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Reading and the Brain: What Early Childhood Educators Need
to Know
Nancy Frey •Douglas Fisher
Published online: 13 April 2010
Springer Science+Business Media, LLC 2010
Abstract This manuscript focuses on neuroscience
research that may have applicability for early childhood
educators. Beginning with cautions about the usefulness of
neurosciences, we offer reviews of several ideas that can
inform the practice of early childhood educators. We begin
with the understanding that reading is not innate, meaning
that every brain must be taught to read. We continue with
the idea that language learning physically changes the brain
to remind early childhood educators that their instruction
can be powerful. We note the research focused on repeti-
tion that leads to automaticity, a key finding from reading
research that results in skilled readers. We also discuss the
importance that visuals play in learning and then note that
children are hardwired to imitate others, which is why
teacher modeling is so important. We conclude the article
with future research needs and implications for educators.
Keywords Reading Brain Neuroscience Learning
Introduction
The education field is awash in findings about brain
development and its implications for the classroom. In
addition to dozens of new books, there are national con-
ferences dedicated to helping teachers understand so-called
‘‘brain-based education.’’ We are both fascinated by and
skeptical of the evidence collected thus far concerning the
significance of brain research for teaching. Fascinated
because a better understanding of the very organ we’re
trying to influence could transform teaching and answer
many of the questions educators have; skeptical due to the
generalizations and leaps of faith some people are making
based on limited data collected in clinical settings. How-
ever, in a decade where ‘‘scientifically based reading
instruction’’ has become increasingly valued, we are
interested in locating neuroscience research that validates
and extends our understanding of quality reading instruc-
tion for young children. This article draws from neurosci-
entific research regarding how reading acquisition occurs
and some possible explanations for why it doesn’t happen
for some children. Accordingly, our hope is that readers
become informed consumers of the research in this field. In
addition, we make recommendations for future research
that could extend knowledge in both the educational and
neuroscientific fields, as well as expand the knowledge
base regarding developmentally appropriate practice for
children in preschool and primary education (National
Association for the Education of Young Children 2009).
The Emerging Field of Neuroscience: What is Its Role
in Early Childhood Education?
Neuroscience is a loose collection of specialties and
includes neurobiology, neuroimaging, neuropsychology,
neuropharmocology, and even neuroeconomics (the study
of why you buy things). Language arts, on the other hand,
has more defined fields such as reading, vocabulary, writ-
ing, and oral language development. Current findings
emanating from neuroscientific fields can inform, but also
confuse. Neuroscientists readily acknowledge that the field
of reading research is much more advanced in its study of
reading behaviors. Eden et al. (2004) cautions, ‘‘it should
be noted that brain imaging studies…have not yet
N. Frey D. Fisher (&)
San Diego State University, 3910 University Ave.,
San Diego, CA 92105, USA
e-mail: dfisher@hshmc.org
123
Early Childhood Educ J (2010) 38:103–110
DOI 10.1007/s10643-010-0387-z
employed the reading-level matched design that is pre-
valent in behavioral studies of reading disabilities’’ (p.
417). However, cognitive neurosciences can serve the
useful purpose of informing biologically what we under-
stand behaviorally. And just as importantly, the reading
research field must understand the clinical work of the
neurosciences community in order to contribute insights at
the applied level.
There is debate about the application of neuroscience
research in education, and it is complicated by the diversity
of specialties within the neurosciences. In 1997, Bruer
argued that linking neuroscience to education was ‘‘a
bridge too far’’ (p. 4). An emergent field of research, called
neuroeducational studies, focuses on bridging the fields of
psychology, neuroscience, and education to create a set of
methodological, ethical, and empirical practices for con-
ducting and reporting findings (Howard-Jones 2009). The
Harvard School of Education introduced an interdisciplin-
ary master’s degree program in Mind, Brain, and Education
earlier in the decade to address practice and public policy
issues related to this emergent field, especially as it applies
to reading and reading disorders (Fischer et al. 2007).
Related programs are offered at Dartmouth College and
Cambridge University.
The rapid changes in the technological tools available in
the neurosciences mean that as educators we should not fail
to revisit the possibilities that lie in the educational neu-
rosciences (Varma et al. 2008). There are ideas from the
neurosciences that are important for teachers to understand.
In the same regard, there are ideas in reading research that
are important for neuroscientists to understand. An analysis
of the research suggests five topics worthy of attention. For
each of the topics, we will make connections to reading
acquisition research and practical guidelines for preschool
and primary grade teachers.
