Cognitive neuroscience
Vogue1988
document8.pdf
Training the Brain Practical Applications of Neural Plasticity From the Intersection of
Cognitive Neuroscience, Developmental Psychology,
and Prevention Science
Richard L. Bryck and Philip A. Fisher University of Oregon and Oregon Social Learning Center
Prior researchers have shown that the brain has a remark- able ability for adapting to environmental changes. The positive effects of such neural plasticity include enhanced functioning in specific cognitive domains and shifts in cortical representation following naturally occurring cases of sensory deprivation; however, maladaptive changes in brain function and development owing to early develop- mental adversity and stress have also been well docu- mented. Researchers examining enriched rearing environ- ments in animals have revealed the potential for inducing positive brain plasticity effects and have helped to popu- larize methods for training the brain to reverse early brain deficits or to boost normal cognitive functioning. In this article, two classes of empirically based methods of brain training in children are reviewed and critiqued: laboratory- based, mental process training paradigms and ecological interventions based upon neurocognitive conceptual mod- els. Given the susceptibility of executive function disrup- tion, special attention is paid to training programs that emphasize executive function enhancement. In addition, a third approach to brain training, aimed at tapping into compensatory processes, is postulated. Study results show- ing the effectiveness of this strategy in the field of neurore- habilitation and in terms of naturally occurring compen- satory processing in human aging lend credence to the potential of this approach.
Keywords: plasticity, training, intervention, developmental cognitive neuroscience
Supplemental materials: http://dx.doi.org/10.1037/a0024657 .supp
To what extent are children’s brains pliable and train-able? Moreover, what are the most effective tech-niques for training and shaping the brains of children to achieve positive and prevent negative outcomes? What are the advantages and disadvantages of laboratory-based versus ecologically grounded, family- and community- based approaches? Should we think of these approaches as alternatives to traditional psychotherapeutic techniques or as complementary approaches? At what developmental
points are such programs most beneficially employed, and is there a point past which such programs are not likely to be effective? Are all brain systems equally pliable? If not, which show the greatest degree of plasticity in response to intervention? Last, are there circumstances under which we should be concerned about potential iatrogenic effects of intervention or training programs?
Although the answers to these questions are complex and, in many cases, unresolved, interest in children’s brain plasticity and interventions that promote plasticity appears to be widespread and rapidly growing. Such interest exists among neuroscientists focused on understanding the basic science of brain development, developmental psychologists focused on the emergence of key competencies necessary for healthy adjustment over time, child psychologists and other clinicians focused on understanding and treating psy- chological disorders, prevention scientists and educators focused on designing effective programs for reducing risks and promoting resiliency in high-risk populations, and poli- cymakers focused on allocating funding and resources for such programs. In addition, the media and general public appear to be intrigued by this subject, as indicated by the frequency with which the results of neuroscientific inves- tigations of plasticity published in professional journals are being covered in the popular press. Furthermore, the bur- geoning array of commercial products aimed at “brain fitness”—from online tutorials to computer software pack- ages to object-based games—now available to the general
This article was published Online First July 25, 2011. Richard L. Bryck and Philip A. Fisher, Department of Psychology,
University of Oregon, and Oregon Social Learning Center, Eugene, Or- egon.
This research was supported by the following grants: MH059780 and MH078105 (National Institute of Mental Health, U.S. Public Health Service); HD045894 (Eunice Kennedy Shriver National Institute of Child Health and Human Development, U.S. Public Health Service); DA021424 and DA029320 (National Institute on Drug Abuse, U.S. Public Health Service); and R324A080026 (Institute of Education Science, U.S. Depart- ment of Education). We thank Matthew Rabel for manuscript editing.
Correspondence concerning this article should be addressed to Philip A. Fisher, Oregon Social Learning Center, 10 Shelton McMurphey Bou- levard, Eugene, OR 97401. E-mail: [email protected]
87February–March 2012 ● American Psychologist © 2011 American Psychological Association 0003-066X/12/$12.00 Vol. 67, No. 2, 87–100 DOI: 10.1037/a0024657
T hi
s do
cu m
en t i
s co
py ri
gh te
d by
th e
A m
er ic
an P
sy ch
ol og
ic al
A ss
oc ia
tio n
or o
ne o
f i ts
a lli
ed p
ub lis
he rs
. T
hi s
ar tic
le is
in te
nd ed
s ol
el y
fo r t
he p
er so
na l u
se o
f t he
in di
vi du
al u
se r a
nd is
n ot
to b
e di
ss em
in at
ed b
ro ad
ly .
public marks an important penetration of brain science into the consumer marketplace.1 Put simply, this is an important and much-debated topic.
In this article, we explore the state of the science in this area, with the goal of stimulating dialogue on the topic of training the brain in children, paying particular attention to how research in this area can inform current policy and shape prevention and intervention programs for high-risk children and their families. We focus on efforts to train cognitive control (commonly referred to as executive func- tioning [EF]), reviewing evidence from the two approaches employed in this area among children with attention-deficit/ hyperactivity disorder (ADHD) and children who manifest neurodevelopmental difficulties as a result of exposure to early stressful environments: (a) laboratory-based studies that directly train specific neurocognitive processes and (b) ecologically based interventions informed from a neurobi- ological perspective. We concentrate on studies that pro- vide instructional training rather than rote, unguided learn- ing via repetition only. We then speculate on a possible third route to brain training in children that is grounded in neurorehabilitation and adult aging research: utilization of compensatory neural processes. Finally, we consider the future directions for this work.
Human Neural Development and Plasticity Much of the current state of knowledge about neural plas- ticity in children is embedded in the science of brain development. Until the advent of modern neuroscience, conventional wisdom held that brain development was largely complete relatively early in life, perhaps owing to appreciable anatomy: The human brain has reached ap- proximately 90% of its adult weight by early childhood and
changes very little in size after age five (Durston et al., 2001; Reiss, Abrams, Singer, Ross, & Denckla, 1996). However, the results from histological postmortem studies on humans and nonhuman primates and from in vivo im- aging studies have provided strong evidence that human brain development is far from complete by early childhood. In fact, dynamic and continuing changes in brain architec- ture occur throughout the course of development. For ex- ample, in humans, the ratio of gray matter (unmyelinated neurons) to white matter (myelinated neurons) changes dramatically from birth through adulthood, particularly in the cerebral cortex. Gray matter density follows a nonlinear trend of initial growth during early childhood, with a sub- sequent decrease in density during adolescence and young adulthood. Further, regional differences exist such that primary motor and sensory areas tend to mature the earliest in development, with higher level association and multi- modal areas (e.g., the dorsolateral prefrontal cortex and the superior temporal gyrus) reaching adult levels the latest (Giedd, 2004; Gogtay et al., 2004; Reiss et al., 1996). White matter volume conversely follows a steady linear increase throughout childhood and up to adulthood (Giedd et al., 1999; Gogtay et al., 2004).
The inverse relationship seen between decreases in gray matter and increases in white matter has been postulated to reflect both synaptic pruning (loss of gray matter) and increased myelination (the formation of glial support cells); this pattern is thought to be due to a combination of loss in redundant or unused connections and the strengthening of relevant connections based on environmental input and experience (Huttenlocher, 1990). Functionally, these dynamic changes in brain architecture most likely reflect increased neural effi- ciency and faster network connections that parallel the behavioral changes observed during development.
As with physical development, brain development is genetically programmed. Although there are individual dif- ferences in the exact timing of this programming, there is a great degree of invariance in the sequence of maturation of particular brain regions for typically developing individu- als. For example, auditory and visual regions mature early, language later, and higher order cognitive functions later still. However, the maturing brain is also strongly influ- enced by experience in the prenatal, childhood, and ado- lescent developmental periods; further, there is increasing evidence that brain development continues throughout adult life. This has led to the characterization of the extent to which the environment affects neural development in terms of the idea that experience shapes the architecture of the developing brain (National Scientific Council on the Developing Child, 2007). Indeed, it is within this experi- ential sculpting process that the plasticity of the developing brain might best be understood.
1 It should be noted that many of these products are not based on a solid neuroscience framework; further, empirical evidence supporting the efficacy of some of these programs has recently been called into question (Owen et al., 2010).
Richard L. Bryck
88 February–March 2012 ● American Psychologist
T hi
s do
cu m
en t i
s co
py ri
gh te
d by
th e
A m
er ic
an P
sy ch
ol og
ic al
A ss
oc ia
tio n
or o
ne o
f i ts
a lli
ed p
ub lis
he rs
. T
hi s
ar tic
le is
in te
nd ed
s ol
el y
fo r t
he p
er so
na l u
se o
f t he
in di
vi du
al u
se r a
nd is
n ot
to b
e di
ss em
in at
ed b
ro ad
ly .
Many of the early scientific demonstrations of neural plasticity highlighted the changes in the brain induced from deprivation of normal, experience-expectant stimulus in- put. For example, Wiesel and Hubel (1963) demonstrated the effect of profound sensory deprivation on the develop- ment of the visual cortex. In these experiments, kittens underwent monocular deprivation (i.e., one eye sewn shut) during a critical period of visual development. Normally, this interval (age four weeks to three months) corresponds to a period of preprogrammed ocular dominance column formation, in which alternating bands that are preferentially sensitive to input from either eye form in the primary visual cortex. In the case of monocular deprivation, however, profound visual impairment was seen after the deprived eye was reopened. However, these effects were not seen if the deprivation occurred later (i.e., age three–four months; Hubel & Wiesel, 1970). At the physiological level, the ocular dominance columns representing the deprived eye failed to develop and appeared to be replaced by the col- umns representing the nondeprived eye. This suggests that cortical areas are able to represent the winner of competi- tive interaction between environmental inputs received, at least during certain critical periods, rather than prepro- grammed representation only (LeVay, Wiesel, & Hubel, 1980; Wiesel & Hubel, 1963). The results from these studies provided evidence of the profound ability of the brain to alter so-called hardwired connections as a result of subsequent environmental experiences, demonstrating for the first time the remarkable plasticity of the brain.
