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Abstract

Focused attention meditation (FAM) practices are cognitive control exercises where meditators learn to maintain focus and attention in the face of distracting stimuli. Previous studies have shown that FAM is both activating and causing plastic changes to the mesolimbic dopamine system and some of its target structures, particularly the anterior cingulate cortex (ACC) and striatum. Feedback-based learning also depends on these systems and is known to be modulated by tonic dopamine levels. Capitalizing on previous findings that FAM practices seem to cause dopamine release, the present study shows that FAM experience predicts learning from negative feedback on a probabilistic selection task. Furthermore, meditators exhibited attenuated feedback-related negativity (FRN) as compared with nonmeditators and this effect scales with meditation experience. Given that reinforcement learning and FRN are modulated by dopamine levels, a possible explanation for our findings is that FAM practice causes persistent increases in tonic dopamine levels which scale with amount of practice, thus altering feedback processing.
Meditation experience predicts negative reinforcement learning and is
associated with attenuated FRN amplitude
Paul Knytl
1
&Bertram Opitz
1
#The Author(s) 2018
Abstract
Focused attention meditation (FAM) practices are cognitive control exercises where meditators learn to maintain focus and
attention in the face of distracting stimuli. Previous studies have shown that FAM is both activating and causing plastic changes
to the mesolimbic dopamine system and some of its target structures, particularly the anterior cingulate cortex (ACC) and
striatum. Feedback-based learning also depends on these systems and is known to be modulated by tonic dopamine levels.
Capitalizing on previous findings that FAM practices seem to cause dopamine release, the present study shows that FAM
experience predicts learning from negative feedback on a probabilistic selection task. Furthermore, meditators exhibited atten-
uated feedback-related negativity (FRN) as compared with nonmeditators and this effect scales with meditation experience.
Given that reinforcement learning and FRN are modulated by dopamine levels, a possible explanation for our findings is that
FAM practice causes persistent increases in tonic dopamine levels which scale with amount of practice, thus altering feedback
processing.
Keywords Feedback-learning bias .Feedback-related negativity .FRN .Reinforcement learning .Meditation .Dopamine .
ACC .Striatum
Since the turn of the century meditation has gone from relative
obscurity in Western academia to explosive growth in interest
and research. Only around 60 academic papers were published
on the topic of mindfulness in 2003; by 2013 that number
jumped to 600 (Shonin, Van Gordon, & Griffiths, 2013).
The appeal is understandable; claimed benefits of meditation
range from reduced stress (Kabat-Zinn, 2003) to improved
immune function (Davidson et al., 2003), from improved at-
tention and lower anxiety (Tang et al., 2007) to a novel treat-
ment for depression (Teasdale et al., 2000), and has even been
recommended as a possible way to manage symptoms of psy-
chosis (Shonin, Van Gordon, & Griffiths, 2014).
While some researchers have been pressing forward ex-
ploring the possible applications of meditation, others have
been trying to understand what exactly is occurring in the
nervous system during and after meditation, what systems
are involved, and what the long-term effects of practice might
be. In part due to methodological issues, inconsistent
operationalization, the relative lack of longitudinal studies,
and the infancy of the field, the precise mechanisms and
long- term effects of various meditation styles are still not
entirely clear (Cahn & Polich, 2006;Hölzeletal.,2011;
Tang, Hölzel, & Posner, 2015; Vago & Silbersweig, 2012).
There has been recognition that effective study of these
practices requires precise operationalization of the concept.
The term meditation refers to a diverse group of cognitive
practices which share some similarities and some fundamental
differences (Lutz, Slagter, Dunne, & Davidson, 2008). To the
uninitiated, the matter is further confused by the casual use of
relevant terms by both the public and academia. For example,
the term mindfulness can refer to a type of meditation, a trait,
and a state of mind (Vago & Silbersweig, 2012). To address
this, a framework has been introduced which categorizes med-
itative practices into three groups based on their primary cog-
nitive strategy: focused attention meditation (FAM), open-
monitoring meditation (OMM), and loving-kindness medita-
tion (LKM; Hölzel et al., 2011; Lutz et al., 2008; Vago &
Silbersweig, 2012). Of particular interest in the present study
is FAM. FAM is central to many meditative traditions, such as
Buddhist samatha and vipassana meditation and their deriva-
tive secular mindfulness practices and clinical interventions
such as mindfulness-based stress reduction (MBSR),
*Bertram Opitz
b.opitz@surrey.ac.uk
1
School of Psychology, University of Surrey, Guildford, Surrey GU2
7XH, UK
Cognitive, Affective, & Behavioral Neuroscience
https://doi.org/10.3758/s13415-018-00665-0
mindfulness-based cognitive therapy (MBCT), and other
practices such as transcendental meditation (Harvey, 2015;
Lutz et al., 2008; Vago & Silbersweig, 2012). FAM is charac-
terized by the establishment, monitoring, and maintenance of
attention on a chosen sensory object, such as the sensation of
breathing (Lutz et al., 2008).
What is striking about FAM is that the cognitive processes
invoked during practice bear close resemblance to the process-
es that the brains mesencephalic dopamine system and its
target areas are thought to perform. For instance, others have
hypothesized that the continual establishment, monitoring,
and reestablishment of attention on an object of meditation
during FAM should elicit activity in those brain areas already
associated with conflict monitoring and sustained attention,
such as the dorsolateral prefrontal cortex (dlPFC) and the an-
terior cingulate cortex (ACC; Lutz et al., 2008). An early
review of 12 neuroimaging studies of meditation found nu-
merous brain areas active during meditation, such as the stri-
atum, hippocampus, thalamus, along with the ACC and dlPFC
(Cahn & Polich, 2006). As this review predates Lutz et al.s
(2008) operationalization, it does not directly specify what
kind of meditation may be involved (FAM, OMM, LKM,
etc.), even including some studies of Christian prayer. This
inclusion ofa wide range of practices involved in the reviewed
studies may account for the diverse brain areas reportedly
active in meditators. Studies looking only at practices having
a clear FA component (e.g., Buddhist and secular mindful-
ness) consistently report brain areas involved in attention
and conflict monitoring, including the dlPFC and particularly
the ACC, to be reliably active in meditators (for a review, see
Tang et al., 2015).
