Cognitive Aging and Long-Term Maintenance of Attentional Improvements Following Meditation Training

Abstract

Sustained attention is effortful, demanding, and subject to limitations associated with age-related cognitive decline. Researchers have sought to examine whether attentional capacities can be enhanced through directed mental training, with a number of studies now offering evidence that meditation practice may facilitate generalized improvements in this domain. However, the extent to which attentional gains are maintained following periods of dedicated meditation training and how such improvements are moderated by processes of aging have yet to be characterized. In a prior report (Sahdra et al., Emotion 11, 299–312, 2011), we examined attentional performance on a sustained response inhibition task before, during, and after 3-months of full-time meditation. We now extend this prior investigation across additional follow-up assessments occurring up to 7 years after the conclusion of training. Performance improvements observed during periods of intensive practice were partially maintained several years later. Importantly, aging-related decrements in measures of response inhibition accuracy and reaction time variability were moderated by levels of continued meditation practice across the follow-up period. The present study is the first to offer evidence that intensive and continued meditation practice is associated with enduring improvements in sustained attention and response inhibition, with the potential to alter longitudinal trajectories of cognitive change across the lifespan.

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ORIGINAL ARTICLE
Cognitive Aging and Long-Term Maintenance of Attentional
Improvements Following Meditation Training
Anthony P. Zanesco
1
&Brandon G. King
2,3
&Katherine A. MacLean
3
&Clifford D. Saron
3,4
Received: 29 September 2017 / Accepted: 13 February 2018 / Published online: 28 March 2018
#The Author(s) 2018. This article is an open access publication
Abstract
Sustained attention is effortful, demanding, and subject to limitations associated with age-related cognitive decline. Researchers
have sought to examine whether attentional capacities can be enhanced through directed mental training, with a number of studies
now offering evidence that meditation practice may facilitate generalized improvements in this domain. However, the extent to
which attentional gains are maintained following periods of dedicated meditation training and how such improvements are
moderated by processes of aging have yet to be characterized. In a prior report (Sahdra et al., Emotion 11, 299–312, 2011),
we examined attentional performance on a sustained response inhibition task before, during, and after 3-months of full-time
meditation. We now extend this prior investigation across additional follow-up assessments occurring up to 7 years after the
conclusion of training. Performance improvements observed during periods of intensive practice were partially maintained
several years later. Importantly, aging-related decrements in measures of response inhibition accuracy and reaction time vari-
ability were moderated by levels of continued meditation practice across the follow-up period. The present study is the first to
offer evidence that intensive and continued meditation practice is associated with enduring improvements in sustained attention
and response inhibition, with the potential to alter longitudinal trajectories of cognitive change across the lifespan.
Keywords Aging .Meditation .Response Inhibition .Sustained Attention .Vigilan ce
The human capacity to sustain attention over time is limited
and effortful, and is prone to fatigue, lapses, and fluctuations
with prolonged engagement (Fortenbaugh et al. 2017;
Langner and Eickhoff 2013;Warmetal.2008). These limita-
tions are exacerbated by age-related cognitive decline
(Fortenbaugh et al. 2015; Lustig and Jantz 2015; Smittenaar
et al. 2015), and there is now considerable interest in identi-
fying training interventions that can offer effective remedia-
tion in aging populations and promote cognitive
improvements in healthy individuals at large (e.g., Anguera
et al. 2013; Bavelier and Davidson 2013). Increasingly, re-
searchers have emphasized meditation- and mindfulness-
based approaches for the training of attention. Meditation-
based trainings have been shown to temper transient lapses
in attention that disrupt ongoing task performance (Jha et al.
2015;Lutzetal.2009;Morrisonetal.2014;Mrazeketal.
2013;vanVugtandJha2011; Zanesco et al. 2013,2016), and
improve individuals’ability to sustain attention over time
(MacLean et al. 2010;Sahdraetal.2011; Zanesco et al.
2013). However, the extent to which attentional improve-
ments endure after periods of dedicated training, and how
continued meditation practice is associated with cognitive
change across the lifespan remains unclear and understudied.
The limited, fluctuating, and effortful nature of attention
historically forms a central motivation for improving atten-
tional abilities through meditation among diverse Buddhist
contemplative traditions (e.g., Gunaratana 2011; Wallace
1999). From this perspective, meditation is conceptualized
as a detailed, formalized system of mental training through
which practitioners cultivate specific cognitive capacities over
time, including increased clarity, stability, and duration of
*Anthony P. Zanesco
apz13@miami.edu
1
Department of Psychology, University of Miami, Coral Gables, FL,
USA
2
Department of Psychology, University of California, Davis,
Davis, CA, USA
3
Center for Mind and Brain, University of California, Davis,
Davis, CA, USA
4
The MIND Institute, University of California, Davis, Davis, CA,
USA
Journal of Cognitive Enhancement (2018) 2:259–275
https://doi.org/10.1007/s41465-018-0068-1
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attentional focus (Wallace 1999). While acknowledging that
these traditions are shaped by a multitude of sociohistorical
and soteriological factors, contemporary neurocognitive
frameworks of mindfulness and meditation have endeavored
to characterize families of meditation practice in terms of
known features of attention and cognitive control (Dahl
et al. 2015; Lippelt et al. 2014;Lutzetal.2008,2015; Vago
and Silbersweig 2012), and theories of skill learning and plas-
ticity (Slagter et al. 2011). Yet, the enduring consequences of
continued meditation training have received sparse consider-
ation in this emerging literature. Longitudinal investigations
that track practitioners across periods of training and years of
practice are critical for understanding the durability of trait-
level cognitive changes associated with meditation, and for
broadly characterizing the influence of attentional training
on cognitive development across the lifespan.
Meditation techniques are commonly understood and dis-
seminated by contemplative practitioners, teachers, and
mindfulness-based clinicians as exercises for use in long-
term or life-long personal development. Nevertheless, few
studies have attempted to characterize how meditation-
related cognitive improvements develop over years of prac-
tice, or whether such improvements are maintained following
periods of formal training. Investigations detailing the devel-
opmental trajectory and maintenance of training-related im-
provements are vital to understanding the benefits and limita-
tions of mindfulness-based interventions as traditionally con-
ceptualized. Ideally, such studies would implement repeated
assessments spread across extended time intervals; attempt to
distinguish periods of intensive training from less-intensive
durations of practice; and track both the quality and amount
of time that practitioners dedicate to continued practice over
these extended intervals. It is plausible, for example, that the
benefits of acute periods of training are difficult to maintain
absent an ongoing commitment to meditation orother lifestyle
or behavioral changes that support continued engagement
with contemplative practice. There is a need to examine how
meditation-related improvements manifest in concert with de-
velopmental changes in cognition, and whether meditation
practice can moderate the effects of aging-related cognitive
decline.
Studies of cognitive aging offer compelling evidence that
the ability to sustain attention and inhibit prepotent response
tendencies is diminished in later life (e.g., Fortenbaugh et al.
2015; Smittenaar et al. 2015), and greater reaction time vari-
abilityduringtaskperformancehasbeenproposedasanim-
portant marker of age-related impairment in executive control
(MacDonald et al. 2006; Vasquez et al. 2014;Westetal.
2002). Age-related deficits in these domains have spurred
the development of targeted intervention strategies for im-
proving cognitive performance in older adults, including
computer-based cognitive training programs (e.g., Anguera
et al. 2013; Toril et al. 2014), general lifestyle interventions
(e.g., Park et al. 2014), and mindfulness-based approaches (for
reviews, see Gard et al. 2014; Malinowski and Shalamanova
2017; Kurth et al. 2017). Although several studies have re-
ported cross-sectional differences between meditation practi-
tioners and meditation-naïve controls in older adult samples
(e.g., Laneri et al. 2016; Sperduti et al. 2016; van Leeuwen
et al. 2009), relatively few studies have investigated medita-
tion training as a directed intervention for older populations
(e.g., Malinowski et al. 2017). Overall, research tentatively
supports the claim that meditation practice can protect against
age-related deficits in attention and executive function.