Much of the early evidence for neural plasticity came from sensory deprivation experiments using animal mod- els, but parallel studies of plasticity in humans could not employ such methods for obvious reasons. Instead, scien- tists have relied on experiments of nature. An indication of
the profound malleability of the human brain is given in the following examples. Neville and Lawson (1987) showed that, compared with individuals with normal hearing, indi- viduals with congenital deafness exhibit an early attention effect when tracking motion presented in the peripheral visual field. Similarly, Bavelier et al. (2000) conducted a neuroimaging study that revealed greater activity in the motion-sensitive portion of visual cortex (the middle tem- poral cortex) among participants with congenital deafness than among participants with normal hearing when tracking motion. Similar results have been obtained with individuals with congenital blindness in response to auditory stimuli (Röder et al., 1999). Individuals who are deaf have also been shown to have tactile ability superior to that in non- deprived control samples; further, the primary visual cortex has been shown to be activated when participants who are blind perform tactile discrimination tasks, such as reading Braille (Levänen & Hamdorf, 2001). Thus, these results demonstrate cross-modal plasticity. That is, the blind or deaf participants showed enhanced use of brain regions typically associated with the deprived sense, suggesting that the cortical areas involved in a particular sensory modality, if unused, may be recruited by neighboring cor- tical areas (Pascual-Leone, Amedi, Fregni, & Merabet, 2005).
Negative Consequences of Neural Malleability Neural plasticity facilitates healthy development across a vast continuum of rearing conditions and might help to account for resiliency even when children experience non- optimal parenting or conditions of social and economic adversity. However, adaptive neural plasticity might also represent vulnerability under certain circumstances. In par- ticular, there is increasing evidence that exposure to stress at levels that overwhelm the organism’s ability to manage that stress may negatively affect brain development (P. A. Fisher & Gunnar, 2010).
For example, there is over half a century of research evidence involving animal models that stressful rearing environments are associated with changes in key neural regulatory systems (Levine, 2005). Evidence from parallel studies in human populations has documented similar ef- fects. Research findings involving children reared in insti- tutions in developing countries, which offer extremely ne- glectful early care, have shown long-term alterations in brain development (Pollak et al., 2010). Converging evi- dence has been obtained in studies of maltreated children (Cicchetti, Rogosch, Gunnar, & Toth, 2010) and foster children (P. A. Fisher, Gunnar, Dozier, Bruce, & Pears, 2006). Notably, stressful experiences do not have to be extreme to alter the course of brain development. Shonkoff, Boyce, and McEwen (2009) noted that one pathway for stress effects over time might be cumulative in nature (i.e., allostatic load) and that chronic exposure to moderate stres- sors might result in changes in the developing brain. In a review of the literature in this area, P. A. Fisher and Gunnar (2010) noted that the timing (especially in the first 24
Philip A. Fisher
89February–March 2012 ● American Psychologist
T hi
s do
cu m
en t i
s co
py ri
gh te
d by
th e
A m
er ic
an P
sy ch
ol og
ic al
A ss
oc ia
tio n
or o
ne o
f i ts
a lli
ed p
ub lis
he rs
. T
hi s
ar tic
le is
in te
nd ed
s ol
el y
fo r t
he p
er so
na l u
se o
f t he
in di
vi du
al u
se r a
nd is
n ot
to b
e di
ss em
in at
ed b
ro ad
ly .
months), duration, and severity of stress might require consideration by those examining the association between early adversity and alterations in brain development.
Intervention Efforts to Promote Neural Plasticity
If negative life events, such as early stress, can alter and remodel brain development, is the converse also true? Is it possible to leverage neural plasticity to promote healthy development and to remediate the effects of early stress via intervention efforts? Early evidence along these lines came from investigations of the effects of rearing rodents in enriched environments following exposure to early stress. In these studies, researchers have documented neuroanat- omical changes after exposure to enriched environments, including increased brain weight and size, increased den- dritic branching and length, changes in synaptic size and number, and behavioral improvements on long-term spatial memory tasks such as the Morris water maze and the T maze (see Nithianantharajah & Hannan, 2006; van Praag, Kempermann, & Gage, 2000). The two key components of an enriched environment seem to be complexity and nov- elty (Sale, Berardi, & Maffei, 2009).
Important questions arise, however, when attempting to apply these findings to humans. Certainly, the animal studies provide proof of concept that the brain is malleable in response to environmental intervention and that im- proved outcomes are possible following early stress. How- ever, there is considerable cross-species variation in the rate of development. Moreover, the architecture of the human brain is vastly more complex than that of rodent brains, and the neural systems in which changes might be most desired in humans involve higher order cognitive processing. Thus, the limits, approaches, and effectiveness of interventions to promote positive neural plasticity in humans are uncertain.
It is worth highlighting the growing body of conver- gent animal and human literature that demonstrates bene- ficial changes in brain morphology and cognition after physical exercise. The findings from animal studies in rodents, for example, have consistently shown increased neurogenesis—the formation of new neurons—in the adult following induced or voluntary exercise.2 Behavioral ef- fects, such as improved acquisition and retention of spatial memory, have also been reliably observed in rodents after exercise (see van Praag, 2008); it should be noted, how- ever, that there is insufficient evidence that the observed performance improvements result from exercise-induced neurogenesis (Hillman, Erickson, & Kramer, 2008; van Praag, 2008). Comparable effects of exercise on cognition have been found in human studies; in a recent review, Hillman et al. (2008) highlighted moderate but positive overall effects of physical activity on a range of child cognitive abilities, including academic performance. Sim- ilar findings in aging populations have shown beneficial relationships between exercise and various cognitive do- mains, particularly executive control (Colcombe &
Kramer, 2003). Recent evidence indicates that exercise might also contribute to the continued plasticity of brain structures in old age. Erickson et al. (2011), for example, showed a gain in anterior hippocampus volume in a group of older adults who participated in a yearlong exercise regimen and a comparable loss in volume in those who practiced only stretching and toning. These changes in volume were positively related to changes in fitness level, blood levels of brain-derived neurotrophic factor (a puta- tive mediator of neurogenesis), and improvements in a spatial memory task. Additionally, the results from func- tional neuroimaging studies have shown activation differ- ences between physically fit individuals and their less fit peers in key control-related cortical areas (e.g., the dorsal lateral prefrontal cortex and anterior cingulate, areas im- plicated in the allocation of attention and conflict detection, respectively). Further, advancement in the field will no doubt be aided by the recent development of an in vivo imaging marker (cerebral blood volume) of exercise-in- duced neurogenesis in humans (Pereira et al., 2007). This literature is highlighted to illustrate an example of the profound neural and behavioral remodeling that can arise after accompanying environmental enrichment. Similar cortical plasticity is thought to occur following intense intervention or cognitive training regiments.
To capitalize on the adaptability of the brain, some of the earliest cognitive training researchers incorporated principles of cortical plasticity and basic neuroscience into their designs (e.g., the importance of competitive processes in driving change in neural networks). For example, basic research findings have implicated language-based learning impairments in children as a deficit in the temporal dynam- ics of auditory processing, so researchers devised a training program designed to improve basic perceptual processing (i.e., the spatiotemporal aspects of sounds) in young chil- dren diagnosed with language-based learning impairments. This program successfully improved speech discrimination and language comprehension abilities (Tallal et al., 1996). Similar plasticity-based training programs have proved ef- fective at enhancing memory performance in older adults and verbal memory in individuals with schizophrenia (M. Fisher, Holland, Merzenich, & Finogradov, 2009; Mahncke et al., 2006).
Existing EF Training Paradigms
In this section, we review the major findings from brain and cognitive training and intervention studies to shed light on questions about the potential and limits of training the brain. In particular, we highlight training programs and findings focused on EF, the cognitive processes that allow for the flexible selection of behavior based on internal goals or rules (e.g., Koechlin & Summerfield, 2007; Zelazo,
2 Exercise is just one way neurogenesis can be promoted; other factors implicated in regulating new cell growth in adults include expo- sure to enriched environments and exposure to hippocampal-dependent learning (e.g., spatial navigation tasks).
90 February–March 2012 ● American Psychologist
T hi
s do
cu m
en t i
s co
py ri
gh te
d by
th e
A m
er ic
an P
sy ch
ol og
ic al
A ss
oc ia
tio n
or o
ne o
f i ts
a lli
ed p
ub lis
he rs
. T
hi s
ar tic
le is
in te
nd ed
s ol
el y
fo r t
he p
er so
na l u
se o
f t he
in di
vi du
al u
se r a
nd is
n ot
to b
e di
ss em
in at
ed b
ro ad
ly .
Müller, Frye, & Marcovitch, 2003).3 These processes in- clude related but separate components such as updating representations in working memory, shifting between men- tal representations (cognitive flexibility), and inhibiting competing, prepotent representations or responses (Huiz- inga, Dolan, & van der Molen, 2006; Miyake, Friedman, Emerson, Witzki, & Howerter, 2000). The anatomical basis of EF has been intricately linked to the prefrontal cortex in humans (Miller & Cohen, 2001). The development of EF follows a protracted course, with EF components emerging in infancy, exhibiting pronounced changes during the pre- school period, and reaching full competency in adolescence or young adulthood (Carlson, 2005; Huizinga et al., 2006; Zelazo, Carlson, & Kesek, 2008). We chose to focus on EF for a number of reasons. First, EF deficits are one of the chief effects manifested in relation to early adversity. For example, researchers working with foster children (Lewis, Dozier, Ackerman, & Sepulveda-Kozakowski, 2007; Pears, Kim, & Fisher, 2008) and low-income children (Hackman & Farah, 2009) have found poorer performance on EF tasks in these high-risk samples. Second, the devel- opment of EF is clearly important to healthy adjustment in the context of family, school, peers, and community. Pro- ficient EF capabilities play a critical role in a multitude of other domains, including theory of mind (Carlson & Mo- ses, 2001), self-regulation (Blair, 2002), and long-term memory retrieval (Levy & Anderson, 2002). Additionally, EF deficits appear to be at the core of developmental psychopathologies such as autism (Ozonoff, 1995), ADHD (Pennington & Ozonoff, 1996), and substance abuse dis- orders (Giancola & Tarter, 1999). Thus, EF might be particularly susceptible to disruption yet amendable via training.
The current EF training methods generally involve two approaches: laboratory-based training and neurobio- logically informed ecological interventions. Improvements on psychosocial, behavioral, and/or physiological measures have been demonstrated with both strategies, and each strategy has specific advantages, limitations, and implicit assumptions.
