MEDITATING WITH A FRIEND ENHANCES MEDITATION-
RELATED IMPROVEMENTS TO VAGAL TONE 
A Thesis
Presented to the Faculty of the Graduate School
of Cornell University
in Partial Fulfillment of the Requirements for the Degree of
Masters of Arts
by
Sydney Lynn Shohan
December 2021
i
© 2021 Sydney Lynn Shohan 
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ABSTRACT 
Social support has been shown to improve both physical and mental health. Previous research 
suggests social resources provide metabolic relief by dampening vigilance and threat response. 
However, the extent to which social support influences autonomic nervous system function is 
unclear. Motivated by Polyvagal Theory and Social Baseline Theory, the current study examines 
social support and meditation as potential resources to moderate the influence of the vagal nerve 
over the cardiovascular system. Time domain measures of heart rate variability (RMSSD) and 
subjective units of distress (SUDS) were examined using a linear mixed-effects model 
(contemplative practice*social condition) while controlling for nuisance variables. Data analysis 
revealed no significant main effect between contemplative practice or the presence of social 
support and changes to vagal tone. However, an interaction was found between social support 
and meditation such that when meditating, those with social support showed the greatest heart 
rate variability. 
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BIOGRAPHICAL SKETCH 
In May 2019, Sydney received her B.A. in Cognitive Science from the University of Virginia 
where she worked as an undergraduate researcher in Dr. Xiaorong Liu’s Neurobiology Lab. In 
December 2021, Sydney will graduate with an M.A. in Developmental Psychology from Cornell 
University in collaboration with Dr. Marlen Gonzalez’s Life History Lab and Dr. Adam 
Anderson and Dr. Eve De Rosa’s Affect and Cognition Lab. 
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ACKNOWLEDGEMENTS 
I want to thank Dr. Marlen Gonzalez for her overflowing support and guidance, and Dr. Eve De 
Rosa and Dr. Adam Anderson for their insightful feedback and inspiring ideas. I would also like 
to thank Stephen Parry for his wonderful statistical consulting. Finally, I want to thank Jing Luo, 
who has been an amazing friend through these difficult times. Your support has been invaluable 
this past year and a half. Thank you all! 
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TABLE OF CONTENTS 
BIOGRAPHICAL SKETCH ..........................................................................................................iv 
ACKNOWLEDGEMENTS .............................................................................................................v 
INTRODUCTION ...........................................................................................................................1 
Social Baseline Theory ........................................................................................................1 
Polyvagal Theory .................................................................................................................3 
Effects of Contemplative Practice .......................................................................................5 
Estimating Vagal Tone .........................................................................................................7 
The Present Study ................................................................................................................8 
METHOD ........................................................................................................................................9 
Participants and Data Collection ..........................................................................................9 
Measures ............................................................................................................................11 
Analysis ..............................................................................................................................13 
RESULTS .......................................................................................................................................13 
Heart Rate Variability ........................................................................................................14 
Subjective Units of Distress ...............................................................................................20 
DISCUSSION ................................................................................................................................22 
Interaction Effects ..............................................................................................................23 
Baseline HRV Measures ....................................................................................................24 
HRV Increase During CO2 Inhalation ...............................................................................25 
Further Work and Directions ..............................................................................................27 
REFERENCES ..............................................................................................................................29
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INTRODUCTION 
 Strong, reliable social relationships have long been foundational to humans’ well-being. 
Desires of having loving support systems composed of closely knit friends and family can be 
seen globally and throughout human history. This year, we were able to witness the necessity for 
social relationships. To top off the added fear of COVID-19, the effects of social isolation for 
many were devastating and debilitating. A study surveying faculty and students at universities 
found more than 90% reported having been affected by the shutdown and unable to perform 
work or studies (Leal Filho et al., 2021). Others have found large increases in depression and 
anxiety among isolated first-year college students following the lockdown (Fruehwirth et al., 
2021, Son et al., 2020). 
  The presence of social support is considered to be strongly related to physical health 
outcomes (Cohen, 1997; Uchino, 2009; Compare et al., 2013). Low levels of social support have 
higher mortality rates, especially cardiovascular disease (Compare et al., 2013). Social isolation 
itself has been long deemed a high-risk factor for all types of morbidity. Social support can be 
further understood by parsing the concept of perceived social support from received social 
support. Perceived social support describes the self-reported access to potential social support 
(Uchino, 2009) and has a strong physiological effect on reducing plaque build-up, cardiovascular 
reactivity, and blood pressure (Howard et al. 2017).
Social Baseline Theory
 How and why perceived social support has such a strong influence over health can be 
explained by Social Baseline Theory (Beckes & Coan, 2011, Coan & Sbarra, 2015) and the 
phenomenon of “yielding” (Gonzalez et al., 2021). The Social Baseline Theory (SBT) suggests 
1
the presence of others helps lower vigilance and conserve often costly metabolic and neural 
resources devoted to the brain.  In this way, the human brain at baseline assumes a predictable 
social environment. Emotional regulation, in this instance, is not characterized by the active 
neural effort, but by an absence of effort altogether returning the brain to a baseline state.  
 In this context, perceived social support can be thought of as a measure of social 
predictability. When it is expected that support will not be readily available, the body will take 
on more costly metabolic and neural functions to prepare for unexpected threats (Coan et al., 
2017). When alone, this can manifest through increased stress hormones, an overactive 
sympathetic nervous system, and heightened anxiety (Reblin & Uchino, 2008). 
