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Personalizing the Training of Attention: Predicting Effectiveness of Meditation using Traits and Abilities
by
Thomas Anderson
A thesis submitted in conformity with the requirements for the degree of Master of Arts
Department of Psychology University of Toronto
© Copyright by Thomas Anderson 2016
ii
Personalizing the Training of Attention
Thomas Anderson
Master of Arts
Department of Psychology University of Toronto
2016
Abstract
Precision medicine involves tailoring interventions to the individual, but superior health outcomes
are only possible if individuals follow the advice of healthcare professionals. Current meditation
interventions have high drop-out rates despite the great benefits continued practice offers. Secular
meditation interventions have heretofore used somatosensory objects as the anchor of attention, but
other less-studied modalities may be preferred by certain individuals. Investigating the influence of
individual differences on preference of meditation modality is the purpose of this research. In this
study I use personality traits and sensory discriminability to predict preferences among three
modalities of meditation anchor: breath, phrase, and image. Results indicate that sensory
discriminability predicts preference, as do incoming bias and motivation. These results imply
multiple anchor modalities should be made accessible and that new meditators should be involved in
anchor-selection. This study begins a line of research into personalizing meditation instruction and
will allow more precise individualized recommendations.
iii
Acknowledgments
The author wishes to acknowledge the members of the Regulatory and Affective Dynamics
Laboratory at the University of Toronto, Mississauga for support and advice. Thanks especially to
Norman Farb for providing a space for freedom, exploration, and growth, as well as financial
support during data acquisition. Your trust in me will not be forgotten, and I will endeavor never to
abuse it. Thanks also to Michael Inzlicht for acting as my subsidiary advisor and Geoff MacDonald
for agreeing to sit on my committee. You are both adept at asking deep and probing questions that
are both challenging and stimulating. Thanks also to my research assistants Mallika Suresh, Youssef
Rachid, and Gurinder Cheema. Mallika was a saint and I wonder if I could have made it here
without her dedication. Thanks to Nicole Cosentino for bearing with my verbose ramblings on all
matters scholastic and otherwise. Your comments on my draft were insightful and invaluable. This
research was supported by the Social Science and Humanities Research Council of Canada and I
wish to thank everyone who was a part of getting me that support, from the psychology department
award committee to SGS to Roxane Itier and Dan Nemrodov for trusting a wide-eyed
undergraduate with the complex study that led to my first conference presentation and journal
publication. Finally I want to take a moment to acknowledge the sheer improbability of being here,
now, and appreciate this multifaceted ride we call life in its entirety. Thanks, universe.
iv
Table of Contents
Acknowledgments ............................................................................................................................................. iii
Table of Contents.............................................................................................................................................. iv
List of Tables .................................................................................................................................................... vii
List of Figures ..................................................................................................................................................viii
List of Appendices ............................................................................................................................................ ix
Introduction and Rationale ......................................................................................................................... 1
1.1 Intervention Adherence and Preference ........................................................................................... 1
1.2 Meditation .............................................................................................................................................. 2
1.2.1 Breath-Based Meditation ....................................................................................................... 2
1.2.2 Phrase-Based Meditation ....................................................................................................... 3
1.2.3 Image-Based Meditation ........................................................................................................ 3
1.2.4 Secularization of Meditation Objects ................................................................................... 3
1.2.5 Preferences for Particular Meditations ................................................................................ 4
1.3 What Could Predict Meditation Preference? .................................................................................... 5
1.3.1 Prior preference, Motivation, and Preference .................................................................... 5
1.3.2 Trait Mindfulness .................................................................................................................... 5
1.3.3 Mind-Wandering ..................................................................................................................... 6
1.3.4 Sensory Discriminability ........................................................................................................ 6
1.3.5 Personality ................................................................................................................................ 7
1.3.6 Physiological Efficacy ............................................................................................................. 7
Methods.......................................................................................................................................................... 8
2.1 Participants ............................................................................................................................................ 8
2.2 Design .................................................................................................................................................... 8
2.3 Measures ................................................................................................................................................ 8
2.3.1 Trait Mindfulness and Personality ........................................................................................ 8
v
2.3.2 Mind-Wandering ..................................................................................................................... 9
2.3.3 Sensory Discriminability ........................................................................................................ 9
2.3.4 Subjective Preference ...........................................................................................................10
2.3.5 Physiological Efficacy ...........................................................................................................11
2.4 Meditation Intervention .....................................................................................................................11
2.5 Data Analysis .......................................................................................................................................11
2.5.1 Just Noticeable Differences .................................................................................................11
2.5.2 HR and HRV Analysis .........................................................................................................11
2.5.3 Modelling................................................................................................................................12
Results ..........................................................................................................................................................13
3.1 Planned Modelling ..............................................................................................................................14
3.1.1 What Predicts Meditation Preference?...............................................................................14
3.2 Exploratory Modelling .......................................................................................................................14
3.2.1 What Might Predict Motivation? ........................................................................................14
3.3 Physiological Efficacy ........................................................................................................................15
3.3.1 Correlation with Preference ................................................................................................16
3.3.2 What Predicts Decreased Heart Rate? ...............................................................................16
3.3.3 What Predicts Increased High Frequency Heart Rate Variability? ................................16
3.4 Speculative Exploration .....................................................................................................................17
3.4.1 Do Physiological Changes Influence Preference Updating? ..........................................17
Discussion ....................................................................................................................................................18
4.1 Preference and Motivation ................................................................................................................18
4.2 Physiological Efficacy ........................................................................................................................19
4.3 The Effect of Experience ..................................................................................................................20
4.4 Limitations ...........................................................................................................................................20
4.5 Future Directions................................................................................................................................22
vi
4.6 Conclusions .........................................................................................................................................23
References .........................................................................................................................................................25
Tables .................................................................................................................................................................32
Figures ................................................................................................................................................................47
Appendix A: Glossary of Acronyms .............................................................................................................55
Appendix B: MAAS-5 .....................................................................................................................................56
Appendix C: CAMS-R .....................................................................................................................................57
Appendix D: Preference Questionnaire........................................................................................................58
Appendix E: Meditation Instructions ...........................................................................................................59
Appendix F: Example Free Form Reponses ................................................................................................61
vii
List of Tables
Table 1. Demographic variables of participants included in analysis ...................................................... 32
Table 2. Hierarchical Linear Modelling of Preference ............................................................................... 33
Table 3. Hierarchical Linear Modelling of Motivation .............................................................................. 35
Table 4. Hierarchical Linear Modelling of Prior-Preference .................................................................... 37
Table 5. Hierarchical Linear Modelling of Heart-Rate Decrease in the First-Half of Meditation ...... 39
Table 6. Hierarchical Linear Modelling of Heart-Rate Decrease in the Second-Half of Meditation . 40
Table 7. Hierarchical Linear Modelling of High-Frequency Heart-Rate-Variability Increase in the
First-Half of Meditation ................................................................................................................................. 42
Table 8. Hierarchical Linear Modelling of High-Frequency Heart-Rate-Variability Increase in the
Second-Half of Meditation ............................................................................................................................ 44
Table 9. Hierarchical Linear Modelling of Updating ................................................................................. 46
viii
List of Figures
Figure 1. Meditation Preference by Meditation Type ................................................................................ 47
Figure 2. Meditation Preference by Just-Noticeable-Difference Score ................................................... 48
Figure 3. Motivation by Meditation Type .................................................................................................... 49
Figure 4. Prior-Preference by Meditation Type .......................................................................................... 50
Figure 5. Increase in High-Frequency Heart-Rate-Variability by Just-Noticeable-Difference score . 51
Figure 6. Increase in High-Frequency Heart-Rate-Variability by Meditation Type .............................. 52
Figure 7. Update from bias by Decrease in Heart-Rate and Meditation Type ....................................... 53
Figure 8. Dispositional Change Across The Study..................................................................................... 54
ix
List of Appendices
Appendix A: Glossary of Acronyms .............................................................................................................55
Appendix B: MAAS-5 .....................................................................................................................................56
Appendix C: CAMS-R .....................................................................................................................................57
Appendix D: Preference Questionnaire........................................................................................................58
Appendix E: Meditation Instructions ...........................................................................................................59
Appendix F: Example Free Form Reponses ................................................................................................61
1
Introduction and Rationale
This study investigates personality traits and sensory discriminability as predictors of preference
across three types of meditation. The goal of this line of research is to enable the personalization of
meditation interventions, which is a special case of personalized or "precision medicine"(Lu,
Goldstein, Angrist, & Cavalleri, 2014). Precision medicine involves tailoring interventions to the
particular patient through understanding personal history, genetics, environment, and lifestyle with
the aim of decreasing side-effects and improving outcomes, which naturally involves enhancing
adherence (‘White House Precision Medicine Initiative’, 2015).
1.1 Intervention Adherence and Preference
Adherence is defined as "the extent to which a person’s behaviour – taking medication, following a
diet, and/or executing lifestyle changes, corresponds with agreed recommendations from a health
care provider."(Sabaté, 2003, p. 17). Adherence is difficult to obtain and there is little research on
how to improve it (Aronson, 2007). Indeed, the World Health Organization estimates that in
developed nations an average of 50% of prescribed treatments are not followed (Sabaté, 2003). The
implications of poor adherence include worse health outcomes, relapse, and even suicide in the case
of mental health treatments (Vuckovich, 2010). Meditation interventions for depression (Lau &
Segal, 2007) and other clinical conditions (Goldin & Gross, 2010) have proliferated, but up to a third
of participants may drop out before finishing the treatment. Crane and Williams (2010) found that
30% of participants dropped out during MBCT interventions, even with the generous definition that
completing only four of nine sessions was enough. Similar drop-out rates plague MBSR and while
the problem is known few if any solid predictors exist for categorizing participants as likely drop-
outs (Dobkin, Irving, & Amar, 2012). A meta-analysis of clinical interventions revealed a large
significant effect (0.58 at p < 0.05) of participant preference on intervention drop-out rates (Swift &
Callahan, 2009) such that participants were half as likely to drop out if they were randomized into
their preferred intervention. Participant preference must be taken into account if meditation
interventions are to be personalized, especially if the goal includes long-term adherence where the
benefits of meditation may be most profound.
2
1.2 Meditation
There are uncounted different types of meditation and numerous definitions of meditation used by
different researchers, and while no precise consensus has been reached, meditations are generally
considered “complex emotional and attentional regulatory strategies” and “mental and emotional
control practices” (Lutz, Slagter, Dunne, & Davidson, 2008, p. 163; Thomas & Cohen, 2014, p. 1).
These complex practices can be understood by considering common components found across
practices. Four essential components create a workable definition: meditation 1) uses a defined
technique 2) involving a self-induced state that 3) lacks the intention to analyse, judge, or expect and
that 4) brings about mental calmness and physical relaxation by suspending the stream of thoughts
that normally occupy the mind (Bond et al., 2009). One additional component could be considered
important, though perhaps not essential: the use of an “anchor” or "object of meditation",
sometimes involving concentration, other times involving the deliberate disengagement of
concentration (Bond et al., 2009). Meditation objects may direct concentration to one or more
sensory system - tactile, auditory, visual - and may interact with sensory abilities in that domain. One
such example is tactile sensitivity increasing after body-scan meditation practice (Mirams, Poliakoff,
Brown, & Lloyd, 2013). It is to the consideration of anchors in different sensory modalities that we
now turn our attention.
1.2.1 Breath-Based Meditation
A wealth of research on meditation involving the breath as an anchor of attention has emerged over
the past decades. Breath-focused mindfulness training has been shown to decrease detrimental
mind-wandering (Mrazek, Franklin, Phillips, Baird, & Schooler, 2013; Mrazek et al., 2014) and
clinical and non-clinical benefits of breath-focused mindfulness-training are abundantly documented.
These include improvements in conditions such as depression (including relapse reduction), anxiety,
chronic pain, stress, and substance abuse as well as direct and indirect improvement of well-being,
affect regulation, and health-related quality of life, such as improvements on blood-pressure
measures (see Brown, Ryan, & Creswell, 2007 for a review). Breath-focused mindfulness training has
even been suggested as a potential method to improve workplace performance and relationships
(Good et al., 2016). Reactions are not universally positive, however, and harm can be an accidental
outcome (Dobkin et al., 2012). Indeed, presently ongoing research is investigating adverse reactions
to meditation practice (Britton, 2011).
