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Younger Adults Show Long-Term Effects of Cognitive Training on Broad Cognitive Abilities Over 2 Years

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In the COGITO study (Schmiedek, Lövdén, & Lindenberger, 2010), 101 younger adults practiced 12 tests of perceptual speed, working memory, and episodic memory for over 100 daily 1-hr sessions. The intervention resulted in positive transfer to broad cognitive abilities, including reasoning and episodic memory. Here, we examine whether these ability-based transfer effects are maintained over time. Two years after the end of the training, 80 participants returned for follow-up assessments of the comprehensive battery of transfer tasks. We found reliable positive long-term transfer effects for reasoning and episodic memory, controlling for retest effects by including participants from the original control group. This shows, for the first time, that intensive cognitive training interventions can have long-term broad transfer at the level of cognitive abilities. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
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BRIEF REPORT
Younger Adults Show Long-Term Effects of Cognitive Training on Broad
Cognitive Abilities Over 2 Years
Florian Schmiedek
Max Planck Institute for Human Development, Berlin, Germany,
and German Institute for International Educational Research
(DIPF), Frankfurt am Main, Germany
Martin Lövdén
Max Planck Institute for Human Development, Berlin, Germany;
Karolinska Institutet; and Stockholm University
Ulman Lindenberger
Max Planck Institute for Human Development, Berlin, Germany
In the COGITO study (Schmiedek, Lövdén, & Lindenberger, 2010), 101 younger adults practiced 12
tests of perceptual speed, working memory, and episodic memory for over 100 daily 1-hr sessions. The
intervention resulted in positive transfer to broad cognitive abilities, including reasoning and episodic
memory. Here, we examine whether these ability-based transfer effects are maintained over time. Two
years after the end of the training, 80 participants returned for follow-up assessments of the compre-
hensive battery of transfer tasks. We found reliable positive long-term transfer effects for reasoning and
episodic memory, controlling for retest effects by including participants from the original control group.
This shows, for the first time, that intensive cognitive training interventions can have long-term broad
transfer at the level of cognitive abilities.
Keywords: cognitive training, cognitive abilities, transfer effects, latent change score models, long-term
effects
Attempts to improve cognitive functioning with training inter-
ventions have a long history in psychology. For many years,
interventions used strategy instruction and practice on tasks from
psychometric test batteries of cognitive abilities, and at most these
interventions produced transfer effects (i.e., improvements on un-
trained tasks) that must be considered narrow (Noack, Lövdén,
Schmiedek, & Lindenberger, 2009). More recently, however, cog-
nitive training research has produced a number of findings that
paint a more positive picture of the effectiveness of practice-
induced changes of cognitive functioning. The most promising
findings come from trainings that (a) build on self-guided practice,
rather than instruction of strategies (cf. Hofland, Willis, & Baltes,
1981); (b) focus on the core capacities of working memory (WM;
e.g., Dahlin, Stigsdotter-Neely, Larsson, Bäckman, & Nyberg,
2008;Jaeggi, Buschkuehl, Jonides, & Perrig, 2008;Klingberg et
al., 2005; see Morrison & Chein, 2011, for review) or executive
functions like task switching (Karbach & Kray, 2009); and (c) use
computerized setups that adapt task difficulties to a continuously
challenging level. Holding individualized task-difficulty up high
creates a continuous mismatch of cognitive demands and individ-
ual functional supplies. Such mismatches, if present for a pro-
longed period, could have the potential to improve cognitive
processing efficiency rather than merely exploiting the available
behavioral flexibility with effective, but typically task-specific,
strategies (Lövdén, Bäckman, Lindenberger, Schaefer, & Schmie-
dek, 2010). As of recently, failed replications of WM training
studies have also been reported (Chooi & Thompson, 2012; Redick
et al., 2012), and critical reviews on WM training have appeared
(Melby-Lervåg & Hulme, 2013;Shipstead, Hicks, & Engle, 2012;
Shipstead, Redick, & Engle, 2012). Thus, the jury on the effec-
tiveness and efficiency of cognitive training is still out and await-
ing further empirical evidence that allows evaluating its useful-
ness.
This article was published Online First July 14, 2014.
Florian Schmiedek, Center for Lifespan Psychology, Max Planck Insti-
tute for Human Development, Berlin, Germany, and German Institute for
International Educational Research (DIPF), Frankfurt am Main; Martin
Lövdén, Center for Lifespan Psychology, Max Planck Institute for Human
Development, Berlin, Germany, and Aging Research Center, which be-
longs to the Karolinska Institutet and Stockholm University; Ulman Lin-
denberger, Center for Lifespan Psychology, Max Planck Institute for
Human Development, Berlin, Germany.
The COGITO Study was supported by the Max Planck Society, includ-
ing a grant from the innovation fund of the Max Planck Society (M.FE.
A.BILD0005); the Sofja Kovalevskaja Award (to Martin Lövdén) of the
Alexander von Humboldt Foundation donated by the German Federal
Ministry for Education and Research (BMBF); the German Research
Foundation (DFG; KFG 163); and the German Federal Ministry for Edu-
cation and Research (BMBF; CAI). Ulman Lindenberger was supported by
the Gottfried Wilhelm Leibniz Award of the DFG.
