How People Learn:
Brain, Mind,
Experience, and School
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Part II: Learners and Learning
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5
Mind and Brain
As the popular press
has discovered, people have a keen appetite for research information
about how the brain works and how thought processes develop
(Newsweek, 1996, 1997; Time, 1997a, b). Interest runs
particularly high in stories about the neuro-development of babies and
children and the effect of early experiences on learning. The fields of
neuroscience and cognitive science are helping to satisfy this
fundamental curiosity about how people think and learn.
In considering which
findings from brain research are relevant to human learning or, by
extension, to education, one must be careful to avoid adopting faddish
concepts that have not been demonstrated to be of value in classroom
practice. Among these is the concept that the left and right
hemispheres of the brain should be taught separately to maximize the
effectiveness of learning. Another is the notion that the brain grows
in holistic "spurts," within or around which specific educational
objectives should be arranged: as discussed in this chapter, there is
significant evidence that brain regions develop asynchronously, although
any specific educational implications of this remain to be determined.
Another widely held misconception is that people use only 20 percent of
their brains--with different percentage figures in different
incarnations--and should be able to use more of it. This belief appears
to have arisen from the early neuroscience finding that much of the
cerebral cortex consists of "silent areas" that are not activated by
sensory or motor activity. However, it is now known that these silent
areas mediate higher cognitive functions that are not directly coupled
to sensory or motor activity.
Advances in
neuroscience are confirming theoretical positions advanced by
developmental psychology for a number of years, such as the importance
of early experience in development (Hunt, 1961). What is new, and
therefore important for this volume, is the convergence of
evidence from a number of scientific fields. As the sciences of
developmental psychology, cognitive psychology, and neuroscience, to
name but three, have contributed vast numbers of research studies,
details about learning and development have converged to form a more
complete picture of how intellectual development occurs. Clarification
of some of the mechanisms of learning by neuroscience has been advanced,
in part, by the advent of non-invasive imaging technologies, such as
positron emission tomography (PET) and functional magnetic resonance
imaging (FMRI). These technologies have allowed researchers to observe
human learning processes directly.
This chapter reviews
key findings from neuroscience and cognitive science that are expanding
knowledge of the mechanisms of human learning. Three main points guide
the discussion in this chapter:
1. Learning changes the physical structure of the brain.
2. These structural changes alter the functional organization of the
brain; in other words, learning organizes and reorganizes the brain.
3. Different parts of the brain may be ready to learn at different
times.
We first explain some
basic concepts of neuroscience and new knowledge about brain
development, including the effects of instruction and learning on the
brain. We then look at language in learning as an example of the
mind-brain connection. Lastly, we examine research on how memory is
represented in the brain and its implications for learning.
From a neuroscience
perspective, instruction and learning are very important parts of a
child's brain development and psychological development processes.
Brain development and psychological development involve continuous
interactions between a child and the external environment--or, more
accurately, a hierarchy of environments, extending from the level of the
individual body cells to the most obvious boundary of the skin. Greater
understanding of the nature of this interactive process renders moot
such questions as how much depends on genes and how much on environment.
As various developmental researchers have suggested, this question is
much like asking which contributes most to the area of a rectangle, its
height or its width (Eisenberg, 1995)?
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THE BRAIN: FOUNDATION FOR LEARNING |
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Neuroscientists study
the anatomy, physiology, chemistry, and molecular biology of the nervous
system, with particular interest in how brain activity relates to
behavior and learning. Several crucial questions about early learning
particularly intrigue neuroscientists. How does the brain develop? Are
there stages of brain development? Are there critical periods when
certain things must happen for the brain to develop normally? How is
information encoded in the developing and the adult nervous systems?
And perhaps most important: How does experience affect the brain?
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Some Basics |
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A nerve cell, or
neuron, is a cell that receives information from other nerve cells or
from the sensory organs and then projects that information to other
nerve cells, while still other neurons project it back to the parts of
the body that interact with the environment, such as the muscles. Nerve
cells are equipped with a cell body--a sort of metabolic heart--and an
enormous treelike structure called the dendritic field, which is the
input side of the neuron. Information comes into the cell from
projections called axons. Most of the excitatory information comes into
the cell from the dendritic field, often through tiny dendritic
projections called spines. The junctions through which information
passes from one neuron to another are called synapses, which can be
excitatory or inhibitory in nature. The neuron integrates the
information it receives from all of its synapses and this determines its
output.