 SBT can be better understood through a 2006 handholding fMRI study (Coan et al., 
2006). Sixteen married women were subject to the threat of shock while holding their husband’s 
hand, the hand of an experimenter, or no hand at all. They found that when holding hands, 
especially that of their husband’s, threat response was attenuated by dampening brain activation. 
Areas involved in emotional regulation like the dorsolateral prefrontal cortex, caudate, and areas 
involved emotional-related homeostasis like the superiors colliculus also showed less activation 
when holding hands. In this way, the social regulation of emotions doesn’t occur through a 
process of emotional regulation, but instead through returning the brain to a baseline state that 
doesn’t require additional neural effort for emotional regulation. It is theorized that in the 
presence of others, we relax our metabolic and neural investments, and conserve resources 
through a process called yielding (Gonzalez et al. 2021). This recent study by Gonzalez et al. 
(2021), used the handholding paradigm and found that metabolic resources are conserved during 
a subsequent stress task when in the presence of a relational partner.  
2
Polyvagal Theory 
 Porges’ Polyvagal Theory (PVT) suggests similar mechanisms for how social support can 
reduce physiological costs (Porges 2007). According to Porges, increased influence of the vagal 
nerve over the heart, characterized by increased heart rate variability (HRV), supports 
spontaneous social engagement. He suggests that as a result of mammalian evolution, the 
primary vagal regulation of the heart shifted from unmyelinated pathways originating in the 
dorsal motor nucleus (DMX) of the vagus to myelinated pathways originating from the nucleus 
ambiguus (NA) of the vagus. The newly developed myelinated pathways function as a fast-
acting vagal brake, rapidly inhibiting and disinhibiting vagal tone to the heart. When the vagal 
brake is released heart rate speeds up, whereas increasing inhibitory vagal control of the heart 
slows down heart rate. This allows for either rapid mobilization when the brake is removed 
(fight-flight) or calming, self-soothing, and socially-engaging behaviors when the brake is 
maintained. Therefore it can help or hinder behaviors depending on its weakness or strength. For 
instance, strong vagal innervation, which can be thought of by how quickly the driver can switch 
between the brake and gas, will allow the heart to be more flexible; mobilizing, and relaxing 
quickly. Weak vagal innervation results in an inflexible heart, unable to move quickly from an 
elevated state to a relaxed state, resulting in more often than not, sympathetic dominance over 
the nervous system characterized by heightened anxiety and vigilance (Porges 2007). When 
vagal innervation is weak the switch between the gas and the break is delayed.   
 The theory proposes that the myelinated vagus is related to prosocial behaviors via a 
mechanism called the social-engagement system. In humans, the vagal nerve is anatomically 
connected to muscles that control eye gaze, create facial expressions, and tune hearing to the 
3
frequency of the human voice, all of which are strongly tied to prosocial behavior (Porges, 
2007). Engagement with these social indicators fosters calm behavioral states by inhibiting 
sympathetic influence over the heart. Through a process Porges calls “neuroception” the vagus 
elicits physiological states that can support social engagement or fight-flight. Neuroception refers 
to the neural processes that assess risk and vigilance, in mammals specifically.  
 What Porges describes here is similar to what SBT describes as the predictability of 
social relationships. The process of neuroception first requires the evaluation of risk, and then, 
depending on the physical or visceral environment, elects a state to support either social 
engagement or fight-flight. The brain distinguishes safe from unsafe, responding to safe 
environments by not engaging in metabolically costly sympathetic activation, allowing for 
engagement in social behaviors. Similarly, in SBT, being alone requires more costly metabolic 
activity because of heightened threat vigilance, while being around others dampens threat 
vigilance and metabolic spending.  
 Familiarity and predictability go hand-in-hand with conserving metabolic resources in 
SBT. Being more familiar with the potential outcome of someone else’s actions allows for a 
more accurate evaluation of risk. It’s safer to yield metabolic resources to someone who is 
known to be a reliable source of support, rather than someone who is a reliably poor source of 
support or a stranger, whose behaviors are unknown. The effectiveness of the social-engagement 
system should be affected by the predictability of social resources, although how the system 
differentiates “safe” social stimuli from “unsafe social stimuli” is not yet understood.  
 Additionally, from the perspective of SBT, activation of the social-engagement system 
would be the baseline assumption of the brain. This may seem contradictory since SBT 
4
emphasizes that overall less activation characterizes the baseline state. However, in PVT it isn't 
the amount of vagal activation that accounts for vagal tone, but the strength of vagal projections 
to the heart. Therefore, activation of the social-engagement system doesn’t require more neural 
effort. Instead, it inhibits sympathetic activation, which results in an overall decreased neural 
effort.  
Effects of Contemplative Practice 
 Contemplative practices like meditation have also been found to influence vagal tone and 
increase heart rate variability (HRV); a measure of vagal tone (Peng et al., 2004; Krygier et al., 
2013). Although there are hundreds of different types of meditation, they are broadly understood 
as “a family of complex emotional and attentional regulatory strategies developed for various 
ends (Lutz et al., 2008).  
