Neural and Cognitive Plasticity
Media
Part of Neural and Cognitive Plasticity
- Title
- Neural and Cognitive Plasticity
- extracted text
-
Neural and Cognitive Plasticity
EDUARDO MERCADO III
Abstract
Modern humans spend much of their early lives participating in formal educational
programs designed to increase their cognitive competencies. Despite this concerted
effort to maximize individuals intellectual capacities, scientists and educators know
relatively little about the neural factors that determine when and how learning experiences lead to improvements in cognitive abilities. Current theories of how brains
are changed by learning focus on incremental adjustments to connections between
neurons that are driven by increases in neural activity. This article summarizes past
theoretical and experimental research on the relationship between neural plasticity
and experience-dependent changes in cognition, briefly describes recent technological advances in measuring and inducing brain plasticity mechanisms, and outlines
key questions that researchers must address to provide a more complete understanding of the factors that enable people to learn new cognitive skills. Answering such
questions will require the combined efforts of neuroscientists, psychologists, and
educational researchers, as well as the development of new technologies for monitoring neural changes in humans and other animals as they learn to perform a variety
of cognitive tasks.
INTRODUCTION
The intellectual capacities of adult humans depend on numerous cognitive
skills acquired through years of practice, including reading, writing, and
problem-solving abilities. Although it is well known that individuals vary
considerably in their capacity to gain proficiency in such skills, the specific
qualities of brain structure and function that enable certain individuals to
excel in situations where others struggle remain mysterious. Historically,
cognitive prowess has been viewed as an intrinsic trait, a kind of mental
talent endowed at birth. More recently, however, it has become clear that
how a person’s brain functions can be strongly experience-dependent and
that the structure of brain circuitry is much more dynamic than previously
assumed. Here, I summarize seminal ideas and findings that have led to this
new understanding of how experience changes brain function, and consider
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.
1
2
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
how brain plasticity may contribute to (or constrain) an individual’s ability
to master cognitive skills.
Past studies of brain plasticity have focused on changes that occur during development, in response to brain injuries, and during learning. For the
past century, scientists have heavily emphasized changes that occur in the
connections between neurons. Such changes are possible because of neural
plasticity, which is the capacity of neurons to morph over time. Neuroscience
studies have revealed that neural circuits can be highly mutable, even in
adults, and that changes in circuitry are correlated with changes in functional
capacity. Psychological studies of learning and cognition have long assumed
that learning experiences can generate internal traces of those experiences,
but it has only been recently that researchers have gained access to technologies that enable them to monitor and manipulate brain changes in parallel
with changes in behavior. Studies of cognitive plasticity, the capacity of individuals to acquire or improve cognitive skills, seek to identify the factors that
determine the range of plasticity as well as ways of expanding this range.
Understanding the relationship between neural and cognitive plasticity is
key for maximizing the benefits of educational and rehabilitation programs,
as well as for understanding the neural substrates of cognitive abilities.
Identifying how variations in brain structure and function contribute to
individual differences in cognitive capacity will require the development
of advanced technologies for measuring and modifying brain structure
and activity, as well as collaborative interdisciplinary efforts between
psychologists, educators, rehabilitation scientists, and neuroscientists.
FOUNDATIONAL RESEARCH
BRAIN PLASTICITY
The phenomenon of neural plasticity was hypothesized long before any
empirical evidence of developmental or experiential changes in neural
circuits was collected. William James (1890) popularized the use of the term
plasticity as a property of neural circuits and theorized that all perceptual
and cognitive abilities were a function of experience-driven changes in
neural circuits that occurred continuously throughout an organism’s life.
The neuroanatomist Ramón y Cajal proposed that neurons were connected
by discrete junctions (synapses) and suggested that plasticity of such connections might be an important component of regenerative processes in the
nervous system (DeFelipe, 2006; Stahnisch & Nitsch, 2002). Cajal described
neural plasticity as a process that enabled the brain to compensate for
damaged circuits, whereas James theorized that changes in neural circuits
Neural and Cognitive Plasticity
3
were an inherent feature of their normal functioning and a fundamental
component of all learning and memory abilities.
COGNITIVE CHANGE
A third perspective on neural plasticity came from researchers studying the
early development of behavior (Bischof, 1983). Lorenz (1937) observed that
young birds would preferentially follow moving objects that they experienced soon after hatching, including specific people, and that this preference
persisted throughout development. This type of early imprinting suggested
that experiences within a developmentally restricted period (referred to as a
critical or sensitive period) could have profound impacts on an individual’s
brain and behavior. It was later found that preferences created during such
sensitive periods were largely irreversible (Bischof, 1983). Studies of imprinting in birds thus provided the first clear evidence that behavioral plasticity
could vary considerably across an organism’s lifespan and strong indications
that experience could lead to significant shifts in the way that an individual’s
brain responded to highly specific events such as the visual features associated with a particular human.
Early theories of learning assumed that the acquisition of new behavioral
patterns required the modification of cortical circuits (Pavlov, 1927). However, some initial neuroscience studies raised doubts about the contributions
of cortical plasticity and theories of learning quickly became divorced from
any assumptions about underlying neural substrates (Thompson, 1965).
Studies of perceptual learning in the late 1960s set the stage for renewed
interest in links between neural plasticity and learning mechanisms (Hall,
1991). In particular, Gibson and Walk’s (1956) demonstrations that adults
could improve their ability to distinguish visual stimuli simply by being
repeatedly exposed to those stimuli revived questions about the underlying
mechanisms of perceptual skill acquisition. Such behavioral results, in combination with increasing knowledge of the cortical substrates of perception,
ultimately reunited the study of learning and cognition with neurobiological
studies of cortical plasticity as originally conceived by James and Pavlov.
MEASURING NEURAL CORRELATES OF LEARNING
A key breakthrough in scientific studies of experience-dependent neural
plasticity came when Hubel and Wiesel (1970) showed that the firing
patterns of visual cortical neurons could be systematically changed by
controlling the early visual experiences of kittens. They found that some
neurons responded most strongly to specific visual inputs such as lines
oriented at a particular angle. In other words, the neurons acted as if they
4
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
were detecting visual features. Hubel and Wiesel showed that the selectivity
of cortical neurons could be biased toward particular features by limiting
what kittens saw. These studies established that processing of specific visual
inputs early in development could change how cortical circuits functioned,
as suggested by earlier studies of imprinting.
Hebb (1949) noted that rats raised by children as pets seemed to have a
greater capacity to learn than his laboratory rats. This observation was later
confirmed in studies comparing the maze learning abilities of rats raised in
enriched environments to those of rats raised in standard laboratory housing (Rosenzweig, 1984). Researchers found that the brains of enriched rats
contained cortical neurons with more extensive connections than seen in the
brains of rats raised in more sterile environments (Globus, Rosenzweig, Bennett, & Diamond, 1973; Greenough, West, & DeVoogd, 1978); overall brain
volume was also greater. Thus, differences in early experiences can not only
change the response properties of cortical neurons, they may also lead to
structural changes in neural circuits that are correlated with individual differences in learning capacity.
Studies of neural plasticity initially focused on developmental plasticity
and effects of the environment on cortical structure and function. In contrast, links between associative learning and neural plasticity were sparse
until the early 1970s when researchers discovered techniques for inducing
long-lasting changes in the electrical activity of mammalian hippocampal
circuits (Bliss & Lomo, 1973), and for observing changes in simple neural circuits of sea snails that were associated with incremental changes in learned
responses (Kandel & Schwartz, 1982). The availability of these new methods
for inducing and measuring changes in the neural circuits of adult animals
led to a renaissance of research on the neural substrates of learning mechanisms. However, the range of learned skills that could be explored using both
techniques was severely limited. Consequently, the increased understanding of mechanisms of synaptic plasticity derived from these new methods
provided few insights into the factors that constrain an individual’s learning
capacity.
The kinds of neural plasticity postulated by James and Cajal were first
demonstrated in adult monkeys. Merzenich and colleagues showed that
reducing the functionality of a monkey’s hand by removing a finger, or
by surgically joining two fingers, led to rapid and extensive changes in
cortical representations of the affected finger (Merzenich et al., 1983). They
interpreted these changes as evidence of compensatory plasticity within
the cortex that served to counteract the loss of function. Subsequent studies
showed that when monkeys learned to make fine distinctions between
tactile or auditory stimuli, changes in cortical sensitivities were observed
(Recanzone, Merzenich, Jenkins, Grajski, & Dinse, 1992), suggesting that
Neural and Cognitive Plasticity
5
learning experiences could change sensory cortical processing in adults.