Correspondence concerning this article should be addressed to Florian
Schmiedek, German Institute for International Educational Research
(DIPF), Schloßstr. 29, 60486 Frankfurt am Main, Germany. E-mail:
schmiedek@dipf.de
This document is copyrighted by the American Psychological Association or one of its allied publishers.
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Developmental Psychology © 2014 American Psychological Association
2014, Vol. 50, No. 9, 2304–2310 0012-1649/14/$12.00 http://dx.doi.org/10.1037/a0037388
2304
To be of practical relevance for everyday competencies,
training-induced changes need to meet two criteria. First, changes
need to be located at the level of broad cognitive abilities, that is,
they have to reach beyond the acquisition of task-specific skills.
Second, changes need to be enduring, that is, maintained for some
time after the training intervention has ended (cf. Sternberg, 2008).
Ideally, training interventions enhance the long-term trajectory of
cognitive development, foster success in educational and profes-
sional settings, and extend the period in old age during which
individuals are able to live independently (Hertzog, Kramer, Wil-
son, & Lindenberger, 2008).
Empirically, the first criterion can be evaluated by investigating
the range of transfer effects. Effects observed on individual trans-
fer tasks, however, provide only weak evidence for improvements
in general cognitive abilities. If an ability (e.g., reasoning) had
indeed improved, one would expect that performance on indicator
tasks (e.g., Raven’s Advanced Progressive Matrices; Raven &
Horn, 2009) of this ability should improve. However, because
performance on observed tasks can be influenced by factors be-
yond the underlying ability, like measurement error or task-
specific skills, the practice of relying on individual indicators of a
given ability can easily lead to false positive findings (e.g., im-
provements due to the acquisition of task-specific skills) as well as
negative findings (e.g., due to lack of power because of improve-
ments in ability being blurred by task-specific variance and mea-
surement error) regarding the question of whether the underlying
ability has improved.
Therefore, studies on transfer of training need to investigate
whether transfer can be discerned at the level of cognitive abilities
(Lövdén et al., 2010;Noack et al., 2009;Schmiedek, Lövdén, &
Lindenberger, 2010;Shipstead et al., 2012). This requires assess-
ing transfer with broad selections of heterogeneous tasks that cover
the range of the target ability in a comprehensive manner and test
changes at the level of common factors of these tasks. Such
common factors represent sources of variance that are shared
across tasks and are therefore free from measurement error and
task-specific influences. Demonstrating transfer at this level pro-
vides a more solid basis for concluding that ability has improved
than focusing on the task level.
Using data from the COGITO study, in which 101 younger and
103 older adults practiced a battery of 12 cognitive tasks over 100
daily sessions, Schmiedek et al. (2010) could show that a cognitive
intervention can result in transfer at the ability level for reasoning
(i.e., fluid intelligence) and episodic memory in healthy younger
adults. In addition, transfer was observed on a factor of WM tasks
in both age groups. The tasks comprising this factor were struc-
turally similar to the trained ones but differed in task content.
Transfer of training was not reliable for reasoning and episodic
memory in the older adults, and for perceptual speed as well as for
a factor of complex span tasks of WM in both age groups.
Regarding the criterion of temporal preservation, there is evi-
dence that improvements can be maintained up to several years,
particularly for improvements on the trained tasks (e.g., Ball et al.,
2002) and for specific strategies and skills (e.g., Brehmer et al.,
2008;Klauer & Phye, 2008;Stigsdotter-Neely & Bäckman, 1993).
For long-term transfer effects, empirical evidence is scarcer. There
is some indication that transfer effects can be maintained up to 18
months (e.g., Borella, Carretti, Riboldi, & De Beni, 2010;Dahlin,
Nyberg, Bäckman, & Stigsdotter-Neely, 2008;Holmes, Gather-
cole, & Dunning, 2009;Li et al., 2008). Regarding the question of
transfer breadth, earlier studies are of limited value because they
were either confined to near transfer or to single indicator tasks per
target ability.
It is completely unknown whether transfer at the level of latent
ability factors induced by cognitive interventions can be main-
tained over longer periods of time (e.g., years). The COGITO
study provides an opportunity to address this question because
participants of the training and control groups came back for
follow-up assessments of the transfer tasks about 2 years after
posttest. Sample sizes at follow-up were sufficiently large to
investigate long-term transfer effects at the ability level using
latent change score models (McArdle, 2009;McArdle & Prindle,
2008). These models have the advantage of allowing to directly
test transfer effects at the latent factor level, which no longer
contains task-specific sources of variance or measurement error
(see Figure 1). We predicted that the pattern of positive transfer at
the factor level at follow-up (i.e., changes from pretest to
follow-up for the training group minus corresponding changes for
the control group) that we observed at posttest would be main-
tained at follow-up. As no reliable transfer effects for the abilities
of episodic memory and reasoning could be demonstrated for the
older adults at posttest, we restricted our analyses to the younger
adults.
Method
Participants and Procedure
During the training phase, 101 younger adults (51.5% women,
M
age
25.6 years, SD
age
2.7, range: 20–31 years) completed an
average of 101 practice sessions (SD 2.6, range: 87–109).