 The amount of meditative practices that have been the subject of scientific studies isn’t 
quite as vast and can be understood via three categories (Lutz et al., 2008, Kok, Waugh, et al., 
2013). The first, focused attention meditation, involves the meditator focusing their attention on 
a certain sense or stimulus, like breath or other bodily sensations. Eventually, it becomes easier 
and takes less energy for the meditator to focus and direct attention. Open monitoring meditation 
builds upon the core of focused attention meditation. In this next practice, there is no longer a 
singular point of focus. Instead, the meditator observes any thoughts and feelings that arise but 
does not engage with them. Rather than being pulled into a memory or thought, the meditator 
observes the memory but does not engage with it, remaining present in the moment. Finally, 
kindness and compassion meditation alters the focus of meditation from oneself to one’s 
connection with others. This practice again builds upon skills learned in focused attention 
5
meditation and open-monitoring meditation. The meditator can begin by focusing on breath and 
slowly shift focus towards a person or group with the intention to cultivate feelings of closeness 
and kindness (Kok, Waugh, et al., 2013). 
 Meditation has been shown to reduce inflammatory immune responses and improve 
cardiovascular health (Ospina et al., 2007; Anderson et al., 2008; Kok, Coffey, et al., 2008). It is 
theorized that the driving mechanism behind health improvements of focused attention 
meditation and open monitoring comes from the ability to more effectively self-regulate negative 
emotions (Kok, Waugh, et al., 2013). Mechanisms underlying kindness and compassion 
meditation are thought to occur by increasing the likelihood of positive emotions and feelings of 
social closeness, both of which are associated with a decreased risk of cardiovascular disease 
(Kok & Fredrickson, 2010) and increased cardiovascular recovery from stress (Fredrickson & 
Levenson, 1998).  
 The connection between meditation and feelings of social closeness and support can be 
further understood by a study that found through love and kindness meditation (a form of 
kindness and compassion meditation) positive emotions improve and build positive perceptions 
of social connectedness which improve vagal tone (Kok et al. 2013). Improved vagal tone, in 
turn, improves positive emotions, and the cycle continues in an upward spiral, showing that 
vagal tone can be improved by using meditation techniques to consistently improve positive 
emotions and perceived social connections. This is consistent with the PVT social-engagement 
system, as it shows improvement in vagal tone through engagement in social connections. 
Because it was perceived social connections that were found to be tied to increases in vagal tone 
and positive emotions, it is also consistent with SBT.  
6
Estimating Vagal Tone 
 Heart rate variability (HRV), or the variance of length between successive heartbeats, is 
an indirect measure of autonomic nervous system function and has been commonly used as a 
metric of general health (Porges, 1995; Cacioppo et al., 2001). A more variable heart rate (high 
HRV) reflects flexibility to appropriately speed up when in trouble and to slow down when 
resting. These two responses are controlled by two branches of the autonomic nervous system; 
the sympathetic and parasympathetic nervous systems (Shaffer et al., 2014). 
 In psychopathology like PTSD, schizophrenia, and anxiety, we usually see an overactive 
sympathetic system and a weakened parasympathetic system, characterized by a weakened 
innervation to the vagus nerve, the primary source of parasympathetic projections to the heart 
(Young & Benton 2018). Cardiac vagal tone is the strength of these parasympathetic projections 
to the heart that we can indirectly measure using HRV (Laborde et al., 2017). High HRV and 
more variability indicate higher vagal tone and parasympathetic presence, while low HRV and 
less variability indicate lower vagal tone and sympathetic presence. 
 Reduced HRV is associated with poor cognitive function, an increased risk of death, the 
development of diabetes, and cardiovascular disease (Dekker et al., 2000; Young & Benton 
2018). Additionally, psychopathology including bipolar disorder, anxiety disorders (Scott & 
Weems, 2014), and PTSD (Gentzler et al., 2009) are associated with low HRV. Improving 
general health through exercise, nutrition, and sleep has been shown to increase short-term HRV 
and cognitive functioning, and therapeutic solutions like meditation, mindfulness (Krygier et al., 
2013), and expressive writing (Bourassa et al. 2017) have shown similar results. 
7
The Present Study 
 This current study looks to further understand how social support and meditation could 
have potentially additive improvements to cardiovascular health and resilience to stress. We 
analyzed respiratory data, ECG data, and subjective units of distress (SUDS) of participants 
either practicing meditation or breathing deeply with either a source of social support alone. This 
allows us to see the separate and additive effects of meditation and social support.  We expected 
increased HRV for all groups after contemplative practice and greater increases in HRV for 
participants that meditated and had access to social support. To test the resilience provided by 
our conditions, we followed this with a task where participants were inundated with CO2 
enriched air to elicit a stress response. As the sympathetic system took over, we expected 
decreased HRV for all groups, but less of a decrease in HRV for participants that had the buffer 
of meditation and social support prior to the CO2. Subjective distress was expected to increase 
for all groups during the CO2 stress task, while a smaller increase was expected for participants 
that meditated and had access to social support. 
Figure 1 
2x2 Design
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METHOD 
Participants and Data Collection 
 Participants included both meditation-naive and meditation-experienced students. 
Undergraduate students experienced in meditation were recruited from the Buddhism in the 
Modern World class offered through the Religious Studies Department at the University of 
Virginia. While enrolled in the Buddhism class, students practiced one hour of meditation a week 
with a group. Undergraduates inexperienced in meditation were recruited from the School of Arts 
and Sciences. Students were excluded from the meditation-naive group if they practiced 
meditation for an hour or more a week. All participants came in pairs. In total 128 
undergraduates were recruited to the study; 26 experienced meditators who completed the 
contemplative+social phase alone, 26 experienced meditators who completed the 
contemplative+social phase with a friend, 40 mediation-naïve participants who completed the 
contemplative+social phase without a friend, and 36 meditation-naïve participants who 
completed the contemplative+social phase with a friend. 