Notably, however, no studies have longitudinally tracked
meditation practitioners over years of continued practice to
examine the moderation of age-related decline.
In our ongoing work, we have employed resource-
demanding vigilance tasks—such as our sustained response
inhibition task (RIT)—to assess skilled attentional perfor-
mance in cognitively healthy adults (MacLean et al. 2009;
Sahdra et al. 2011). In the RIT, participants are asked to dis-
criminate between rare target and frequent non-target stimuli
(small vertical lines) while inhibiting behavioral responses to
targets over the course of performance. Using this task, we
have demonstrated increases in response inhibition accuracy
and attenuation of the vigilance decrement across a 3-month
period of intensive training in focused attention meditation
(Sahdra et al. 2011), and replicated these results in an inde-
pendent 1-month training study incorporating arelated style of
practice (Zanesco et al. 2013). In addition to measures of
performance accuracy, reductions in reaction time variability
were also observed in this latter study. We now revisit our
previous data (Sahdra et al. 2011) in light of an extensive
long-term follow-up investigation. Our goal is to characterize
the maintenance of training-related improvements across an
extended post-training interval, and to examine the influence
of continued meditation practice on age-related cognitive de-
cline and longitudinal training trajectories.
In our prior report, training and wait-list control partici-
pants were assessed on the RIT at the beginning, middle,
and end of a 3-month intensive meditation training (Sahdra
et al. 2011). Follow-up assessments were conducted approxi-
mately 6 months, 1.5 years, and 7 years following the inter-
vention. During training, practitioners engaged in shamatha
meditation practices (Wallace 2006) that are thought to in-
crease the clarity, stability, and duration of an individual’s
concentration and to reduce the felt cognitive effort required
to maintain attention in a sustained manner (Lutz et al. 2015).
From a neurocognitive perspective, the features of concentra-
tion targeted by shamatha practice share considerable concep-
tual overlap with measures of attention derived from vigilance
tasks such as our RIT. These tasks place substantial demand
on supporting cognitive systems, leading to a monotonic de-
cline in one’s capacity to detect and appropriately respond to
target stimuli over time (i.e., vigilance decrement; Mackworth
260 J Cogn Enhanc (2018) 2:259–275
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1948). Studies of vigilance, moreover, have demonstrated that
increased task discrimination difficulty can lead to a corre-
sponding increase in the magnitude of this performance dec-
rement and in the amount of subjective effort, distress, and
demand reported by an individual (see for review, Langner
and Eickhoff 2013; See et al. 1995;Warmetal.2008). In order
to minimize the potential influence of individual differences in
discrimination capacity on observed training effects, we im-
plemented a visual thresholding procedure designed to control
levels of task difficulty across participants and assessments.
Lapses in attentional performance may also result from
graded variation in attentional control on a moment-to-
moment basis (e.g., Adam et al. 2015), or from shifts in atten-
tion to task-unrelated thought (e.g., mind wandering;
Smallwood and Schooler 2015). Indeed, increased research
on mind wandering and related phenomena (e.g., Cheyne
et al. 2009;Selietal.2013; Smallwood and Schooler 2015),
and the behavioral consequences of variability in attention and
associated functional brain networks (e.g., Adam et al. 2015;
Bellgrove et al. 2004; Weissman et al. 2006), has motivated
attempts to reconcile accounts of the vigilance decrement with
these more transient attentional lapses and fluctuations that
occur during ongoing task performance (Langner and
Eickhoff 2013; Thomson et al. 2015). This work suggests that
variability of response times may partly reflect graded fluctu-
ations in attention or episodes of task-unrelated thought. We
therefore examined response time variability over the course
of performance to characterize how ongoing fluctuations in
attentional stability are influenced by longitudinal processes
of aging and continued practice.
The primary study aims were to investigate the mainte-
nance of training-related changes in response inhibition, reac-
tion time variability, and vigilance over a 7-year period, and to
assess the moderating influences of aging-related declines in
performance and individual differences in continued medita-
tion practice. We hypothesized that practitioners would main-
tain some attentional benefits of training, and that continued
practice of meditation following training would be associated
with this maintenance. We also predicted an interaction be-
tween the amount of continued practice and age-related de-
clines in sustained attention and response inhibition. Older
practitioners who devoted greater time to meditation practice
in the years following training were expected to show reduced
effects of age-related decline, in contrast to individuals who
engaged in comparatively less continued practice.
Method
Participants
Sixty experienced meditation practitioners were assigned to
either an initial training (N= 30) or wait-list control (N=30)
group through stratified random assignment; groups were
matched on age (M= 48.96 years at study assignment, range
=22–69), gender, prior meditation experience, and baseline
personality variables (see MacLean et al. 2010;Sahdraetal.
2011, for full recruitment and matching criteria). During an
initial 3-month retreat (retreat 1), training participants resided
and practiced meditation at Shambhala Mountain Center
(SMC) in Red Feather Lakes, Colorado. During retreat 1,
wait-list control participants traveled to SMC for week-long
assessment periods but otherwise maintained their daily rou-
tines at home between assessments. Approximately 3 months
after retreat 1, these same wait-list control participants re-
ceived formally identical training during a second 3-month
residential retreat at SMC (retreat 2; n=29).
1
All participants
were invited to participate in three follow-up assessments con-
ducted approximately 6 months (M= 6.6 months, range =
4.7–11.9), 1.5 years (M= 17.9 months, range = 15.6–20.2),
and 7 years (M= 81.9 months, range = 73.3–93.9) following
the conclusion of their respective training periods. Follow-up
attrition was generally low (>70% retention at each assess-
ment; see Table 1for sample size at each assessment). All
participants were compensated $20 per hour of data
collection.
Meditation Training
Meditation training occurred under guidance of B. Alan
Wallace, a Buddhist teacher and contemplative scholar.
Training included shamatha techniques designed to foster
calm sustained attention on a chosen object, and complemen-
tary techniques, known as the Four Immeasurables (compas-
sion, loving-kindness, empathetic joy, and equanimity), aimed
at generating benevolent aspirations for the well-being of one-
self and others (Wallace 2006,2011). Primary practice in-
volved mindfulness of breathing, in which attention is drawn
to the tactile sensations of the breath. Participants also prac-
ticed attending to the arising of mental content (e.g., thoughts,
perceptions, sensations), a technique known as settling the
mind into its natural state, and focusing attention on the sense
of awareness itself, known as shamatha without a sign
(Wallace 2006,2011). Participants met twice daily for group
practice and discussion, devoted about 6 h of their remaining
day to solitary shamatha meditation, and about 45 min to Four
Immeasurables meditation. In addition to these formal practice
sessions, participants were encouraged to maintain mindful,
present-centered awareness throughout their day, and met with
Dr. Wallace privately once a week for guidance and advice.
Full details regarding the techniques employed and training
time dedicated to each practice can be found in Sahdra et al.
(2011) and Rosenberg et al. (2015).
1
One wait-list control participant withdrew prior to retreat 2 for reasons un-
related to the intervention.