During the development
process, the "wiring diagram" of the brain is created through the
formation of synapses. At birth, the human brain has in place only a
relatively small proportion of the trillions of synapses it will
eventually have; it gains about two-thirds of its adult size after
birth. The rest of the synapses are formed after birth, and a portion
of this process is guided by experience.
Synaptic connections
are added to the brain in two basic ways. The first way is that
synapses are overproduced, then selectively lost. Synapse
overproduction and loss is a fundamental mechanism that the brain uses
to incorporate information from experience. It tends to occur during
the early periods of development. In the visual cortex--the area of the
cerebral cortex of the brain that controls sight--a person has many more
synapses at 6 months of age than at adulthood. This is because more and
more synapses are formed in the early months of life, then they
disappear, sometimes in prodigious numbers. The time required for this
phenomenon to run its course varies in different parts of the brain,
from 2 to 3 years in the human visual cortex to 8 to 10 years in some
parts of the frontal cortex.
Some neuroscientists
explain synapse formation by analogy to the art of sculpture. Classical
artists working in marble created a sculpture by chiseling away
unnecessary bits of stone until they achieved their final form. Animal
studies suggest that the "pruning" that occurs during synapse
overproduction and loss is similar to this act of carving a sculpture.
The nervous system sets up a large number of connections; experience
then plays on this network, selecting the appropriate connections and
removing the inappropriate ones. What remains is a refined final form
that constitutes the sensory and perhaps the cognitive bases for the
later phases of development.
The second method of
synapse formation is through the addition of new synapses--like the
artist who creates a sculpture by adding things together until the form
is complete. Unlike synapse overproduction and loss, the process of
synapse addition operates throughout the entire human life span and is
especially important in later life. This process is not only sensitive
to experience, it is actually driven by experience. Synapse addition
probably lies at the base of some, or even most, forms of memory. As
discussed later in this chapter, the work of cognitive scientists and
education researchers is contributing to our understanding of synapse
addition.
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Wiring the Brain |
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The role of experience
in wiring the brain has been illuminated by research on the visual
cortex in animals and humans. In adults, the inputs entering the brain
from the two eyes terminate separately in adjacent regions of the visual
cortex. Subsequently, the two inputs converge on the next set of
neurons. People are not born with this neural pattern. But through the
normal processes of seeing, the brain sorts things out.
Neuroscientists
discovered this phenomenon by studying humans with visual abnormalities,
such as a cataract or a muscle irregularity that deviates the eye. If
the eye is deprived of the appropriate visual experience at an early
stage of development (because of such abnormalities), it loses its
ability to transmit visual information into the central nervous system.
When the eye that was incapable of seeing at a very early age was
corrected later, the correction alone did not help--the afflicted eye
still could not see. When researchers looked at the brains of monkeys
in which similar kinds of experimental manipulations had been made, they
found that the normal eye had captured a larger than average amount of
neurons, and the impeded eye had correspondingly lost those connections.
This phenomenon only
occurs if an eye is prevented from experiencing normal vision very early
in development. The period at which the eye is sensitive corresponds to
the time of synapse overproduction and loss in the visual cortex. Out
of the initial mix of overlapping inputs, the neural connections that
belong to the eye that sees normally tend to survive, while the
connections that belong to the abnormal eye wither away. When both eyes
see normally, each eye loses some of the overlapping connections, but
both keep a normal number.
In the case of
deprivation from birth, one eye completely takes over. The later the
deprivation occurs after birth, the less effect it has. By about 6
months of age, closing one eye for weeks on end will produce no effect
whatsoever. The critical period has passed; the connections have
already sorted themselves out, and the overlapping connections have been
eliminated.
This anomaly has helped
scientists gain insights into normal visual development. In normal
development, the pathway for each eye is sculpted (or "pruned") down to
the right number of connections, and those connections are sculpted in
other ways, for example, to allow one to see patterns. By overproducing
synapses then selecting the right connections, the brain develops an
organized wiring diagram that functions optimally. The brain
development process actually uses visual information entering from
outside to become more precisely organized than it could with intrinsic
molecular mechanisms alone. This external information is even more
important for later cognitive development. The more a person interacts
with the world, the more a person needs information from the world
incorporated into the brain structures.