 All participants were accompanied to the lab with a partner to complete a series of 
questionnaires accounting for known moderators before psychophysical data collection. 
Meditators and non-meditators were randomly assigned to either an alone or with partner 
condition. At baseline recording, all participants were separated from their partners. Following 
baseline readings, meditation naïve participants were instructed to breathe deeply for 10 minutes 
while experienced meditators were asked to meditate for 10 minutes. During this phase, 
participants in the friend condition had their partners rejoin them for a contemplative practice; 
either meditation or deep-breathing. Following the contemplative practice, partners were 
9
separated and all participants completed a respiratory stress task in which they breathed in 
carbon dioxide enriched air (7%). They were not alerted as to when the CO2 task would start. 
Both subjective distress and psychophysiology data were recorded during the CO2 
administration. After a set amount of time, normal levels of oxygen returned to the air for the 
CO2 recovery phase. Psychophysiology was collected using the BIOPAC data acquisition 
system. 
Figure 2 
Experimental Structure
Notes: Experimental Phase: a) Baseline Phase: participants’ respiratory and ECG data and subjective 
units of distress (SUDS) are recorded before any manipulations. Contemplative+Social Phase: 
participants meditated or deep-breathed either with or without a friend in the room. b) Pre-CO2 Phase: 
participants with partners are separated. Respiratory, ECG and SUDS data recording resumes. c) CO2 
Inhalation Phase: without being alerted, the participant begins to breath in C02 enriched air. d) CO2 
Recovery Phase: without being alerted, CO2 enrichment stops and air returns to normal levels of 
oxygenation 
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Measures 
 ECG Data Cleaning. Raw ECG data was cleaned using the ARTiiFACT HRV processing 
tool. IBI’s were extracted using the ecgExtract tool using a sampling rate of 1000 Hz.  R-peak 
detection threshold was set to 200µV with a global threshold. The ibiArtifactProcessing tool was 
then used to correct and detect artifacts in IBI data using the Berntson detection method and the 
cubic spline interpolation processing method. Undetected R-peaks were manually corrected. 
Frequency and time domains were calculated using the hrvAnalysis tool with an interpolation 
rate of 4 Hz, window width of 120 s during the baseline period, 240 s during the contemplative 
practice period, 480s during the CO2 enrichment period,  and 480s during the post-CO2 recovery 
period. Window overlap was set to 50% with frequency bands 0.04,0.15,0.40.  Fifteen 
participants of the 128 were excluded due to missing or corrupted data. Because certain 
participants were not able to complete the CO2 enrichment stage of the experiment, 8 
participants were included with HRV measures from only the baseline phase and the 
contemplative-practice/social phase. In total, data from 113 participants were collected from the 
baseline and contemplative practice conditions, and data from 105 participants were collected 
from the CO2 enrichment condition and the recovery condition. 
 Heart Rate Variability. HRV was calculated from raw ECG data as RMSSD. HRV can 
be measured in many ways and one of them is through calculating a root mean square of 
successive deviations (RMSSD) between adjacent heartbeats. RMSSD is a time-domain measure 
which is resilient to changes in respiration rate (Shaffer & Ginsberg, 2017). To compare 
experimental phases, time-domain measures were chosen as they are resilient respiratory 
11
changes. Frequency domain measures, specifically high-frequency HRV (HF), were not used for 
this reason. 
 Subjective Units of Distress Scale (SUDS). Participants were asked, “Imagine this is a 
distress thermometer to measure your feelings. Using a 0 to 100 scale where 0 is totally relaxed 
and 100 is the highest distress/anxiety/discomfort that you have felt, how anxious/distressed are 
you right now?” SUDS were collected from participants every 2 minutes over the course of a 24 
minutes period. Participants with at least 6 of 12 SUDS reports were included in the analysis. 
Out of 128 participants, 10 participants were disqualified for under 6 SUDS reports, and 12 
participants were disqualified for having missing or unlabelled SUDS reports. The data from a 
total of 106 participants were used in this analysis. In total, there was 1 SUDS report taken at 
baseline, 2 during the contemplative/social condition, 4 during the CO2 challenge, 4 during post-
CO2 recovery, and 1 after the task. Due to the missing data in both the ECG files (15 from 
baseline and contemplative+social phase, 23 from CO2 and recovery phase) and the SUDS files 
(22), there were participants used in the SUDS analysis that weren’t used in the HRV analysis 
due to missing ECG files or attrition. Likewise, there were participants included in the HRV 
analysis whose SUDS data was lost. 
 Nuisance Variables. 
 ASI-3: Anxiety Sensitivity Index (Taylor et al. 2007): A 16-item scale measuring the 
degree to which a person is concerned about the physical symptoms of their anxiety. 
 ECR_S: Experience in Close Relationship Scale (Wei, Russel, et al. 2007): A 12 item 
scale measuring attachment style of close relationships. 
12
 IOS: Inclusion of Other in the Self Scale (Aron, Aron, et al. 1992): A single-item scale to 
measure how close the respondent feels with another person. 
Analysis 
 A linear mixed-effects model was run on the HRV and SUDS data where the social and 
contemplative conditions were independent variables and the experimental phase and gender 
were categorical predictors along with additional continuous covariates (ASI, ECR_S, IOS). 
HRV and SUDS scores varied randomly by participant. 
