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Mindfulness
ISSN 1868-8527
Mindfulness
DOI 10.1007/s12671-011-0083-0
The Disciplined Mind: Associations
Between the Kentucky Inventory of
Mindfulness Skills and Attention Control
Brian M.Galla, T.Sigi Hale, Anshu
Shrestha, Sandra K.Loo & Susan
L.Smalley
1 23
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ORIGINAL PAPER
The Disciplined Mind: Associations Between the Kentucky
Inventory of Mindfulness Skills and Attention Control
Brian M. Galla & T. Sigi Hale & Anshu Shrestha &
Sandra K. Loo & Susan L. Smalley
#
Springer Science+Business Media, LLC 2011
Abstract In an attempt to replicate and clarify previous
research, we examined the associations between the
Kentucky Inventory of Mindfulness Skills (KIMS) and
measures of sustained (Continuous Performance Test;
CPT) and executive (Stroop) attention in a community
sample of adults (n=106). After controlling for age,
gender, e ducation, socio-economic status, IQ, and depres-
sion and anxiety, analyses indicated that the KIMS-
Observe scale predicted enhanced Stroop performance
and reduced variability in attentional processing on the
CPT. Post hoc analyses also provided evidence that the
associative strength between KIMS-Observe and reduced
CPT reaction time variability increased as a function of
task block, suggestive of a protective effect against
attentional lapses due to prolonged exposure to the CPT.
While the present study failed to replicate previously
reported associations between KIMS and attentional
functioning, the consistency of current findings to con-
ceptualizatio ns of mindfulness sug gests that KIMS-
Observe taps important attentional processes thought to
underlie mindfulness.
Keywords Mindfulness
.
Self-report
.
Attention
.
Cognitive
control
Introduction
Mindfulness refers to a state of consciousness involving a
receptive attention to and awareness of present moment
experiences (Analayo 2003; Brown and Ryan 2003). Newly
developed self-report measures attempt to capture individ-
ual differences in various qualities of mind hypothesized to
underlie such a mindful state of consciousness (Baer et al.
2004; Baer et al. 2008; Brown and Ryan 2003). Some of
the qualities assessed include the perceived ability to notice
subtle perceptual events, to act in a deliberate, conscien-
tious fashion, and to maintain a non-reactive attitude toward
unpleasant emotional experiences. While these question-
naires do appear sensitive to improvements resulting from
mindfulness training (Carmody et al. 2009), a variety of
health-related outcomes (Brow n et al. 2007), as well as
aspects of improved neurobiological functioning (Creswell
et al. 2007), the construct validity of these scales as
measures of “mindfulness ” remains unclea r (Davidson
2010; Grossman 2008; Grossman and Van Dam 2011;
Rosch 2007). This is partly attribut able to the fact that there
is curren tly no consensus regarding the inventory of skills
that constitute mindfulness (Baer 2011), and that self-report
measures of such psychological states are highly vulnerable
to bias and recall error. Ideally, these instruments should be
tested against objective behavioral and physiologi c indices
of mindfulness (Robertson et al. 1997; Smallwood et al.
2007); however, it is not yet clear what measures are best
suited to serve in this capacity (Davidson 2010).
Because mindfulness is fundamentally about paying
attention (Analayo 2003), one possible avenue to further
explore the nature of self-report mindfulness scales might
be to examine their relation to attentional capacities. While
“mindfulness” is not simply reducible to “attention” (Bodhi
2011; Siegel 2007), it clearly is a compo nent part of
B. M. Galla (*)
:
T. S. Hale
:
S. L. Smalley
Mindful Awareness Research Center, Semel Institute for
Neuroscience and Human Behavior, University of California,
760 Westwood Plaza, Rm 47-444, Box 951759, Los Angeles, CA
90095, USA
e-mail: gallabrian@gmail.com
A. Shrestha
:
S. K. Loo
Center for Neurobehavioral Genetics, Semel Institute for
Neuroscience and Human Behavior, University of California,
Los Angeles, CA, USA
Mindfulness
DOI 10.1007/s12671-011-0083-0
Author's personal copy
mindfulness, and there fore may usefully serve as a proxy
that is also amenable to quantitative assessment. The fact
that individuals naturally vary in attentional control (Posner
and Rothbart 2007), coupled with the existence of multiple
objective measures of attention, greatly facilitates this
approach. Indeed, Davidson (2010) proposed that a link
between self-reported mindfulness and performance on
multiple objective measures of attention might help verify
the self-report measures’ utility as an index of “mindful”
functioning. As mindfulness seems to be especially reliant
upon both sustained and executive attention (Bishop et al.
