Mindfulness meditation experience is associated with ... · 04.02.2020 · 21 1 Introduction 22 The goal of this study is to investigate whether extensive meditation experience can

Mindfulness meditation experience is associated with increased

ability to monitor covert somatosensory attention

Stephen Whitmarsh1, Ole Jensen2, and Henk Barendregt1

1Foundations of mathematics and computer science, Faculty of Science, Radboud

UNiversity, Nijmegen, The Netherlands

2Department of Psychology, University of Birmingham, Edgbaston, Birmingham, B15 2TT,

United Kingdom

Abstract1

The distinguishing practice of mindfulness meditation is the intentional regulation of attention to-2

wards the present moment. Mindfulness meditation therefore emphasizes metacognitive functions, in3

particular the ability to monitor the attentional focus on a moment-by-moment basis. In this study4

we set out to test whether mindfulness meditation experience is associated with an increased ability5

to monitor moment-by-moment fluctuation in the attentional state. In response to auditory cues, par-6

ticipants maintained somatosensory attention to either their left or right hand. At random moments,7

trials were terminated by a probe sound to which participants reported their level of attention at that8

moment. MEG was recorded during the attention interval preceding probe onset. Using a beamformer9

approach, alpha activity in contralateral primary somatosensory regions was quantified. Alpha activity10

for self-reported high versus low attention trials was compared both within and between groups of either11

highly experienced experienced mindfulness meditators, novice meditators or meditation-naive partic-12

ipants (controls). As predicted, generally contralateral alpha power was associated with self-reported13

attention. Novice meditators (< 1000 h of meditation) showed temporal profiles similar to controls,14

displaying a correspondence between self-report and alpha power preceding probe onset. Expert med-15

itators (� 1000 h) showed a strikingly different pattern, however. Their self-reported attentional state16

corresponded with alpha power during a more extended time interval preceding those of controls and17

novice meditators. In addition, self-reported low attention trials showed a distinctive alpha suppression18

preceding probe onset, suggesting that the ability for moment-by-moment monitoring of the attentional19

state permitted greater attentional control.20

1

(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted February 4, 2020. ; https://doi.org/10.1101/2020.02.04.933606doi: bioRxiv preprint

https://doi.org/10.1101/2020.02.04.933606

1 Introduction21

The goal of this study is to investigate whether extensive meditation experience can be associated with22

changes in meta-metacognitive monitoring of attention. While meditation practices are generally considered23

to incorporate the practice of attentional control, mindfulness meditation distinguishes itself by an emphasis24

on continuous metacognitive monitoring of mental contents and processes (Lutz, Slagter, Dunne, & David-25

son, 2008; Lutz, Dunne, & Davidson, 2007). Mindfulness based interventions have been proven successful26

in treating a wide range of clinical syndromes ranging from depression (Chiesa & Serretti, 2011; Piet &27

Hougaard, 2011; van Aalderen et al., 2012) to anxiety (Roemer & Orsillo, 2006; Wurtzen et al., 2013) and28

ADHD (Zylowska et al., 2008). Several clinical interventions have integrated mindfulness, e.g. in Mind-29

fulness Based Stress Reduction (MBCT, (Kabat-Zinn, 2014, 2013), Mindfulness Based Cognitive Therapy30

((Segal, Williams, & Teasdale, 2002; Teasdale, Segal, & Williams, 1995) and Acceptance and Commitment31

Therapy (Hayes, 2004). The mechanisms by which mindfulness meditation might benefit the mental health32

of the practitioner remain unclear, however. Initial proposals have been put forward from cognitive psycho-33

logical (Baer, 2003; Brown, Ryan, & Creswell, 2007) or neuroscientific perspectives (e.g. (Chiesa & Serretti,34

2011; Holzel et al., 2011; Tang & Posner, 2013). in which mindfulness has been defined in terms of “pay-35

ing attention in a particular way: on purpose, in the present moment, and nonjudgmentally” (Kabat-Zinn,36

2014) or “the state of being attentive to and aware of what is taking place in the present” (Brown & Ryan,37

