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Post-training Meditation Promotes Motor Memory Consolidation


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Following training, motor memory consolidation is thought to involve either memory stabilization or off-line learning processes. The extent to which memory stabilization or off-line learning relies on post-training wakeful periods or sleep is not clear and thus, novel research approaches are needed to further explore the conditions that promote motor memory consolidation. The present experiment represents the first empirical test of meditation as potential facilitator of motor memory consolidation. Twelve adult residents of a yoga center with a mean of 9 years meditation experience were trained on a sequence key pressing task. Three hours after training, the meditation group completed a 30 min session of yoga nidra meditation while a control group completed 30 min of light work duties. A wakeful period of 4.5 h followed meditation after which participants completed a test involving both trained and untrained sequences. Training performance did not significantly differ between groups. Comparison of group performance at test, revealed a performance benefit of post-training meditation but this was limited to trained sequences only. That the post-training meditation performance benefit was specific to trained sequences is consistent with the notion of meditation promoting motor memory consolidation as opposed to general motor task performance benefits from meditation. Further, post-training meditation appears to have promoted motor memory stabilization as opposed to off-line learning. These findings represent the first demonstration of meditation related motor memory consolidation and are consistent with a growing body of literature demonstrating the benefits of meditation for cognitive function, including memory.
Content may be subject to copyright.
fpsyg-07-01698 October 27, 2016 Time: 16:44 # 1
published: 01 November 2016
doi: 10.3389/fpsyg.2016.01698
Edited by:
Krishna P. Miyapuram,
Indian Institute of Technology
Gandhinagar, India
Reviewed by:
Attila J. Kovacs,
University of Wisconsin–La Crosse,
Arnaud Boutin,
Research Center at the Geriatric
Institute of the University of Montreal,
Maarten A. Immink
Specialty section:
This article was submitted to
Movement Science and Sport
a section of the journal
Frontiers in Psychology
Received: 01 July 2016
Accepted: 14 October 2016
Published: 01 November 2016
Immink MA (2016) Post-training
Meditation Promotes Motor Memory
Front. Psychol. 7:1698.
doi: 10.3389/fpsyg.2016.01698
Post-training Meditation Promotes
Motor Memory Consolidation
Maarten A. Immink*
School of Health Sciences, Centre for Sleep Research and Cognitive Neuroscience Laboratory, University of South Australia,
Adelaide, SA, Australia
Following training, motor memory consolidation is thought to involve either memory
stabilization or off-line learning processes. The extent to which memory stabilization
or off-line learning relies on post-training wakeful periods or sleep is not clear and thus,
novel research approaches are needed to further explore the conditions that promote
motor memory consolidation. The present experiment represents the first empirical
test of meditation as potential facilitator of motor memory consolidation. Twelve adult
residents of a yoga center with a mean of 9 years meditation experience were trained
on a sequence key pressing task. Three hours after training, the meditation group
completed a 30 min session of yoga nidra meditation while a control group completed
30 min of light work duties. A wakeful period of 4.5 h followed meditation after
which participants completed a test involving both trained and untrained sequences.
Training performance did not significantly differ between groups. Comparison of group
performance at test, revealed a performance benefit of post-training meditation but this
was limited to trained sequences only. That the post-training meditation performance
benefit was specific to trained sequences is consistent with the notion of meditation
promoting motor memory consolidation as opposed to general motor task performance
benefits from meditation. Further, post-training meditation appears to have promoted
motor memory stabilization as opposed to off-line learning. These findings represent the
first demonstration of meditation related motor memory consolidation and are consistent
with a growing body of literature demonstrating the benefits of meditation for cognitive
function, including memory.
Keywords: Meditation, memory consolidation, motor learning, sequence learning, human performance, learning,
Motor memory consolidation has been described as processes that provide for either motor
memory stabilization or further off-line learning in the period that follows training (Walker et al.,
2003a;Robertson et al., 2004a;Press et al., 2005). Memory stabilization related consolidation
has been demonstrated by reduced susceptibility to interference from exposure to other tasks.
(Brashers-Krug et al., 1996;Shadmehr and Brashers-Krug, 1997;Muellbacher et al., 2002;Walker
et al., 2003a). Consolidation resulting in off-line learning has been demonstrated as improvements
in performance gains following a period of time that does not involve training (Walker et al., 2002,
2003a,b). This off-line learning form of consolidation appears to be specific to tasks or effectors
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Immink Meditation and Motor Memory Consolidation
that have been trained since off-line learning does not appear to
benefit transfer performance for new tasks or untrained effectors
(Fischer et al., 2002;Korman et al., 2003).
Whether consolidation processes reduce susceptibility
interference from competing memories or provide for off-line
learning has been argued to depend on temporally dissociable
stages of consolidation (Walker et al., 2003a,b;Walker and
Stickgold, 2004). Training initiates learning resulting in large
improvements in performance. However, following training,
nascent motor memory is thought to be in a fragile state due
to susceptibility for disruption, competition or interference
(Brashers-Krug et al., 1996;Shadmehr and Brashers-Krug, 1997;
Krakauer and Shadmehr, 2006). In the wakeful period that
follows training, between 10 min to 6 h (Shadmehr and Brashers-
Krug, 1997;Walker et al., 2003a), motor memory is thought to
undergo a first stage of consolidation where it is stabilized against
interfering or competing memories. Importantly, this first
stage does not result in further performance improvements but
supports maintenance of performance relative to end of training
levels. The second stage of consolidation is thought to occur
during the period of sleep (Karni et al., 1994;Stickgold et al., 2000;
Fischer et al., 2002;Walker et al., 2002) or napping (Mednick
et al., 2003;Nishida and Walker, 2007) that follows training
and it is this sleep-dependent consolidation stage that enhances
motor memory providing off-line gains in performance. Despite
the support for the two stages of consolidation, there is some
debate against this view (Peigneux et al., 2005;Brawn et al., 2010).
Even if motor memory has undergone stages of consolidation
that stabilize and enhance the memory, motor memory may
once again be rendered fragile to interference by re-introduction
of training, involving recall from long-term memory, and
this process of re-training, memory instability and memory
re-consolidation is thought to be important for the ongoing
development and refinement of motor skills (Walker et al.,
2003a;Monfils et al., 2009).
Further research is needed to provide a greater understanding
of motor memory consolidation. For example, it is not yet
clear how motor memory is stabilized following training and
what factors mediate these stabilization processes (Korman
et al., 2007). The introduction of memory interference following
training has been the prevalent research paradigm used to
address motor memory stabilization and clearly other research
approaches are needed in order to gain a broader understanding
of what memory stability entails. The common paradigm for
investigating off-line learning has involved comparison of test
performance to end of training performance with respect to
whether a period of sleep or wakefulness occurred between
training and test. Using this paradigm, studies have demonstrated
that off-line motor performance gains rely on a period of
sleep or more specifically, on certain stages of sleep (Walker
et al., 2002, 2003a,b;Walker and Stickgold, 2004). However,
the requirement of sleep for off-line gains has been questioned
and the effects of specific sleep stages on consolidation has
been suggested to be dependent on the type of motor task
(Stickgold, 2005;Marshall and Born, 2007;Squire, 2009). The
uncertainty in this literature includes demonstrations of off-line
gains when only a wakeful period has followed training (Denny
et al., 1955;Cohen et al., 2005;Brown and Robertson, 2007).
Further, sleep has not always provided off-line gains (Brawn et al.,
Another debate that has surrounded motor memory
consolidation relates to whether or not the participant practiced
with awareness of underlying motor task features or what task
features are being learned. It has been proposed that when
motor tasks are practiced under explicit conditions or with
awareness of task features, motor memory consolidation requires
a period of sleep (Robertson et al., 2004b). In contrast, practice
under implicit conditions or with little or no awareness of
task features, a wakeful period following practice is sufficient
for consolidation (Robertson et al., 2004b, 2005;Press et al.,
2005). However, not all findings align with this notion as
consolidation of implicitly learned motor tasks has been
demonstrated after sleep (Maquet et al., 2003;Peigneux
et al., 2003) and off-line performance gains after a wakeful
period have been demonstrated with explicit motor practice
conditions (Spencer et al., 2006). More broadly, delineation
of implicit versus explicit learning has not been entirely clear
(Cleeremans et al., 1998;Frensch and Runger, 2003) leading
some to argue that awareness is not the key distinguishing
factor between these modes of motor learning (Whittlesea
and Dorken, 1997) while others have argued for abandoning
this delineation altogether (Willingham and Preuss, 1995;
Cleeremans, 1997). Rather than being distinct processes, it
might be that implicit and explicit learning processes interact
or work in parallel during motor task acquisition. For example,
Willingham and Goedert-Eschmann (1999) demonstrated
that sequence learning under explicit or implicit instruction
conditions resulted in equivalent learning outcomes. Thus, it
is clear that novel research approaches are needed to further
the understanding of what motor memory consolidation entails
and requires with respect to memory stabilization and off-line
Meditation might represent a novel approach to further
our understanding of motor memory consolidation.