There is also evidence of morphological differences and
changes in plasticity in FAM practitioners. A recent anatomical
likelihood estimation (ALE) meta-analysis of 21 morphometric
brain imaging studies revealed higher grey and white matter
density in the ACCs of meditators compared with
nonmeditators (Fox et al., 2014). Neuroplastic changes may
also happen relatively quickly after beginning FAM training;
one intervention with an FAM component resulted in higher
connectivity between the ACC and the brain stem after only 11
hours of practice (Tang et al., 2010). These findings suggest
that meditation not only activates areas vital to attention but can
also quickly induce neuroplastic growth in these brain regions.
Neuroimaging studies have also revealed that the striatum,
a core component of the dopamine system, is active during
meditation. In one study, [
11
C]raclopride, a radio ligand that
binds competitively with dopamine D2 receptors, had been
used to measure participantsdopamine tone in the striatum,
first while they listened to speech with their eyes closed and
then while they were actively meditating (Kjaer et al., 2002).
Compared with the speech state, the meditation state was as-
sociated with a 65% increase in endogenous dopamine re-
lease. In another investigation, fMRI was used to carry out a
case study of an experienced Buddhist meditator while he
used various meditative techniques including FAM (Hagerty
et al., 2013). The authors reported that this participant
displayed increased activity in the ACC and in the striatum
during the FAM phase. Both of these studies suggest that
practicing FAM activates the dopaminergic system in the cor-
tex and basal ganglia.
The dopaminergic system has been shown to play an im-
portant role in reward, learning, error correction, motor con-
trol, and attention (Schultz, 2002). More precisely, phasic
(fast) bursts of dopamine act as a learning signal which serve
to orient organisms to novel or unexpected stimuli and en-
codes the difference between the expected and actual outcome
of an action, a reward prediction error (RPE), in the form of
either a spike in dopamine if the event was rewarding, or a dip
if the event was not (Schultz, 2002,2013,2016). A seminal
study demonstrated that dysfunctional dopamine levels ex-
plain some perplexing cognitive differences between people
with Parkinsons disease (PD), where dopamine production is
severely reduced, and age and IQ matched controls (Frank,
Seeberger, & OReilly, 2004). While off their dopaminergic
medication, patients suffering from PD performed worse than
controls on a probabilistic selection task (PST)but only
when they received positive feedback. When they received
negative feedback they outperformed controls. When placed
on L-DOPA medication, a dopamine precursor which in-
creases tonic dopamine levels, the pattern reversed with PD
patients outperforming controls on positive feedback-learning
but underperforming on negative feedback-learning (Frank
et al., 2004). Since a person can be said to have a feedback-
learning bias (FLB) if they learn more effectively from either
positive or negative feedback, this study demonstrated that up
or down regulating tonic dopamine levels respectively creates
a positive or negative feedback-learning bias.
The interaction between phasic dopamine bursts and per-
sistent tonic dopamine levels provides a possible reason for
this phenomenon. FLB has been explained using a neuro-
computational model whereby an up-regulated or down-
regulated tonic dopamine signal respectively enhanced or
inhibited sensitivity to a particular type of feedback, thus cre-
ating a FLB (Frank, 2005). In this model, depressed tonic
dopamine levels, such as those found in PD patients off med-
ication, effectively increased the amplitude of phasic dopa-
mine bursts required to activate the Bgo^pathway in the basal
ganglia. At the same time, a smaller magnitude of phasic dip is
required to trigger the threshold that leads to negative feed-
back learning, resulting in a negative FLB. Increasing tonic
dopamine seems to have the opposite effect, amplifying the
effect of phasic bursts, inhibiting the Bno-go^pathway, and
thus leading to a positive feedback-learning bias (Frank,
2005).
This phasic dopaminergic activity is widely believed to be
the cause of one component of the event-related potential
Cogn Affect Behav Neurosci
(ERP) known as feedback-related negativity (FRN). The FRN
is typically calculated as the difference between negative-
feedback and positive-feedback trial waveforms and is char-
acterized by a frontocentral scalp distribution which peaks
about 250 ms after feedback presentation (San Martín, 2012;
Walsh & Anderson, 2012). The highly influential reinforce-
ment learning (RL) theory of the FRN holds that phasic dips in
dopaminergic activity (negative RPEs) disinhibit the ACC,
which in turn generates the FRN (Holroyd & Coles, 2002).
Hierarchical reinforcement learning (HRL) builds on the suc-
cess of RL theory by breaking complex tasks into simpler sets
of subroutines. This helps to manage the computational de-
mand of real-world learning and decision-making scenarios,
ensuring they are still tractable using the RL algorithm
(Botvinick, 2012). The RL theory of the FRN has also been
updated to leverage the insights gained from HRL (Holroyd &
Yeun g , 2012). This account elaborates on the role of the ACC,
proposing that it is responsible for learning the values of, and
maintaining engagement in, HRL subroutines. It draws on a
number of more recent studies to argue that the ERN/FRN are
in fact a function of an underlying component, sensitive to
reward, termed the reward positivity (RewP), which reflects
a positive, as opposed to negative, RPE. Although there has
been some debate over whether the FRN reflects an underly-
ing RPE, surprise, or response conflict, what remains un-
changed and what is of paramount importance in the present
study, is the abundance of evidence which supports the theory
that the FRN is generated when the ACC responds to phasic
dopaminergic inputs fromthe midbrain and basal ganglia (San
Martín, 2012; Walsh & Anderson, 2012)
Given the tonicphasic interaction in the dopamine system,
it is not surprising that the FRN has also been linked to tonic
dopamine levels. In one study, participants who displayed a
positive FLB on the PST also presented a lower amplitude
FRN than participants who were negatively biased (Frank,
Woroch, & Curran, 2005). Other researchers have shown that
people who are MET homozygous for the val158met COMT
polymorphism, which elevates tonic dopamine levels, also
present an attenuated FRN (Marco-Pallarés et al., 2009), and
that dopamine agonists likewise reduce FRN amplitude
(Santesso et al., 2009). These studies suggest that higher tonic
dopamine levels result in not only a more positive feedback-
learning bias but also a reduction in FRN amplitude.