JCognEnhanc(2018)2:259–275 261
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At the 7-year follow-up assessment, participants were
asked to estimate the amount of time they spent meditating
outside of formal retreat settings (i.e., daily, non-intensive
practice) across the follow-up period. Participants estimated
their current weekly practice time, then adjusted these values
to estimate their total practice hours for the previous year,
before finally providing adjusted estimates for each preceding
year. Practice estimates were then summed over the entire
follow-up period (M= 3127.95 h, median = 1896, range =
249–14,900). In addition, participants were asked to estimate
the total number of days they spent in formal retreat (i.e.,
intensive) practice across this period (M=293.19daysonre-
treat, range = 0–2125). Among participants with available
data (n= 40), all reported some form of continued meditation
practice across this follow-up interval; 85% attended at least
one meditation retreat; 55% reported that Dr. Wallace
remained one of their primary meditation teachers; and 60%
directly identified shamatha meditation or mindfulness of
breathing as one of their primary meditation practices.
Procedure
Retreat 1 training participants were tested at the beginning
(preassessment), middle (midassessment), and end
(postassessment) of retreat 1. Wait-list control participants
were assessed at the beginning, middle, and end of both med-
itation retreats, first serving as control participants for retreat
1, then as active training participants for retreat 2. Finally,
participants in both groups were tested at each of the three
follow-up assessments (6-month, 1.5-year, and 7-year). See
Table 1for an overview of the assessment schedule and RIT
task parameters.
At each assessment, participants completed an initial dis-
crimination threshold procedure followed by the 32-min con-
tinuous RIT.
2
Participants responded to commonly occurring
non-target (long line) stimuli while withholding behavioral
responses to rare target (short line) stimuli. All procedures
were approved by the institutional review board of the
University of California, Davis, and all participants gave full
informed consent.
Threshold The discrimination threshold procedure (~ 10 min)
was designed to equate task demand across participants and to
calibrate individual task difficulty (see MacLean et al. 2009).
Participants maintained fixation on a small dot at the center of
the screen while single gray vertical lines appeared one at a
time against a black background. Each line stimulus was pre-
sented for 150 ms. Avisual mask pattern was presented before
and after the line stimulus for 100 ms. The mask was com-
prised of small lines (0.07° wide and 0.28° to 0.45° long)
positioned throughout a 5.0°× 1.0° space surrounding the fix-
ation point. The mask pattern varied randomly on each trial
and was not presented concurrently with line stimulus presen-
tation. The inter-stimulus interval varied randomly but was
2
The full RIT was administered at all assessments, excluding the 2-year fol-
low-up assessment for retreat 1 training participants (see Table 1and BFollow-
up Assessments^section in Method).
Table 1 Task parameters and descriptive statistics for RIT-dependent measures
Measure Training Follow-up
Pre Mid Post 6-Month 1.5-Year 7-Year
R1 Training group
N30 30 30 28 22 21
Age 50.25 (12.6) 50.34 (12.6) 50.44 (12.6) 50.35 (12.5) 50.69 (11.9) 56.19 (12.2)
Re-parameterized Yes Yes Yes Yes Yes Yes
Discrimination 3.77 (.39) 4.11 (.18) 4.14 (.27) 3.89 (.23) 4.10 (.44) 3.90 (.55)
Accuracy (A) 0.80 (.05) 0.88 (.06) 0.88 (.06) 0.88 (.07) 0.90 (.07)
RTCV 0.25 (.07) 0.24 (.06) 0.24 (.06) 0.26 (.06) 0.26 (.06)
R2 Training group
N29 29 29 27 22 23
Age 47.51 (15.5) 47.60 (15.5) 47.70 (15.5) 48.46 (14.9) 51.38 (14.9) 54.79 (15.4)
Re-parameterized Yes No No No No Yes
Discrimination 4.03 (.31) 4.08 (.22) 4.11 (.23) 4.15 (.24) 4.11 (.28) 3.96 (.43)
Accuracy (A) 0.88 (.05) 0.91 (.05) 0.92 (.05) 0.91 (.05) 0.92 (.05) 0.89 (.07)
RTCV 0.26 (.08) 0.22 (.06) 0.20 (.07) 0.25 (.06) 0.26 (.06) 0.27 (.07)
Note: means and standard deviations (in parentheses) are provided for participant age and RIT performance measures. The number (N) and mean age of
participants with complete data at each assessment are indicated for each group. Re-parameterized indicates whether the RIT target was pre-set (No) or
re-thresholded (Yes) at a given assessment. Discrimination is the achieved mean visual angle of the PEST procedure. Accuracy (perceptual sensitivity, A)
and RTCV (reaction time coefficient of variation) are computed across the full 32-min RIT
262 J Cogn Enhanc (2018) 2:259–275
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constrained to a mean of 1850 ms and a range (rectangular
distribution) of 1550–2150 ms. Participants were asked to
respond as quickly and accurately as possible with the left
mouse button (right index finger) to frequent long lines
(70% of stimuli) and to withhold responses to rare short lines
(30% of stimuli). Sound feedback was provided for correct
and incorrect responses.
The length of the short-line target was adjusted according
to parameter estimation through sequential testing (PEST;
Taylor and Creelman 1967). This procedure was used to es-
tablish the target line length that can be correctly discriminated
at a pre-determined accuracy rate for a given participant,
which defined an individual’sdiscrimination threshold in
units of visual angle. A larger threshold value indicates a lon-
ger short-line target, and thus better discrimination. PEST ac-
curacy was set to 85% at the retreat 1 preassessment; at all
other assessments, accuracy was set at 75%. This change in
procedure was implemented to ensure high task demand for
the remainder of assessments, after we failed to observe reli-
able vigilance decrements at 85% difficulty (Sahdra et al.
2011).
RIT Participants completed the 32-min RIT immediately fol-
lowing the PEST threshold procedure (960 trials in total).
Stimulus and response parameters were identical to the thresh-
old procedure except that (1) the length of the target line
remained constant throughout the task, (2) targets occurred
less frequently (10% of all stimuli totaling 96 target lines),
and (3) sound feedback was not present.
For each group and assessment, the short-line target was
individually determined based on a participant’s PEST dis-
crimination threshold using one of two target-setting manipu-
lations: either (1) difficulty was adjusted by re-parameterizing
the target stimulus to a participant’s current PEST discrimina-
tion threshold, or (2) difficulty was pre-set to a participant’s
previously measured discrimination threshold. When re-pa-
rameterized, the RIT target length was determined from the
PEST immediately preceding the RIT at that same assessment
point. This manipulation of target length across assessments
was informed by our observation (described in MacLean et al.
2010) that systematic improvements in discrimination thresh-
olds may have limited our ability to observe training-related
performance improvements in performance accuracy.
Response inhibition accuracy was quantified using the
non-parametric index of perceptual sensitivity, A(Zhang and
Mueller 2005). Hits were defined as correct inhibitions to
targets and false alarms as incorrect inhibitions to non-targets.
Aranges from 0 to 1, with 0.5 indicating chance performance
and 1 perfect performance. To compare accuracy from retreat
1 preassessment (set at 85% threshold level) to all remaining
assessments (set at 75% threshold level), levels of Aat the
initial assessment were adjusted to estimate performance at
75% [adjusted A= (original A× .75) / .85], an approach
consistent with the methods of our prior report (Sahdra et al.
2011). Reaction time variability was quantified as the reaction
time coefficient of variability (RTCV = standard deviation RT
/ mean RT) for non-target trials (864 trials), where lower
RTCV values indicate lower reaction time variability. For each
participant, perceptual sensitivity (A) and RTCV were calcu-
lated for the overall task and for each of eight 4-min contigu-
ous trial blocks (120 trials per block).
Training Assessments Retreat assessments were conducted in
darkened, sound-attenuated testing chambers located in the
dormitory where participants resided and practiced medita-
tion. Stimuli were delivered on an LCD monitor (Viewsonic
VX-922) while participants maintained a viewing distance of
57 cm from the screen. In retreat 1, the target stimulus was re-
parameterized at each assessment; in retreat 2, the target was
pre-set to each individual’s retreat 2 preassessment PEST
threshold. Thus, target length was not re-parameterized at
the retreat 2 mid- or postassessments, but was instead held
constant to equate stimulus parameters across the second
retreat.