Meditation has been defined as a complex set of cognitive
processes (Newberg and Iversen, 2003;Cahn and Polich,
2006;Sperduti et al., 2012;Malinowski, 2013;Nash and
Newberg, 2013) that are brought under voluntary control in
a comfortable, relaxed but alert state (Craven, 1989;Walsh
and Shapiro, 2006). The unique and complex cognitive
processes and states associated with meditation highlight
the importance of investigating meditation as a valuable
opportunity to further understand of brain, cognition and
consciousness (Cahn and Polich, 2006;Raffone and Srinivasan,
Meditation has been shown to influence or enhance
cognitive function (Cahn and Polich, 2006;Tang et al.,
2007;Zeidan et al., 2010;Lippelt et al., 2014;Colzato et al.,
2015a,b, 2016). Specifically for memory, regular meditators
outperform demographically matched adults on both short
and long-term memory tasks (Lykins et al., 2012). In addition,
brief periods of meditation practice have been shown to
benefit performance on memory tasks (Mrazek et al., 2013;
Xin et al., 2013;Quach et al., 2016). Demonstrations that
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Immink Meditation and Motor Memory Consolidation
meditation specifically enhances memory suggest that engaging
in meditation following training might benefit memory
processes including those associated with motor memory
Further support for the potential of meditation to benefit
motor memory consolidation is based on studies identifying
neurophysiological processes associated with meditation that
seem particularly relevant to memory consolidation. For
example, neuroimaging work by Kjaer et al. (2002) has
demonstrated increases in striatal dopamine, a neurotransmitter
thought to be important for regulation of cognitive function
(Nieoullon, 2002), working memory (Cools and D’Esposito,
2011) and memory consolidation (Karunakaran et al., 2016),
following a single session of meditation. Findings from Kruis
et al. (2016), which investigated changes in dopamine activity
based spontaneous eye blink rate, suggest that meditation
effects on dopamine might require long term practice with
meditation techniques. A second point of support for the role
of meditation in memory consolidation is based on research
investigating the effects of meditation on cortical activity
using electroencephalography (EEG) techniques. This work has
demonstrated increases in theta band frequencies in anterior
and frontal regions (Baijal and Srinivasan, 2010;Lomas et al.,
2014, 2015). These findings are of particular interest with
respect to reports of post-training gains in motor performance
following a bout of theta-wave training using EEG neurofeedback
(Reiner et al., 2014;Rozengurt et al., 2016). Finally, a basis
for considering a role of meditation in memory consolidation
lies in findings linking meditation to increased activity in the
hippocampus (Lou et al., 1999;Lazar et al., 2000;Newberg
and Iversen, 2003;Luders et al., 2009), a region important for
motor sequence memory consolidation (Albouy et al., 2008,
2012, 2013), including during wakefulness (Karlsson and Frank,
There is empirical evidence to suggest that experiencing
meditation and its associated cognitive processes and states
following training can lend benefits for motor memory
consolidation. The present experiment set out to test this
proposition by having experienced meditators complete a single-
session of meditation in the hours that followed a bout
of motor sequence learning. Later on the same day, test
performance on trained and untrained (novel) sequences was
compared to a group of experienced meditators, who did
not complete meditation after training. It was predicted that
if meditation provides for consolidation in terms of motor
memory stabilization, then post-training meditation would
benefit trained sequence performance relative to the control
group and trained sequence performance in the meditation
group would be comparable between the end of training and
test. On the other hand, if meditation engenders consolidation
related to off-line learning, then performance would be improve
between the end of training and test when compared for
those who completed meditation after training. Finally, if
consolidation associated with meditation is specific to trained
tasks, then the retention or improvement of performance
would only be observed with trained sequences and not novel
Twelve right-handed individuals (seven females; aged
35.6 ±9.9 years) participated in the present experiment,
which was conducted at a yoga center located in New South
Wales, Australia, where the participants resided. Participants
were experienced meditators with a mean of 9.0 years (±8.6,
range 2–35) of self-reported regular meditation practice and
a mean of 190 min (±92.9, range 90–420) of self-reported
weekly meditation practice. All participants provided written
informed consent prior to initiating their participation
and the research protocol for this study was approved by
the University of South Australia Human Research Ethics
Apparatus and Stimuli
Stimuli for the motor sequence task were presented on a 48.3 cm
display with 1024 ×768 pixel resolution and a refresh rate of
75 Hz. A PC with IntelR
CoreTM 2 Quad Q8300 CPU processor
running at 2.53 GHz running E-Prime 2 (Psychological Software
Tools Inc., Sharpsburg, PA, USA) on Windows 7 controlled
stimulus presentation and recorded key press responses via a
QWERTY keyboard. Participants sat with a viewing distance of
60 cm to the display but this was not strictly enforced. All stimuli
were presented a black background field. At the start of each trial,
an alerting stimulus based on a row of six dashes ( _ _ _ _ _ _ )
was presented in the center of the screen. The alerting stimulus
represented the spatial position of the three left hand response
keys (S, D, F, pressed by the ring, middle and index fingers of the
left hand, respectively) and the three right hand response keys
(J, K, L, pressed by the index, middle and ring fingers of the
right hand, respectively) and also indicated the location where the
response stimulus would be subsequently presented. Each dash
was 2visual angle in length, the left and right set of dashes were
spaced 4apart and dashes within each set was spaced 1apart.
Following presentation of the alerting stimulus, the response
stimulus was then presented and this consisted of a set of five
digits, each 2visual angle in size and numbering between 1
and 5. These digits were each presented above a corresponding
key position (e.g., 5 1 3 - 4 2 ), representing the order that each
response key was to be pressed so that the key position with a “1”
above meant that key was to be pressed first, the position with a
“2” was the second key to be pressed and so on up to the fifth key
of the sequence was. The key position with a “-” above indicated
that key was not to be pressed in the sequence.
At 08:00 h (Figure 1) participants complete a training phase
on the motor sequence task (Immink and Wright, 1998) in
an administrative office of the yoga center that included a
workstation with the apparatus and where the participant was
alone with the experimenter. The commencement of the training
phase was a mean of 3.1 (±0.46) h after awakening from a
mean of 7.0 (±0.88) h of sleep. All participants had participated
in a 90-min yoga class at 05:30 as was part of the yoga
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FIGURE 1 | Procedure for training, meditation and test phases.
center’s daily routine. To start the training phase, participants
received written instructions for the sequence production task.
The instructions included information describing mapping of
fingers with response keys and mapping of numeric digits with
sequence ordering of response keys. In addition, the instructions
indicated that 5-key sequences would be produced in each trial
and that the aim of the task was to enter the sequence as
accurately and as fast as possible. Following, presentation of
instructions, participants completed eight familiarization trials
using a practice sequence (S–L–D–K–F) to ensure participants
understood the mapping between the stimuli and the sequence
response. If by the end of the familiarization trials, participants
could not accurately complete two trials, the familiarization trials
were completed again. Next participants completed training on
three unique sequences (D–L–F–K–S, K–D–L–F–J, J–S–K–D–L)
over four blocks of 30 trials, where in each block, sequences were
presented in a pseudo-random fashion based on randomizing the
order every three trials without repetition of the same sequence
on successive trials. At the start of the trial, participants were
presented with a “Ready” message in the center of the screen
for 2,000 ms. Next, the alerting stimulus was presented for a
random delay period between 1,500 and 2,500 ms. Then, the
response stimulus was presented and remained on the screen
until the participant pressed the fifth key of the sequence. If one or
more key presses in the sequence were incorrect, the participant
received a response error message on the screen for 1,000 ms
and the trial was repeated. Following accurate completion of the
sequence, visual augmented feedback was presented for 1,500 ms
on the monitor indicating that the response was accurate and
also indicating their response time for the trial, which was based
on the latency between presentation of the response stimulus
and pressing the fifth key in seconds. While participant feedback
involved response time, for the purpose of this experiment,
sequence performance was recorded as reaction time (RT), the
latency between response stimulus presentation and pressing the
first key, and sequence entry time (SET), the latency between
pressing the first key and the fifth key. Both RT and SET
were recorded in milliseconds. A sixty-second rest interval was
provided following blocks 1, 2, and 3. The time to complete the
training phase was about 60 min.
Participants were randomly allocated to one of two
experimental conditions that took place between 12:00
and 12:30 on the same day as the training and test phases.
Participants allocated to the meditation condition participated
in a 30-min yoga nidra meditation while participants allocated
to the control condition participated in 30 min of light
work duties (termed karma yoga) at various locations of
the yoga center including the kitchen, gardening, grounds
maintenance, and housekeeping. A historical account of yoga
nidra meditation, existing descriptions of the technique and
associated physiological correlates have been reviewed elsewhere
by Parker et al. (2013). Based on the taxonomy proposed by
Nash and Newberg (2013) and for the purpose of this study,
this meditation was classified as being a cognitive-directed
type of meditation because of its emphasis on purposefully
attending to body sensations and generating body experiences
and visual imagery. Its aim is described as inducing a state of deep
relaxation while maintaining alertness. The 30-min technique
includes eight stages: (1) preparation and internalizing attention,
(2) mental repetition of a personal resolution statement or
affirmation, (3) purposeful direction of attention to body regions,
(4) awareness of sensations and experiences associated with
breathing naturally, (5) imagining opposite body experiences
(e.g., heavy vs. light, hot vs. cold), (6) visualization of natural
scenes (e.g., a forest, waves on the beach), (7) mental repetition
of a personal resolution statement or affirmation and (8)
externalizing attention and closure as described by (Saraswati,
2001). Yoga nidra meditation was practiced while keeping the
body still in a supine position with the eyes closed and verbal
instructions were provided by an experienced instructor who
also resided at the yoga center but who was not involved in
the present experiment. Participants completed yoga nidra
meditation in a group class format with other individuals who
were also residents at the center. Participants in both conditions
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were informed that they would participate in one of two types of
yoga activities between 12:00 and 12:30 but were not informed of
what the alternative activity was. Except for their mid-day yoga
activity, participants were instructed to not participate in any
other type of yoga or meditation activity following the training
At 17:00 h, participants completed a test phase involving
performance of the three trained sequences and two untrained
sequences (L-S-F-K-J, S-L-J-F-D) in a block of 20 trials with a
pseudo-random order every five trials with a condition of no
sequence repetition on successive trials. Reminder instructions
about the task were presented in the test phase, and the trial
procedure was the same as that described for the training phase
with the exception that here, no response feedback was provided.
Instead, participants were presented with an interval of 1,500 ms
before the next trial. The test phase was about 20 min in duration.
