Exercising your brain: a review of human brain plastici...

Exercising your brain: a review of human brain plasticity and training-induced learning

Human beings have an amazing capacity to learn new skills and adapt to new environments. However, several obstacles remain to be overcome in designing paradigms to broadly improve quality of life. Arguably, the most notable impediment to this goal is that learning tends to be quite specific to the trained regimen and does not transfer to even qualitatively similar tasks. This severely limits the potential benefits of learning to daily life. This review discusses training regimens that lead to the acquisition of new knowledge and strategies that can be used flexibly across a range of tasks and contexts. Possible characteristics of training regimens are proposed that may be responsible for augmented learning, including the manner in which task difficulty is progressed, the motivational state of the learner, and the type of feedback the training provides. When maximally implemented in rehabilitative paradigms, these characteristics may greatly increase the efficacy of training.


Green CS, Bavelier D. Exercising your brain: a review of human brain plasticity and training-induced learning. Psychol Aging. 2008 Dec;23(4):692-701.

The ability to learn, or, in other words, to acquire skills and alter
behavior as a result of experience, is fundamentally important to
the survival of all animals. Humans are certainly no exception; our
incredible capacity to learn is certainly one of the principle vari-
ables explaining the success of our species. Whereas the term
learning is extremely broad, and interesting work exists on its
every aspect from the relatively simple (e.g. nonassociative learn-
ing) to the much more complex (e.g. social learning), this review
focuses mainly on skill learning. This research, which has been
predominantly carried out with young adults, provides compelling
evidence for common principles of learning and learning transfer.
We review this work and its implications for the design of training
regimens in older adults.

Skill learning is defined here as a change, typically an improve-
ment, in perceptual, cognitive, or motor performance that comes
about as a result of training and that persists for several weeks or
months, thus distinguishing it from effects related to adaptation or
other short-lived effects. Perhaps the most notable finding from the
past century or more of research in the field is that humans have
demonstrated some amount of learning in virtually every paradigm
tested. Evidence for the principle that, given appropriate practice,
humans improve on essentially every task, is prevalent throughout
the psychology literature, ranging from the domain of perceptual
learning (Fahle & Poggio, 2002) to that of motor learning (Karni
et al., 1998) and cognitive training (Willis et al., 2006). An
important distinction in the field concerns the time course of the
learning. Many researchers have differentiated between an early,
fast stage of learning that occurs on the order of minutes as the
participant becomes familiar with the task and stimulus set and a
much slower stage of learning triggered by practice but which
requires hours and sometime days to become effective. This dis-
tinction is observed in both the perceptual (Karni & Sagi, 1993)
and the motor (Karni et al., 1998) learning domains (but see Karni
& Bertini, 1997, for examples of “slow” learning in purportedly
“fast-learning only” paradigms and vice versa). The review herein
focuses principally on the latter slow-learning effects. Further-
more, with an eye toward the potential impact of training regimens
on daily life, our emphasis will be not only on durable learning
effects but also on general ones. We use the term general learning
to refer to learning effects that, at the time of retention testing, not
only provide high savings on the trained task but also transfer to
new tasks and new contexts (Schmidt & Bjork, 1992).


The Problem of Skill Learning

Whereas the exceptional capacity of humans to learn should
certainly give heart to those seeking to design rehabilitative train-
ing paradigms (whether focused on retraining vision or language
after neurological damage or slowing/reversing the normal decline
in visual and cognitive skills associated with aging), several key
obstacles still must be overcome. First, although it is true that the
skill learning literature is filled with examples that suggest that
humans can improve on virtually any perceptual or motor task
(Ball & Sekuler, 1982; Brashers-Krug, Shadmehr, & Bizzi, 1996;
Fahle, 2004; Fine & Jacobs, 2000; Fiorentini & Berardi, 1980;
Gandolfo, Mussa-Ivaldi, & Bizzi, 1996; Karni et al., 1995; Karni
& Sagi, 1991; Mackrous & Proteau, 2007; Martin, Keating, Good-
kin, Bastian, & Thach, 1996; Ramachandran & Braddick, 1973;
Seidler, 2004; Shiu & Pashler, 1992; Sireteanu & Rettenbach,
1995, 2000; Sowden, Rose, & Davies, 2002), most of these same
instances also demonstrate a remarkable specificity of learning. In
other words, improvement is observed only in the trained task,
with little to no transfer of learning being observed even for very
similar untrained tasks. This is obviously a potentially severe
impediment for rehabilitative programs, where the goal is to in-
crease the quality of everyday life and which thus necessarily
require a general improvement in skills. Second, the fact that
training tasks are often boring and unpleasant may decrease the
probability of full compliance with the regimen, which in turn will
negatively affect final result. Third, improvement in performance
is not always due to training-induced learning. Instead, changes in
mood, level of motivation, or even desire to please the investigator
can all lead to temporary improvements in performance, which
without care in experimental design, can easily be mistaken for
true learning effects. The key questions in the field of training-
induced learning are therefore the following: First, can training
regimens be identified that lead to performance improvements that
generalize beyond the training context and persist over time?
Second, what exact factors contribute to a more general learning
outcome?