2004; Lutz et al. 2008), the degree to which self-report
measures capture “mindful” functioning should be most
evident in these capacities. To this end, the current study
examined the associations between one widely used self-
report mindfulness measure, the Kentucky Inventory of
Mindfulness Skills (KIMS; Baer et al. 2004), and aspects of
sustained and executive attention to further assess the
nature of this self-report mindfulness scale , and to better
understand whether, and how, it maps onto various features
of attention.
As of this writing there is a paucity of studies examining
the relations between the KIMS and attentional processes.
Schmertz et al. (2009) found no evidence for associations
between the KIMS-Act with Awareness scale and indices of
sustained a ttention (omission errors and reaction time
variability) on the Continuous Performance Test (CPT;
Conners 2000), although they did not report on the other
mindfulness facets assessed by the KIMS. Moore and
Malinowski (2009) found that KIMS-Act with Awareness
and A ccept scores most strong ly related to executive
attention performance on a paper-and-pencil Stroop test
(Golden 1978), and sustained attention performance on the
d2-concentration and endurance test (Bricke nkamp and
Zilmer 1988). Finally, Josefsson and Broberg (2010)
explored the relations between the Five Factor Mindfulness
Questionnaire (FFMQ; Baer et al. 2008)—the second
iteration of the KIMS—and measures of both sustained
(Sustained Attention to Response Test, SART; Robertson et
al. 1997) and executive (computerized Stroop test) atten-
tion. Higher FFMQ-Describe scores significantly predicted
fewer errors of sustained attention (FFMQ-Observe and
Non-judge were also marginally significance), and pre-
dicted fewer Stroop errors.
Taken together, these studies indicate somewhat incon-
sistent and unexpected findings. Schmertz et al. (2009)
found no association between the CPT and the Act with
Awareness scale, but as noted, they did not report any other
KIMS scales, namely “Observe”, which is also thought to
be attention related (Baer et al. 2004). Furthermore, they
reported on only two of four common CPT performance
measures. Moreover, the two studies that found significant
effects surprisingly indicated that “non-attentional” KIMS
measures were associated with attention performance on the
Stroop task, with each implicating a different scale (e.g.,
Describe, Accept). In order to help clarify and extend this
work, the current study reexamined the relationship
between the CPT and KIMS subscales, but included all
standard performance scales; and it reexamined the rela-
tionship between KIMS and the Stroop test. Furthermore,
whereas the previous three studies relied on relatively small
student samples (e.g. , Josefss on and Br oberg 2010;
Schmertz et al. 2009), the current study was able to utilize
a larger community sample and control for additional and
important factors that might influence cognit ive func-
tioning, such as age, educational attainment, IQ, socio-
economic status, and depression and anxiety. Controlling
for factors known to be related to cognitive functioning
will help reduce Type I errors, and can provide an
additional degree of confidence in any significant
findings. Of the three studies cited above, only two
controlled for the effects of age (Josefsson and Broberg
2010; Moore and Malinowski 2009), and none explored
the effects of other demographic and psychological
variables. Due t o the scarcity and inconsistency of
previous findings on this topic, we did not make specific
predictions regarding the associations between the KIMS
and measures of sustained and executive attention. Our
goal was to clarify and extend previous findings in a larger
and more carefully controlled community sample.
Method
Participants and Procedure
Subjects were 106 parents (51% female, M
age
=44.7 years,
SD=6.29) drawn from a larger sample of 241 adults (130
families) identified from an ongoing molecular genetics
study of ADHD sibling pairs and their families (Smalley et
al. 2000). A previous study using the KIMS included the
larger sample of 241 (Smalley et al. 2009); however in the
current study, individuals were excluded from current
analyses if they met criteria for a lifetime diagnosis of
ADHD (n=106, 44%), bringing the total sample to 135.