Reading is Not Innate
Oral language and written language are fundamentally
different. This can best be demonstrated by two recurrent
findings; first, that even though most young children
without disabilities learn to speak or listen, not all become
fluent readers and writers (Schultz 2003). One finding from
neuroscience confirms the complex nature of reading
acquisition and forwards the theory of neuronal recycling
(Dehaene and Cohen 2007). Unlike speech, which develops
uniformly across languages and cultures and is directly
associated with specific brain and motor structures (Tom-
asello 2008), reading occurs only through the intentional
appropriation of existing structures within the brain. While
many thousands of spoken languages have existed during
the course of human history, not all have a written
language component. Reading is a complex, rule-based
system that must be imposed on biological structures that
were designed or evolved for other reasons. Most children
are born with the right structures, but these structures do
not inherently know how to read.
The brain has evolved for hundreds of thousands of
years as a speaking and listening brain, while written lan-
guage as only existed for 6,000 years (Wolf 2007). Pinker
observed, ‘‘children are wired for sound, but print is an
optional accessory that must be bolted on’’ (Pinker 1999,p.
ix). The human brain is able to accomplish this through the
repurposing of brain structures, through the process of
neuronal recycling. For example, the reading brain must
figure out a way to convert the occipital region of the brain,
which is designed to recognize objects, into one that rec-
ognizes letters and words. Letter and word recognition
must be further coordinated with the auditory areas of the
brain that process the sounds of language and assemble
them into meaningful strings. This loop between the
occipital lobe, Broca’s area in the left frontal lobe (lan-
guage processing), and Wernicke’s area in the left temporal
lobe (language comprehension) must be trained to coordi-
nate efficiently. Any disruption in this pathway can
potentially interfere with reading comprehension (e.g.,
Perfetti 1985). A portion of this coordinated system, called
the dorsal stream, links the visual cortex with a spatial
attention area that locates objects in space. In a study of
entering kindergarten students that used reading behavior
measures and functional magnetic resonance imaging
(fMRI), Kevan and Pammer (2009) found that difficulty
within this pathway was a predictor of reading problems
18 months later.
The behavioral reading literature confirms the impor-
tance of early experiences with print to prepare young
children for reading instruction. These serve as a means for
establishing and strengthening the coordination of the
phonological loop, which perceives and produces the
sounds of meaningful language, with the long term mem-
ory systems needs to acquire and sustain reading behaviors
(Cunningham and Stanovich 1997; Swanson 1999). Young
children need to be read to and talked with, even before
they enter formal schooling (e.g., Hart and Risley 1999).
As Duursma et al. (2008) note, bedtime reading stimulates
a wide range of a child’s development, from language to
motor skills to memory. Children require a rich set of
experiences that ensure that they hear, process, and pro-
duce language. In addition to advocating for family reading
time, early childhood teachers must implement intentional
instruction that ensures students have lots of opportunities
to engage with oral and written language in ways that allow
them to explore the sounds, sights, and meanings of words.
This is consistent with the NAEYC position statement’s
advice that ‘‘language interactions throughout the day’’
104 Early Childhood Educ J (2010) 38:103–110
123
offer a ‘‘linguistic payoff [through] extended discourse’’
(2009, p. 7).
Wolf adds, ‘‘The more young children are read to, the
more them will understand the language of books and
increase their vocabulary, their knowledge of grammar,
and their awareness of the tiny but very important sounds
inside words’’ (2007, p. 223). Being read to builds the
neural pathways critical to written language comprehen-
sion and production. By connecting these reading expe-
riences with reinforcing activities such as eating, being
held, and receiving attention, a pleasure pathway is
formed that connects reading with enjoyment in the
brain.
Language Learning Physically Changes the Brain
The second implication from neurosciences is that experi-
ence changes neural connections. When we experience
something, neurons fire. Repeated firings lead to physical
changes that, over time and with repetition, become more
permanent. The functional organization of an individual’s
brain is the result of intense and relentless competition for
space on the cortical map. Because the brain is not as
hardwired as was previously thought, these brain maps can
be noticeably altered in days or weeks. For example, Mark
et al. (2006) summarized a number of transcranial mag-
netic stimulation (TMS), (fMRI), and other neuroimaging
approaches that document the changes that occur in a
stroke-damaged brain due to therapy.