Specificity of Learning

In the field of skill learning, transfer of learning from the trained
task to even other very similar tasks is generally the exception
rather than the rule. This fact is well documented in the field of
perceptual learning. For instance, Fiorentini and Berardi (1980)
trained participants to discriminate between two complex gratings
that differed only in the relative spatial phase of the two compo-
nent sinusoids. Performance on this task improved very rapidly
over the course of a single training session and remained consis-
tently high when participants were tested on two subsequent days.
However, when the gratings were rotated by 90° or the spatial
frequency was doubled, no evidence of transfer was observed.
Karni and Sagi (1991) trained participants on the discrimination of
oriented texture objects always presented in a certain region of the
visual field within an array of oriented background objects. Al-
though learning occurred for the trained stimulus, no transfer was
observed if the object was moved to a new location or if the
orientation of the object was changed. Learning in some types of
hyperacuity tasks can also be specific for the trained retinal loca-
tion, orientation, and even eye (Fahle, 2004). Ball and Sekuler
(1982) trained participants to discriminate small differences in the
direction of dot motion. A linear increase in performance was
observed over the course of seven sessions, but following training,
the authors found no effect of training on orientations more than
45° different than the trained orientation, and the effect at 45° was
approximately half of that seen in the training orientation. There is
also evidence that such motion training can be speed specific as
well (Saffell & Matthews, 2003). Finally, in psychophysics, where
visual stimuli are often backward masked, learning can be specific
to even the particular structure of the given mask (Maehara &
Goryo, 2003).

Similar examples of specificity can also be found in the motor
domain (Bachman, 1961). For instance, Rieser, Pick, Ashmead,
and Garing (1995) induced a recalibration of the motor system by
altering the normal relationship between a given motor command
and its result in the world. They accomplished this by towing a
treadmill, on which a participant was walking, behind an automo-
bile. By driving the car faster than the movement of the treadmill,
the researchers recalibrated the system in line with the belief that
less biomechanical force was required to move a given distance
and vice versa when the car was driven slower than the movement
of the treadmill. This recalibration was demonstrated by having
participants walk to targets while blindfolded, where they observed
undershoots or overshoots consistent with the training condition.
Interestingly, recalibrations of walking speed did not transfer to
throwing or to turning in place (although they did transfer to
another form of forward locomotion: sidestepping). Comparable
recalibrations of throwing likewise did not transfer to walking.
Similar specificity has also been found in prism adaptation,
wherein participants wear goggles that displace the visual world
laterally, thus requiring a recalibration of the motor system to bring
it back in alignment with the nondisplaced real world. In the prism
adaptation literature, there is evidence for learning that is specific
to the trained limb (Martin et al., 1996), to the start and end
position of the learned movement, and to the action performed
(Redding, Rossetti, & Wallace, 2005; Redding & Wallace, 2006).
Further examples of specificity in motor learning include manual
aiming practice. Participants trained to aim at a target with the
aiming hand visibly improve in their aiming movement in terms of
accuracy and speed. However, these improvements do not transfer
to conditions in which the hand is not visible (Proteau, 1992). A
related finding is observed in young adults in stimulus–response
mapping learning studies. For instance, Pashler and Baylis (1991)
trained participants to associate one of three keys with certain
visually presented symbols (left key = P or 2, middle key = V or
8, right key = K or 7). Over the course of multiple training blocks,
participant reaction time decreased significantly. However, when
new symbols were added that needed to be mapped to the same
keys in addition to the learned symbols (left key = P, 2, F, 9;
middle key = V, 8, D, 3; right key = K, 7, J, 4), no evidence of
transfer was apparent (and reaction times to the previously learned
symbols increased to pretraining levels).

Specificity of learning is also a feature of cognitive training. For
example, a wealth of studies now exists on the impact of cognitive
training in older adults. By and large, these studies demonstrate
improvements on attention, memory, and reasoning tasks follow-
ing training (Basak, Boot, Voss, & Kramer, 2008; Bherer et al.,
2005; Plemons, Willis, & Baltes, 1978; Verhaeghen, Marcoen, &
Goossens, 1992; Willis, Blieszner, & Baltes, 1981; Winocur et al.,
2007). However, training differences are typically specific to the
ability trained, with those individuals trained in attention experi-
encing gains in attention but not in memory or reasoning and vice
versa (see, for example, Allaire & Marsiske, 2005; Ball et al.,
2002).


Training Regimens and General Learning

Although myriad examples of highly specific learning exist,
only a handful of training paradigms have been established where
learning seems more general. These learning paradigms are typi-
cally more complex than laboratory manipulations and correspond
to real-life experiences, such as action video game training, mu-
sical training, or athletic training.