Group Differences in Participant
Characteristics and Performance Error
To evaluate if random allocation of participants to meditation or
control conditions resulted in group differences for participant
characteristics, independent t-test analyses were conducted
indicating no significant group differences for age (p=0.13),
years of self-reported meditation experience (p=0.25), and
weekly self-reported volume of meditation practice (p=0.12).
Chi-square analysis indicated no significant group differences in
gender distribution (p=0.56). In addition, group differences
for the number of error trials that were re-run in training was
tested using independent t-test analyses. In training, the number
of error trials for the meditation group (M=7.2, SD =5.2)
was not significantly different than the number of error trials
for the control group (M=6.2, SD =5.6; p=0.76). Group
differences in error trials for trained (meditation, M=1.8,
SD =1.8; control, M=2.3, SD =2.4) and untrained (meditation,
M=1.5, SD =0.83; control, M=1.8, SD =3.0) sequences
at test was analyzed using a 2 (Group: meditation, control) ×2
(Sequence: trained, untrained) analysis of variance (ANOVA)
with repeated measures on the second factor. This analysis
revealed no significant main effects of Group (p=0.71) or
Sequence (p=0.43) and no significant Group ×Sequence
interaction (p=0.89). Accurate trials where RT or SET
performance was 3 standard deviations above the participant
mean were classified as outlier data and these trials were removed
from further analyses. In training, 1% of the trials were removed
while at test 1.3% of trials were removed.
Training Performance
Mean RT and SET for accurate trials was calculated for each
participant according to eight trial blocks. As these eight trial
blocks are based on dividing each of the four training blocks
in half, they allowed evaluation of performance in the first half
(or first 15 trials) versus the second half (trials 16–30) of each
training block. Each of the eight trial blocks was based on 15 trials,
or five trials of training on each of the three trained sequences.
Participant mean RT and SET were separately submitted to 2
(Group: meditation, control) ×8 (Trial Block: 1–8) ANOVA with
repeated measures on the second factor. For RT, the main effect
of Group (p=0.95) and the Group ×Trial Block interaction
(p=0.89) were not significant while, the main effect of Trial
Block was significant, F(7,70) =13.7, p<0.0001, η2
Post hoc analysis using Duncan’s multiple range test identified the
source of the main effect to be based on RT being significantly
longer at Trial Block 1 and Trial Block 2 but RT was not
significantly different between Trial Blocks 3 to 8. Analysis of
SET revealed no significant main effect of Group (p=0.94)
and no significant Group ×Trial Block interaction (p=0.97).
A significant main effect of Trial Block for SET, F(7,70) =12.6,
p<0.0001, η2
p=0.56, was based on significantly longer SET
at Trial Blocks 1 and 2, which did not significantly differ, than
subsequent Trial Blocks. RT and SET performance at training are
presented in Figures 2 and 3, respectively.
Test Performance
Mean RT and SET for accurate test trials was calculated for each
participant for trained and untrained sequences. RT and SET
were separately submitted to 2 (Group: meditation, control) ×2
(Sequence: trained, untrained) ANOVA with repeated measures
on the second factor. Analysis of RT revealed no significant main
effect of Group (p=0.58) or Sequence (p=0.11) while the
Group ×Sequence interaction was significant, F(1,10) =8.35,
p<0.05, η2
p=0.45. Post hoc analysis revealed that RT for the
meditation group was significantly shorter for trained sequences
(M=1,437.9 ms, SD =422.5) than for untrained sequences
(M=1,858.5 ms, SD =346.8). Meditation group RT for
untrained sequences was not significantly different than control
group RT for trained (M=1,888.0 ms, SD =715.9) and
untrained sequences (M=1,786.7 ms, SD =791.3), which
also did not significantly differ. RT for trained sequences in
the meditation group was significantly shorter than trained
and untrained RT in the control group. For SET, the main
effect of Group was not significant (p=0.59) but the main
effect of Sequence was significant, F(1,10) =25.94, p<0.001,
p=0.72. This main effect was superseded by a significant
Group ×Sequence interaction, F(1,10) =17,61, p<0.01,
p=0.64. For the meditation group, SET was significantly
shorter with trained sequences (M=988.0 ms, SD =405.0)
than untrained sequences (M=1,307.0 ms, SD =343.1) and
was significantly shorter than SET for trained (M=1,281,1 ms,
SD =515.4) and untrained (M=1,311.9 ms, SD =568.1)
sequences in the control group. SET for untrained sequences
in the meditation group and trained and untrained sequences
in the control group did not significantly differ. RT and SET
performance at test are presented in Figures 2 and 3, respectively.
Trained Sequence Performance at Test
Compared to End of Training
To compare test performance relative to end of training
performance within each experimental group, the percentage
change (Kuriyama et al., 2004) in RT and SET was separately
calculated for each participant. The percentage change was
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FIGURE 2 | Reaction time performance at training and test. Error bars represent standard error of the mean.
FIGURE 3 | Sequence entry time performance at training and test. Error bars represent standard error of the mean.
calculated by subtracting trial block 8 from test performance,
dividing by trial block 8 and then multiplying by 100,
where positive percentage values reflect a proportional slowing
in RT and SET at test and negative percentage values
reflect performance gains (i.e., shorter sequence initiation and
completion times) at test with these measures. Univariate analysis
of the percentage change in RT revealed a significant Group
effect, F(1,10) =5.41, p<0.05, η2
p=0.35. The percentage
change in RT for the meditation group (M=0.2%, SD =24.2)
was significantly lower than the control group (M=34.2%,
SD =26.3). Furthermore, the percentage change in RT for
the meditation group was not significantly different than 0%
(p=0.99) while for the control group the percentage change
in RT was significantly higher than 0% (p=0.025). Univariate
analysis of percentage change in SET between the meditation
(M=3.6%, SD =30.5) and control (M=37.7%, SD =21.9)
approached but did not reach significance (p=0.052). The
meditation group’s percentage change in SET did not significantly
differ from 0% (p=0.78) while percentage change in SET in the
control group was significantly higher than 0% (p=0.008).
The purpose of the present experiment was to investigate if
meditation can promote motor memory consolidation processes
following training. Three hours after completion of training on
three key-pressing sequences, experienced meditators completed
either a 30-min period of meditation or light work duties as the
control condition. Then, 4.5 h after completion of meditation or
control conditions, motor memory consolidation was tested with
three previously trained and two untrained sequences.
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Meditation does appear to promote motor memory
consolidation since at test, trained sequence RT and SET
was significantly shorter for the meditation group than the
control group. RT reflects response planning processes and
SET reflects response execution processes (Diedrichsen and
Kornysheva, 2015) and in this case, both types of processes
appear to have benefited from post-training meditation. More
specifically, the observed benefits of post-training meditation
for test performance can be explained by considering that
meditation promoted memory consolidation to the extent
that motor chunking was enhanced. Motor chunking, where
successive movement elements are concatenated into a response
unit (Verwey, 1996, 1999), is thought to be an important
component of sequence learning and associated performance
improvements (Boutin et al., 2010, 2014;Verwey and Abrahamse,
2012). The meditation group demonstrated significantly shorter
RT and SET performance on trained sequences than on
untrained sequences. In contrast, test performance in the
control group did not significantly differ between trained and
untrained sequences. This pattern of results suggests that the
consolidation promoted by meditation was limited to previously
trained sequences and did not afford transfer to the performance
on untrained sequences, which is consistent with the notion
that consolidation is specific to trained tasks and does not
benefit transfer performance (Fischer et al., 2002;Korman
et al., 2003). The absence of transfer effects in the meditation
group is consistent with the interpretation that greater motor
chunking was a product of the consolidation processes promoted
by meditation. The performance benefit of motor chunking
is sequence specific because concatenated response units are
derived from the specific order of the movement elements that
have been learned. The untrained sequences at test had different
sequence structures to the trained sequences, which prevented
utilization of trained sequence chunks with untrained sequences
(Verwey et al., 2009).
That meditation group test performance on untrained
sequences did not differ from control group test performance
on trained and untrained sequences appears to rule out the
explanation that meditation provided a general advantage for test
performance, through greater alertness or processing capacity,
for example. Had meditation provided general performance
benefits then performance on both trained and untrained
sequences would have favored the meditation group. Instead, the
benefits of meditation for test performance are limited to trained
sequences giving strength to the interpretation meditation
promoted motor memory consolidation (Fischer et al., 2002;
Korman et al., 2003). These results thus represent the first
demonstration of motor memory consolidation following a
single-session of meditation.
In the meditation group, performance on trained sequences at
test is comparable to that present at the end of training. Thus, it is
important to note that meditation did not promote consolidation
in the sense of ‘off line’ performance gains (Walker et al., 2002,
2003a,b;Robertson et al., 2004a). That meditation did not provide
‘off line’ learning like sleep (Walker et al., 2002, 2003a,b) and
wakeful periods (Denny et al., 1955;Cohen et al., 2005;Brown
and Robertson, 2007) suggests that the form of consolidation
observed at present following meditation is closer to the notion
of stabilizing newly acquired information (McGaugh, 2000;
Robertson et al., 2004a), which can occur independently from
sleep (Donchin et al., 2002) in the first 6 h that follow training
(Shadmehr and Brashers-Krug, 1997;Walker et al., 2003a).
Because the test included both trained and untrained sequences,
the potential existed for untrained sequences to interfere with
the performance of trained sequences. This interference might
explain why test performance in the control group did not
differ between trained and untrained sequences and why trained
sequence performance for the control group at test appears to
revert back to levels observed at the start of training. The effects
of interference at test might have been exacerbated for the control
group by the fact that motor memory for trained sequences
was rendered more fragile following memory recall activity
necessary at test (Walker et al., 2003a;Monfils et al., 2009).
Trained sequence test performance for the meditation group
did not suffer from the same level of recall induced memory
fragility or interference from untrained sequences because of the
consolidation that the meditation afforded.