Participants in the no-contact control group were 44 younger
adults (47.7% women, M
age
25.2 years, SD
age
2.5, range:
21–29 years). Before and after the training, participants completed
pre- and posttests during 10 sessions that consisted of 2–2.5 hr of
comprehensive cognitive test batteries and self-report question-
naires. On average, time elapsing between pre- and posttest was
197 versus 193 days for the training and control groups, respec-
tively. Additional information on sample characteristics and study
dropout can be found in Schmiedek, Lövdén, and Lindenberger
(2010) and Schmiedek, Bauer, Lövden, Brose, and Lindenberger
(2010).
The cognitive assessment of the posttest sessions was repeated
at the 2-year follow-up (time from posttest to follow-up: M
time
755 days, Mdn 749 days, range: 679–927 days, for the training
group; M
time
745 days, Mdn 742 days, range: 693–798 days,
for the control group). Participation rates at follow-up were satis-
factory (80 younger adults in the training and 32 in the control
group, corresponding to 79% and 73% of the original sample sizes,
respectively). Comparisons of pretest performance on the transfer
tasks and on the Digit-Symbol Substitution Test (Wechsler, 1981)
showed that the follow-up sample did not differ significantly from
the dropouts between posttest and follow-up (ps.05), with the
exception of numerical reasoning, for which the follow-up sample
had significantly higher performance at pretest than did the dropouts,
t(99) 2.22, p.028. The present analyses were confined to the
follow-up sample. Within this sample, pretest differences on the trans-
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2305
LONG-TERM EFFECTS OF COGNITIVE TRAINING
fer tasks and the Digit-Symbol Substitution Test between the
trained and control groups were not significant (ps.05).
Tasks
In each session, participants practiced 12 different computerized
tasks with two to eight blocks each. For perceptual speed, those
were three two-choice reaction tasks (odd vs. even numbers;
consonants vs. vowels; symmetric vs. asymmetric figures) and
three comparison tasks (two strings of digits/consonants, or two
three-dimensional figures). For episodic memory, tasks required
participants to memorize word lists, number–word pairs, or object
positions in a grid. WM tasks were adapted versions of the alpha
span, numerical memory updating, and spatial n-back tasks (for
details of all tasks, see Schmiedek, Lövdén, & Lindenberger,
2010). Difficulty levels for the choice-reaction, episodic memory,
and WM tasks were individualized using different presentation
times based on pretest performance.
Transfer tasks included computerized tasks as well as 27 tasks
from the paper-and-pencil Berlin Intelligence Structure (BIS) test
(Jäger, Süß, & Beauducel, 1997). The three near transfer WM tasks
were based on the same three paradigms as the practiced WM tasks,
but used different content material. The far transfer WM tasks were
established complex span tasks (reading span, counting span, and
rotation span). For episodic memory, one computerized word
paired-associates task and nine tasks from the BIS (three for each
content domain) were used. Transfer in reasoning was measured
with 15 items from the Raven’s Advanced Progressive Matrices
(Raven & Horn, 2009) as well as with nine tasks from the BIS,
three for each content domain.
Data Analysis
Effect sizes (d) for single tasks were calculated as mean pre-post
(pre-follow-up) differences in accuracy divided by the SD of the
experimental group at pretest. Net effects provided in Table 1 were
obtained by subtracting the effect sizes for the control from those
of the training group. Whether these net effects were statistically
significant was investigated by testing the interaction of occasion
and group with linear mixed effect models (using PROC MIXED
in SAS 9.3; Kenward-Roger degrees of freedom; see Littell, Mil-
liken, Stroup, Wolfinger, & Schabenberger, 2006) that allowed for
different variances at pre- and posttest (Ftests for the interaction
are provided in Table 1). Effects at the latent level were analyzed
with latent change score models (McArdle, 2009;McArdle &
Prindle, 2008). In these models, latent factors were defined by a set
of transfer tasks. Improvements at the latent factor level were
captured by the means of latent change score factors (see Figure 1).
In order for these means to be readily interpretable, it is necessary
that factor loadings and intercepts are constrained to be equal
across occasions and experimental groups (strong measurement
invariance). Here, we even aimed for strict measurement invari-
ance (i.e., residual variances also fixed across occasions and ex-
perimental groups). Tests of whether mean changes at the latent
Figure 1. Latent change score model for modeling training-induced changes at the latent factor level. Squares
represent observed variables, circles represent latent factors, and the triangle serves to represent information
regarding means and intercepts. A1–A3, B1–B3, C1–C3 observed indicator variables A, B, and C (i.e., tasks
of one ability) measured at the three time points; F1–F3: latent factor of ability at the three time points; LC1:
latent change factor from pretest to posttest; LC2: latent change factor from pretest to follow-up; : latent mean
of ability factor at pretest; : mean change of latent ability factors from pre- to posttest; : mean change of latent
ability factors from pretest to follow-up; : variance (individual differences) in latent ability at pretest; variances
of the latent change factors was fixed to zero, because they were not significant. Loadings of observed variables
on latent factors, intercepts of observed variables, and residual variances were fixed to be the same across the
three time points and across training and control groups (i.e., strict measurement invariance). Residuals for the
same observed variable were allowed to correlate across time points.