Recent work indicates that action video game experience leads
to enhanced performance on a number of tasks. For example,
action game players outperform their peers on the multiple-object
tracking task, wherein participants must track many independently
moving objects, therefore displaying an enhanced capacity of the
attentional system (Green & Bavelier, 2006b). They also perform
better on the useful field of view task, wherein participants must
localize a quickly flashed target amongst a host of distracting
objects (Green & Bavelier, 2006a). This skill indexes the ability to
deploy attention over space (Ball, Beard, Roenker, Miller, &
Griggs, 1988) and is one of the best perceptual predictors of
driving accident rates in older persons, far outperforming standard
measures of acuity (Myers, Ball, Kalina, Roth, & Goode, 2000).
Action game players demonstrate superior capabilities on the
attentional blink task, wherein participants must parse a stream of
letters presented one after another at a fast pace (10 Hz), indicating
faster temporal characteristics of visual attention (Green & Bave-
lier, 2003). Participants skilled in action game playing can also
resolve visual details in the context of tightly packed distractors, as
in the crowding task. In this task, flanking objects above and below
a center target negatively affects the ability to identify the center
target. In doing so, such participants exhibit higher spatial resolu-
tion of visual processing (Green & Bavelier, 2007). Action video
game players also demonstrate enhanced mental rotation abilities
(Feng, Spence, & Pratt, 2007). Action video game experience has
been shown to transfer to even high-level real-world tasks, such as
piloting procedures (Gopher, Weil, & Bareket, 1994).

Critically, in each of the cases above, the causative link between
action video game experience and enhanced performance was
demonstrated through a training study in which non–game-playing
individuals were specifically trained on an action video game, and
the skill in question (e.g., attentional capacity) was assessed before
and after training and compared with the performance of a control
group that played a non–action game for the same period of time.
This point is of great importance, as properly conducted training
studies are critical to advancing the level of understanding in this
field. Although many individuals play video games, music, or
sports as part of their everyday lives, we can only infer so much by
comparing the performance of these “experts” with “nonexperts”
who do not ordinarily engage in these activities. Population bias is
a constant concern; it is likely that individuals with some type of
inherent talent and/or skill will flock to those activities that reward
their particular skill set. For instance, individuals born with supe-
rior hand–eye coordination may be quite successful at some types
of video games and thus preferentially tend to play these types of
games, whereas individuals born with poor hand–eye coordination
may tend to avoid playing games that require this skill. It is
essential to demonstrate a definitive causative link between a given
form of experience and any enhancement in skills by training
non-experts on the experience in question and observing the ef-
fects of this training.

Furthermore, it is not sufficient to test only an experimental
group. Training studies should also include a group that controls
for test–retest effects (i.e., how much improvement can be ex-
pected simply from taking the test a second time) and, just as
importantly, for psychological and motivational effects. Indeed, it
is well documented that individuals who experience an active
interest taken in their performance tend to increase their perfor-
mance more than do individuals who experience no interest taken
in their performance, an effect often dubbed the Hawthorne effect
(Lied & Karzandjian, 1998). This effect can lead to powerful
improvements in performance that have little to do with the spe-
cific cognitive training regimen under study but rather reflect
social and motivational factors on performance. The impact of
these factors on learning is important in and of itself and should
certainly be the subject of careful studies. However, the many
studies that include only a no-intervention, no-contact control
group cannot distinguish between the cognitive content of the
training regimen and social stimulation as the source of improve-
ment (Drew & Waters, 1986; Goldstein et al., 1997; Kawashima et
al., 2005; Willis et al., 2006).

Although a training study is lacking, and thus the question of
causation remains unanswered, there are also a host of other
reports in the literature (for a review, see Green & Bavelier, 2006c)
that those individuals who naturally play action video games
outperform their non–game-playing peers on other measures of
visual attention (Bialystok, 2006; Castel, Pratt, & Drummond,
2005; Greenfield, DeWinstanley, Kilpatrick, & Kaye, 1994; Grif-
fith, Voloschin, Gibb, & Bailey, 1983; Trick, Jaspers-Fayer, &
Sethi, 2005), visuomotor skills, and even job-specific skills such as
laparoscopic maneuvers (Rosser et al., 2007).

Furthermore, and of particular relevance to the field of geron-
tology, several reports have demonstrated that video game play can
improve perceptual, motor, and cognitive function in older per-
sons. For instance, Drew and Waters (1986) reported significant
improvements in both measures of manual dexterity (Purdue peg-
board, rotary pursuit) as well as general cognitive function (Wech-
sler Adult Intelligence Scale—Revised Full Scale, Verbal, and
Performance scores). Several groups (Clark, Lanphear, & Riddick,
1987; Dustman, Emmerson, Steinhaus, Shearer, & Dustman, 1992;
Goldstein et al., 1997) have also reported significant decreases in
reaction time as a result of video game experience in older persons.
Although it is unfortunate that the studies listed above largely did
not include intervention control groups, the results are certainly
noteworthy and encouraging of further investigation. In particular,
it is interesting to speculate that given the growing popularity of
the Nintendo Wii, which attracts a much wider population than
standard video games, including older persons, an interesting
convergence may soon occur between researchers examining the
effects of video games and those examining the effects of physical
activity on perceptual and cognitive skills (see below).

