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

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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.

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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.

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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

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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

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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

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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

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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.

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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).

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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

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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”

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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

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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

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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

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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

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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."