The present demonstration of motor memory consolidation
effects following meditation are based on a small sample of
experienced meditators, even though quite substantial effect sizes
were observed in test effects (Levine and Hullett, 2002). The
small sample reflected the limited availability of experienced
meditators who resided at the yoga center at the time of this
experiment. Inclusion of these residents was an advantage for
the present experiment since for the most part, participants
shared similar lifestyle behaviors such as regular practice of yoga
and meditation and daily schedules in terms of waking, meal
and sleep times. Accordingly and importantly, no differences
in training performance were observed between groups. That
consolidation effects following meditation were shown with
experienced meditators brings in to question what extent of
meditation experience or training might be required to derive
these types of consolidation benefits. Future research should
address this question as well as test the generalizability of
these effects by including a larger and more representative
Delineation of the mechanisms underlying meditation-
based motor memory consolidation was beyond the scope of
the present experiment but nonetheless the present findings
pose important questions for future research. In the present
experiment, it was assumed that those in the meditation group
were able to reach high levels of engagement in the meditation
technique given the high level of meditation experience in
these participants. However, meditation engagement or depth
of meditation experience was not objectively measured, which
does somewhat limit interpretation of the influence of meditation
on consolidation. In addition, it is not possible to rule out the
possibility that participants might have slept during all or parts
of the meditation, even as the explicit aim of the meditation
technique is to remain awake and aware while following the
instructions. Measurement of neural correlates of meditation,
through EEG, for example, is thus needed in future work to
characterize meditation as an agent for consolidation and to
ensure consolidation effects are not attributable to those effects
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Immink Meditation and Motor Memory Consolidation
demonstrated with potentially similar agents such as napping
(Mednick et al., 2003;Korman et al., 2007;Nishida and Walker,
The present results provide the first demonstration of
meditation-based promotion of motor memory consolidation
in a wakeful period. Specifically, the introduction of meditation
3 h after training appears to have promoted motor memory
stabilization as opposed to off-line learning. This stabilization
was only evident in previously trained motor task variations
suggesting that meditation-based promotion of motor memory
consolidation does not support transfer performance. Research
is needed to further investigate meditation promotion of motor
memory consolidation with a larger sample size representing a
range of meditation experience levels. Furthermore, the neural
correlates of the meditation experienced after training need to be
described in order to understand the underlying mechanisms by
which meditation promotes motor memory consolidation.
MI was responsible for conceiving, developing, and conducting
the experiment reported in this manuscript and MI drafted all
sections of this manuscript.
The author would like to thank Mangrove Yoga and the Academy
of Yoga Science in New South Wales, Australia for their
assistance in this research including recruitment of participants
and provision of facilities.
Albouy, G., King, B. R., Maquet, P., and Doyon, J. (2013). Hippocampus
and striatum: dynamics and interaction during acquisition and sleep-related
motor sequence memory consolidation. Hippocampus 23, 985–1004. doi:
Albouy, G., Sterpenich, V., Balteau, E., Vandewalle, G., Desseilles, M., Dang-
Vu, T., et al. (2008). Both the hippocampus and striatum are involved
in consolidation of motor sequence memory. Neuron 58, 261–272. doi:
Albouy, G., Sterpenich, V., Vandewalle, G., Darsaud, A., Gais, S., Rauchs, G., et al.
(2012). Neural correlates of performance variability during motor sequence
acquisition. Neuroimage 60, 324–331. doi: 10.1016/j.neuroimage.2011.12.049
Baijal, S., and Srinivasan, N. (2010). Theta activity and meditative states: spectral
changes during concentrative meditation. Cogn. Process. 11, 31–38. doi:
Boutin, A., Blandin, Y., Massen, C., Heuer, H., and Badets, A. (2014). Conscious
awareness of action potentiates sensorimotor learning. Cognition 133, 1–9. doi:
Boutin, A., Fries, U., Panzer, S., Shea, C. H., and Blandin, Y. (2010). Role of action
observation and action in sequence learning and coding. Acta Psychol. 135,
240–251. doi: 10.1016/j.actpsy.2010.07.005
Brashers-Krug, T., Shadmehr, R., and Bizzi, E. (1996). Consolidation in human
motor memory. Nature 382, 252–255. doi: 10.1038/382252a0
Brawn, T. P., Fenn, K. M., Nusbaum, H. C., and Margoliash, D. (2010).
Consolidating the effects of waking and sleep on motor-sequence learning.
J. Neurosci. 30, 13977–13982. doi: 10.1523/Jneurosci.3295-10.2010
Brown, R. M., and Robertson, E. M. (2007). Inducing motor skill improvements
with a declarative task. Nat. Neurosci. 10, 148–149. doi: 10.1038/nn1836
Cahn, B. R., and Polich, J. (2006). Meditation states and traits: EEG, ERP,
and neuroimaging studies. Psychol. Bull. 132, 180–211. doi: 10.1037/0033-
Cleeremans, A. (1997). “Principles for implicit learning,” in How Implicit is Implicit
Learning?, ed. D. C. Berry (Oxford: Oxford University Press), 195–234.
Cleeremans, A., Destrebecqz, A., and Boyer, M. (1998). Implicit learning:
news from the front. Trends Cogn. Sci. 2, 406–416. doi: 10.1016/S1364-
Cohen, D. A., Pascual-Leone, A., Press, D. Z., and Robertson, E. M. (2005). Off-line
learning of motor skill memory: a double dissociation of goal and movement.
Proc. Natl. Acad. Sci. U.S.A. 102, 18237–18241. doi: 10.1073/pnas.0506072102
Colzato, L. S., Sellaro, R., Samara, I., Baas, M., and Hommel, B. (2015a).
Meditation-induced states predict attentional control over time. Conscious.
Cogn. 37, 57–62. doi: 10.1016/j.concog.2015.08.006
Colzato, L. S., Sellaro, R., Samara, I., and Hommel, B. (2015b). Meditation-induced
cognitive-control states regulate response-conflict adaptation: evidence from
trial-to-trial adjustments in the Simon task. Conscious. Cogn. 35, 110–114. doi:
Colzato, L. S., van der Wel, P., Sellaro, R., and Hommel, B. (2016).
A single bout of meditation biases cognitive control but not attentional
focusing: evidence from the global-local task. Conscious. Cogn. 39, 1–7. doi:
Cools, R., and D’Esposito, M. (2011). Inverted-U-shaped dopamine actions on
human working memory and cognitive control. Biol. Psychiatry 69, E113–E125.
doi: 10.1016/j.biopsych.2011.03.028
Craven, J. L. (1989). Meditation and psychotherapy. Can. J. Psychiatry 34, 648–653.
Denny, M. R., Frisbey, N., and Weaver, J. Jr (1955). Rotary pursuit performance
under alternate conditions of distributed and massed practice. J. Exp. Psychol.
49, 48–54. doi: 10.1037/h0041724
Diedrichsen, J., and Kornysheva, K. (2015). Motor skill learning between selection
and execution. Trends Cogn. Sci. 19, 227–233. doi: 10.1016/j.tics.2015.02.003
Donchin, O., Sawaki, L., Madupu, G., Cohen, L. G., and Shadmehr, R.
(2002). Mechanisms influencing acquisition and recall of motor memories.
J. Neurophysiol. 88, 2114–2123. doi: 10.1152/jn.00033.2002
Fischer, S., Hallschmid, M., Elsner, A. L., and Born, J. (2002). Sleep forms
memory for finger skills. Proc. Natl. Acad. Sci. U.S.A. 99, 11987–11991. doi:
Frensch, P. A., and Runger, D. (2003). Implicit learning. Curr. Dir. Psychol. Sci. 12,
13–18. doi: 10.1111/1467-8721.01213
Immink, M. A., and Wright, D. L. (1998). Contextual interference: a response
planning account. Q. J. Exp. Psychol. A 51, 735–754. doi: 10.1080/71375
Karlsson, M. P., and Frank, L. M. (2009). Awake replay of remote experiences in
the hippocampus. Nat. Neurosci. 12, 913–918. doi: 10.1038/nn.2344
Karni, A., Tanne, D., Rubenstein, B. S., Askenasy, J. J. M., and Sagi, D. (1994).
Dependence on rem-sleep of overnight improvement of a perceptual skill.
Science 265, 679–682. doi: 10.1126/science.8036518
Karunakaran, S., Chowdhury, A., Donato, F., Quairiaux, C., Michel, C. M., and
Caroni, P. (2016). PV plasticity sustained through D1/5 dopamine signaling
required for long-term memory consolidation. Nat. Neurosci. 19, 454–464. doi:
Kjaer, T. W., Bertelsen, C., Piccini, P., Brooks, D., Alving, J., and Lou, H. C. (2002).
Increased dopamine tone during meditation-induced change of consciousness.
Cogn. Brain Res. 13, 255–259. doi: 10.1016/S0926-6410(01)00106-9
Korman, M., Doyon, J., Doljansky, J., Carrier, J., Dagan, Y., and Karni, A. (2007).
Daytime sleep condenses the time course of motor memory consolidation. Nat.
Neurosci. 10, 1206–1213. doi: 10.1038/nn1959
Korman, M., Raz, N., Flash, T., and Karni, A. (2003). Multiple shifts
in the representation of a motor sequence during the acquisition of
skilled performance. Proc. Natl. Acad. Sci. U.S.A. 100, 12492–12497. doi:
Frontiers in Psychology | 8November 2016 | Volume 7 | Article 1698
fpsyg-07-01698 October 27, 2016 Time: 16:44 # 9
Immink Meditation and Motor Memory Consolidation
Krakauer, J. W., and Shadmehr, R. (2006). Consolidation of motor memory. Trends
Neurosci. 29, 58–64. doi: 10.1016/j.tins.2005.10.003
Kruis, A., Slagter, H. A., Bachhuber, D. R. W., Davidson, R. J., and Lutz, A. (2016).