The effects of playing video games on perceptual and cognitive
skills are particularly remarkable given the typical specificity of
skill learning. Indeed, in the case of action video game training, the
tasks used to measure the various perceptual, attentional, and
visuomotor skills are quite a departure from the “training para-
digm” (i.e., action video games). There are few obvious links
between chasing monsters across a star-spotted “spacescape” and
determining the orientation of a single black ‘T’ on a uniform gray
background, or between driving a car through a crowded cityscape
while shooting at rival vehicles and counting the number of white
squares that are quickly flashed against a black background. Al-
though one can certainly argue that individuals are making use of
similar underlying processes in action video games and in the
psychophysical tasks (rapid object identification for instance), this
argument flies in the face of the great many articles demonstrating
that no transfer is observed if something as seemingly minor as
spatial frequency or orientation is changed. Along a continuum of
task similarity, it seems natural to consider orientation discrimi-
nation around 45° as closer to orientation discrimination around
135° than to avoiding laser blasts from spaceships.

However, it is not the case that action video game experience
leads to enhancements in every perceptual, attentional, and/or
visuomotor skill. For instance, Castel et al. (2005) showed that the
attentional orienting system appears to be similar in action video
game players and in nonplayers. Furthermore, it is essential to
convey the fact that not all types of video games lead to similar
effects. Our work and, to some extent, the majority of the litera-
ture, has focused specifically on the effect of action video games,
that is, games that are fast paced and unpredictable, require effec-
tive monitoring of the entire screen, and necessitate that decisions
be made extremely rapidly. Other game types, such as puzzle
games, fantasy games, or role-playing games do not have similar
effects (although they may influence other types of processing).

Other types of activities in addition to video game play have
also been observed to lead to reasonably generalized effects, in
particular, musical and athletic training. In the music domain for
instance, Schellenberg (2004) assessed the effect of music lessons
on IQ. Children from a large sample were randomly assigned to
one of four groups. Two groups received music training (keyboard
or vocal), one control group received drama training, and the final
group received no training. The primary measures of interest were
scores on the Wechsler Intelligence Scale for Children, Third
Edition before and after training. Whereas IQ scores increased in
all groups, the largest increases were observed in the two music
training groups (an effect that further held in all but 2 of the 12
subtests of the full scale). Rauscher et al. (1997) monitored the
spatiotemporal reasoning skills of children (3–4 years old) who
were given 6 months of keyboard lessons. Significantly larger
improvements in spatiotemporal reasoning were noted in the
keyboard-trained children than in two control groups: a computer
training group and a no-training group (see also Hetland, 2000).
Researchers have also suggested that music training enhances
mathematical ability and verbal memory (Gardiner, Fox, Knowles,
& Jefferey, 1996; Graziano, Peterson, & Shaw, 1999; Ho, Cheung,
& Chan, 2003). Perhaps the best known and most popularized
effect related to music is the so-called “Mozart effect” (Rauscher,
Shaw, & Ky, 1993), wherein listening to only 10 min of a Mozart
sonata was found to lead to significant increases in IQ. Unfortu-
nately, in addition to proving difficult to consistently replicate
(Fudin & Lembessis, 2004; McCutcheon, 2000; Rauscher & Shaw,
1998; Steele, Brown, & Stoecker, 1999), this effect does not
constitute true learning, as any positive effects last only a few
minutes, potentially as a result of short-term arousal or mood
changes (Thompson, Schellenberg, & Husain, 2001).

In the athletic domain, Kioumourtzoglou, Kourtessis, Michalo-
poulou, and Derri (1998) compared athletes with expertise in
various games (basketball, volleyball, and water polo) on a number
of measures of perception and cognition. The experts demonstrated
enhancements (compared with novices) in skills that are intuitively
important to performance in their given games. Basketball players
exhibited superior selective attention and eye–hand coordination,
volleyball players outperformed novices at estimating the speed
and direction of a moving object, and water polo players had faster
visual reaction times and better spatial orienting abilities. Several
groups have observed similar sports-related differences in the
Posner cueing task (Lum, Enns, & Pratt, 2002; Nougier, Azemar,
& Stein, 1992), and Kida, Oda, and Matsumura (2005) demon-
strated that trained baseball players responded faster than novices
in a go/no-go task (“press the button if you see Color A”; “do not
press the button if you see Color B”) but, interestingly, showed no
enhancements in a simple reaction time task (“press the button
when a light turns on”). In the future, training studies that establish
the causal effects of athletic training would be highly beneficial.