2003) and similarly in (Bishop et al., 2004). The practice of mindfulness meditation is therefore understood38

beyond the ability to maintain attention upon a specific object or to safeguard oneself from (especially39

negative) distraction. Rather, mindfulness meditation emphases metacognitive awareness of experience re-40

gardless of its desirability or relevance. In fact, it is this explicit awareness of ongoing mental content, called41

meta-awareness (J. W. Schooler et al., 2011) or metacognition (Fleming, Dolan, & Frith, 2012), that allows42

distracting mental content to be noticed so that attention can subsequently be reoriented (J. W. Schooler43

et al., 2011; Smallwood, Beach, Schooler, & Handy, 2007; Smallwood & Schooler, 2006). Mindfulness, or44

“attending to the present moment”, can therefore be operationalized as the metacognitive monitoring of the45

focus of attention.46

It is well known that mind-wandering often occurs without the awareness that one’s mind has drifted47

(Giambra, 1995; J. Schooler & Schreiber, 2004). Importantly, recent work has show that the tendency to48

mind-wander correlates negatively with a mindful disposition (Mrazek, Smallwood, & Schooler, 2012). Fur-49

thermore, successful outcome after MBCT has been associated with increased availability of metacognitive50

reflection on negative experiences (Teasdale et al., 2002). Although metacognition is understood to be an51

important aspect of mindfulness, experimental evidence for improved metacognitive functioning after mind-52

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fulness meditation has been only inferred indirectly from studies showing increased performance in tasks53

requiring cognitive control and response inhibition. Jha and colleagues (2007) found that mindfulness med-54

itation improved conflict monitoring in the Attention Network task. Allen and colleagues (2012) showed a55

reduction in affective Stroop conflict and in Heeren and colleagues (2009), mindfulness training was associated56

with increased cognitive inhibition in go/nogo task. Metacognition has been a traditional topic of interest57

in memory research for decades (see: (Metcalfe & Shimamura, 1996) and (Dunlosky & Bjork, 2014) for an58

overview). Recently, metacognition of action and perceptual processes have become a topic of interest as well59

(see (Fleming et al., 2012) for an overview). Since mindfulness meditation is first and foremost an attentional60

practice, recent work on metacognition of attention, or conversely of mind-wandering, could be a promising61

direction of neuroscientific research. Previous experiments on metacognition of attention or mind-wandering62

have used control tasks or external stimulation (Braboszcz & Delorme, 2011; Christoff, Gordon, Smallwood,63

Smith, & Schooler, 2009; Macdonald, Mathan, & Yeung, 2011; Wilimzig, Tsuchiya, Fahle, Einhauser, &64

Koch, 2008). When studying neural correlates of attention during meditation, however, the performance of65

a concomitant task can be expected to interfere with attentional monitoring, as well as preventing ecologi-66

cally valid conclusions regarding meditation. Furthermore, task performance and stimulus processing might67

provide information about the attentional state during stimulus processing or task performance, preventing68

conclusions in terms of metacognition of attention per se (Fleming et al., 2012). Importantly however, the69

attentional state can be determined without a control task or external stimulus. Cortical alpha activity has70

been shown to reflect both the degree and the location of covert somatosensory attention (Haegens, Handel, &71

Jensen, 2011; Haegens, Luther, & Jensen, 2012; van Ede, de Lange, Jensen, & Maris, 2011; van Ede, Jensen,72

& Maris, 2010). Furthermore, occipital alpha power was shown to be strongly correlated with self-reported73

attention during a visual detection task (Macdonald et al., 2011). We have reported that contralateral alpha74

power corresponds to self-reported attentional focus as well (Whitmarsh, Barendregt, Schoffelen, & Jensen,75

2014; Whitmarsh, Oostenveld, Almeida, & Lundqvist, 2017). In this study we used an identical paradigm76

as in Whitmarsh et al. (2014) to investigate whether metacognitive awareness of attention is improved after77

extensive mindfulness meditation. Participants were presented with auditory cues to attend either to their78

left or right hand while their brain activity was measured using MEG. After a random time interval trials79

were terminated with a probe sound to which they reported the degree of attentional focus. Self-reported80

attentional focus was compared with contralateral somatosensory alpha preceding probe onset, in eleven one81

second intervals. We compared the degree of correspondence over time between novice and expert mindful-82

ness meditators as well as with that previously reported for meditation-naive participants (Whitmarsh et83

al., 2014). We predicted that meditators would show a stronger and more sustained correspondence between84

self-reported attentional focus and contralateral alpha power over time. Furthermore, we expected these85

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differences to be more pronounced for expert meditators compared to novices.86