Since the main goal of this study was to replicate , as closely
as possible, previously reported findings linking KIMS with
cognitive functioning, we limited our analyses to a
normative sample of adults. Because ADHD is class ified
as a neurodevelopmental disorder that c arries distinct
genetic, neural, and clinical properties (Li et al. 2006),
individuals with a lifetime diagnosis were excluded from all
analyses. Due to time constraints some participants were
unable to complete the entire battery of tests, leaving 106
(out of 135) individuals with complete data. Of the included
participants, 6.8% met criteria for one or more mood
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disorders (major depressive disorder, dysthymic disorder,
mania, hypomania, and/or cyclothymia), and 24.3% met
criteria for one or more anxiety disorders (generalized anxiety
disorder, separation anxiety disorder, social phobia, simple
phobia, panic disorder with or without agoraphobia, and/or
post-traumatic stre ss disorder). The current sample was
composed primarily of Caucasians (79%), but also included
Hispanics (6%), Black/African Americans (5%), Asian/
Pacific Islanders (8%), and individuals from mixed/other
ethnic backgrounds (2%). A majority of participants in the
current sample had 4-year college or professional degrees
(77%), and were in the middle range of socio-economic status
according to the Hollingshead (1957) scale where 1 is the
highest and 7 is the lowest (M
ses
=3.22, SD=1.99).
Detailed information regarding informed consent and the
assessments for ADHD and other DSM-IV Axis I disorders
has been previously published (Smalley et al. 2000).
During a single laboratory session, participants completed
a battery of self-report measures and standardized neuro-
psychological tests assessing executive and sustained
attention (Loo et al. 2008). The CPT paradigm described
in “Measures” was added later in the course of the study, so
only a subset of participants (n=82 ) were able to complete
the task. Participants were also administered the Vocabulary
and Block Design portions of the Wechsle r Adult Intelli-
gence Scale, Third edition (WAIS-III; Wechsler 1997),
allowing for estimation of full IQ for control purposes.
Measures
Mindfulness w as assessed using the Kentucky Inventory
of Mindfulness Skills (Baer et al. 2004), a 39-item
questionnaire that taps four mindfulness-related s kills:
(1) Acting with Awareness (e.g., “When I do t hings, my
mind wander s off an d I’m easily distracted”), (2) Observ-
ing ( e.g., “I notice changes in my body, such as whether
my breathing slows down or speeds up”), (3) Describing
(e.g., “I’mgoodatfindingwordstodescribemyfeel-
ings”), and (4) Non-judgmental Acceptance (e.g., “Itell
myself that I shouldn’tbefeelingthewayI’mfeeling”).
The scales can also be combined to form a composite
score of mindfulness. The items are rated on a 5-point
Likert scale, from 1 (never or very rarely true) to 5 (very
often or always true) with higher scores reflecting greater
mindfulness. The KIMS-Total score (α=0.85), as well as
the subscales Act with Awareness (α=0.74), Observe (α=
0.84), Describe (α=0.88), and Accept (α=0.83) showed
high internal consistency.
Executive a ttention was assessed using the Golden
version (1978) of the Stroop test. The Stroop test assesses
participants’ ability to overcome habitual responding and
activate more subdominant response tendencies by naming
the font color of a written word rather than the semantic
meaning of the color word (MacLeod 1991). The Golden
version consists of three blocks (word-naming, color-
naming, and color–word-naming), although the third block
(color–word-naming) specifically relates to executive atten-
tion. The total number of items processed during the 45-s
time limit on each task block was included as measures of
participants’ performance.
Sustained attention was assessed using a 14-min Con-
tinuous Performance Test (Conners 1994). During the task,
subjects monitor a central fixation on a computer screen
while single capital letters are sequentially and centrally
presented during six continuous blocks of 20 trials with
varying interstimulus intervals (ISI). The order of ISI-block
presentation is randomized within subjects. Subjects press
the space bar using their dominant hand with every letter
presentation except for the letter “X”, which occurs on 10%
of the trials within a given ISI-block. Subjects’ sustained
attention performance was assessed using the following
CPT measures: (1) commission errors: a failure to inhibit
response when an “X” is presented, (2) omission errors: a
failure to respond when any letter other tha n “X” is
presented, (3) mean reaction time, and (4) hit reaction time
standard deviation: trial-by-trial variability in response
times.