While strokes in early childhood are rare, the evidence
that learning leads to changes at the biological level
expands our understanding the effect of teaching on the
learner. In an fMRI study of elementary students with
reading difficulties before and after 100 h of sentence
comprehension instruction, Meyler et al. (2008) found
brain activation changes that persisted when analyzed
1 year later. Neuroplasticity, the brain’s ability to physi-
cally change, is an important consideration given that our
actions can permanently alter the learner’s brain.
Neuroplasticity is an important concept in early child-
hood education because of the role of background knowl-
edge and wide reading in learning. As background
knowledge is built through direct and indirect experiences
and wide reading experiences, physical changes occur in
the brain. These new neural pathways are used in later
reading-related tasks, such as making connections and
visualizing. Engaging instruction that reinforces specific
pathways makes it easier for new knowledge to be
acquired, learned, and recalled (Draganski et al. 2006).
Even among learners who have more significant issues
that may impede their reading acquisition, it can and does
occur. Over the past decade, education researchers have
come to understand that many children with disabilities did
not learn how to read because they did not receive
instruction in how to do so (Kliewer et al. 2006). The
implication for early childhood educators is that students
with significant disabilities should not be excluded from
reading instruction, as a growing body of evidence is
demonstrating that those who are taught well do in fact
acquire knowledge in how to read. This should serve as a
confirmation to classroom teachers who may wonder
whether the time doing so is well spent. The good news is
that it is.
Repetition Leads to Automaticity
Squire and Kandel (2000) demonstrated that there are three
areas of the brain involved in the early stages of learning a
new skill or procedure: the prefrontal cortex, the parietal
cortex, and the cerebellum. These three areas allow the
learner to pay attention, execute the correct movements,
and sequence steps. Their research, and the research of
others they summarize, suggests that as a task or procedure
is learned, these brain areas become less involved as the
sensory-motor cortex takes over. In other words, more
cognitive space is needed when learning a new skill, and
needed space is reduced over time as the skill becomes
more automatic.
Hebb (1949) suggested that as neuronal pathways are
used repeatedly, they begin to change physically and form
steadily faster networks. Hebb’s principle that ‘‘neurons
that fire together, wire together’’ is echoed in the theory of
automaticity (LaBerge and Samuels 1974). As these path-
ways are used in ever-increasing efficiency, the reader
becomes more fluent, creating the necessary ‘‘think time’’
to form new connections. Fluent reading is also associated
with an expanding working memory (WM), believed to be
key in growth from novice to expert (Ericsson and Kintsch
1995). In other words, as specific tasks become automatic,
working memory is available for meaning making or
comprehension.
Automaticity is an important tool for teachers because
of its relationship to fluency and understanding. Fluent
reading is positively associated with comprehension and
is thought to contribute to the learner’s ability to process
the meaning of the text because less effort is required to
recognize symbols, decode, and assign meaning to words
(e.g., Bell and Perfetti 1994). Consistent with the idea of
automaticity, reading teachers know from experience that
just getting students to read faster does not lead directly
to higher levels of understanding or engagement. Auto-
maticity is not about speed reading; it’s about creating
pathways that fire consistently so that the reader’s work-
ing memory can focus on meaning making.
Early Childhood Educ J (2010) 38:103–110 105
123
This explains why some weak readers can comprehend
well in spite of their poor reading speed. Walczyk and
Griffith-Ross (2007) propose that when less able readers
are taught other tasks such as slowing down, pausing, and
looking back, they are able to compensate and thus com-
prehend. The Compensatory-Encoding Theory (C-ET),
which is based in neuropsychology, suggests less fluent
readers can be taught to use compensatory reading strate-
gies to enhance their understanding of the text. In a large-
scale study of C-ET, ‘‘less fluent readers compensated
more often, and older readers compensated most effi-
ciently’’ (Walczyk et al. 2007, p. 867). These findings
merge with current discussions of skilled versus strategic
readers, led by Afflerbach et al. (2008), who place
emphasis on the reader’s actions, and whether they are
automatic or deliberate. In their words, ‘‘reading skills
operate without the reader’s deliberate control or conscious
awareness …[t]his has important, positive consequences
for each reader’s limited working memory’’ (p. 368).
Strategies, on the other hand, are ‘‘effortful and deliberate’’
and occur during initial learning, and when the text
becomes more difficult for the reader to understand
(p. 369). The findings on the use of compensatory reading
strategies by Walczyk et al. (2007) support this stance of
shifting attention from the labeling of strategies to one that
emphasizes how and when a reader applies them
automatically.