2 Method87

2.1 Participants88

The control group consisted of fifteen healthy participants (9 female, mean = 29.4 years, SD = 10.4). One89

control participant was excluded from the analysis due to excessive movement artifacts. The meditation90

group consisted of sixteen healthy experienced mindfulness meditators (5 female, mean = 48.0 years, SD91

= 16.1). They were recruited via personal network of mindfulness teachers or contacted directly. To be92

included in our study they had to be familiar with mindfulness meditation through a meditation retreat and93

had to practice mindfulness mediation regularly for at least a year at the time of the study. Participants94

were enrolled after providing written informed consent and were paid in accordance with guidelines of the95

local ethics committee (CMO Committee on Research Involving Humans subjects, region Arnhem-Nijmegen,96

the Netherlands). The experiment was in compliance with national legislation as well as the code of ethical97

principles (Declaration of Helsinki).98

2.2 Procedure99

Participants were instructed to attend continuously to the cued hand while remaining aware of their attentive100

state until a probe sound (2000 Hz tone) was presented (Figure 1A). Cues consisted of two sequential tones101

of 400 ms each, 200 ms apart, either ascending in pitch for the right hand (2000 Hz followed by 2500 Hz)102

or descending for the left hand (2000 Hz followed by 1500 Hz). Cue side was determined pseudo-randomly.103

The subjective evaluation of attention was done by button press, selecting one out of four options: (1)104

not at all, (2) little, (3) much, (4) fully/maximally attentive. To minimize head movements and provide105

more comfort participants were measured in supine position. To minimize eye movements and blinks and106

increase the chance of fluctuations in attentional focus, participants were instructed to remain with their eyes107

closed throughout the experiment. The experiment started with a training session until participants were108

familiar with the paradigm, followed by three continuous experimental blocks of 125 trials (≈ 25 min each)109

separated by self-paced breaks. Non-evaluation trials (10% ad random) were indicated by the presentation of110

an extra third cue of similar pitch to the second. Participants were instructed that no evaluation of attention111

was needed at these trials, but to respond as quickly as possible with their index finger. On these trials112

only, participants received a short electrical stimulation of the cued hand after probe offset, to provide an113

additional functional localizer (which was not used in the analysis). Non-evaluation trials were discarded114

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from all further analysis. Cue-probe intervals were generated according to an exponential distribution with115

mean of 3 s and a cut-off time of 27 s seconds, providing a flat hazard rate. In other words, the chance of the116

probe occurring after trial onset was held constant. A minimal cue-probe interval of 5 s was added, resulting117

in an average cue-probe interval of 8 s seconds, and maximal of 32 s.118

Cue

5 seconds

max. 32 seconds

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right highright low

trialnr.block1block2

response timetrial length

coil1 xcoil1 ycoil1 zcoil2 xcoil2 ycoil2 zcoil3 xcoil3 ycoil3 z

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example design matrixparadigmA B

Figure 1: Schematic of paradigm and example of the design matrix used for the source level General LinearModel. A) Schematic depiction of paradigm showing timing parameters of trial. B) Example design matrixshowing regressors for conditions (0’s and 1’s) and confound regressors (normalized).

2.3 Data Preprocessing119

Continuous MEG data was recorded using a 275-sensor axial gradiometer system (CTF MEG TM Systems120

Inc., Port Coquitlam, BC, Canada) placed in a magnetically shielded room. The ongoing MEG signals were121

low-pass filtered at 300 Hz, digitized at 1200 Hz, and stored for off-line analysis. The subjects’ head position122

was continuously recorded relative to the gradiometer array using coils positioned at the subject’s nasion123

and at the left and right ear canals. High-resolution anatomical images (1 mm isotropic voxel size) were124

acquired using a 1.5 T Siemens Magnetom Sonata system (Erlangen, Germany). The same earplugs, using125

vitamin E instead of the coils, were used for co-registration with the MEG data.126

2.4 Data Analysis127

MEG data was analyzed using the Matlab-based Fieldtrip toolbox, developed at the Donders Institute for128

Brain, Cognition and Behavior (Oostenveld, Fries, Maris, & Schoffelen, 2011). Trials containing movement,129

muscle, and superconducting quantum interference device (SQUID) jumps were discarded by visual inspec-130

tion. Independent component analysis (ICA) was used to remove eye and heart artifacts.131