Results
Correlations Between KIMS and At tentional Control
Major study variables were first checked for normality and
outliers using SPSS (2007). CPT omission errors failed to
meet assumptions of normality and were log transformed to
approximate normal distribution. Furthermore, three out-
liers (scoring more than 3 SDs below the mean) were
identified on the KIMS composite score, and these
individuals were removed f rom subsequent analyses,
leaving a final sample of 103 individuals for analyses.
Significant correlations emerged between various
demographic variables and major study variables, and
were therefore included in all regression analyses
reported in the next section. Correlations between the
KIMS scales and attentional control indices are presented
in Table 1. Positive correlations were noted between
KIMS-Observe and Describe (r=0.38, p=0.000), KIMS-
Act with Awareness and Accept (r=0.19, p=0.06). Negative
correlations were observed between KIMS-Observe and
Accept (r=−0.41, p=0.000). KIMS-Describe scale was
unrelated to the Acting with Awareness (r=0.10, p=0.31)
and Accept (r=0.07, p=0.50) scales in our sample.
Few significant correlations emerged between KIMS scale
scores and attentional control indices. The KIMS-Observe
scale was positively correlated with the number of items
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processed on the Stroop color-naming (r=0.21, p=0.04) and
color–word-naming (r=0.22, p=0.03) blocks, and negatively
correlated with hit reaction time (RT) variability (SD) on the
CPT (r=−0.24, p=0.03). Finally, the KIMS-Accept scale
was inversely correlated with the number of items processed
on the Stroop word-naming block (r=−0.24, p=0.01).
Regression Analyses
To further explore the significant correlations noted in the
previous section, multiple regressions were conducted with
controls for age, gender, education, socio-economic status,
and full IQ (results are summarized in Table 2). KIMS-
Observe scores remained a significant predictor for the
number of items processed on the Stroop color–word block
(β=0.22, t=2.41, p=0.02) and CPT RT variability (β=−0.26,
t=−2.30, p= 0.03). KIMS-Observe scores no longer predicted
the number of items processed on the Stroop color-naming
block (β=0.16, t=1.70, p=0.09). Similarly, KIMS-Accept no
longer predicted items processed on the Stroop word-naming
block (β=−0.18, t=1.93, p=0.06).
To account for other transient factors that might
influence performance on attention tasks, we conducted
two further regression analyses, this time with added
controls for depression and a nxiety. The inclusion of
psychiatric variables did not alter the significant associa-
tions between KIMS-Observe and performance indices. It
should be noted that anxiety did contribute a negative main
effect on items processed on the Stroop color–word block
(β=−0.23, t=−2.31, p=0.02). No other significant effects
between depression and anxiety and cognitive task perfor-
mance emerged.