Such learning-related changes in cortical sensitivities were also reported
during learning by nonprimates (Weinberger & Diamond, 1987). Taub (1980)
found that monkeys that lost the use of an arm after sensation in that arm
was surgically blocked could be rehabilitated if they were forced to use the
disabled arm. Recovery was mediated in part by cortical reorganization
(Taub, Uswatte, & Elbert, 2002). Collectively, these studies with adult
mammals provided convincing evidence that plasticity in cortical circuits
contributed to learning-related changes in perceptual and motor abilities.
MEASURING COGNITIVE PLASTICITY IN HUMANS
Unlike neural plasticity research, which historically has focused primarily
on the developing brains of nonhumans, cognitive plasticity studies have
emphasized the intellectual capacities of humans, concentrating heavily on
factors that led to cognitive deficits at later stages of the lifespan. Baltes (1987)
proposed that there were large variations in the capacity of elderly individuals to benefit from cognitive training and showed that extended training
on a cognitive task could increase an older adult’s abilities to levels closer to
those seen in younger adults. He also developed techniques for measuring
differences in individual learning capacity in an effort to identify the range
of learning abilities and the biological and sociocultural limits on what individuals can learn. Studies of cognitive plasticity build on a long history of
efforts to understand the factors that constrain human intellectual abilities
(Mercado, 2008), but shift the emphasis from one-shot measures of cognitive
performance to more longitudinal measures of changes in cognitive capacity
over time.
CUTTING-EDGE RESEARCH
In the past, observations of neural plasticity have been limited to either
imaging of microscopic structural features within post-mortem brain tissue or recordings of electrical activity from neurons in animals or brain
tissue. Recently, however, new techniques for genetically engineering
brain structure in animal models and for imaging tissue with lasers has
made it possible to measure and modify changes in neural circuits with
unprecedented precision. For instance, optogenetics enables researchers to
visualize dynamic structural changes in synapses, dendrites, and dendritic
spines in behaving animals (Bernstein & Boyden, 2011; Fenno, Yizhar, &
Deisseroth, 2011). Used in combination with sophisticated laser technologies
(Holtmaat, Randall, & Cane, 2013; Knott & Holtmaat, 2008), researchers can
now artificially stimulate and inhibit specific types of neurons in particular
6
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
areas (Andrasfalvy, Zemelman, Tang, & Vaziri, 2010; Smith & Graybiel,
2013), and can also remove parts of individual neurons, including pruning
individual axons, dendrites, and dendritic spines (Holtmaat et al., 2013).
Furthermore, light-based manipulations tend to cause less damage than
more traditional electrophysiological and pharmacological techniques,
increasing the repeatability of manipulations to neural circuits (Smith &
Graybiel, 2013). In some cases, this has made it possible to use colored light
to control an organism’s developmental trajectory (Schultheis, Liewald,
Bamberg, Nagel, & Gottschalk, 2011). Techniques involving introducing
nanoparticles into the membranes of neurons provide yet another way of
precisely controlling the activity of individual neurons (Huang, Delikanli,
Zeng, Ferkey, & Pralle, 2010). These new technologies provide a wealth of
opportunities for exploring detailed interactions within neural circuits as
well as how synapses are affected by different experiences.
The most impressive examples of neural plasticity occur during the early
stages of development, as noted in the description of sensitive periods. Originally, periods of increased sensitivity were thought to be limited to specific
stages along an individual’s developmental trajectory. Recent research has
revealed, however, that it is possible to manipulate the timing, duration, and
closure of critical periods in sensory systems and that in some cases such
periods can be reactivated in adulthood (Hensch, 2004; Hooks & Chen, 2007).
Surprisingly, researchers have discovered that reactivation of critical periods
in adults does not require invasive surgery, neurostimulation, or pharmacological interventions, but can be achieved simply by systematically changing the sensory stimulation that an individual receives (Duffy & Mitchell,
2013; He, Hodos, & Quinlan, 2006; de Villers-Sidani, Simpson, Lu, Lin, &
Merzenich, 2008; Zhou, Panizzutti, de Villers-Sidani, Madeira, & Merzenich,
2011). The ability to control levels of brain plasticity has important implications for the development of new educational strategies, therapies, and
approaches to minimizing deficits associated with cognitive aging. As techniques for controlling brain plasticity become more sophisticated, this will
afford new opportunities for studying how variations in neural plasticity
impact an individual’s ability to learn new cognitive skills.
The capacity for brain plasticity to vary over time, either as a function
of development, experience, or artificial manipulations has been described
as metaplasticity (Abraham, 2008; Hulme, Jones, & Abraham, 2013; Sehgal,
Song, Ehlers, & Moyer, 2013). Recent research suggests that mechanisms of
metaplasticity provide a way for brains to dynamically adjust the capacity
of neural circuits to change, thereby potentially increasing or decreasing
learning capacity in a context-dependent manner (Hulme et al., 2013). Degradation of neural plasticity within and across individuals may be associated
with abnormalities in learning abilities (Dovgopoly & Mercado, 2013), and
Neural and Cognitive Plasticity
7
a loss of cognitive abilities (Hulme et al., 2013). Conversely, techniques for
globally increasing neural plasticity, such as those described in relation
to sensitive periods, may potentially provide new ways of enhancing
rehabilitation after brain damage. In addition to possible implications
for treating disorders, manipulations of metaplasticity are also becoming
more relevant for the general population. Specifically, there is increasing
interest in the potential for typically functioning individuals to use drugs
to enhance their learning capacity (Greely et al., 2008; Smith & Farah, 2011).
Researchers are also beginning to explore new techniques for noninvasively
modulating metaplasticity in adults, such as transcranial magnetic and
electrical stimulation (Cappelletti et al., 2013; Kadosh, Levy, O’Shea, Shea,
& Savulescu, 2012). Such studies suggest that the relative benefits and costs
of artificially controlling levels of neural plasticity vary considerably across
individuals and that extensive research will be needed to determine how
one might use manipulations of metaplasticity to optimize an individual’s
learning potential without leading to unintended negative consequences.
Neuroimaging studies of cognitive processing have focused heavily
on localizing brain regions that are differentially activated during the
performance of specific tasks, including cognitive skills such as reading
(Turkeltaub, Gareau, Flowers, Zeffiro, & Eden, 2003), calculating (Butterworth & Walsh, 2011), and musical processing (Ellis et al., 2012). There has
been developing interest in understanding how activation in such regions
changes with practice (Chein & Schneider, 2005; Ellis et al., 2012; Ischebeck
et al., 2006), and most recently in how cognitive training can change brain
structure in adults (He et al., 2006; Lovden, Wenger, Martensson, Lindenberger, & Backman, 2013; Welcome, Chiarello, Thompson, & Sowell, 2011).
More attention is also being given to identifying the role of genetics versus
experience in determining individual variations in cognitive plasticity (He
et al., 2006; Mercado, 2008; Pinel & Dehaene, 2013; Welcome et al., 2011),
and to clarifying the role of neural plasticity in cognitive aging (Greenwood
& Parasuraman, 2010). Future efforts that combine interventions such as
neurostimulation and cognitive training with neuroimaging (e.g., Cappelletti et al., 2013) will be important for understanding how processing can be
modified in human brains.
KEY ISSUES FOR FUTURE RESEARCH
The dynamic nature of neural processes undoubtedly shapes the ways in
which people and other animals are able to think and learn. Although neuroscientists have conclusively established that experience can modify neural
circuits, the understanding of how such changes may impact cognitive processes remains limited. A basic assumption of current educational systems
8
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
is that schooling at any age can promote positive changes in intellectual
competence and that accumulation of knowledge, in particular, is critical
to maximizing an individual’s intellectual potential. However, essentially
nothing is known about the direct impact of this practice on brain circuitry
and function, making it difficult to evaluate the actual neural costs or benefits of current educational approaches. Furthermore, it is now known that
levels of neural plasticity are themselves dependent on experience, such that
periods of high plasticity can be either shortened or extended depending on
the types of events that an individual experiences. Discovering new ways of
fostering and assessing the brain changes that give rise to improvements in
cognitive abilities is a major challenge for future research on the efficacy of
different educational approaches.