3
1.2.2 Phrase-Based Meditation
Less commonly researched is meditation based on phrase-repetition. The mere silent repetition of a
word has been shown to produce wide-ranging reductions in fMRI BOLD signal (Berkovich-Ohana,
Wilf, Kahana, Arieli, & Malach, 2015). Though a review of the neuroscience of meditation is well
beyond the scope of this paper, it is worth noting that these areas showing reduction - the anterior
and posterior cingulate cortex, superior and inferior parietal lobule, medial frontal gyrus, and insular
cortex - are involved in a wide array of processing including working-memory and executive-control,
emotion- and self-processing, sensory integration and interoception, and autonomic functions that
regulate blood pressure and heart-rate. These areas overlap heavily with areas found to be
structurally different in meditators as compared to controls (Fox et al., 2014) and overlap with but
are distinct from those identified in mindfulness meditation (Dickenson, Berkman, Arch, &
Lieberman, 2013). This partial overlap is mirrored by the partial overlap in Buddhist-inspired
mindfulness meditations and Hindu-inspired phrase-based absorptive meditations (Tomasino,
Chiesa, & Fabbro, 2014). Berkovich-Ohana et al. (2015) also collected qualitative reports that offer
salient experiential descriptors of phrase-meditating participants ranging from “focused”, “no
thoughts”, “deeper than rest”, to “easy”, “monotonous”, and “boring”.
1.2.3 Image-Based Meditation
Visualization meditations are perhaps the least studied type of meditation while also being the most
varied in content, as three examples will demonstrate. Buddhist “kasina” meditation involves
focusing the attention on a simple coloured disk (Amihai & Kozhevnikov, 2014). In contrast, the
broad category of qigong meditation includes images of energy fields in, around, and extending from
the body (Burke A., 2012). Visualization in Tantric-Buddhist "deity meditation" involves numerous
complex multi-coloured multi-armed three-dimensional supernatural figures holding various
spiritually-meaningful objects (Amihai & Kozhevnikov, 2014). One might speculate that performing
deity meditation in the laboratory for the first time could be quite demanding for a first-year
psychology undergraduate; perhaps it is no surprise that there is far less research on this kind of
visualization when compared to the breath.
1.2.4 Secularization of Meditation Objects
Breath-based meditations are well documented (Brown et al., 2007), and while it would be rash to
assume that the breath is the optimal meditative anchor for every person, the breath is an inherently
4
non-sectarian object of meditation (Harris, 2011) and thus relatively easy to study. In contrast, many
images used in traditional meditation practices involve visualizing deities, chakras, or other
symbolically rich religious constructs (Deleanu, 2010). Similarly, traditional meditation phrases -
“mantras” - are often devotional recitations toward a deity or sacred word-like sounds, such as the
syllable “om”, thought to hold mystical power and significance (Burchett, 2008; Gurjar & Ladhake,
2009). A demystification of image- and phrase-based meditations must be undertaken in order to
study them with an eye toward a broader understanding of specific effects and wider application of
benefits in non-religious contexts. Few efforts have been undertaken in this regard, but one example
is the aforementioned phrase-meditation study by Berkovich-Ohana et al. (2015). The authors note
that the wide-spread BOLD changes were elicited by the word ֶאָחד, (phonetically “ekh-awd'”,
meaning “one” in Hebrew) and claim that this word is not a mantra in any known spiritual context
(Berkovich-Ohana et al., 2015), but spiritual and religious connotations of “one” are certainly
numerous. Regarding image-based meditations, a search of the literature has been able to uncover
no secularization of visualization meditation for research (and very little non-secularized image-
based meditation). One simple suggestion is to secularize the "kasina" practice by choosing a colour
of disk not originally included in Buddhist practice. Whether specific colours, images, or phrases
elicit different responses may be the subject of future investigation. Whether certain elements of
traditional images - complexity, variability in colour, implied motion, meaning in context - and
sounds - meaning or meaninglessness, syllable count, intonation and prosody - are important for
reaping the full benefit or practice is an empirical question, as is the case with certain suggestive
elements of meditation instruction (Farb, 2012). The relative utility of these elements may further be
tied to religious or philosophical orientation, individual abilities, and participant preference.
1.2.5 Preferences for Particular Meditations
Historically meditators have been self-selected and, while this continues to be the most common
case, modern therapeutic interventions have led to the proliferation of courses such as MBSR
(Kabat-Zinn, 1990) and MBCT (Lau & Segal, 2007) that bring breath-based meditation to those who
may be otherwise disinclined to practice. There are many options other than the breath, however,
for potential meditators to choose from and in the spirit of concordance clients should be consulted
regarding their options (de Almeida Neto & Aslani, 2008). Burke (2012) found differences in
participant preference ratings by contrasting four types of meditation: 1) Vipassana (Mindfulness), 2)
Mantra, 3) Qigong, and 4) Zen. Specifically, the anchoring objects of meditation were, respectively:
1) the breath and a practice of mentally labelling thoughts and sensations, 2) the word “Hum”
5
silently repeated on inhalation of the breath followed by “Sah” on exhalation, 3) a complex
visualization of the movement of imagined light, tied again to inhalation and exhalation of the
breath, and 4) “general awareness” anchored in a traditional seated posture (Burke, 2012).
Unfortunately severe methodological limitations and reporting issues prevent the results from being
easily summarized. Regardless, the objects of meditation are severely overlapping as three of the four
practices are tied to the breath and the fourth is tied to body-posture. In contrast, the current study
investigates three meditations with different specific sensory modalities.
1.3 What Could Predict Meditation Preference?
Individual differences in traits and abilities may predict which object of meditation will be most
preferred. By investigating the link between preference and motivation, personality traits, trait-
mindfulness, behavioural mind-wandering, and sensory abilities this study hopes to inform
improvements in intervention prescription and thereby adherence to encourage long-term
commitment to beneficial practices.
1.3.1 Prior preference, Motivation, and Preference
In a meta-analysis of clinical interventions participant preference was linked to likelihood of
beneficial outcome (0.2 at p < 0.01 for randomized control trial experiments) (Swift & Callahan,
2009). Preference prior to practice and motivation to engage in the particular meditation are thus
expected to strongly predict post-intervention preferences:
Hypothesis 1: prior-preference and motivation will positively predict
preference
1.3.2 Trait Mindfulness
Predisposition to mindful behaviour is expected to enhance preferences for meditation. We measure
this "trait mindfulness" through two questionnaires, The Mindful Attention Awareness Scale
(MAAS; (Brown & Ryan, 2003)) and The Cognitive and Affective Mindfulness Scale Revised
(CAMS-R; (Feldman, Hayes, Kumar, Greeson, & Laurenceau, 2006)), discussed in more detail in
section 2.3.1 below.
Hypothesis 2: Trait-mindfulness (MAAS and CAMS-R scores) will
positively predict preference
6
1.3.3 Mind-Wandering
In contrast to trait-mindfulness, a strong tendency to mind-wander is expected to make meditation
feel more difficult and thus detract from preferences. As mind-wandering differs by task-
engagement participants may be less inclined to mind-wander during meditations for which they are
more motivated (Kane et al., 2007) thus motivation and mind-wandering may interact. The measure
of mind-wandering in this study is a short version of the Metronome Response Task (MRT, 2.3.2
below; Seli, Cheyne, & Smilek, 2013). During the MRT participants tap along to a steady beat and
variability in tapping is considered a measure of mind-wandering (Bastian & Sackur, 2013; Seli et al.,
2013).
Hypothesis 3a: Mind-wandering (MRT variability) will predict
decreased preference
Hypothesis 3b: Mind-wandering (MRT variability) will interact with
motivation to mitigate the negative impact of mind-wandering on
preference
1.3.4 Sensory Discriminability
Different objects of meditation engage distinct sensory modalities. These sensory systems - tactile,
auditory, visual - show variability in sensitivity across participants and discrimination thresholds
reflect these differences in psychophysical abilities (Garcı́a-Pérez, 1998). We measure sensory
discrimination thresholds by a psychophysical staircase procedure (2.3.3 below2.3.2 below) in which
two similar stimuli are presented and the participant must discriminate between them. The similarity
gradually increases, honing in on the point at which the participant can just barely discriminate
between the two stimuli, their individual Just Noticeable Difference (JND). We measure JND in
three sensory modalities: physical vibration, auditory pitch, and visual colour saturation. These
sensory modalities were chosen to reflect the three types of meditation objects used in this study -
breath, phrase, and image. Meditation preference is expected to be predicted by JND scores such
that participants will prefer meditations using an anchor that draws on a sensory system where they
have superior discriminability.
Hypothesis 4: JND scores will positively predict preference for
within-modality meditation objects
7
1.3.5 Personality
When considering personalized medicine one must take into account individual differences, such as
personality variables. The construct of conscientiousness implies socially prescribed impulse control
and rule-following (John, Naumann, & Soto, 2008) thus particularly good adherence is expected.
Research has shown that the factor of conscientiousness predicts health-beneficial behaviours (Hall,
Fong, & Epp, 2013; Hampson, Edmonds, Goldberg, Dubanoski, & Hillier, 2013; Murray & Booth,
2015; Turiano, Chapman, Gruenewald, & Mroczek, 2015) and given that meditation practice is
generally considered health-beneficial (Brown et al., 2007) those with high conscientiousness are
expected to report greater preference. Likewise, given the conceptual nature of the personality
construct of Openness as involving the "inner life" of the individual (John et al., 2008) Openness is
expected to increase preference for meditation. Conversely, due to opposition between the free-
running negative emotionality of Neuroticism (John et al., 2008) and the common hallmarks of
meditation, emotional control and stability, Neuroticism is expected to predict decreased preference.
The personality factors of Extraversion and Agreeableness are exploratory.
Hypothesis 5a: Conscientiousness will positively predict preference
Hypothesis 5b: Openness will positively predict preference
Hypothesis 5c: Neuroticism will negatively predict preference
1.3.6 Physiological Efficacy
Research suggests that heart-rate (HR) and high-frequency heart-rate-variability (HF-HRV) may be
considered physiological outcomes of efficacious of meditation (Olex, Newberg, & Figueredo, 2013;
Shearer, Hunt, Chowdhury, & Nicol, 2015). Specifically decreases in HR and increases in HF-HRV
have been considered physiological markers of deeper meditative experience (Olex et al., 2013). It is
expected that participant experience during the meditation will be the main driving force behind
their preference and as such post-intervention preference (2.3.4 below) is expected to show a strong
positive zero-order correlation with measures of physiological efficacy (2.3.5 below).
Hypothesis 6: preference will be positively correlated with
physiological efficacy
8
Methods
2.1 Participants
Meditation-naïve undergraduates from the University of Toronto, Mississauga campus participated
in exchange for course-credit or monetary remuneration. In total 46 have participated; 36 have been
retained for analysis after 10 (22%) were removed after comments collected during the study
revealed they were not following the meditation instructions (see Limitations, section 4.4 below).
Collection shall continue through September and October until 130 participants total have been
collected. Demographic variables of included subjects are summarized in Table 1.
2.2 Design
After obtaining informed consent participants were equipped with a respiration and heart-rate
monitor (2.3.5 below). Participants then completed a computer-based questionnaire on trait
mindfulness and personality (2.3.1 below) and rated their predicted enjoyment of each object of
meditation (breath, phrase, and image). Following this they engaged in tasks assessing pitch-
discrimination, colour saturation-discrimination, and vibration-detection (2.3.3 below), as well as a
behavioural measure of mind-wandering (2.3.2 below). Participants then read general meditation
instructions before reading object-specific instructions (2.4 below). In random order they completed
three 10-minute meditation interventions (breath, phrase, and image). Following each meditation
participants completed an experience questionnaire (2.3.4 below). After completing all of the
meditations participants filled out demographic information and were debriefed.
2.3 Measures
2.3.1 Trait Mindfulness and Personality
The Mindful Attention Awareness Scale (MAAS; (Brown & Ryan, 2003)) is commonly used to
assess present-minded awareness by reverse-scoring items that tap the construct of “automatic-ness”
(Osman, Lamis, Bagge, Freedenthal, & Barnes, 2015; Van Dam, Earleywine, & Borders, 2010); a
five-item short-form of the MAAS was administered (Osman et al., 2015; Van Dam et al., 2010).
