Page 1

The Educational Forum, 73: 333–46, 2009 Copyright © Kappa Delta Pi

What Brain Research Suggests for Teaching Reading Strategies by Judy Willis

Abstract How the brain learns to read has been the subject of much neuroscience educational research. Evidence is mounting for identifiable networks of connected neurons that are particularly active during reading processes such as response to visual and auditory stimuli, relating new information to prior knowledge, long-term memory storage, comprehension, and memory retrieval. This article offers strategies that build on current research showing the correlation of brain structure and literacy development, providing interventions for educators. The way in which the brain learns to read has been the subject of much neuroscience educational research. Neuroimaging and the other brain monitoring systems used for reading research offer suggestive rather than completely empirical links between how the brain learns and metabolizes oxygen or glucose, conducts electricity, or changes its cellular density. We can see on scans which sensory input is associated with increased brain activity in consistent regional networks of connecting neurons, but there is no one reading center, which means that localization is still imprecise. Reading is the behavioral product of the interaction of multiple structures in the brain through distributed networks. Evidence is mounting for networks that appear particularly metabolically active to visual and auditory responses, relational processing, long-term memory storage, and for executive function processing. Neuroimaging, electrical monitoring, and human genome atlases also are building databases that are likely to give rise to techniques for earlier identification of children who would benefit from specific interventions to help them reach their optimal reading potentials.

The Educational Forum • Volume 73 • 2009 • 333

Willis For now, a combination of the art of teaching and the science of how the brain responds to stimuli guides educators in finding the best ways to prepare lessons and use strategies to promote students’ success as they learn to read effectively and joyfully. The strategies I review in this article are, to the best of my understanding of the brain, compatible with the research on how the brain responds to the presentation of sensory stimuli in the complex processes of reading. Most of these strategies are not original and have been used successfully by teachers for decades. What is different now is that the brain-based research is building to suggest why and where in the brain these strategies may be working.

Brain Plasticity Research and Implications for Reading Once, it was believed that only young brains were capable of cell growth and that the connections between the brain’s neurons developed in the first few years of childhood and then became permanent. The neural research of the past decades has revealed that human brains are plastic in that they can change with growth of connecting nerve fibers (dendrites) in apparent response to learning and manipulation of information (practice) or with reduction of nerve fiber density from neglect of stimulation (Draganski et al. 2004; Panchev and Wermter 2004). The brain’s potential for learning throughout life appears to be associated with information (sensory input) that is successfully recognized as relating to patterns existing in the brain, which is then encoded into these neural networks (Sjöström et al. 2008). Once the new sensory input is consolidated into relational memories in the hippocampus, it is transported electrically and chemically along neuronal circuits to be encoded and stored in neurons in the cerebral cortex, the outer layers of the brain (Martin and Morris 2002). This layer is called gray matter because of the color from the high density of these neurons. The human cerebral cortex is so densely packed that if it were unfolded to the width of individual cells, this gray matter could be spread over 500 square inches (Perron et al. 2008). In the adult brain, these cortical neurons are connected to one another by more than one million nerve fibers. Most of these nerve fibers are dendrites (branches that sprout from neurons) and axons. Information is carried in these neural networks at speeds up to 300 feet per second. Each neuron typically will make 1,000 to 50,000 connections with other neurons, and it is the development of these new connections that represents brain growth due to plasticity (Trachtenberg et al. 2002). There is increasing support for a correlation of active mental manipulation, represented in neuroimaging with increased metabolic activity, and more successful memory of the information that is manipulated consciously. This conscious manipulation can include responding to the information students hear or read by using it for activities or analyzing, discussing, or writing about it. This use (active mental manipulation) of information in “thinking” activities may be the cause of the increased neuronal activity in the prefrontal cortex that is associated the executive functions of higher level cognition. This is where neuroimaging (Wagner et al. 1998) and animal research (Nelson, Sjöström, and Turrigiano 2002; Pratt et al. 2003; Turrigiano and Nelson 2004) suggest that sustained or repeated mental processing of information then may build and strengthen the neural networks engaged in the thinking activities. In theory, practice builds permanence. 334 • The Educational Forum • Volume 73 • 2009