Follow-up Assessments Each participant was provided a 14′′
IBM T-40 ThinkPad laptop, with detailed instructions for as-
sembling an in-home testing environment, setting dim ambi-
ent lighting, and maintaining a viewing distance of 57 cm. For
retreat 1 training participants, target length was again re-
parameterized at the 6-month and 7-year follow-up assess-
ments; these participants did not complete the RIT at the 1.5-
year follow-up assessment, instead completing only the
threshold procedure (see Table 1). For retreat 2 participants,
target length was again pre-set to each individual’sretreat2
preassessment threshold for the 6-month and 1.5-year follow-
up assessments; at the 7-year assessment, however, target
length was re-parameterized. The decision to re-parameterize
the target length for both participant groupsat the final follow-
up was based on the supposition that visual acuity or executive
function may have changed substantially since the retreat 2
preassessment, thus making participants’previous target line
length inordinately challenging.
3
For follow-up assessments
where targets were pre-set, stimuli sizes were scaled to main-
tain the same visual angle for both laptop and laboratory ver-
sions of the task.
Analysis
Multi-level models implemented with SAS PROC MIXED
version 9.4 were used to analyze longitudinal changes in dis-
crimination, accuracy (perceptual sensitivity, A), and RTCV.
3
Target length at the 7-year assessment did not significantly differ from the
pre-set threshold used at all other assessments for retreat 2 participants, t(22) =
0.45, p=.658.
JCognEnhanc(2018)2:259–275 263
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Significance of random effects was evaluated using log-
likelihood tests of change in model fit (−2ΔLL), estimated
using restricted maximum likelihood. Reported models were
estimated using full maximum likelihood, and fixed effects
were evaluated using Satterthwaite approximated degrees of
freedom (reported to the nearest integer).
Growth curve models describe the mean trajectory of
change in terms of an intercept (i.e., starting point) and slope
(i.e., rate of change), with random effects representing
between-person variability in these parameters (Ferrer and
McArdle 2010; Hoffman 2015). Retreat measurements were
spaced at equal intervals, whereas the timing of follow-up
assessments varied both within and between individuals. To
characterize change during retreat, we modeled a slope across
fixed training assessments (pre- = 0, mid- = 1, and
postassessment = 2), with a statistically significant slope indi-
cating group-level change. To characterize change across fol-
low-up, we modeled a slope over years since retreat (YSR;
scaled in years), where YSR reflects the yearly change in
performance since end of retreat (postassessment = 0 years).
A statistically significant YSR slope indicates group-level
change (e.g., improvement or decline) across follow-up as-
sessments. For models including both retreat and follow-up
assessments, these parameters were included as piecewise
slopes representing separable components of change attribut-
able to training and YSR respectively (Hoffman 2015).
Random effects were included when significant to allow
for individual differences in the intercept, or slope of perfor-
mance across blocks, training, or YSR. The effect of block
(block 1 = 0) represents the linear rate of change (magnitude
of the vigilance decrement) across the eight 4-min segments of
the RIT. Age was centered such that 0 indicates a participant
who was 65 years old at the end of training (age at
postassessment=65 years). When included in models with
follow-up assessments, the effect of age represents between-
person effects of participant age, whereas YSR reflects within-
person change across years of follow-up (Hoffman 2015).
Visual inspection of age trends suggested quadratic trajecto-
ries in performance as participants aged. Quadratic effects are
reported where significant.
Results
Summary statistics for perceptual discrimination, accuracy
(perceptual sensitivity, A), and reaction time variability across
all assessments are reported in Table 1.
Longitudinal Training and Maintenance in Retreat 1
We first analyzed longitudinal change across training (pre-,
mid-, and postassessment) and years of follow-up (6 month,
1.5 year, 7 year) for retreat 1 training participants. For each
measure, we fit an initial model describing change across
block, retreat, and YSR (reported in Table 2). For accuracy
and RTCV, we then examined interactions between block and
training, and block and YSR. Finally, we examined age as a
predictor of performance. Figure 1depicts mean Aand RTCV
for the retreat 1 training group across RIT blocks at each
assessment. Figure 2depicts observed changes in discrimina-
tion, A, and RTCV across YSR for each individual, with the
intercept indicating performance at postassessment (YSR = 0)
and the trajectory representing performance over YSR.
Discrimination The inclusion of a random slope for YSR (−
2ΔLL(3) = 21.9, p< .001) significantly improved model fit.
There were significant linear, β= 0.545, p< .001, and qua-
dratic, β=−0.201, p< .001, trajectories across retreat, but
no significant linear yearly change over YSR, β=−0.019,
p= .184 (see Fig. 2a). Training participants’discrimination
threshold was estimated to increase (p< .001) by .344° of
visual angle from pre- to midassessment. This rate then
slowed over time such that participants increased (p<.001)
a total of .286° of visual angle from pre- to postassessment.
Finally, we included age as a model predictor. Age did not
significantly predict discrimination, β=−0.0013, p= .689,
and there were no higher-order interactions of age with train-
ing or YSR.
Accuracy The inclusion of random slopes for block (−
2ΔLL(2) = 20.8, p< .001), training (−2ΔLL(3) = 34.8,
p<.001),YSR (−2ΔLL(4) = 112.7, p< .001), and quadratic
training (−2ΔLL(5) = 39.4, p< .001) significantly improved
model fit. We observed a significant vigilance decrement (i.e.,
effect of block) with an estimated decline (p<.001)of−.0084
units of Aat each RIT block. Random effects confidence in-
tervals (CI) suggested that 95% of participants showed a dec-
rement between −.017 and .0004 units of Afor each block. In
addition, we observed significant linear, β=0.126, p<.001,
and quadratic, β=−0.045, p< .001, slopes across training
assessments, indicating significant increases in accuracy over
retreat. Compared to preassessment, participants increased
(p< .001) an estimated total of .081 units of Aby
midassessment, and an estimated total (p< .001) of .071 units
by postassessment.
There were, however, no significant linear changes in A
across YSR, β= 0.004, p= .119, 95% CI [−.0012, .0097]
(see Fig. 2b). This non-significant change across YSR offers
no affirmative statistical evidence in support of maintenance.
We therefore further evaluated the estimated change across
YSR using TOST equivalence procedures (Lakens 2017).
Specifically, we examined the years for which the total accu-
mulated change was significantly smaller than a minimally
meaningful effect, defined as half the total increase in accura-
cy over retreat (Δ
L
=−0.035, Δ
U
= 0.035). The accumulated
change across follow-up was practically equivalent to zero
264 J Cogn Enhanc (2018) 2:259–275
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from the end of retreat to at least year 4, after which the 90%
CI of the remaining years’estimates overlapped with the up-
per equivalence bound (see Fig. 3). Maintenance over years 5
to 7 was thus statistically undetermined.
We next examined whether the within-task performance
decrement changed across retreat assessments or YSR.
There was no significant interaction between block and linear,
β=−0.004, p= .390, or quadratic training, β= 0.002,
p= .439, suggesting that the vigilance decrement was unaf-
fected by training in retreat 1. The effect of block, however,
was significantly attenuated across YSR, β= 0.0007,
p= .037. Finally, we examined the effects of aging on re-
sponse inhibition accuracy. Age did not significantly predict
A,β=−0.0003, p= .611, and there were no higher-order inter-
actions between age, block, training, and YSR.