Laboratory-Based Training
Laboratory-based training approaches often involve repeated performance, typically computer-based, on speeded-choice tasks. These approaches tend to target a particular cognitive domain (often labeled a specific process or direct interven- tion) rather than take a domain-general approach aimed at improving overall psychosocial well-being, reducing be- havioral problems, or reducing clinical symptomatology.
Improving working memory capacity, for example, has been the goal of several recent training studies in typically developing and developmentally delayed child populations. Targeting working memory ability, espe- cially in children, is a naturally attractive goal, given the relationship between individual differences on measures of working memory and fluid intelligence in adults
(Engle & Kane, 2004) in addition to the relationship seen between working memory scores and scholastic performance in children (Gathercole, Brown, & Picker- ing, 2003). Early researchers investigating training on working memory, however, found marginal improvements at best in working memory capacity (Kristofferson, 1972) or highly task specific improvements (Ericcson, Chase, & Faloon, 1980). However, perhaps owing to the recent de- velopment of more precise intervention techniques and/or measurement methods, promising results have begun to emerge. The results of these studies, as well as those targeting other EF domains, are summarized in Table 1. (Direct comparison of effect sizes from different designs and statistics in Tables 1– 4 is cautioned against; Morris & DeShon, 2002. See supplemental materials for procedures and formulas for calculating effect sizes, additional demo- graphics, and additional details on the studies listed in Tables 1– 4.) As shown in Table 1, laboratory-based train- ing intervention methods excel at enhancing specific neu- robehavioral processes (or highly related processes) of interest, and some produce broader transfer of training effects. Further, the rigorous methodological consider- ations in these studies limit nuisance or confounding vari- ables. Given the specificity of the processes targeted for improvement, the design and objectives of these studies are driven by relatively precise neurobehavioral theoretical bases.
The laboratory nature of the training in these studies inevitably raises questions of ecological or external validity and the generalizability of improvements. At present, there is only limited evidence that training on computerized, laboratory-based tests of specific cognitive abilities gener- alizes to real-world situations. Understanding of the degree to which cognitive improvement from such training appli- cations enhances daily cognitive functioning is clearly needed. Similarly, little is known of the long-term effects of these training paradigms. Retest improvements have been shown over a matter of months in several studies (Holmes, Gathercole, & Dunning, 2009; Klingberg et al., 2005), but an understanding of the long-term efficacy of these methods is clearly needed to move beyond the proof- of-concept phase.
Moreover, questions remain as to how effective such training is for the extreme ends of the impaired EF spec- trum. Although some researchers have investigated training effects with developmentally delayed populations, such as children with ADHD (see Table 1), little is known about the effectiveness of such training methods in populations demonstrating more severe behavioral, cognitive, or emo- tional deficits.
3 Several of the studies reviewed here target the training of attention. On the surface, attention training might not seem to fit into the EF category; however, various types of attention have been hypothesized, including executive attention. The studies reviewed here contain compo- nents that can be categorized as training this type of attention and/or control over the allocation of attention and thereby broadly fitting the EF construct.
91February–March 2012 ● American Psychologist
T hi
s do
cu m
en t i
s co
py ri
gh te
d by
th e
A m
er ic
an P
sy ch
ol og
ic al
A ss
oc ia
tio n
or o
ne o
f i ts
a lli
ed p
ub lis
he rs
. T
hi s
ar tic
le is
in te
nd ed
s ol
el y
fo r t
he p
er so
na l u
se o
f t he
in di
vi du
al u
se r a
nd is
n ot
to b
e di
ss em
in at
ed b
ro ad
ly .
Ta b le
1 La
bo ra
to ry
-B as
ed EF
Tr ai
ni ng
St ud
ie s
in C
hi ld
re n
St ud
y N
C N
E A
ge Sa
m pl
e Tr
ai ni
ng do
m ai
n
Ef fe
ct si
ze s
C at
eg or
y St
at So
ur ce
Ty pe
Ef fe
ct Si
ze
Ru ed
a et
al .
(2 0
0 5
) 2
5 a
2 4
4 Ty
pi ca
lly de
ve lo
pi ng
A tte
nt io
n IG
p- p
d C
N sm
al l1
fr on
ta lE
RP am
pl itu
de IG
p- p
d C
F 1
re as
on in
g 0
.8 2
b
IG p-
p d
C F
1 IQ
0 .6
3 b
Ru ed
a et
al .
(2 0
0 5
) 1
2 1
2 6
Ty pi
ca lly
de ve
lo pi
ng A
tte nt
io n
IG p-
p d
C N
1 fr on
to pa
ri et
al ER
P am
pl itu
de IG
p- p
d C
F 1
vo ca
bu la
ry Th
or el
le ta
l. (2
0 0
9 )
3 0
a 1
7 4
–5 Ty
pi ca
lly de
ve lo
pi ng
V is
uo sp
at ia
lW M
IG p-
p d
C N
1 sp
at ia
lW M
0 .4
1 IG
p- p
d C
F 1
ve rb
al W
M 1
.4 0
IG p-
p d
C F
1 at
te nt
io n
0 .6
5 K ar
ba ch
& K ra
y (2
0 0 9 )
4 2
1 4
8 –1
0 Ty
pi ca
lly de
ve lo
pi ng
Ta sk
sw itc
hi ng
RM d
R N
2 sw
itc h
co st
s �
1 .2
0 IG
p- p
d C
N 1
in hi
bi to
ry co
nt ro
l 0
.4 4
IG p-
p d
C N
1 w
or ki
ng m
em or
y 0
.6 0
c
IG p-
p d
C F
1 re
as on
in g
0 .4
9 St
ev en
s et
al .
(2 0
0 8
) 1
3 9
6 –8
Ty pi
ca lly
de ve
lo pi
ng La
ng ua
ge vi
a at
te nt
io n
IG p-
p d
C N
1 re
ce pt
iv e
la ng
ua ge
1 .4
3
IG p-
p d
C F
1 at
te nt
io na
lE RP
am pl
itu de
1 .7
6
St ev
en s
et al
. (2
0 0
8 )
1 3
7 6
–8 Sp
ec ifi
c la
ng ua
ge im
pa ir m
en t
La ng
ua ge
vi a
at te
nt io
n IG
p- p
d C
N 1
re ce
pt iv
e la
ng ua
ge 3
.9 1
IG p-
p d
C F
1 at
te nt
io na
lE RP
am pl
itu de
6 .7
3
H ol
m es
et al
. (2
0 0
9 )
2 0
2 2
8 –1
1 Lo
w W
M V
er ba
la nd
vi su
os pa
tia lW
M IG
p- p
d C
N 1
ve rb
al W
M 1
.0 6
c ,d
IG p-
p d
C N
1 vi
su os
pa tia
l W
M 1
.2 0
c ,d
RM d
R F
1 m
at h
at fo
llo w
- up
0 .4
9 c ,e
M ac
ke y
et al
. (2
0 1
1 )
1 1
1 7
7 –9
Lo w
SE S
Fl ui
d re
as on
in g
IG p-
p d
C N
1 re
as on
in g
1 .3
2 IG
p- p
d C
F 1
W M
0 .8
8 K er
ns et
al .
(1 9
9 9
) 7
7 7
–1 1
A D
H D
A tte
nt io
n RM
d C
N 1
at te
nt io
n 2
.6 4
c
RM d
C N
2 in
te rf er
en ce
3 .0
6 RM
d C
F 1
m at
h 1
.9 4
K lin
gb er
g et
al .
(2 0
0 2
) 7
7 7
–1 5
A D
H D
V er
ba la
nd vi
su os
pa tia
lW M
IG p-
p d
C N
1 vi
su os
pa tia
l W
M 4
.6 6
c
IG p-
p d
C F
1 re
as on
in g
1 .8
9 IG
p- p
d C
F 2
A D
H D
1 .4
4
92 February–March 2012 ● American Psychologist
T hi
s do
cu m
en t i
s co
py ri
gh te
d by
th e
A m
er ic
an P
sy ch
ol og
ic al
A ss
oc ia
tio n
or o
ne o
f i ts
a lli
ed p
ub lis
he rs
. T
hi s
ar tic
le is
in te
nd ed
s ol
el y
fo r t
he p
er so
na l u
se o
f t he
in di
vi du
al u
se r a
nd is
n ot
to b
e di
ss em
in at
ed b
ro ad
ly .
Neurobiologically Informed Ecological Interventions
Numerous researchers conducting more traditional, contex- tually based school- and family-centered prevention and intervention studies have demonstrated evidence of brain training via changes in behavioral and psychosocial func- tioning. Such ecological approaches acknowledge the im- portance of understanding the neural underpinnings of be- havior, especially nonnormative behavior, as a critical factor in designing and implementing prevention and in- tervention paradigms. In discussing the rationale for these approaches, Blair (2002) argued that the extent to which children successfully navigate the transition to primary school depends on a set of social– emotional skills that are based in the EF neural substrates. These skills include self-regulation, effortful control, and working memory. Ecological interventions that target these domains typically integrate high rates of employing EF-based skills in school or family settings. Several of these interventions have resulted in impressive findings (see Table 2).
Ecological intervention approaches have a number of advantages over the laboratory-based training methods, in particular, real-world applicability. They are relatively easy to implement and require no special equipment. Moreover, because these approaches are conducted in real-world set- tings, the skills learned might be more likely to generalize to similar real-world contexts. In addition, these ap- proaches appear to be effective with multiproblem children who might have fairly large deficits in the targeted inter- vention areas.
Despite these potential advantages, these approaches tend to be more intensive, longer lasting, and more costly than the laboratory-based training methods. In addition, inasmuch as many of these interventions consist of multiple components, it can be challenging to deconstruct and distill the effective components. In addition, these approaches are typically less specific with regard to the targeted brain systems. Although neural and biological indices of func- tioning are acquired, precise theories of how the interven- tions affect the underlying neural circuitry are often less well articulated. Isolating particular neurocognitive sys- tems affected by such interventions is difficult in the con- text of these studies, in part because of their domain- general, multimodal natures. As such, one goal of future ecological interventions targeting EF is to more adequately identify the targeted neurocognitive systems.