The Cognitive and Affective Mindfulness Scale Revised (CAMS-R; (Feldman et al., 2006)) was also
used to assess trait mindfulness; the CAMS-R combines several positive aspects of mindfulness -
including attention regulation, present-minded awareness, and non-judgemental acceptance - into a
single score. The Big Five Inventory (BFI; McCrae & John, 1992) measures personality using the
9
commonly recognized five-factor model - Extraversion, Agreeableness, Conscientiousness,
Neuroticism, and Openness. In order to retain uniformity across all items the MAAS and CAMS-R
items were reworded to conform to the style of the BFI ("I see myself as someone who…",
Appendix B: MAAS-5 & Appendix C: CAMS-R). All items were rated using a sliding 0-100 scale
with nominal descriptors at 0 ("Strongly Disagree"), 50 ("Neutral"), and 100 ("Strongly Agree").
2.3.2 Mind-Wandering
The Metronome Response Task (MRT) is a task in which participants tap along to a steady aural
beat (Seli et al., 2013). While mind-wandering is most commonly measured by verbal probes asking
participants about their current degree of mind-wandering (Schooler et al., 2014) response time
variability in the MRT has also been linked to mind-wandering (Bastian & Sackur, 2013; Seli et al.,
2013). Unlike other behavioural markers of mind-wandering the MRT does not require specialized
equipment such as EEG (Broadway, Franklin, & Schooler, 2015), eye-trackers (Foulsham, Farley, &
Kingstone, 2013; Franklin, Broadway, Mrazek, Smallwood, & Schooler, 2013; Schad, Nuthmann, &
Engbert, 2012; Uzzaman & Joordens, 2011), or balance-boards (Seli et al., 2014). MRT variability
thus acts as an indirect behavioural measure of mind-wandering.
As in Seli et al. (2013) MRT trials were as follows: 650 ms of silence followed by a 440 Hz tone
lasting 75 ms followed by another 575 ms of silence, resulting in a total trial duration of 1300 ms.
Participants were instructed to use the spacebar to "tap along with the tone". In contrast to the
lengthy MRT used in previous research participants completed two shorter blocks of 45 trials
(approximately 1 min) and 230 trials (approximately 5 min). Response variability was computed for
each block with missed trials dropped. Five participants did not follow the instructions and as such
were not included in MRT analysis.
2.3.3 Sensory Discriminability
Three Just Noticeable Differences (JND) staircases measured discrimination thresholds in three
sensory domains: physical vibration, auditory pitch, and visual colour saturation. Each staircase,
programmed in PsychoPy (Peirce, 2007, 2009), followed a 2-Alternative Forced-Choice (2AFC) 3-
Down/1-Up procedure as per (Garcı́a-Pérez, 1998). In this implementation of the 2AFC procedure
participants were directed via on-screen instructions to indicate whether the first or second of two
randomly presented sequential stimuli - a target and a foil - represented the reference stimulus,
which was demonstrated at the beginning of the staircase. In the first trial the difference between
10
target and foil is great and with each correct trial the difference decreases until the first incorrect
trial. From this point onward each incorrect trial results in the difference between target and foil
increasing one step (1-Up); three consecutive correct trials results in the difference between target
and foil decreasing one step (3-Down). A change in direction - from increasing difference to
decreasing or vice versa - is called a reversal, which represents a crossing of the participants'
discrimination threshold. For each two reversals the step size decreased by half so as to hone in on
the specific boundary of the threshold. Ten reversals were used and the last six reversals were
averaged to create a JND score (Garcı́a-Pérez, 1998). If participants failed to discriminate the stimuli
adequately the instructions and reference were automatically reviewed and the staircase would begin
anew. If participants continued failing to discriminate the stimuli then the staircase quit and no value
was collected; this happened in the case of 5 participants. During pre-processing of data JND error-
values were trimmed (3 vibration, 3 auditory, and 4 visual) and remaining values winsorized to
reduce the impact of outliers (5% vibration, 15% auditory, and 5% visual).
In the case of vibration-detection, participants were asked to indicate which of the two intervals
contained a vibrating stimulus, an index of bodily awareness (Mirams et al., 2013). Participants were
given a bone-conductor and told to hold the device lightly but firmly between the thumb and index-
finger of their left hand. Intensity of vibration increased or decreased until ten staircase reversals
were made and the threshold was determined. In the case of pitch-discrimination, participants were
asked to indicate which of the two intervals contained the reference tone, a 440 Hz sine wave or
concert-A. Participants wore headphones and foil tones were randomly higher or lower in pitch. In
the case of colour saturation-discrimination, participants were asked to indicate which of the two
intervals contained the reference circle, an on-screen circle filled with a dim green (RGB values (0%,
50%, 0%)). Foil colours were randomly more or less saturated.
2.3.4 Subjective Preference
A search of the literature revealed no standard measure of preference thus a new questionnaire was
developed. Some items were created by the author for this study while others were drawn from the
two subscales of the Toronto Mindfulness Scale, Curiosity and Decentering (Lau et al., 2006), as well
as the Meditation Depth Questionnaire (Piron, 2001). All items were formatted to use the same scale
ranging from experienced “not at all” to “very much” as in the TMS (Lau et al., 2006). The final
scale can be seen in Appendix D: Preference Questionnaire.
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2.3.5 Physiological Efficacy
During the experiment participants were equipped with a Zephyr BioHarness 3, a heart- and breath-
monitoring belt (Ainsworth, Cahalin, Buman, & Ross, 2015; Hailstone & Kilding, 2011; Johnstone,
Ford, Hughes, Watson, & Garrett, 2012a, 2012b). Heart-rate (HR) and high-frequency heart-rate-
variability (HF-HRV) as well as breathing-rate act as behavioural markers of the efficacy of each
meditation intervention (Shearer et al., 2015). Specifically lower HR and breathing-rate as well as
HF-HRV have been considered physiological markers of deeper meditative experience (Olex et al.,
2013). Breathing-rate data is not reported here.
2.4 Meditation Intervention
The meditation interventions are based loosely on the instruction manual for Natural Stress Relief
meditation (Coppola, 2007; Coppola & Spector, 2009), adjusting as needed such that the object of
meditation is one of the breath, the phrase, or the visual image. The phrase meditation used a
meaningless word-like phrase developed for this study; this phrase - "ay-lo-ra" - was played through
headphones during the instructions. The image meditation used an image developed for this study;
this image - a dim green circle - was likewise shown during the instructions. The intervention
instructions, including audio and visual stimuli, are available in Appendix E: Meditation Instructions
and for other researchers to simplify replication.
2.5 Data Analysis
2.5.1 Just Noticeable Differences
The last six reversals were averaged to create a JND score (Garcı́a-Pérez, 1998). Participants unable
to complete the staircase were excluded from analysis (5 participants). During pre-processing of data
JND error-values were removed (3 vibration, 3 auditory, and 4 visual) and remaining values
winsorized to reduce the impact of outliers (5% vibration, 15% auditory, and 5% visual). To allow
comparison of scores across modalities JND scores were Z-scored with lower scores being a finer
level of discrimination.
2.5.2 HR and HRV Analysis
During the experiment participants were equipped with a Zephyr BioHarness 3. The BioHarness
records electrocardiogram (ECG) at 250 Hz and performs online R-to-R interval measurement,
which is generally preferred over offline calculation (Berntson et al., 1997). Unfortunately a large
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subset of the data was problematic (not collected, intermittent connectivity, excessively noisy) thus
only a small subset is available (19 participants) for present analysis.
Heart-rate was filtered to automatically remove rates lower than 25bpm and higher than 200bpm.
Further outliers were removed by manual inspection. The first and last minutes of meditation were
dropped and two four minute epochs were created as the first and second half of meditation,
following the literature (Berntson et al., 1997). Within-subjects baselines were calculated as the
average of two four-minute epochs, one during the personality questionnaire and the other during
the JND tasks. Heart-rate was interpolated at 4 Hz and HRV transformations were computed
separately using both Fourier and Wavelet transforms; results discussed here are on the Fourier
transformed HRV. HRV was split into four frequency ranges as per (Berntson et al., 1997): Ultra-
low frequency (ULF, 0 to 0.03 Hz), Very Low frequency (VLF, 0.03 to 0.05 Hz), Low frequency
(LF, 0.05 to 0.15 Hz), and High frequency (HF, 0.15 to 0.4 Hz). Due to the dubious interpretations
of most frequency bands (Berntson et al., 1997) only High-frequency band values are discussed.
These HF-HRV values were winsorized prior to modelling to reduce the impact of outliers (10%).
2.5.3 Modelling
Multilevel hierarchical linear regression modelling with participant as level-2 and meditation-type as
level-1 was used. Multilevel modelling allows for a finer parcellation of variance when using a within-
subjects design and allows for the retention of participants when cells are missing non-modelled
data-points: when a participant is missing behavioural or physiological data they are dropped only
from models requiring that value as a predictor or outcome. Hierarchical linear regression allows for
principled step-by-step addition of new variables to a model, testing at each step whether novel
predictors improve the model fit. At any point where a predictor was added to a model and the new
model did not significantly improve the fit over the previous model that new predictor was dropped
from further analysis; when multiple predictors were added simultaneously and only a subset reached
significance the model was compared to a model where non-significant predictors were omitted. If
omission did not decrease model fit the non-significant predictors were dropped.
For each series of models a simple intercept-only model acted as the starting point. Potential effects
of meditation order were investigated and then the three-categories of mediation were added. Next,
motivation and prior-preference were added to test Hypothesis 1 (1.3.1 above). The two measures of
trait-mindfulness (MAAS and CAMS-R) were added next (Hypothesis 2, 1.3.2 above). The
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behavioural measure of mind-wandering - MRT variability - was then added (Hypothesis 3, 1.3.3
above).
The primary experimental model testing Hypothesis 4 (1.3.4 above) added the modality-specific just-
noticeable-difference scores: haptic sensitivity as a predictor for breath-meditation preference, pitch-
discrimination for phrase-meditation, and visual-JND for image-meditation. Next,
Conscientiousness, Neuroticism, and Openness were added to the model to assess Hypothesis 5
(1.3.5 above). The other personality factors - Extraversion and Agreeableness - were then tested as
exploratory predictors. This same series of model steps was used to predict three outcome variables:
meditation preference (3.1.1 below), heart-rate decrease (3.3.2 below), and high-frequency heart-rate-
variability increase (3.3.3 below). Based on the findings, a similar series of models was applied post-
hoc to predict motivation and prior-preference (see 3.2.1 below for further details).
Prior to modelling all variables were grand-mean centred (Enders & Tofighi, 2007). An unstructured
covariance matrix and the between-within method of estimating degrees of freedom were used in
model building. Effect sizes were estimated with semi-partial R2 (Edwards, Muller, Wolfinger,
Qaqish, & Schabenberger, 2008) and overall model effects using Pseudo R2 (Snijders & Bosker,
1994). The intraclass correlation coefficient was calculated as a measure of how critical multilevel
modelling was to correctly parcelling variance within the dataset (Mathieu, Aguinis, Culpepper, &
Chen, 2012).
Results
Due to the small sample size there is presently insufficient power to find all but the strongest effects.
For this reason, there will be four subsections of results: planned modelling, exploratory modelling,
physiological efficacy, and speculation. During the first I will describe the results of the a priori
models for preference and the significant findings thereof. Due to the unexpectedly powerful
influence of motivation as a predictor of preference I will turn to exploratory modelling using
motivation as the outcome variable and testing the fit of models originally intended to predict
preference. Physiological effects are then considered. Further exploration on the differences
between prior preferences, motivation, and post-intervention preference and how they relate to
physiological response to meditation is beyond the scope of the a priori predictions and thus
demarcated as speculative exploration with the purpose of generating future testable hypotheses.
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3.1 Planned Modelling
For the full hierarchical linear regressions consult Table 2-9.
3.1.1 What Predicts Meditation Preference?
Meditation Preference is the primary subjective outcome variable, measured by questionnaire (see
Appendix D: Preference Questionnaire) and recorded as the average value of the responses. The
intraclass correlation coefficient (ρ = 0.226, t(33) =1.333, p = 0.096) does not affirm the absolute
necessity of multilevel modelling; regardless this method was maintained. Pseudo R2 for the final
models reduced prediction error compared to the intercept-only by a medium amount (Pseudo-R2 =
0.338).