Reaction Time Variability Random slopes for block (−
2ΔLL(2) = 42.9, p< .001), training (−2ΔLL(3) = 53.4,
p<.001),linearYSR(−2ΔLL(4) = 54.6, p< .001), and qua-
dratic YSR (−2ΔLL(5) = 64.6, p< .001) all significantly im-
proved model fit. We observed a significant linear effect of
block, indicating an average increase (p< .001) of .006 units
of RTCV per block. Random effects CI suggested that 95% of
participants showed a per-block change in RTCV between
−.005 and .017 units. We also observed a significant linear
effect of training, β=−0.013, p= .003, indicating significant
Table 2 Growth models of
longitudinal training and
maintenance in retreat 1 training
participants
Model effects Parameter estimate (SE)
Discrimination Accuracy RTCV
Fixed effects
Intercept 3.766 (.056)** 0.829 (.008)** 0.233 (.011)**
Linear block −0.008 (.001)** 0.006 (.001)**
Linear training 0.545 (.115)** 0.126 (.017)** −0.0125 (.004)**
Quadratic training
2
−0.201 (.053)** −0.045 (.008)**
Linear YSR −0.019 (.014) 0.004 (.003) 0.056 (.018)**
Quadratic YSR
2
−0.007 (.003)**
Random effects
Intercept variance 0.032 (.013)** 0.001 (.001)** 0.003 (.001)**
Block variance 0.00002 (.00001)* 0.00003 (.00001)**
Training variance 0.006 (.002)** 0.0003 (.0001)*
YSR variance 0.002 (.001)* 0.0001 (.0001)** 0.007 (.003)**
Training
2
variance 0.002 (.001)**
YSR
2
variance 0.0001 (.0001)**
Intercept-block covariance 0.0001 (.0001)* 0.0001 (.0001)
Intercept-training covariance −0.0003 (.0008) −0.0006 (.0003)
Intercept-YSR covariance 0.005 (.003) −0.00002 (.0001) 0.002 (.001)
Intercept-training
2
covariance −0.00005 (.0004)
Intercept-YSR
2
covariance −0.0002 (.0002)
Block-training covariance −0.0002 (.0001) 0.0000004 (.00003)
Block-YSR covariance −0.00002 (.00002) −0.0002 (.0001)
Block-training
2
covariance 0.0001 (.0001)*
Block-YSR
2
covariance 0.00002 (.00002)
Training-YSR covariance −0.0001 (.0002) −0.0008 (.001)
Training-training
2
covariance −0.003 (.001)*
Training-YSR
2
covariance 0.0001 (.0001)
YSR-training
2
covariance −0.00004 (.0001)
YSR-YSR
2
covariance −0.001 (.0004)**
Residual variance 0.062 (.008)** 0.004 (.0002)** 0.002 (.0001)**
Fit statistics
−2 Log-likelihood −69.5 −2831.4 −3522.6
Note: maximum likelihood estimates are reported for piecewise longitudinal analyses of RIT measures across
retreat and years since retreat (YSR). Standard errors are reported in parentheses
2
denotes a quadratic parameter
*p< .05; **p<.01
JCognEnhanc(2018)2:259–275 265
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reductions in RTCV. Ninety-five percent of individuals had a
slope between −0.044 and .019 units of RTCV across retreat
assessments. Finally, we observed significant linear, β=
0.056, p= .006, and quadratic, β=−0.007, p= .008, slopes
across YSR (see Fig. 2c), indicating that participants lost the
benefits of training in the years following retreat, but that this
rate of loss slowed and then reversed over time. One year after
postassessment (YSR = 1), participants were estimated to
have increased (p= .005) .048units of RTCV, whereas 7 years
later, the estimated total increase (p= .031) was .031 units.
No significant interaction between block and training,
β= 0.0012, p=.202,wasobservedwhenincludedinthe
model. There were significant interactions between block
and both the linear, β=−0.009, p= .001, and quadratic,
β= 0.0012, p= .001, YSR trends, however, indicating that
the within-task increase in RTCV was significantly re-
duced in the years following training. Finally, age did
not significantly predict RTCV, β=−0.0004, p= .556, in
retreat 1 participants. There were no higher-order interac-
tions between age, block, training, or YSR.
Fig. 1 Mean performance trajectories for accuracy (A) and reaction time variability (RTCV) across the eight contiguous 4-min blocks of the RIT. Retreat
1 and retreat 2 training participants are shown across training (black) and follow-up (red) assessments
266 J Cogn Enhanc (2018) 2:259–275
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Summary Significant increases in accuracy were observed
across retreat 1 training assessments, which were then defini-
tively maintained for at least 4 years following retreat.
Improvements (i.e., reductions) in reaction time variability
were also observed across retreat, but were lost over the course
of follow-up. Interestingly, within-task decrements in perfor-
mance accuracy and RTCV were attenuated across years of
follow-up, but not during retreat, suggesting possible benefits
of long-term continued practice. No significant effects of ag-
ing were observed.
Fig. 2 Observed individual performance trajectories for discrimination
threshold (in units of visual angle), accuracy (perceptual sensitivity, A),
and reaction time variability (RTCV) across years since retreat (YSR).
The intercept represents performance at retreat postassessment (YSR = 0)
and the model-estimated group trajectory is indicated in red
JCognEnhanc(2018)2:259–275 267
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Longitudinal Training and Maintenance in Retreat 2
We next examined longitudinal change across blocks, training,
and YSR in retreat 2 training participants. Parameter estimates
arereportedinTable3,andFig.1depicts mean Aand RTCV
across blocks at each assessment. In retreat 2, the RIT target was
pre-set to each participant’s preassessment discrimination thresh-
old for all remaining assessments, excluding the 7-year follow up
assessment, for which target length was re-parameterized (see
Tab le 1).
Discrimination Model fit was significantly improved by inclu-
sion of a random slope for YSR (−2ΔLL(3) = 31.8, p<.001)
only. We observed a significant linear increase in discrimina-
tion across training, β= 0.060, p= .002, and a significant
yearly decrease over YSR, β=−0.027, p= .029 (see Fig.
2d), suggesting training-related improvements in discrimina-
tion capacity that were then lost over years of follow-up. There
were no significant effects of age on discrimination threshold,
β=−0.004, p=.101.
Accuracy Inclusion of random slopes for block (−2ΔLL(2) =
23.9, p<.001), and YSR (−2ΔLL(4) = 139.5, p<.001), sig-
nificantly improved model fit; the random effect of training (−
2ΔLL(3) = 5.1, p= 0.139), however, did not improve fit, sug-
gesting minimal influence of individual differences on change
in accuracy across training. The fixed effect of block was
significant, β=−0.008, p< .001, indicating an average per-
block reduction of −.008 units of A. The random effects CI
suggested that 95% of participants had a vigilance decrement
between −.016 and −.0005 units of A. In addition, we ob-
served significant linear, β=0.045, p< .001, and quadratic,
β=−0.012, p=.012, trends across training, such that partic-
ipants improved in accuracy during retreat, but that the rate of
improvement slowed across assessments. Compared to
preassessment, participants increased (p< .001) an estimated
total of .033 units of Aby midassessment, and an estimated
total (p< .001) of .043 units by postassessment.
Although no overall significant yearly changes in Awere
observed following retreat, β=−0.004, p=.128, 95% CI[−
0.0094, 0.0013] (see Fig. 2e), we observed significant indi-
vidual differences in rates of yearly change: 95% of individ-
uals demonstrated changes ranging from −0.028 to .020 units
of Aper each year of follow-up. To formally evaluate mainte-
nance, TOST equivalence procedures were used to examine
the years for which the total accumulated change in accuracy
over YSR was significantly smaller than half the total increase
accrued over retreat (Δ
L
=−0.022, Δ
U
= 0.022).
Accumulated change was equivalent to zero until at least the
second year following retreat, after which maintenance was
statistically undetermined (see Fig. 3).
We next investigated whether within-task decrements in A
changed across retreat assessments or YSR. There was a sig-
nificant interaction between block and the linear effect of
training, β=0.0022, p= .030, suggesting that the magnitude
of the vigilance decrement was attenuated across training.