A key question that researchers have yet to adequately address is what specific factors determine when and how much neural circuits change. While
it is known that a structured sequence of gene expression gives rise to the
basic organizational structure of each individual’s brain, it is less clear how
sensitive different time points along these trajectories might be to different
environmental influences. Similarly, the extent to which an individual’s cognitive abilities might be enhanced (or degraded) through targeted behavioral,
pharmaceutical, or technological interventions delivered at particular points
in development is unknown. Ethical considerations preclude detailed studies
of such issues in humans, making it important to develop new ways of investigating processes of neural and cognitive plasticity in nonhumans. A major
challenge for this approach is to discover ways of increasing the complexity
of cognitive skills that can be learned by animals so that cognitive training
in animals leads to mental processing that more closely approximates the
kinds of cognitive acts typically engaged in by modern humans. This may
also require creating new kinds of animals (e.g., through genetic engineering or neuroengineering) with neural circuitry and brain organization that
is more similar to that of humans (an approach which raises its own set of
ethical dilemmas).
Along with efforts to clarify how changes in neural plasticity can naturally impact levels of cognitive plasticity, there is interest in developing
techniques for artificially accelerating brain changes, including changes
that may increase the efficacy of compensatory responses to brain damage
or the benefits of behavioral training for cognitive performance. More
refined techniques for controlling when particular circuits become engaged,
and to what extent, can profoundly impact how rapidly neural circuits
reorganize as well as which circuits change. Given the large individual
differences in brain circuits across the lifespan and between individuals,
techniques for directly measuring the impact of different interventions and
for adaptively customizing treatments based on the efficacy of a particular
Neural and Cognitive Plasticity
9
approach are sorely needed. It will also be important to develop models
of the long-term impact of artificially remodeling neural circuits to determine when such interventions might lead to unintended negative side
effects.
The potential benefits of understanding mechanisms of neural and cognitive plasticity are far reaching in both educational and medical contexts. Such
knowledge is also fundamentally important for understanding human cognition more generally. Philosophers, theologians, and scientists alike have often
pointed to the superior intellectual capacities of humans as evidence of their
unique status among living things, and theories abound about the properties
of human brains and minds that confer humans with these enhanced abilities.
The current consensus view is that human brains can give rise to cognitive
processes that the brains of other animals cannot. However, whether such
constraints arise because of fundamental differences in neural architectures
or because of more subtle differences in the availability or capacity of particular circuitry remains a topic of debate. Consequently, it remains to be seen
what type of brain circuits are necessary to endow an individual with a given
level of cognitive plasticity or what levels of plasticity are necessary for an
individual to learn any given cognitive skill at a level comparable to modern
humans.
Many of the cognitive skills commonly taught in elementary schools are
relatively recent cultural inventions that arose through social trial-and-error
long before there was any scientific awareness of the role of brain circuitry
in cognition. As scientific understanding of the neural constraints on cognition grows, it is likely that new ways of overcoming existing constraints
on cognitive development will be discovered, as well as new cognitive
skills that enable humans to think in ways that they have never thought
before.
REFERENCES
Abraham, W. C. (2008). Metaplasticity: Tuning synapses and networks for plasticity.
Nature Reviews Neuroscience, 9, 387. doi:10.1038/nrn2356
Andrasfalvy, B. K., Zemelman, B. V., Tang, J., & Vaziri, A. (2010). Two-photon
single-cell optogenetic control of neuronal activity by sculpted light. Proceedings of the National Academy of Sciences, USA, 107, 11981–11986. doi:10.1073/pnas.
1006620107
Baltes, P. B. (1987). Theoretical propositions of life-span developmental psychology: On the dynamics between growth and decline. Developmental Psychology, 23,
611–626.
Bernstein, J. G., & Boyden, E. S. (2011). Optogenetic tools for analyzing the neural circuits of behavior. Trends in Cognitve Sciences, 15, 592–600. doi:10.1016/
j.tics.2011.10.003
10
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
Bischof, H. J. (1983). Imprinting and cortical plasticity: A comparative review. Neuroscience and Biobehavioral Reviews, 7, 213–225.
Bliss, T. V., & Lomo, T. (1973). Long-lasting potentiation of synaptic transmission in
the dentate area of the anaesthetized rabbit following stimulation of the perforant
path. Journal of Physiology, 232, 331–356.
Butterworth, B., & Walsh, V. (2011). Neural basis of mathematical cognition. Current
Biology, 21, R618–621. doi:10.1016/j.cub.2011.07.005
Cappelletti, M., Gessaroli, E., Hithersay, R., Mitolo, M., Didino, D., Kanai, R., … ,
Walsh, V. (2013). Transfer of cognitive training across magnitude dimensions
achieved with concurrent brain stimulation of the parietal lobe. Journal of Neuroscience, 33, 14899–14907. doi:10.1523/JNEUROSCI.1692-13.2013
Chein, J. M., & Schneider, W. (2005). Neuroimaging studies of practice-related
change: fMRI and meta-analytic evidence of a domain-general control network
for learning. Brain Research: Cognitive Brain Research, 25, 607–623. doi:10.1016/
j.cogbrainres.2005.08.013
de Villers-Sidani, E., Simpson, K. L., Lu, Y. F., Lin, R. C., & Merzenich, M. M. (2008).
Manipulating critical period closure across different sectors of the primary auditory cortex. Nature Neuroscience, 11, 957–965. doi:10.1038/nn.2144
DeFelipe, J. (2006). Brain plasticity and mental processes: Cajal again. Nature Reviews
Neuroscience, 7, 811–817. doi:10.1038/nrn2005
Dovgopoly, A., & Mercado, E., III, (2013). A connectionist model of category learning
by individuals with high-functioning autism spectrum disorder. Cognitive, Affective, and Behavioral Neuroscience, 13, 371–389. doi:10.3758/s13415-012-0148-0
Duffy, K. R., & Mitchell, D. E. (2013). Darkness alters maturation of visual cortex and
promotes fast recovery from monocular deprivation. Current Biology, 23, 382–386.
doi:10.1016/j.cub.2013.01.017
Ellis, R. J., Norton, A. C., Overy, K., Winner, E., Alsop, D. C., & Schlaug, G. (2012). Differentiating maturational and training influences on fMRI activation during music
processing. Neuroimage, 60, 1902–1912. doi:10.1016/j.neuroimage.2012.01.138
Fenno, L., Yizhar, O., & Deisseroth, K. (2011). The development and application
of optogenetics. Annual Review of Neuroscience, 34, 389–412. doi:10.1146/annurevneuro-061010-113817
Gibson, E. J., & Walk, R. D. (1956). The effect of prolonged exposure to visually
presented patterns on learning to discriminate them. Journal of Comparative and
Physiological Psychology, 49, 239–242.
Globus, A., Rosenzweig, M. R., Bennett, E. L., & Diamond, M. C. (1973). Effects of
differential experience on dendritic spine counts in rat cerebral cortex. Journal of
Comparative and Physiological Psychology, 82, 175–181.
Greely, H., Sahakian, B., Harris, J., Kessler, R. C., Gazzaniga, M., Campbell, P., &
Farah, M. J. (2008). Towards responsible use of cognitive-enhancing drugs by the
healthy. Nature, 456, 702–705. doi:10.1038/456702a
Greenough, W. T., West, R. W., & DeVoogd, T. J. (1978). Subsynaptic plate perforations: Changes with age and experience in the rat. Science, 202, 1096–1098.
Greenwood, P. M., & Parasuraman, R. (2010). Neuronal and cognitive plasticity: A
neurocognitive framework for ameliorating cognitive aging. Frontiers in Aging
Neuroscience, 2, 150. doi:10.3389/fnagi.2010.00150
Neural and Cognitive Plasticity
11
Hall, G. (1991). Perceptual and associative learning. New York, NY: Oxford University
Press.
He, H. Y., Hodos, W., & Quinlan, E. M. (2006). Visual deprivation reactivates rapid
ocular dominance plasticity in adult visual cortex. Journal of Neuroscience, 26,
2951–2955. doi:10.1523/JNEUROSCI.5554-05.2006
Hebb, D. O. (1949). The organization of behavior. New York, NY: Wiley.