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2306 SCHMIEDEK, LÖVDÉN, AND LINDENBERGER
factor level were significant were conducted by comparing
the 2LL of models in which means of the latent change factor
were estimated separately for the training and control groups with
models in which both means were constrained to be equal, result-
ing in a
2
test with one df. Testing whether effects at follow-up
differed from those at posttest were conducted by comparing the
unconstrained model to one in which the differences training
minus control were constrained to be equal for both latent change
factors, resulting in a
2
test with one df.
Model fits were acceptable for reasoning,
2
(75) 83.91,
root-mean-square error of approximation (RMSEA) .05, and
episodic memory,
2
(75) 93.61, RMSEA .07, but not for the
model of WM near transfer tasks, even if only strong measurement
invariance was modeled,
2
(60) 106.73, RMSEA .12. We
therefore refrain from interpreting results for WM at the latent
factor level.
Latent effect sizes were calculated by dividing the latent mean
differences by the latent SDs at pretest. For analyses of the BIS
test, tasks were parceled for each ability construct by calculating
composites of standardized scores for the three tasks of each
content domain. As these scores were thus already standardized
based on pretest SDs, mean differences are in effect-size metric
and do not need to be divided by SDs.
Results
In the following, we focus on long-term transfer effects at the
latent factor level and restrict our analyses to those transfer effects
for which we found significant results at posttest for the younger
adults (Schmiedek, Lövdén, & Lindenberger, 2010); that is, for
latent factors of reasoning and episodic memory. Results on trans-
fer effects at the observed task level are reported in Table 1.
For the latent factor of reasoning, there was a significant inter-
action of experimental group and occasion,
2
(2) 15.54, p
.001. The latent net effect sizes were .17,
2
(1) 7.41, p.006,
at posttest and .23,
2
(1) 14.57, p.001, at follow-up. The
difference of these effects was not reliable,
2
(1) 1.12, ns.As
shown in Figure 2, this was due to relative stability of latent means
for both the trained and the control group. For the latent factor of
episodic memory, there was a significant interaction of experimen-
tal group and occasion,
2
(2) 31.45, p.001. The latent net
effect sizes were .47,
2
(1) 30.48, p.001, at posttest and .18,
2
(1) 3.88, p.041, at follow-up. The difference of these
effects was reliable,
2
(1) 11.54, p.001. The reduction of the
effect was mainly due to a reduction of the effect in the trained
group (see Figure 2).
In sum, the results at the latent factor level show that the
improvements at the ability level for reasoning and episodic mem-
ory were (a) significant at posttest for the reduced follow-up
sample, (b) significant at the 2-year follow-up, and (c) signifi-
cantly reduced at follow-up, in comparison to transfer at posttest,
for episodic memory, but not for reasoning. Group differences in
motivation are unlikely to be the cause of these effects, as self-
reported motivation to work on the tasks did not differ signifi-
cantly between the training and control groups (see Figure 3).
Discussion
The present results show that far transfer to broad cognitive
abilities can be maintained over several years. The sizes of the
observed reliable effects were not large. However, their breadth
renders them beneficial for a number of real-life outcomes. As
reasoning and episodic memory are abilities of high predictive
validity for everyday competency (Tucker-Drob, 2011), even
small effects can have a substantial impact on performance in
educational, professional, and leisure activity settings. Training
interventions that lead to small effects of wide scope and high
temporal stability may pay off more than interventions that lead to
strong but specific effects that do not last for long.
Regarding reasoning, transfer effects at follow-up were signif-
icant at the observed task as well as at the latent ability level and
of comparable size as at posttest. While for episodic memory,
Table 1
Transfer Effects for Follow-Up Sample and Individual Tasks at Posttest and Follow-Up
Task Pre-post net
effect size Pre-Post
Experimental Group Pre-follow-up net
effect size Pre-Follow-up
Experimental Group
Working memory—Near
Animal span .02 F(1, 110) 0.01, ns .06 F(1, 110) 0.11, ns
N-back numerical .41 F(1, 110) 6.21, p.014 .46 F(1, 110) 9.07, p.003
Memory updating spatial .07 F(1, 124) 0.18, ns .05 F(1, 124) 0.06, ns
Working memory—Far
Reading span .00 F(1, 124) 0.00, ns .31 F(1, 124) 1.72, ns
Counting span .03 F(1, 124) 0.03, ns .24 F(1, 124) 1.24, ns
Rotation span .08 F(1, 124) 0.28, ns .04 F(1, 124) 0.08, ns
Reasoning
Verbal .12 F(1, 110) 1.38, ns .22 F(1, 110) 4.14, p.044
Numerical .25 F(1, 110) 5.40, p.022 .32 F(1, 110) 7.11, p.009
Figural/spatial .23 F(1, 110) 3.68, ns .27 F(1, 110) 7.30, p.008
Raven .21 F(1, 109) 1.58, ns .40 F(1, 107) 3.90, ns
Memory
Verbal .49 F(1, 110) 17.09, p.0001 .15 F(1, 110) 1.68, ns
Numerical .53 F(1, 110) 11.15, p.001 .16 F(1, 110) 1.20, ns
Figural/spatial .20 F(1, 110) 3.42, ns .21 F(1, 110) 3.43, ns
Word pairs .22 F(1, 110) 2.20, ns .16 F(1, 110) 0.92, ns
Note. Pre pretreatment; post posttreatment.
