughout the two week period, the
participants self-reported the level of their
anxiety on a scale of 0 to 100. At the end of
each day between 6 pm and 8 pm, the
participants gave an overall score of their
feelings of anxiety for that day using this
anxiety level scale: 0 = no feelings of
anxiety, 25 = minimal feelings of anxiety,
50 = moderate feelings of anxiety, 75 = high
levels of anxiety, and 100 = extreme levels
of anxiety. Then the participants recorded
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their results in the individual research
journals.
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
(levels of anxiety) with their outcome
variable (hemispheric bias, decrease in the
amygdala, increase in the right
hippocampus, and increase in diastolic blood
pressure). To test hypothesis #1, we
correlated the average levels of anxiety with
the average Global and Local errors from the
Navon task that the participants completed
daily. To test hypothesis #2, we correlated
the average levels of anxiety with the
average response of the amygdala of each
participant on each day during the threat
monitoring phase. To test hypothesis #3, we
correlated the average levels of anxiety with
the average level in the hippocampal-insula
connection activity of each participant on
each day. To test hypothesis #4, we
correlated the average levels of anxiety with
the average blood pressure results of each
participant on each day. 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 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).
2.2.2 Experimental Study Methods

Based on the strength of the correlation
between anxiety levels and levels of
hippocampal-insula connection 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 #3.
We manipulated the independent
variable, levels of anxiety, over a twelve-day
period by randomly assigning participants
each day to either a guided meditation
experimental condition or a social media
control condition. On guided meditation
experimental days, participants completed a
five-minute mindful breathing meditation
YouTube video (See Appendix C). On the
control days, participants spent five minutes
on social media. All conditions were
completed within 30 minutes of the
participants waking up each day. Between 6
pm and 8 pm, the participants recorded their
overall anxiety levels and the amount of
memory induced gut feelings they had
throughout the day.
To avoid order effects, participants were
randomly assigned to a condition each day
by choosing the top card from a pre-shuffled
14-card deck that had equal amounts of each
color (black suit = experimental condition
and red suit = control condition). To avoid
experimenter expectancy effects, the
participants tried to be extremely objective
about what they considered to be memory
induced gut feelings.
To assess the statistical significance of
differences seen in hippocampal-insula
connection on guided meditation
experimental days vs. social media 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
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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 two-tailed distribution
(i.e., allowing this difference to be positive
or negative), 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
As shown in Table 1, hippocampal-insula
activity was significantly correlated with
anxiety levels. Although not statistically
significant for half the participants,
hippocampal-insula activity was
significantly correlated with anxiety levels
using both pooled raw data (r = .49, p =
.00009) and pooled standardized data (r =
.49, p = .00009; see Figure 3). In addition,
although not statistically significant for half
the participants, blood pressure was
significantly correlated with anxiety levels
using both pooled raw data (r = .42, p =
.001) and pooled standardized data (r = .43,
p = .0008; see Figure 4). In contrast, no
statistically significant correlations were
found between anxiety level and
hemispheric bias using any single
participant’s data (all r ≤ .28, all p ≥ .33),
pooled raw data (r = .05, p = .70), or pooled
standardized data (r = .04, p = .78; see
Figure 1). Similarly, no statistically
significant correlations were found between
anxiety level and amygdala activity using
any single participant’s data (all r ≤ .25, all p
≥ .39), pooled raw data (r = .08, p = .58), or
pooled standardized data (r = .10, p = .46;
see Figure 2). Based on a comparison of the
correlation coefficients using either the

pooled raw data or the pooled standardized
data, both hippocampal-insula activity and
blood pressure showed the strongest
correlation with anxiety levels.
3.2 Experimental Study Results
As shown in Table 2, only one participant
(#3) was shown to have a significant effect.
No other participants had a significant
difference that was found in memory
induced gut feeling between the meditation
condition and the social media condition.
Statistically significant differences between
the conditions were seen using participant’s
#3 data (all p =.08). All other participants'
data were not statistically significant
individually (p ≥ .09), with pooled raw data
(p = .53; See Figure 5), or with pooled
standardized data (p = .42).
4. Discussion
4.1 Summary of Results
Based on previous research, we
hypothesized that increases in anxiety levels
would result in increases in the four
variables: left hemispheric bias (Hypothesis
#1), amygdala activity (Hypothesis #2), the
activity in the hippocampal-insula
(Hypothesis #3), and the diastolic blood
pressure (Hypothesis #4). Data pooled
across participants in our correlational study
supported the prediction relationship of daily
level of anxiety and the activity in the
hippocampal-insula and blood pressure but
only with half of the participants
(Hypothesis #3&4) but not with hemispheric
bias or amygdala activity (Hypotheses
#1&2).
4.2 Relation of Results to Past Research