In addition to enhancements as a result of experience with
specific sports, a rapidly growing body of work suggests that
aerobic exercise of any sort may benefit a range of cognitive
abilities, particularly in older persons, with consistently positive
results having been found in many cross-sectional studies (i.e.,
comparing individuals who normally exercise with those who do
not). Positive effects have been documented on tasks as varied as
dual-task performance or executive attention/distractor rejection
(for recent reviews, see Colcombe & Kramer, 2003; Hillman,
Erickson, & Kramer, 2008; Kramer & Erickson, 2007). Unfortu-
nately, as is true in the video game and music literatures, many
experimental studies in this literature either have not included a
control condition (Elsayed, Ismail, & Young, 1980; Stacey,
Kourma, & Stones, 1985) or have included control conditions
where the groups were not matched in terms of experimenter
involvement (Hawkins, Kramer, & Capaldi, 1992). Furthermore,
results in this literature are not always in agreement, with some
groups showing positive results (Dustman et al., 1984; Hawkins et
al., 1992) and others failing to show such effects (Blumenthal et
al., 1991; Hill, Storandt, & Malley, 1993). Yet, several recent
reviews and meta-analyses (Colcombe & Kramer, 2003; Etnier,
Nowell, Landers, & Sibley, 2006; Hillman et al., 2008; Kramer &
Erickson, 2007) have demonstrated that across studies, designs,
and dependent measures, older adults that perform aerobic activity
exhibit enhanced cognitive performance as compared with those
who do not. This point finds support beyond behavioral measures,
as aerobic fitness has also been linked with neuroanatomical and
neurophysiological changes, including increased gray matter vol-
ume in the prefrontal and temporal areas (Colcombe & Kramer,
2003); changes in cerebral blood volume in the hippocampus
(Pereira et al., 2007); and functional brain activity in a variety of
areas, including superior parietal areas and the anterior cingulate
cortex (Colcombe et al., 2004). Taken together with the mounting
evidence that proper nutrition facilitates cognitive abilities (see
Gomez-Pinilla, 2008, for a thorough review), the emerging picture
confirms the old saying “mens sana in corpore sano [a healthy
mind in a healthy body].”

In addition to the types of everyday experience outlined above,
several groups have developed training regimens specifically de-
signed to improve cognitive abilities, targeting, in particular, aging
baby boomers and older adults. Small and large companies have
been attracted to this high potential market, including Nintendo,
with the BrainGames series, and smaller companies like the one
developing POSIT (Mahncke, Bronstone, & Merzenich, 2006), to
cite only a few. These training regimens typically use a variety of
standard psychological tests, meaning that individuals are asked to
perform small tests that are highly similar in content and structure
with tests used on psychological assessment scales (e.g., list learn-
ing to enhance semantic memory, pattern identification to enhance
visual form recognition, visual search to enhance the efficiency of
visual attention, matching easily confusable consonant–vowel–
consonant words to enhance appropriate use of inhibitory mecha-
nisms, n-back tasks to increase working memory abilities). These
regimens have shown clear improvements in abilities specific to
those trained as well as maintenance of those gains from 3 months
(Mahncke, Connor, et al., 2006) to 5 years (Willis et al., 2006). A
main issue for future work remains the extent to which these gains
generalize outside of the laboratory situation to improve the ev-
eryday life of the participants. Evidence for substantial transfer
effects between training and testing has been elusive thus far. The
training paradigm used by Mahncke, Connor, et al. (2006) resulted
in improvements in an untrained auditory memory task, and one
version of the paradigm used by Willis et al. (2006) resulted in
self-reported reductions in the difficulty of complex home activi-
ties such as meal preparation and shopping. Winocur et al. (2007)
reported more substantial transfer to untrained tasks applicable to
real-life situations; however, the use of a no-intervention control
group leaves the interpretation of their effects open (particularly
given the extensive and highly personal interactions that occurred
between the experimental group and experimenters). As is the case
in the field of brain plasticity, the greatest effects of training are
observed on tasks that most closely mirror the trained task, with
transfer of gains to other skills or to everyday competence rarely
documented.

It is interesting to note one key difference between the “natural”
training regimens discussed above (sports, music, video games)
and those that have been designed for the specific purpose of brain
training. The natural training regimens are exceedingly complex
and tap many systems in parallel. In video games developed for
entertainment, for instance, one may be simultaneously engaged in
memory tasks (e.g., spatial memory for the route to the enemy
fortress, semantic memory for weapons at one’s disposal or ene-
mies still active), executive tasks (e.g., resource and weapon
allocation, dual tasking), visual attention tasks (multiple object
tracking, distractor rejection), visuomotor tasks (e.g., steering,
piloting), and rapid object recognition, to cite just a few. The same
need for highly parallel processing across domains is prevalent in
athletics and, to varying degrees, in learning to play a musical
instrument. Conversely, when researchers have designed training
regimens for the purpose of brain/cognitive training, they have
purposefully separated these tasks or domains. The training is
typically broken down into subdomains, with semantic memory
being trained entirely separately from inhibition control, which, in
turn, is trained separately from speed of processing. The existing
research suggests that such blocked learning leads to faster learn-
ing during the acquisition phase, yet it can be detrimental during
the retention phase, leading to less robust retention and to lesser
transfer across tasks (Ahissar & Hochstein, 2004; Schmidt &
Bjork, 1992). For example, Clopper and Pisoni (2004) asked two
groups of participants to classify sentences according to the dialect
region of the speakers’ native region. A first group of participants
was trained with each dialect being represented by a single
speaker. A second group of participants was trained with three
different speakers for each dialect. The group that received
the more variable training learned more slowly initially but was
more accurate in a retention test involving new speakers with new
sentences.