Automaticity is dependent on working memory. Despite
attempts to cram lots of information into a brain all at once,
neuroscience research confirms Miller’s (1956) finding that
humans can work with about seven new and previously
unassociated bits of information at a time. Accordingly,
teachers need to chunk information in ways that are con-
sistent with working memory and long-term transfer. One
of the ways to do this is through work with schemas, or
mental structures that represent content. Importantly,
schema are involved in background knowledge and
vocabulary. Tools such as concept maps, word webs, and
graphic organizers provide students with schemata that
they can use to organize information (e.g., Guthrie et al.
2004).
LaBerge and Samuels (1974) suggested that a key to
automaticity is in building reader’s capacity to shift their
attention from decoding to comprehension. This is
accomplished through fluency development that frees up
working memory (Kintsch 2004). The challenge, of course,
with automaticity is to not allow repetition to turn into a
rut. Samuels (1979) and others have advocated for repeated
reading experiences that provide students with the neces-
sary repetition with text passages. Readers Theater is one
way to causing repeated readings because they are moti-
vated to perform their scripts expressively for their audi-
ence (Martinez et al. 1999). Inexperienced students may
want to memorize their scripts, which defeats the fluency-
building intention of the activity. Therefore, the parameters
of the activity must be clear:
•Performances conducted by groups of students
•Expressive reading, but little if any movement or use of
props.
•Use of the script, even during the performance.
•Performance goal is to create an entertaining experi-
ence for the audience.
Nested within automaticity is the subskill of phonemic
awareness. There has been an abundance of research in
neurosciences in this area, one that most reading
researchers would say has limited usefulness. While there
has been considerable debate about how phonemic
awareness should be taught, there is strong evidence from a
neuroscience perspective that the sound system is impor-
tant in learning to read (e.g., Eden et al. 2004; Sousa 2004).
For example, Paulesu et al.’s (2001) study of English,
French, and Italian children who were poor readers found
commonalities across languages—a reduced activation in
the superior temporal gyrus, which forms part of the pho-
nological loop between Broca’s and Wernicke’s areas.
Furthermore, when a person who once had phonemic
awareness suddenly loses it, his or her reading ability is
compromised. Conduction aphasia, which results when a
stroke occurs in this region of the brain, leaves adult
readers struggling, and many will transpose phonemes
within a word (e.g., ‘‘pisghetti’’ for ‘‘spaghetti’’) (Sch-
mahmann and Pandya 2006).
A contributory skill in automaticity is phonemic
awareness, the ability to recognize and differentiate among
the 44 sounds of the English language. There is evidence in
the reading research that phonemic awareness instruction is
important for young children (e.g., Snow et al. 1998). For
example, in their study of 1509 first-grade students, Hoein
et al. (1995) used regression analysis of reading achieve-
ment and found that the phonemic identification factor was
the strongest predictor of reading achievement. While
others have raised methodological questions about this
study in terms of participants, length of study, and such,
there appears to be a relationship between phonemic
awareness and reading for young children. Having said
that, we also recognize that phonemic awareness itself is
nested within the larger reading picture. Paris (2005) calls
phoneme identification a ‘‘constrained skill’’ because it has
a finite range—once you know the sounds of a language,
you know them (unless something occurs to cause con-
duction aphasia). In a meta-analysis of US studies, the
findings of Bus and van IJzendoorn (1999) did not support
the predictive nature of phonemic awareness on later
reading achievement. Our point is not to engage in a debate
about phonemic awareness per se, but rather to point out
106 Early Childhood Educ J (2010) 38:103–110
123
that as educators we need to apply the same kind of
nuanced analysis to neuroscientific studies that we rou-
tinely do to educational ones. As early childhood educa-
tors, it is necessary to be sensitive to the acquisition of this
constrained skill, acknowledging its importance during the
years when phonemic awareness is critical, but also
understanding that once fully acquired (typically around
the age of seven) its predictive power rapidly diminishes.
Visuals Play an Important Role in Learning
A fourth area of neuroscientific research that has implica-
tions for early childhood education concerns the role of
visual information in learning. Medina (2008) argues that
vision trumps all other senses and is ‘‘probably the best
single tool we have for learning anything’’ (p. 233). In other
words, visual stimuli will be attended to over other stimuli
most of the time, especially when the visual stimulus moves.
Medina argues that attending to visual information is a
survival mechanism, which is why it takes up so much neural
real estate and resources (about 50% according to Medina).