Hensch, T. K. (2004). Critical period regulation. Annual Review of Neuroscience, 27,
549–579. doi:10.1146/annurev.neuro.27.070203.144327
Holtmaat, A., Randall, J., & Cane, M. (2013). Optical imaging of structural and functional synaptic plasticity in vivo. European Journal of Pharmacology, 719, 128–136.
doi:10.1016/j.ejphar.2013.07.020
Hooks, B. M., & Chen, C. (2007). Critical periods in the visual system: Changing views for a model of experience-dependent plasticity. Neuron, 56, 312–326.
doi:10.1016/j.neuron.2007.10.003
Huang, H., Delikanli, S., Zeng, H., Ferkey, D. M., & Pralle, A. (2010). Remote control of ion channels and neurons through magnetic-field heating of nanoparticles.
Nature Nanotechnology, 5, 602–606. doi:10.1038/nnano.2010.125
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.
Hulme, S. R., Jones, O. D., & Abraham, W. C. (2013). Emerging roles of metaplasticity in behaviour and disease. Trends in Neuroscience, 36, 353–362. doi:10.1016/
j.tins.2013.03.007
Ischebeck, A., Zamarian, L., Siedentopf, C., Koppelstatter, F., Benke, T., Felber,
S., & Delazer, M. (2006). How specifically do we learn? Imaging the learning of multiplication and subtraction. Neuroimage, 30, 1365–1375. doi:10.1016/
j.neuroimage.2005.11.016
James, W. (1890). The principles of psychology. New York, NY: Dover.
Kadosh, R. C., Levy, N., O’Shea, J., Shea, N., & Savulescu, J. (2012). The neuroethics
of non-invasive brain stimulation. Current Biology, 22, R108–R111.
Kandel, E. R., & Schwartz, J. H. (1982). Molecular biology of learning: Modulation of
transmitter release. Science, 218, 433–443.
Knott, G., & Holtmaat, A. (2008). Dendritic spine plasticity—Current understanding from in vivo studies. Brain Research Reviews, 58, 282–289. doi:10.1016/
j.brainresrev.2008.01.002
Lorenz, K. (1937). Imprinting. Auk, 54, 245–273.
Lovden, M., Wenger, E., Martensson, J., Lindenberger, U., & Backman, L. (2013).
Structural brain plasticity in adult learning and development. Neuroscience and
Biobehavioral Reviews, 37, 2296–2310. doi:10.1016/j.neubiorev.2013.02.014
Mercado, E., III (2008). Neural and cognitive plasticity: From maps to minds. Psychological Bulletin, 134, 109–137. doi:10.1037/0033-2909.134.1.109
Merzenich, M. M., Kaas, J. H., Wall, J., Nelson, R. J., Sur, M., & Felleman, D. (1983).
Topographic reorganization of somatosensory cortical areas 3b and 1 in adult monkeys following restricted deafferentation. Neuroscience, 8, 33–55.
12
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
Pavlov, I. P. (1927). Conditioned reflexes. London, England: Oxford University Press.
Pinel, P., & Dehaene, S. (2013). Genetic and environmental contributions to brain
activation during calculation. Neuroimage, 81, 306–316. doi:10.1016/j.neuroimage.
2013.04.118
Recanzone, G. H., Merzenich, M. M., Jenkins, W. M., Grajski, K. A., & Dinse, H. R.
(1992). Topographic reorganization of the hand representation in cortical area 3b
of owl monkeys trained in a frequency-discrimination task. Journal of Neurophysiology, 67, 1031–1056.
Rosenzweig, M. R. (1984). Experience, memory, and the brain. American Psychologist,
39, 365–376.
Schultheis, C., Liewald, J. F., Bamberg, E., Nagel, G., & Gottschalk, A. (2011). Optogenetic long-term manipulation of behavior and animal development. PLoS One,
6, e18766. doi:10.1371/journal.pone.0018766
Sehgal, M., Song, C., Ehlers, V. L., & Moyer, J. R., Jr. (2013). Learning to learn — Intrinsic plasticity as a metaplasticity mechanism for memory formation. Neurobiology
of Learning and Memory, 105, 186–199. doi:10.1016/j.nlm.2013.07.008
Smith, K. S., & Graybiel, A. M. (2013). Using optogenetics to study habits. Brain
Research, 1511, 102–114. doi:10.1016/j.brainres.2013.01.008
Smith, M. E., & Farah, M. J. (2011). Are prescription stimulants “smart pills”? The
epidemiology and cognitive neuroscience of prescription stimulant use by normal
healthy individuals. Psychological Bulletin, 137(5), 717–741. doi:10.1037/a0023825
Stahnisch, F. W., & Nitsch, R. (2002). Santiago Ramón y Cajal’s concept of neuronal
plasticity: The ambiguity lives on. Trends in Neuroscience, 25, 589–591.
Taub, E. (1980). Somatosensory deafferentation research with monkeys: Implications
for rehabilitation medicine. In L. P. Ince (Ed.), Behavioral psychology in rehabilitation
medicine: Clinical applications (pp. 371–401). New York, NY: Williams & Wilkins.
Taub, E., Uswatte, G., & Elbert, T. (2002). New treatments in neurorehabilitation
founded on basic research. Nature Reviews Neuroscience, 3, 228–236. doi:10.1038/
nrn754
Thompson, R. F. (1965). The neural basis of stimulus generalization. In D. I. Mostofsky (Ed.), Stimulus generalization (pp. 154–178). Stanford, CA: Stanford University
Press.
Turkeltaub, P. E., Gareau, L., Flowers, D. L., Zeffiro, T. A., & Eden, G. F. (2003).
Development of neural mechanisms for reading. Nature Neuroscience, 6, 767–773.
doi:10.1038/nn1065
Weinberger, N. M., & Diamond, D. M. (1987). Physiological plasticity in auditory
cortex: Rapid induction by learning. Progress in Neurobiology, 29, 1–55.
Welcome, S. E., Chiarello, C., Thompson, P. M., & Sowell, E. R. (2011). Reading skill
is related to individual differences in brain structure in college students. Human
Brain Mapping, 32, 1194–1205. doi:10.1002/hbm.21101
Zhou, X., Panizzutti, R., de Villers-Sidani, E., Madeira, C., & Merzenich, M.
M. (2011). Natural restoration of critical period plasticity in the juvenile and
adult primary auditory cortex. Joural of Neuroscience, 31, 5625–5634. doi:10.1523/
JNEUROSCI.6470-10.2011
Neural and Cognitive Plasticity
13
FURTHER READING
Ganguly, K., & Poo, M. M. (2013). Activity-dependent neural plasticity from bench
to bedside. Neuron, 80, 729–741. doi:10.1016/j.neuron.2013.10.028
Li, S.-C. (2013). Neuromodulation and developmental contextual influences on neural and cognitive plasticity across the lifespan. Neuroscience and Biobehavioral
Reviews, 37, 2201–2208. doi:10.1016/j.neubiorev.2013.07.019
Mercado, E., III, (2011). Mapping individual variations in learning capacity. International Journal of Comparative Psychology, 24, 4–35.
Merzenich, M. M. (2013). Soft-wired: How the new science of brain plasticity can change
your life. San Francisco, CA: Parnassus Publishing.
EDUARDO MERCADO III SHORT BIOGRAPHY
Eduardo “Eddie” Mercado III is Associate Professor of Psychology at the
University at Buffalo, The State University of New York. Mercado has written and coauthored books and articles on the neurobiology of learning and
memory (Learning and Memory: From Brain to Behavior, 2013), cetacean cognition and bioacoustics, and computational models of psychological processes.
His research focuses on how different brain systems interact to develop representations of experienced events and how these representations change
over time.
RELATED ESSAYS
Misinformation and How to Correct It (Psychology), John Cook et al.
Four Psychological Perspectives on Creativity (Psychology), Rodica Ioana
Damian and Dean Keith Simonton
Multitasking (Communications & Media), Matthew Irwin and Zheng Wang
Construal Level Theory and Regulatory Scope (Psychology), Alison Ledgerwood et al.
Resource Limitations in Visual Cognition (Psychology), Brandon M. Liverence and Steven L. Franconeri
Models of Duality (Psychology), Anand Krishna et al.