Effects of meditation practice on spontaneous eyeblink rate. Psychophysiology
53, 749–758. doi: 10.1111/psyp.12619
Kuriyama, K., Stickgold, R., and Walker, M. P. (2004). Sleep-dependent learning
and motor-skill complexity. Learn. Mem. 11, 705–713. doi: 10.1101/lm.76304
Lazar, S. W., Bush, G., Gollub, R. L., Fricchione, G. L., Khalsa, G., and Benson, H.
(2000). Functional brain mapping or the relaxation response and meditation.
Neuroreport 11, 1581–1585. doi: 10.1097/00001756-200005150-00042
Levine, T. R., and Hullett, C. R. (2002). Eta squared, partial eta squared, and
misreporting of effect size in communication research. Hum. Commun. Res. 28,
612–625. doi: 10.1093/hcr/28.4.612
Lippelt, D. P., Hommel, B., and Colzato, L. S. (2014). Focused attention,
open monitoring and loving kindness meditation: effects on attention,
conflict monitoring, and creativity – A review. Front. Psychol. 5:1083. doi:
Lomas, T., Edginton, T., Cartwright, T., and Ridge, D. (2014). Men developing
emotional intelligence through meditation? integrating narrative, cognitive and
electroencephalography (EEG) evidence. Psychol. Men Masc. 15, 213–224. doi:
Lomas, T., Ivtzan, I., and Fu, C. H. Y. (2015). A systematic review of the
neurophysiology of mindfulness on EEG oscillations. Neurosci. Biobehav. Rev.
57, 401–410. doi: 10.1016/j.neubiorev.2015.09.018
Lou, H. C., Kjaer, T. W., Friberg, L., Wildschiodtz, G., Holm, S., and Nowak, M.
(1999). A (15)O-H(2)O PET study of meditation and the resting state of
normal consciousness. Hum. Brain Mapp. 7, 98–105. doi: 10.1002/(SICI)1097-
Luders, E., Toga, A. W., Lepore, N., and Gaser, C. (2009). The underlying
anatomical correlates of long-term meditation: larger hippocampal
and frontal volumes of gray matter. Neuroimage 45, 672–678. doi:
Lykins, E. L. B., Baer, R. A., and Gottlob, L. R. (2012). Performance-based
tests of attention and memory in long-term mindfulness meditators and
demographically matched nonmeditators. Cogn. Ther. Res. 36, 103–114. doi:
Malinowski, P. (2013). Neural mechanisms of attentional control in mindfulness
meditation. Front. Neurosci. 7:8. doi: 10.3389/fnins.2013.00008
Maquet, P., Schwartz, S., Passingham, R., and Frith, C. (2003). Sleep-related
consolidation of a visuomotor skill: brain mechanisms as assessed by functional
magnetic resonance imaging. J. Neurosci. 23, 1432–1440.
Marshall, U., and Born, J. (2007). The contribution of sleep to hippocampus-
dependent memory consolidation. Trends Cogn. Sci. 11, 442–450. doi:
McGaugh, J. L. (2000). Memory – a century of consolidation. Science 287, 248–251.
doi: 10.1126/science.287.5451.248
Mednick, S., Nakayama, K., and Stickgold, R. (2003). Sleep-dependent learning: a
nap is as good as a night. Nat. Neurosci. 6, 697–698. doi: 10.1038/nn1078
Monfils, M. H., Cowansage, K. K., Klann, E., and LeDoux, J. E. (2009). Extinction-
reconsolidation boundaries: key to persistent attenuation of fear memories.
Science 324, 951–955. doi: 10.1126/science.1167975
Mrazek, M. D., Franklin, M. S., Phillips, D. T., Baird, B., and Schooler, J. W.
(2013). Mindfulness training improves working memory capacity and GRE
performance while reducing mind wandering. Psychol. Sci. 24, 776–781. doi:
Muellbacher, W., Ziemann, U., Wissel, J., Dang, N., Kofler, M., Facchini, S.,
et al. (2002). Early consolidation in human primary motor cortex. Nature 415,
640–644. doi: 10.1038/nature712
Nash, J. D., and Newberg, A. (2013). Toward a unifying taxonomy and definition
for meditation. Front. Psychol. 4:806. doi: 10.3389/fpsyg.2013.00806
Newberg, A. B., and Iversen, J. (2003). The neural basis of the complex mental
task of meditation: neurotransmitter and neurochemical considerations. Med.
Hypotheses 61, 282–291. doi: 10.1016/S0306-9877(03)00175-0
Nieoullon, A. (2002). Dopamine and the regulation of cognition and attention.
Prog. Neurobiol. 67, 53–83. doi: 10.1016/S0301-0082(02)00011-4
Nishida, M., and Walker, M. P. (2007). Daytime naps, motor memory
consolidation and regionally specific sleep spindles. PLoS ONE 2:e341. doi:
Parker, S., Bharati, S. V., and Fernandez, M. (2013). Defining yoga-nidra:
traditional accounts, physiological research, and future directions. Int. J. Yoga
Ther. 23, 11–16.
Peigneux, P., Destrebecqz, A., Hotermans, C., and Cleeremans, A. (2005). Filling
one gap by creating another: memory stabilization is not all-or-nothing, either.
Behav. Brain Sci. 28, 78. doi: 10.1017/S0140525X05350023
Peigneux, P., Laureys, S., Fuchs, S., Destrebecqz, A., Collette, F., Delbeuck, X.,
et al. (2003). Learned material content and acquisition level modulate cerebral
reactivation during posttraining rapid-eye-movements sleep. Neuroimage 20,
125–134. doi: 10.1016/S1053-8119(03)00278-7
Press, D. Z., Casement, M. D., Pascual-Leone, A., and Robertson, E. M. (2005). The
time course of off-line motor sequence learning. Cogn. Brain Res. 25, 375–378.
doi: 10.1016/j.cogbrainres.2005.05.010
Quach, D., Mano, K. E. J., and Alexander, K. (2016). A randomized
controlled trial examining the effect of mindfulness meditation on working
memory capacity in adolescents. J. Adolesc. Health 58, 489–496. doi:
Raffone, A., and Srinivasan, N. (2010). The exploration of meditation in the
neuroscience of attention and consciousness. Cogn. Process. 11, 1–7. doi:
Reiner, M., Rozengurt, R., and Barnea, A. (2014). Better than sleep: theta
neurofeedback training accelerates memory consolidation. Biol. Psychol. 95,
45–53. doi: 10.1016/j.biopsycho.2013.10.010
Robertson, E. M., Pascual-Leone, A., and Miall, R. C. (2004a). Current concepts in
procedural consolidation. Nat. Rev. Neurosci. 5, 576–582. doi: 10.1038/nrn1426
Robertson, E. M., Pascual-Leone, A., and Press, D. Z. (2004b). Awareness
modifies the skill-learning benefits of sleep. Curr. Biol. 14, 208–212. doi:
Robertson, E. M., Press, D. Z., and Pascual-Leone, A. (2005). Off-line
learning and the primary motor cortex. J. Neurosci. 25, 6372–6378. doi:
Rozengurt, R., Barnea, A., Uchida, S., and Levy, D. A. (2016). Theta EEG
neurofeedback benefits early consolidation of motor sequence learning.
Psychophysiology 53, 965–973. doi: 10.1111/psyp.12656
Saraswati, S. S. (2001). Yoga Nidra, 6 Edn. Munger: Yoga Publications Trust.
Shadmehr, R., and Brashers-Krug, T. (1997). Functional stages in the formation of
human long-term motor memory. J. Neurosci. 17, 409–419.
Spencer, R. M. C., Sunm, M., and Ivry, R. B. (2006). Sleep-dependent consolidation
of contextual learning. Curr. Biol. 16, 1001–1005. doi: 10.1016/j.cub.2006.03.094
Sperduti, M., Martinelli, P., and Piolino, P. (2012). A neurocognitive model of
meditation based on activation likelihood estimation (ALE) meta-analysis.
Conscious. Cogn. 21, 269–276. doi: 10.1016/j.concog.2011.09.019
Squire, L. R. (2009). Memory and brain systems: 1969-2009. J. Neurosci. 29,
12711–12716. doi: 10.1523/Jneurosci.3575-09.2009
Stickgold, R. (2005). Sleep-dependent memory consolidation. Nature 437,
1272–1278. doi: 10.1038/nature04286
Stickgold, R., Whidbee, D., Schirmer, B., Patel, V., and Hobson, J. A. (2000).
Visual discrimination task improvement: a multi-step process occurring
during sleep. J. Cogn. Neurosci. 12, 246–254. doi: 10.1162/08989290056
Tang, Y. Y., Ma, Y. H., Wang, J., Fan, Y. X., Feng, S. G., Lu, Q. L., et al. (2007).
Short-term meditation training improves attention and self-regulation. Proc.
Natl. Acad. Sci. U.S.A. 104, 17152–17156. doi: 10.1073/pnas.0707678104
Verwey, W. B. (1996). Buffer loading and chunking in sequential keypressing.
J. Exp. Psychol. Hum. Percept. Perform. 22, 544–562. doi: 10.1037/0096-
Verwey, W. B. (1999). Evidence for a multistage model of practice in a sequential
movement task. J. Exp. Psychol. Hum. Percept. Perform. 25, 1693–1708. doi:
Verwey, W. B., and Abrahamse, E. L. (2012). Distinct modes of executing
movement sequences: reacting, associating, and chunking. Acta Psychol. 140,
274–282. doi: 10.1016/j.actpsy.2012.05.007
Verwey, W. B., Abrahamse, E. L., and Jimenez, L. (2009). Segmentation of short
keying sequences does not spontaneously transfer to other sequences. Hum.