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2307
LONG-TERM EFFECTS OF COGNITIVE TRAINING
transfer effects were not significant anymore at the observed task
level for verbal, numerical, and figural-spatial memory at
follow-up (see Table 1), the effect at the level of their common
factor was reduced in comparison to the posttest effects, but still
maintained reliable. This further demonstrates the usefulness of
investigating transfer at the latent factor level. At the observed task
level, performance is measured with imperfect reliability due to
measurement error and might be influenced by task-specific strat-
egies that have been acquired during the training, but could not be
reactivated in an effective manner after 2 years. As the latent level
only captures sources of variance that have a general influence on
all indicator tasks of the factor, general effects, if present, are more
easily detectable there.
How did transfer to broad cognitive abilities come about, and
how was it maintained over the considerable period of 2 years? We
hold that plasticity at the neural level requires a sustained chal-
lenge of the cognitive system produced by a mismatch between
cognitive demands and functional supplies (Lövdén et al., 2010).
The breadth (12 heterogeneous tasks that differed in content and
paradigms), intensity (high difficulty due to adjustment to individ-
ual performance levels), and dosage (100 sessions of about 1 hr
duration) of the training fulfills this requirement and could thereby
lead to plastic brain changes, for example, in gray matter (Dra-
ganski et al., 2006), white matter (Scholz, Klein, Behrens, &
Johansen-Berg, 2009), and neurotransmitter systems (Bäckman et
al., 2011;McNab et al., 2009). For a subsample of COGITO
participants, Lövdén, Bodammer, et al. (2010) have found indica-
tions of improved white-matter microstructure as well as increased
volumes of the anterior corpus callosum at posttest. Little is known
about the temporal stability of plastic neural changes, and we do
Figure 2. Latent means and associated standard errors for the training and control groups at pretest, posttest,
and follow-up. Training group shown with solid lines, control with dashed lines. A: latent factor of reasoning;
B: latent factor of episodic memory. As the indicator tasks of the latent factors were standardized by SDsat
pretest, latent means are in effect size metric.
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2308 SCHMIEDEK, LÖVDÉN, AND LINDENBERGER
not know whether and how they help to preserve positive transfer
in broad cognitive abilities.
In addition to plastic changes at the neural level, we also need
to consider rather complex reciprocal effects among the develop-
mental trajectories of cognitive and other psychological variables.
Improved cognitive abilities may open opportunities in the educa-
tional and professional paths of younger adults that in turn lead to
continuously raised levels of cognitive demand, which may help to
perpetuate the beneficial effects of the training. Similarly, in-
creased cognitive capacities might lead to an increased need for
cognition (Cacioppo, Petty, Feinstein, & Jarvis, 1996) or openness
to experience (Jackson, Hill, Payne, Roberts, & Stine-Morrow,
2012) that makes participants seek and face cognitive challenges in
their lives. Findings of long-term benefits of early education pro-
grams that sometimes last decades after the intervention programs
have ended (Barnett, 2011) underscore the importance of taking a
developmental perspective on cascading outcomes of training in-
terventions.
The finding that latent transfer effects were reduced at follow-up
for episodic memory, but not reasoning, speaks to the possibility
that the acquisition of general strategies might also have contrib-
uted to the findings for episodic memory at posttest. Besides the
influence of task-specific strategies, which should not influence
findings at the latent factor level, our participants might also have
acquired and practiced more general strategies, like mental imag-
ery, that are supportive for a broad selection of episodic memory
tasks. Difficulties with an ad-hoc reactivation of these strategies at
follow-up might explain the reduction of transfer effects. As no
reasoning tasks were included in the training and as potential
strategies used with the practiced WM tasks are much less likely to
be of help for performance on the transfer reasoning tasks, a
strategy-based explanation of the transfer to reasoning is difficult
to entertain.
In sum, the present findings provide room for cautious optimism
(cf. Hertzog et al., 2008). Cognitive trainings can produce transfer
effects that are sufficiently large in scope and stable over time to
justify the considerable effort that is needed to produce them.
Future studies should hold up the proposed standard of investigat-
ing transfer at the level of latent ability factors and improve on the
investigation of the mechanisms that produce transfer and main-
tenance. Future research will need to take close and continuous
looks at postintervention developmental trajectories on behavioral,
social, and neural dimensions to better understand the conditions
under which cognitive training interventions can trigger a cascade
of changes that result in improved or maintained cognitive com-
petence.
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
2309
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Received February 4, 2013
Revision received January 23, 2014
Accepted May 20, 2014
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
2310 SCHMIEDEK, LÖVDÉN, AND LINDENBERGER
... These studies show promise, but in computerized programs, it is difficult to automate the adaptive nature needed to meet the skill progression of students (Astle et al., 2015;Bergman-Nutley & Klingberg, 2014;Bigorra et al., 2015;Sohlberg et al., 2014). Interestingly, a comparison between one-onone brain training and a combination approach of one-on-one with computerized training showed gains in GIA with only a long-term memory discrepancy between the two approaches (Moore, Carpenter, Miller, & Ledbetter, in press;Schmiedek et al., 2014). ...