The Intrinsic Dynamics of Development (Psychology), Paul van Geert and
Marijn van Dijk
-
Neural and Cognitive Plasticity
EDUARDO MERCADO III
Abstract
Modern humans spend much of their early lives participating in formal educational
programs designed to increase their cognitive competencies. Despite this concerted
effort to maximize individuals intellectual capacities, scientists and educators know
relatively little about the neural factors that determine when and how learning experiences lead to improvements in cognitive abilities. Current theories of how brains
are changed by learning focus on incremental adjustments to connections between
neurons that are driven by increases in neural activity. This article summarizes past
theoretical and experimental research on the relationship between neural plasticity
and experience-dependent changes in cognition, briefly describes recent technological advances in measuring and inducing brain plasticity mechanisms, and outlines
key questions that researchers must address to provide a more complete understanding of the factors that enable people to learn new cognitive skills. Answering such
questions will require the combined efforts of neuroscientists, psychologists, and
educational researchers, as well as the development of new technologies for monitoring neural changes in humans and other animals as they learn to perform a variety
of cognitive tasks.
INTRODUCTION
The intellectual capacities of adult humans depend on numerous cognitive
skills acquired through years of practice, including reading, writing, and
problem-solving abilities. Although it is well known that individuals vary
considerably in their capacity to gain proficiency in such skills, the specific
qualities of brain structure and function that enable certain individuals to
excel in situations where others struggle remain mysterious. Historically,
cognitive prowess has been viewed as an intrinsic trait, a kind of mental
talent endowed at birth. More recently, however, it has become clear that
how a person’s brain functions can be strongly experience-dependent and
that the structure of brain circuitry is much more dynamic than previously
assumed. Here, I summarize seminal ideas and findings that have led to this
new understanding of how experience changes brain function, and consider
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.
1
2
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
how brain plasticity may contribute to (or constrain) an individual’s ability
to master cognitive skills.
Past studies of brain plasticity have focused on changes that occur during development, in response to brain injuries, and during learning. For the
past century, scientists have heavily emphasized changes that occur in the
connections between neurons. Such changes are possible because of neural
plasticity, which is the capacity of neurons to morph over time. Neuroscience
studies have revealed that neural circuits can be highly mutable, even in
adults, and that changes in circuitry are correlated with changes in functional
capacity. Psychological studies of learning and cognition have long assumed
that learning experiences can generate internal traces of those experiences,
but it has only been recently that researchers have gained access to technologies that enable them to monitor and manipulate brain changes in parallel
with changes in behavior. Studies of cognitive plasticity, the capacity of individuals to acquire or improve cognitive skills, seek to identify the factors that
determine the range of plasticity as well as ways of expanding this range.
Understanding the relationship between neural and cognitive plasticity is
key for maximizing the benefits of educational and rehabilitation programs,
as well as for understanding the neural substrates of cognitive abilities.
Identifying how variations in brain structure and function contribute to
individual differences in cognitive capacity will require the development
of advanced technologies for measuring and modifying brain structure
and activity, as well as collaborative interdisciplinary efforts between
psychologists, educators, rehabilitation scientists, and neuroscientists.
FOUNDATIONAL RESEARCH
BRAIN PLASTICITY
The phenomenon of neural plasticity was hypothesized long before any
empirical evidence of developmental or experiential changes in neural
circuits was collected. William James (1890) popularized the use of the term
plasticity as a property of neural circuits and theorized that all perceptual
and cognitive abilities were a function of experience-driven changes in
neural circuits that occurred continuously throughout an organism’s life.
The neuroanatomist Ramón y Cajal proposed that neurons were connected
by discrete junctions (synapses) and suggested that plasticity of such connections might be an important component of regenerative processes in the
nervous system (DeFelipe, 2006; Stahnisch & Nitsch, 2002). Cajal described
neural plasticity as a process that enabled the brain to compensate for
damaged circuits, whereas James theorized that changes in neural circuits
Neural and Cognitive Plasticity
3
were an inherent feature of their normal functioning and a fundamental
component of all learning and memory abilities.
COGNITIVE CHANGE
A third perspective on neural plasticity came from researchers studying the
early development of behavior (Bischof, 1983). Lorenz (1937) observed that
young birds would preferentially follow moving objects that they experienced soon after hatching, including specific people, and that this preference
persisted throughout development. This type of early imprinting suggested
that experiences within a developmentally restricted period (referred to as a
critical or sensitive period) could have profound impacts on an individual’s
brain and behavior. It was later found that preferences created during such
sensitive periods were largely irreversible (Bischof, 1983). Studies of imprinting in birds thus provided the first clear evidence that behavioral plasticity
could vary considerably across an organism’s lifespan and strong indications
that experience could lead to significant shifts in the way that an individual’s
brain responded to highly specific events such as the visual features associated with a particular human.
Early theories of learning assumed that the acquisition of new behavioral
patterns required the modification of cortical circuits (Pavlov, 1927). However, some initial neuroscience studies raised doubts about the contributions
of cortical plasticity and theories of learning quickly became divorced from
any assumptions about underlying neural substrates (Thompson, 1965).
Studies of perceptual learning in the late 1960s set the stage for renewed
interest in links between neural plasticity and learning mechanisms (Hall,
1991). In particular, Gibson and Walk’s (1956) demonstrations that adults
could improve their ability to distinguish visual stimuli simply by being
repeatedly exposed to those stimuli revived questions about the underlying
mechanisms of perceptual skill acquisition. Such behavioral results, in combination with increasing knowledge of the cortical substrates of perception,
ultimately reunited the study of learning and cognition with neurobiological
studies of cortical plasticity as originally conceived by James and Pavlov.
MEASURING NEURAL CORRELATES OF LEARNING
A key breakthrough in scientific studies of experience-dependent neural
plasticity came when Hubel and Wiesel (1970) showed that the firing
patterns of visual cortical neurons could be systematically changed by
controlling the early visual experiences of kittens. They found that some
neurons responded most strongly to specific visual inputs such as lines
oriented at a particular angle. In other words, the neurons acted as if they
4
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
were detecting visual features. Hubel and Wiesel showed that the selectivity
of cortical neurons could be biased toward particular features by limiting
what kittens saw. These studies established that processing of specific visual
inputs early in development could change how cortical circuits functioned,
as suggested by earlier studies of imprinting.
Hebb (1949) noted that rats raised by children as pets seemed to have a
greater capacity to learn than his laboratory rats. This observation was later
confirmed in studies comparing the maze learning abilities of rats raised in
enriched environments to those of rats raised in standard laboratory housing (Rosenzweig, 1984). Researchers found that the brains of enriched rats
contained cortical neurons with more extensive connections than seen in the
brains of rats raised in more sterile environments (Globus, Rosenzweig, Bennett, & Diamond, 1973; Greenough, West, & DeVoogd, 1978); overall brain
volume was also greater. Thus, differences in early experiences can not only
change the response properties of cortical neurons, they may also lead to
structural changes in neural circuits that are correlated with individual differences in learning capacity.
Studies of neural plasticity initially focused on developmental plasticity
and effects of the environment on cortical structure and function. In contrast, links between associative learning and neural plasticity were sparse
until the early 1970s when researchers discovered techniques for inducing
long-lasting changes in the electrical activity of mammalian hippocampal
circuits (Bliss & Lomo, 1973), and for observing changes in simple neural circuits of sea snails that were associated with incremental changes in learned
responses (Kandel & Schwartz, 1982). The availability of these new methods
for inducing and measuring changes in the neural circuits of adult animals
led to a renaissance of research on the neural substrates of learning mechanisms. However, the range of learned skills that could be explored using both
techniques was severely limited. Consequently, the increased understanding of mechanisms of synaptic plasticity derived from these new methods
provided few insights into the factors that constrain an individual’s learning
capacity.
The kinds of neural plasticity postulated by James and Cajal were first
demonstrated in adult monkeys. Merzenich and colleagues showed that
reducing the functionality of a monkey’s hand by removing a finger, or
by surgically joining two fingers, led to rapid and extensive changes in
cortical representations of the affected finger (Merzenich et al., 1983). They
interpreted these changes as evidence of compensatory plasticity within
the cortex that served to counteract the loss of function. Subsequent studies
showed that when monkeys learned to make fine distinctions between
tactile or auditory stimuli, changes in cortical sensitivities were observed
(Recanzone, Merzenich, Jenkins, Grajski, & Dinse, 1992), suggesting that
Neural and Cognitive Plasticity
5
learning experiences could change sensory cortical processing in adults.