Essays Patterning activities, through neuroplasticity, build and strengthen the networks of phonologic processing and hold potential for reading fluency, comprehension, and memory retention of the information. The strategies I consider reasonable in response to this preliminary neuroimaging in animal plasticity research include offering multisensory, engaging, and thought-provoking lessons followed by opportunities to practice and process sensory information with conscious thinking, verbalizing, writing, or creating something that uses the reading skill. These mental manipulations stimulate the new circuits which results in more strength and growth in the memories they contain. The implication is that the more opportunities students have to receive, pattern, and consciously manipulate new information, the greater will be the neural network stimulation and development. One example of brain plasticity research is the “juggling study.” This research project demonstrated that after several weeks of practicing juggling, subjects’ brain scans reflected increased neural connections (dendrites) in the brain regions that were stimulated (metabolically active on scans) during juggling practice sessions (Draganski et al. 2004). Taken into the classroom, this research suggests to this author that, if greater brain region stimulation may promote the growth of synapses and dendrites, and more areas of the brain are stimulated when information is presented through multiple senses (each sense has its own sensory receptive and processing region), then multisensory presentations of lesson material could stimulate the growth of more brain connections and multiple, mutually reinforcing information storage sites and neuronal networks to carry in and retrieve the stored information.

The implication is that the

more opportunities students have to receive, pattern, and consciously manipulate new information, the greater will be the neural network stimulation and development.

Additional brain growth or plasticity research also comes from investigation of the brain’s response to enriched environments. There is as yet no definitive neuroimaging to cognitive research to classroom study that proves that enriched environments result in “better brains.” The evidence is suggestive and again it is this author’s interpretation of the information that leads to recommend strategies that may influence the development of the neural networks involved in reading. One study raised mice in “enriched” environments with other mice (social stimulation) and a variety of stimulating “toys” (objects to manipulate that stimulated their senses). The enriched environment mouse brains displayed increased amounts of nerve growth factor and greater, more sustained dendrite growth than did the brains of inactive, isolated mice. The enriched environment mice also performed faster and more accurately on learning tasks (Kempermann, Kuhn, and Gage 1997). The Educational Forum • Volume 73 • 2009 • 335

Willis The brain’s plasticity response to human interaction and enrichment also was studied in the 1990s PET scans of the Romanian orphans who lived from infancy with little human interaction, toys, or other environmental stimuli. By age three, these children had 25 percent less brain development (size and density) than normal on CT scans (Perry et al. 1995). In the United States, the Abecedarian Project correlated academic cognitive outcomes with the amount of enrichment in children’s environments. The study followed cognitive development in children of poor, borderline mentally retarded mothers. The children ranged in age at the onset of participation in the program from four months to eight years and were followed through age 15. Children in the control group were provided good health care and nutrition, but had no other intervention once they entered the program. The experimental group of children had similar backgrounds but spent five days a week in enriched environments. The enriched environment included frequent interaction with caregivers, who conversed with them, told stories, played games, and responded to their emotional behaviors. The control group of children remained low functioning, whereas the experimental group of children developed what was determined to be average intelligence. By the age of 15, 50 percent of the children in the control group had failed one or more grades in school, but only 13 percent of the experimental group failed any grades. The enriched environment group who entered the program before age five scored higher in math and reading at age 15 than the control group (Ramey and Campbell 1991). Another possible manifestation of neuroplasticity has been suggested for the findings that the speed and efficiency for acquiring language begins to diminish for most people around 10 to 12 years of age. Obviously, one can still acquire a new language after that age, but it takes more effort and time. It is theorized that the new language acquisition networks are spatially separated in the brain from the native language networks (Baynes et al. 1998). PET scans of children who grew up learning two languages showed that language receptive and producing activities correlated with increased metabolic activity in the same areas of their brains. In contrast, subjects who learn a second language after age 12 showed two spatially separate brain areas activated during language reception and production (Kim and Ug˘urbil 1997).