Due to the overlapping systems involved in reinforcement
learning and FAM, we hypothesized that FAM might induce a
positive FLB and attenuated FRN by upregulating tonic do-
pamine levels. To investigate whether, compared with
nonmeditators, the amount of FAM experience a person has
would predict a more positive FLB and attenuated FRN, we
carried out a study using a version of the probabilistic selec-
tion task (PST) employed by Frank et al. (2005). In this task
participants were required to learn to choose the more reward-
ing of two symbols via trial and error. Positive and negative
feedback was provided on a probabilistic basis, with some
symbols being on average more rewarding than others.
Therefore, when choosing a more rewarding symbol, partici-
pants were more likely to receive positive feedback than they
would from a less rewarding one. We measured FLB and
recorded their FRN while they completed this task. We rea-
soned that if FAM is causing long-term increases to tonic
dopamine levels, meditators should exhibit characteristics of
positively biased feedback learners on the PSTas well as have
an attenuated FRN. We predicted that, if this effect is caused
by FAM practice, both the FLB and the FRN effects should
scale with FAM experience.
Method
Participants
Thirty-five people were recruited from the local student pop-
ulation, Buddhist groups, and the general public. In keeping
with the FAM/OMM/LKM framework, the 23 meditators
among our participants consisted of Buddhist (N= 9), secular
(N= 12), and Qi Gong (N= 2) FAM practitioners. Since the
measures we used are sensitive to tonic dopamine levels, vol-
unteers were prescreened using a self-report questionnaire for
current episodes of major depression or anxiety, and mono-
amine oxidase inhibitors or recreational drug use.
The participants were grouped into a nonmeditator group
(N= 12, mean age = 34.6 years, SD = 14.8 years, range: 22
60, six female), a novice meditator group (N= 12, mean age =
38.6 years, SD =14.3years,range:1859, 10 female) and an
experienced meditator group (N= 11, mean age = 38.2 years,
SD = 13.7 years, range: 2356, six female) based on a median
split
1
on their self-reported meditation experience. We chose
to use years of experience as our grouping variable in keeping
with other studies (Kang et al., 2013). Participants were con-
sidered experienced if they meditated for more than 30 mi-
nutes a week for at least 4 years; this criterion reflects a me-
dian split of our data based on years of experience with the
added proviso that meditators practiced on a weekly basis for
a reasonable amount of time. Mean age was not significantly
different between groups F(2, 32) < 1. A self-report
prescreening questionnaire revealed that, except for one am-
bidextrous participant, all were right-handed. Everyone was a
volunteer, and no compensation was provided beyond reim-
bursement of travel costs.
1
Our findings are relatively robust to alternative quantifications of FAM ex-
perience. See the Appendix for details and discussion.
Cogn Affect Behav Neurosci
Apparatus
Participants sat in a dimly lit, electrically shielded, sound at-
tenuated booth. On a desk in front of them was a 17-in. LCD
monitor used to display stimuli at a screen resolution of 1280
× 1024 at 60 Hz. The screen was approximately 1 meter from
the subjects face at eye level. Responses were recorded via
keyboard. Besides the EEG amplifier and screen, all EMR
sources were located outside the booth, and DC lighting was
used.
Design and procedure
This experiment used a between-subjects design examining
the effect of three levels of FAM experience (control, novice,
experienced) on two feedback learning measures: feedback-
learning bias (FLB) and FRN amplitude (FRN).
We employed a version of the probabilistic selection task
(PST) procedure used by Frank et al. (2005). During the EEG
recording, participants were shown pairs of stimuli consisting
of Japanese hiragana characters. Following the procedure
from cited above, after a short practice block, stimuli were
presented in three blocks each containing a training and test
phase. Each block used one of three stimuli sets whose pre-
sentation order was randomized across participants.
In the training phase, participants were instructed to learn
via trial-and-error to select the hiragana symbol most likely to
provide positive feedback from each of three pairs (represent-
ed here using roman letters: AB, CD, EF). Pairs were present-
ed in random order, and the side that the superior symbol
appeared on was pseudorandomized such that symbols ap-
peared equally often on either side of the screen. If no selec-
tion was made within 1,000 ms, the message Bno response
detected^was displayed. Participants were provided with pos-
itive or negative feedback to guide their learning; however, as
summarized in Table 1, this feedback was probabilistic. For
example, choosing Ain the AB pair would result in positive
feedback (+10 points, green colour, Arial, size matched to the
hiragana characters) 80% of the time, whereas choosing B
would result in positive feedback 20% of the time. In order
to maximize their score in each training block, participants
should have learned to choose Aover B,Cover D,andEover
F. Note that this could be accomplished by either learning to
select the winners (A, C, E), avoid the losers (B, D, F), or both.
We enforced the same training criterion as Frank et al. (2005);
after 60 trials, performance levels were checked (65% Ain the
AB pair, 60% Cin CD, and 50% Ein EF), and if the criterion
was met, participants were released to the test phase. If the
criterion was not met, participants would continue up to a
maximum of 120 trials.