A Promising New Approach
Given the limitations of the aforementioned approaches, we propose a third possible approach to brain training in this area: strengthening compensatory processes. Rather than being aimed only at restoring neurocognitive functions that are not operating optimally, this method is aimed at leveraging other neural systems to accomplish complemen- tary cognitive and behavioral outcomes. As is shown be- low, this is not a novel concept in of itself; however, to our knowledge, this type of approach has not been employed asTa
b le
1 (c
o n ti n u e d
)
St ud
y N
C N
E A
ge Sa
m pl
e Tr
ai ni
ng do
m ai
n
Ef fe
ct si
ze s
C at
eg or
y St
at So
ur ce
Ty pe
Ef fe
ct Si
ze
K lin
gb er
g et
al .
(2 0
0 5
) 2
4 2
0 7
–1 2
A D
H D
V er
ba la
nd vi
su os
pa tia
lW M
IG p-
p d
C N
1 ve
rb al
W M
0 .6
8 d
IG p-
p d
C N
1 vi
su os
pa tia
l W
M 0
.6 6
d
IG p-
p d
C N
2 in
te rf er
en ce
0 .2
5 IG
p- p
d C
F 1
re as
on in
g 0
.1 8
IG p-
p d
C F
2 A
D H
D 0
.9 0
d
Sh al
ev et
al .
(2 0
0 7
) 1
6 2
0 6
–1 3
A D
H D
A tte
nt io
n IG
p- p
� 2
R F
1 ac
ad em
ic ac
hi ev
em en
t 0
.1 5
c
IG p-
p �
2 R
F 2
A D
H D
0 .2
9 c
N ot
e. A
ge is
gi ve
n in
ye ar
s. Se
e su
pp le
m en
ta lm
at er
ia ls
fo r
de fin
iti on
s an
d pr
oc ed
ur es
fo r
de te
rm in
in g
th e
ef fe
ct si
ze s
ba se
d on
th e
pa ra
m et
er s
gi ve
n in
th e
or ig
in al
st ud
ie s.
EF �
ex ec
ut iv
e fu
nc tio
n; N
C �
sa m
pl e
si ze
of th
e co
nt ro
lg ro
up ;
N E
� sa
m pl
e si
ze of
th e
ex pe
ri m
en ta
l/ tra
in in
g gr
ou p;
St at
� th
e ef
fe ct
si ze
st at
is tic
gi ve
n; IG
p- p
� in
de pe
nd en
tg ro
up s,
pr e–
po st
; d
� C
oh en
’s d;
C �
ef fe
ct si
ze va
lu e
ca lc
ul at
ed ;
N �
ne ar
(im pr
ov em
en to
n tra
in ed
ta sk
s or
no nt
ra in
ed ,
st ru
ct ur
al ly
si m
ila r
ta sk
s) ;
ER P
� ev
en t-r
el at
ed po
te nt
ia l;
F �
fa r
(im pr
ov em
en to
n st
ru ct
ur al
ly di
ss im
ila r
ta sk
s) ;
W M
� w
or ki
ng m
em or
y; RM
� re
pe at
ed m
ea su
re s,
ch an
ge w
ith in
gr ou
p; SE
S �
so ci
oe co
no m
ic st
at us
; A
D H
D �
at te
nt io
n- de
fic it/
hy pe
ra ct
iv ity
di so
rd er
; R
� ef
fe ct
si ze
va lu
e as
re po
rte d
in th
e or
ig in
al st
ud y;
� 2
� et
a sq
ua re
d. a
M ul
tip le
co nt
ro lg
ro up
s us
ed , co
m bi
ne d
N gi
ve n.
b Se
e no
te in
su pp
le m
en ta
lm at
er ia
ls fo
r ca
lc ul
at io
n of
th es
e ef
fe ct
si ze
s. c
A ve
ra ge
ef fe
ct si
ze ca
lc ul
at ed
fr om
m ul
tip le
de pe
nd en
tv ar
ia bl
es m
ea su
ri ng
th e
sa m
e co
ns tru
ct .
d Ef
fe ct
st ab
le at
fo llo
w -u
p te
st .
e Ef
fe ct
re po
rte d
fr om
fo llo
w -u
p te
st .
93February–March 2012 ● American Psychologist
T hi
s do
cu m
en t i
s co
py ri
gh te
d by
th e
A m
er ic
an P
sy ch
ol og
ic al
A ss
oc ia
tio n
or o
ne o
f i ts
a lli
ed p
ub lis
he rs
. T
hi s
ar tic
le is
in te
nd ed
s ol
el y
fo r t
he p
er so
na l u
se o
f t he
in di
vi du
al u
se r a
nd is
n ot
to b
e di
ss em
in at
ed b
ro ad
ly .
Ta b le
2 N
eu ro
bi ol
og ic
al ly
In fo
rm ed
Ec ol
og ic
al In
te rv
en tio
ns
St ud
y N
C N
E A
ge Sa
m pl
e In
te rv
en tio
n
Ef fe
ct si
ze s
C at
eg or
y St
at So
ur ce
Ty pe
Ef fe
ct Si
ze
D ia
m on
d et
al .
(2 0
0 7
) 6
2 8 5
5 Lo
w SE
S Se
lf- re
gu la
tio n,
pl ay
, m
em or
y, at
te nt
io n,
an d
lis te
ni ng
sk ill
s
IG po
st N
1 in
hi bi
to ry
co nt
ro l
IG po
st a
N 1
EF r
R F
EF co
rr el
at io
n w
ith ac
ad em
ic sc
or es
0 .4
1 b
Ra ve
r et
al .
(2 0
0 9
) 2
1 8
2 3 1
3 –5
Lo w
SE S
Te ac
he r
tra in
in g:
cl ea
r ru
le s,
IG p-
p d
R F
2 in
te rn
al iz
in g
0 .7
6 b
m on
ito ri ng
, an
d po
si tiv
e re
in fo
rc em
en t
IG p-
p d
R F
2 ex
te rn
al iz
in g
0 .5
9 b
To m
in ey
& M
cC le
lla nd
(2 0
1 1
) 3
7 2 8
4 –5
1 ⁄2
lo w
SE S
Pl ay
gr ou
p ga
m es
: at
te nt
io n,
IG po
st d
C N
1 in
hi bi
to ry
co nt
ro l
0 .6
9 c
m em
or y,
an d
in hi
bi to
ry co
nt ro
l IG
p- p
d C
F 1
re ad
in g
0 .4
4
Br uc
e et
al .
(2 0
0 9
) 2
4 d
1 0
5 –7
Fo st
er ca
re Pa
re nt
tra in
in g
an d
th er
ap eu
tic pl
ay gr
ou p
IG po
st �
p2 R
F 1
fe ed
ba ck
-re la
te d
ER P
am pl
itu de
0 .2
5
N ot
e. A
ge is
gi ve
n in
ye ar
s. Se
e su
pp le
m en
ta lm
at er
ia ls
fo r
de fin
iti on
s an
d pr
oc ed
ur es
fo r
de te
rm in
in g
th e
ef fe
ct si
ze s
ba se
d on
th e
pa ra
m et
er s
gi ve
n in
th e
or ig
in al
st ud
ie s.
N C
� sa
m pl
e si
ze of
th e
co nt
ro lg
ro up
; N
E �
sa m
pl e
si ze
of th
e ex
pe ri m
en ta
l/ tra
in in
g gr
ou p;
St at
� th
e ef
fe ct
si ze
st at
is tic
gi ve
n; SE
S �
so ci
oe co
no m
ic st
at us
; IG
po st
� in
de pe
nd en
tg ro
up s,
po st
; N
� ne
ar (im
pr ov
em en
to n
tra in
ed ta
sk s
or no
nt ra
in ed
, st
ru ct
ur al
ly si
m ila
r ta
sk s)
; EF
� ex
ec ut
iv e
fu nc
tio n;
r �
Pe ar
so n’
s pr
od uc
t– m
om en
tc or
re la
tio n;
R �
ef fe
ct si
ze va
lu e
as re
po rte
d in
th e
or ig
in al
st ud
y; F
� fa
r (im
pr ov
em en
to n
st ru
ct ur
al ly
di ss
im ila
r ta
sk s)
; IG
p- p
� in
de pe
nd en
tg ro
up s,
pr e–
po st
; d
� C
oh en
’s d;
C �
ef fe
ct si
ze ca
lc ul
at ed
; �
p2 �
pa rti
al et
a sq
ua re
d. a
M ea
su re
no t
ad m
in is
te re
d to
co nt
ro l
gr ou
p. b
A ve
ra ge
ef fe
ct si
ze ca
lc ul
at ed
fr om
m ul
tip le
de pe
nd en
t va
ri ab
le s
m ea
su ri ng
th e
sa m
e co
ns tru
ct .
c Ef
fe ct
fo un
d in
su bs
et w
ith lo
w in
iti al
ex ec
ut iv
e fu
nc tio
n sc
or es
. d
M ul
tip le
co nt
ro lg
ro up
s us
ed ,
co m
bi ne
d N
gi ve
n.
94 February–March 2012 ● American Psychologist
T hi
s do
cu m
en t i
s co
py ri
gh te
d by
th e
A m
er ic
an P
sy ch
ol og
ic al
A ss
oc ia
tio n
or o
ne o
f i ts
a lli
ed p
ub lis
he rs
. T
hi s
ar tic
le is
in te
nd ed
s ol
el y
fo r t
he p
er so
na l u
se o
f t he
in di
vi du
al u
se r a
nd is
n ot
to b
e di
ss em
in at
ed b
ro ad
ly .
a therapeutic intervention for remedying EF deficits and other neurocognitive deficits in children.
Numerous neurorehabilitation therapies for patients with traumatic brain injuries are aimed at improving cog- nitive functioning by using strategic training techniques to offset rather than restitute lost functionality. In these ap- proaches, the underlying neural systems supporting a given function are generally assumed to be damaged beyond repair. These approaches thus target dormant, complemen- tary neural systems (i.e., redundant pathways) unaffected by the brain insult or wholly different brain systems capa- ble of carrying out functions formerly handled by damaged brain regions. The conceptual models driving these ap- proaches involve rerouting the neural circuitry so individ- uals learn new methods of handling old problems or goals. In general, these compensatory approaches target specific cognitive deficits incurred after brain damage (e.g., atten- tional, memorial, or visual abilities; see Table 3).