But all visual information isn’t equal. Pictures consis-
tently trump text or oral presentations. This is so common
that cognitive scientists have a name for it: pictorial
superiority effect (Stenberg 2006). For example, there is
evidence that people can remember 2,500 pictures with
about 90 percent accuracy several days after seeing them
(Standing et al. 1970). In another study, adults were able to
recognize pictures of Dick and Jane (from the readers)
decades after they completed elementary school (Read and
Barnsley 1977). It’s not just that pictures are easier to
remember, they’re significantly more likely to be stored
and much more likely to be retrieved.
This has profound implications for a print-based society.
Could we really improve student achievement with the
addition of visual/pictorial information? Might the work on
comics, the Internet, videos, and other highly visual repre-
sentations confirm the neuroscience research on the primacy
of images? As teachers consider the multiple and new lit-
eracies of their students, they recognize that they are
accountable for their students’ traditional literacy
achievement.
Given that research in this area is relatively new, it
seems prudent to ensure that students have access to visual
information paired with text and are taught how to interpret
visual stimuli. For example, Sipe (1998) identified five
scaffolds that preschool and primary teachers use to focus
students on meaning making of print and visual informa-
tion (reading the text, managing/encouraging, clarifying/
probing, speculating/wondering, and extending/refining).
It is also possible that people may process visual
information differently. There is preliminary evidence that
adults with reading difficulties take in more visual infor-
mation peripherally and less at the fovea (focus area),
which may interfere with their ability to process print
information, but may be an incredible boon in other fields
(Schneps et al. 2007). For example, there are a dispropor-
tionate number of astronomers and astrophysicists labeled
as dyslexic, a discipline that demands pattern recognition
across wide star fields (Schneps et al. 2007).
As brain researchers focus on the human visual system
(e.g., Tove
´e2008) a growing body of evidence suggests
that text should be paired with illustrations. This has
implications for the Internet, picture books, and the com-
prehension strategy of visualizing. Again, combining
reading and neuroscience research could yield instructional
implications we can all use, and can assist us in recog-
nizing the skills and strengths of our students. However,
the necessary voices of reading researchers who are leading
the way in how digital media are understood from a
learning standpoint must collaborate with neuroscientists to
explore this nexus.
Hardwired to Imitate
A final subfield of neuroscientific research concerns the
role of imitation in learning. An aspect of intentional
instruction is teacher modeling, demonstration, and think-
ing aloud. These teaching acts form the core of what occurs
in early childhood education, especially in the use of
speculative and observational language made public and
apparent to learners. From the time we are born, we learn
by imitating and mimicking. The brain makes use of spe-
cialized cells called mirror neuron systems (Cattaneo and
Rizzolati 2009). These unique cells are active when we do
something or when we watch someone do something. In
terms of reading and language acquisition, students’ neu-
rons are firing as they watch teachers perform or think
through information, such as reading for meaning. The way
that students experience modeling affects how they per-
form and execute human actions from imitation to empathy
to language learning and use. New evidence shows a
similar phenomenon occurs as a reader reads text about an
action a character in a story. Speer et al. (2009) offer
intriguing new evidence that the pathways that fire while
reading about an action are nearly identical to those that are
fired in the commission of the action.
Research on mirror neuron systems, while still in its
infancy, adds further evidence to support teacher modeling.
Beginning with the work of Holdaway (1979,1983), who
developed big books as a way for teachers to model while
young students watched, teacher modeling has become a
staple in most literacy frameworks. Simply said, teacher
modeling is one of the best ways to introduce skills and
Early Childhood Educ J (2010) 38:103–110 107
123
strategies for readers (Fisher et al. 2008). The use of these
transactional strategies that allow students to witness and
participate in making meaning has been demonstrated to be
an effective means for fostering reading comprehension
among primary-aged students, especially for those who are
struggling (Brown 2008).