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2.5 Source Reconstruction132

Source reconstruction was performed using a frequency-domain beamformer approach (Dynamic Imaging of133

Coherent Sources) which uses adaptive spatial filters to localize power in the entire brain (Gross et al., 2001;134

Liljestrom, Kujala, Jensen, & Salmelin, 2005). The brain volume of each individual subject was discretized135

to a grid with a 0.8 cm resolution. For every grid point a spatial filter was constructed from the cross-spectral136

density matrix and the lead field. The lead fields were calculated from a subject specific realistic single-shell137

model of the brain (Nolte, 2003), based on the individual anatomical MRIs. We calculated the cross-spectral138

density matrix based upon the full interval between cue offset and probe onset to obtain the most accurate139

estimation of the alpha sources. Individual alpha frequencies were used, determined by the maximum log140

power 7 Hz to 15 Hz on all trials and sensors.141

Separate analyses were performed for one second segments preceding probe onset, to a maximum of 11142

seconds before probe onset. A sufficient number of trials (≈ 100) had trial lengths of at least 11 seconds143

to enable source statistics at those intervals. After calculating the spatial filter for each grid point and144

one-second time segment, alpha activity was estimated using a (Slepian) multitaper approach to accomplish145

accurate frequency smoothing for the alpha band (±2 Hz) around the subject-specific alpha peaks. To enable146

valid voxel-by-voxel comparisons in the face of the beamformer depth bias, alpha estimates were standardized147

over trials.148

A voxel-by-voxel first-level GLM approach was then used for every subject and time segment. Figure149

1B shows an example design matrix, including cue side (left/right) and self-report (high/low), dichotomized150

according to a median split per subject and time-point. In addition, trial number as well as the mean X, Y151

and Z position of the three fiducial coils were entered as separate regressors. The locations of the fiducial152

coils indicate the position of anatomical landmarks of the subject’s head (nasion and pre-auricular points) in153

the MEG helmet. By including the average fiducial positions during each trial, variance that was caused by154

differences in head position over trials was also reduced. To further reduce variance that could be explained155

by response preparation, regressors for evaluation response times and cue-probe duration were added to the156

GLM, together with separate regressors for the response hand, which was switched between each block and157

randomized over subjects. The cue side and self-report predictors consisted of 0’s and 1’s, thereby yielding158

mean standardized alpha power after multiplication with the standardized data. By standardizing the159

remaining covariates (response time, trial length and fiducial position) multiplication with the standardized160

data resulted in correlation values (ρ). Prior to averaging and group statistics, the resulting beta-values161

and correlations values were spatially normalized using SPM2 to the International Consortium for Brain162

Mapping template (Montreal Neurological Institute, MNI, Montreal, QC, Canada).163

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2.6 Functional localization of somatosensory regions164

After the reconstruction of alpha power for each voxel and time-point, somatosensory regions of interest165

(ROIs) were determined based on alpha power during the last second preceding probe onset. A voxel-166

by-voxel comparison was made between left and right attention trials. A cluster-based permutation test167

(Maris & Oostenveld, 2007) was then used to identify significant spatial clusters. This resulted in a distinct168

somatosensory alpha-ROI for each hemisphere. Each ROI therefore depended on cue condition (left versus169

right), but remained independent of the self-reported evaluation of attention.170

2.7 Region of Interest analysis171

Alpha power values within the left and right ROI voxels were averaged according to cue condition (ipsi versus172

contra), evaluation (high versus low) and time-point (eleven one-second intervals preceding probe onset). The173

effects of cue condition and evaluation on mean alpha power were tested over time using repeated measures174

ANOVA. Differences in these effects over time were tested using post-hoc t-tests per time-point.175

3 Results176

Attentional focus fluctuated over trials, as was reflected by the use of the full range of responses (Figure177

2A). Participants were generally confident about their ability to perform the task, as was shown by the large178

fraction of high evaluations. A repeated measures ANOVA with one within-subject factor with four levels179

(evaluation) and one group factor (meditators vs. controls) showed that the number of trials per level of180

attention were significantly different (F (3) = 30.931, p < 0.001), and assumed a negative linear relationship181

(F (1) = 73.889, p =< 0.001). No effect of group on evaluation was found (F (3) = 1.451, p = .234).182