Specifically, the performance decrement over blocks (β=−
0.008) was estimated to diminish by.002 units at each assess-
ment. The interaction between block and the quadratic effect
of training was not significant, β= 0.0009, p=.644,andthere
was no change in the vigilance decrement over YSR, β=−
0.0006, p=.118,95% CI [−0.0013, 0.00015]. These patterns
suggest that meditation training improved performance and
moderated the vigilance decrement, and that these benefits
did not change over years of the follow-up.
Finally, we examined age as a predictor of response inhi-
bition accuracy. There was a significant main effect of age on
A,β= 0.0007, p= .048. We next explored interactions be-
tween age and other model effects. Age was unrelated to block
Fig. 3 Model estimated change in accuracy (perceptual sensitivity, A)
based on the linear slope across years since retreat (YSR) for aretreat 1
and bretreat 2.Ninety and 95% confidence intervals are displayed around
each yearly estimate with a thick line and thin line respectively.
Horizontal black dotted lines indicate the lower (Δ
L
) and upper (Δ
U
)
equivalence bounds for a meaningful effect, defined as half the total
increase in accuracy accrued during training for retreat 1 (Δ
L
=−0.035,
Δ
U
= 0.035) and retreat 2 (Δ
L
=−0.022, Δ
U
=0.022)
268 J Cogn Enhanc (2018) 2:259–275
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or to the rate of improvement across training, but was a sig-
nificant moderator of change after retreat, β=−.0004,
p= .026. Specifically, older participants declined at a greater
rate across years of follow-up than did younger participants.
Moreover, although retreat 2 participants retained training im-
provements across the follow-up on average, yearly losses
were estimated to occur specifically in older (i.e., age = 65)
participants, β=−0.009, p= .036. Figure 4adepicts
Table 3 Growth models of
longitudinal training and
maintenance in retreat 2 training
participants
Model effects Parameter estimate (SE)
Discrimination Accuracy RTCV
Fixed effects
Intercept 4.023 (.045)** 0.900 (.007)** 0.248 (.013)**
Block −0.008 (.001)** 0.004 (.001)**
Linear training 0.060 (.019)** 0.045 (.010)** −0.067 (.011)**
Quadratic training
2
−0.012 (.005)* 0.019 (.005)**
Linear YSR −0.027 (.012)* −0.004 (.003) 0.045 (.009)**
Quadratic YSR
2
−0.006 (.001)**
Random effects
Intercept variance 0.035 (.011)** 0.001 (.0003)** 0.004 (.001)**
Block variance 0.00002 (.00001)*
Training variance 0.0006 (.0002)**
YSR variance 0.002 (.001)** 0.0002 (.0001)** 0.002 (.001)**
YSR
2
variance 0.00004 (.00001)**
Intercept-block covariance 0.0001 (.00004)**
Intercept-training covariance −0.001 (.0004)*
Intercept-YSR covariance 0.0008 (.002) −0.0002 (.0001) −0.0001 (.001)
Intercept-YSR
2
covariance 0.00003 (.0001)
Block-YSR covariance 0.00001 (.00001)
Training-YSR covariance −0.0004 (.0003)
Training-YSR
2
covariance 0.00005 (.00004)
YSR-YSR
2
covariance −0.0002 (.0001)**
Residual variance 0.027 (.004)** 0.004 (.0002)** 0.003 (.0001)**
Fit statistics
−2 Log-likelihood −29.6 −3403.4 −3473.5
Note: maximum likelihood estimates are reported for piecewise longitudinal analyses of RIT measures across
retreat and years since retreat (YSR). Standard errors are reported in parentheses
2
denotes a quadratic parameter
*p< .05; **p<.01
Fig. 4 Observed individual
performance trajectories for
accuracy (A) and reaction time
variability (RTCV) as a function
of age for retreat 2 follow-up
assessments. The model-
estimated aging slope of best fit is
indicated in red
JCognEnhanc(2018)2:259–275 269
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individual subject trajectories of Aat each follow-up assess-
ment as a function of age.
Reaction Time Variability Random effects for the linear slope
of training (−2ΔLL(3) = 55.2, p< .001), and both linear (−
2ΔLL(4) = 78.5, p< .001) and quadratic slopes of YSR (−
2ΔLL(6) = 50.5, p< .001), significantly improved model fit.
We observed a significant within-task increase of .004 RTCV
units across blocks, β=0.004,p< .001. There were also sig-
nificant linear, β=−0.067, p< .001, and quadratic, β=0.019,
p< .001, decreases in RTCV across retreat assessments. The
quadratic trend indicates that RTCV was reduced across train-
ing, but that the rate of decrease slowed across assessments. At
midassessment, participants showed an estimated −.048
(p< .001) unit reduction in RTCV compared to
preassessment, while the estimated reduction (p< .001) from
pre- to postassessment was −.058 units. Significant linear,
β= 0.045, p< .001, and quadratic, β=−0.006, p< .001,
trends in YSR (see Fig. 2f) were also observed. Although
participants gradually lost the benefits of training over fol-
low-up, the rate of loss slowed over time: 1 year after
postassessment, participants showed an estimated increase
(p< .001) of .039 units of RTCV, whereas 7 years later the
estimated increase (p= .025) was .044 units.
We observed no significant interactions between block and
any other linear or quadratic trajectories, indicating that the
per-block increase in RTCV was unaffected by training or
YSR. Finally, although there was no significant linear effect
of age, β=0.003,p= .145, we observed a significant quadrat-
ic, β=0.00012,p= .026, effect of age on RTCV. The relation-
ship between RTCVand age was ∪-shaped, such that RTCV
was reduced in middle age and increased in older age.
Figure 4b depicts individual subject trajectories of RTCV at
each follow-up assessment as a function of age.
Summary Training participants demonstrated overall im-
provements in discrimination and performance accuracy dur-
ing retreat 2, and significant attenuation of the vigilance dec-
rement. No statistically significant changes in overall accuracy
and vigilance were observed over years of follow-up, with
equivalence testing suggesting that changes in accuracy were
maintained below half the level of total retreat gains for ap-
proximately 2 years. However, when pooled across both re-
treats, total change in accuracy across YSR was closer to zero,
such that the weighted estimate (β= 0.0004, 90% CI [−
0.021,0.021]) remained within the equivalence bounds up to
7 years following retreat. Thus, the true degree of maintenance
was likely underestimated across individual retreats. As in
retreat 1, RTCV was reduced during training in retreat 2, but
improvements were then lost following retreat. Age was a
significant predictor of RTCV, and interacted with rate of
change over YSR such that losses in performance accuracy
were estimated to occur specifically in older participants.
Meditation Practice Moderates Age-Related Decline
in Performance
In a final set of analyses, we examined whether the observed
age-related declines in performance among retreat 2 partici-
pants were moderated by meditation practice across the
follow-up period. Estimates of continued practice (M=
2834.2 h, range = 406–11,900) and intensive retreat participa-
tion (M= 176.7 days on retreat, range = 0–1460) were avail-
able for 19 participants. These variables were entered sepa-
rately into models for Aand RTCV across YSR, after remov-
ing participants for whom no practice estimates were avail-
able. Hours of practice were rescaled to aid interpretation of
model parameters (1 unit represents 100 h).
We first included estimates of continued practice (in hours)
over follow-up as a predictor of performance accuracy.
Parameter estimates from this model are reported in Table 4.
We observed a significant interaction between hours and YSR,
β= 0.001, p= .029, and a significant three-way interaction
between age, hours, and YSR, β= 0.00004, p= .018.