In line with Hypothesis 1 (1.3.1 above) there was a significant, moderate main effect of Motivation
(b=0.291, SE=0.065, F(58)=20.424, p<0.0001, semi-partial R2=0.260) such that greater motivation
predicted greater preference. A Type by JND interaction was found (b=6.427, SE=3.184,
F(58)=5.266, p=0.008, semi-partial R2=0.154) and small main effects of Type (b=-7.840, SE=2.946,
F(58)=4.757, p=0.012, semi-partial R2=0.141) and JND (b=-4.757, SE=2.219, F(58)=8.224,
p=0.006, semi-partial R2=0.124) were also uncovered. Figure 1 shows the baseline difference in the
preferences for different meditation types, specifically that participants prefer Breath (M: 59.10, SD:
15.60) and Phrase (M: 56.72, SD: 13.13) meditations over the Image (M: 50.38, SD: 16.58)
meditation (Breath: t(35)=2.9558, p=0.006, Phrase: t(35)=2.2136, p=0.033) and also that greater
modality-specific discriminatory ability predicts greater preference for meditations of that modality,
strong support for Hypothesis 4 (1.3.4 above). The interaction, however, hints that some
discriminatory abilities are more impactful than others (see Figure 2); exploration shows that the
relationship between visual-JND and preference for the Image meditation is stronger (F(31)=6.195,
p=0.018, R2=0.167) than for the other meditations where the relationship was not significant
(Breath: F(31)=2.551, p=0.120, R2=0.076; Phrase: F(31)=0.594, p=0.447, R2=0.019).
3.2 Exploratory Modelling
3.2.1 What Might Predict Motivation?
Motivation to engage in a meditation was uncovered as a primary predictor of preference for that
meditation. While an interesting and expected finding (Hypothesis 1, 1.3.1 above), this results begs
the question of what predicts motivational bias. In order to explore this question new models of the
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same form as those planned for meditation preference were run with motivation as the outcome
variable. While based on planned modelling the application of these models to this outcome variable
were not planned and as such this analysis must be taken as explicitly exploratory. A Pseudo-R2 of
0.377 was accomplished by the final model, a medium reduction in error. The intraclass correlation
coefficient (ρ = 0.457, t(33) = 2.952, p < 0.01) affirmed the utility of multilevel modelling for
motivation.
A series of significant, moderate and small main effects were found including Prior Preference
(b=0.339, SE=0.079, F(68)=23.947, p<0.0001, semi-partial R2=0.260), Type (b=-9.645, SE=3.429,
F(68)=7.353, p=0.001, semi-partial R2=0.178), and Neuroticism (b=0.444, SE=0.142, F(34)=9.709,
p=0.004, semi-partial R2=0.222). A less than small main effect of Order was also found (b=-4.017,
SE=1.717, F(68)=5.965, p=0.017, semi-partial R2=0.081) suggesting that motivation dropped
slightly over the course of the experiment duration. Much like the main effect of meditation type on
preference the main effect of type on motivation revealed that participants again differ at baseline.
Figure 3 shows motivation bias in favour of the Breath (M: 70.94, SD: 21.88) over the Image (M:
62.22, SD: 17.16) and Phrase (M: 58.56, SD: 25.71) meditation (Image: t(35)=2.4296, p=0.020;
Phrase: t(35)=2.9127, p=0.006) was uncovered. Curiously greater neuroticism predicted greater
motivation, possibly refuting Hypothesis 5c (1.3.5 above).
As with predicting preference by motivation, predicting motivation via prior-preference begs the
question of what predicts prior-preference. Exploratory analyses (not reported here) used the same
technique to predict Prior-Preference and, as with Motivation, Type and Neuroticism were the main
predictors (model Pseudo-R2 of 0.192; Type: b=-16.639, SE=4.733, F(70)=9.179, p=0.0003, semi-
partial R2=0.208; Neuroticism: b=0.302, SE=0.120, F(34)=6.367, p=0.017, semi-partial R2=0.158).
Investigating prior-preference by type of meditation revealed the biased baseline, shown in Figure 4,
this time with Breath (M: 67.69 SD: 22.07) and Image (M: 69.42, SD: 18.55) greatly outclassing the
Phrase (M: 51.06, SD: 20.98) meditation (Breath: t(35)=3.7155, p<0.001; Image: t(35)= 3.7145,
p<0.001). Again greater neuroticism predicted greater prior preference; the implications of this are
touched upon in the discussion of future directions (4.5 below).
3.3 Physiological Efficacy
Percent-decrease in heart-rate (HR) from baseline and percent-increase in high-frequency heart-rate-
variability (HF-HRV) were split into two epochs, the first and second halves of meditation. The
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sample size of the physiological data is extremely limited at 19 participants, less with both
physiological and JND data. The Zephyr BioHarness 3.0 (2.3.5 above) fits around the torso, under
the clothing, and as such participants affixed it themselves. Research assistants made sure the device
was powered on, but this alone was not enough to ensure good quality data collection. As such,
these results should be considered as preliminary.
3.3.1 Correlation with Preference
Counter to Hypothesis 6 (Error! Reference source not found.) physiological efficacy and
subjective preference measures were not significantly correlated (Heart-Rate: first half: r(55) = -
0.067, p = 0.620; second half: r(55) = 0.088, p = 0.5153; HF-HRV, first half: r(55) = 0.050, p =
0.7102, second half: r(55) = 0.120, p = 0.3748). In a larger sample this finding would raise serious
questions about what we should measure and optimize in the application of meditation
interventions: How does a subjective measure of fit compare to an objective measure of fit? Which
is more fundamental? If we prioritize subjective preference, are we measuring the wrong
physiological markers? If we prioritize physiology, should we endeavour to make the meditation
more subjectively enjoyable or is difficulty part of the benefit? Due to the very small sample,
however, these questions will be tabled for now.
3.3.2 What Predicts Decreased Heart Rate?
Percent-decrease in heart-rate from baseline was split into two epochs, the first and second halves of
meditation. The intraclass correlation coefficient was significant for both first (ρ = 0.612, t(17) =
3.191, p < 0.01) and second halves (ρ = 0.695, t(16) = 3.866, p < 0.05) suggesting that multilevel
analysis was indeed appropriate for these data. On the other hand, Pseudo R2 for the final model in
both halves of meditation failed to meaningfully reduce prediction error compared to the intercept-
only model. Cohen (1992) considers 0.1 a small amount and no model could attain even that (first:
Pseudo-R2 = 0.067; second: Pseudo-R2 = 0.099). This is not surprising given that less than 20
participant data-points were obtained; for this reason no further discussion on heart-rate is presently
warranted.
3.3.3 What Predicts Increased High Frequency Heart Rate Variability?
Percent-increase in high-frequency heart-rate-variability from baseline was also split into epochs
consisting of the first and second halves of meditation. Pseudo R2 for the final models reduced
prediction error from intercept-only by a medium amount (first: Pseudo-R2 = 0.431; second:
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Pseudo-R2 = 0.300). Intraclass correlation coefficients were significant for both halves (first: ρ =
0.767, t(16) = 4.781, p < 0.001; second: ρ = 0.651, t(17) = 3.536, p < 0.01) affirming the utility of
multilevel analysis.
Significant, medium effects on HF-HRV came from Extraversion and were consistent across both
epochs (first: b=0.036, SE=0.012, F(16)= 9.619, p=0.007, semi-partial R2=0.375; second: b=0.044,
SE=0.014, F(17)=9.122, p=0.008, semi-partial R2=0.349) such that higher Extraversion predicted
greater increase in HF-HRV. During the first epoch performance on the JND measures were
impactful as revealed by a significant, moderate main effect (b=-0.306, SE= 0.121, F(32)= 9.851,
p=0.004, semi-partial R2=0.235) such that, as predicted in Hypothesis 3 (1.3.4 above), superior
sensory discriminability predicted greater HF-HRV for same-modality meditation. The interpretation
of this result is complex, however, as shown in Figure 5 and discussed in section 4.2 below. During
the second epoch, on the other hand, the specific type of meditation was revealed to exert a
significant small main effect, particularly that the Phrase meditation (b= 0.794, SE=0.309, F(36)=
3.563, p=0.039, semi-partial R2=0.165) showed the greatest increase in HF-HRV over baseline (see
Figure 6).
3.4 Speculative Exploration
3.4.1 Do Physiological Changes Influence Preference Updating?
To this point the results have shown that participants' prior-preference predict their motivation, and
their motivation predicts their preference. Participants had never meditated before the study,
however, thus prior-preference and motivation represent a bias. Here we explore whether
experience can overcome that bias and help "update" participant preference. Indeed, the degree to
which participant incoming bias differs from preferences reported after participating reflects the
effect of experience; we refer to this derived exploratory measure as "updating".
Multilevel hierarchical linear regression modelling was again performed. For these models
meditation type, personality variables, and physiological measures were tested as predictors of
updating (see Table 9). The updating outcome was a derived difference score: the difference of the
final preference from the mean of prior preference and motivation with positive values reflecting
meditations preferred more than expected. The final model reduced error by a moderate amount
(Pseudo-R2=0.243) and multilevel modelling was maintained even though the intraclass correlation
coefficient (ρ = 0.067, t(17) =0.277, p = 0.785) did not affirm its necessity. Two significant,
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moderate main effects were found regarding Type (b= 12.475, SE=5.609, F(2,35)= 5.993, p=0.006,
semi-partial R2=0.255) and Heart-Rate reduction in the second half of meditation (b=131.962, SE=
41.906, F(35)= 9.916, p= 0.003, semi-partial R2=0.221) such that greater reductions predicted more
updating (see Figure 7). The phrase meditation saw the greatest updating showing that it was
preferred more than anticipated. While these findings must be noted as exploratory and conclusions
limited until this model is replicated with the complete sample, and in future studies, the potential
impact of type of meditation and heart-rate change for updating bias through experience will be
discussed (4.2 below).
Discussion
The present study investigated personality traits and sensory discriminability as predictors of
meditation preference, motivation, and physiological efficacy using multilevel hierarchical regression
modelling. Findings indicate that sensory ability measures may be useful in screening new meditators
with the goal of increasing assignment of practices novices will prefer. If preference leads to
adherence, as Swift & Callahan's (2009) meta-analysis suggests, then the precision tailoring of
meditation intervention to individual ability will improve outcomes gained from meditation.
4.1 Preference and Motivation
The strongest predictor of preference was motivation (semi-partial R2=0.260), an unsurprising
confirmation that willingness to engage with a meditation is linked to subjective preference.
Motivation was further explored by applying the same hierarchical linear multilevel modelling used
to predict preference and another foreseeable result was exposed: prior-preference was the strongest
predictor of motivation (semi-partial R2=0.260). While other measures were able to enhance the
model somewhat, if participant bias is the best predictor of meditation outcomes then simply asking
new meditators what they think they might like could be a boon for directing them to the most
preferable practice. Though simple this in itself is a notable finding as teaching meditation
commonly involves uniform instruction in one technique, in the case of mindfulness meditation by
beginning with a body-scan or with the breath as the object. The breath or body are only one
modality of many, however, and these results suggest that potential meditators should be presented
with options and involved in the decisions-making process.
JND performance generally enhanced preference and there was a synergy (see Figure 2) such that
subtler visual discriminability was particularly important for enjoyment of the image meditation,
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which was otherwise preferred less on average than the other meditations (Figure 1). This findings
suggests that for most people the image meditation would not be a good choice, but those with
superior visual discriminability may still be able to enjoy it.
4.2 Physiological Efficacy
Models predicting heart-rate decrease were unable to convey meaningful predictions thus they
warrant little discussion at present; perhaps with more participants some findings can be uncovered,
but with the small sample null results are no surprise. Interestingly heart-rate decrease in the second
half of meditation predicted updating preference from initial bias; this is discussed more below (4.3).
While the consistent medium effect-size impact of participant Extraversion on high-frequency heart-
rate variability during meditation may appear interesting these results should be interpreted with
caution. The existing literature relating HF-HRV and personality is somewhat underwhelming.
Shepherd, Mulgrew, and Hautus (2015) modelled a negative associated between HF-HRV and
Neuroticism in a sample of 106 postgraduate students, though the R2 value was less than 0.1.