... Further, when participants were allowed to choose activities, levels, and games, the cognitive changes were disappointing. Some near transfer effects, or transfer to similar skills, were observed with adults who participated in brain training with the use of games (Ballesteros et al., 2014;Ballesteros et al., 2015;Ballesteros et al., 2017;Dunning & Holmes, 2014;Nouchi et al., 2013;Oei & Patterson, 2013;Schmiedek et al., 2014;Strenziok et al., 2014;West et al., 2017). When choosing a program to administer brain training to older adults, trainers should consider things such as dexterity, vision, hearing, and the ability or willingness to use a computer. ...
... The more comprehensive the brain training program, the more significant the results and signs of transfer (Carpenter et al., 2016;Gibson, Carpenter, Moore, & Mitchell, 2015;Schmiedek et al., 2014). Directly after training and 2 years after training, both reasoning and episodic memory showed significant improvements. ...
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The purpose of this study was to examine the perceptions of adults who completed a LearningRx ThinkRx program. Organizations and employees often deal with low levels of self-efficacy, which can lead to lower levels of performance. Specifically, this study question was an examination of perceptions related to workplace self-efficacy. Brain training research has been shown to help increase academic skills, but little research has been conducted with self-efficacy and adults. The sample in this study were adults who completed their program between the years 2015 and 2018. The sample was chosen using purposeful sampling procedures among all participants who are now adults and completed their program between 2015 and 2018 at the Northern Colorado LearningRx center. Three forms of data were collected: artifact data, participant interviews, and member checking interviews. This qualitative case study examined the data using a thematic data analysis. The five themes found in this study were self-awareness, problem solving, emotional control, achievement, and leadership. There was direct support for the research question from the themes of self-awareness, problem solving, and emotional control. Though achievement and leadership did not directly support this research, futures researchers could explore if there is a connection between workplace self-efficacy and the these final two themes. Keywords: brain training, cognitive training, LearningRx, adult self-efficacy, self- efficacy
... The second strongest effect was from the cortico-spinal tract, which affected three speed measures above and beyond the variance already captured by Forceps Minor (see also Duering et al., 2013, andLövdén et al., 2014). Together, these findings provide significant support for the watershed model: white matter, processing speed and fluid intelligence stand in a hierarchical, many-to-one relationship that requires measuring a broad spectrum of variables at each explanatory level. ...
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... Other theoretical work also pointed in this direction (Cacioppo et al., 1996;Cattell, 1987;Chamorro-Premuzic & Furnham, 2004;Hill et al., 2013). Regarding Gc, individuals with higher Gc might become curious to learn more and their higher semantic knowledge might enable them to pursue certain activities (e.g., theatre, reading) or educational career pathways that, in turn, train persistence in dealing with cognitive challenges (see Schmiedek et al., 2014). ...
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In the present study, we investigated the effects of a four-week working memory (WM) and attention training program using commercial brain training (Synaptikon GmbH, Berlin). Sixty young healthy adults were assigned to the experimental and active control training programs. The training was conducted in a naturalistic home-based setting, while the pre- and post-examinations were conducted in a controlled laboratory setting. Transfer effects to an untrained WM task and to an untrained episodic memory task were examined. Furthermore, possible influences of personality, i.e., the five-factor model (FFM) traits and need for cognition (NFC), on training outcomes were examined. Additionally, the direct relationship between improvement in single trained tasks and improvement in the transfer tasks was investigated. Our results showed that both training groups significantly increased performance in the WM task, but only the WM training group increased their performance in the episodic memory transfer task. One of the training tasks, a visuospatial WM task, was particularly associated with improvement in the episodic memory task. Neuroticism and conscientiousness showed differential effects on the improvement in training and transfer tasks. It needs to be further examined whether these effects represent training effects or, for example, retest/practice or motivation effects.
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Numerous studies have investigated the efficacy of various cognitive trainings, with working memory being the most often trained cognitive aspect. In this regard, executive aspects of working memory have received the most attention, with updating training being vastly explored. In this study, we aimed to examine the differential contribution of some individual characteristics to the efficacy of updating training using a well-established n-back training paradigm. More specifically, we examined the contribution of fluid reasoning (gf), and personality (neuroticism, conscientiousness) to training efficacy. Participants (N = 47) took part in a 15-session, dual n-back training, spread over 4 weeks. They were pretested for fluid reasoning (CFT-3), personality (IPIP-100), and performed the initial testing on the OSPAN task. OSPAN was measured in three additional measurement points (after 5th, 10th, 15th session). The data was analyzed within the multilevel modeling approach. Initial hypotheses were partly confirmed, in that: 1) training was efficient in terms of OSPAN score, which grew linearly over time and the trajectory was similar between participants, 2) although the growth was similar for all participants, differences were found in intercepts, and 3) these differences could be partly explained by differences in fluid reasoning, but not with personality traits of conscientiousness and neuroticism.