Figure 5depicts model-estimated simple slopes across YSR
Table 4 Effects of aging and practice hours across follow-up on retreat
2 performance accuracy
Model effects Parameter estimate (SE)
Fixed effects
Intercept 0.999 (.047)**
Linear block −0.010 (.002)**
Linear YSR −0.033 (.011)*
Age 0.002 (.002)
Hours of practice −0.003 (.002)
Age-YSR interaction −0.001 (.0004)*
Age-hours interaction −0.0001 (.0001)
YSR-hours interaction 0.001 (.001)*
YSR-age-hours interaction 0.00004 (.00002)*
Random effects
Intercept variance 0.001 (.001)
Block variance 0.00003 (.00002)
YSR variance 0.0001 (.00004)*
Intercept-block covariance 0.0002 (.0001)
Intercept-YSR covariance −0.0003 (.0001)
Block-YSR covariance 0.00002 (.00002)
Residual variance 0.004 (.0003)**
Fit statistics
−2 Log-likelihood −1014.5
Note: maximum likelihood estimates are reported for RIT accuracy (A)
across years since retreat (YSR) in retreat 2 training (n= 19) participants.
Age is centered to postassessment and 65 years. Hours of practice are
scaled to 100 h per unit. Standard errors are reported in parentheses
*p<.05; **p<.01
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at low (1250 h), medium (2000 h), and high (2750 h) values of
continued practice for middle-aged (45 years) and older indi-
viduals (65 years). As can be seen in Fig. 5, older individuals
who engaged in a relativelysmaller amount of continued prac-
tice over YSR were predicted to experience greater losses of
training-related benefits in A. Middle-aged individuals did not
experience training losses over YSR, irrespective of their con-
tinued practice acrossthis interval. For older individuals, how-
ever, there was a marginally significant slope across YSR at
lower practice estimates (1250 h), β=−0.052, p= .099, that
reached statistical significance at approximately 750 h of es-
timated practice.
We next examined whether continued practice moderat-
ed the effects of aging on RTCV (see Table 5for
parameter estimates). We observed significant linear, β=
0.017, p< .001, and quadratic, β=0.0004, p< .001, trends
across age, a significant effect of total practice hours, β=
−0.008, p< .001, and a significant interaction between
hours and the linear, β=−0.0004, p=.004, and quadratic
age parameters, β=−.000007, p= .019, for RTCV. Thus,
in contrast to performance accuracy, continued meditation
practice appeared to directly moderate age-related declines
in reaction time variability. Figure 6illustrates model-
estimated simple slopes for low (1250 h), medium
(2000 h), and high (2750 h) values of practice across
continuous age. As shown in Fig. 6, individuals who en-
gaged in relatively fewer hours of practice over YSR dem-
onstrated greater age-related impairments in RTCV.
Finally, intensive retreat practice (in days) over the follow-
up period was examined as a predictor of Aand RTCV.
Although there were no significant effects on performance
accuracy, more reported days on retreat over follow-up was
a marginally significant predictor of lower overall RTCV, β=
−.00007, p= .058. There were no significant interactions.
Discussion
The present study represents the most extensive longitudinal
examination of meditation training-related improvements in
sustained attention to date. Using a sustained response inhibi-
tion task, performance was examined across six assessment
waves over more than a 7-year training and follow-up interval.
Table 5 Effects of aging and practice hours across follow-up on retreat
2 RTCV
Model effects Parameter estimate (SE)
Fixed effects
Intercept 0.401 (.029)**
Linear block 0.004 (.002)**
Linear YSR 0.003 (.003)
Linear age 0.017 (.003)**
Quadratic age
2
0.0004 (.0001)**
Hours of practice −0.008 (.001)**
Age-hours interaction −0.0004 (.0001)**
Age
2
-hours interaction −0.000007 (.000003)*
Random effects
Intercept variance 0.003 (.001)**
YSR variance 0.0001 (.0001)**
Intercept-YSR covariance −0.0006 (.0003)*
Residual variance 0.005 (.0003)**
Fit statistics
−2 Log-likelihood −1004.6
Note: maximum likelihood estimates are reported for RIT RTCV across
years since retreat (YSR) in retreat 2 training (n= 19) participants. Age is
centered to postassessment and 65 years. Hours of practice are scaled to
100 h per unit. Standard errors are reported in parentheses
2
denotes a quadratic parameter
*p<.05; **p<.01
Fig. 5 Model estimates of linear change in accuracy (A) across years
since retreat for retreat 2 training participants at low (1250 h), medium
(2000 h), and high (2750 h) levels of practice hours for middle-aged
(45 years) and older individuals (65 years)
Fig. 6 Model estimates of reaction time variability (RTCV)atlow
(1250 h), medium (2000 h), and high (2750 h) levels of practice hours
as a function of age for retreat 2 training group participants
JCognEnhanc(2018)2:259–275 271
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We observed robust improvements in perceptual discrimina-
tion, response inhibition, vigilance, and RTCV across medita-
tion retreat assessments, and extended our prior investigation
(Sahdra et al. 2011) to examine the long-term maintenance of
training-related improvements. We observed no significant
changes in response inhibition accuracy across the 7-year fol-
low-up interval, and improvements were maintained above
half the level of overall training gains for several years follow-
ing the end of retreat. Furthermore, aging-related performance
deficits were moderated by continued meditation practice:
older participants who reported engaging in more meditation
practice following formal training demonstrated attenuated
aging-related performance deficits.
Our previous report (Sahdra et al. 2011) detailed relations
between increases in performance accuracy on retreat and
growth in self-reported adaptive psychological functioning. In
the present study, we incorporated reaction time variability as
an additional outcome of training, reporting significant reduc-
tions in RTCVacross both intensive retreat interventions. These
findings appear to generalize to other training styles and pe-
riods of intensive training (i.e., 1-month training; Zanesco et al.
2013), and provide additional support to the growing body of
evidence that meditation training influences the stability of
goal-directed attention. Increased stability of attention is a cen-
tral organizing feature of the benefits of many meditation train-
ing regimens (Lutz et al. 2015). Measures of attentional stabil-
ity, including reaction time variability, therefore hold promise
for informing emerging models of the distinctive phenomeno-
logical qualities and neurocognitive processes that characterize
meditation-related development of cognitive capacities.
Attempts to sustain attention over time can impose system-
wide cognitive consequences on processes underlying perfor-
mance on tasks like the RIT, including discrimination of percep-
tually challenging stimuli, inhibition of competing response ten-
dencies, and maintenance of attention in an ongoing and stable
manner. Consistent with theories of resource depletion (Langner
and Eickhoff 2013), our findings support the well-characterized
decline in perceptual sensitivity (i.e., performance accuracy) ob-
served in tasks of sustained attention, and further suggest that
increases in reaction time variability across task duration are a
measurable behavioral consequence of sustaining attention
(Wang et al. 2014; Zanesco et al. 2013). This increased variabil-
ity may reflect graded variation in participants’ability to main-
tain attention and regulate behavior over time, or increases in the
frequency or disruptiveness of task-unrelated thought (Seli et al.
2013). Importantly, however, training was associated with atten-
uated within-task performance decrements for accuracy only.
Data characterizing the long-term maintenance of
meditation-related improvements are critical for understand-
ing the generalized benefits of contemplative or mindfulness-
based approaches to cognitive training. We employed growth
curve models to examine separable sources of change across
an extensive longitudinal duration: changes over the course of
retreat, changes over years since retreat, and changes associ-
ated with aging. Using this approach, we observed distinct
patterns of maintenance across dependent measures. No sig-
nificant changes in response inhibition accuracy were ob-
served over the 7-year follow-up period for either retreat in-
tervention. Moreover, equivalence tests supported the asser-
tion that performance accuracy was definitively maintained
for several years following retreat; the accumulated change
in accuracy was significantly smaller than any meaningful
amount (or half the total improvement accrued during retreat)
up to 5 years later, demonstrating the durability of training
effects well beyond the intervention itself.