Evaluation times were also significantly different between levels of attention (F (3) = 55.390, p < 0.001) and183

showed a positive linear relationship (F (1) = 69.862, p < 0.001). No effect of group on evaluation time was184

found (F (3) = 2.038, p = .116). Furthermore, evaluation times correlated negatively with cue-probe duration185

for both controls (ρ = −0.130, t(13) = −5.505, p < 0.001) and meditators (ρ = −0.078, t(15) = −2.648, p =186

0.018), showing that longer cue-probe durations did not result in a loss of vigilance. The correlation between187

evaluation times and cue-probe duration did not differ between the groups (t(28) = −1.3450, p = 0.189).188

The behavioral analysis therefore showed no differences between meditators and controls, and both groups189

were confident in their performance of the task.190

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controlsmeditators

0

100

0

1

2

high lowevaluation of attention

high lowevaluation of attention

eval

uatio

n tim

e (s

)

num

ber o

f tria

ls

A B

Figure 2: Behavioral differences between evaluations. A Distribution of number of trials per evaluation ofattention show that attention was generally rated high, showing a negative relationship with level of attention.B Evaluation times were reduced when attention was evaluated higher, showing a positive relationship withlevel of attention. No differences between groups were found.

3.1 Regions of interest analysis191

Somatosensory alpha regions of interest (ROIs) were determined for controls and meditators on the basis of192

the lateralization of alpha power (alpha cue left minus alpha cue right) during the last second before probe193

onset. A cluster-based permutation test (Maris & Oostenveld, 2007) was used to identify the significant194

clusters responsive to cue direction. In both groups the analysis resulted in two significant clusters, one in195

each hemisphere, including primary sensorimotor areas (see Figure 3). A cluster-based permutation test of196

alpha lateralization showed no significant differences between groups. Mean values of alpha power within197

the group-specific regions-of-interest were used in further analysis. We then computed contralateral alpha198

power preceding probe onset. Evaluations were dichotomized according to a median split per subject and199

time-point, resulting in time courses for high versus low attention trials (Figure 4).200

Differences between high and low attention trials were shown to be uncorrelated with age at any of the201

significant time points for either controls ([−3 : −2], ρ = −.31, p = .915; [−2 : −1], ρ = −.016, p = .956; [−1 :202

0], ρ = .313, p = .275) or meditators: ([−10 : −9], ρ = −.912, p = .476; [−6 : −5], ρ = .096, p = .724; [−5 :203

−4], ρ = −.191, p = .478; [−4 : −3], ρ = .188, p = .486).204

3.2 Controls versus meditators205

We performed a repeated measures ANOVA with time and self-report (high vs. low) as within-subject factors206

and group (meditators vs. controls) as a between-subjects factor. Self-report showed a significant effect on207

contralateral alpha power (F (1, 28) = 9.509, p = 0.005). Time showed to be a significant factor as well208

(F (10, 19) = 2.983, p = 0.019). No significant interaction with group or between time and self-report effects209

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0.4

-0.4

0

mea

n st

anda

rdiz

ed a

lpha

cu

e le

ft -

cue

right

CONTROLS MEDITATORS

0.4

-0.4

0

Figure 3: Somatosensory alpha power lateralized in response to cue direction in both groups. Source recon-structed alpha activity during one second preceding probe-onset shows clear lateralization in areas includingprimary somatosensory regions. Significant voxels were thresholded based on cluster-based permutation test(p¡ 0.05) (Maris & Oostenveld, 2007)

were found. As expected, no effect of self-report was found for the ipsilateral ROI (F (1, 28) = 2.318, p =210

0.139). We took a closer look at the temporal profile of alpha in the high versus low evaluated trials. In211

controls, the last three second interval before probe onset showed significantly lower alpha power in high212

versus low attention trials (Figure 4A; paired t-tests, two-tailed: [−3 : −2], t(13) = −2.357, p = 0.036; [−2 :213

−1], t(13) = −2.746, 0.017; [−1 : 0], t(13) = −2.648, p = 0.020). Although the full model ANOVA did not214

show a significant interaction between time and group, differences in alpha power preceded those of controls215

(paired t-tests, two-tailed: [−10 : −9], t(15) = −2.806, p = 0.04; [−6 : −5], t(15) = −2.156, p = 0.048; [−5 :216