Other examples of compensatory processing are more spontaneous in nature. Recruitment of right hemisphere areas, homologous to left hemisphere language centers, has been shown to aid individuals with aphasia in recovering language function. It has been suggested that these right hemisphere activations reflect the recruitment of additional processes to support language recovery rather than the restoration of language functions per se (see Raymer et al., 2008). Similarly, neuroimaging researchers conducting ag- ing studies have shown overactivation patterns (i.e., more diffuse activity) in the brains of older than of young adults during complex cognitive tasks. This distributed pattern in older adults is often seen as bilateral activity, often in the prefrontal cortex, homologous to the unilateral activity seen in young adults, and positive correlations between additional activity and performance have been reported. Such overactivation patterns have been demonstrated even when performance levels between groups were matched. The claim that increased activation is necessary for im- proved performance is supported by a pair of transcranial magnetic stimulation (TMS) studies. In one study, older adults performed more poorly when deactivating TMS was applied to either prefrontal hemisphere during a recognition memory task (older adults normally show bilateral activa- tion in this task), whereas younger adults performed more poorly when deactivating TMS was applied to only one hemisphere (Rossi et al., 2004). Conversely, in a subgroup of low-performing older adults, activating TMS increased task performance. Moreover, the functional magnetic res- onance imaging (fMRI) activation patterns in this low- performing group were more unilateral before and more bilateral after the activating TMS (Solé-Padullés et al., 2006), which is consistent with improved performance. Taken together, this evidence supports the compensation account of overactivation: Increased activation reflects neural processes that aid older adults during task perfor- mance (see Table 4; see Reuter-Lorenz & Lustig, 2005). However, bilateral activity during performance on inhibi- tory tasks has also been linked to poorer performance in older adults, suggesting that overactivation patterns might
not universally indicate the presence of compensatory pro- cessing (Colcombe, Kramer, Erickson, & Scalf, 2005).
As is shown in Tables 3 and 4, compensatory or strategic processes can be effective remedies for amelio- rating lost or degraded cognitive functioning. As such, this form of brain training should be further explored as a potential model to consider for application with vulnerable populations. One such compensatory strategy, broadly de- fined, might involve teaching self-verbalization strategies (i.e., using one’s inner voice to stay on task) to children with attention or inhibitory control deficits. This strategy is based on the Vygotskian hypothesis that inner language helps to guide action, particularly during development, and on recent empirical evidence supporting the role of self- verbalization in the performance of demanding EF situa- tions like task switching (Bryck & Mayr, 2005; Emerson & Miyake, 2003).
Valuable lessons can be gleaned from the aforemen- tioned compensatory rehabilitation techniques when ad- dressing severe early adversity (e.g., childhood physical and emotional trauma and neglect), which can greatly impact neurodevelopment. The results from neuroscience research are increasingly demonstrating the existence of redundant and complementary neural systems, making it plausible that similar compensatory systems could be tapped in children who have endured similar alterations in neural functioning. Much work is needed to tailor these training programs to meet the specific needs and impair- ments of vulnerable childhood populations and to design clinical trial examinations of the effectiveness of compen- satory training in young and/or at-risk populations. Despite this, the compensatory processes brain-training approach is worth exploring, especially for individuals who are less responsive to more traditional intervention approaches.
Discussion The findings from research on neural plasticity provide the impetus for interventions designed to reverse the effects of early adverse environments on child brain development, in particular with regard to EF. Although experimental eval- uations of the impact of interventions on EF neural plas- ticity are relatively new, the research findings to date are promising. Laboratory-based training and neurobiologi- cally informed ecological interventions have been shown to be efficacious and have great promise for improving out- comes for high-risk children. Moreover, enhanced out- comes may be obtained by hybridizing these two approaches.
Compensatory processes brain training has not yet been applied to prevention and intervention programs for high-risk children, despite its analogues in neural rehabil- itation research. Such methods might prove to be effica- cious on their own or as supplementary components ap- pended to emerging methods with documented efficacy. It is clear that the structure and components involved in such approaches should be specified and evaluated.
In addition, there is a need for parallel development of innovative measurement methodologies. For example, ex- amining fMRI activation on tasks known to recruit EF
95February–March 2012 ● American Psychologist
T hi
s do
cu m
en t i
s co
py ri
gh te
d by
th e
A m
er ic
an P
sy ch
ol og
ic al
A ss
oc ia
tio n
or o
ne o
f i ts
a lli
ed p
ub lis
he rs
. T
hi s
ar tic
le is
in te
nd ed
s ol
el y
fo r t
he p
er so
na l u
se o
f t he
in di
vi du
al u
se r a
nd is
n ot
to b
e di
ss em
in at
ed b
ro ad
ly .
Ta b le
3 N
eu ro
re ha
bi lit
at io
n St
ra te
gy /C
om pe
ns at
or y
Tr ai
ni ng
St ud
ie s
St ud
y N
C N
E A
ge Sa
m pl
e D
ef ic
it St
ra te
gy
Ef fe
ct si
ze s
C at
eg or
y St
at Ef
fe ct
Si ze
N ie
m ei
er (1
9 9
8 )
1 5
1 6
4 5
–7 7
St ro
ke V
is ua
li na
tte nt
io n/
ne gl
ec t
V is
ua li
m ag
er y
(s w
ee pi
ng ey
e m
ov em
en ts
) RM
d 2
vi su
al se
ar ch
er ro
rs 0
.9 1
IG p-
p d 1
at te
nt io
n 1
.0 4
K er
kh of
f et
al .
(1 9
9 4
) 2
2 1
6 –7
7 St
ro ke
H om
on ym
ou s
vi su
al fie
ld de
fe ct
sa C
om pe
ns at
or y
ey e
sa cc
ad es
an d
vi su
al se
ar ch
st ra
te gi
es RM
2 vi
su al
se ar
ch er
ro rs
RM 1
vi su
al se
ar ch
RM 1
ac tiv
iti es
of da
ily liv
in g
Pa m
ba ki
an et
al .
(2 0
0 4
) 2
9 2
4 –7
5 St
ro ke
H om
on ym
ou s
vi su
al fie
ld de
fe ct
sa V
is ua
ls ea
rc h
RM 2
vi su
al se
ar ch
tim e
RM 1
ac tiv
iti es
of da
ily liv
in g
K as
ch el
et al
. (2
0 0
2 )
1 2
9 2
0 –6
0 TB
I M
em or
y V
is ua
li m
ag er
y m
ne m
on ic
s IG
p- p
d 1
de la
ye d
re ca
ll 1
.1 7
b
IG p-
p d 1
ev er
yd ay
m em
or y
0 .7
3 b
Be rg
et al
. (1
9 9
1 )
1 1
1 7
1 9 –5
8 TB
I M
em or
y M
ak in
g as
so ci
at io
ns ,
or ga
ni zi
ng ,
IG p-
p f2 1
ac qu
is iti
on m
em or
y 0
.0 4
c
an d
m at
ch in
g en
co di
ng an
d re
tri ev
al co
nt ex
ts IG
p- p
f2 1
de la
ye d
re ca
ll 0
.0 6
c
Fa so
tti et
al .
(2 0
0 0
) 1
0 1
2 1
8 –4
5 TB
I M
en ta
ls lo
w ne
ss D
ec is
io n
pl an
ni ng
, ta
sk IG
p- p
d 1
m em
or y
0 .4
0 m
an ag
em en
t, an
d de
lin ea
tin g
ta sk
s hi
er ar
ch ic
al ly
IG p-
p d 1
at te
nt io
n 0
.5 2
va n
H eu
gt en
et al
. (1
9 9
8 )
3 3
3 9 –9
1 St
ro ke
A pr
ax ia
V er
ba liz
in g
th e
st ep
s in
an ac
tio n
RM d 1
ac tiv
iti es
of da
ily liv
in g
1 .3
0
RM d 1
m ot
or fu
nc tio
ni ng
0 .5
7 RM
d 2
ap ra
xi a
sy m
pt om
s 0
.5 9
N ot
e. A
ge is
gi ve
n in
ye ar
s. Se
e su
pp le
m en
ta lm
at er
ia ls
fo r
de fin
iti on
s an
d pr
oc ed
ur es
fo r
de te
rm in
in g
th e
ef fe
ct si
ze s
ba se
d on
th e
pa ra
m et
er s
gi ve
n in
th e
or ig
in al
st ud
ie s.
A ll
ef fe
ct si
ze s
w er
e ca
lc ul
at ed
. N
C �
sa m
pl e
si ze
of th
e co
nt ro
lg ro
up ;
N E
� sa
m pl
e si
ze of
th e
ex pe
ri m
en ta
l/ st
ra te
gy gr
ou p;
St at
� th
e ef
fe ct
si ze
st at
is tic
gi ve
n; RM
� re
pe at
ed m
ea su
re s,
ch an
ge w
ith in
gr ou
p; d
� C
oh en
’s d;
IG p-
p �
in de
pe nd
en t
gr ou
ps ,
pr e–
po st
; TB
I� tra
um at
ic br
ai n
in ju
ry ;
f2 �
C oh
en ’s
f2 .
a A
lo ss
of vi
su al
pe rc
ep tio
n in
on e
ha lf
of th
e fie
ld of
vi si
on .
b Ef
fe ct
st ab
le at
fo llo
w -u
p te
st .
c Ef
fe ct
re po
rte d
fr om
fo llo
w -u
p te
st .
96 February–March 2012 ● American Psychologist
T hi
s do
cu m
en t i
s co
py ri
gh te
d by
th e
A m
er ic
an P
sy ch
ol og
ic al
A ss
oc ia
tio n
or o
ne o
f i ts
a lli
ed p
ub lis
he rs
. T
hi s
ar tic
le is
in te
nd ed
s ol
el y
fo r t
he p
er so
na l u
se o
f t he
in di
vi du
al u
se r a
nd is
n ot
to b
e di
ss em
in at
ed b
ro ad
ly .
neural systems before and after the application of strategy training could be applied as a useful tool in determining whether, where, and how much compensatory processing has occurred as a result of the training. Similarly, employ- ing measures of connectivity among regions of interest, in terms of both resting-state and task-related functional con- nectivity, is likely to be highly promising (see Fair et al., 2007). The hypothesis that compensatory mechanisms can be recruited to overcome early deficits in children with early adversity is an exciting and potentially highly reward- ing avenue that necessitates exploration.