Such learning-related changes in cortical sensitivities were also reported
during learning by nonprimates (Weinberger & Diamond, 1987). Taub (1980)
found that monkeys that lost the use of an arm after sensation in that arm
was surgically blocked could be rehabilitated if they were forced to use the
disabled arm. Recovery was mediated in part by cortical reorganization
(Taub, Uswatte, & Elbert, 2002). Collectively, these studies with adult
mammals provided convincing evidence that plasticity in cortical circuits
contributed to learning-related changes in perceptual and motor abilities.
MEASURING COGNITIVE PLASTICITY IN HUMANS
Unlike neural plasticity research, which historically has focused primarily
on the developing brains of nonhumans, cognitive plasticity studies have
emphasized the intellectual capacities of humans, concentrating heavily on
factors that led to cognitive deficits at later stages of the lifespan. Baltes (1987)
proposed that there were large variations in the capacity of elderly individuals to benefit from cognitive training and showed that extended training
on a cognitive task could increase an older adult’s abilities to levels closer to
those seen in younger adults. He also developed techniques for measuring
differences in individual learning capacity in an effort to identify the range
of learning abilities and the biological and sociocultural limits on what individuals can learn. Studies of cognitive plasticity build on a long history of
efforts to understand the factors that constrain human intellectual abilities
(Mercado, 2008), but shift the emphasis from one-shot measures of cognitive
performance to more longitudinal measures of changes in cognitive capacity
over time.
CUTTING-EDGE RESEARCH
In the past, observations of neural plasticity have been limited to either
imaging of microscopic structural features within post-mortem brain tissue or recordings of electrical activity from neurons in animals or brain
tissue. Recently, however, new techniques for genetically engineering
brain structure in animal models and for imaging tissue with lasers has
made it possible to measure and modify changes in neural circuits with
unprecedented precision. For instance, optogenetics enables researchers to
visualize dynamic structural changes in synapses, dendrites, and dendritic
spines in behaving animals (Bernstein & Boyden, 2011; Fenno, Yizhar, &
Deisseroth, 2011). Used in combination with sophisticated laser technologies
(Holtmaat, Randall, & Cane, 2013; Knott & Holtmaat, 2008), researchers can
now artificially stimulate and inhibit specific types of neurons in particular
6
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
areas (Andrasfalvy, Zemelman, Tang, & Vaziri, 2010; Smith & Graybiel,
2013), and can also remove parts of individual neurons, including pruning
individual axons, dendrites, and dendritic spines (Holtmaat et al., 2013).
Furthermore, light-based manipulations tend to cause less damage than
more traditional electrophysiological and pharmacological techniques,
increasing the repeatability of manipulations to neural circuits (Smith &
Graybiel, 2013). In some cases, this has made it possible to use colored light
to control an organism’s developmental trajectory (Schultheis, Liewald,
Bamberg, Nagel, & Gottschalk, 2011). Techniques involving introducing
nanoparticles into the membranes of neurons provide yet another way of
precisely controlling the activity of individual neurons (Huang, Delikanli,
Zeng, Ferkey, & Pralle, 2010). These new technologies provide a wealth of
opportunities for exploring detailed interactions within neural circuits as
well as how synapses are affected by different experiences.
The most impressive examples of neural plasticity occur during the early
stages of development, as noted in the description of sensitive periods. Originally, periods of increased sensitivity were thought to be limited to specific
stages along an individual’s developmental trajectory. Recent research has
revealed, however, that it is possible to manipulate the timing, duration, and
closure of critical periods in sensory systems and that in some cases such
periods can be reactivated in adulthood (Hensch, 2004; Hooks & Chen, 2007).
Surprisingly, researchers have discovered that reactivation of critical periods
in adults does not require invasive surgery, neurostimulation, or pharmacological interventions, but can be achieved simply by systematically changing the sensory stimulation that an individual receives (Duffy & Mitchell,
2013; He, Hodos, & Quinlan, 2006; de Villers-Sidani, Simpson, Lu, Lin, &
Merzenich, 2008; Zhou, Panizzutti, de Villers-Sidani, Madeira, & Merzenich,
2011). The ability to control levels of brain plasticity has important implications for the development of new educational strategies, therapies, and
approaches to minimizing deficits associated with cognitive aging. As techniques for controlling brain plasticity become more sophisticated, this will
afford new opportunities for studying how variations in neural plasticity
impact an individual’s ability to learn new cognitive skills.
The capacity for brain plasticity to vary over time, either as a function
of development, experience, or artificial manipulations has been described
as metaplasticity (Abraham, 2008; Hulme, Jones, & Abraham, 2013; Sehgal,
Song, Ehlers, & Moyer, 2013). Recent research suggests that mechanisms of
metaplasticity provide a way for brains to dynamically adjust the capacity
of neural circuits to change, thereby potentially increasing or decreasing
learning capacity in a context-dependent manner (Hulme et al., 2013). Degradation of neural plasticity within and across individuals may be associated
with abnormalities in learning abilities (Dovgopoly & Mercado, 2013), and
Neural and Cognitive Plasticity
7
a loss of cognitive abilities (Hulme et al., 2013). Conversely, techniques for
globally increasing neural plasticity, such as those described in relation
to sensitive periods, may potentially provide new ways of enhancing
rehabilitation after brain damage. In addition to possible implications
for treating disorders, manipulations of metaplasticity are also becoming
more relevant for the general population. Specifically, there is increasing
interest in the potential for typically functioning individuals to use drugs
to enhance their learning capacity (Greely et al., 2008; Smith & Farah, 2011).
Researchers are also beginning to explore new techniques for noninvasively
modulating metaplasticity in adults, such as transcranial magnetic and
electrical stimulation (Cappelletti et al., 2013; Kadosh, Levy, O’Shea, Shea,
& Savulescu, 2012). Such studies suggest that the relative benefits and costs
of artificially controlling levels of neural plasticity vary considerably across
individuals and that extensive research will be needed to determine how
one might use manipulations of metaplasticity to optimize an individual’s
learning potential without leading to unintended negative consequences.
Neuroimaging studies of cognitive processing have focused heavily
on localizing brain regions that are differentially activated during the
performance of specific tasks, including cognitive skills such as reading
(Turkeltaub, Gareau, Flowers, Zeffiro, & Eden, 2003), calculating (Butterworth & Walsh, 2011), and musical processing (Ellis et al., 2012). There has
been developing interest in understanding how activation in such regions
changes with practice (Chein & Schneider, 2005; Ellis et al., 2012; Ischebeck
et al., 2006), and most recently in how cognitive training can change brain
structure in adults (He et al., 2006; Lovden, Wenger, Martensson, Lindenberger, & Backman, 2013; Welcome, Chiarello, Thompson, & Sowell, 2011).
More attention is also being given to identifying the role of genetics versus
experience in determining individual variations in cognitive plasticity (He
et al., 2006; Mercado, 2008; Pinel & Dehaene, 2013; Welcome et al., 2011),
and to clarifying the role of neural plasticity in cognitive aging (Greenwood
& Parasuraman, 2010). Future efforts that combine interventions such as
neurostimulation and cognitive training with neuroimaging (e.g., Cappelletti et al., 2013) will be important for understanding how processing can be
modified in human brains.
KEY ISSUES FOR FUTURE RESEARCH
The dynamic nature of neural processes undoubtedly shapes the ways in
which people and other animals are able to think and learn. Although neuroscientists have conclusively established that experience can modify neural
circuits, the understanding of how such changes may impact cognitive processes remains limited. A basic assumption of current educational systems
8
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
is that schooling at any age can promote positive changes in intellectual
competence and that accumulation of knowledge, in particular, is critical
to maximizing an individual’s intellectual potential. However, essentially
nothing is known about the direct impact of this practice on brain circuitry
and function, making it difficult to evaluate the actual neural costs or benefits of current educational approaches. Furthermore, it is now known that
levels of neural plasticity are themselves dependent on experience, such that
periods of high plasticity can be either shortened or extended depending on
the types of events that an individual experiences. Discovering new ways of
fostering and assessing the brain changes that give rise to improvements in
cognitive abilities is a major challenge for future research on the efficacy of
different educational approaches.