−4]t(15) = −4.413, p < 0.001; [−4 : −3], t(15) = −2.275, p = 0.036). As predicted, no time points showed217

a difference between high and low attention on the ipsilateral ROI, for either the control group or the218

meditation group.219

3.3 Novice versus expert meditators220

To explore differences between novices and expert meditators we applied a median split of the meditation221

group on the basis of total hours of meditation experience. Hours of meditation experience were calculated222

from the self-reported estimated time spend in meditation per week, multiplied by the total duration of223

consistent meditation practice. For meditation retreats an estimated ten hours per day were added. The224

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******

−0.1

0

0.15

PROBE ONSET

-1-10 TIME (s)

−0.1

0

0.15HIGH ATTENTIONLOW ATTENTION

CONTROLS MEDITATORS

PROBE ONSET

STA

ND

ARD

IZED

AL

PHA

POW

ER

HIGH ATTENTIONLOW ATTENTION

-1-10 TIME (s)

STA

ND

ARD

IZED

AL

PHA

POW

ER

*

A B

Figure 4: Time-resolved analysis of contralateral alpha power for controls (A) and meditators (B). Asteriskdepict significant differences between high- and low-attention at individual time points. Shaded surfacerepresents standard error of mean.

median split resulted in two very separate groups for novices (n = 7, mean = 555 hrs, range 220-885 hrs)225

and expert meditators (n = 8, mean = 12962 hrs, range 3275-24819 hrs). One meditator did not reply226

to our follow-up questions and could not be included in the analysis. As can be seen in Figure 5A, the227

temporal profile of the novice meditators showed a remarkable similarity with those of controls, showing a228

significant effect of self-report at a distinct period directly preceding probe onset ([−2 : −1]t(6) = −4.057, p =229

.006; [−5 : −4]t(6) = −2.298, p = .062; [−6 : −5]t(6) = −3.865, p = 0.008). Expert meditators, however,230

showed a dramatically different profile (Figure 5B), with significant differences in alpha power preceding the231

differences found in novices ([−10 : −9]t(7) = −2.391, p = 0.048; [−8 : −7]t(7) = −2.201, p = 0.064; [−7 :232

−6]t(7) = −2.008, p = 0.085; [−5 : −6]t(7) = −2.580, p = 0.036; [−5 : −4]t(7) = −3.769, p = 0.008).233

Furthermore, the last interval in high meditators showed a significant difference in the opposite direction234

after what seems to be a gradual reduction of alpha power in low attention trials during the last seconds of235

the trial (t(7) = 3.593, p = 0.009). We therefore divided the temporal profile midway in time and averaged236

alpha power for high and low self-report within the late and early period preceding probe onset. An ANOVA237

with self-report (high vs. low) and time (early vs. late) as within-subjects factors and group (novice vs.238

expert) as between-subjects factor showed a significant main effect of self-report (F (1, 13) = 7.672, p = 0.016)239

as well as significant interaction between self-report, time and group (F (1, 13) = 8.908, p = 0.011).240

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STA

ND

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IZED

AL

PHA

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PROBE ONSET

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−0.1

0

0.1

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*******HIGH ATTENTION

LOW ATTENTION

HIGH ATTENTIONLOW ATTENTION

NOVICES EXPERTSA Bearly lateearly late

Figure 5: Time-resolved analysis of contralateral alpha power for novice meditators (A) and expert meditators(B). Asterisk depict significant differences between high- and low-attention at individual time points. Shadedsurface represents standard error of mean.

4 Discussion241

Compared to both meditation-naive controls and novice meditators, expert mindfulness meditators showed242

a distinctly different temporal profile of metacognitive awareness. While the self-reported attentional state243

of controls and novices were reflected by differences in alpha power preceding probe-onset, expert meditators244

showed a correspondence between self-report and alpha power during an extended period of time preceding245

that of both controls and novice meditators. Furthermore, expert meditators displayed a pronounced sup-246

pression of contralateral alpha power before probe onset, even surpassing those of high attention trials. Both247

these results are unexpected and warrant only tentative conclusions. Given these reservations, the results248

suggest shed light on previously unexplored territory and suggest new avenues of exploring neurocognitive249

mechanisms of mindfulness meditation. Firstly, the findings suggest that while novice meditators and con-250

trols were able to report on their attentional state retrospectively (i.e. after probe onset), expert meditators251

monitored their attention continuously and used it proactively rather than reactively. The ability maintain252

moment-by-moment evaluation of attention is in accordance with the proposed aim of mindfulness medi-253

tation. This might have enabled expert meditators to reestablish their attentional focus when distraction254

was noticed, as reflected by the suppression of alpha power in the latter half of low attention trials. How-255

ever, since these trials were still reported as low attention trials, expert meditators might have confounded256

metacognitive awareness of their attentional state with that of attentional control. Future research could257

test this hypothesis by using additional self-report probes of both attentional state as well as effort/control.258