Synapse overproduction
and selection may progress at different rates in different parts of the
brain (Huttenlocher and Dabholkar, 1997). In the primary visual cortex,
a peak in synapse density occurs relatively quickly. In the medial
frontal cortex, a region clearly associated with higher cognitive
functions, the process is more protracted: synapse production starts
before birth and synapse density continues to increase until 5 or 6
years of age. The selection process, which corresponds conceptually to
the main organization of patterns, continues during the next 4-5 years
and ends around early adolescence. This lack of synchrony among
cortical regions may also occur upon individual cortical neurons where
different inputs may mature at different rates (see Juraska, 1982, on
animal studies).
After the cycle of
synapse overproduction and selection has run its course, additional
changes occur in the brain. They appear to include both the
modification of existing synapses and the addition of entirely new
synapses to the brain. Research evidence (described in the next
section) suggests that activity in the nervous system associated with
learning experiences somehow causes nerve cells to create new synapses.
Unlike the process of synapse overproduction and loss, synapse addition
and modification are life-long processes, driven by experience. In
essence, the quality of information to which one is exposed and the
amount of information one acquires is reflected throughout one's life in
the structure of the brain. This process is probably not the only way
that information is stored in the brain, but it is a very important way
that provides insight into how people learn.
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EXPERIENCES AND ENVIRONMENTS FOR BRAIN DEVELOPMENT |
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Alterations in the
brain that occur during learning seem to make the nerve cells more
efficient or powerful. Animals raised in complex environments have a
greater volume of capillaries per nerve cell--and therefore a greater
supply of blood to the brain--than the caged animals, regardless of
whether the caged animal lived alone or with companions (Black et al.,
1987). (Capillaries are the tiny blood vessels that supply oxygen and
other nutrients to the brain.) In this way experience increases the
overall quality of functioning of the brain. Using astrocytes (cells
that support neuron functioning by providing nutrients and removing
waste) as the index, there are higher amounts of astrocyte per neuron in
the complex-environment animals than in the caged groups. Overall,
these studies depict an orchestrated pattern of increased capacity in
the brain that depends on experience.
Other studies of
animals show other changes in the brain through learning; see Box 5.1. The weight and thickness
of the cerebral cortex can be measurably altered in rats that are reared
from weaning, or placed as adults, in a large cage enriched by the
presence both of a changing set of objects for play and exploration and
of other rats to induce play and exploration (Rosenzweig and Bennett,
1978). These animals also perform better on a variety of
problem-solving tasks than rats reared in standard laboratory cages.
Interestingly, both the interactive presence of a social group and
direct physical contact with the environment are important factors:
animals placed in the enriched environment alone showed relatively
little benefit; neither did animals placed in small cages within the
larger environment (Ferchmin et al., 1978; Rosenzweig and Bennett,
1972). Thus, the gross structure of the cerebral cortex was altered
both by exposure to opportunities for learning and by learning in a
social context.
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Does Mere Neural Activity Change the Brain or Is Learning
Required? |
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Are the changes in the
brain due to actual learning or to variations in aggregate levels of
neural activity? Animals in a complex environment not only learn from
experiences, but they also run, play, and exercise, which activates the
brain. The question is whether activation alone can produce brain
changes without the subjects actually learning anything, just as
activation of muscles by exercise can cause them to grow. To answer
this question, a group of animals that learned challenging motor skills
but had relatively little brain activity was compared with groups that
had high levels of brain activity but did relatively little learning
(Black et al., 1990). There were four groups in all. One group of rats
was taught to traverse an elevated obstacle course; these "acrobats"
became very good at the task over a month or so of practice. A second
group of "mandatory exercisers" was put on a treadmill once a day, where
they ran for 30 minutes, rested for 10 minutes, then ran another 30
minutes. A third group of "voluntary exercisers" had free access to an
activity wheel attached directly to their cage, which they used often.
A control group of "cage potato" rats had no exercise.
What happened to the
volume of blood vessels and number of synapses per neuron in the rats?
Both the mandatory exercisers and the voluntary exercisers showed higher
densities of blood vessels than either the cage potato rats or the
acrobats, who learned skills that did not involve significant amounts of
activity. But when the number of synapses per nerve cell was measured,
the acrobats were the standout group. Learning adds synapses; exercise
does not. Thus, different kinds of experience condition the brain in
different ways. Synapse formation and blood vessel formation
(vascularization) are two important forms of brain adaptation, but they
are driven by different physiological mechanisms and by different
behavioral events.