Personality and HF-HRV were not related in a sample of more than 200 undergraduates (Silvia,
Jackson, & Sopko, 2014) and in a very recent paper by Sloan et al. (2016) HRV in a representative
sample of almost 1000 participants was not associated with any of the Big Five personality variables.
For this reason further consideration of any associations between personality and physiological
measures will be left until the full sample is obtained.
Similar caution should be taken when considering the other predictors of HF-HRV, but some
speculative discussion could prove interesting. During the first half of meditation participant
performance on the JND measures significantly predicted a moderate effect (semi-partial R2=0.235)
such that finer discriminatory ability predicted increased HF-HRV. Examining Figure 5, however,
suggests that the effect may be driven by a few poor performances on the JND rather than a more
robust and meaningful effect. Likewise, in the latter half the particular type of meditation object
showed a small effect (semi-partial R2=0.165), though examining Figure 6 suggests that the data may
still be too noisy to confidently interpret and the relationship may become clearer with more
participants.
Caveats stated, speculation offers that a lack of discriminability slows or limits participants' initial
move into meditative states; as time in session continues the meditation (and object of meditation)
drives the effect beyond baseline ability. If this suggestion holds true two suggestions result. First, if
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a participant performs very poorly on a JND task then the corresponding meditation object could be
eliminated as a possible suggestion, at least for beginners. Second, it may be prudent to recommend
a minimal meditation time no less than ten minutes. The first five minutes of meditation may or may
not be useful if a person already meets baseline modality-specific discrimination; the benefits of
meditation may require devoting enough time to practice, especially while the skill is
underdeveloped. If this finding holds it could call into question the utility of 3-minute and 5-minure
meditations for novices that are often "homework" in mindfulness courses.
4.3 The Effect of Experience
Over the course of the experiment, average dispositions toward the different types of meditations
changed (Figure 8). Indeed, at baseline participants reported favouring the Breath and Image
meditations quite starkly over the Phrase. When reporting motivation participants had already
shifted and were favouring the Breath over both other meditations. After engaging in each
participants finally favoured Breath and Phrase meditations. While Breath meditation maintained its
high status, the Image meditation fell from most favoured to least, and favour for Phrase gained a
considerable positive update.
In order to explore this update to participant preference exploratory models were fit and found that
the predictors were meditation type and a heart-rate decrease. As with all physiological data in this
study the sample size is small and noisy. With the caveat that these results need replication we
speculate that the reduction in heart rate may reflect a relaxation response participants find pleasant
as they move into a meditative state: the deeper the response the more the change in preference
(Figure 7). An effect of type was also significant wherein favour for Breath and Image dropped and
favour for Phrase increased. This finding could reflect a regression to the mean due to the difference
in bias at the beginning of the experiment. Where this bias originates is a question for future studies
(4.5 below).
4.4 Limitations
This study suffers a number of limitations, most obviously its small sample size. With so few
participants the results may be unstable when compared to the larger complete sample. The sample
was reduced further as 20% of participants either did not understand or did not follow meditation
instructions (available in Appendix E: Meditation Instructions). After reading the instructions
participants were required to describe what they were about to do as a screen for misunderstanding,
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and after each preference questionnaire the participant had the option of including additional details
about their experience. Some illustrative example are recounted in Appendix F: Example Free Form
Reponses. This finding is quite disillusioning as it highlights how the simple assumption that
participants understand and follow instructions is likely a great source of noise and possibly quite
detrimental to statistical power. That participants either do not follow or do not understand
relatively banal meditation instructions raises serious questions about participant understanding in
more complex formal meditation classes. The complexity of meditation instructions given to
patients who have been prescribed mindfulness could be a great barrier to adherence, as is the case
with patients given complex medicinal regimens (Osterberg & Blaschke, 2005). If a new meditator
does not understand and implement instructions correctly then there is little reason to expect that
they will reap the rewards of their practice and adherence would surely suffer. Including a qualitative
comprehension check should become standard operating procedure anywhere meditation is taught
or researched. Follow-up studies will include a mandatory, rather than optional, post-meditation
qualitative report to assess participant adherence.
The sample size of the physiological data is also severely limited. Due to the monitoring device
fitting under clothing and around the torso each participant was entrusted with affixing it
themselves. The research assistants made sure the device was powered on but no further procedure
was undertaken to ensure good, low-noise data collection as recordings are stored on the device until
uploaded after the experiment. In the future the wireless Bluetooth capabilities will be explored as a
possible way of assessing recording quality before beginning.
Additional participants were lost due to difficulty in completing the Just-Noticeable-Differences
task, particularly the auditory task. Reinitiating the procedure upon too many failures seems to have
helped, but in the future a small number of practice trials will be introduced. There were a few
participants who had issues with the Metronome Response Task so a catch will also be put in place
in future implementations of the MRT.
The utility of the MRT as a behavioural measure of mind-wandering, at least on the short time-scales
used in this experiment, may be in question. It was not a meaningful predictor in any model, refuting
Hypotheses 3a and b (1.3.3 above) and was not correlated with either of the trait-mindfulness
questionnaires, which were also not meaningful predictors in any model, refuting Hypothesis 2 (1.3.2
above). Replication of the original MRT studies should be undertaken before further attempting to
use this measure.
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4.5 Future Directions
In addition to addressing the limitations above, some methodological updates and additions are in
order. Follow-up studies will include the BFI-2 (Soto & John, 2016) as an updated model of
personality. Additionally, due to the predictive utility of Neuroticism found in this study future
iterations will include measures to probe negative emotionality with more granularity, including but
not limited to anxiety and depression. A relevant personality predictor missing from the present
study is "absorption", the tendency to fully engage with situations and events (Menzies, Taylor, &
Bourguignon, 2008). Indeed, in contrast to some Buddhist meditations, Hindu-inspired meditations,
including phrase-based practices, often have absorptive aspects (Tomasino et al., 2014). For this
reason, future iterations will include the Tellegen Absorption Scale (Tellegen & Atkinson, 1974) and
a scale measuring the related construct of "boundaries of the mind" (Harrison & Singer, 2013;
Houran, Thalbourne, & Hartman, 2003). It has also been argued that "psychological reactance", the
tendency to oppose influences when freedom and autonomy are threatened, may negatively impact
adherence to instruction, including medical adherence (de Almeida Neto & Aslani, 2008), and as
such it may be prudent to include a measure of reactance, such as the Salzburger State Reactance
Scale (Sittenthaler, Traut-Mattausch, Steindl, & Jonas, 2015).
In addition to new trait-predictors, new ability-predictors will be investigated, particularly working
memory span (Conway et al., 2005). There exist working-memory span tasks for both verbal and
spatial modalities and while these may be quite highly correlated (Engle, 2010) the question of
discriminant preference remains a possibility. Even in the case of no additional discriminant value,
inclusion of such a measure could probe an additional question: do those with higher working
memory benefit more quickly from meditation? Working memory capacity correlates highly with
general intelligence (Conway, Cowan, Bunting, Therriault, & Minkoff, 2002; Dang, Braeken, Colom,
Ferrer, & Liu, 2014; Engle, Laughlin, Tuholski, & Conway, 1999; Hall et al., 2013; Kane et al., 2004;
Wongupparaj, Kumari, & Morris, 2015) and working memory is known to mitigate the deleterious
effect of mind-wandering on task performance (Kane et al., 2007). Given the task of gently
stabilizing concentration upon an object of meditation one might hypothesize that participants with
higher working memory could perform better. Indeed, Engle (2010, pp. S23–S24) argues that
working memory differences reflect "differences in ability to effectively select representations that
are relevant to the task at hand and to deselect, inhibit, or suppress competing representations."
Such differences would surely enhance meditative ability. Indeed, meditation may also improve
working memory capacity (Mrazek et al., 2013; Posner, Rothbart, & Tang, 2015).
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Longitudinal research could also be used to investigate possible benefits of changing the object of
practice over time and with the aforementioned measures included participant drop-out could be
investigated in a more detailed manner. To address non-adherence we need to understand why it
happens (Vuckovich, 2010). I have proposed an exploratory survey study that combines personality
measures with qualitative responses asking former meditators what meditation types they tried and
why they quit, including timelines. Current meditators will also be recruited and asked about their
specific practice and why they continue. One online poll found 41% of 413 votes reported
"Emotional well-being (less stress, anxiety, depression)" as the main reason for meditating, followed
by "Spiritual reasons (Awakening/connection with God)" and " Personal growth/self-knowledge" at
27% and 22% respectively (Giovanni, 2015). This simple online poll may help shed light on why
Neuroticism was a positive predictor of both prior preference and motivation: it may seem counter-
intuitive that greater neuroticism predicted greater prior preference and greater motivation but
perhaps, having heard about possible emotional benefits, the opportunity of learning to meditate
attracted particularly neurotic persons to self-select for this study. The reasons people chose to take
up or quit meditation and the underlying bias in picking one meditation type over another must be
further considered scientifically, and there is no reason to stop at meditation. Indeed, meditation is
likely contraindicated for certain individuals (Dobkin et al., 2012) and ongoing research is
investigating adverse reactions to meditation practice (Britton, 2011). Many people have practices
they consider "meditative" that fall outside the particular domain of meditation: yoga, tai chi,
journaling or drawing, playing or listening to music, burning sacred plants, etc (McKay, 2016).
Perhaps prescribing one or more of these "meditative" practices could be an alternative to
meditation per se in those individuals who are either disinclined or contraindicated to practice.
4.6 Conclusions
The goal of superior health outcomes through precision medicine is necessarily mediated by
adherence. The present study serves as a starting point for understanding intervention adherence
and predicting meditation preference. Personalized instruction will be informed by understanding
the predictive power of biases and personality on meditation preference and efficacy in the lab. Our
findings suggest superior sensory discriminability increases preference for within-modality
meditation objects and that a minimal meditation time no less than ten minutes may be prudent. We
should capitalise on the self-knowledge of new meditators and offer meditative objects in a wide
array of sensory modalities beyond the mere breath. We need to do more qualitative research
regarding biases, and most of all we need to do research that blends both qualitative and quantitative
24
measures. The importance of checking participant understanding of instructions and implementation
of those instructions is clearly indicated, both in the lab and in the community. The precise
personalization of meditation and other interventions is an area ripe for scientific research.