During the test phase, participants were told to use their gut
feeling to choose the superior symbol from the pairs present-
ed, and this time no feedback would be provided. During this
phase novel combinations (e.g., AC or BD) were presented to
participants. This was done in order to assess which feedback
type was most effective in teaching them and how accurate
their learning was. Since Amost reliably provided positive
feedback and Bmost reliably provided negative feedback,
participants should have learned to choose Awhenever it
was presented in a novel pair while avoiding Bwhenever it
was presented in a novel pair. Our paradigm differed from the
procedure in Frank et al. (2005) in two ways. First, we used
points (+10 or 10 points for positive or negative feedback,
respectively) instead of smiley faces, and Xicons as the feed-
back graphics, as preexperimental paradigm testing revealed
higher task engagement when a points system was used.
Second, the number of pairings in the test phase containing
Aor Bwere increased by one, while all other pairings were
reduced by one. This increased reliability without increasing
task duration.
After participants had finished the task, they were provided
with facilities to clean themselves up. They were then
debriefed regarding the full nature and purpose of the
experiment.
Data recording and analysis
Behavioural data
The PST was created and run using the E-Prime 2.0 stimulus
presentation suite. Responses from the training and test phase
were recorded via keyboard. The mean accuracy scores across
all pairs and blocks involving Aand Bwere calculated. The
FLB score is the mean difference, as a percentage, between
correct Choose Aand Avoid Bresponses. The AB trial was
excluded from this calculation since the preferred strategy
(Choose Aor Avoid B)usedtoansweritcannotbedissociated.
Electrophysiological data
During the behavioural task, a 32-channel continuous EEG
recording of each participant was made using Ag/AgCl elec-
trodes. Electrodes were affixed to an appropriately sized EEG
cap (EASYCAP GmbH) worn by each participant. The
Table 1 Summary of the probability a participant would receive
positive feedback when selecting a given character
Character Probability of receiving positive feedback
A 0.80
B 0.20
C 0.70
D 0.30
E 0.60
F 0.40
Cogn Affect Behav Neurosci
extended 1020 electrode layout (Klem, Otto, Jasper, & Elger,
1999) was employed. Signals from two additional electrodes
placed on the participants left and right mastoid were record-
ed. All electrodes recorded relative to a common average ref-
erence. Horizontal and vertical electrooculograms were record-
ed with electrodes located above and below the participants
left eye, and on the outer canthi of both eyes. Interelectrode
impedances were kept below 10 kΩ. All channels were ampli-
fied with a band pass from DC to 70 Hz and A/D converted
with 16-bit resolution at a rate of 500 Hz.
ERPs were calculated using the off-line data processing
suite included in the BrainAnalyzer2 program (Brain
Products GmbH, Gilching, Germany). Data preprocessing
consisted of a digital band-pass filter from 0.3 Hz to 30 Hz
(3 dB cut-off) to eliminate low-frequency signal drifts and
high-frequency artefacts. Eye-movement artefacts were elim-
inated using an automated independent components approach
as implemented in BrainAnalyzer2. Automatic artefact rejec-
tion (gradient criterion: voltage variation of more than 75 μV
in two subsequent time points, amplitude criterion: any volt-
age exceeding ± 100 μV and low activity criterion: 0.5 μV/50
ms) was applied to all channels to mark segments contaminat-
ed by additional artefacts. These recording epochs were ex-
cluded from further analysis. Recordings were segmented
with epochs ranging from 100 to +800 ms with respect to
the onset of the feedback. Artefact-free segments from the
training phases were averaged separately for each participant
to form 4 ERPs by feedback type and valence (true positive,
true negative, false positive, false negative). The 100-ms prior
to feedback onset served as the baseline. These averages were
digitally re-referenced to average mastoid activity.
We created the FRN difference wave by subtracting the
true positive ERP from true negative ERP for each participant
at the FCz electrode (e.g., Pfabigan, Alexopoulos, Bauer, &
Sailer, 2011). We excluded false feedback trials from the anal-
ysis as recent work by Ernst and Steinhauser (2018)has
shown that the FRN is attenuated in unreliable (i.e., false)
feedback trials. As participants were informed in the present
study that stimuli behaviour would be reliable mostbut not
allof the time, we improved signal-to-noise ratio by exclud-
ing false feedback trials. The proportion of true feedback trials
was constant at 70% across all participants.
Since persons exhibiting a positive bias on behavioural
measures may have delayed FRNs (Frank et al., 2005), auto-
matic peak detection was employed to pick the most negative
value of the difference wave within an epoch ranging between
220 and 320 ms postfeedback. This epoch was selected as it
should encapsulate peak FRN activity, approximately 265 ms
poststimulus (Gehring & Willoughby, 2002; San Martín,
2012; Walsh & Anderson, 2012). The mean amplitude in a
window ±50 ms of this peak was exported for analysis. To
assess whether any change in the FRN across groups was
driven by the ERP response to positive or negative feedback,
the mean amplitude of the same time window around the FRN
peak was also analysed for each condition specific ERP
separately.
Results
Behavioural data
Average training-phase performance across all blocks and
groups was 64%, indicating learning occurred. A 3 × 3
(Block × Group) GreenhouseGeisser corrected (ɛ= .75)
mixed-design ANOVA was conducted revealing no signifi-
cant effect of block, F(1.50, 47.81) = 0.91, p=.38,group,
F(2, 32) = 0.69, p=.51,oranyinteractionF(2.99, 47.81) =
0.56, p= .65, on training phase performance. There were no
significant group differences for the ratio of true positive to
true negative feedback received, F(2, 32) = 1.39, p=.26,or
the ratio of total positive to total negative feedback received,
F(2, 32) = 1.11, p= .34. Likewise, no group differences in the
total number of trials performed F(2, 32) = 0.27, p=.76,were
observed. In addition, overall test-phase performance did not
differ between groups F(2, 32) = 0.05, p=.95.