Mov. Sci. 28, 348–361. doi: 10.1016/j.humov.2008.10.004
Walker, M. P., Brakefield, T., Hobson, J. A., and Stickgold, R. (2003a). Dissociable
stages of human memory consolidation and reconsolidation. Nature 425,
616–620. doi: 10.1038/nature01930
Frontiers in Psychology | 9November 2016 | Volume 7 | Article 1698
fpsyg-07-01698 October 27, 2016 Time: 16:44 # 10
Immink Meditation and Motor Memory Consolidation
Walker, M. P., Brakefield, T., Morgan, A., Hobson, J. A., and Stickgold, R. (2002).
Practice with sleep makes perfect: sleep-dependent motor skill learning. Neuron
35, 205–211. doi: 10.1016/S0896-6273(02)00746-8
Walker, M. P., Brakefield, T., Seidman, J., Morgan, A., Hobson, J. A., and
Stickgold, R. (2003b). Sleep and the time course of motor skill learning. Learn.
Mem. 10, 275–284. doi: 10.1101/lm.58503
Walker, M. P., and Stickgold, R. (2004). Sleep-dependent learning and memory
consolidation. Neuron 44, 121–133. doi: 10.1016/j.neuron.2004.08.031
Walsh, R., and Shapiro, S. L. (2006). The meeting of meditative disciplines and
Western psychology – A mutually enriching dialogue. Am. Psychol. 61, 227–239.
doi: 10.1037/0003-066x.61.3.227
Whittlesea, B. W. A., and Dorken, M. D. (1997). Implicit learning: indirect, not
unconscious. Psychon. Bull. Rev. 4, 63–67. doi: 10.3758/Bf03210775
Willingham, D. B., and Goedert-Eschmann, K. (1999). The relation between
implicit and explicit learning: evidence for parallel development. Psychol. Sci.
10, 531–534. doi: 10.1111/1467-9280.00201
Willingham, D. B., and Preuss, L. (1995). The death of implicit memory. Psyche
2, 1–10.
Xin, X., Deng, Y., Ding, X., and Tang, Y.-Y. (2013). Short-term meditation
improves working memory performance through changing frontal-parietal
network efficiency. Psychophysiology 50, S124–S124.
Zeidan, F., Johnson, S. K., Diamond, B. J., David, Z., and Goolkasian, P.
(2010). Mindfulness meditation improves cognition: evidence of brief
mental training. Conscious. Cogn. 19, 597–605. doi: 10.1016/j.concog.2010.
Conflict of Interest Statement: The author declares that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2016 Immink. This is an open-access article distributed under the terms
of the Creative Commons Attribution License (CC BY). The use, distribution or
reproduction in other forums is permitted, provided the original author(s) or licensor
are credited and that the original publication in this journal is cited, in accordance
with accepted academic practice. No use, distribution or reproduction is permitted
which does not comply with these terms.
Frontiers in Psychology | 10 November 2016 | Volume 7 | Article 1698
... Motor memory consolidation is characterized as involving two types of offline processes that contribute to either stabilization or enhancement of a newly acquired motor skill (Immink, 2016;Korman et al., 2007). New motor memory is labile and, therefore, susceptible to interference for about 6 hr after learning (Shadmehr & Brashers-Krug, 1997;Walker et al., 2003). ...
... In addition to aerobic exercise (Rhee et al., 2016;Roig et al., 2012) and cognitive tasks (Brown & Robertson, 2007), mindfulness meditation has been shown to support wakeful forms of motor memory consolidation (Immink, 2016). While meditation is commonly thought of as a form of relaxation, empirical evidence has instead shown mindfulness meditation to be a goal-oriented cognitive task that emphasizes attention control (Colzato et al., 2013De Bruin et al., 2016;Malinowski, 2013;Tang et al., 2015). ...
... Mindfulness meditation following explicit motor sequence practice has been shown to promote motor memory consolidation (Immink, 2016). In this work, 12 participants with a mean of 9 years meditation experience first received training on three discrete key pressing sequences in the morning. ...
Posttraining meditation has been shown to promote wakeful memory stabilization of explicit motor sequence information in learners who are experienced meditators. We investigated the effect of single-session mindfulness meditation on wakeful and sleep-dependent forms of implicit motor memory consolidation in meditation naïve adults. Immediately after training with a target implicit motor sequence, participants ( N = 20, eight females, 23.9 ± 3.3 years) completed either a 10-min mindfulness meditation ( N = 10) or a control listening task before exposure to task interference induced by training with a novel implicit sequence. Target sequence performance was tested following 5-hr wakeful and 15-hr postsleep periods. Bayesian inference was applied to group comparisons of mean reaction time (RT) changes across training, interference, wakeful, and postsleep timepoints. Relative to control conditions, posttraining meditation reduced RT slowing between target sequence training and interference sequence introduction (BF 10 [Bayes factors] = 6.61) and supported RT performance gains over the wakeful period (BF 10 = 8.34). No group differences in postsleep RT performance were evident (BF 10 = 0.38). These findings illustrate that posttraining mindfulness meditation expedites wakeful, but not sleep-dependent, offline learning with implicit motor sequences. Previous meditation experience is not required to obtain wakeful consolidation gains from posttraining mindfulness meditation.
... One additional post-training wakeful intervention that has recently been shown to support motor memory consolidation, mindfulness meditation, appears to more directly implicate attention control as important for offline gain (Immink, 2016). Rather than relaxation, meditation has been described as a goal-oriented cognitive task (Chan et al., 2018;Immink et al., 2017). ...
... Importantly, meditation explicitly targets the manipulation of attention, since the goal of meditation is to direct and sustain attention to an object or experience such as body sensations or breathing (Malinowski, 2013;Tang et al., 2015), and thus meditation can be considered a form of attentional cognitive training. To achieve this goal, meditation techniques rely on engagement of attention control processes (Chan et al., 2020;Chan et al., 2017;Chan et al., 2018;Immink, 2016;Immink et al., 2017;Malinowski, 2013;Tang et al., 2015). As cognitive tasks, bouts of exercise and mindfulness meditation can be conceived as goal-oriented tasks, and these post-training interventions might have a shared capacity to support wakeful motor memory consolidation through eliciting increased attention control (Colzato et al., 2013;Colzato et al., 2017;De Bruin et al., 2016). ...
... The emergence of theta waves in frontal regions during meditation highlights involvement in attentional processes (Baijal & Srinivasan, 2010), particularly, in areas such as the ACC, medial prefrontal cortex and DLPFC which are importantly linked with attention-demanding tasks (Cahn & Polich, 2006). Immink (2016) demonstrated that motor memory stabilization could be induced by meditation following training on an explicit motor sequence task. Twelve experienced meditators were trained on a sequential key pressing task in the morning and then completed a 30-minute session of FAM or light work duties (control group) at midday. ...
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Post-training meditation has been shown to promote wakeful motor memory stabilization in experienced meditators. We investigated the effect of single-session mindfulness meditation on wakeful and sleep-dependent forms of implicit motor memory consolidation in mediation naïve adults. Immediately after implicit sequence training, participants (N = 20, 8 females, Mage = 23.9 years ± 3.3) completed either a 10-minute focused attention meditation (N = 10), aiming to direct and sustain attention to breathing, or a control listening task. They were then exposed to interference through novel sequence training. Trained sequence performance was tested following a 5-hour wakeful period and again after a 15-hour period, which included sleep. Bayesian inference was applied to group comparison of mean reaction time (MRT) changes across training, interference, wakeful and post-sleep time points. Relative to control conditions, post-training meditation reduced novel sequence interference (BF 10 = 6.61) and improved wakeful motor memory consolidation (BF 10 = 8.34). No group differences in sleep consolidation were evident (BF 10 = 0.38). These findings illustrate that post-training mindfulness meditation expedites wakeful offline learning of an implicit motor sequence in meditation naïve adults. Interleaving mindfulness meditation between acquisition of a target motor sequence and exposure to an interfering motor sequence reduced proactive and retroactive inference. Post-training mindfulness meditation did not enhance nor inhibit sleep-dependent offline learning of a target implicit motor sequence. Previous meditation training is not required to obtain wakeful consolidation gains from post-training mindfulness meditation.
... When comparing the effects of pro-and retroactive interference, it is perceived that retroactive interference could have a stronger effect on memory formation [52]. Contrary to inhibitory interference effects, findings from the field of meditation and eastern martial arts provide evidence that pre-and postactive meditation can have positive effects on memory consolidation [53][54][55]. In addition, more passive approaches such as sleeping or napping after an activity also indicate positive effects on just that [56][57][58][59]. ...
... The first of these concerns category 2b on the chronological order of exercise and learning: the influence of physical exercise following a cognitive learning unit has not yet been studied. This is somehow surprising, since active approaches like retroactive interference [49][50][51] and meditation [53] or more passive approaches like napping and sleeping [56][57][58][59] have already shown that an intervention subsequent to learning can positively influence learning. ...
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Numerous studies have shown cognitive enhancement through sport and physical exercise. Despite the variety of studies, the extent to which physical activity before or after a cognitive learning session leads to more effective cognitive enhancement remains largely unresolved. Moreover, little attention has been paid to the dependence of the motor learning approach then applied. In this study, we compare the influence of differential with uniformly rope skipping directly succeeding an acquisition phase in arithmetic mathematics. For three weeks 26 pupils, 14 female, 12 male, and 13.9 ± 0.7 years old, completed nine 15 min exercises in arithmetic math, each followed by 3 min rope skipping with heart rate measurement. Arithmetic performance was tested in a pre-, post-and retention test design. The results showed a statistically significant difference between the differential and the control groups within the development of arithmetic performance, especially in the retention test. There was no statistical difference in heart rate. It is suggested that the results provide evidence for sustainable improvements of cognitive learning performance by means of highly variable rope skipping.
... The influence of sleep has also been shown in consolidation of motor skills in music (Allen, 2007) and in auditory learning (Gaab et al., 2004). Meditation has also been seen to have a positive effect on motor learning (Immink, 2016) and we hypothesized that sleep and meditation habits could have an impact on practice outcome. ...