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The lack of correlation between anxiety
levels and hemispheric bias is not in line
with previous research. Keller et al. (2000)
found that those with high levels of selfreported anxiety also had a significant
correlation with a left hemispheric bias
based on measurements of a chimeric face
task electroencephalogram (EEG) to
measure brain activity. The methodology of
our correlational study differs from that of
Keller et al. (2000) that may account for the
discrepant results. Our study used a Navon
task, which used global and local features
within letters to measure hemispheric bias. It
was predicted that those with higher levels
of anxiety would have fewer local errors
compared to global errors, suggesting an
increased left-hemisphere bias. One of the
major errors of this correlational study could
be a result of carryover effects. As the
participants completed this task daily, it is
possible that they became increasingly better
at the Navon task due to learning. Future
studies may want to examine this
phenomenon of hemispheric bias and
anxiety in such a way that carryover effects
will not have the ability to confound the
study.
A strong relationship between anxiety
level during the threat monitoring phase and
amygdala activity was not found in our
correlational study. These results contradict
past research. Choi et al. (2012) found that
threat monitoring decreased response in the
amygdala. While the Choi et al. (2012) study
showed a negative correlation between these
two variables, our correlational study
demonstrated a weak positive correlation.
This difference in findings can be related to
the way we conducted our studies. In
addition to threat monitoring, Choi et al.
(2012) also used a response-conflict task
where participants needed to perform
cognitive actions and respond by using
either index finger or middle finger. Our

study only included the threat monitoring
phase. Future studies should test whether the
addition of response-conflict tasks
influences the relationship between anxiety
and amygdala activity.
The moderate correlation between
the anxiety levels and the hippocampusinsula connection is partially in line with
previous research. The previous research by
Huo et al. (2020) found that trait anxiety can
accredit to risk-taking behaviors throughout
the connection with the hippocampus and
the right insula. The research measured
connections between anxiety and risktaking, and the connection to the
hippocampus and the right insula. The Huo
et al. (2020) participants all took a selfreported trait anxiety report paired with the
BART, whereas, our research was a daily
self-report tally of memory induced gut
feelings. The similarities of both our
conclusions despite using different research
designs suggest a generalized relationship
with the level of anxiety and between risktaking behaviors and memory induced gut
feelings.
The strong correlation between
anxiety level and blood pressure is
completely in line with previous research.
Mucci et al. (2016) found out that anxiety
improves the sympathetic response and
easily activates the sympathetic nervous
system. The activation reduces the renal
blood flow along with increasing renal water
and sodium retention. This elevates blood
pressure. Thus, anxiety levels up blood
pressure. The Mucci et al. research
participants conducted a survey where the
blood pressures were recorded and other
factors such as lifestyle variables (smoker,
alcoholic, etc.) were also considered,
however, we did not consider all these
factors in our research. Our research was
about self-reported anxiety levels where
participants measured their blood pressure
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daily. The similarities of both our
conclusions despite not considering the
lifestyle variables suggest that anxiety
elevates blood pressure.
4.3 Implications of Results
Possible practical applications of our
current findings are that increased memory
induced gut feeling and blood pressure
might be the indicators of higher than usual
levels of anxiety in some people.
We originally conducted a current study
to find out how anxiety affects our body and
brain in order to learn how to better manage
anxiety and improve our general well-being.
Based on our experimental study we cannot
recommend using memory-induced gut
feeling for monitoring anxiety level. It
remains for future studies to find more
precise ways of testing the effects of anxiety
on the body and brain given our limited
access to comprehensive testing equipment.

References
Choi, J. M., Padmala, S., & Pessoa, L.
(2012). Impact of state anxiety on the
interaction between threat monitoring and
cognition. NeuroImage, 59(2), 1912–
1923. https://doiorg.libsecure.camosun.bc.ca:2443/10.101
6/j.neuroimage.2011.08.102
Huo, H., Zhang, R., Seger, C. A., Feng, T.,
& Chen, Q. (2020). The effect of trait
anxiety on risk‐taking: Functional
coupling between right hippocampus and
left insula. Psychophysiology, 57(10), 1–
10.
https://onlinelibrary-wileycom.libsecure.camosun.bc.ca:2443/doi/fu
ll/10.1111/psyp.13629
Keller, J., Nitschke, J. B., Bhargava, T.,
Deldin, P. J., Gergen, J. A., Miller, G. A.,
& Heller, W. (2000). Neuropsychological
differentiation of depression and anxiety.
Journal of Abnormal Psychology, 109(1),
3–10. https://doiorg.libsecure.camosun.bc.ca:2443/10.103
7/0021-843X.109.1.3
Mucci, N., Giorgi, G., De Pasquale Ceratti,
S., Fiz-Pérez, J., Mucci, F., & Arcangeli,
G. (2016). Anxiety, stress-related factors,
and blood pressure in young adults.
Frontiers in Psychology, 7, 1682.