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Localized Changes |
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Learning specific tasks
brings about localized changes in the areas of the brain appropriate to
the task. For example, when young adult animals were taught a maze,
structural changes occurred in the visual area of the cerebral cortex
(Greenough et al., 1979). When they learned the maze with one eye
blocked with an opaque contact lens, only the brain regions connected to
the open eye were altered (Chang and Greenough, 1982). When they
learned a set of complex motor skills, structural changes occurred in
the motor region of the cerebral cortex and in the cerebellum, a
hindbrain structure that coordinates motor activity (Black et al., 1990;
Kleim et al., 1996).
These changes in brain
structure underlie changes in the functional organization of the brain.
That is, learning imposes new patterns of organization on the brain, and
this phenomenon has been confirmed by electrophysiological recordings of
the activity of nerve cells (Beaulieu and Cynader, 1990). Studies of
brain development provide a model of the learning process at a cellular
level: the changes first observed in rats have also proved to be true
in mice, cats, monkeys, and birds, and they almost certainly occur in
humans.
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ROLE OF INSTRUCTION IN BRAIN DEVELOPMENT |
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Clearly, the brain can
store information, but what kinds of information? The neuroscientist
does not address these questions. Answering them is the job of
cognitive scientists, education researchers, and others who study the
effects of experiences on human behavior and human potential. Several
examples illustrate how instruction in specific kinds of information can
influence natural development processes. This section discusses a case
involving language development.
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Language and Brain Development |
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Brain development is
often timed to take advantage of particular experiences, such that
information from the environment helps to organize the brain. The
development of language in humans is an example of a natural process
that is guided by a timetable with certain limiting conditions. Like
the development of the visual system, parallel processes occur in human
language development for the capacity to perceive phonemes, the "atoms"
of speech. A phoneme is defined as the smallest meaningful unit of
speech sound. Human beings discriminate the "b" sound from the "p"
sound largely by perceiving the time of the onset of the voice relative
to the time the lips part; there is a boundary that separates "b" from
"p" that helps to distinguish "bet" from "pet." Boundaries of this sort
exist among closely related phonemes, and in adults these boundaries
reflect language experience. Very young children discriminate many more
phonemic boundaries than adults, but they lose their discriminatory
powers when certain boundaries are not supported by experience with
spoken language (Kuhl, 1993). Native Japanese speakers, for example,
typically do not discriminate the "r" from the "l" sounds that are
evident to English speakers, and this ability is lost in early childhood
because it is not in the speech that they hear. It is not known whether
synapse overproduction and elimination underlies this process, but it
certainly seems plausible.
The process of synapse
elimination occurs relatively slowly in the cerebral cortical regions
that are involved in aspects of language and other higher cognitive
functions (Huttenlocher and Dabholkar, 1997). Different brain systems
appear to develop according to different time frames, driven in part by
experience and in part by intrinsic forces. This process suggests that
children's brains may be more ready to learn different things at
different times. But, as noted above, learning continues to affect the
structure of the brain long after synapse overproduction and loss are
completed. New synapses are added that would never have existed without
learning, and the wiring diagram of the brain continues to be
reorganized throughout one's life. There may be other changes in the
brain involved in the encoding of learning, but most scientists agree
that synapse addition and modification are the ones that are most
certain.
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Examples of Effects of Instruction on Brain
Development |
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Detailed knowledge of
the brain processes that underlie language has emerged in recent years.
For example, there appear to be separate brain areas that specialize in
subtasks such as hearing words (spoken language of others), seeing words
(reading), speaking words (speech), and generating words (thinking with
language). Whether these patterns of brain organization for oral,
written, and listening skills require separate exercises to promote the
component skills of language and literacy remains to be determined. If
these closely related skills have somewhat independent brain
representation, then coordinated practice of skills may be a better way
to encourage learners to move seamlessly among speaking, writing, and
listening.
Language provides a
particularly striking example of how instructional processes may
contribute to organizing brain functions. The example is interesting
because language processes are usually more closely associated with the
left side of the brain. As the following discussion points out,
specific kinds of experiences can contribute to other areas of the brain
taking over some of the language functions. For example, deaf people
who learn a sign language are learning to communicate using the visual
system in place of the auditory system. Manual sign languages have
grammatical structures, with affixes and morphology, but they are not
translations of spoken languages. Each particular sign language (such
as American Sign Language) has a unique organization, influenced by the
fact that it is perceived visually. The perception of sign language
depends on parallel visual perception of shape, relative spatial
location, and movement of the hands--a very different type of perception
than the auditory perception of spoken language (Bellugi, 1980).