From Lab to Classroom
The relevance of neuroscientific knowledge is likely to
increase in the coming decade, and therefore it is critical to
be informed about the current state of the field and where
early childhood education research can contribute to their
knowledge base (Dehaene 2009). In the same regard, neu-
roscientific findings can be applied to the education field to
confirm or disconfirm teaching practices, as well as to
expand and strengthen them. As we have noted, there is an
explosion of research related to the human brain. Not all of it
will be helpful nor will all of it be confirmed. The relation-
ship between neuroscience and education is a tenuous one,
and researchers in both fields caution that findings should not
be extrapolated beyond the limitations of any one study
(Varma et al. 2008). Becoming an informed consumer of this
growing body of knowledge is an important role of the
language arts teacher. Having said that, there are particular
areas of future research that we can recommend, in light of
the many unanswered questions and intriguing findings in
the fields of reading and the neurosciences:
1. Develop imaging techniques that allow for studies that
involving reading longer passages. The early (1990s)
brain imaging studies were limited to very brief events
of less than 1-s. Therefore, much of the reading
research in the neurosciences involved reading single
words in isolation. As educators, we know that this is
insufficient for our purposes—reading in the classroom
is far more complex. With the development of newer
imaging techniques, particularly Transcranial Mag-
netic Stimulation (TMS), this window is expanding.
However, further development of technologies that
address this issue, as well as very real limitations
regarding the size of the machinery and the high cost,
must be addressed if neuroscientific results are to be
useful in education.
2. Create collaborative partnerships to create cross-
disciplinary research. The neuroscience community
readily acknowledges that their lack of expertise in
understanding reading behaviors limits the scope of
their work. Innovative partnerships such as the Mind,
Brain, and Education graduate program at Harvard are
creating a new field of research that draws on the
cognition research of psychology, neuroscience, and
education to reach new understandings. Education
research is notable for its history of consolidating
many fields of study. No educator could imagine being
prepared for the profession without being grounded in
child development, psychology, communication, soci-
ology, as well as specific disciplines such as mathe-
matics and the sciences. These collaborations have
come about because education researchers have
reached out to other fields to utilize and apply what
has been learned. In the same regard, educational
applications inform and enrich these disciplines. Early
childhood reading researchers like Wolf are formally
collaborating with neuroscientists to inform and extend
knowledge by asking questions of one another.
3. Focus research on new literacies. This is perhaps the
most exciting area of emerging research. Just as
notable researchers in the reading field are building our
understanding of how digital environments change the
act of reading, so can the neurosciences. For example,
does the brain process print-based and digital based
texts differently? Does age and experience affect these
changes? What are the effects of near-constant expo-
sure to screen-based literacies on reading acquisition?
How does learning occur in a virtual environment?
None of these questions can be solely answered by
behavioral or neuroscientific research alone—they are
dependent on one another.
While not directly controlled by the early childhood
classroom teacher, our collective voices influence the
development and funding of such neuroscientific research.
As attention in our society has shifted from viewing pre-
school experiences as a caretaking one, to a view of early
educational experiences as foundational to later academic
achievement, so have research monies. For instance, the
work of Petitto and colleagues at the University of Toronto
focuses on early childhood language acquisition and
development and includes implications for making policy
and programmatic decisions (see Petitto 2005; Petitto and
Dunbar 2004 for examples of this line of cross-disciplinary
research.) In addition, the reported findings from clinical
settings must be further examined in the applied environ-
ment. Early childhood educators play a vital role in testing,
challenging, and confirming laboratory results in vivo—the
living organism that is the preschool and primary classroom.
Conclusion
Regardless of the age of child we teach, we’re all ‘‘brain
workers.’’ Teachers spend their days trying to influence
that which is stored in students’ brains. It’s really quite
simple. There are a limited number of inputs that the brain
108 Early Childhood Educ J (2010) 38:103–110
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accepts and a limited number of outputs that the brain can
produce. Inputs can come in the form of sight, hearing,
taste, smell, and touch. Outputs include such modes as
speaking, writing, and moving, but reading teachers have
known that for decades. Where brain research might help
us is in how information is stored and retrieved. Under-
standing the neural basis of reading will likely validate
many of the instructional routines and cognitive strategies
teacher and students already use as well as provide guid-
ance on effective and less-than-effective approaches to
reading and language acquisition.
References
Afflerbach, P., Pearson, P. D., & Paris, S. (2008). Clarifying
differences between reading skills and reading strategies. The
Reading Teacher, 61, 364–373.
Bell, L. C., & Perfetti, C. A. (1994). Reading skill: Some adult
comparisons. Journal of Educational Psychology, 86, 244–255.
Brown, R. (2008). The road not yet taken: A transactional strategies
approach to comprehension instruction. The Reading Teacher,
61(7), 538–547.
Bruer, J. T. (1997). Education and the brain: A bridge too far.
Educational Researcher, 26(8), 4–16.
Bus, A. G., & van IJzendoorn, M. H. (1999). Phonological awareness
and early reading: A meta-analysis of experimental training
studies. Journal of Educational Psychology, 91(3), 403–414.