Limitations and Assumptions of Current Methods There are consistent limitations seen across the methods of brain training reviewed here. Foremost among these is that a training regimen designed for a particular group or pop- ulation can have limited success in dissimilar populations. The most straightforward solution is to tailor interventions or training protocols to the population of interest. Similarly, within any population, a large degree of variation in the deficits observed is likely, and there are inherent challenges in targeting particular skills or functions that might benefit only a subset of the targeted population.
Additionally, much of the work to date has been on preschool- and kindergarten-age children. Given the consid- erable behavioral and neural changes throughout the course of development, more work is needed to understand whether interventions of this nature (especially after services have ceased) are efficacious throughout development. The existing evidence to this end is limited. More data on the long-term persistence of these effects are needed to determine whether booster sessions would be appropriate. The efficacy of such booster sessions would require evaluation as well.
When considering the intended beneficial effects of these approaches for high-risk children, one must also examine the implicit assumptions made by each method- ology. Laboratory-based approaches are generally aimed at improving functioning within one cognitive domain. Such research findings suggest that improvements in a given domain can mediate real-world improvements in classroom behavior and achievement or reduce psychopathology symptoms. The implicit assumptions are that strengthening a specific cognitive process via laboratory training allows for more efficient use of this pathway when it is called upon in ecological settings and that the given neural pathway has been strengthened. The problem with these assumptions is that few of the laboratory-based training studies have tested the potential positive effects of training outside of the laboratory, although some exceptions exist (e.g., Holmes et al., 2009; Klingberg et al., 2005; Klingberg, Forssberg, & Thomson, 2002; Shalev, Tsal, & Mevorach, 2007; Stevens, Fanning, Coch, Sanders, & Neville, 2008). Future training studies in children should include pre–post measures of academic achievement and teacher reports of classroom behavior to allow for an assessment of the potential broad effects training might induce in ecologically valid contexts.
The assumption that neural processing is somehow strengthened after training has been examined previously. For example, Olesen, Westerberg, and Klingberg (2004) found increased prefrontal activity resulting from working memory training in adults. Additionally, Rueda, Rothbart, McCandliss, Saccomanno, and Posner (2005) and Stevens et al. (2008) demonstrated evidence of enhanced neural processing after training via changes in event-related potential attentional com- ponents. However, more evidence is needed from neuroim- aging studies to delineate the mechanisms behind these ob- served changes; for example, it is unclear whether training
Table 4 Overactivation Patterns of Activity in Senior Participants Reflect Compensatory Processing
Study NC NE Age Tasks Results
Morcom et al. (2003) 14 14 63–74 Memory Successful encoding activates left prefrontal cortex in young adults but homologous left and right prefrontal cortex in older adults
Cabeza et al. (2004) 20 20 70 (M) Working memory and attention
In both tasks, older adults showed greater bilateral prefrontal cortex activity (compensation) and less occipital activity (sensory decline) than young adults did
Rossi et al. (2004) 37 29 50–80 Memory 2 memory retrieval in older adults with disruptive TMS at the left or right prefrontal cortex and 2 memory retrieval in young adults with disruptive TMS only at the right prefrontal cortex
Solé-Padullés et al. (2006) 19 20 67 (M)a Memory Preactivating TMS: unilateral activity. Postactivating TMS: 1 bilateral activity and 1 memory
Note. Age is given in years. Due to the complexity and descriptive nature of these effects, effects sizes are not reported. NC � sample size of the control group; NE � sample size of the experimental/senior group; TMS � transcranial magnetic stimulation. a Participants demonstrated low memory scores at pretesting.
97February–March 2012 ● American Psychologist
T hi
s do
cu m
en t i
s co
py ri
gh te
d by
th e
A m
er ic
an P
sy ch
ol og
ic al
A ss
oc ia
tio n
or o
ne o
f i ts
a lli
ed p
ub lis
he rs
. T
hi s
ar tic
le is
in te
nd ed
s ol
el y
fo r t
he p
er so
na l u
se o
f t he
in di
vi du
al u
se r a
nd is
n ot
to b
e di
ss em
in at
ed b
ro ad
ly .
results in stronger connectivity between regions, the recruit- ment of more diffuse neural areas, and more available cogni- tive resources or whether neural processing in a given path- way becomes more efficient after training. Collecting a full range of neural indices, such as resting-state connectivity patterns, electroencephalogram power and source localization analyses, fMRI activation, and regional connectivity analyses before and after training paradigms are implemented, could help answer such questions.
It might appear that neurobiologically based ecologi- cal interventions and laboratory-based training interven- tions make opposing assumptions because the generality of the former typically prevents defining the precise systems affected. For example, although EF deficits are thought to contribute heavily to classroom behavioral regulation prob- lems and poorer academic performance, many interven- tions lack appropriate or well-defined markers of such deficits or the potential for improving them. Incorporating a working memory task known to activate the EF systems targeted by many of these interventions, for example, would allow for a pre–post estimation of EF change. Such a marker—particularly if it is a neural index—would sup- port the claim that these systems are enhanced or might show that these systems are affected differently after the intervention than in normal populations (e.g., through the use of compensatory pathways to achieve the same result). Regardless, such knowledge is critical for developing effi- cacious brain training techniques.
A recent study, Mackey, Hill, Stone, and Bunge (2011; see Table 1), took a unique approach to training by incorporating aspects of laboratory-based and of ecologi- cally based intervention methods. Computerized and non- computerized games that required relational integration, the simultaneous processing of multiple relations between stimuli, were chosen. Relational integration is thought to be integral to fluid reasoning ability, which is a strong predic- tor of school performance. In this study, the children (age seven–nine; low socioeconomic status) participated in an eight-week session of fluid reasoning training or of pro- cessing speed training in a classroom setting where games were played individually and in groups. The children in the reasoning group showed significant improvement in the number of matrix reasoning problems, a measure of fluid intelligence, completed after training. This large effect is particularly impressive, given the widely held belief that fluid reasoning is a static trait that is not amendable to training (cf. Jaeggi, Buschkuehl, Jonides, & Perrig, 2008, for a similar training effect on fluid reasoning in adults). These results provide a first step toward integrating eco- logically valid approaches with rigorous laboratory-tested methodologies to achieve promising changes in mental abilities.
Summary Training the brain, specifically in at-risk populations, is a difficult undertaking with many factors to consider, includ- ing the program type for a given population, the skills or abilities to target, and the program cost and duration. Given the difficulties and the limitations involved in effective
brain training, we advocate for a more collaborative effort. Continued advancements in neuroscience will allow greater insight into the neural processes underlying learning and training, particularly in brains having undergone damage, insult, or abnormal development. Such advances will con- tinue to inform intervention, prevention, and training ef- forts as to the specific deficits affected and the particular brain systems to target in differing populations. Under- standing the neural mechanisms affected will also advance our understanding of the most malleable brain systems for training or intervention. Conversely, intervention science will continue to make advancements regarding the best means of implementing training and applications for real- world contexts. With regard to effective policy, the most critical factor will undoubtedly be the ability of neurosci- entists and intervention scientists to listen, communicate, and collaborate with each other.
REFERENCES
Bavelier, D., Tomann, A., Hutton, C., Mitchell, T., Corina, D., Liu, G., & Neville, H. (2000). Visual attention to the periphery is enhanced in congenitally deaf individuals. Journal of Neuroscience, 20(17), 1– 6. Retrieved from http://www.jneurosci.org
Berg, I. J., Koning-Haanstra, M., & Deelman, B. G. (1991). Long-term effects of memory rehabilitation: A controlled study. Neuropsycholog- ical Rehabilitation, 1, 97–111. doi:10.1080/09602019108401384
Blair, C. (2002). School readiness: Integrating cognition and emotion in a neurobiological conceptualization of children’s functioning at school entry. American Psychologist, 57, 111–127. doi:10.1037/0003- 066X.57.2.111
Bruce, J., McDermott, J. M., Fisher, P. A., & Fox, N. A. (2009). Using behavioral and electrophysiological measures to assess the effects of a preventive intervention: A preliminary study with preschool-aged foster children. Prevention Science, 10, 129 –140. doi:10.1007/s11121-008- 0115-8
Bryck, R. L., & Mayr, U. (2005). On the role of verbalization during task set selection: Switching or serial order control? Memory & Cognition, 33, 611– 623. doi:10.3758/BF03195328
Cabeza, R., Daselaar, S. M., Dolcos, F., Prince, S. E., Budde, M., & Nyberg, L. (2004). Task-independent and task-specific age effects on brain activity during working memory, visual attention and episodic retrieval. Cerebral Cortex, 14, 364 –375. doi:10.1093/cercor/bhg133
Carlson, S. M. (2005). Developmentally sensitive measures of executive function in preschool children. Developmental Neuropsychology, 28, 595– 616. doi:10.1207/s15326942dn2802_3
Carlson, S. M., & Moses, L. J. (2001). Individual differences in inhibitory control and children’s theory of mind. Child Development, 72, 1032– 1053. doi:10.1111/1467-8624.00333
Cicchetti, D., Rogosch, F. A., Gunnar, M. R., & Toth, S. L. (2010). The differential impacts of early physical and sexual abuse and internalizing problems on daytime cortisol rhythm in school-aged children. Child Development, 81, 252–269. doi:10.1111/j.1467-8624.2009.01393.x
Colcombe, S., & Kramer, A. (2003). Fitness effects on the cognitive function of older adults: A meta-analytic study. Psychological Science, 14, 125–130. doi:10.1111/1467-9280.t01-1-01430
Colcombe, S. J., Kramer, A. F., Erickson, K. I., & Scalf, P. (2005). The implications of cortical recruitment and brain morphology for individ- ual differences in cognitive performance in aging humans. Psychology and Aging, 20, 363–375. doi:10.1037/0882-7974.20.3.363
Diamond, A., Barnett, W. S., Thomas, J., & Munro, S. (2007, November 30). Preschool program improves cognitive control. Science, 318, 1387–1388. doi:10.1126/science.1151148
Durston, S., Hulshoff Pol, H. E., Casey, B. J., Giedd, J. N., Buitelaar, J. K., & van Engeland, H. (2001). Anatomical MRI of the developing human brain: What have we learned? Journal of the American Academy of Child & Adolescent Psychiatry, 40, 1012–1020. doi:10.1097/ 00004583-200109000-00009
98 February–March 2012 ● American Psychologist
T hi
s do
cu m
en t i
s co
py ri
gh te
d by
th e
A m
er ic
an P
sy ch
ol og
ic al
A ss
oc ia
tio n
or o
ne o
f i ts
a lli
ed p
ub lis
he rs
. T
hi s
ar tic
le is
in te
nd ed
s ol
el y
fo r t
he p
er so
na l u
se o
f t he
in di
vi du
al u
se r a
nd is
n ot
to b
e di
ss em
in at
ed b
ro ad
ly .