In the nervous system
of a hearing person, auditory system pathways appear to be closely
connected to the brain regions that process the features of spoken
language, while visual pathways appear to go through several stages of
processing before features of written language are extracted (Blakemore,
1977; Friedman and Cocking, 1986). When a deaf individual learns to
communicate with manual signs, different nervous system processes have
replaced the ones normally used for language--a significant achievement.
Neuroscientists have
investigated how the visual-spatial and language processing areas each
come together in a different hemisphere of the brain, while developing
certain new functions as a result of the visual language experiences.
In the brains of all deaf people, some cortical areas that normally
process auditory information become organized to process visual
information. Yet there are also demonstrable differences among the
brains of deaf people who use sign language and deaf people who do not
use sign language, presumably because they have had different language
experiences (Neville, 1984, 1995). Among other things, major
differences exist in the electrical activities of the brains of deaf
individuals who use sign language and those who do not know sign
language (Friedman and Cocking, 1986; Neville, 1984). Also, there are
similarities between sign language users with normal hearing and sign
language users who are deaf that result from their common experiences of
engaging in language activities. In other words, specific types of
instruction can modify the brain, enabling it to use alternative sensory
input to accomplish adaptive functions, in this case, communication.
Another demonstration
that the human brain can be functionally reorganized by instruction
comes from research on individuals who have suffered strokes or had
portions of the brain removed (Bach-y-Rita, 1980, 1981; Crill and
Raichle, 1982). Since spontaneous recovery is generally unlikely, the
best way to help these individuals regain their lost functions is to
provide them with instruction and long periods of practice. Although
this kind of learning typically takes a long time, it can lead to
partial or total recovery of functions when based on sound principles of
instruction. Studies of animals with similar impairments have clearly
shown the formation of new brain connections and other adjustments, not
unlike those that occur when adults learn (e.g., Jones and Schallert,
1994; Kolb, 1995). Thus, guided learning and learning from individual
experiences both play important roles in the functional reorganization
of the brain.
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MEMORY AND BRAIN PROCESSES |
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Research into memory
processes has progressed in recent years through the combined efforts of
neuroscientists and cognitive scientists, aided by positron emission
tomography and functional magnetic resonance imaging (Schacter, 1997).
Most of the research advances in memory that help scientists understand
learning come from two major groups of studies: studies that show that
memory is not a unitary construct and studies that relate features of
learning to later effectiveness in recall.
Memory is neither a
single entity nor a phenomenon that occurs in a single area of the
brain. There are two basic memory processes: declarative memory, or
memory for facts and events which occurs primarily in brain systems
involving the hippocampus; and procedural or nondeclarative memory,
which is memory for skills and other cognitive operations, or memory
that cannot be represented in declarative sentences, which occurs
principally in the brain systems involving the neostriatum (Squire,
1997).
Different features of
learning contribute to the durability or fragility of memory. For
example, comparisons of people's memories for words with their memories
for pictures of the same objects show a superiority effect for pictures.
The superiority effect of pictures is also true if words and pictures
are combined during learning (Roediger, 1997). Obviously, this finding
has direct relevance for improving the long-term learning of certain
kinds of information.
Research has also
indicated that the mind is not just a passive recorder of events,
rather, it is actively at work both in storing and in recalling
information. There is research demonstrating that when a series of
events are presented in a random sequence, people reorder them into
sequences that make sense when they try to recall them (Lichtenstein and
Brewer, 1980). The phenomenon of the active brain is dramatically
illustrated further by the fact that the mind can "remember" things that
actually did not happen. In one example (Roediger, 1997), people are
first given lists of words:
sour-candy-sugar-bitter-good-taste-tooth-knife-honey-photo-chocolate-heart-cake-tart-pie.
During the later recognition phase, subjects are asked to respond "yes"
or "no" to questions of whether a particular word was on the list. With
high frequency and high reliability, subjects report that the word
"sweet" was on the list. That is, they "remember" something that is not
correct. The finding illustrates the active mind at work using
inferencing processes to relate events. People "remember" words that
are implied but not stated with the same probability as learned words.
In an act of efficiency and "cognitive economy" (Gibson, 1969), the mind
creates categories for processing information. Thus, it is a feature of
learning that memory processes make relational links to other
information.