The vigilance decrement and the within-task increase in
RTCV were attenuated across years of follow-up for retreat
1, whereas we observed no significant changes in vigilance
across follow-up for retreat 2. Improvements in perceptual
discrimination, however, were lost at a gradual rate over years
of follow-up, while improvements in RTCV observed in both
retreats were lost shortly following the end of training. It is
possible that measures of accuracy and vigilance reflect com-
ponents of sustained attention that are more robust to long-
term maintenance than reaction time variability. Once im-
proved through training, cognitive capacities relating to the
global control of attention, target detection, stimulus process-
ing, and response execution may endure to a greater degree
than those supporting reductions in ongoing attentional and
behavioral fluctuations. It is also presently unclear how factors
relating to the training intervention itself, such as individual
differences in duration and intensity of practice during the
retreat, may have supported long-term maintenance. Clearly,
more research is needed to better clarify the relative suscepti-
bility of attentional markers to training interventions.
Growth curve models are characterized by higher levels of
statistical power than more traditional analyses (e.g., mean
comparisons between assessment waves; Muthén and
Curran 1997), and offer analytic advantages when measure-
ment intervals vary between assessments or individuals, or
when individuals lack complete data across measurement oc-
casions. Nevertheless, limited sample variability may have
reduced our ability to detect significant changes in some pa-
rameters over the course of the 7-year follow-up, or to observe
direct associations between maintenance of training improve-
ments and continued meditation practice. For example, it is
possible that additional assessment waves or greater sampling
density may have increased our ability to statistically detect
longitudinal changes in accuracy over the follow-up.
Moreover, our sample was comprised of experienced medita-
tors who all engaged in considerable amounts of ongoing
practice. Future studies should therefore investigate associa-
tions between performance and practice hours using samples
with greater variability in practice times, or with designs that
encourage practitioners to engage in different amounts or
styles of practice after periods of formal training to better
272 J Cogn Enhanc (2018) 2:259–275
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

explore the association between continued practice and main-
tenance. Finally, procedurally altering the stimulus parameter-
ization at the 7-year follow-up for the retreat 2 group may
have partially confounded longitudinal estimates. However,
while there was a noticeable drop in performance accuracy
between the 1.5- and 7-year assessments, levels of accuracy
at 1.5 years were nearly identical to those at the conclusion of
retreat.
Consistent with prior research (e.g., Fortenbaugh et al. 2015),
we observed curvilinear age-related deficits in RTCV, such that
reaction time variability improved through middle-age then de-
clined in later life. In contrast, no age-related declines were found
for performance accuracy overall. These findings are in line with
cross-sectional evidence noting a relative lack of negative asso-
ciation between age and cognition in meditation practitioners
(Gard et al. 2014). Indeed, continued meditation practice ap-
peared to moderate aging effects in performance accuracy and
RTCV in our sample. Although older individuals failed to main-
tain training-related accuracy improvements on average, older
practitioners reporting larger amounts of continued practice
maintained improvements over the follow-up. These findings
provide initial, yet provocative, evidence that continued medita-
tion practice may be associated with a moderation of age-related
decline in attentional components known to be sensitive to aging
(Fortenbaugh et al. 2015; MacDonald et al. 2006; Smittenaar
et al. 2015).
Taken together, the procedural differences in threshold pa-
rameterization across our interventions highlight an interest-
ing dichotomy between corrective approaches for ameliorat-
ing aging-related cognitive deficits. One approach is to tailor
demanding tasks to match individuals’cognitive capabilities,
thereby countering or offsetting losses attributable to age-
related decline; an alternative approach might attempt targeted
improvement of cognitive deficits through cognitive training,
meditation, or related interventions. The present findings offer
tentative evidence that age-related influences on response in-
hibition accuracy and reaction time variability may be buff-
ered through both of these mechanisms, at least among expe-
rienced practitioners: in retreat 1, we observed no age-related
decline in training participants when the resource demands of
the task were adaptively adjusted across assessments to an
individual’s performance threshold; in retreat 2—when exter-
nal demands were held constant—we observed a typical pat-
tern of age-related decline, which was moderated by levels of
continued meditation practice.
It is possible that other aspects of participants’lifestyle or
personality might have contributed to the observed modera-
tion of age-related deficits by continued meditation practice.
Although groups were matched on multiple demographic and
personality factors prior to study onset (see MacLean et al.
2010), our sample presumably differed from the general pop-
ulation on various attributes relevant to ongoing cognitive
health. Indeed, socio-economic status and other lifestyle
factors have long been thought to influence the rate of cogni-
tive decline across the lifespan, motivating researchers to in-
vestigate these factors as potential targets for intervention. Yet
recent work has suggested otherwise (e.g., Early et al. 2013;
Salthouse 2014), in that lifestyle factors may primarily influ-
ence individuals’baseline scores, rather than rates of cognitive
decline. Nevertheless, causation cannot be attributed to the
moderation of aging-related decline with continued medita-
tion practice in our sample. It is therefore critical that more
research is conducted before advocating meditation practice as
an intervention for cognitive aging.
Indirectly, our findings also present a sobering appraisal of
the viability of short-term or non-intensive mindfulness inter-
ventions for improving sustained attention in a lasting manner.
The participants in our sample were experienced practitioners
who engaged in amounts of practice across the 7-year follow-
up far in excess of standardized mindfulness-based interven-
tions (Creswell 2017), which are clearly not feasible for the
wide application of interventions targeting cognitive aging.
Although participants reported engaging in periods of inten-
sive retreat practice—as well as non-intensive daily practice—
across the 7-year follow-up interval, systematic group-level
improvements were largely constrained to the targeted 3-
month intervention. These findings support the principle that
continued practice over long-term intervals (even large
amounts of regular practice across 7 years’time as in our
sample) may not be sufficient to improve sustained attention
in experienced practitioners. Instead, periods of intensive
training, coupled with well-timed assessments, may be neces-
sary to produce and reveal robust and lasting cognitive im-
provements. In contrast to these group-wide patterns, we ob-
served significant individual differences in the rates of change
across the 7-year follow-up, indicating that performance im-
proved over time for some individuals. Future research should
continue to investigate the factors that underlie long-term
maintenance and cognitive change.
In conclusion, the present study suggests that intensive and
continued meditation is associated with enduring improve-
ments in sustained attention, supporting the notion that the
cognitive benefits of dedicated mental training may persist
over the long-term when promoted by a regimen of continued
practice. Although participants did not generally improve over
years of daily meditation practice, continued meditation ap-
pears to benefit practitioners by preserving gains accrued dur-
ing periods of intensive formal training and by altering trajec-
tories of age-related cognitive decline. Continued meditation
practice seems to be associated with substantial experiential
and developmental influences on practitioners’attentional ca-
pacities over the lifespan. These findings have broad implica-
tions for meditation and mindfulness-based approaches to
cognitive training and raise important questions regarding
the limits of meditation practice on the plasticity of human
cognition.
JCognEnhanc(2018)2:259–275 273
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Acknowledgements We thank Jennifer Pokorny, Stephen Aichele, Tonya
Jacobs, David Bridwell, Quinn Conklin, and Sarah Huffman for their help
in collecting a portion of these follow-up data. Major support for original
data collection and follow-up analyses were provided by Fetzer Institute
Grant #2191 and John Templeton Foundation Grant #39970 to C.D.S.,
gifts from the Hershey Family, Baumann, Tan Teo, Yoga Science, and
Mental Insight Foundations, anonymous and other donors all to C.D.S.,
and the Santa Barbara Institute for Consciousness Studies.
Compliance with Ethical Standards
Conflict of Interest The authors declare that they have no conflict of
interest.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appro-
priate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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