Emerson, M. J., & Miyake, A. (2003). The role of inner speech in task switching: A dual-task investigation. Journal of Memory and Lan- guage, 48, 148 –168. doi:10.1016/S0749-596X(02)00511-9
Engle, R. W., & Kane, M. J. (2004). Executive attention, working memory capacity, and a two-factor theory of cognitive control. In B. H. Ross (Ed.), The psychology of learning and motivation (pp. 145–199). New York, NY: Academic Press.
Ericcson, K. A., Chase, W. G., & Faloon, S. (1980, June 6). Acquisition of a memory skill. Science, 208, 1181–1182. doi:10.1126/sci- ence.7375930
Erickson, K. I., Voss, M. W., Prakash, R. S., Basak, C., Szabo, A., Chaddock, L., . . . Kramer, A. F. (2011). Exercise training increases size of hippocampus and improves memory. Proceedings of the Na- tional Academy of Sciences, USA, 108, 3017–3022. doi:10.1073/ pnas.1015950108
Fair, D. A., Dosenbach, N. U. F., Church, J. A., Cohen, A. L., Brahmbhatt, S., Miezin, F. M., . . . Schlaggar, B. L. (2007). Development of distinct task control networks through segregation and integration. Proceedings of the National Academy of Sciences, USA, 104, 13507–13512. doi: 10.1073/pnas.0705843104
Fasotti, L., Kovacs, F., Eling, P. A. T. M., & Brouwer, W. H. (2000). Time pressure management as a compensatory strategy training after closed head injury. Neuropsychological Rehabilitation, 10, 47– 65. doi: 10.1080/096020100389291
Fisher, M., Holland, C., Merzenich, M. M., & Finogradov, S. (2009). Using neuroplasticity-based auditory training to improve verbal mem- ory in schizophrenia. American Journal of Psychiatry, 166, 805– 811. doi:10.1176/appi.ajp.2009.08050757
Fisher, P. A., & Gunnar, M. R. (2010). Early life stress as a risk factor for disease in adulthood. In E. Vermetten, R. Lanius, & C. Pain (Eds.), The impact of early life trauma on health and disease (pp. 133–141). Cambridge, England: Cambridge University Press.
Fisher, P. A., Gunnar, M. R., Dozier, M., Bruce, J., & Pears, K. C. (2006). Effects of a therapeutic intervention for foster children on behavior problems, caregiver attachment, and stress regulatory neural systems. In B. M. Lester, A. Masten, & B. McEwen (Eds.), Annals of the New York Academy of Sciences: Vol. 1094. Resilience in children (pp. 215–225). doi:10.1196/annals.1376.023
Gathercole, S. E., Brown, L., & Pickering, S. J. (2003). Working memory assessments at school entry as longitudinal predictors of National Curriculum attainment levels. Educational and Child Psychology, 20, 109 –122.
Giancola, P. R., & Tarter, R. E. (1999). Executive cognitive functioning and risk for substance abuse. Psychological Science, 10, 203–205. doi:10.1111/1467-9280.00135
Giedd, J. N. (2004). Structural magnetic resonance imaging of the ado- lescent brain. Adolescent brain development: Vulnerabilities and op- portunities. In R. E. Dahl & L. P. Spear (Eds.), Annals of the New York Academy of Sciences: Vol. 1021. Adolescent brain development: Vul- nerabilities and opportunities (pp.77– 85). doi:10.1196/annals.1308 .009
Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., . . . Rappoport, J. L. (1999). Brain development during childhood and adolescence: A longitudinal MRI study. Nature Neuro- science, 2, 861– 863. doi:10.1038/13158
Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., . . . Thompson, P. M. (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences, USA, 101, 8174 – 8179. doi:10.1073/pnas.0402680101
Hackman, D. A., & Farah, M. J. (2009). Socioeconomic status and the developing brain. Trends in Cognitive Sciences, 13, 65–73. doi: 10.1016/j.tics.2008.11.003
Hillman, C. H., Erickson, K. I., & Kramer, A. F. (2008). Be smart, exercise your heart: Exercise effects on brain and cognition. Nature Reviews Neuroscience, 9, 58 – 65. doi:10.1038/nrn2298
Holmes, J., Gathercole, S. E., & Dunning, D. L. (2009). Adaptive training leads to sustained enhancement of poor working memory in children. Developmental Science, 12, F9 –F15. doi:10.1111/j.1467-7687.2009 .00848.x
Hubel, D. H., & Wiesel, T. N. (1970). The period of susceptibility to the
physiological effects of unilateral eye closure in kittens. Journal of Physiology, 206, 419 – 436.
Huizinga, M., Dolan, C. V., & van der Molen, M. W. (2006). Age-related change in executive function: Developmental trends and a latent vari- able analysis. Neuropsychologia, 44, 2017–2036. doi:10.1016/j.neuro- psychologia.2006.01.010
Huttenlocher, P. R. (1990). Morphometric study of human cerebral cortex development. Neuropsychologia, 28, 517–527. doi:10.1016/0028- 3932(90)90031-I
Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Pro- ceedings of the National Academy of Sciences, USA, 105, 6829 – 6833. doi:10.1073/pnas. 0801268105
Karbach, J., & Kray, J. (2009). How useful is executive control training? Age differences in near and far transfer of task-switching training. Developmental Science, 12, 978 –990. doi:10.1111/j.1467- 7687.2009.00846.x
Kaschel, R., Della Sala, S., Cantagallo, A., Fahlböck, A., Laaksonen, R., & Kazen, M. (2002). Imagery mnemonics for the rehabilitation of memory: A randomised group controlled trial. Neuropsychological Rehabilitation, 12, 127–153. doi:10.1080/09602010143000211
Kerkhoff, G., Münssinger, U., & Meier, E. K. (1994). Neurovisual reha- bilitation in cerebral blindness. Archives of Neurology, 51, 474 – 481.
Kerns, K. A., Eso, K., & Thomson, J. (1999). Investigation of a direct intervention for improving attention in young children with ADHD. Developmental Neuropsychology, 16, 273–295. doi:10.1207/ S15326942DN1602_9
Klingberg, T., Fernell, E., Olesen, P. J., Johnson, M., Gustafsson, P., Dahlström, K., . . . Westerberg, H. (2005). Computerized training of working memory in children with ADHD: A randomized, controlled trial. Journal of the American Academy of Child & Adolescent Psychi- atry, 44, 177–186. doi:10.1097/00004583-200502000-00010
Klingberg, T., Forssberg, H., & Westerberg, H. (2002). Training of working memory in children with ADHD. Journal of Clinical and Experimental Neuropsychology, 24, 781–791. doi:10.1076/ jcen.24.6.781.8395
Koechlin, E., & Summerfield, C. (2007). An information theoretical approach to prefrontal executive function. Trends in Cognitive Science, 11, 229 –235. doi:10.1016/j.tics.2007.04.005
Kristofferson, M. W. (1972). Effects of practice on character-classifica- tion performance. Canadian Journal of Psychology/Revue canadienne de psychologie, 26, 54 – 60. doi:10.1037/h0082415
Levänen, S., & Hamdorf, D. (2001). Feeling vibrations: Enhanced tactile sensitivity in congenitally deaf humans. Neuroscience Letters, 301, 75–77. doi:10.1016/S0304-3940(01)01597-X
LeVay, S., Wiesel, T. N., & Hubel, D. H. (1980). The development of ocular dominance columns in normal and visually deprived monkeys. Journal of Comparative Neurology, 191, 1–51. doi:10.1002/ cne.901910102
Levine, S. (2005). Stress: An historical perspective. In T. Steckler, N. H. Kalin, & J. M. H. M. Reul (Eds.), Handbook on stress and the brain (pp. 3–23). Amsterdam, the Netherlands: Elsevier Science.
Levy, B. J., & Anderson, M. C. (2002). Inhibitory processes and the control of memory retrieval. Trends in Cognitive Sciences, 6, 299 –305. doi:10.1016/S1364-6613(02)01923-X
Lewis, E. E., Dozier, M., Ackerman, J., & Sepulveda-Kozakowski, S. (2007). The effect of placement instability on adopted children’s inhib- itory control abilities and oppositional behavior. Developmental Psy- chology, 43, 1415–1427. doi:10.1037/0012-1649.43.6.1415
Mackey, A. P., Hill, S. S., Stone, S. I., & Bunge, S. A. (2011). Differential effects of reasoning and speed training in children. Developmental Science, 14, 582–590.
Mahncke, H. W., Connor, B. B., Appelman, J., Ahsanuddin, O. N., Hardy, J. L., Wood, R. A., . . . Merzenich, M. M. (2006). Memory enhance- ment in healthy older adults using a brain plasticity-based training program: A randomized, controlled study. Proceedings of the National Academy of Sciences, USA, 103, 12523–12528. doi:10.1073/ pnas.0605194103
Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167–202. doi: 10.1146/annurev.neuro.24.1.167
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., & Howerter,
99February–March 2012 ● American Psychologist
T hi
s do
cu m
en t i
s co
py ri
gh te
d by
th e
A m
er ic
an P
sy ch
ol og
ic al
A ss
oc ia
tio n
or o
ne o
f i ts
a lli
ed p
ub lis
he rs
. T
hi s
ar tic
le is
in te
nd ed
s ol
el y
fo r t
he p
er so
na l u
se o
f t he
in di
vi du
al u
se r a
nd is
n ot
to b
e di
ss em
in at
ed b
ro ad
ly .
A. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analy- sis. Cognitive Psychology, 41, 49 –100. doi:10.1006/cogp.1999.0734
Morcom, A. M., Good, C. D., Frackowiak, R. S. J., & Rugg, M. D. (2003). Age effects on the neural correlates of successful memory encoding. Brain, 126, 213–229. doi:10.1093/brain/awg020
Morris, S. B., & DeShon, R. P. (2002). Combining effects size estimates in meta-analysis with repeated measures and independent-groups de- signs. Psychological Methods, 7, 105–125. doi:10.10371082- 989X.7.1.105
National Scientific Council on the Developing Child. (2007). The timing and quality of early experiences combine to shape brain architecture (Working Paper No. 5). Retrieved from http://developingchild.harvard- .edu/library/reports_and_working_papers/working_papers/wp5/
Neville, H. J., & Lawson, D. (1987). Attention to central and peripheral visual space in a movement detection task: An event-related potential and behavioral study: II. Congenitally deaf adults. Brain Research, 405, 268 –283. doi:10.1016/0006-8993(87)90296-4
Niemeier, J. P. (1998). The lighthouse strategy: Use of a visual imagery technique to treat visual inattention in stroke patients. Brain Injury, 12, 399 – 406. doi:10.1080/026990598122511
Nithianantharajah, J., & Hannan, A. J. (2006). Enriched environments, experience-dependent plasticity and disorders of the nervous system. Nature Reviews Neuroscience, 7, 697–709. doi:10.1038/nrn1970
Olesen, P. J., Westerberg, H., & Klingberg, T. (2004). Increased prefron- tal and parietal activity after training of working memory. Nature Neuroscience, 7, 75–79. doi:10.1038/nn1165
Owen, A. M., Hampshire, A., Grahn, J. A., Stenton, R., Dajani, S., Burns, A. S., . . . Ballard, C. G. (2010, June 10). Putting brain training to the test. Nature, 465, 775–778. doi:10.1038/nature09042
Ozonoff, S. (1995). Executive functions in autism. In E. Schopler & G. B. Mesibov (Eds.), Learning and cognition in autism: Current issues in autism (pp. 199 –219). New York, NY: Plenum Press.
Pambakian, A. L. M., Mannan, S. K., Hodgson, T. L., & Kennard, C. (2004). Saccadic visual search training: A treatment for patients with homonymous hemianopia. Journal of Neurology, Neurosurgery & Psy- chiatry, 75, 1443–1448. doi:10.1136/jnnp.2003.025957
Pascual-Leone, A., Amedi, A., Fregni, F., & Merabet, L. B. (2005). The plastic human brain cortex. Annual Review of Neuroscience, 28, 377– 401. doi:10.1146/annurev.neuro.27.070203.144216
Pears, K. C., Kim, H. K., & Fisher, P. A. (2008). Psychosocial and cognitive functioning of children with specific profiles of maltreat- ment. Child Abuse & Neglect, 32, 958 –971. doi:10.1016/ j.chiabu.2007.12.009
Pennington, B. F., & Ozonoff, S. (1996). Executive functions and devel- opmental psychopathology. Journal of Child Psychology and Psychi- atry, 37, 51– 87. doi:10.1111/j.1469-7610.1996.tb01380.x
Pereira, A. C., Huddleston, D. E., Brickman, A. M., Sosunov, A. A., Hen, R., McKhann, G. M., . . . Small, S. A. (2007). An in vivo correlate of exercise-induced neurogenesis in the adult dentate gyrus. Proceedings of the National Academy of Sciences, USA, 104, 5638 –5643. doi: 10.1073/pnas.0611721104
Pollak, S. D., Nelson, C. A., Schlaak, M. F., Roeber, B. J., Wewerka, S. S., Wiik, K. L., . . . Gunnar, M. R. (2010). Neurodevelopmental effects of early deprivation in postinstitutionalized children. Child Development, 81, 224 –236. doi:10.1111/j.1467-8624.2009.01391.x
Raver, C. C., Jones, S. M., Li-Grining, C., Zhai, F., Metzger, M. W., & Solomon, B. (2009). Targeting children’s behavior problems in pre- school classrooms: A cluster-randomized controlled trial. Journal of Consulting and Clinical Psychology, 77, 302–316. doi:10.1037/ a0015302
Raymer, A. M., Beeson, P., Holland, A., Kendall, D., Maher, L. M., Martin, N., . . . Rothi, L. J. G. (2008). Translational research in aphasia: From neuroscience to neurorehabilitation. Journal of Speech, Lan- guage, and Hearing Research, 51, S259 –S275. doi:10.1044/1092- 4388(2008/020)
Reiss, A. L., Abrams, M. T., Singer, H. S., Ross, J. L., & Denckla, M. B. (1996). Brain development, gender and IQ in children: A volumetric imaging study. Brain, 119, 1763–1774. doi:10.1093/brain/119.5.1763
Reuter-Lorenz, P. A., & Lustig, C. (2005). Brain aging: Reorganizing
discoveries about the aging mind. Current Opinion in Neurobiology, 15, 245–251. doi:10.1016/j.conb.2005.03.016
Röder, B., Teder-Sälejärvi, W., Sterr, A., Rösler, F., Hillyard, S. A., & Neville, H. J. (1999, July 8). Improved auditory spatial tuning in blind humans. Nature, 400, 162–166. doi:10.1038/22106
Rossi, S., Miniussi, C., Pasqualetti, P., Babiloni, C., Rossini, P. M., & Cappa, S. F. (2004). Age-related functional changes of prefrontal cortex in long- term memory: A repetitive transcranial magnetic stimulation study. Jour- nal of Neuroscience, 24, 7939 –7944. doi:10.1523/JNEUROSCI.0703- 04.2004
Rueda, M. R., Rothbart, M. K., McCandliss, B. D., Saccomanno, L., & Posner, M. I. (2005). Training, maturation, and genetic influences on the development of executive attention. Proceedings of the National Academy of Sciences, USA, 102, 14931–14936. doi:10.1073/ pnas.0506897102
Sale, A., Berardi, N., & Maffei, L. (2009). Enrich the environment to empower the brain. Trends in Neurosciences, 32, 233–239. doi: 10.1016/j.tins.2008.12.004
Shalev, L., Tsal, Y., & Mevorach, C. (2007). Computerized progressive attentional training (CPAT) program: Effective direct intervention for children with ADHD. Child Neuropsychology, 13, 382–388. doi: 10.1080/09297040600770787
Shonkoff, J. P., Boyce, W. T., & McEwen, B. S. (2009). Neuroscience, molecular biology, and the childhood roots of health disparities: Build- ing a new framework for health promotion and disease prevention. JAMA, 301, 2252–2259. doi:10.1001/jama.2009.754
Solé-Padullés, C., Bartrés-Faz, D., Junqué, C., Clemente, I. C., Molin- uevo, J. L., Bargalló, N., . . . Valls-Solé, J. (2006). Repetitive transcra- nial magnetic stimulation effects on brain function and cognition among elders with memory dysfunction: A randomized sham-con- trolled study. Cerebral Cortex, 16, 1487–1493. doi:10.1093/cercor/ bhj083
Stevens, C., Fanning, J., Coch, D., Sanders, L., & Neville, H. (2008). Neural mechanisms of selective auditory attention are enhanced by computerized training: Electrophysiological evidence from language- impaired and typically developing children. Brain Research, 1205, 55– 69. doi:10.1016/j.brainres.2007.10.108
Tallal, P., Miller, S. L., Bedi, G., Byma, G., Wang, X., Nagarajan, S., . . . Merzenich, M. M. (1996, January 5). Language comprehension in language-learning impaired children improved with acoustically mod- ified speech. Science, 271, 81– 84. doi:10.1126/science.271.5245.81
Thorell, L. B., Lindqvist, S., Nutley, S. B., Bohlin, G., & Klingberg, T. (2009). Training and transfer effects of executive functions in preschool children. Developmental Science, 12, 106 –113. doi:10.1111/j.1467- 7687.2008.00745.x
Tominey, S. L. M., & McClelland, M. M. (2011). Red light, purple light: Findings from a pilot intervention using classroom games to improve behavioral self-regulation. Manuscript submitted for publication.
van Heugten, C. M., Dekker, J., Deelman, B. G., van Dijk, A. J., Steh- mann-Saris, J. C., & Kinebanian, A. (1998). Outcome of strategy training in stroke patients with apraxia: A phase II study. Clinical Rehabilitation, 12, 294 –303. doi:10.1191/026921598674468328
van Praag, H. (2008). Neurogenesis and exercise: Past and future direc- tions. Neuromolecular Medicine, 10, 128 –140. doi:10.1007/s12017- 008-8028-z
van Praag, H., Kempermann, G., & Gage, F. H. (2000). Neural conse- quences of environmental enrichment. Nature Reviews Neuroscience, 1, 191–198. doi:10.1038/35044558
Wiesel, T. N., & Hubel, D. H. (1963). Single-cell responses in striate cortex of kittens deprived of vision in one eye. Journal of Neurophys- iology, 26, 1003–1017.
Zelazo, P. D., Carlson, S. M., & Kesek, A. (2008). Development of executive function in childhood. In C. A. Nelson & M. Luciana (Eds.), Handbook of developmental cognitive neuroscience (pp. 553–574). Cambridge, MA: MIT Press.
Zelazo, P. D., Müller, U., Frye, D., & Marcovitch, S. (2003). The development of executive function: Cognitive complexity and con- trol—revised. Monographs of the Society for Research in Child Devel- opment, 68, 93–119. doi:10.1111/j.0037-976X.2003.00266.x
100 February–March 2012 ● American Psychologist
T hi
s do
cu m
en t i
s co
py ri
gh te
d by
th e
A m
er ic
an P
sy ch
ol og
ic al
A ss
oc ia
tio n
or o
ne o
f i ts
a lli
ed p
ub lis
he rs
. T
hi s
ar tic
le is
in te
nd ed
s ol
el y
fo r t
he p
er so
na l u
se o
f t he
in di
vi du
al u
se r a
nd is
n ot
to b
e di
ss em
in at
ed b
ro ad
ly .