Table 2 Regression analyses predicting attention performance from
KIMS-Observe Score
Model B SE β t
Stroop performance (color–word block)
(Constant) 41.26 7.46 5.53**
Age −0.52 0.12 −0.40 −4.21**
Gender 2.42 1.61 0.15 1.5
SES 0.25 0.39 0.06 0.64
Education 2.39 1.88 0.13 1.27
Full IQ 0.12 0.05 0.24 2.52*
KIMS-Observe 0.22 0.09 0.22 2.41*
CPT reaction time variability (SD)
a
(Constant) 100.23 50.64 1.98*
Age 0.53 0.76 0.08 0.7
Gender −2.62 9.62 −0.03 −0.27
SES −5.23 2.35 −0.25 −2.22*
Education −28.24 11.31 −0.31 −2.50*
Full IQ 0.71 0.34 0.25 2.10*
KIMS-Observe −1.25 0.54 −0.26 −2.30*
*p<0.05; **p<0.01
CPT Continuous Performance Test, KIMS Kentucky Inventory of
Mindfulness Skills
a
N=82
Table 1 Descriptive statistics and correlations between major study variables ( N=103)
Variable M SD123456789
Age 44.7 6.29 –
Gender 0.51 0.5 0.33** –
SES 3.22 1.99 −0.07 −0.26** –
Education 2.21 1.15 0.07 0.02 −0.25* –
Full-scale IQ 110.04 16.10 0.14 0.05 −0.14 0.37** –
KIMS-Observe 34.54 8.07 0.01 −0.18† −0.05 0.09 0.09 –
KIMS-Describe 29.11 5.95 0.01 −0.06 −0.06 −0.09 0.06 0.38** –
KIMS-Act 32.67 5.50 0.14 0.30** −0.18† −0.02 −0.09 −0.04 0.10 –
KIMS-Accept 32.88 6.22 0.16† 0.16† −0.09 0.02 −0.05 −0.41** 0.07 0.19 –
Stroop items processed (word) 102.36 16.34 −0.19 −0.20* 0.07 0.21* 0.28** 0.11 0.08 0.06 −0.24*
Stroop items processed (color) 72.85 12.07 −0.23* −0.26** 0.09 0.17† 0.26** 0.21* 0.17 −0.04 −0.01
Stroop items processed (color–word) 42.88 8.11 −0.31** −0.03 −0.02 0.19† 0.25* 0.22* 0.14 −0.01 −0.11
CPT omission errors
a
7.20 9.34 0.04 0.21* −0.02 −0.16 −0.06 −0.08 0.01 −0.11 0.11
CPT commission errors
a
10.26 6.36 0.08 0.23* 0.10 −0.11 0.00 −0.09 0.08 −0.03 0.00
CPT mean reaction time
a
434.33 60.18 0.03 −0.18 −0.09 −0.10 0.06 −0.08 −0.03 −0.02 0.07
CPT RT variability (SD)
a
118.12 40.99 0.09 0.11 −0.19† −0.14 0.12 −0.24* −0.16 −0.09 0.12
CPT Continuous Performance Test, KIMS Kentucky Inventory of Mindfulness Skills
†p<0.10; *p<0.05; **p<0.01
a
N=82
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Post Hoc Analyses: Relations Between KIMS-Observe
and CPT RT Variability as a Function of CPT Task Block
Evidence suggests that the ability to maintain a stable,
sustained attention wanes over time, with attention being
subject to a number of vulnerabilities, including fatigue
(Davies and Parasuraman 1982; Parasuraman 1984) and
mind wandering (Smallwood and Schooler 2006). Increases
in lapses of attention can be seen, in part, through increased
trial-by-trial RT variability over time on the CPT. Given
recent empirical evidence that intensive meditation training
was associated with improved vigilance (reduced attention-
al fatigue) over time (MacLean et al. 2010), we further
examined whether the associations observed between
KIMS-Observe and CPT RT variability were a function of
task block. As shown in Table 3, both the range and mean
levels of RT variability (SD) increased as a function of CPT
task block, suggestive of an increase in attentional lapses
over time.
Figure 1 shows the correlation coefficients between
KIMS-Observe and CPT RT variability as a function of
task block. With each successive task block, the relations
between KIMS-Observe and CPT RT variability became
increasingly stronger and reached marginal significance in
block 5 (r=−0.20, p=0.07) and full significance in block 6
(r=−0.24, p=0.03). This finding suggests that individuals
higher in KIMS-Observe were less vulnerable to attentional
lapses over time as indexed by reduced trial-by-trial
reaction time variability.
Discussion
In the current study, we attempted to replicate, as closely as
possible, and to clarify previously reported findings on the
associations between a widely used self-report measure of
mindfulness (KIMS) and tests of sustained (CPT) and
executive (Stroop) attention. We sought to do this while
also addressing several methodological limitations of
previous research. Overall, the current results indicated that
individuals with high scores on KIMS-Observe were better
able to resolve cognitive conflict as measured by the Stroop
test, and showed less variability in their attentional
processing on the CPT. These associations remained
significant even after controlling for age, gender, education,
socio-economic status, full IQ, and mood and anxiety,
providing a measure of confidence in the results. Post hoc
analyses also provided evidence that the relations between
KIMS-Observe and CPT RT variability increased as a
function of task block.