The primary measure of interest from the behavioural data
was feedback-learning bias (FLB). This quantity measures
whether participants are biased towards learning Choose A
or Avoid Bbehaviour. The value is computed by subtracting
correct test-phase performance on Avoid Btrials from Choose
Atrials. AB trial performance is excluded from this calcula-
tion, as it is not possible to disentangle which strategy (Choose
A, or Avoid B) was used to learn the correct response. Thus,
when FLB has a positive value, participants learned better
from positive feedback; likewise, a negative value indicates
negative feedback was more effective in training a participant.
The nonmeditators had the most negative FLB (M=0.11,
SE = 0.03). Meditators were positively biased, with the novice
group (M=0.00,SE = 0.04) being less positively biased than
the experienced group (M=0.05,SE = 0.06). An independent
ANOVA was performed to determine the effect of meditation
experience on FLB. As hypothesized, there was an effect of
meditation experience on FLB, F(2, 32) = 3.41, p=.045,ƞ
p
2
=
.18. Planned contrasts revealed that meditators were signifi-
cantly more positively biased than were nonmeditators, t(32)
=2.51,p=.017,r= .41. However, no significant difference
between the novice and experienced meditation groups, t(32)
=0.80,p=.43,r= .14, was found.
We added participant age to the model to determine if this
had an effect on FLB. The ANOVA revealed that while there
was still a significant effect of meditation experience on FLB,
F(2, 31) = 3.42, p=.045,ƞ
p
2
= .19, there was no significant
effect of age, F(1, 31) = 0.22, p=.64,ƞ
p
2
=.007.
A post hoc analysis was carried out in order to determine
whether this shift in feedback processing was driven by an
Cogn Affect Behav Neurosci
increase or decrease in Choose Aor Avoid Bperformance.
Approach and avoid performance is summarized by group in
Fig. 1. The control group had the lowest Choose Atest per-
formance (M=0.53, SE = 0.04), followed by the novice group
(M=0.59, SE = 0.03), while the experienced meditators had
the best performance (M=0.60, SE = 0.03). In terms of Avoid
Bperformance, the control group had the highest performance
(M=0.64, SE = 0.03), followed by the novice group (M=
0.59, SE = 0.03), while the experienced meditators performed
worst (M=0.55, SE = 0.04). A 2 × 3 (Approach/Avoid ×
Meditation Group) mixed-design ANOVA was performed to
determine whether there was an interaction between response
behaviour type and meditation experience on participant test
performance. There was no main effect of behaviour type,
F(1, 32) = 0.50, p= .49, or meditation experience, F(2, 32)
=0.05,p= .95; however, there was a significant interaction
between behaviour type and meditation experience on test
performance F(2, 32) = 3.41, p=.045,ƞ
p
2
=.18.Basedon
our hypothesis that meditation may cause these effects, we
conducted a post hoc linear regression to examine whether
meditation experience independently predicts Choose Aand
Avoi d Bperformance. Mediation experience did not signifi-
cantly predict Choose Aperformance, F(1, 33) = 1.06, p=.31,
R
2
= .03 (see Fig. 2). However, meditation experience did
predict Avoid Bperformance, F(1, 33) = 5.23, p=.029,R
2
= .14 (see Fig. 3), indicating that as meditation experience
increases, participants do not learn as well from negative feed-
back trials.
Overall, these results indicate that feedback-learning bias
becomes more positive with meditation experience and that
this cannot be explained by age. However, despite an upward
trend, the fact that novice and experienced meditators were not
significantly different on FLB is not supportive of our hypoth-
esis that there would be a significant increase of this effect
with meditation experience. Post hoc analysis revealed a sig-
nificant interaction between approach and avoid performance
and meditation experience, but despite trend-level evidence
suggestive of equal contribution of changes in both Choose
Aand Avoid Bperformance, our sample lacked the power to
further explicate the interaction at the group level.
EEG data
For each participant, the main EEG measure was the scalp
voltage at FCz, averaged across a 100-ms window centred
on the peak of the FRN difference wave between 220 and
320 ms postfeedback. The difference wave was created by
subtracting the true positive-feedback-locked waveform from
the true negative-feedback-locked waveform at FCz.
The nonmeditation group had the highest amplitude FRN
(M=4.07 μV, SE =0.57μV; see Fig. 2a), peaking at 279 ms
poststimulus, followed by the novice group (M=2.70 μV,
SE =0.44μV; see Fig. 2b), peaking at 272 ms poststimulus,
and finally the experienced group (M=1.14 μV, SE =0.27
μV; see Fig. 2c), peaking at 263 ms poststimulus. The FRN
was significantly different from zero for all groups:
nonmeditators, t(11) = 7.09, p< .001; novice meditators,
t(11) = 6.09, p< .001; experienced meditators, t(10) =
4.19, p= .002. An independent ANOVA was performed to
examine the effect of meditation experience on FRN ampli-
tude. As expected, analysis of these data revealed that there
was a main effect of meditation experience on FRN ampli-
tude, F(2, 32) = 10.18, p<.001,ƞ
p
2
= .39 (see Fig. 3). The
timing and frontocentral scalp distribution (see Figs. 4and 5)
of these ERPs is characteristic of the classic FRN (Baker &
Holroyd, 2011; Walsh & Anderson, 2012). There was no ef-
fect of meditation experience on FRN latency, F(32) < 1.
Fig. 1 Choose Aand Avoid Bperformance by group and feedback type. Error bars represent SE
Cogn Affect Behav Neurosci
Planned contrasts revealed that there was a significant dif-
ference between nonmeditators and meditators, t(32) = 3.88,
p<.001,r= .57, and also between novice and experienced
meditators, t(32) = 2.40, p=.022,r= .39. We added partic-
ipant age to the model to determine if this had an effect on the
FRN. The ANOVA revealed that while there was still a sig-
nificant effect of meditation experience on FRN amplitude,
F(2, 31) = 9.87, p<.001,ƞ
p
2
= .39, there was no significant
effect of age, F(1, 31) = 2.92, p=.10,ƞ
p
2
= .09. These results
indicate that the FRN attenuates as meditation experience in-
creases, and this effect is not explained by participant age. It is
also worth noting that although weaker, when false feedback
trials are included in our analysis, the direction of our results
remain unchanged.