Chapter
The focus of this chapter is on a selected class of statistical models: latent change models. They are especially eligible for typical applications in cognitive training research with two or three groups (e.g., training, active control, passive control) and two or three time points (pretest, posttest, follow-up). Latent variable models have a long tradition in cognitive science because they can separate task-, paradigm-, and ability-specific variance in performance tasks. Latent change modeling allows to study latent means, latent intraindividual mean changes, and interindividual differences in both. This chapter addresses how the effectiveness of training programs can be evaluated with latent change models and typical misunderstandings in this context. Statistical power considerations and measurement invariance across experimental groups and time points are discussed. The benefits and risks of analyzing predictors and correlates of latent change variables are particularly relevant for cognitive training research. They provide valuable correlative information about possible mechanisms moderating training outcomes (e.g., compensation or magnification effects) but are no causal test of these mechanisms. Taken together, latent change modeling does not only allow testing whether a cognitive training works on average, but also studying interindividual differences in training outcomes.
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Researchers have examined inductive reasoning to identify different cognitive processes when participants deal with inductive problems. This article presents a prescriptive theory of inductive reasoning that identifies cognitive processing using a procedural strategy for making comparisons. It is hypothesized that training in the use of the procedural inductive reasoning strategy will improve cognitive functioning in terms of (a) increased fluid intelligence performance and (b) better academic learning of classroom subject matter. The review and meta-analysis summarizes the results of 74 training experiments with nearly 3,600 children. Both hypotheses are confirmed. Further, two moderating effects were observed: Training effects on intelligence test performance increased over time, and positive problem-solving transfer to academic learning is greater than transfer to intelligence test performance. The results cannot be explained by placebo or test-coaching effects. It is concluded that the proposed strategy is theoretically and educationally promising and that children of a broad age range and intellectual capacity benefit with such training.
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The authors examined lifespan differences in the maintenance of skilled episodic memory performance by assessing 100 individuals (10 –11, 12–13, 21–26, and 66 –79 years old) 11 months after termination of an intensive multisession mnemonic training program (Y. Brehmer, S.-C. Li, V. Müller, T. von Oertzen, & U. Lindenberger, 2007). Skill maintenance was tested in 2 follow-up sessions, the first without and the second with mnemonic reinstruction. Younger and older adults' average performance levels were stable across time. In contrast, both younger and older children's memory performance improved beyond originally attained levels. Older adults' performance improved from the first to the second follow-up session, presumably profiting from instruction-induced skill reactivation. Results suggest that (a) skill maintenance is largely intact in healthy older adults, (b) older adults need environmental support to fully reactivate their former skill levels (cf. F. I. M. Craik, 1983), and (c) children adapt a skill learned 11 months ago to their increasing cognitive capabilities.
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Need for cognition in contemporary literature refers to an individual's tendency to engage in and enjoy effortful cognitive endeavors. Individual differences in need for cognition have been the focus of investigation in over 100 empirical studies. This literature is reviewed, covering the theory and history of this variable, measures of interindividual variations in it, and empirical relationships between it and personality variables, as well as individuals' tendencies to seek and engage in effortful cognitive activity and enjoy cognitively effortful circumstances. The article concludes with discussions of an elaborated theory of the variable, including antecedent conditions; interindividual variations in it related to the manner information is acquired or processed to guide perceptions, judgments, and behavior; and the relationship between it and the 5-factor model of personality structure. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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Cogmed working memory training is sold as a tool for improving cognitive abilities, such as attention and reasoning. At present, this program is marketed to schools as a means of improving underperforming students’ scholastic performance, and is also available at clinical practices as a treatment for ADHD. We review research conducted with Cogmed software and highlight several concerns regarding methodology and replicability of findings. We conclude that the claims made by Cogmed are largely unsubstantiated, and recommend that future research place greater emphasis on developing theoretically motivated accounts of working memory training.
Article
Jaeggi and her colleagues claimed that they were able to improve fluid intelligence by training working memory. Subjects who trained their working memory on a dual n-back task for a period of time showed significant improvements in working memory span tasks and fluid intelligence tests such as the Raven's Progressive Matrices and the Bochumer Matrices Test after training compared to those without training. The current study aimed to replicate and extend the original study in a well-controlled experiment that could explain the cause or causes of such transfer if indeed the case. There were a total of 93 participants who completed the study, and they were assigned to one of three groups—passive control group, active control group and experimental group. Half of the participants were assigned to the 8-day condition and the other half to the 20-day condition. All participants completed a battery of tests at pre- and post-tests that consisted of short timed tests, a complex working memory span and a matrix reasoning task. Although participants' performance on the training task improved, results from the current study did not suggest any significant improvement in the mental abilities tested, especially fluid intelligence and working memory capacity, after training for 8 days or 20 days. This does not support the notion that increasing one's working memory capacity by training and practice could transfer to improvement on fluid intelligence as asserted by Jaeggi and her colleagues.