Regarding performance on the CPT, our findings were
similar to that of Schmertz et al. (2009) in that we both
found no evidence for an association between KIMS-Act
with Awareness scores and CPT omission errors or reaction
time variability. However, our findings indicated that other
mindfulness facets assessed by the KIMS which were
excluded by Schmertz et al. (2009) were associated with
sustained attention performance. We showe d that scores on
KIMS-Observe, the perceived tendency to observe and pay
careful attention to subtle perceptual experiences, were
associated with reduced variability in attentional processing
on a measure of sustained attention. Variability in trial-by-
trial response times on a vigilance task is indi cative of
momentary lapses in attention from current processing
goals (Castellanos et al. 2006), due in part to task-unrelated
processes, like mind wandering (Smallwood et al. 2007).
Close monitoring of subjective experience includes the
ability to notice subtle fluctuations or lapses in attention (or
alertness) from goal-directed processes, and individuals
who are naturally more attuned to subtl e sensory experi-
ences are likely to be also more sensitive to the location and
quality of their attention. When current processing goals
demand the maintenance of a steady, sustained attention
across time, such awareness is likely to facilitate these
efforts. This steady and vivi d watchfulness, or monitoring,
Fig. 1 Correlation coefficients between KIMS-Observe and Contin-
uous Performance Test Reaction Time Variability (SD) as a function of
task block. †p<0.10; *p<0.05
Table 3 CPT reaction time variability (SD) across the six task blocks
Variable Minimum Maximum Range Mean SD
Block 1 52.67 260.08 207.41 111.49 43.04
Block 2 42.33 236.48 194.15 101.72 36.77
Block 3 54.19 255.36 201.17 107.07 45.93
Block 4 41.59 328.28 286.69 110.04 52.71
Block 5 43.67 374.46 330.79 112.04 55.52
Block 6 50.81 490.63 439.82 119.89 70.14
CPT Continuous Performance Test
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of current experience is also part and parcel of mindfulness
practice (Bishop et al. 2004; Bodhi 2011; Wallace and
Shapiro 2006). For example, Lutz et al. (2009) convinc-
ingly showed that intensive retreat practice resulted in
reduced trial-by-trial reaction time variability on a dichotic
listening task compared to a control group (but, interest-
ingly, did not result in group differences in mean reaction
time or increased target detection).
Post hoc analyses also provided evidence that the
associative strength between KIMS-Observe and CPT RT
variability increased as a function of task block. This
finding further supports the argument that close observation
of sensory experiences, as measured by the KIMS-Observe,
can facilitate more efficient and sustained attentional
functioning in demanding contexts, and may prevent
attentional lapses or fatigue (MacLean et al. 2010). Whether
these individuals are actually more resistant to attentional
fatigue over time, or are more capable of mustering limited
resources in the midst of fati gue (more resilient) is
unknown. Either way, it do es appear that individuals
scoringhighonKIMS-Observeshowedanincreased
capacity for sustained attention as the CPT progressed. It
should be kept in mind that these results are unique to this
study and until more research can confirm these findings,
they should be considered preliminary. Furthermore, it is
uncertain whether Schmertz et al. (2009) would have found
these results even if they had included the KIMS-Observe
scale in their study because the CPT utilized in their study
consisted of three blocks of 20 trials compared with six
blocks of 20 trials used in our study. Their task might not
have been long enough to produce a sufficient amount of
attentional fatigue we reported.
Regarding performance on the Stroop test, our findings
were inconsistent with previous research. We were unable
to replicate the findings of the two previous studies that
examined the associations between the KIMS and the
Stroop test (Josefsson and Broberg 2010; Moore and
Malinowski 2009). Furthermore, neither of those studies
revealed significant associations between KIMS-Observe
and Stroop performance that we showed in our analyses.