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Table 1
Correlation coefficient (r) values, with the number of daily trials (n) per correlation in brackets.

Variables correlated
Anxiety level &
hemispheric bias
Anxiety level & amygdala
activity
Anxiety level &
hippocampal-insula
connection

Participant Participant Participant Participant
#1
#2
#3
#4
0.09(14)
0.00(14)
-0.22(14)
0.28(14)

Pooled
raw data
0.05(56)

Pooled
standardized
data
0.04(56)

-0.09(14)

0.03(14)

0.21(14)

0.25(14)

0.08(56)

0.10(56)

0.21(14)

0.36(14)

0.60(14)*

0.80(14)*

0.49(56)*

0.49(56)*

*p < .05.

Table 2
Descriptive statistics on memory induced gut feeling for meditation and social media conditions.

Condition
5 minutes of
guided breathing
focused
meditation
5 minutes of
social media

Statistic
Mean
S.D.
n
Mean
S.D.
n

Participant Participant Participant Participant
#1
#2
#3
#4
1.0
1.20
2.83*
1.57
0.89
0.84
0.75*
0.53
6
5
6
7
1.33
0.82
6

2.14
0.9
7

1.83*
0.75*
6

2.0
0.71
5

Pooled
raw
data
1.67
1.01
24

Pooled
standardized
data
-0.11
0.95
24

1.83
0.11
24

0.82
0.99
24

* p < .05 for comparison of high-congener condition with its respective low-congener condition.

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Figure 1
Scatterplot of levels of anxiety and hemispheric bias using pooled standardized data across
participants.

Marker color indicates which participant data is from: red = participant #1, orange = participant
#2, yellow = participant #3, and light green = participant #4. Some data might not be visible in the
figure due to overlapping markers.

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Figure 2
Scatterplot of levels of anxiety and amygdala activity using pooled standardized data across
participants.

Marker color indicates which participant data is from: red = participant #1, orange = participant
#2, yellow = participant #3, and light green = participant #4. Some data might not be visible in the
figure due to overlapping markers.

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Figure 3
Scatterplot of anxiety and Hippocampal-insula activity using pooled standardized data across
participants.

Marker color indicates which participant data is from: red = participant #1, orange = participant
#2, yellow = participant #3, and light green = participant #4. Some data might not be visible in the
figure due to overlapping markers.

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Figure 4
Scatterplot of anxiety level and blood pressure using pooled standardized data across participants.

Marker color indicates which participant data is from: red = participant #1, orange = participant
#2, yellow = participant #3, and light green = participant #4. Some data might not be visible in the
figure due to overlapping markers.

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Figure 5
Bar graph of average memory induced gut feelings across guided meditation condition and social
media condition using pooled raw data from participants, with error bars showing ± 95%
confidence levels, and with an overlapping scatterplot of data from each participant.

Marker color indicates which participant data is from: red = participant #1, orange = participant
#2, and yellow = participant #3, light green = participant #4.

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Merrick, Muratova, Rebalkin, & Kaur - J Camosun Psyc Res. (2021). Vol. 3(1), pp. 1-15.

Appendix A
Link to Navon Task: https://www.psytoolkit.org/experiment-library/navon.html
Appendix B
The list of videos used for measuring heart rate during the threat monitoring.
1. https://www.youtube.com/watch?v=104f8ofoeh4
2. https://www.youtube.com/watch?v=HrbR8Y6XpuY
3. https://www.youtube.com/watch?v=XPeBqO4yyGk
4. https://www.youtube.com/watch?v=Rv1YvQbkPBY
5. https://www.youtube.com/watch?v=zQYcc5cDywY
6. https://www.youtube.com/watch?v=xEbNulX-xds
7. https://www.youtube.com/watch?v=U49r1MmyuIw
8. https://www.youtube.com/watch?v=zV_XnEMjA74
9. https://www.youtube.com/watch?v=pp9iU5RAp20
10. https://www.youtube.com/watch?v=104f8ofoeh4
11. https://www.youtube.com/watch?v=HrbR8Y6XpuY
12. https://www.youtube.com/watch?v=XPeBqO4yyGk
13. https://www.youtube.com/watch?v=Rv1YvQbkPBY

14. https://www.youtube.com/watch?v=zQYcc5cDywY
Appendix C
Link to guided breathing meditation:
http://youtube.com/watch?v=nmFUDkj1Aq0&feature=youtu.be&ab_channel=MyLife

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