Mechanisms of Learning

Learning mechanisms surely vary in their specific implementa-
tion across different perceptual and cognitive domains. Learning to
recognize faces or to speak seems effortless and occurs naturally
during the course of development; in contrast, learning to read has
to be taught in an explicit fashion. However, it remains that some
mechanisms of learning appear to be shared across domains. Their
further characterization will be critical to our general understand-
ing of learning principles.

The reverse hierarchy theory, which was initially proposed by
Ahissar and Hochstein (2004) to account for learning in the per-
ceptual domain, has such general appeal. The authors hypothesized
that information flows in a feed-forward manner through hierar-
chically organized structures, with information at the lower levels
of processing decaying as information flows upward. Yet, if in-
formation at the higher level is insufficient to sustain task perfor-
mance, feedback searches can be initiated downward in the hier-
archical structure to locate the most informative levels of
representation. In short, this view holds that learning is a top-down
guided process, wherein learning occurs at the highest level that
suffices for the given task. Specificity of learning and the degree
of generalization are naturally accounted for in this framework.
Tasks handled at high levels of the hierarchical organization will
demonstrate transfer of learning. Tasks that require backward
searches and lower levels of representation will lead to highly
specific learning. Although originally designed to account for
results in the perceptual learning literature, the reverse hierarchy
model captures many features of learning beyond those in the field
of psychophysics. Recently, the model has been successfully ap-
plied to the field of word recognition and sentence processing
(Ahissar & Hochstein, 2004). It also naturally predicts the fact that
variability in learning experience will result in less extensive
learning during the acquisition phase but larger transfer to new
tasks during retention tests. Furthermore, the model predicts that
tasks that require very low-level representations will show less
generalization of learning than those that rely on higher levels of
representation. A potential weakness of reverse hierarchy theory is
that it has not always been easy to predict, from the study design,
which level of processing will be the highest one sufficient to carry
out the task (and without such clear a priori predictions, the
argument can quickly become circular). As the hierarchical level at
which learning occurs and the amount of generalization are diffi-
cult to manipulate independently, validation of the theory is still
pending.

Other models of complex human learning, such as those derived
from connectionism or machine learning, also provide clues about
the general mechanisms of bottom-up learning. Much is based on
inferring the statistical structure of the world with which the
learner is faced. Recently, the framework of Bayesian inference
has been proposed to provide a good first-order model of how
participants learn to optimize behavior in dynamic complex tasks,
whether perceptual or cognitive in nature (Courville, Daw, &
Touretzky, 2006; Ernst & Banks, 2002; Orbán, Fiser, Aslin, &
Lengyel, 2008; Tenenbaum, Griffiths, & Kemp, 2006). Another
key feature of recent advances has been the realization that
actions and the feedback they provide about the next step to be
computed can greatly reduce the computational load of a task as
well as facilitate learning and generalization (Ballard, Hayhoe,
Pook, & Rao, 1997; Taagten, 2005). More generally, the field of
reinforcement learning has been instrumental in promoting the
development of general principles for learning rules and pro-
viding clues as to the factors that promote learning transfer. The
determinants of learning discussed below are largely inspired
from this work.


Determinants of Learning and Learning Transfer

As a whole, the literature on the effects of video game, music,
and sports experience demonstrate that general learning is possi-
ble. However, each of these training regimens is exceedingly
complex and differs from traditional training regimens in many
ways. A major challenge for future work is to pinpoint what
aspects or combination of aspects inherent to these activities is
responsible for enhancement in learning and learning transfer. This
point is exceedingly important, both theoretically, in terms of
designing models of human learning and behavior, and practically,
for those seeking to devise rehabilitation paradigms to ameliorate
daily life (Schmidt & Bjork, 1992). Fortunately, on the basis of the
literatures from a variety of somewhat disparate fields, there are
characteristics inherent to these complex training regimens that
seem more likely to be at the root of the observed enhancements
than others. We discuss these characteristics from the vantage
point of video games, but musical and sports training also embody
these characteristics to some extent.