... However, the participants' self-reported general sleep duration showed no significant relationship with neither performance improvements nor overall performance scores, which may be explained by one study (Tucker et al., 2016) suggesting that musicians have the capacity to consolidate a motor skill across waking hours. Meditation has also been seen to have a positive effect on motor learning (Immink, 2016), but surprisingly we found negative effects on improvement of rhythm and intonation in those who reported regular use of meditation compared to those who reported no use of meditation. ...
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Classical musicians face a high demand for flawless and expressive performance, leading to highly intensified practice activity. Whereas the advantage of using mental strategies is well documented in sports research, few studies have explored the efficacy of mental imagery and overt singing on musical instrumental learning. In this study, 50 classically trained trumpet students performed short unfamiliar pieces. Performances were recorded before and after applying four prescribed practice strategies which were (1) physical practice, (2) mental imagery, (3) overt singing with optional use of solfege, (4) a combination of 1, 2 and 3 or a control condition, no practice. Three experts independently assessed pitch and rhythm accuracy, sound quality, intonation, and musical expression in all recordings. We found higher gains in the overall performance, as well as in pitch accuracy for the physical practice, and the combined practice strategies, compared to no practice. Furthermore, only the combined strategy yielded a significant improvement in musical expression. Pitch performance improvement was positively correlated with previous solfege training and frequent use of random practice strategies. The findings highlight benefits from applying practice strategies that complement physical practice in music instrument practice in short term early stages of learning a new piece. The study may generalize to other forms of learning, involving cognitive processes and motor skills.
... Some evidence offers more direct support for a role of meditation in memory formation. Immink (2016) found that, in experienced meditators, a 30-min yoga nidra meditation session (a cognitively engaged type of meditation involving attention to bodily sensations and visual imagery) resulted in enhanced motor memory consolidation relative to an AW condition (light physical work). In unpublished experiments (Dastgheib, 2020), we have assessed the effects of a 60-min post-learning period of self-guided mindfulness meditation, often conceptualized as focused attention on, and monitoring of, bodily sensations (Lomas et al., 2015). ...
Substantial empirical evidence suggests that sleep benefits the consolidation and reorganization of learned information. Consequently, the concept of “sleep-dependent memory consolidation” is now widely accepted by the scientific community, in addition to influencing public perceptions regarding the functions of sleep. There are, however, numerous studies that have presented findings inconsistent with the sleep-memory hypothesis. Here, we challenge the notion of “sleep-dependency” by summarizing evidence for effective memory consolidation independent of sleep. Plasticity mechanisms thought to mediate or facilitate consolidation during sleep (e.g., neuronal replay, reactivation, slow oscillations, neurochemical milieu) also operate during non-sleep states, particularly quiet wakefulness, thus allowing for the stabilization of new memories. We propose that it is not sleep per se, but the engagement of plasticity mechanisms, active during both sleep and (at least some) waking states, that constitutes the critical factor determining memory formation. Thus, rather than playing a "critical" role, sleep falls along a continuum of behavioral states that vary in their effectiveness to support memory consolidation at the neural and behavioral level.
... Taken together, the present results enrich scientific knowledge on yoga nidra, indicating that it is a powerful practice for eliciting altered states of consciousness, sustained by specific psychophysiological and phenomenological correlates, whose therapeutic potential needs further study. Finally, we want to stress that even if yoga nidra has been classified within the cognitive domain of the taxonomy proposed by Nash et al., 8 the voluntary directing of topdown attention, body-scan, and guided visual imagery 68 mean that it also shares important features with the socalled null domain (i.e., methods that purport to create a noncognitive/nonaffective state, an enhanced empty state devoid of phenomenological content). In fact, cognitive training and focused attention are present only during the initial stages of the practice, whose final aim is to reach the dissolution of all conscious contents and the experience of nothing but pure awareness. ...
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Nidrâ yoga is an ancient yogic practice capable of inducing altered states of consciousness characterized by deep relaxation, strong concentration, acute self-awareness, and joy. In modern contemplative neuroscience language, it is known by the name yoga nidra, and few studies have investigated its phenomenological and psychophysiological effects. Six healthy volunteers (four females aged 31–74) performed 12 yoga nidra sessions guided by an expert during a 6-day retreat. Each session consisted of 10 minutes in a resting state (baseline) followed by 2 hours of yoga nidra. Psychometric data regarding dissociative experiences (Clinician Administered Dissociative States Scale) and the state of consciousness (Phenomenology of Consciousness Inventory) were collected after baseline and yoga nidra, while high-density EEG was recorded during the entire session. During nidra sessions, no sleep hallmarks (i.e., K-complexes and sleep spindles) were detected by the EEG in any subject. Psychometric data we re analyzed using a Wilcoxon signed-rank test corrected with the false discovery rate approach for multiple comparisons. Compared to baseline, yoga nidra practice was related to: (1) increased dissociative effects (p = 0.022); (2) perception of being in an altered state of consciousness (p = 0.026); (3) alterations in perceived body image (p = 0.022); (4) increased “meaningfulness” attributed to the experience (p = 0.026); (5) reduced rational thinking (p = 0.029); and (6) reduced volitional thought control (p = 0.026). First-person experience is discussed in relation to descriptive EEG power spectral density analysis, which was performed in one subject because of severe EEG artifacts in the other recordings; that subject showed, compared to baseline: (1) early increase of alpha and beta power, followed by a progressive widespread reduction; (2) widespread early increase of theta power, followed by a progressive reduction; and (3) widespread increase of gamma power in the latest stages. The present preliminary results enrich the knowledge of yoga nidra, elucidating its phenomenology and suggesting some psychophysiological correlates that future studies may address.
... Both fast and slow learning stages are experience dependent since the performance improvements only occur with actual task practice. An intermediate stage highlights offline forms of motor learning and includes motor memory consolidation processes (Robertson et al., 2004;Walker & Stickgold, 2004;Immink, 2016). The fast learning stage is characterized by large performance improvements during initial skill practice. ...
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Skill acquisition represents a progression from high to low reliance on the conscious control of the action. The ability to produce action without drawing upon limited attentional resources has traditionally been the defining characteristic of skill automaticity. As such, learning represents a progression from low to high efficiency in the cognitive processes needed to plan, execute, and update skilled movement. In this chapter, we summarize neuroimaging findings that illustrate the evolution of such efficiency in terms of the neural adaptations that underlie skill learning automatization. As a backdrop to these findings, we first review the cognitive characteristics of skill automaticity as well as a contemporary theoretical framework for how we perform action based on sequencing movement elements. This provides a vantage point from which neural basis of skill automaticity can be considered in terms of associative and sensorimotor learning processes that provide for more efficient action in terms of cognitive requirements. We then contrast this with a summary of the contextual interference effect, which represents a cautionary account for the negative learning consequences associated with training protocols that appear to expedite skill automaticity.
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Human performance applications of mindfulness-based training have demonstrated its utility in enhancing cognitive functioning. Previous studies have illustrated how these interventions can improve performance on traditional cognitive tests, however, little investigation has explored the extent to which mindfulness-based training can optimise performance in more dynamic and complex contexts. Further, from a neuroscientific perspective, the underlying mechanisms responsible for performance enhancements remain largely undescribed. With this in mind, the following study aimed to investigate how a short-term mindfulness intervention (one week) augments performance on a dynamic and complex task (target motion analyst task; TMA) in young, healthy adults (n = 40, age range = 18 - 38). Linear mixed effect modelling revealed that increased adherence to the mindfulness-based training regime (ranging from 0-21 sessions) was associated with improved performance in the second testing session of the TMA task, controlling for baseline performance. Further analyses of resting-state electroencephalographic (EEG) metrics and additional individual factors demonstrated enhancements associated with training adherence remained relatively consistent across varying levels of participants’ resting-state EEG metrics, personality measures (i.e., trait mindfulness, neuroticism, conscientiousness), self-reported enjoyment and timing of intervention adherence. Our results thus indicate that mindfulness-based cognitive training leads to performance enhancements in distantly related tasks, irrespective of several individual differences. We also revealed nuances in the magnitude of cognitive enhancements contingent on the timing of adherence, regardless of total volume of training. Overall, our findings suggest that mindfulness-based training could be used in a myriad of settings to elicit transferable performance enhancements.
Aim: The purpose of this paper was to review and synthesize published research articles that have utilized yoga nidra as an intervention. Background: Yoga nidra is a form of guided meditation that has emerged in the literature in the past two decades as an intervention for a variety of medical conditions such as stress and mental health. It differs from traditional yoga, in that it does not require yoga poses. It is a noninvasive, cost-effective approach that is also easily accessible so it can be done in the privacy and comfort of the home. Design: The integrative review methodology by Whittemore and Knafl (2005) provided the framework for this review. Methods: The databases CINAHL, PubMed, SCOPUS, and PsycINFO were used to search for articles. Inclusion criteria consisted of journal articles in English with no limitations on dates of publication. Studies were excluded if any form of traditional yoga requiring poses was used as an intervention. Also excluded were all types of meditation that were not yoga nidra, systematic reviews, studies that utilized multiple intervention types (i.e., traditional yoga and yoga nidra), and commentaries/brief reports. Twenty-nine studies met the inclusion criteria. Quality appraisal was completed for each study. Results: The 29 studies that were reviewed consisted of 12 randomized controlled trials, 13 quasi-experimental studies, 3 mixed-methods studies, and 1 qualitative study. Outcome variables were categorized according to themes and results were systemically synthesized and reported by theme: (a) stress, (b) mood, (c) well-being, (d) psychologic dysfunction, (e) biomarkers, (f) sleep, and (g) miscellaneous. Conclusion: Yoga nidra was found to be effective in most of these studies. However, there was some clinical heterogeneity in the sample populations and intervention session lengths, frequencies, and durations, making it difficult to draw conclusions about yoga nidra intervention based solely on the findings presented in this review. More studies are needed overall, particularly ones with larger sample sizes and stronger experimental designs. Clinical relevance: Yoga nidra has the potential to be a useful, noninvasive, nonpharmacologic treatment or adjunct for a variety of conditions, particularly mental health.