25
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Tables
Table 1. Demographic variables of participants included in analysis
Demographic variables of participants included in analysis
Age (in Years) Mean (SD) Min - Max
21.2 (2.2) 19 - 29
Gender Count Percent
Male 12 33%
Female 24 67%
Orientation
Hererosexual 29 81%
Homosexual 2 6%
Bi/Multisexual 2 6%
Prefer Not To Answer 3 8%
Ethnic Heritage
South Asian 16 44%
East Asian 6 17%
European 6 17%
Mixed 3 8%
White 2 6%
Middle Eastern 1 3%
Other 2 6%
SES
Upper-middle class 9 25%
Middle class 17 47%
Lower-middle class 6 17%
Skilled working class 1 3%
Working class 2 6%
Prefer Not To Answer 1 3%
Religious Affiliation
Non-religious, atheist,
or agnostic10 28%
Christianity 9 25%
Islam 6 17%
Sikhism 3 8%
Hinduism 3 8%
Budhism 3 8%
Judaism 1 3%
Other 1 3%
Spirituality (0-100 scale) Mean (SD) Min - Max
45.7 (26.9) 0 - 95
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Table 2. Hierarchical Linear Modelling of Preference
Hierarchical Linear Modelling of Preference (Part I)
Beta Std Error DF T-Value P-Value
Step 0. Intercept Only
Intercept 0.000 1.813 72 0.000 1.000
Step 1. Order Effects
Intercept 0.000 1.822 71 0.000 1.000
Order -0.386 1.612 71 -0.240 0.811
Step 2. Type
Intercept 4.275 2.529 70 1.690 0.095
Type (Image) -9.585 3.023 70 -3.170 0.002
Type (Phrase) -3.242 3.023 70 -1.072 0.287
Step 3. Motivation and Prior-Preference
Intercept 2.512 2.388 69 1.052 0.296
Motivation 0.251 0.066 69 3.799 0.000
Type (Image) -7.399 3.036 69 -2.437 0.017
Type (Phrase) -0.137 3.091 69 -0.044 0.965
Intercept 2.151 2.475 67 0.869 0.388
Motivation 0.302 0.107 67 2.831 0.006
Type (Image) -7.044 3.116 67 -2.261 0.027
Type (Phrase) 0.040 3.150 67 0.013 0.990
Motivation x Type
(Image)-0.055 0.167 67 -0.329 0.743
Motivation x Type
(Phrase)-0.086 0.135 67 -0.635 0.528
Intercept 2.329 2.380 68 0.978 0.331
Motivation 0.202 0.073 68 2.760 0.007
Type (Image) -8.000 3.042 68 -2.630 0.011
Type (Phrase) 1.015 3.169 68 0.320 0.750
Pref_Pre 0.105 0.072 68 1.463 0.148
Step 4. Trait-Mindfulness
Intercept 2.563 2.409 69 1.064 0.291
Motivation 0.243 0.068 69 3.554 0.001
Type (Image) -7.463 3.066 69 -2.434 0.018
Type (Phrase) -0.228 3.125 69 -0.073 0.942
MAAS -0.475 1.929 33 -0.246 0.807
CAMS -1.679 3.807 33 -0.441 0.662
Step 5. Behavioural Mind-Wandering
Intercept 4.047 2.729 57 1.483 0.144
Motivation 0.223 0.076 57 2.937 0.005
Type (Image) -8.575 3.488 57 -2.458 0.017
Type (Phrase) -0.706 3.543 57 -0.199 0.843
MRTt 96.968 169.987 28 0.570 0.573
34
Hierarchical Linear Modelling of Preference (Part II)
Beta Std Error DF T-Value P-Value
Step 6. Sensory Discrimination
Intercept 2.998 2.454 60 1.222 0.227
Motivation 0.283 0.067 60 4.213 0.000
Type (Image) -7.844 3.117 60 -2.517 0.015
Type (Phrase) -0.387 3.164 60 -0.122 0.903
JND -3.774 1.381 60 -2.733 0.008
Intercept 3.021 2.365 58 1.277 0.207
Motivation 0.291 0.065 58 4.465 0.000
Type (Image) -7.840 2.946 58 -2.661 0.010
Type (Phrase) -0.257 2.994 58 -0.086 0.932
JND -4.757 2.219 58 -2.144 0.036
JND x Type
(Image)-3.448 3.183 58 -1.083 0.283
JND x Type
(Phrase)6.427 3.184 58 2.018 0.048
Step 7. Personality
Intercept 3.096 2.417 58 1.281 0.205
Motivation 0.281 0.073 58 3.876 0.000
Type (Image) -7.991 3.015 58 -2.650 0.010
Type (Phrase) -0.444 3.080 58 -0.144 0.886
JND -4.758 2.254 58 -2.111 0.039
Conscientiousness 0.045 0.125 31 0.356 0.724
Neuroticism 0.053 0.115 31 0.462 0.648
Openness 0.010 0.145 31 0.069 0.945
JND x Type
(Image)-3.452 3.235 58 -1.067 0.290
JND x Type
(Phrase)6.309 3.251 58 1.941 0.057
Intercept 3.073 2.359 58 1.303 0.198
Motivation 0.290 0.065 58 4.467 0.000
Type (Image) -7.771 2.991 58 -2.598 0.012
Type (Phrase) -0.197 3.037 58 -0.065 0.949
JND -4.157 2.283 58 -1.821 0.074
Extraversion -0.031 0.114 32 -0.272 0.787
Agreeableness 0.140 0.115 32 1.224 0.230
JND x Type
(Image)-4.268 3.255 58 -1.311 0.195
JND x Type
(Phrase)5.682 3.264 58 1.741 0.087
35
Table 3. Hierarchical Linear Modelling of Motivation
Hierarchical Linear Modelling of Motivation (Part I)
Beta Std Error DF T-Value P-Value
Step 0. Intercept Only
Intercept 0.000 2.903 72 0.000 1.000
Step 1. Order Effects
Intercept 0.000 2.917 71 0.000 1.000
Order -4.181 1.952 71 -2.142 0.036
Step 2. Type
Intercept 7.286 3.610 69 2.018 0.047
Order -4.484 1.808 69 -2.480 0.016
Type (Image) -9.096 3.616 69 -2.516 0.014
Type (Phrase) -12.763 3.616 69 -3.530 0.001
Step 3. Motivation and Prior-Preference
Intercept 5.467 3.248 68 1.683 0.097
Order -3.988 1.715 68 -2.326 0.023
Type (Image) -9.675 3.424 68 -2.826 0.006
Type (Phrase) -6.727 3.669 68 -1.834 0.071
Pref_Pre 0.360 0.079 68 4.557 0.000
Intercept 4.827 3.263 66 1.479 0.144
Order -4.711 1.767 66 -2.666 0.010
Type (Image) -7.661 3.625 66 -2.113 0.038
Type (Phrase) -5.772 3.826 66 -1.509 0.136
Pref_Pre 0.497 0.128 66 3.885 0.000
Type (Image) x
Pref_Pre-0.345 0.206 66 -1.675 0.099
Type (Phrase) x
Pref_Pre-0.108 0.179 66 -0.604 0.548
Step 4. Trait-Mindfulness
Intercept 5.476 3.164 68 1.731 0.088
Order -3.990 1.731 68 -2.305 0.024
Type (Image) -9.672 3.456 68 -2.798 0.007
Type (Phrase) -6.755 3.701 68 -1.825 0.072
Pref_Pre 0.359 0.079 68 4.519 0.000
MAAS -5.272 2.861 33 -1.843 0.074
CAMS -1.052 5.774 33 -0.182 0.857
Step 5. Behavioural Mind-Wandering
Intercept 7.813 3.643 56 2.145 0.036
Order -5.191 1.859 56 -2.793 0.007
Type (Image) -10.687 3.718 56 -2.874 0.006
Type (Phrase) -8.424 3.904 56 -2.158 0.035
Pref_Pre 0.304 0.090 56 3.395 0.001
MRTt 63.077 280.257 28 0.225 0.824
36
Hierarchical Linear Modelling of Motivation (Part II)
Beta Std Error DF T-Value P-Value
Step 6. Sensory Discrimination
Intercept 5.757 3.429 59 1.679 0.098
Order -5.182 1.790 59 -2.895 0.005
Type (Image) -9.309 3.535 59 -2.634 0.011
Type (Phrase) -7.304 3.839 59 -1.903 0.062
Pref_Pre 0.362 0.086 59 4.222 0.000
JND 1.009 1.732 59 0.583 0.562
Step 7. Personality
Intercept 5.610 3.052 68 1.838 0.070
Order -4.027 1.732 68 -2.324 0.023
Type (Image) -9.630 3.459 68 -2.784 0.007
Type (Phrase) -7.200 3.709 68 -1.941 0.056
Pref_Pre 0.332 0.080 68 4.140 0.000
Conscientiousness -0.117 0.178 32 -0.657 0.516
Neuroticism 0.433 0.147 32 2.940 0.006
Openness 0.125 0.204 32 0.613 0.544
Intercept 5.572 3.036 68 1.835 0.071
Order -4.017 1.717 68 -2.339 0.022
Type (Image) -9.642 3.429 68 -2.812 0.006
Type (Phrase) -7.075 3.672 68 -1.927 0.058
Pref_Pre 0.339 0.079 68 4.304 0.000
Neuroticism 0.444 0.142 34 3.116 0.004
Intercept 5.577 3.050 68 1.829 0.072
Order -4.018 1.734 68 -2.317 0.024
Type (Image) -9.640 3.462 68 -2.784 0.007
Type (Phrase) -7.091 3.708 68 -1.912 0.060
Pref_Pre 0.339 0.080 68 4.249 0.000
Neuroticism 0.474 0.148 32 3.216 0.003
Extraversion 0.130 0.167 32 0.782 0.440
Agreeableness 0.038 0.166 32 0.229 0.820
Intercept 3.073 2.359 58 1.303 0.198
Motivation 0.290 0.065 58 4.467 0.000
Type (Image) -7.771 2.991 58 -2.598 0.012
Type (Phrase) -0.197 3.037 58 -0.065 0.949
JND -4.157 2.283 58 -1.821 0.074
Extraversion -0.031 0.114 32 -0.272 0.787
Agreeableness 0.140 0.115 32 1.224 0.230
JND x Type
(Image)-4.268 3.255 58 -1.311 0.195
JND x Type
(Phrase)5.682 3.264 58 1.741 0.087
37
Table 4. Hierarchical Linear Modelling of Prior-Preference
Hierarchical Linear Modelling of Prior-Preference (Part I)
Beta Std Error DF T-Value P-Value
Step 0. Intercept Only
Intercept 0.000 2.120 72 0.000 1.000
Step 2. Type
Intercept 4.972 3.432 70 1.449 0.152
Type (Image) 1.722 4.814 70 0.358 0.722
Type (Phrase) -16.639 4.814 70 -3.457 0.001
Step 4. Trait-Mindfulness
Intercept 4.972 3.442 70 1.444 0.153
Type (Image) 1.722 4.860 70 0.354 0.724
Type (Phrase) -16.639 4.860 70 -3.423 0.001
MAAS -2.684 2.348 33 -1.143 0.261
CAMS 1.466 4.750 33 0.309 0.760
Step 5. Behavioural Mind-Wandering
Intercept 6.611 3.615 58 1.829 0.073
Type (Image) 0.733 5.051 58 0.145 0.885
Type (Phrase) -14.600 5.051 58 -2.890 0.005
MRTt 199.846 206.709 28 0.967 0.342
Step 6. Sensory Discrimination
Intercept 6.352 3.507 61 1.811 0.075
Type (Image) -0.441 4.805 61 -0.092 0.927
Type (Phrase) -15.784 4.807 61 -3.284 0.002
JND -0.921 2.046 61 -0.450 0.654
38
Hierarchical Linear Modelling of Prior-Preference (Part II)
Beta Std Error DF T-Value P-Value
Step 7. Personality
Intercept 4.972 3.317 70 1.499 0.138
Type (Image) 1.722 4.691 70 0.367 0.715
Type (Phrase) -16.639 4.691 70 -3.547 0.001
Conscientiousness -0.180 0.150 32 -1.203 0.238
Neuroticism 0.292 0.123 32 2.383 0.023
Openness 0.303 0.171 32 1.777 0.085
Intercept 4.972 3.347 70 1.486 0.142
Type (Image) 1.722 4.733 70 0.364 0.717
Type (Phrase) -16.639 4.733 70 -3.515 0.001
Neuroticism 0.302 0.120 34 2.523 0.017
Intercept 4.972 3.369 70 1.476 0.144
Type (Image) 1.722 4.764 70 0.361 0.719
Type (Phrase) -16.639 4.764 70 -3.492 0.001
Neuroticism 0.316 0.125 32 2.535 0.016
Extraversion 0.040 0.143 32 0.282 0.780
Agreeableness 0.108 0.142 32 0.761 0.452
Intercept 4.972 3.323 68 1.496 0.139
Type (Image) 1.722 4.699 68 0.366 0.715
Type (Phrase) -16.639 4.699 68 -3.541 0.001
Neuroticism 0.527 0.206 34 2.561 0.015
Type (Image) x
Neuroticism-0.529 0.291 68 -1.817 0.074
Type (Phrase) x
Neuroticism-0.146 0.291 68 -0.502 0.617
39
Table 5. Hierarchical Linear Modelling of Heart-Rate Decrease in the First-Half of
Meditation
Hierarchical Linear Modelling of Heart-Rate Decrease in the First-Half of Meditation
Beta Std Error DF T-Value P-Value
Step 0. Intercept Only
Intercept 0.000 0.013 38 0.000 1.000
Step 1. Order Effects
Intercept 0.000 0.013 37 0.000 1.000
Order 0.013 0.007 37 1.859 0.071
Step 2. Type
Intercept -0.002 0.016 36 -0.139 0.890
Type (Image) 0.000 0.015 36 0.018 0.986
Type (Phrase) 0.006 0.015 36 0.430 0.670
Step 3. Motivation and Prior-Preference
Intercept 0.000 0.013 37 0.038 0.970
Motivation 0.000 0.000 37 -1.385 0.174
Intercept 0.001 0.013 37 0.083 0.934
Pref_Pre -0.001 0.000 37 -2.649 0.012
Step 4. Trait-Mindfulness
Intercept -0.001 0.013 37 -0.090 0.929