Prior Knowledge Activation for Patterning and Relational Memory Construction Thinking of the brain as a pattern-seeking device in search of meaning correlates with thinking of learning as the acquisition of mental programs for using what we understand (Olsen 1995). Information-processing theories based upon neuroimaging and time sequencing of brain regions of electrical activation propose that sensory input from receptive regions travels along neuronal pathways through the thalamus and amygdala to the hippocampus. Here the new input is linked to related information that has simultaneously been activated and pulled in from memory storage networks in the cerebral cortex (Davachi and Wagner 2002). The resulting consolidation of newly coded data with previously stored information is termed relational memory (Shadmehr and Holcomb 1997; 336 • The Educational Forum • Volume 73 • 2009

Essays Grabowski, Damasio, and Damasio 1998; McGaugh, McIntyre, and Power 2002; Sherman and Guillery 2002). It may be that through plasticity the more these relationships are practiced and connections of prior knowledge (such as remembered words or letter sounds) to new input are made, the more efficiently the stored information will be available to be linked with new information. For example, extrapolating from plasticity and patterning theory, one could see the benefit of practice with the suffix “ate” and multiple roots to potentially increase the network of nerve cell connections that respond to new input of words with that suffix. Thus, when a word such as “navigate” is read, the already stored category of words ending in “ate” could be activated. If the student has also developed a storage center patterned with words relating to navigation and boats, that storage center also could be activated as part of the relational memory patterning. All those relations may not be needed to read or comprehend the word “navigate,” but the more neural connections there are that relate the word “navigate” to prior knowledge, the more successfully the new information may be linked to the appropriate related information to create new relational patterns. The region of greatest metabolic activity in animal research, following the hippocampal activity burst associated with the forming of relational connections, is the prefrontal cortex (Lee and Solivan 2008). The prefrontal cortex is where it is theorized that the newly patterned relational memories that have been linked in the hippocampus are categorized for long-term memory storage. Functional MRI (fMRI) investigations support the theory that the prefrontal cortex is particularly active during the use of semantic strategies for reading (Gabrieli, Brewer, and Poldrack 1998) and during fluency processing (Phelps et al. 1997). Children with damage to the prefrontal cortex can learn new information, but tend to do so in a disordered fashion. For example, they might show normal recognition of a word or object they have seen in the past, but be unable to recall in what context they saw it (Janowsky, Shimamura, and Squire 1989). This correlates with the proposal that the prefrontal cortex helps organize new information for efficient storage and retrieval.

Memory Consolidation Strategies for Reading The human brain appears to be a pattern-building and detecting mechanism, not a passive processor of random input that creates networked connections among its 100 billion neurons that allow us to successfully navigate our world. In this interpretation, new stimuli would be evaluated for clues with which to connect the incoming information with stored patterns or past experiences. Seeking patterns can be the brain’s way of making sense of information and experiences, evaluating the personal and emotional significance of events, predicting subsequent actions, and selecting responses to the input. In the human brain, a considerable portion of the sensory intake area is dedicated to receiving data from our eyes (the occipital lobes). This is compatible with the fact that 80 percent of the information entering the brain comes in via visual pathways. It is reasonable that the teaching of reading benefits from a repertoire of strategies that powerfully The Educational Forum • Volume 73 • 2009 • 337

Willis engage different intake regions of the brain. With the predominance of visual sensory receptor sites in the brain, perhaps the most critical components that teachers can bring to early reading instruction are those that promote pattern recognition through the visual system. Considering the different storage regions of the brain for the different sensory modalities, engaging the auditory, tactile, kinesthetic, or olfactory senses to store the same information presented through different senses would reasonably provide greater opportunities for subsequent recognition of new, related data that match the patterns in stored memory.