Task Difficulty

The principle of utilizing small incremental increases in task
difficulty is implicit in nearly every video game. As players
progress through levels, they learn new skills and techniques that
allow the player to master game circumstances that would have
been impossible at the game outset. In our own work on video
game training, we have acknowledged this principle explicitly, by
advancing players to the next level of difficulty during training
only when they have demonstrated sufficient mastery of their
current level (Green & Bavelier, 2006a, 2006b, 2007). Similarly,
albeit with barn owls rather than human participants, Linkenhoker
and Knudsen (2002) demonstrated that adult barn owls could
adjust to sizable shifts in visual experience (using prism goggles)
when the shifts were made in small enough increments. In contrast,
large shifts led to no learning in these adult barn owls. The idea of
manipulating task difficulty appropriately has also been noted by
Sireteanu and Rettenbach (1995, 2000) and Ahissar Hochstein
(2000). Ahissar and Hochstein (2004), in particular, have remarked
upon the conditions of difficulty in which learning seems to
transfer most. Their basic task involved asking participants to view
arrays of oriented lines and to determine which contained a single
oddly oriented line. This task can be made arbitrarily difficult by
limiting exposure time as well as the time between exposure and a
subsequent mask. With practice, the time between target and mask
onsets that could be tolerated by the participants decreased sub-
stantially. Interestingly, when the task was started at a difficult
level (short times between target and mask, small difference in
orientation between oddball and background, and/or greater eccen-
tricity), learning was slow and specific for the trained orientation
and location. When the task was made easier (in particular by
starting with long intervals between stimulus and mask), learning
progressed quickly and transferred to novel orientations. In the
same vein, Liu and Weinshall (2000), using much the same motion
direction discrimination paradigm as Ball and Sekuler (1982),
where no transfer was observed, demonstrated that learning an
“easy” discrimination (discrimination of 9° of motion direction
rather than 3°) transferred substantially to novel orientations.


Motivation and Arousal

The concept of setting a proper task difficulty leads to the
consideration of additional factors that could potentially influence
the outcome of training: motivation and arousal. Whereas motiva-
tion and arousal have been largely (if not completely) ignored in
the field of skill learning (but see Ackerman & Cianciolo, 2000;
Ackerman, Kanfer, & Goff, 1995), these factors have been and
continue to be actively considered in social psychology, education,
and many other fields concerned with learning. For instance,
motivation is a critical component of most major theories of
learning in these fields, with motivation level being posited to
depend highly on the individual’s internal belief about his or her
ability to meet the current challenge. Vygotsky’s (1978) “zone of
proximal development” corresponds well with the skill learning
literature discussed above. According to this theory, motivation is
highest and learning is most efficient when tasks are made just
slightly more difficult than can be matched by the individual’s
current ability. Tasks that are much too difficult or much too easy
will lead to lower levels of motivation and thus substantially
reduced learning. This is not to say that no learning will ever occur
if the task is too difficult or too easy (Amitay, Irwin, & Moore,
2006; Seitz & Watanabe, 2003; Watanabe, Nanez, & Sasaki,
2001), but learning rate should be at a maximum when the task is
challenging, yet still doable.

Like motivation, arousal is at the heart of many learning theories
in the social sciences, but for the most part has also been over-
looked in the field of skill learning. Although it is difficult to find
a consistent operational definition for the term (Anderson, 1990;
Neiss, 1988), arousal is often thought of as an abstract construct
encompassing a variety of processes, including those that mediate
alertness and wakefulness, and has been defined in terms of
autonomic responses (e.g., changes in heart and breathing rate,
pupil dilation, changes in skin conductivity), neurophysiological
responses (e.g., activity in the reticular formation, as well as in
cholinergic ponto-mesencephalic, noradrenergic locus coeruleus
and dopaminergic ventral mesencephalic neurons), and/or behav-
ioral responses (e.g. increased attentiveness). The term arousal is
also often treated as being synonymous with the stress response
(i.e., fight or flight), although some authors have proposed distinc-
tions between the two (e.g., that stress only occurs when task
demands exceed an individual’s ability; see Westman & Eden,
1996). Video games are known to strongly elicit both the auto-
nomic responses (Hebert, Beland, Dionne-Fournelle, Crete, &
Lupien, 2005; Segal & Dietz, 1991; Shosnik, Chatterton, Swisher,
& Park, 2000) and the neurophysiological responses (Koepp et al.,
1998) characteristic of arousal, with these responses constituting a
subjectively salient difference between traditional learning para-
digms and video games. The Yerkes–Dodson law (Yerkes &
Dodson, 1908) predicts that learning is a U-shaped function of
arousal level. Training paradigms that lead to low levels of arousal
will tend to lead to low amounts of learning as will training
paradigms that lead to excessively high levels of arousal (Fran-
kenhaeuser & Gardell, 1976). Between these extremes is some
level of arousal that leads to a maximum amount of learning,
which no doubt differs greatly among individuals. In light of this
theory, it is interesting to note that the level of arousal in standard
skill learning paradigms is almost certainly toward the very lowest
end of the spectrum, which again predicts very low amounts of
learning, whereas action video games, for example, likely lead to
a much more optimal level of arousal and thus greater amounts of
learning. In this vein, although again investigated with owls,
Bergan, Ro, Ro, and Knudsen (2005) observed that owls who were
forced to hunt (an activity that involves motivation and arousal, as
well as reward, which is discussed subsequently) while wearing
displacing prisms demonstrated significant increases in accuracy
compared with owls who wore the prisms for the same period of
time but were fed dead prey.