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Positive findings on both indexes would provide objective evidence for the claim that mindfulness meditation259

increases the ability to differentiate internal cognitive processes (Bishop et al., 2004).260

Alpha values for each voxel and time interval were standardized over all trials to remove the beamformer261

depth bias. Because we were interested in the correspondence between self-reports and alpha power over262

time, our data was standardized per time-interval as well. This made our paradigm particularly sensitive to263

metacognitive awareness of attention, but might have missed other effect of mindfulness meditation such as264

an increased ability to modulate somatosensory alpha (Kerr et al., 2011). Our findings are, however, in line265

with recent studies reporting improvements in attentional stability (Lutz et al., 2009) and improved conflict266

monitoring (Jha et al., 2007) as both are considered to be under metacognitive control (Fleming et al., 2012).267

The experiment required a minimum of behavioral responses, and these were not experienced as inter-268

fering with the usual practice of mindfulness meditation. In fact, the paradigm was well received by the269

expert meditators we measured. The benefit of a less intrusive paradigm in meditation research cannot be270

overstated. The absence of any external stimuli or behavioral measurements too often precludes unequivocal271

interpretation of psychophysical findings (see e.g. (Cahn & Polich, 2006)), while at the same time, exogenous272

perceptual and behavioral tasks might interfere with the introspective practice.273

A possible limitation to our study is the fact that the meditation and control groups were not matched in274

age, with the meditation group being significantly older. However, for both controls and meditators, age did275

not correlate with the differences in alpha power between high and low attention trials. It is therefore highly276

unlikely that results were dependent on age. As a between-subjects cross-sectional study our results might277

also suffer from a selection bias. The fact that our findings depended on the hours of meditation experience278

speaks against an interpretation in terms of differences preceding their meditation practice. Lastly, we cannot279

exclude any ‘third variable’ that might correlate both with meditation experience as well as metacognitive280

ability. For instance, it is shown that metacognition is impaired under conditions such as cigarette craving281

(Sayette, Schooler, & Reichle, 2010) or inebriation (Sayette, Reichle, & Schooler, 2009). Since meditation282

is often practiced in a social and religious context, it cannot be excluded that influences of life-style might283

have played a role.284

Mindfulness meditation has been theorized to distinguish itself from meditation practices that focus285

on sustained and selective attention through its emphasis on metacognitive monitoring (Lutz, Dunne, &286

Davidson, 2006; Lutz et al., 2008). While there is growing evidence that mindfulness meditation improves287

attentional abilities, convincing evidence for improved ability for metacognitive monitoring has been lacking288

(Chiesa & Serretti, 2011). This is unfortunate, since these metacognitive abilities are proposed to underlie289

its clinical efficacy (Baer, 2003; Teasdale et al., 2002). Our findings therefore provide important preliminary290

evidence for the involvement of metacognitive monitoring in mindfulness meditation. Furthermore, our291

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https://doi.org/10.1101/2020.02.04.933606

results suggest that such proactive attentional monitoring abilities could improve the ability to manage292

attentional resources on a moment-by-moment basis. However, in line with the conclusion of Chiesa and293

colleagues (2011), our results also show that measurable effects might require extensive practice.294

5 Acknowledgements295

We would like to express our gratitude to David A. Holmes for his invaluable insights in understanding the296

practices and potential of mindfulness meditation, and for his help in proofreading the manuscript. The297

authors also gratefully acknowledge the support of the BrainGain Smart Mix Programme of the Netherlands298

Ministry of Economic Affairs and the Netherlands Ministry of Education, Culture and Science the Nether-299

lands Initiative Brain and Cognition, a part of the Organization for Scientific Research (NWO) under grant300

number SSM0611,301

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Mindfulness meditation experience is associated with ... · 04.02.2020 · 21 1 Introduction 22 The goal of this study is to investigate whether extensive meditation experience can