As with the FLB data, we decomposed the difference wave
by feedback type to determine whether the group differences
in FRN were driven by an increase or decrease in either the
positive-feedback-elicited or negative-feedback-elicited
waveforms. The condition specific voltage at the FRN differ-
ence wave peak is summarized by feedback type for each
group in Fig. 6and Table 2.A2×3(FeedbackTyp
Meditation Group) mixed-design ANOVA was conducted
and revealed a main effect of feedback type, F(1, 32) =
84.04, p<.001,ƞ
p
2
= 0.72, confirming that negative feedback
elicited a more negative-going waveform than positive feed-
back did in all groups. While there was no main effect of
meditation experience on FRN voltage, F(2, 32) = 0.44, p=
.65, there was a significant feedback type by meditation group
interaction, F(2, 32) = 10.18, p<.001,ƞ
p
2
=.39,indicating
that meditation experience differently affects positive-
feedback-elicited and negative-feedback-elicited voltage.
Post hoc independent ANOVA was performed on the
Fig. 2 Choose Aaccuracy as a function of years of meditation experience.
Fig. 3 Av oid Baccuracy as a function of years of meditation experience
Cogn Affect Behav Neurosci
Fig. 4 Feedback-locked FRN difference wave and condition specific
ERPs at FCz elicited by true feedback trials for (a) the nonmeditation
group, (b) the novice group, and (c) the experienced group. Following
EEG convention, up is negative. Feedback stimulus occurs at zero ms.
Negative-feedback-elicited waveform in blue, positive-feedback elicited
waveform in red. Note the difference wave attenuates between 220 and
320 ms as meditation experience increases. Also shown, scalp distribu-
tion of the difference wave for each group. Peak activity has the
frontomedial distribution and timing characteristic of the FRN. (Colour
figure online)
Cogn Affect Behav Neurosci
condition specific ERPs to attempt to explicate the significant
interaction. Neither the positive feedback ERP, F(2, 32) =
0.05, p=.95,ƞ
p
2
< .01, nor the negative feedback ERP, F(2,
32) = 1.07, p=.36,ƞ
p
2
= .06, revealed a significant effect of
meditation experience on ERP voltage. Thus, despite a possi-
ble trend-level effect of meditation experience on negative
feedback voltage, our sample lacks the power to explain the
interaction further.
As with the FLB, to investigate whether meditation expe-
rience predicts FRN amplitude, a linear regression was con-
ducted (see Fig. 7and Table 3). This analysis reveals a trend of
reduced FRN amplitude as a function of increased meditation
experience. Meditation experience did not significantly pre-
dict condition-specific ERP amplitudes, suggesting that this
effect is driven by changes in both positive-feedback and
negative-feedback processing.
Discussion
The aim of this study was to determine whether or not there
was an association between FAM experience and behavioural
and electrophysiological measures of reinforcement learning.
As predicted, the behavioural data revealed that meditators
were more positively biased feedback learners. These differ-
ences appear to be driven primarily by differences in negative
rather than positive feedback processing. The ERP data re-
vealed that the amplitude of the FRN was significantly smaller
Fig. 5 FRN amplitude at FCz as a function of FAM experience; error bars are SEM. All three groups differed significantly from one another, indicating
that FRN amplitude decreases with meditation experience. *p.05. **p.01. ***p.001
Fig. 6 Condition-specific (true positive and negative feedback) elicited voltages, 50 ms either side of the difference wave peak at FCz, grouped by
meditation experience. Error bars are SEM
Cogn Affect Behav Neurosci
in meditators than in controls, and that these effects scale with
meditation experience. Furthermore, these group differences
cannot be explained by age, disease, or medication.
One potential explanation for these data is that striatal do-
pamine signalling varies as a function of meditation experi-
ence. First, PET has revealed that in experienced meditators,
the act of meditating is associated with a significant increase in
tonic dopamine levels in the striatum (Kjaer et al., 2002).
Second, others using the probabilistic selection task (PST)
reported a shift towards a positive FLB after using l-dopa to
increase dopamine levels in Parkinsonspatients(Franketal.,
2004). Despite some controversy surrounding the reliability of
the PST (Baker, Stockwell, & Holroyd, 2013) and its sensi-
tivity to the pharmacological manipulation of dopamine levels
in the brain in Parkinsons patients (Grogan et al., 2017), there
are a large number of studies demonstrating a trend of in-
creased dopamine levels being associated with a morepositive
feedback-learning bias (FLB) and relatively lower dopamine
levels being associated with more negative FLB (Cox et al.,
2015; Frank & Hutchison, 2009; Frank & Kong, 2008; Frank
&OReilly, 2006; Frank et al., 2004; Klein et al., 2007;
Lighthall, Gorlick, Schoeke, Frank, & Mather, 2013;
Smittenaar et al., 2012; Voon et al., 2010). Third, a recent
PETstudy of healthy individuals has demonstrated that striatal
D1 and D2 signalling respectively predicts learning from pos-
itive and negative outcomes on the PST (Cox et al., 2015). D1
receptors have low affinity and therefore respond more to
phasic DA activity and less to tonic dopamine activity. D2
receptors, meanwhile, have high affinity and so are more
strongly influenced by changes in tonic DA (Frank, 2005).