A key question that researchers have yet to adequately address is what specific factors determine when and how much neural circuits change. While
it is known that a structured sequence of gene expression gives rise to the
basic organizational structure of each individual’s brain, it is less clear how
sensitive different time points along these trajectories might be to different
environmental influences. Similarly, the extent to which an individual’s cognitive abilities might be enhanced (or degraded) through targeted behavioral,
pharmaceutical, or technological interventions delivered at particular points
in development is unknown. Ethical considerations preclude detailed studies
of such issues in humans, making it important to develop new ways of investigating processes of neural and cognitive plasticity in nonhumans. A major
challenge for this approach is to discover ways of increasing the complexity
of cognitive skills that can be learned by animals so that cognitive training
in animals leads to mental processing that more closely approximates the
kinds of cognitive acts typically engaged in by modern humans. This may
also require creating new kinds of animals (e.g., through genetic engineering or neuroengineering) with neural circuitry and brain organization that
is more similar to that of humans (an approach which raises its own set of
ethical dilemmas).
Along with efforts to clarify how changes in neural plasticity can naturally impact levels of cognitive plasticity, there is interest in developing
techniques for artificially accelerating brain changes, including changes
that may increase the efficacy of compensatory responses to brain damage
or the benefits of behavioral training for cognitive performance. More
refined techniques for controlling when particular circuits become engaged,
and to what extent, can profoundly impact how rapidly neural circuits
reorganize as well as which circuits change. Given the large individual
differences in brain circuits across the lifespan and between individuals,
techniques for directly measuring the impact of different interventions and
for adaptively customizing treatments based on the efficacy of a particular
Neural and Cognitive Plasticity
9
approach are sorely needed. It will also be important to develop models
of the long-term impact of artificially remodeling neural circuits to determine when such interventions might lead to unintended negative side
effects.
The potential benefits of understanding mechanisms of neural and cognitive plasticity are far reaching in both educational and medical contexts. Such
knowledge is also fundamentally important for understanding human cognition more generally. Philosophers, theologians, and scientists alike have often
pointed to the superior intellectual capacities of humans as evidence of their
unique status among living things, and theories abound about the properties
of human brains and minds that confer humans with these enhanced abilities.
The current consensus view is that human brains can give rise to cognitive
processes that the brains of other animals cannot. However, whether such
constraints arise because of fundamental differences in neural architectures
or because of more subtle differences in the availability or capacity of particular circuitry remains a topic of debate. Consequently, it remains to be seen
what type of brain circuits are necessary to endow an individual with a given
level of cognitive plasticity or what levels of plasticity are necessary for an
individual to learn any given cognitive skill at a level comparable to modern
humans.
Many of the cognitive skills commonly taught in elementary schools are
relatively recent cultural inventions that arose through social trial-and-error
long before there was any scientific awareness of the role of brain circuitry
in cognition. As scientific understanding of the neural constraints on cognition grows, it is likely that new ways of overcoming existing constraints
on cognitive development will be discovered, as well as new cognitive
skills that enable humans to think in ways that they have never thought
before.
REFERENCES
Abraham, W. C. (2008). Metaplasticity: Tuning synapses and networks for plasticity.
Nature Reviews Neuroscience, 9, 387. doi:10.1038/nrn2356
Andrasfalvy, B. K., Zemelman, B. V., Tang, J., & Vaziri, A. (2010). Two-photon
single-cell optogenetic control of neuronal activity by sculpted light. Proceedings of the National Academy of Sciences, USA, 107, 11981–11986. doi:10.1073/pnas.
1006620107
Baltes, P. B. (1987). Theoretical propositions of life-span developmental psychology: On the dynamics between growth and decline. Developmental Psychology, 23,
611–626.
Bernstein, J. G., & Boyden, E. S. (2011). Optogenetic tools for analyzing the neural circuits of behavior. Trends in Cognitve Sciences, 15, 592–600. doi:10.1016/
j.tics.2011.10.003
10
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
Bischof, H. J. (1983). Imprinting and cortical plasticity: A comparative review. Neuroscience and Biobehavioral Reviews, 7, 213–225.
Bliss, T. V., & Lomo, T. (1973). Long-lasting potentiation of synaptic transmission in
the dentate area of the anaesthetized rabbit following stimulation of the perforant
path. Journal of Physiology, 232, 331–356.
Butterworth, B., & Walsh, V. (2011). Neural basis of mathematical cognition. Current
Biology, 21, R618–621. doi:10.1016/j.cub.2011.07.005
Cappelletti, M., Gessaroli, E., Hithersay, R., Mitolo, M., Didino, D., Kanai, R., … ,
Walsh, V. (2013). Transfer of cognitive training across magnitude dimensions
achieved with concurrent brain stimulation of the parietal lobe. Journal of Neuroscience, 33, 14899–14907. doi:10.1523/JNEUROSCI.1692-13.2013
Chein, J. M., & Schneider, W. (2005). Neuroimaging studies of practice-related
change: fMRI and meta-analytic evidence of a domain-general control network
for learning. Brain Research: Cognitive Brain Research, 25, 607–623. doi:10.1016/
j.cogbrainres.2005.08.013
de Villers-Sidani, E., Simpson, K. L., Lu, Y. F., Lin, R. C., & Merzenich, M. M. (2008).
Manipulating critical period closure across different sectors of the primary auditory cortex. Nature Neuroscience, 11, 957–965. doi:10.1038/nn.2144
DeFelipe, J. (2006). Brain plasticity and mental processes: Cajal again. Nature Reviews
Neuroscience, 7, 811–817. doi:10.1038/nrn2005
Dovgopoly, A., & Mercado, E., III, (2013). A connectionist model of category learning
by individuals with high-functioning autism spectrum disorder. Cognitive, Affective, and Behavioral Neuroscience, 13, 371–389. doi:10.3758/s13415-012-0148-0
Duffy, K. R., & Mitchell, D. E. (2013). Darkness alters maturation of visual cortex and
promotes fast recovery from monocular deprivation. Current Biology, 23, 382–386.
doi:10.1016/j.cub.2013.01.017
Ellis, R. J., Norton, A. C., Overy, K., Winner, E., Alsop, D. C., & Schlaug, G. (2012). Differentiating maturational and training influences on fMRI activation during music
processing. Neuroimage, 60, 1902–1912. doi:10.1016/j.neuroimage.2012.01.138
Fenno, L., Yizhar, O., & Deisseroth, K. (2011). The development and application
of optogenetics. Annual Review of Neuroscience, 34, 389–412. doi:10.1146/annurevneuro-061010-113817
Gibson, E. J., & Walk, R. D. (1956). The effect of prolonged exposure to visually
presented patterns on learning to discriminate them. Journal of Comparative and
Physiological Psychology, 49, 239–242.
Globus, A., Rosenzweig, M. R., Bennett, E. L., & Diamond, M. C. (1973). Effects of
differential experience on dendritic spine counts in rat cerebral cortex. Journal of
Comparative and Physiological Psychology, 82, 175–181.
Greely, H., Sahakian, B., Harris, J., Kessler, R. C., Gazzaniga, M., Campbell, P., &
Farah, M. J. (2008). Towards responsible use of cognitive-enhancing drugs by the
healthy. Nature, 456, 702–705. doi:10.1038/456702a
Greenough, W. T., West, R. W., & DeVoogd, T. J. (1978). Subsynaptic plate perforations: Changes with age and experience in the rat. Science, 202, 1096–1098.
Greenwood, P. M., & Parasuraman, R. (2010). Neuronal and cognitive plasticity: A
neurocognitive framework for ameliorating cognitive aging. Frontiers in Aging
Neuroscience, 2, 150. doi:10.3389/fnagi.2010.00150
Neural and Cognitive Plasticity
11
Hall, G. (1991). Perceptual and associative learning. New York, NY: Oxford University
Press.
He, H. Y., Hodos, W., & Quinlan, E. M. (2006). Visual deprivation reactivates rapid
ocular dominance plasticity in adult visual cortex. Journal of Neuroscience, 26,
2951–2955. doi:10.1523/JNEUROSCI.5554-05.2006
Hebb, D. O. (1949). The organization of behavior. New York, NY: Wiley.