 A pairwise analysis of the social conditions and contemplative conditions was used to 
explore interaction effects. To explore potential confounding variables, the differences in 
friendship length were compared between the meditation and deep-breathing groups with a linear 
effects model. The baseline differences in HRV between groups were also explored using a linear 
effects model. Pairwise comparisons were done to further explore the source of any differences. 
 Analyses were done using RStudio. The package lme4 was used to run the linear mixed-
effects model and the package emmeans was used to view pairwise differences and interactions 
within the model. The package ggplot2 was used to visualize the data. The linear mixed-effects 
model was fitted by restricted maximum likelihood (REML). T-tests used Satterthwaite's method 
with an alpha level of 0.95.  
RESULTS 
 HRV analyses were conducted among 113 participants, and the SUDS analyses were 
conducted among 106 participants. The average age for the sample was 19.08, ranging from 18 
to 22. The racial distribution of the sample was approximately 67% white, 24% Asian, 5% 
biracial, 4% black, and 1% unreported.   
13
Heart Rate Variability 
Figure 3 
Line plot visualization of RMSSD changes across phases
Figure 4 
Bar-plot visualization of RMSSD changes across phases with standard error
14
Table 1 
Means and standard deviations of RMSSD across phases
Contemplative Social Baseline Pre-CO2 CO2 Recovery
Condition Context M SD M SD M SD M SD
Alone 34.39 19.54 48.22 22.64 51.06 38.81 42.37 21.63
Meditation With 
Friend 52.84 36.88 65.52 39.14 70.67 50.34 57.46 32.65
Alone 36.93 28.28 50.56 32.48 61.36 36.66 45.65 30.38
DB With 
Friend 36.22 20.44 46.44 20.26 52.32 23.92 42.43 20.57
Note: Means are listed as M, and standard deviations are listed as SD
Table 2 
Linear mixed effects model: RMSSD
SSE MSE NumDF DenDF F-ratio p
Social Context 354.1 354.1 1 101.75 1.9720 0.16328
Contemplative Practice 385.3 385.3 1 101.92 2.1458 0.14604
Phase 21678.5 7226.2 3 313.57 40.2458 <0.0001***
IOS 5.5 5.5 1 102.02 0.0305 0.86161
ASI 60.7 60.7 1 101.67 0.3378 0.56237
ECRR_avoid 272.1 272.1 1 101.34 1.5154 0.22117
Gender 108.4 54.2 2 101.25 0.3019 0.74005
Social:Contemplative 767.1 767.1 1 101.78 4.2722 0.04138*
Note: Linear mixed effects model (Type III Analysis of Variance) fit by restricted maximum likelihood 
(REML). t-tests use Satterthwaite's method. SSE = sum squared error, MSE = mean squared error, 
NumDF = degrees of freedom of the numerator, DenDF = degrees of freedom of the denominator. In 
this model RMSSD is the dependent variable and social context and contemplative practice are 
independent variables, with time and gender as categorical predictors. IOS (Inclusion of other in self 
index), ASI (Anxiety sensitivity index), and ECRR_avoid (Experience in close relationships scale) were 
continuous covariates. Participants were identified as random variables.
15
Table 3  
Pairwise comparison of phase-related RMSSD change
Contrasts
Contemplative Social estimate SE df t-ratio p
Condition Condition Phase
Baseline - Pre-CO2 -13.83 4.03 310 -3.43 0.0534
Alone
Baseline - CO2 -16.14 4.09 311 -3.94 0.0096**
Meditate
Baseline - Pre-CO2 -12.68 3.94 310 -3.21 0.1007
Friend
Baseline - CO2 -21.03 4.07 311 -5.16 <.0001***
Baseline - Pre-CO2 -13.63 3.22 310 -4.23 0.0031**
Alone
Baseline - CO2 -24.62 3.36 312 -7.32 <.0001***
Deep-breathing
Baseline - Pre-CO2 -10.23 3.53 310 -2.90 0.2220
Friend
Baseline - CO2 -15.61 3.57 311 -4.37 0.0018**
Note: This pairwise comparison of means is from a post-hoc ANOVA that sets RMSSD as the 
dependent variable and treatment, or “group assignment”, and phase as the independent variables with 
four levels.
 We calculated the root mean square of successive deviations (RMSSD) in milliseconds 
(ms) for each participant at each of the four stages of the experiment and performed a linear 
mixed-effects analysis. As expected there was a phase related effect on RMSSD  (F(3,313) = 
40.24, p<.0001) (Table 2).  No main effects were found as a result of the social context variable 
or the contemplative practice variable. However an interaction was found between social context 
and contemplative practice (F(1,102) = 4.27,p=.04138.  
 The effect of phase was further explored in Table 3. RMSSD improved from baseline to 
the pre-CO2 phase for the deep breathing+alone condition (t(310)=-4.23, p=.0031). 