Cattaneo, L., & Rizzolati, G. (2009). The mirror neuron system.
Archives of Neurology, 66(5), 557–560.
Cunningham, A. E., & Stanovich, K. E. (1997). Early reading
acquisition and its relation to reading experience and ability ten
years later. Developmental Psychology, 33(6), 934–945.
Dehaene, S. (2009). Reading in the brain. New York: Viking.
Dehaene, S., & Cohen, L. (2007). Cultural recycling of cortical maps.
Neuron, 56(2), 384–398.
Draganski, B., Gaser, C., Kempermann, G., Kuhn, H. G., Winkler, J.,
Bu
¨chel, C., et al. (2006). Temporal and spatial dynamics of brain
structure changes during extensive learning. Journal of Neuro-
science, 26(23), 6314–6317.
Duursma, E., Augustyn, M., & Zuckerman, B. (2008). Reading aloud
to children: The evidence. Archives of disease in childhood
online. Published Online First: 13 May 2008. doi:10.1136/
adc.2006.106336.
Eden, G. F., Jones, K. M., Cappell, K., Gareau, L., Wood, F. B.,
Zeffiro, T. A., et al. (2004). Neural changes following remedi-
ation in adult developmental dyslexia. Neuron, 44, 411–422.
Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory.
Psychological Review, 102, 211–245.
Fischer, K. W., Bernstein, J. H., & Yang, M. H. I. (Eds.). (2007).
Mind, brain, and education in reading disorders. Cambridge,
UK: Cambridge University Press.
Fisher, D., Frey, N., & Lapp, D. (2008). Shared readings, modeling
comprehension, vocabulary, text structures, and text features for
older readers. The Reading Teacher, 61(7), 548–556.
Guthrie, J. T., Wigfield, A., & Barbosa, P. (2004). Increasing reading
comprehension and engagement through concept-oriented
reading instruction. Journal of Educational Psychology, 96(3),
403–423.
Hart, B., & Risley, T. R. (1999). The social world of children learning
to talk. Baltimore, MD: Paul H. Brookes.
Hebb, D. O. (1949). The organization of behavior. New York: Wiley.
Hoein, T., Lundberg, I., Stanovich, K. E., & Bjaalid, I. (1995).
Components of phonological awareness. Reading and Writing:
An Interdisciplinary Journal, 7, 171–188.
Holdaway, D. (1979). The foundations of literacy. Sydney: Ashton
Scholastic.
Holdaway, D. (1983). Shared book experience: Teaching reading
using favorite books. Theory into Practice, 40(4), 293–300.
Howard-Jones, P. (2009). Introducing neuroeducational research:
Neuroscience, education, and the brain from contexts to
practice. New York: Routledge.
Kevan, A., & Pammer, K. (2009). Predicting early reading skills from
pre-reading doral stream functioning. Neuropsychologia, 47,
3174–3181.
Kintsch, W. (2004). The construction-integration model of text
comprehension and its implications for instruction. In R. B.
Ruddell & N. J. Unrau (Eds.), Theoretical models and processes
of reading (5th ed., pp. 1270–1328). Newark, DE: International
Reading Association.
Kliewer, C., Biklen, D., & Kasa-Hendrickson, C. (2006). Who may be
literate? Disability and resistance to the cultural denial of
competence. American Educational Research Journal, 43,
163–192.
LaBerge, D., & Samuels, S. J. (1974). Toward a theory of automatic
information processing in reading. Cognitive Psychology, 6(2),
293–323.
Mark, V. W., Taub, E., & Morris, D. M. (2006). Neuroplasticity and
constraint-induced movement therapy. European Journal of
Physical and Rehabilitation Medicine, 42, 269–284.
Martinez, M., Roser, N., & Strecker, S. (1999). ‘‘I never thought I
could be a star’’: A readers theatre ticket to reading fluency. The
Reading Teacher, 52, 326–334.
Medina, J. (2008). Brain rules: 12 principles for surviving and
thriving at work, home and school. Seattle, WA: Pear Press.
Meyler, A., Keller, T. A., Cherkassky, V. L., Gabrieli, J. D. E., & Just,
M. A. (2008). Modifying the brain activation of poor readers
during sentence comprehension with extended remedial instruc-
tion: A longitudinal study of neuroplasticity. Neuropsychologia,
46(10), 2580–2592.