Moore and Malinowski (2009) did note significant negative
associations between KIMS-Observe and Stroop errors.We
were unable to examine this index in our samp le due to an
extremely low frequency and variation in error rate (Stroop
color–word errors: M=1.12, SD=1.40). It is quite possible
that the Stroop test implemented in our study was simply
too short (45 s per block) to reveal meaningful variation in
error rates that might be associated with KIMS-Observe
scores (compared with the 2-min task blocks used in Moore
and Malinowski’s(2009) study). Also, a unique aspect of
Moore and Malinowski’s(2009) study—that we did not
find in our sample—was the higher than usual correlations
between KIMS facets, indicating a possible lack of
differentiation between the subscales. Together, these
differences might have con tributed to the discre pant
findings between the current stu dy and M oore an d
Malinowski’s(2009) study regarding KIMS-Observe and
Stroop performance.
The associations Moore and Malinowski (2009) noted
between KIMS-Accept and Stroop performance—although
not duplicated in our study—is worthy of mention. Baer et
al. (2004 ) argued that non-eva luative processing of experi-
ence, tapped by the Accept scale, might
“encourage more
adaptive responding to problematic situations by preventing
automat ic, impulsive, maladap tive behavior s” (p. 194).
While the scale items might not be expli citly related to
attentional functioning, it is very plausible, given this
rationale, that Accept scores might track performance on
measures of cognitive inhibition and flexibility. As such,
the FFMQ-Non-reactivity subscale, which purportedly taps
the ability to inhibit emotional and/or behavioral reactivity
(Baer et al. 2008), might be even more suited for indexing
cognitive inhibition/flexibility than Observe or Accept, and
future research would do well to examine this scale for this
purpose.
We, as well as Moore and Malinowski (2009), were also
unable to replicate the associations between KIMS-
Describe and reduced Stroop interference showed by
Josefsson and Broberg ( 2010). These discrepancies might
be due in large part to the different Stroop administration
types (paper-and-pencil vs. computer-based) which have
been shown to produce different outcomes in the same
sample (Salo et al. 2001). In articulating their findings
however, Josefsson and Broberg (2010) argued that KIMS-
Describe might index a tendency to be more conscientious
when completing tasks, which in turn might facilitate
performance on the Stroop. While KIMS-Describe does
seem to be related to trait conscientiousness (Baer et al.
2004), the Act with Awareness scale is more strongly
associated with trait conscientiousness (Baer et al. 2004 )
but was not associated with Stroop performance in
Josefsson and Broberg’s(2010) study. Further replication
studies are needed to authenticate the findings reported by
Josefsson and Broberg (2010), and it will also be necessary
to clarify why exactly Describe should be associated with
attentional control (Grossman and Van Dam 2011).
While the current findings between KIMS-Observe and
Stroop do not e ntirely corroborate previously reporte d
findings, they make clear theoretical sense and help support
the findings from the CPT. More than anything, mindful-
ness is fundamentally about close, repetitive observation of
the experiential field (Bodhi 2011), and this is precisely the
quality of mind that KIMS-Observe attempts to capture.
Our results suggest that KIMS-Obser ve is related to
performance on the Stroop test, which require s the
inhibition of automatic responding. Any prolonged period
Mindfulness
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of sustaine d and vivid awareness on present moment
experience requires monitoring the focus of attention, disen-
gaging from distractions, and redirecting attention back to
current processing goals. As expected then, the practice of
mindfulness has been shown to reinforce a degree of
“deautomatization” from automatic tendencies of mind,
indexed by improved ability to resolve cognitive interference
on the Stroop test (Wenk-Sormaz 2005). Individuals naturally
more sensitive to subtle, low-intensity somatic, and percep-
tual experiences might maintain a more chronically activated
monitoring state (or, meta-awareness; Smallwood and
Schooler 2006), and may thus be more able to gather, when
appropriate, the cognitive resources to resolve the processing
conflict effectively.
Taken together, t he consi sten cy of our findings in
relation to current conceptualizations of mindfulness sug-
gests that KIMS-Observe taps important attentional pro-
cesses though t to underlie mindfulness. Linking self-report
measures of mindfulness to objective measures of attention
appears to represent one promising means to evaluate the
nature of these measures (Davidson 2010). However, this
approach also carries several methodological and concep-
tual issues that must also be addressed in future research.