Unexpectedly, during the CO2 inhalation phase, RMSSD improved for all groups when 
compared to baseline; meditate+alone  (t(311)=-3.94, p =.0096), meditate+friend (t(311)=-5.16, 
16
p<.0001), deep-breathing+alone (t(312)=-7.32, p<.0001), and deep-breathing+friend 
Table 4 
Marginal means of the contemplative practice x social context interaction
Contemplative Social 
Practice Context M SD MM SE df Lower CL Upper CL
Meditate Alone 43.97 27.04 43.0 11.4 101 20.3 65.6
Friend 61.52 40.13 62.3 11.4 101 39.7 84.9
Deep-breathing Alone 46.62 31.42 45.8 10.9 102 24.2 67.3
Friend 44.33 22.23 42.1 10.9 101 22.0 62.3
Note: M is the actual mean of RMSSD across all phases, while MM is the marginal means of RMSSD 
across all phases. SD = Standard deviation, SE = Standard Error, df = degrees of freedom, Lower CL = 
lower confidence limit, Upper CL = upper confidence limit
Table 5 
Pairwise comparison of completive practice x social context interaction
Contemplative 
Practice Social Context estimate SE df t-ratio p
Meditate 19.33 8.32 102 2.324 0.0221*
(Friend - Alone)
Deep-breathing -3.65 7.41 102 -0.492 0.6237
Friend 20.20 8.32 102 2.425 0.0170*
(Meditate - DB)
Alone -2.80 7.93 102 -0.354 0.7244
(t(311)=-4.37, p=.0018).  
 An analysis of the interaction revealed that when the contemplative condition was 
meditation (Table 5), having a friend present predicted higher HRV levels than being alone 
(t(102)=2.324, p=0.0221), and when a friend was present, a contemplative practice of meditation 
predicted higher HRV than deep-breathing (t(102)=2.425, p=0.0170). 
17
Figure 5 
Visualization of the interaction effects between the contemplative practice and 
the social context
Note: Linear prediction is the model’s prediction of RMSSD given the conditions of 
social context and contemplative practice
Table 6 
ANOVA Table for RMSSD at Baseline
DF SSE MSE F-value p
treatment 3 5430 1810.1 2.4728 0.06551
residuals 109 79788 732.0
Note: This post-hoc ANOVA sets RMSSD as the dependent variable and treatment, or “group 
assignment”, as the independent variable with four levels. The test only examines differences in 
RMSSD values at Baseline.
 To further explore potential confound factors, a linear effects model (Table 6) was run to 
determine if there were any significant differences in RMSSD between the groups at baseline. 
Upon investigation, no significant differences were found.  
  
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Table 7 
Mean and standard deviation of friendship length (months)
Contemplative Social 
Practice Context M SD MM SE df Lower CL Upper CL
Meditate Alone 4.54 4.33 4.54 10.24 108 -15.75 24.80
Friend 9.77 16.06 9.77 10.02 108 -10.10 29.6
Deep-breathing Alone 40.17 70.73 42.39 8.18 108 26.17 58.6
Friend 25.02 52.71 25.02 9.12 108 6.94 43.1
Table 8 
ANOVA: friendship length (months)
DF  SSE MSE F-ratio p
Social Context 1 1741 1741.5 0.7330 0.3939
Contemplative 
Practice 1 17897 17867.3 7.5202 0.0072**
Social C: Cont P 1 2803 2803.0 1.1798 0.2798
Residuals 107 254223 1375.9
Note: This analysis of variance set friendship length as the dependent variable, and social context and 
contemplative practice as independent variables. 
Table 9 
Pairwise comparison of differences in friendship length (months)
Contemplative 
Practice Social Context estimate SE DF t-ratio p
Meditate 5.23 14.2 107 0.368 0.7140
Friend - Alone
Deep-breathing -15.15 12.2 107 -1.238 0.2184
Friend -15.2 13.5 107 -1.33 0.2595
Meditate - DB
Alone -35.6 13.6 107 -2.723 0.0076**
19
 Post-hoc ANOVA was run to determine differences between the length of time 
participants had known their friends (Table 8). The linear effects model showed an effect of 
contemplative practice on length of friendship (F(1) = 7.52, p=.0072). Further analysis (Table 9) 
shows the only statistically significant difference exists between the meditate+alone group and 
the deep-breathing+alone group such that the meditation+alone group had known their friend for 
a significantly less time than the deep-breathing+alone group t(107)=-2.72, p=.0076). 
Subjective Units of Distress 
Table 10 
Mean and standard deviation of SUDS across phases
Contemplative Social Baseline Pre-CO2 CO2 Recovery
Condition Context M SD M SD M SD M SD
Alone 20.05 13.88 16.92 15.24 28.88 21.27 15.12 13.97
Meditation With 
Friend 22.55 15.56 20.71 12.95 36.02 21.17 19.41 15.91
Alone 15.50 14.16 17.98 16.75 30.50 23.26 16.34 16.32
Deep-breathing With 
Friend 17.43 16.07 16.91 14.28 26.94 20.45 16.80 13.46
Table 11 
Linear mixed effects model: SUDS
SSE MSE NumDF DenDF F-ratio p
Social Context 46 46.5 1 97.29 0.3413 0.5605
Contemplative 
Practice 28 28.4 1 97.25 0.2082 0.6492
IOS 249 248.7 1 97.53 1.8262 0.1797
ASI 370 369.7 1 98.32 2.7144 0.1026
ECRR_avoid 116 116.4 1 97.49 0.8550 0.3574
Gender 32 32.0 1 97.59 0.2350 0.6289
Phase 45725 11431.3 4 1078.34 83.9352 <.0001***
20
Social:Contemp
lative 268 267.8 1 97.25 1.9660 0.1641
Note: Linear mixed model (Type III Analysis of Variance) fitted by restricted maximum likelihood 
(REML). t-tests use Satterthwaite's method. In this model, SUDS is the dependent variable and social 
context and contemplative practice are independent variables, with phase and gender as categorical 
predictors. IOS (Inclusion of other in self index), ASI (Anxiety sensitivity index), and ECRR_avoid 
(Experience in close relationships scale) were continuous covariates. Participants were identified as 
random variables.