Feedback

Another possible factor in learning is feedback and the utility of
rewards (of which motivation and arousal are likely to serve at
least partially as functions). The exact role that feedback plays in
learning is a subject of much debate within the field. Numerous
examples have demonstrated that feedback is necessary for learn-
ing (Herzog & Fahle, 1997; Seitz, Nanez, Holloway, Tsushima, &
Watanabe, 2006), whereas many counterexamples have demon-
strated that feedback is not necessary for learning (Amitay et al.,
2006; Ball & Sekuler, 1987; Fahle, Edelman, & Poggio, 1995;
Karni & Sagi, 1991). Complicating matters is that even when
experimenter-generated explicit feedback is not provided, if
above-threshold stimuli are used, participants will nevertheless
have varying degrees of confidence that their response was correct,
which could act as a de facto feedback signal (Mollon & Danilova,
1996). Whereas most major theories of learning require that some
type of learning signal be present (often in the form of an error
signal), they do not necessarily require that the signal be explicit
nor do they require that feedback be given on a trial-to-trial basis.
There are many algorithms that can learn quite efficiently even if
feedback is given only after a series of actions has been completed.
The latter case is most analogous to action video games where
feedback (typically in the form of killing an opponent or dying
oneself) becomes available only at the conclusion of a very com-
plicated pattern of actions. How best to solve this credit assign-
ment problem and how this affects the generality of what is learned
is a topic of ongoing research in the field (Fu & Anderson, 2008).

Whereas the role of feedback has been studied extensively (if
not to any conclusive end), the effect of the utility of the feedback
has been largely overlooked (but see Seitz & Dinse, 2007). Utility,
a term most commonly used in economics, describes the relative
desirability of a reward, thereby explicitly recognizing the idea that
the same physical reward is not necessarily worth the same amount
to every person. In fact, there is a large amount of variability in the
utility of given rewards, both between participants and even within
participants (for instance, the utility of an energy bar depends on
both the individual’s like or dislike of energy bars as well as their
current level of hunger). There is a wealth of evidence in the
neurophysiology literature demonstrating that the brain systems
thought to convey the utility of reward, such as the ventral teg-
mental area (VTA) and the nucleus basilis (NB), play a large role
in producing plastic changes in sensory areas. In particular, when
specific auditory tones are paired with stimulation of either the
VTA (dopaminergic) or the NB (cholinergic), the area of primary
auditory cortex that represents the given tone increases dramati-
cally (Bao, Chan, & Merzenich, 2001; Kilgard & Merzenich,
1998). Interestingly, at least some of these same areas have been
shown to be extremely active when individuals play action video
games. For instance, Koepp et al. (1998) demonstrated that
roughly the same amount of dopamine is released in the basal
ganglia when playing an action video game as when methamphet-
amines are injected. How areas involved in the processing of
reward play into learning and neural plasticity will continue to be
an area of active research.


Variability

The final key factor in ensuring flexible learning that we discuss
is variability in task and input. Variability is important at both the
level of the exemplars to be learned and the context in which they
appear (Schmidt & Bjork, 1992). For example, participants learn to
recognize objects in a more flexible way if the objects are pre-
sented in a highly variable context (Brady & Kersten, 2003), which
forces participants to extract more general principles about object
category (rather than focusing on specific features that may be
dependent on viewpoint, lighting conditions, etc.). Work on object
classification and artificial grammar learning shows that low input
variability induces learning at levels of representation that are
specific to the items being learned, which are too rigid to gener-
alize to new stimuli. High variability is crucial in ensuring that the
newly learned informative fragments be at levels of representation
that can flexibly recombine (Gomez, 2002; Onnis, Monaghan,
Christiansen, & Chater, 2004; Newport, & Aslin, 2008).


Conclusion

The capacity of the human brain to learn and adapt is un-
equalled. This review touched on only a few of the hundreds of
skills in which significant learning has been well established. Yet,
learning is typically so specific to the learned skill that it shows
little generalization to related tasks or new environments, limiting
the practical impact of the learning so potently demonstrated in the
laboratory. This specificity of learning is a major obstacle to the
design of efficient rehabilitation paradigms, whether they are tar-
geted to overcoming deficits related to injury or to slowing/
reversing the normal declines associated with aging.
Recently, several types of experience, including action video
game experience, musical training, and athletic training, have been
shown to lead to quite widespread effects on perception, motor
skills, and cognition. Although these experiences are exceedingly
complex, they offer new insights as to what differences between
these and traditional methods of training/testing are most critical
for the differences in learning outcome and, in particular, gener-
alization of learning.


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