Objectives: We aimed to examine trial feasibility plus physiological and psychological effects of a guided meditation practice, Yoga Nidra, in adults with self-reported insomnia. Methods: Twenty-two adults with self-reported insomnia were recruited to attend two visits at our research center. At Visit 1 (V1), participants were asked to lie quietly for ninety minutes. The primary outcome was change in electroencephalography (EEG). Heart rate variability (HRV), respiratory rate and self-reported mood and anxiety were also measured. At Visit 2 (V2), the same protocol was followed, except half of participants were randomized to practice Yoga Nidra for the first 30-min. Results: There were no between-group changes (V1-V2) in alpha EEG power at O1 (Intervention: 13 ± 70%; Control: -20 ± 40%), HRV or sleep onset latency in response to Yoga Nidra. Respiratory rate, however, showed statistically significant difference between groups (Yoga Nidra -1.4 breaths per minute (bpm) change during and - 2.1 bpm afterwards vs. Control +0.2 bpm during and + 0.4 bpm after; p = .03 for both during and after). The intervention displayed good acceptability (well-tolerated) and credibility (perceived benefit ratings) with implementation success (target sample size reached; 5% dropout rate). Conclusions: This preliminary clinical trial provides early evidence that Yoga Nidra is a well-tolerated, feasible intervention for adults reporting insomnia. Decreased respiratory rate in response to Yoga Nidra needs to be confirmed in more definitive studies. Trial registration information: This trial was registered on as "A Closer Look at Yoga Nidra: Sleep Lab Analyses" (NCT#03685227).
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Mindfulness meditation has been purported to be a beneficial practice for wellbeing. It would therefore be expected that the neurophysiology of mindfulness would reflect this impact on wellbeing. However, investigations of the effect of mindfulness have generated mixed reports of increases, decreases, as well as no differences in EEG oscillations in comparison with a resting state and a variety of tasks. We have performed systematic review of EEG studies of mindfulness meditation in order to determine any common effects and to identify factors which may impact on the effects. Databases were reviewed from 1966 to August 2015. Eligibility criteria included empirical quantitative analyses of mindfulness meditation practice and EEG measurements acquired in relation to practice. A total of 56 papers met the eligibility criteria and were included in the systematic review, consisting of a total 1,715 subjects: 1,358 healthy individuals and 357 individuals with psychiatric diagnoses. Studies were principally examined for power outcomes in each bandwidth, in particular the power differentials between mindfulness and the control state, as well as outcomes relating to hemispheric asymmetry and event-related potentials. The systematic review revealed that mindfulness was most commonly associated with enhanced alpha and theta power as compared to an eyes closed resting state, although such outcomes were not uniformly reported. No consistent patterns were observed with respect to beta, delta and gamma bandwidths. In summary, mindfulness is associated with increased alpha and theta power in both healthy individuals and in patient groups. This co-presence of elevated alpha and theta may signify a state of relaxed alertness which is conducive to mental health.
Procedural learning is subject to consolidation processes believed to depend on the modulation of functional connections involved in representing the acquired skill. While sleep provides the most commonly studied framework for such consolidation processes, posttraining modulation of oscillatory brain activity may also impact on plasticity processes. Under the hypothesis that consolidation of motor learning is associated with theta band activity, we used EEG neurofeedback (NFB) to enable participants to selectively increase either theta or beta power in their EEG spectra following the acquisition phase of motor sequence learning. We tested performance on a motor task before and after training, right after the NFB session to assess immediate NFB effects, 1 day after NFB to assess interaction between NFB effects and overnight sleep-dependent stabilization, and 1 week after the initial session, to assess the effects of NFB on long-term stabilization of motor training. We also explored the extent of the influence of single-electrode NFB on EEG recorded across the scalp. Results revealed a significantly greater improvement in performance immediately after NFB in the theta group than in the beta group. This effect continued for testing up to 1 week following training. Across participants, post-NFB improvement correlated positively with theta/beta ratio change achieved during NFB. Additionally, NFB was found to cause widespread band-power modulation beyond the electrode used for feedback. Thus, upregulating postlearning theta power may yield contributions to the immediate performance and subsequent consolidation of an acquired motor skill.
A rapidly growing body of research suggests that meditation can change brain and cognitive functioning. Yet little is known about the neurochemical mechanisms underlying meditation-related changes in cognition. Here, we investigated the effects of meditation on spontaneous eyeblink rates (sEBR), a noninvasive peripheral correlate of striatal dopamine activity. Previous studies have shown a relationship between sEBR and cognitive functions such as mind wandering, cognitive flexibility, and attention-functions that are also affected by meditation. We therefore expected that long-term meditation practice would alter eyeblink activity. To test this, we recorded baseline sEBR and intereyeblink intervals (IEBI) in long-term meditators (LTM) and meditation-naive participants (MNP). We found that LTM not only blinked less frequently, but also showed a different eyeblink pattern than MNP. This pattern had good to high degree of consistency over three time points. Moreover, we examined the effects of an 8-week course of mindfulness-based stress reduction on sEBR and IEBI, compared to an active control group and a waitlist control group. No effect of short-term meditation practice was found. Finally, we investigated whether different types of meditation differentially alter eyeblink activity by measuring sEBR and IEBI after a full day of two kinds of meditation practices in the LTM. No effect of meditation type was found. Taken together, these findings may suggest either that individual difference in dopaminergic neurotransmission is a self-selection factor for meditation practice, or that long-term, but not short-term meditation practice induces stable changes in baseline striatal dopaminergic functioning.
Long-term consolidation of memories depends on processes occurring many hours after acquisition. Whether this involves plasticity that is specifically required for long-term consolidation remains unclear. We found that learning-induced plasticity of local parvalbumin (PV) basket cells was specifically required for long-term, but not short/intermediate-term, memory consolidation in mice. PV plasticity, which involves changes in PV and GAD67 expression and connectivity onto PV neurons, was regulated by cAMP signaling in PV neurons. Following induction, PV plasticity depended on local D1/5 dopamine receptor signaling at 0-5 h to regulate its magnitude, and at 12-14 h for its continuance, ensuring memory consolidation. D1/5 dopamine receptor activation selectively induced DARPP-32 and ERK phosphorylation in PV neurons. At 12-14 h, PV plasticity was required for enhanced sharp-wave ripple densities and c-Fos expression in pyramidal neurons. Our results reveal general network mechanisms of long-term memory consolidation that requires plasticity of PV basket cells induced after acquisition and sustained subsequently through D1/5 receptor signaling.
Recent studies show that a single bout of meditation can impact information processing. We were interested to see whether this impact extends to attentional focusing and the top-down control over irrelevant information. Healthy adults underwent brief single bouts of either focused attention meditation (. FAM), which is assumed to increase top-down control, or open monitoring meditation (. OMM), which is assumed to weaken top-down control, before performing a global-local task. While the size of the global-precedence effect (reflecting attentional focusing) was unaffected by type of meditation, the congruency effect (indicating the failure to suppress task-irrelevant information) was considerably larger after OMM than after FAM. Our findings suggest that engaging in particular kinds of meditation creates particular cognitive-control states that bias the individual processing style toward either goal-persistence or cognitive flexibility.
Purpose To investigate the effectiveness of a mindfulness meditation intervention on working memory capacity (WMC) in adolescents via a randomized controlled trial comparing mindfulness meditation to hatha yoga and a waitlist control group. Methods Participants (N = 198 adolescents) were recruited from a large public middle school in southwest United States and randomly assigned to mindfulness meditation, hatha yoga, or a waitlist control condition. Participants completed a computerized measure of WMC (Automated Operational Span Task) and self-report measures of perceived stress (Perceived Stress Scale) and anxiety (Screen for Childhood Anxiety Related Emotional Disorders) at preintervention and postintervention/waitlist. A series of mixed-design analyses of variance were used to examine changes in WMC, stress, and anxiety at preintervention and postintervention. Results Participants in the mindfulness meditation condition showed significant improvements in WMC, whereas those in the hatha yoga and waitlist control groups did not. No statistically significant between-group differences were found for stress or anxiety. Conclusions This is the first study to provide support for the benefits of short-term mindfulness practice, specifically mindfulness meditation, in improving WMC in adolescents. Results highlight the importance of investigating the components of mindfulness-based interventions among adolescents given that such interventions may improve cognitive function. More broadly, mindfulness interventions may be delivered in an abridged format, thus increasing their potential for integration into school settings and into existing treatment protocols.
Meditation has been increasingly recommended as a practice with potential psychotherapeutic benefit. This paper provides a description of meditative practice and discusses selected issues related to the conceptual and technical integration of meditation with modern psychotherapeutic interventions. Evidence suggests that meditation may contribute to psychotherapeutic change and that the disciplines from which meditation arises are in some respects similar to modern psychological formulations, and in other respects are complimentary. It is hoped that improved understanding of meditation will contribute to an increased acceptance and use of these practices as aids to psychotherapeutic change and will facilitate meaningful research regarding meditation.
Meditation is becoming an increasingly popular topic for scientific research and various effects of extensive meditation practice (ranging from weeks to several years) on cognitive processes have been demonstrated. Here we show that extensive practice may not be necessary to achieve those effects. Healthy adult non-meditators underwent a brief single session of either focused attention meditation (FAM), which is assumed to increase top-down control, or open monitoring meditation (OMM), which is assumed to weaken top-down control, before performing an Attentional Blink (AB) task - which assesses the efficiency of allocating attention over time. The size of the AB was considerably smaller after OMM than after FAM, which suggests that engaging in meditation immediately creates a cognitive-control state that has a specific impact on how people allocate their attention over time. Copyright © 2015 Elsevier Inc. All rights reserved.