First, and most importantly, it is not yet clear which
measures of attention are most suited to tap underlying
mental processes that support mindfulness. The wide array
of attention tasks used, and inconsistent findings in the
literature attest to the current lack of consensus on the
subject. One problem might stem from the fact that most
objective measures of attention are timed tests that direct
participants to respond to stimuli as quickly as possible.
Mindfulness practice seems to have little in common with
speed and fast responding, so research that focuses
exclusively on strict reaction time measures may be sli ghtly
mistargeted. For example, we offered evidence that KIMS-
Observe was related to better trial-by-trial attentional
stability (RT SD) and reduced exhaustion, but was
unrelated to mean reaction time on the CPT. This pattern
of findings has been shown in a host of other studies
examining the attentional impact of mindfulness practice.
Zeidan et al. (2010) found meditation practice improved
the number of consecutive correct responses on a working
memory task, but not processing speed. MacLean et al.
(2010) showed reduced fatigue following intensive retreat
practice, and Semple (2010) found improved target dis-
crimination following regular mindfulne ss practice. Simi-
larly, Lutz et al. (2009) found improved attentional stability
following intensive meditation retreat, but no improvements
in reaction time relative to a control group. The relations
between self-reported mindfulness and attention might also
be more effectively assessed in emotionally evo cative
situations that place a high degree of cognitive load on
participants (Ortner et al. 2007), as higher levels of
mindfulness might attenuate emotional reactivity. In sum,
measures of attention that rely less on speed, or where
speed is secondary to another ability (e.g., fatigue resis-
tance) appear more closely aligned with conceptualizations
of mindfulness as a monitoring faculty. Exploiting this
similarity might advance the field and help to clarify
lingering inconsistencies in the literature.
Second, non-reaction-time measures might be less
susceptible to other confounding factors, like motivation
and effort (Flehmig et al. 2007). It is quite possible to
perform well on certain attention tests through shear
cognitive will power (Tomporowski and Tinsley 1996),
and although mindfulness certainly involves attention, it
likely is not a necessary component to performing well on
tests of attention. Jensen et al. (2011) reported that some of
the results in the meditation literature might be confounded
by unaccounted motivational factors. This is a serious
challenge to existing empirical literature, and another
reason for future research to consider both selecting
attentional measures that are more resistant to effort (e.g.,
attentional blink test; Olivers and Nieuwenhuis 2005) and
controlling for constructs like motivation.
Finally, we must not assume that attentional measures
are the only worthwhile objective measures to examine the
nature of self-report measures. While we assessed the
relations between self-reported mindfulness and active
attentional performance, we do not assume that other facets
do not also measure important qualities of mind related to
mindfulness. There may be other qualities of psychological
functioning related to mindfulness (e.g., curiosity about and
openness to experience, the tendency to approach rather
than avoid discomfort, stress reactivity, and recovery, etc.)
that fell outside the purview of the current study. Future
studies should investigate the associations between
“non-
attentional” facets of mindfulness and related psychological
processes, although we encourage future research to avoid,
where ever possible, strict reliance on self-reports to draw
these linkages.
The current study expanded upon the growing litera-
ture documenting the associ ations between self-reported
mindfulness and attention functioning in several w ays.
First, we used a community sample of adults, rather than
relying on a convenience sample of college under-
graduates. Secondly, we also controlled for factors
significantly related to attentional co ntrol, such as
anxiety (Eysenck et al. 2007), as well as a number of
other potential demographic variables (e.g., IQ, education,
SES) that previous studies failed to include. Despite these
strengths, the study also had several limitations. As
mentioned above, our Stroop administration time was
relatively short and may have prevented us from finding
relations between other KIMS scales and/or error perfor-
mance (Moore and Malinowski (2009)useda2-min
Mindfulness
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Stroop block administration). Secondly, we included only
one measure of mindfulness and cannot speak to the
relations other mindfulness measures (e.g., Mindful
Awareness Attention Scale; Brown and Ryan 2003)may
exhibit with attention tasks. In conclusion, the results of
the current study offer useful data toward an evolving
cognitive understanding of mindfulness questionnaires.
Acknowledgments The research reported herein is supported by
NIMH MH058277 (SLS) and NINDS NS054124 (SKL). We wish to
thank all the families who participated in this research.
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