Hensch, T. K. (2004). Critical period regulation. Annual Review of Neuroscience, 27,
549–579. doi:10.1146/annurev.neuro.27.070203.144327
Holtmaat, A., Randall, J., & Cane, M. (2013). Optical imaging of structural and functional synaptic plasticity in vivo. European Journal of Pharmacology, 719, 128–136.
doi:10.1016/j.ejphar.2013.07.020
Hooks, B. M., & Chen, C. (2007). Critical periods in the visual system: Changing views for a model of experience-dependent plasticity. Neuron, 56, 312–326.
doi:10.1016/j.neuron.2007.10.003
Huang, H., Delikanli, S., Zeng, H., Ferkey, D. M., & Pralle, A. (2010). Remote control of ion channels and neurons through magnetic-field heating of nanoparticles.
Nature Nanotechnology, 5, 602–606. doi:10.1038/nnano.2010.125
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.
Hulme, S. R., Jones, O. D., & Abraham, W. C. (2013). Emerging roles of metaplasticity in behaviour and disease. Trends in Neuroscience, 36, 353–362. doi:10.1016/
j.tins.2013.03.007
Ischebeck, A., Zamarian, L., Siedentopf, C., Koppelstatter, F., Benke, T., Felber,
S., & Delazer, M. (2006). How specifically do we learn? Imaging the learning of multiplication and subtraction. Neuroimage, 30, 1365–1375. doi:10.1016/
j.neuroimage.2005.11.016
James, W. (1890). The principles of psychology. New York, NY: Dover.
Kadosh, R. C., Levy, N., O’Shea, J., Shea, N., & Savulescu, J. (2012). The neuroethics
of non-invasive brain stimulation. Current Biology, 22, R108–R111.
Kandel, E. R., & Schwartz, J. H. (1982). Molecular biology of learning: Modulation of
transmitter release. Science, 218, 433–443.
Knott, G., & Holtmaat, A. (2008). Dendritic spine plasticity—Current understanding from in vivo studies. Brain Research Reviews, 58, 282–289. doi:10.1016/
j.brainresrev.2008.01.002
Lorenz, K. (1937). Imprinting. Auk, 54, 245–273.
Lovden, M., Wenger, E., Martensson, J., Lindenberger, U., & Backman, L. (2013).
Structural brain plasticity in adult learning and development. Neuroscience and
Biobehavioral Reviews, 37, 2296–2310. doi:10.1016/j.neubiorev.2013.02.014
Mercado, E., III (2008). Neural and cognitive plasticity: From maps to minds. Psychological Bulletin, 134, 109–137. doi:10.1037/0033-2909.134.1.109
Merzenich, M. M., Kaas, J. H., Wall, J., Nelson, R. J., Sur, M., & Felleman, D. (1983).
Topographic reorganization of somatosensory cortical areas 3b and 1 in adult monkeys following restricted deafferentation. Neuroscience, 8, 33–55.
12
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
Pavlov, I. P. (1927). Conditioned reflexes. London, England: Oxford University Press.
Pinel, P., & Dehaene, S. (2013). Genetic and environmental contributions to brain
activation during calculation. Neuroimage, 81, 306–316. doi:10.1016/j.neuroimage.
2013.04.118
Recanzone, G. H., Merzenich, M. M., Jenkins, W. M., Grajski, K. A., & Dinse, H. R.
(1992). Topographic reorganization of the hand representation in cortical area 3b
of owl monkeys trained in a frequency-discrimination task. Journal of Neurophysiology, 67, 1031–1056.
Rosenzweig, M. R. (1984). Experience, memory, and the brain. American Psychologist,
39, 365–376.
Schultheis, C., Liewald, J. F., Bamberg, E., Nagel, G., & Gottschalk, A. (2011). Optogenetic long-term manipulation of behavior and animal development. PLoS One,
6, e18766. doi:10.1371/journal.pone.0018766
Sehgal, M., Song, C., Ehlers, V. L., & Moyer, J. R., Jr. (2013). Learning to learn — Intrinsic plasticity as a metaplasticity mechanism for memory formation. Neurobiology
of Learning and Memory, 105, 186–199. doi:10.1016/j.nlm.2013.07.008
Smith, K. S., & Graybiel, A. M. (2013). Using optogenetics to study habits. Brain
Research, 1511, 102–114. doi:10.1016/j.brainres.2013.01.008
Smith, M. E., & Farah, M. J. (2011). Are prescription stimulants “smart pills”? The
epidemiology and cognitive neuroscience of prescription stimulant use by normal
healthy individuals. Psychological Bulletin, 137(5), 717–741. doi:10.1037/a0023825
Stahnisch, F. W., & Nitsch, R. (2002). Santiago Ramón y Cajal’s concept of neuronal
plasticity: The ambiguity lives on. Trends in Neuroscience, 25, 589–591.
Taub, E. (1980). Somatosensory deafferentation research with monkeys: Implications
for rehabilitation medicine. In L. P. Ince (Ed.), Behavioral psychology in rehabilitation
medicine: Clinical applications (pp. 371–401). New York, NY: Williams & Wilkins.
Taub, E., Uswatte, G., & Elbert, T. (2002). New treatments in neurorehabilitation
founded on basic research. Nature Reviews Neuroscience, 3, 228–236. doi:10.1038/
nrn754
Thompson, R. F. (1965). The neural basis of stimulus generalization. In D. I. Mostofsky (Ed.), Stimulus generalization (pp. 154–178). Stanford, CA: Stanford University
Press.
Turkeltaub, P. E., Gareau, L., Flowers, D. L., Zeffiro, T. A., & Eden, G. F. (2003).
Development of neural mechanisms for reading. Nature Neuroscience, 6, 767–773.
doi:10.1038/nn1065
Weinberger, N. M., & Diamond, D. M. (1987). Physiological plasticity in auditory
cortex: Rapid induction by learning. Progress in Neurobiology, 29, 1–55.
Welcome, S. E., Chiarello, C., Thompson, P. M., & Sowell, E. R. (2011). Reading skill
is related to individual differences in brain structure in college students. Human
Brain Mapping, 32, 1194–1205. doi:10.1002/hbm.21101
Zhou, X., Panizzutti, R., de Villers-Sidani, E., Madeira, C., & Merzenich, M.
M. (2011). Natural restoration of critical period plasticity in the juvenile and
adult primary auditory cortex. Joural of Neuroscience, 31, 5625–5634. doi:10.1523/
JNEUROSCI.6470-10.2011
Neural and Cognitive Plasticity
13
FURTHER READING
Ganguly, K., & Poo, M. M. (2013). Activity-dependent neural plasticity from bench
to bedside. Neuron, 80, 729–741. doi:10.1016/j.neuron.2013.10.028
Li, S.-C. (2013). Neuromodulation and developmental contextual influences on neural and cognitive plasticity across the lifespan. Neuroscience and Biobehavioral
Reviews, 37, 2201–2208. doi:10.1016/j.neubiorev.2013.07.019
Mercado, E., III, (2011). Mapping individual variations in learning capacity. International Journal of Comparative Psychology, 24, 4–35.
Merzenich, M. M. (2013). Soft-wired: How the new science of brain plasticity can change
your life. San Francisco, CA: Parnassus Publishing.
EDUARDO MERCADO III SHORT BIOGRAPHY
Eduardo “Eddie” Mercado III is Associate Professor of Psychology at the
University at Buffalo, The State University of New York. Mercado has written and coauthored books and articles on the neurobiology of learning and
memory (Learning and Memory: From Brain to Behavior, 2013), cetacean cognition and bioacoustics, and computational models of psychological processes.
His research focuses on how different brain systems interact to develop representations of experienced events and how these representations change
over time.
RELATED ESSAYS
Misinformation and How to Correct It (Psychology), John Cook et al.
Four Psychological Perspectives on Creativity (Psychology), Rodica Ioana
Damian and Dean Keith Simonton
Multitasking (Communications & Media), Matthew Irwin and Zheng Wang
Construal Level Theory and Regulatory Scope (Psychology), Alison Ledgerwood et al.
Resource Limitations in Visual Cognition (Psychology), Brandon M. Liverence and Steven L. Franconeri
Models of Duality (Psychology), Anand Krishna et al.
The Intrinsic Dynamics of Development (Psychology), Paul van Geert and
Marijn van Dijk