Pref_Pre -0.001 0.000 37 -2.671 0.011
MAAS -0.003 0.014 16 -0.189 0.853
CAMS 0.034 0.031 16 1.079 0.297
Step 5. Behavioural Mind-Wandering
Intercept 0.003 0.014 33 0.229 0.821
Pref_Pre -0.001 0.000 33 -1.897 0.067
MRTt 0.515 1.518 15 0.339 0.739
Step 6. Sensory Discrimination
Intercept 0.004 0.014 31 0.286 0.777
Pref_Pre -0.001 0.000 31 -2.245 0.032
JND -0.008 0.008 31 -0.983 0.333
Step 7. Personality
Intercept 0.004 0.013 37 0.317 0.753
Pref_Pre -0.001 0.000 37 -2.440 0.020
Conscientiousness 0.000 0.001 15 -0.573 0.575
Neuroticism -0.001 0.001 15 -1.594 0.132
Openness 0.000 0.001 15 0.382 0.708
Intercept 0.005 0.013 37 0.405 0.688
Pref_Pre -0.001 0.000 37 -2.462 0.019
Extraversion 0.001 0.001 16 1.121 0.279
Agreeableness -0.002 0.001 16 -1.503 0.152
40
Table 6. Hierarchical Linear Modelling of Heart-Rate Decrease in the Second-Half of
Meditation
Hierarchical Linear Modelling of Heart-Rate Decrease in the Second-Half of Meditation (Part I)
Beta Std Error DF T-Value P-Value
Step 0. Intercept Only
Intercept 0.000 0.014 38 0.000 1.000
Step 1. Order Effects
Intercept 0.000 0.014 37 0.000 1.000
Order -0.003 0.007 37 -0.455 0.652
Step 2. Type
Intercept -0.007 0.016 36 -0.467 0.643
Type (Image) -0.004 0.014 36 -0.294 0.770
Type (Phrase) 0.027 0.014 36 1.894 0.066
Step 3. Motivation and Prior-Preference
Intercept 0.001 0.013 37 0.056 0.955
Motivation -0.001 0.000 37 -2.168 0.037
Intercept 0.002 0.013 36 0.124 0.902
Motivation 0.000 0.000 36 -1.335 0.190
Pref_Pre -0.001 0.000 36 -2.923 0.006
Intercept 0.001 0.013 37 0.103 0.919
Pref_Pre -0.001 0.000 37 -3.457 0.001
Step 4. Trait-Mindfulness
Intercept -0.001 0.013 37 -0.111 0.912
Pref_Pre -0.001 0.000 37 -3.439 0.002
MAAS 0.001 0.014 16 0.041 0.968
CAMS 0.035 0.032 16 1.101 0.287
Step 5. Behavioural Mind-Wandering
Intercept 0.004 0.015 33 0.248 0.805
Pref_Pre -0.001 0.000 33 -2.949 0.006
MRTt 0.342 1.580 15 0.216 0.832
41
Hierarchical Linear Modelling of Heart-Rate Decrease in the Second-Half of Meditation (Part II)
Beta Std Error DF T-Value P-Value
Step 6. Sensory Discrimination
Intercept 0.005 0.014 31 0.385 0.703
Pref_Pre -0.001 0.000 31 -3.682 0.001
JND -0.015 0.007 31 -2.090 0.045
Step 7. Personality
Intercept 0.005 0.014 30 0.341 0.736
Pref_Pre -0.001 0.000 30 -3.754 0.001
JND -0.014 0.007 30 -1.998 0.055
Pref_Pre x JND 0.000 0.000 30 -0.812 0.423
Intercept 0.009 0.014 31 0.621 0.539
Pref_Pre -0.001 0.000 31 -3.342 0.002
JND -0.014 0.007 31 -1.865 0.072
Conscientiousness -0.001 0.001 14 -0.954 0.356
Neuroticism -0.001 0.001 14 -1.280 0.221
Openness 0.000 0.001 14 0.344 0.736
Intercept 0.010 0.014 31 0.704 0.487
Pref_Pre -0.001 0.000 31 -3.460 0.002
JND -0.014 0.007 31 -1.975 0.057
Extraversion 0.000 0.001 15 0.595 0.561
Agreeableness -0.002 0.001 15 -1.453 0.167
42
Table 7. Hierarchical Linear Modelling of High-Frequency Heart-Rate-Variability Increase
in the First-Half of Meditation
Hierarchical Linear Modelling of High-Frequency Heart-Rate-Variability Increase in the First-Half of Meditation (Part I)
Beta Std Error DF T-Value P-Value
Step 0. Intercept Only
Intercept 0.000 0.279 38 0.000 1.000
Step 1. Order Effects
Intercept 0.000 0.281 37 0.000 1.000
Order 0.188 0.117 37 1.604 0.117
Step 2. Type
Intercept -0.034 0.315 36 -0.107 0.916
Type (Image) -0.108 0.238 36 -0.454 0.653
Type (Phrase) 0.209 0.238 36 0.877 0.386
Step 3. Motivation and Prior-Preference
Intercept 0.005 0.278 37 0.018 0.986
Motivation -0.005 0.006 37 -0.821 0.417
Intercept 0.011 0.280 37 0.039 0.969
Pref_Pre -0.007 0.005 37 -1.513 0.139
Step 4. Trait-Mindfulness
Intercept -0.113 0.264 38 -0.429 0.671
MAAS 0.210 0.281 16 0.748 0.466
CAMS 1.028 0.628 16 1.637 0.121
Step 5. Behavioural Mind-Wandering
Intercept -0.012 0.303 34 -0.040 0.968
MRTt -3.140 31.741 15 -0.099 0.923
Step 6. Sensory Discrimination
Intercept 0.021 0.261 32 0.080 0.937
JND -0.331 0.121 32 -2.724 0.010
43
Hierarchical Linear Modelling of High-Frequency Heart-Rate-Variability Increase in the First-Half of Meditation (Part II)
Beta Std Error DF T-Value P-Value
Step 7. Personality
JND -0.301 0.126 32 -2.388 0.023
Conscientiousness 0.008 0.016 14 0.485 0.635
Neuroticism -0.033 0.015 14 -2.177 0.047
Openness -0.010 0.017 14 -0.550 0.591
Intercept 0.125 0.240 32 0.520 0.607
JND -0.306 0.122 32 -2.504 0.018
Neuroticism -0.032 0.015 16 -2.172 0.045
JND -0.284 0.123 32 -2.302 0.028
Neuroticism -0.021 0.015 14 -1.445 0.170
Extraversion 0.029 0.012 14 2.354 0.034
Agreeableness 0.009 0.018 14 0.525 0.608
Intercept -0.065 0.213 32 -0.305 0.762
JND -0.306 0.121 32 -2.535 0.016
Extraversion 0.036 0.012 16 3.101 0.007
44
Table 8. Hierarchical Linear Modelling of High-Frequency Heart-Rate-Variability Increase
in the Second-Half of Meditation
Hierarchical Linear Modelling of High-Frequency Heart-Rate-Variability Increase in the Second-Half of Meditation (Part I)
Beta Std Error DF T-Value P-Value
Step 0. Intercept Only
Intercept 0.000 0.319 38 0.000 1.000
Step 1. Order Effects
Intercept 0.000 0.322 37 0.000 1.000
Order -0.049 0.166 37 -0.296 0.769
Step 2. Type
Intercept -0.331 0.370 36 -0.894 0.377
Type (Image) 0.199 0.307 36 0.648 0.521
Type (Phrase) 0.794 0.307 36 2.589 0.014
Step 3. Motivation and Prior-Preference
Intercept -0.340 0.381 35 -0.894 0.378
Motivation 0.001 0.008 35 0.129 0.898
Type (Image) 0.211 0.324 35 0.652 0.519
Type (Phrase) 0.807 0.325 35 2.481 0.018
Intercept -0.317 0.376 35 -0.843 0.405
Pref_Pre -0.002 0.007 35 -0.283 0.779
Type (Image) 0.199 0.309 35 0.643 0.525
Type (Phrase) 0.761 0.330 35 2.309 0.027
Step 4. Trait-Mindfulness
Intercept -0.428 0.366 36 -1.171 0.249
MAAS 0.057 0.338 16 0.169 0.868
CAMS 1.118 0.757 16 1.478 0.159
Type (Image) 0.199 0.312 36 0.636 0.529
Type (Phrase) 0.794 0.312 36 2.541 0.016
Step 5. Behavioural Mind-Wandering
Intercept -0.332 0.411 32 -0.808 0.425
MRTt 16.983 37.967 15 0.447 0.661
Type (Image) 0.147 0.337 32 0.436 0.666
Type (Phrase) 0.712 0.337 32 2.113 0.043
Step 6. Sensory Discrimination
Intercept -0.352 0.389 30 -0.906 0.372
JND -0.251 0.176 30 -1.426 0.164
Type (Image) 0.242 0.349 30 0.692 0.494
Type (Phrase) 0.806 0.349 30 2.309 0.028
45
Hierarchical Linear Modelling of High-Frequency Heart-Rate-Variability Increase in the Second-Half of Meditation (Part II)
Beta Std Error DF T-Value P-Value
Step 7. Personality
Intercept -0.222 0.361 36 -0.615 0.543
Conscientiousness 0.011 0.021 15 0.537 0.599
Neuroticism -0.037 0.019 15 -1.920 0.074
Openness -0.004 0.022 15 -0.179 0.861
Type (Image) 0.199 0.315 36 0.630 0.533
Type (Phrase) 0.794 0.315 36 2.516 0.017
Intercept -0.433 0.331 36 -1.310 0.199
Extraversion 0.043 0.014 16 2.990 0.009
Agreeableness 0.013 0.023 16 0.558 0.585
Type (Image) 0.199 0.312 36 0.636 0.529
Type (Phrase) 0.794 0.312 36 2.541 0.016
Intercept -0.392 0.321 36 -1.220 0.230
Extraversion 0.044 0.014 17 3.020 0.008
Type (Image) 0.199 0.309 36 0.642 0.525
Type (Phrase) 0.794 0.309 36 2.565 0.015
46
Table 9. Hierarchical Linear Modelling of Updating
Hierarchical Linear Modelling of Updating
Beta Std Error DF T-Value P-Value
Step 0. Intercept Only
Intercept 0.000 2.014 72 0.000 1.000
Step 1. Type Effects
Intercept -1.729 3.081 70 -0.561 0.576
Type (Image) -6.085 4.010 70 -1.517 0.134
Type (Phrase) 11.272 4.010 70 2.811 0.006
Step 2. Personality and Traits
Intercept -1.729 3.005 70 -0.575 0.567
Type (Image) -6.085 4.150 70 -1.466 0.147
Type (Phrase) 11.272 4.150 70 2.716 0.008
Extraversion -0.125 0.154 28 -0.813 0.423
Agreeableness 0.098 0.146 28 0.674 0.506
Conscientiousness 0.279 0.165 28 1.688 0.103
Neuroticism -0.313 0.130 28 -2.412 0.023
Openness -0.194 0.168 28 -1.156 0.258
MAAS 0.406 2.311 28 0.176 0.862
CAMS -6.988 4.924 28 -1.419 0.167
Step 2b. Reduction of Terms
Intercept -1.729 3.019 70 -0.573 0.569
Type (Image) -6.085 4.029 70 -1.510 0.136
Type (Phrase) 11.272 4.029 70 2.798 0.007
Neuroticism -0.250 0.119 34 -2.098 0.043
Step 3. Physiological Measures
Intercept -8.273 4.721 32 -1.752 0.089
Type (Image) 1.398 5.446 32 0.257 0.799
Type (Phrase) 13.832 5.682 32 2.434 0.021
Neuroticism 0.047 0.237 17 0.198 0.845
HR_1 -63.380 64.777 32 -0.978 0.335
HR_2 212.624 68.076 32 3.123 0.004
HFHRV_1 4.254 3.523 32 1.208 0.236
HFHRV_2 -5.025 2.884 32 -1.742 0.091
Step 3b. Reduction of Terms
Intercept -7.082 4.523 35 -1.566 0.126
Type (Image) -0.411 5.500 35 -0.075 0.941
Type (Phrase) 12.475 5.609 35 2.224 0.033
HR_2 131.962 41.906 35 3.149 0.003
47
Figures
Figure 1. Meditation Preference by Meditation Type
Figure 1. Subjective Meditation Preference by Type of Meditation showing the preference bias for Breath and Phrase meditations over the Image meditation
Type of Meditation
Meditation Preference by Meditation Type
Grand-Mean Centred
Meditation Preference
48
Figure 2. Meditation Preference by Just-Noticeable-Difference Score
Figure 2. Just-Noticeable-Difference score by Type interaction predicting Meditation Preference. Note that lower JND scores reflect subtler sensory discrimination. Lines are linear regressions on Preference for the different types of meditation by the different meditation-specific modalities of JND. Colour of points reflects different types of JND: Red: Haptic, Blue: Auditory, Green: Visual Colour of lines reflects different types of Meditation: Black: Overall, Red: Breath, Blue: Phrase, Green: Image
Meditation Preference by Just-Noticeable-Difference Score
Just-Noticeable-Difference Z-Score
Grand-Mean
Centred Meditation Preference
49
Figure 3. Motivation by Meditation Type
Figure 3. Motivation by Type of Meditation showing the bias for Breath meditation over the Image and Phrase meditations
Motivation by Meditation Type
Grand-Mean Centred
Motivation
Type of Meditation
50
Figure 4. Prior-Preference by Meditation Type
Figure 4. Prior Preference by Type of Meditation showing the bias for Breath and Image meditations over the Phrase meditation.