In this monograph, we ask whether various kinds of intellectual, physical, and social activities produce cognitive enrichment effects—that is, whether they improve cognitive performance at different points of the adult life span, with a particular emphasis on old age. We begin with a theoretical framework that emphasizes the potential of behavior to influence levels of cognitive functioning. According to this framework, the undeniable presence of age-related decline in cognition does not invalidate the view that behavior can enhance cognitive functioning. Instead, the course of normal aging shapes a zone of possible functioning, which reflects person-specific endowments and age-related constraints. Individuals influence whether they function in the higher or lower ranges of this zone by engaging in or refraining from beneficial intellectual, physical, and social activities. From this point of view, the potential for positive change, or plasticity, is maintained in adult cognition. It is an argument that is supported by newer research in neuroscience showing neural plasticity in various aspects of central nervous system functioning, neurochemistry, and architecture. This view of human potential contrasts with static conceptions of cognition in old age, according to which decline in abilities is fixed and individuals cannot slow its course. Furthermore, any understanding of cognition as it occurs in everyday life must make a distinction between basic cognitive mechanisms and skills (such as working-memory capacity) and the functional use of cognition to achieve goals in specific situations. In practice, knowledge and expertise are critical for effective functioning, and the available evidence suggests that older adults effectively employ specific knowledge and expertise and can gain new knowledge when it is required. We conclude that, on balance, the available evidence favors the hypothesis that maintaining an intellectually engaged and physically active lifestyle promotes successful cognitive aging. First, cognitive-training studies have demonstrated that older adults can improve cognitive functioning when provided with intensive training in strategies that promote thinking and remembering. The early training literature suggested little transfer of function from specifically trained skills to new cognitive tasks; learning was highly specific to the cognitive processes targeted by training. Recently, however, a new generation of studies suggests that providing structured experience in situations demanding executive coordination of skills—such as complex video games, task-switching paradigms, and divided attention tasks—train strategic control over cognition that does show transfer to different task environments. These studies suggest that there is considerable reserve potential in older adults' cognition that can be enhanced through training. Second, a considerable number of studies indicate that maintaining a lifestyle that is intellectually stimulating predicts better maintenance of cognitive skills and is associated with a reduced risk of developing Alzheimer's disease in late life. Our review focuses on longitudinal evidence of a connection between an active lifestyle and enhanced cognition, because such evidence admits fewer rival explanations of observed effects (or lack of effects) than does cross-sectional evidence. The longitudinal evidence consistently shows that engaging in intellectually stimulating activities is associated with better cognitive functioning at later points in time. Other studies show that meaningful social engagement is also predictive of better maintenance of cognitive functioning in old age. These longitudinal findings are also open to important rival explanations, but overall, the available evidence suggests that activities can postpone decline, attenuate decline, or provide prosthetic benefit in the face of normative cognitive decline, while at the same time indicating that late-life cognitive changes can result in curtailment of activities. Given the complexity of the dynamic reciprocal relationships between stimulating activities and cognitive function in old age, additional research will be needed to address the extent to which observed effects validate a causal influence of an intellectually engaged lifestyle on cognition. Nevertheless, the hypothesis that an active lifestyle that requires cognitive effort has long-term benefits for older adults' cognition is at least consistent with the available data. Furthermore, new intervention research that involves multimodal interventions focusing on goal-directed action requiring cognition (such as reading to children) and social interaction will help to address whether an active lifestyle enhances cognitive function. Third, there is a parallel literature suggesting that physical activity, and aerobic exercise in particular, enhances older adults' cognitive function. Unlike the literature on an active lifestyle, there is already an impressive array of work with humans and animal populations showing that exercise interventions have substantial benefits for cognitive function, particularly for aspects of fluid intelligence and executive function. Recent neuroscience research on this topic indicates that exercise has substantial effects on brain morphology and function, representing a plausible brain substrate for the observed effects of aerobic exercise and other activities on cognition. Our review identifies a number of areas where additional research is needed to address critical questions. For example, there is considerable epidemiological evidence that stress and chronic psychological distress are negatively associated with changes in cognition. In contrast, less is known about how positive attributes, such as self-efficacy, a sense of control, and a sense of meaning in life, might contribute to preservation of cognitive function in old age. It is well known that certain personality characteristics such as conscientiousness predict adherence to an exercise regimen, but we do not know whether these attributes are also relevant to predicting maintenance of cognitive function or effective compensation for cognitive decline when it occurs. Likewise, more information is needed on the factors that encourage maintenance of an active lifestyle in old age in the face of elevated risk for physiological decline, mechanical wear and tear on the body, and incidence of diseases with disabling consequences, and whether efforts to maintain an active lifestyle are associated with successful aging, both in terms of cognitive function and psychological and emotional well-being. We also discuss briefly some interesting issues for society and public policy regarding cognitive-enrichment effects. For example, should efforts to enhance cognitive function be included as part of a general prevention model for enhancing health and vitality in old age? We also comment on the recent trend of business marketing interventions claimed to build brain power and prevent age-related cognitive decline, and the desirability of direct research evidence to back claims of effectiveness for specific products.
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Reports on 2 studies that examined intra-individual variability (plasticity) in performance on measures of fluid intelligence (figural relations, induction), varying either practice (retesting) or testing time (standard vs power) conditions. Ss were 70 community residents (55 females, 15 males; ages 60–84 yrs). Substantial improvement in level of correct performance (with no evidence for changes in test validity) was obtained for both retest and power conditions. Error patterns, however, differed for the 2 conditions, with a high proportion of commission errors occurring under power conditions. Results are interpreted as contributing to the positions (a) that older persons continue to show learning capacity and (b) that studying the range of performance under varying conditions is critical to an understanding of intellectual aging. (37 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)