Miller, G. A. (1956). The magical number seven, plus or minus two:
Some limits on our capacity for processing information.
Psychological Review, 63, 81–97.
National Association for the Education of Young Children. (2009).
Developmentally appropriate practice in early childhood pro-
grams serving children from birth through age 8. Washington,
DC: Author.
Paris, S. G. (2005). Reinterpreting the development of reading skills.
Reading Research Quarterly, 40(2), 184–202.
Paulesu, E., Demonet, J. F., Fazio, F., McCrory, E., Brunswick, N.,
Cappa, S. F., et al. (2001). Dyslexia: Cultural diversity and
biological unity. Science, 291(5511), 2165–2167. doi:10.1126/
science.1057179.
Perfetti, C. A. (1985). Reading ability. New York: Oxford University
Press.
Petitto, L. A. (2005). How the brain begets language: On the neural
tissue underlying human language acquisition. In J. McGilvray
(Ed.), The cambridge companion to chomsky (pp. 84–101).
London, England: Cambridge University Press.
Petitto, L. A., & Dunbar, K. (2004). New findings from educational
neuroscience on bilingual brains, scientific brains, and the
educated mind:Paper presented at the Conference on Building
Usable Knowledge of Mind, Brain, and Education, Harvard
Graduate School of Education.
Pinker, S. (1999). Foreword. In D. McGuinness (Ed.), Why our
children can’t read and what we can do about it (pp. ix–x). New
York: Touchstone/Simon & Schuster.
Early Childhood Educ J (2010) 38:103–110 109
123
Read, J. D., & Barnsley, R. H. (1977). Remember Dick & Jane?
Memory for elementary school readers. Canadian Journal of
Behavioral Science, 9, 361–370.
Samuels, S. J. (1979). The method of repeated reading. The Reading
Teacher, 32, 403–408.
Schmahmann, J. D., & Pandya, D. N. (2006). Fiber pathways of the
brain. Oxford: Oxford University Press.
Schneps, M. H., Rose, L. T., & Fischer, K. W. (2007). Visual learning
and the brain: Implications for dyslexia. Mind, Brain, and
Education, 1(3), 128–139.
Schultz, K. (2003). Listening: A framework for teaching across
differences. New York: Teachers College Press.
Sipe, L. R. (1998). The construction of literary understanding by first
and second graders in response to picture storybook read-alouds.
Reading Research Quarterly, 33, 376–378.
Snow, C. E., Burns, S., & Griffin, P. (1998). Preventing reading
difficulties in young children. Washington, DC: National Acad-
emy Press.
Sousa, D. A. (2004). How the brain learns to read. Thousand Oaks,
CA: Corwin.
Speer, N. K., Reynolds, J. R., Swallow, K. M., & Zacks, J. M. (2009).
Reading stories activates neural representations of visual and
motor experiences. Psychological Science, 20(8), 989–999.
Squire, L. R., & Kandel, E. R. (2000). Memory: From mind to
molecules. New York: W. H. Freeman.
Standing, L., Conezio, J., & Haber, R. N. (1970). Perception and
memory for pictures—single-trial learning of 2,500 visual
stimuli. Psychonomic Science, 19, 73–74.
Stenberg, G. (2006). Conceptual and perceptual factors in the picture
superiority effect. European Journal of Cognitive Psychology,
18, 813–847.
Swanson, H. L. (1999). Reading comprehension and working memory
in learning-disabled readers: Is the phonological loop more
important than the executive system? Journal of Experimental
Child Psychology, 72(1), 1–31.
Tomasello, M. (2008). The origins of human communication.
Cambridge, MA: MIT Press.
Tove
´e, M. J. (2008). An introduction to the visual system. Cambridge
University Press: New York.
Varma, S., McCandliss, B., & Schwartz, D. L. (2008). Scientific and
pragmatic challenges for bridging education and neuroscience.
Educational Researcher, 37, 140–152.
Walczyk, J. J., & Griffith-Ross, D. A. (2007). How important is
reading skill fluency for comprehension? The Reading Teacher,
60, 560–569.
Walczyk, J. J., Mei, M., Griffith-Ross, D. A., Goubert, S. E., Cooper,
A. L., & Zha, P. (2007). Development of the interplay between
automatic processes and cognitive resources in reading. Journal
of Educational Psychology, 99, 867–887.
Wolf, M. (2007). Proust and the squid: The story and science of the
reading brain. New York: HarperCollins.
110 Early Childhood Educ J (2010) 38:103–110
123