Table 12 
Post-hoc pairwise comparison SUDS across phases
Contrast estimate SE df t-ratio p
Contemplative Social 
Condition Condition Phase
Pre-CO2 - CO2 -11.95 2.26 1064 -5.294 <.0001***
Alone
Baseline - CO2 -8.8250 2.91 1064 -3.029 0.2183
Meditate
Pre-CO2 - CO2 -15.98 2.21 1065 -7.227 <.0001***
Friend
Baseline - CO2 -13.4773 2.78 1064 -4.851 0.0003***
Pre-CO2 - CO2 -12.30 1.80 1067 -8.827 <.0001***
Alone
Baseline - CO2 -15.8223 2.38 1067 -6.658 <.0001***
Deep-breathing
Pre-CO2 - CO2 -10.35 1.94 1066 -5.341 <.0001***
Friend
Baseline - CO2 -9.3708 2.48 1065 -3.779 0.0232*
 There were no main effects found for SUDS for the social support condition or the 
contemplative practice variable. Similarly, there was a main effect found for the experimental 
phase (F(4,1078.34)=83.9, p<.0001) (Table 11), but there was no interaction found between 
social support and contemplative practice. SUDS did not significantly change for any groups 
from baseline to the pre-CO2 stage.  
 As expected from the baseline phase to the CO2 inhalation phase (Table 12), SUDS 
increased for the following groups; meditation+friend (t(1064)=4.85, p=.0003), deep-
21
breathing+alone (t(1067)=6.65, p<.0001), and deep-breathing+friend (t(1065)=3.78, p=.0232). 
Although the meditation+alone group did not show a statistically significant increase in SUDS 
from baseline to the CO2 phase, it did show a significant increase in SUDS from the 
contemplative practice stage to the CO2 phase (t(1064)=5.294, p=<.0001). 
Figure 6 
Visualization of SUDS changes over phases
DISCUSSION 
 This analysis sought to further reveal how social support from a trusted conspecific could 
help strengthen vagal tone and psychobiological resilience to stressors.  Contemplative practice 
and social context did not differ from each other in the strength of their impact on HRV and 
22
SUDS. However, there was an interaction effect found, such that when meditating, having a 
friend present predicted the highest HRV outcome across experimental stages. SUDS data across 
groups was as expected, with an increase in distress when CO2 was inhaled, and a decrease when 
normal air was returned. However, during CO2 inhalation, HRV increased across groups.  
Interaction Effects 
 The meditation+friend group was unique in that the friends brought to the experiment 
were also members of the Introduction to Buddhism course and were fellow meditators in their 
course’s weekly meditation group. This meant that there was a greater likelihood that the friend 
they brought was one that they had made recently while in the class. When compared to the 
deep-breathing+friend group, the meditation+friend group had known their friends for 15.2 
fewer months. Despite the deep-breathing+friend group having brought friends they had known 
for much longer, it was the meditation+friend group that benefited most from social context.  
 The interaction may be explained in part by practice effects. The act of meditation is a 
practice that can be improved upon over time (Huppert & Johnson, 2017). Cardiovascular and 
mental benefits from meditating are also usually observed after a training period that ranges from 
a week to a month (Kok, Coffey, et al., 2013). Because the meditation+friend group was able to 
practice in their meditation course, the practice effects could, in part, account for the interaction. 
However, because the same effect was not found when the social condition was “alone”, practice 
effects would only be one part of the puzzle. 
 The vagal nerve is anatomically connected to muscles that control eye gaze, create facial 
expressions, and tune hearing to the frequency of the human voice, all strongly tied to pro-social 
behavior and coined by Porges as the social-engagement system. While the practice of 
23
meditation incorporates breath control and deep-breathing, its core lies in directing attention; 
attention to and from emotions, bodily sensations, thoughts, awareness, and other people (Lutz et 
al., 2008). These aspects of meditation differentiate it from the deep-breathing control. By having 
been trained to direct attention, it's possible the meditation+friend group was better able to focus 
on their friend and activate their social engagement system than the deep-breathing+friend 
group. This interaction suggests the social aspect of meditation, potentially via the social-
engagement system, is an important component of meditation-related improvements to HRV. It’s 
possible that by having meditated weekly with a certain friend, the sounds and social cues that 
arise during joint meditation enhanced HRV via the social-engagement system. By this logic, it 
was the implementation of a long-term meditation intervention coupled with a social trigger from 
the social-engagement system that may account for the meditation+friend group’s increased 
HRV.  By meditating weekly with a friend, the practice could have tuned the social-engagement 
system for future socially-facilitated meditation.  
Baseline HRV Measures 
 Although the difference was not found to be statistically significant, the baseline HRV 
measure for the meditation+social group was greater than that of the other three groups. No 
differences were found between the meditation+friend group and the other three groups when it 
came to sex, age, or racial distribution. Past exercise experience also did not differ between the 
meditation+social group and the other three groups. Because the meditation+alone group did not 
show the same elevated baseline HRV as the meditation+social group, this finding cannot be 
attributed to the practice effects of the meditation condition. Groups did differ drastically in 
terms of friendship length, with meditation-naive participants having longer relationships with 
24
the friend they brought in than the experienced meditators who brought in their classmates (Table 
7). Therefore, familiarity can not explain baseline differences or the interaction. Baseline 
conditions were the same for all groups, so it remains to be seen if there was a variable 
unaccounted for that could be behind the heightened baseline. It’s also possible this could be an 
effect of random sampling, and coincidentally people with higher HRV levels were randomly 
selected to the meditation+friend group.  