Since phasic signalling is largely driven by D1 receptor activ-
ity which is correlated with Choose A,notAvoidB,perfor-
mance on the PST (Cox et al., 2015), in the present study if
differencesinphasicsignallingweresolelyormostlyrespon-
sible for the observed differences in feedback processing, we
would expect to see a stronger correlation between meditation
experience and variation in positive feedback processing than
in negative feedback processing. However, the opposite ap-
pears to be the case: meditation experience seems to affect
only negative-feedback learning. This suggests that the effect
is driven by differences in striatal D2 signallingnot D1 sig-
nalling. Since D2 signalling is sensitive to changes in tonic
dopamine or D2 receptor availability, our findings could indi-
cate that one or both of these may increase as meditation
experience increases. In other words, since tonic DA in the
striatum increases during meditation and relatively higher ton-
ic DA manifests as poorer Avoid Bperformance on the PST,
our behavioural data could reflect persistent increases in
striatal tonic DA or D2 receptor availability as a function of
meditation experience.
Table 2 Condition-specific ERP voltages
Condition Group Voltage (μV) SE
Negative feedback Nonmeditators 0.95 1.63
Novice meditators 2.68 1.17
Experienced meditators 3.65 1.04
Positive feedback Nonmeditators 5.02 1.75
Novice meditators 5.38 1.17
Experienced meditators 4.79 0.94
Fig. 7 Linear regression models of the effect of meditation experience on FRN amplitude at FCz
Table 3 Summary of linear regression fit to condition-specific and dif-
ference wave FRN voltages at FCz at difference wave peak
Feedback type Fpr
Negativepositive 4.03 .053 .33
Positive .08 .78 .05
Negative .29 .60 .09
Cogn Affect Behav Neurosci
This interpretation of our behavioural data is supported by
our ERP data. There are a number of competing theories on
FRN/ERN generation (San Martín, 2012; Walsh & Anderson,
2012); however, the highly influential reinforcement learning
theory of the FRN links the FRN to the dopaminergic process-
ing of feedback. The theory holds that errors and negative
feedback result in dips in phasic dopaminergic activity, which
disinhibits the anterior cingulate cortex (ACC) and generates
the negative-going condition-specific component of the FRN,
while positive feedback causes bursts of phasic dopaminergic
activity, which inhibit the ACC and results in the more
positive-going condition-specific component of the FRN
(Holroyd & Coles, 2002; San Martín, 2012;Walsh&
Anderson, 2012). Due to the tonic/phasic DA interaction in
the basal ganglia (Frank, 2005), tonic dopamine levels affect
this process. Higher levels bias toward the Bgo^pathway and
inhibition ofthe ACC, thus attenuated FRN. Lower levels bias
toward the Bno-go^pathway and disinhibition of the ACC,
leading to a larger FRN. For instance, Frank et al. (2005)
found that, similar to our data, healthy participants who were
positively biased (go) feedback learners on the PST also
displayed an attenuated FRN compared with negatively bi-
ased (no-go) learners. Another study measuring the effect of
a Gene × Feedback-Valence interaction on FRN generation
during a gambling task reported a lower amplitude FRN in
participants whose tonic DA levels were presumably higher
due to having the MET/MET allele of the val158met COMT
polymorphism (Marco-Pallarés et al., 2009). This pattern is
also seen in a study which found that at baseline MET homo-
zygotes had a lower amplitude FRN as compared with VAL
carriers, and this pattern reversed when participants were giv-
en sulpiride, a D2 antagonist (Mueller et al., 2014). Taken
together, these findings demonstrate that an attenuated FRN
is observed in healthy individuals who have relatively higher
tonic dopamine levels. In the present study, since FRN ampli-
tude decreased as meditation experience increased, and at a
trend level, at least, this appears to be driven mainly by differ-
ences in negative-feedback processing, our data may be indic-
ative of meditators having higher tonic dopamine than do
nonmeditators.
The possibility that meditation could elevate tonic dopa-
mine over time is intriguing. Here, we would like to postulate
a potential mechanism for future investigation: It may be that
the continuous and sustained application of top-down atten-
tion to (and conflict monitoring of) internal and external sen-
sory inputs during FAM practice results in the activation and
maintenance of a feedback loop between the dlPFC, ACC,
and basal ganglia. Activation of this feedback loop could ac-
count for the release of dopamine and activity in dopaminergic
structures reported during various meditation practices
(Hagerty et al., 2013; Kjaer et al., 2002). Over time, medita-
torsrepeated activation of this loop would result in Hebbian
potentiation of the circuit, which could account for the
increased density in the ACC (Fox et al., 2014; Tang et al.,
2015) and the increased connectivity between the ACC and
striatum that has been reported in recent structural imaging
studies of FAM (Tang et al., 2010). The resulting increase in
dopaminergic synapses would then result in higher tonic do-
pamine levels. These elevated tonic dopamine levels could
partially account for the improved mood reported by medita-
tors (Brown & Ryan, 2003;Davidsonetal.,2003;Hagerty
et al., 2013; Singleton et al., 2014). The effect of disrupting
this dopaminergic corticolimbic-striatal circuitry has been
demonstrated in a rat model where chronic exposure to dopa-
mine receptor antagonists resulted in reductions in ACC vol-
ume over time (Vernon et al., 2014). From this view, our
findings fit well with these previous studies and the notion
of meditation potentiating an attention-activated dopaminer-
gic corticolimbic-striatal feedback loop.
Despite the agreement of our data with the above outlined
view, they are, however, in apparent contrast with the results
of a study demonstrating that trait mindfulness did not predict
the FRN (Teper & Inzlicht, 2014). It is important to note that
their study correlated the FRN to trait mindfulness (as mea-
sured by the Philadelphia Mindfulness Scale; PMS) instead of
FAM experience. Although practices with a FAM component
have been shown to increase trait mindfulness, trait mindful-
ness alone does not necessarily imply any FAM experience
the PMS measures self-reported present-moment awareness
and acceptance which may be influenced by FAM, but is also
argued to be a naturally occurring trait (Cardaciotto, Herbert,
Forman, Moitra, & Farrow, 2008). Using trait mindfulness to
infer FAM experience would be a conflation of trait/state/prac-
tice, as mentioned in the introduction.