Type of Meditation
Grand-Mean Centred Prior-
Preference
Prior-Preference by Meditation Type
51
Figure 5. Increase in High-Frequency Heart-Rate-Variability by Just-Noticeable-Difference score
Figure 5. High-Frequency Heart-Rate Variability in the second half of meditation by standardized Just-Noticeable-Difference score. Note that higher HF-HRV is proposed to be reflective of deeper meditative efficacy whereas lower JND scores reflect subtler sensory discrimination. Boxes show two proposed clusters of data, the left being average and better scores on JND with corresponding variable HF-HRV. The right box shows average and worse scores on the JND with correspondingly low HF-HRV. Note the lack of any data-points in the upper-right quadrant. Perhaps poor modality-specific discrimination hinders the ability to enter a same-modality meditative state while better discrimination gives little if any particular benefit. Colour of points reflects different types of JND: Red: Haptic, Blue: Auditory, Green: Visual
Just-Noticeable-Difference Z-Score
Percent Increase HF-HRV
(first half of meditation)
Increase in High-Frequency Heart-Rate-Variability by Just-Noticeable-Difference score
52
Figure 6. Increase in High-Frequency Heart-Rate-Variability by Meditation Type
Figure 6. High-frequency heart-rate variability in the second half of meditation by Type of Meditation showing the increase for Phrase meditation over the Breath and Image meditations, though this effect of type may be unstable given the small sample size.
Percent Increase HF-HRV
(second half
of meditation)
Type of Meditation
Increase in High-Frequency Heart-Rate-Variability by Meditation Type
53
Figure 7. Update from bias by Decrease in Heart-Rate and Meditation Type
Figure 7. Hear-Rate Decrease in the second half of meditation predicting Update amount. Note that higher Heart-Rate scores reflect greater decrease from baseline with decrease indicating relaxation. Lines are linear regressions of Heart-Rate Decreases on Update for the different Types of Meditation Colour of points and lines reflects different types of Meditation: Black: Overall, Red: Breath, Blue: Phrase, Green: Image
Percent Decrease in Heart-Rate (second half of meditation)
Update from bias by Decrease in Heart-Rate and Meditation Type
Grand-Mean Centred Update
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Figure 8. Dispositional Change Across The Study
Figure 8. Dispositional change across the study showing stable disposition toward Breath, increasing disposition toward Phrase, and decreasing disposition toward Image. While relative position is informative, measures at different time-points are on different scales thus absolute values are not directly comparable. Colour of boxes reflects different types of Meditation: Red: Breath, Blue: Phrase, Green: Image
Prior Preference Motivation Preference
Type of Meditation
Dispositional Change Across The Study
Grand-Mean Centred Prior-
Preference, Motivation,
and Preference
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Appendix A: Glossary of Acronyms
BFI - Big Five Inventory (2.3.1)
CAMS-R - Cognitive and Affective Mindfulness Scale Revised (2.3.1, Appendix C: CAMS-R)
HR - Heart-Rate (2.3.5)
HRV - Heart-Rate Variability (2.3.5)
HF-HRV - High-Frequency Heart-Rate Variability (2.3.5)
JND - Just Noticeable Difference (2.3.3)
MAAS - Mindful Attention Awareness Scale (2.3.1, Appendix B: MAAS-5)
MRT - Metronome Response Task (2.3.2)
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Appendix B: MAAS-5
The following five items are taken from the original MAAS and are suggested by both (Osman et al.,
2015) and (Van Dam et al., 2010):
Item 7: It seems I am ‘running on automatic’ without much awareness
Item 8: I run through activities without being really attentive to them
Item 9: I get so focused on the goal I want to achieve that I lose touch with what I am doing right
now to get there
Item 10: I do jobs or tasks automatically, without being aware of what I’m doing
Item 14: I find myself doing things without paying attention
These items were reworded as follows:
Item 7: I see myself as someone who seems to be ‘running on automatic’ without much awareness
Item 8: I see myself as someone who runs through activities without being really attentive to them
Item 9: I see myself as someone who gets so focused on the goal I want to achieve that I lose touch
with what I am doing right now to get there
Item 10: I see myself as someone who does jobs or tasks automatically, without being aware of what
I am doing
Item 14: I see myself as someone who finds myself doing things without paying attention
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Appendix C: CAMS-R
The following ten items are taken from the CAMS-R (Feldman et al., 2006):
It is easy for me to concentrate on what I am doing.
I can tolerate emotional pain.
I can accept things I cannot change.
I can usually describe how I feel at the moment in considerable detail.
I am easily distracted.
It’s easy for me to keep track of my thoughts and feelings.
I try to notice my thoughts without judging them.
I am able to accept the thoughts and feelings I have.
I am able to focus on the present moment.
I am able to pay close attention to one thing for a long period of time.
These items were reworded as follows:
I see myself as someone who finds it easy to concentrate on what I am doing
I see myself as someone who can tolerate emotional pain
I see myself as someone who can accept things I cannot change
I see myself as someone who can usually describe how I feel at the moment in considerable detail
I see myself as someone who is easily distracted
I see myself as someone who finds it easy to keep track of my thoughts and feelings
I see myself as someone who tries to notice my thoughts without judging them
I see myself as someone who is able to accept the thoughts and feelings I have
I see myself as someone who is able to focus on the present moment
I see myself as someone who is able to pay close attention to one thing for a long period of time
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Appendix D: Preference Questionnaire
A preference questionnaire was developed with items drawn from the Toronto Mindfulness Scale
(Lau et al., 2006) and the Meditation Depth Questionnaire (Piron, 2001). Others were created by the
author for this study. All items were reformatted to use the same scale ranging from experienced
“not at all” to “very much” and reported on the same 100 point slider as the ther questionnaires.
Toronto Mindfulness Scale
I was curious to see what my mind was up to from moment to moment.
I was curious about my reactions to things.
I was curious about what I might learn about myself by just taking notice of what my attention gets
drawn to.
I experienced myself as separate from my changing thoughts and feelings.
I was more concerned with being open to my experiences than controlling or changing them.
I was aware of my thoughts and feelings without overidentifying with them.
Meditation Depth Questionnaire (reworded to match style)
I experienced equanimity and inner peace
My sense of time disappeared
I completely stopped thinking
Preferences (Custom)
I was able to understand and implement the meditation instructions
I was confused by the meditation instructions or had a hard time following them
I found the meditation easy
I found the meditation difficult
I found the meditation interesting
I found the meditation boring
I had pleasant experiences
I found the experience mentally calming or focused
I found the experience physically relaxing or restful
I had unpleasant experiences
I found the experience mentally agitating or annoying
I found the experience physically tiring or bothersome
I am willing to try this meditation again, perhaps when I get stressed
I would consider practising this meditation regularly, at least weekly but perhaps even daily
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Appendix E: Meditation Instructions
You are now beginning the meditation phase of the experiment. These are the general instructions for the meditations, but each will have more specific instructions. Before beginning each meditation you will close your eyes and sit comfortably for a little bit. The exact amount of time is not important, but do take a moment to allow your mind a little time to quiet down before beginning. There will likely be thoughts passing through your mind during these times, of course, and that is expected and okay. After sitting for this little bit of time you will start to turn your attention toward a particular "object" of meditation calmly and effortlessly. Each of the three meditations will ask that you focus on a different object or sensation. One will be your breath. Another will be a certain phrase you will listen to with the headphones before attending to it in your mind. Another will be a certain image you will first see on-screen and then imagine in your mind.
<new page>
With each meditation object, keep an open and calm attitude, accepting whatever happens. Of course some thoughts will probably come into your mind, and you may be doubtful whether you are doing well, but do not worry about these thoughts or doubts. Do not make an effort to avoid or stop thoughts. Simply allow thoughts to pass without special effort as you would if you were watching the sidewalk pass from inside a car or bus.
You will likely have moments where you notice that your mind has wandered away from the meditation object. The only "effort" in this style of meditation is the decision to return attention to the particular object after realizing that the mind has wandered. This decision takes no real effort at all and is done with no concern for having wandered.
<new page>
When returning to the object of meditation always find it where it is and simply become aware of it. Qualities of the object may change while you are meditating and any variation is correct, and there is no need to worry about these details in any case. Do not attempt to control the mind or the breath.
In fact, the goal of this meditation is not to keep concentrated or to control anything. Trying hard is not the point.
The goal, the whole practice, is calmly returning to the object of meditation after the mind has wandered.
<new page>
Each meditation will continue for ten minutes, after which you will be presented with a questionnaire about your experiences. There will be three meditations total for about a half-hour of meditation time. Do not concern yourself with the time. When it feels like about ten minutes has passed you may open your eyes and check the on-screen timer. If it has not been ten minutes yet you can close your eyes again and continue meditating. When the time is up, the experiment will continue automatically.
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<new page>
Before each meditation more specific instructions will be presented and changes and reminders will be bolded. The main difference between each meditation is the "object" of meditation, which will act as an anchor for your attention. Feel free to re-read the instructions before each meditation, and be sure to read about and use the new object each time.
<new page>
So, in summary, you will meditate three times for ten minutes each. Close your eyes and sit comfortably for a little bit Turn your mind to the specific object or "anchor" calmly and effortlessly Keep an open and calm attitude, accepting whatever happens Return attention to the object of meditation when noticing that your mind has wandered When you are ready to see the first meditation go to the next page.
Phrase Stimulus available at https://soundcloud.com/user-740689772/ay-lo-ra
Image Stimulus:
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Appendix F: Example Free Form Reponses
The following are some examples of the free-form responses given by participants. First,
participants were required to report the instructions of the meditation after reading them (Appendix
E: Meditation Instructions). Second, participants had the option of reporting details about their
experience of each meditation immediately after the preference questionnaire.
Appropriate Reports of Instructions:
"Relax and have your eyes closed before I begin. Focus on my breathing in a comfortable way. Be
open minded and simply return back the calmness of breathing if I get distracted."
"close eyes, think of the phrase Ay Lo Ra"
"Imagine a green circle as you meditate. Come back to the image if the mind starts to wander."
Inappropriate Reports of Instructions:
"comfortable, relax"
"heavily focused on creative thinking"
"Looks to be interesting. May have difficulty keeping the image in my mind the whole time though."
Appropriate Reports of Experience:
"This was the best one out of the 3"
"Not as effective, a little annoying."
"It was harder to focus on the phrase than it was on the visual image of the last meditation and my
wandered simultaneously while saying the phrase."
Inappropriate Reports of Experience:
"I usually use this type of relaxing to go to sleep, so I became worried I might fall asleep."
"I was able to piece together a story using the image of the green circle and that story helped me get
a better sense of my mental state and thoughts. "
"I found this extremely difficult, I couldn't visualize the plain green circle at all. When I tried to, all I
could do was imagine myself painting a big green circle, or the green circle turning into tree heads,
or shape shifting into hexagons. Because I couldn't visualize the circle, I decided to just close my
eyes and not think about anything, at first my mind wandered, but then I didn't think about
anything, and I wasn't asleep. It was a peaceful experience."