In view of the fact
that experience alters brain structures and that specific experiences
have specific effects on the brain, the nature of "experience" becomes
an interesting question in relation to memory processes. For example,
when children are asked if a false event has ever occurred (as verified
by their parents), they will correctly say that it never happened to
them (Ceci, 1997). However, after repeated discussions around the same
false events spread over time, the children begin to identify these
false events as true occurrences. After about 12 weeks of such
discussions, children give fully elaborated accounts of these fictitious
events, involving parents, siblings, and a whole host of supporting
"evidence." Repeating lists of words with adults similarly reveals that
recalling non-experienced events activates the same regions of the brain
as events or words that were directly experienced (Schacter, 1997).
Magnetic resonance imaging also shows that the same brain areas are
activated during questions and answers about both true and false events.
This may explain why false memories can seem so compelling to the
individual reporting the events.
In sum, classes of
words, pictures, and other categories of information that involve
complex cognitive processing on a repeated basis activate the brain.
Activation sets into motion the events that are encoded as part of
long-term memory. Memory processes treat both true and false memory
events similarly and, as shown by imaging technologies, activate the
same brain regions, regardless of the validity of what is being
remembered. Experience is important for the development of brain
structures, and what is registered in the brain as memories of
experiences can include one's own mental activities.
These points about
memory are important for understanding learning and can explain a good
deal about why experiences are remembered well or poorly. Particularly
important is the finding that the mind imposes structure on the
information available from experience. This parallels descriptions of
the organization of information in skilled performance discussed in Chapter 3: one of the primary differences between
the novice and the expert is the manner in which information is
organized and utilized. From the perspective of teaching, it again
suggests the importance of an appropriate overall framework within which
learning occurs most efficiently and effectively (see evidence discussed
in Chapters 3 and 4).
Overall, neuroscience
research confirms the important role that experience plays in building
the structure of the mind by modifying the structures of the brain:
development is not solely the unfolding of preprogrammed patterns.
Moreover, there is a convergence of many kinds of research on some of
the rules that govern learning. One of the simplest rules is that
practice increases learning; in the brain, there is a similar
relationship between the amount of experience in a complex environment
and the amount of structural change.
In summary,
neuroscience is beginning to provide some insights, if not final
answers, to questions of great interest to educators. There is growing
evidence that both the developing and the mature brain are structurally
altered when learning occurs. Thus, these structural changes are
believed to encode the learning in the brain. Studies have found
alterations in the weight and thickness of the cerebral cortex of rats
that had direct contact with a stimulating physical environment and an
interactive social group. Subsequent work has revealed underlying
changes in the structure of nerve cells and of the tissues that support
their function. The nerve cells have a greater number of the synapses
through which they communicate with each other. The structure of the
nerve cells themselves is correspondingly altered. Under at least some
conditions, both astrocytes that provide support to the neurons and the
capillaries that supply blood may also be altered. The learning of
specific tasks appears to alter the specific regions of the brain
involved in the task. These findings suggest that the brain is a
dynamic organ, shaped to a great extent by experience--by what a living
being does, and has done.
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CONCLUSION |
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It is often popularly
argued that advances in the understanding of brain development and
mechanisms of learning have substantial implications for education and
the learning sciences. In addition, certain brain scientists have
offered advice, often with a tenuous scientific basis, that has been
incorporated into publications designed for educators (see, e.g.,
Sylwester, 1995:Ch. 7). Neuroscience has advanced to the point where it
is time to think critically about the form in which research information
is made available to educators so that it is interpreted appropriately
for practice--identifying which research findings are ready for
implementation and which are not.
This chapter reviews
the evidence for the effects of experience on brain development, the
adaptability of the brain for alternative pathways to learning, and the
impact of experience on memory. Several findings about the brain and
the mind are clear and lead to the next research topics:
1. The functional
organization of the brain and the mind depends on and benefits
positively from experience.
2. Development is not
merely a biologically driven unfolding process, but also an active
process that derives essential information from experience.
3. Research has shown
that some experiences have the most powerful effects during specific
sensitive periods, while others can affect the brain over a much longer
time span.
4. An important issue
that needs to be determined in relation to education is which things are
tied to critical periods (e.g., some aspects of phonemic perception and
language learning) and for which things is the time of exposure less
critical.
From these findings, it
is clear that there are qualitative differences among kinds of learning
opportunities. In addition, the brain "creates" informational
experiences through mental activities such as inferencing, category
formation, and so forth. These are types of learning opportunities that
can be facilitated. By contrast, it is a bridge too far, to paraphrase
John Bruer (1997), to suggest that specific activities lead to neural
branching (Cardellichio and Field, 1997), as some interpreters of
neuroscience have implied.
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