In a similar vein, a recent study reported evidence for lower
striatal dopamine signalling in experienced meditators (Kruis,
Slagter, Bachhuber, Davidson, & Lutz, 2016). They recorded
the spontaneous eye-blink rate (sEBR) of 27 experienced
meditators and 118 nonmeditators at rest and found that
experienced meditators compared with nonmeditators had a
significantly lower sEBR, which is associated with low striatal
dopamine. At first glance this suggests that we should expect
meditators to have low tonic dopamine, contrary to our view.
However, in an earlier study Slagter, Georgopoulou, and
Frank (2015) found that sEBR is inversely correlated with
Avoi d Bperformance on the PST, suggesting that low levels
of dopamine are associated with high Avoid Bperformance,
and, vice versa, high tonic dopamine is associated with poorer
Avo i d Blearning, which is consistent with our findings.
Crucially, however, sEBR in both studies is recorded at rest,
not under the task-related cognitive load of the PST. Kruis
et al. (2016) argue that low sEBR at rest in meditators may
be indicative of increased stability (requiring lower levels of
dopamine),while under cognitive load (i.e., task performance)
this would imply more cognitive flexibility related to higher
dopamine levels. Our data support this possibility; however,
Cogn Affect Behav Neurosci
our paradigm did not provide the resting state needed for
us to examine sEBR in our sample. Although further work
is needed to investigate whether this rest/load hypothesis
can explain the apparent discrepancy between our find-
ings and those of Kruis et al., these studies provide evi-
dence that striatal dopamine signalling may be altered in
experienced meditators.
Despite the our data demonstrating a relationship between
total amount of mediation practice and differences in
reinforcement learning and feedback processing, further
research is needed to determine whether these differences
are caused by meditation. Given the literature demonstrating
neuroplastic changes in the dopamine system with meditation
practice, and that meditators need to practice to become
proficient, we find the idea that meditation is responsible for
these changes most credible. However, since we did not
manipulate meditation experience in the current study, we
cannot rule this out as an alternative explanation. Indeed,
Kruis et al. (2016)failedtofindaneffectofan8-weekmed-
itation intervention on sEBR in a sample of 36 naïve partici-
pants. They also failed to find a relationship between medita-
tion experience in experienced meditators and sEBR. This
could be seen as evidence contrary to our hypothesis that
meditation causes differences in striatal dopamine signalling.
It may be that preexisting differences in striatal dopamine lead
meditators to self-select for meditation practice. However, this
would not explain the relationship in the present study be-
tween total amount of practice time and Avoid Bperformance
or FRN amplitude. It is possible that their intervention was not
long enough, a point Kruis et al. raise, and that their experi-
enced meditators had too much experience (minimum experi-
ence in Kruis et al. was 1,439 hours). For comparison, our
sample ranged from 4 to 4,004 hours of practice, with a me-
dian of 260 hours. It may be that our data captures a critical
cross-section in meditation-induced plasticity missed in Kruis
et al.s sample. Another possibility is that type of meditation
mayalsoplayarole.Mindfulness-based stress reduction
(Kabat-Zinn, 1990), used in the Kruis et al. study, combines
both focused attention (FA) and open monitoring meditation
(OM) styles (Lutz et al., 2008), as well as yoga. The emphasis
on single pointed attention in FA meditation (e.g., samatha,
jhana meditation practices) may be more strongly associated
with classically dopaminergic phenomenological effects of
intense pleasure (e.g., Hagerty et al., 2013).
Finally, despite a growing body of evidence implicating
alterations in dopaminergic activity in meditators, it is possible
that our data could be explained by differences in other neu-
rotransmitter systems. For example, while dopamine is be-
lieved to play a key role in FRN generation, increased levels
of norepinephrine have been shown to amplify the ERN while
serotonin may also have a modulatory effect (Jocham &
Ullsperger, 2009). Serotonin has also been shown to influence
the processing of reward value (Seymour, Daw, Roiser,
Dayan, & Dolan, 2012).
Thus there is a clear need to address these questions in
future work which manipulates both striatal dopamine and
(preferably focused attention) meditation experience in a me-
dium to long-term randomized controlled intervention.
Determining whether meditation practice elevates tonic dopa-
mine levels is crucial to our understanding of a widespread
behaviour, how it may interact with disease and drugs, and
presents the possibility of potentially low-cost behavioural
interventions which could be used as an adjunct to pharmaco-
logical treatment of certain dopaminergic disorders.
In closing, the present study is, to the best of our knowl-
edge, the first study to demonstrate that reinforcement learn-
ing and FRN amplitude vary as a function of total meditation
experience. To explain these findings in the context of the
literature, we posit a theory that meditation causes increases
in tonic dopamine levels (or D2 receptor availability) in the
striatum. We then consider alternate explanations, and thus
reveal the need for further study to determine whether we
might increase our tonic dopamine levels by learning to pay
better attention.
Author contributions statements P.K. conceived and designed the exper-
iment, collected and analysed the data, and wrote the manuscript. B.O.
conceived and designed the experiment, coded the task, supervised the
project, and wrote the manuscript.
Appendix: Reported effects are robust
to alternate quantifications of FAM
experience
Since retrospective self-reported mediation experience can
vary in intensity that is not entirely captured by the number
of years of practice alone, we investigated the effect of two
other quantifications of meditation experience on our results.
The first was to multiply participants years of experience with
their self-reported weekly meditation frequency and duration
to get an estimate of their total hours of experience. Our third
method calculates meditation experience, in hours, in the last
6 months, assuming self-report frequency and duration was
constant. In all cases, a median split on meditation experience
was used to separate participants into the novice and experi-
enced meditators categories. The results of these different ap-
proaches can be seen in Table 4. Ultimately there is no perfect
way to retrospectively assess the amount or quality of medi-
tation experience, and this is a limitation of this type of study.
However, it is worth noting that for both measures total years
of experience accounted for the most variance in the data, and
also that the effects reported were relatively robust to how
meditation experience was quantified.
Cogn Affect Behav Neurosci
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