HRV Increase During CO2 Inhalation 
 Unexpectedly, during the CO2 inhalation phase, HRV increased in all groups. Current 
literature measuring vagal tone using the CO2 task has shown frequency domain measures to be 
sensitive to the CO2, and time-domain measures to be resilient to changes in respiration rate and 
heart rate (Grossman & Taylor, 2007; Laborde et al., 2017). It’s thought that because CO2 
inhalation induces rapid and fast-paced breathing, frequency domain measures, like HF which 
are very sensitive to changes in respiratory rate, will be affected. Unlike frequency-domain HRV 
measures, time-domain measures (in our case RMSSD) are thought of as resilient to changes in 
heart rate and respiration rate. However, it appears uncommon for time-domain measures to be 
reported in studies measuring HRV during CO2. Some studies have found time-domain measures 
unable to detect the changes in HRV caused by the CO2 task (Brown et al., 2007, 2014), 
therefore frequency domain measures are more often reported.  
 A recent article (Martino et al. 2020) also found increased RMSSD during the CO2 task 
when compared to baseline. While excessive breathing explains the jumps in HF, the changes in 
RMSSD are ambiguous. The authors suggested that this difference could be explained by the 
participants’ sitting positions during HRV recording. In their case, and ours, participants’ HRV 
25
was recorded in the sitting position rather than while laying on their backs (supine). RMSSD has 
been found to decrease at baseline when sitting compared to laying in the supine position (Young 
& Leicht, 2011). They suggest that because baseline measures of RMSSD are greater while in 
supine, increases will be less likely to be detected during the CO2 condition. It is possible that 
because participants were sitting down in our experiment, baseline RMSSD measures were lower 
than they would have been if participants were sitting in a supine position, allowing for the 
detection of more dynamic changes in RMSSD. The increase in RMSSD during the CO2 phase 
could have been a result of low baseline recordings. 
 Alternatively, the stress induced by CO2 could be a unique visceral threat that doesn’t 
take the traditional fight or flight pathway and instead activates a freezing response.  
Freezing responses are often confused with involuntary immobility. It can be a precursor to 
immobility as well as fight-flight responses (flight more commonly) and shares the same 
pathway as fight-flight responses, only freezing evokes parasympathetic dominance via vagal 
innervations from the dorsal motor cortex (DMX), while fight-flight responses evoke 
sympathetic dominance (Roelofs, 2017).   
 Similar to the previously mentioned vagal brake, the ventrolateral periaqueductal grey 
matter (vlPAG) also serves as a brake for the sympathetic threat response system. During 
freezing, the vlPAG acts as a brake for threat-related systems, putting the fight-flight responses 
of the dorsolateral PAG (dlPAG) on hold, leaving the animal prepared for action until the brake is 
released. The vlPAG activates a vagal pathway via the dorsal motor nucleus (DMX), which 
regulates parasympathetic heart rate decelerations that are the autonomic equivalent of freezing 
response. The DMX is part of the dorsal vagal complex: the older, more ancient system of two 
26
vagal circuits. While the DMX has unmyelinated projections to the heart, the other vagal circuit, 
the ventral vagal complex which houses the nucleus ambiguus (NA), has myelinated projections 
to the heart. According to PVT, the development of the myelinated vagal projections is relatively 
new, unique to mammals, and is closely tied to social behaviors. Activation of this pathway is 
what we would expect to see during socially mediated meditation.  
 Activation of the vlPAG evokes a freezing response by increasing parasympathetic vagal 
control of the heart via the DMX while the dlPAG evokes a fight-flight response by sympathetic 
activation (Porges, 2007). It’s possible that the jump in HRV during the CO2 induction phase 
was a result of parasympathetic vagal control over the heart via the DMX, instead of the socially 
mediated parasympathetic vagal control we’d expect from the NA. This means that the increase 
in HRV isn’t socially mediated, but is instead a knee-jerk reaction to the visceral threat of 
breathing difficulty. While the Pre-CO2 phase may have been affected by the socially-mediated 
NA vagal pathway, it is doubtful that is this pathway is responsible for the increased HRV levels 
during the CO2 inhalation phase, which is more likely a stress response. 
Further Work and Directions 
 This is the first study to our knowledge showing that social support has the potential to 
enhance the beneficial health effects of meditation. The interaction between meditation and 
social support should be further investigated to better understand the mechanisms driving this 
effect.  
 To further understand our data, we would need to further process high-frequency and 
low-frequency HRV measures. This would allow us to more accurately pinpoint vagal tone and 
compare our results to Martino et al., 2020. Regardless of what RMSSD was found to measure 
27
during the CO2 induction phase, with only one measure, we’re limited in what we can say is 
representative of vagal tone. Computing RSA by controlling for tidal volume and respiration rate 
by calculating HF and LF would help draw stronger conclusions. 
  How meditation can attune the social-engagement system and how the social-
engagement system can improve meditation are still unexplored. Future studies should further 
investigate the effects of visual and auditory social stimuli, both of which direct the social 
engagement system, on the effectiveness of meditation. To further understand what structures 
could be responsible for the increase in HRV during the CO2 phase, neuroimaging studies could 
explore the difference in activation between the two vagal structures: the DMX, the NA, and 
their projections.  
28
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