Pattern-Building Teaching Strategies Many successful current teaching strategies now can be supported by interpretation of brain research. For example, the implications of neuroplasticity research support the value of the patterning activities used to teach reading successfully. Experienced teachers of reading know that early patterning practice can be as simple as sorting objects into categories. Accumulating research supports the correlation of these sorting and categorizing activities to the development of enlarging neural circuits through neuroplasticity. For less experienced teachers, consideration of the following series of activities can be understood as neuro-logical, in light of the implications of neuroscience research.

Considering the different

storage regions of the brain for the different sensory modalities, engaging the auditory, tactile, kinesthetic, or olfactory senses to store the same information presented through different senses would reasonably provide greater opportunities for subsequent recognition of new, related data that match the patterns in stored memory.

Examples of Pattern Building Used in Teaching Reading Starting with obvious categories, such as a collection of pictures or small plastic animals or vehicles, students can work in pairs to sort the items into categories and give names to the categories. Next they can be shown three items together to figure out which one does not belong and explain why not. This even can be done using small plastic objects on the overhead projector. When students are proficient with these obvious categories, they can move to more subtle shapes, which also work well on overhead projectors, followed by partner pair practice. When the class energy level is ready for a more kinesthetic activity, the teacher can select an undisclosed pattern or category, such as class members wearing sneakers, something green, long or short pants, long- or short-sleeved shirts, and so forth, and call those students to the front of the room one at a time until someone recognizes the commonality. Follow-up homework could be a treasure hunt at home or with parents in the park, making (or sketching) items that are in the same category. These could then become 338 • The Educational Forum • Volume 73 • 2009

Essays bulletin board displays, with the names of the categories covered with a flip-up card so classmates can challenge themselves to name the categories. Creating Their Own Categories Because readers will not be given category choices when they need to identify new words, and because categories sometimes overlap, it is valuable for children to practice developing more than one category system using the same objects or words. You can do this with a bag of mixed buttons. The modeling would be followed by students’ opportunities to work on their own or in pairs and sketch their discoveries. This activity also could work as a language arts “learning center” activity. With a bag of buttons, students can decide how to categorize them. They are likely to start with more obvious classifications, such as size or color. With prompting they will move to other patterns such as two- or four-hole or smooth or indented buttons. Building Word Recognition Speed Letter and word naming speed (more so than object naming) appears to be associated with reading success. Event-related potential (electrophysiological recording through event-related potentials) studies and fMRI scans correlate rapid naming with brain activation and in the prefrontal cortex. This is the same region described as the frontal reading system in the Broca’s area of the frontal lobe as implicated in phonological processing (Devlin, Matthews, and Rushworth 2003). An interpretation of this correlation suggests that practice of oral naming could improve word recognition skills as the brain builds pattern recognition (Misra et al. 2004).

Visual Tracking Interventions Another interesting implication of plasticity and patterning relates to the neural networks between the cerebellum, thalamus, and the frontal cortex. The still unconfirmed magnocellular hypothesis suggests that the ability to coordinate gaze to stabilize fixation on letters and words is impaired in some dyslexic readers (Borsting et al. 1996). Other research suggests impairments in visual and auditory processing networks as having the greater impact on reading disorders (Amitay et al. 2002). Some children may have a decreased number of or abnormal function in their neural networks from the visual reception areas projecting to the prefrontal cortex region that is active in eye movement control (and attention). For these children, there may be benefit from interventions that stimulate these tracts. A still controversial intervention designed to compensate for lower visual magnocellular activity and gaze instability proposes increasing print size and spaces between letters and sentences to reduce the chances of uncoordinated eye movements that may result in merged or transposed letters. Using similar logic, some therapies have students stabilize visual perception by using only one eye for some reading to reduce unstable binocular fixation by eliminating binocular vision (Cornelissen et al. 1998). The confirmatory cognitive and neuroimaging studies are not yet available for these interventions, but this will be an area to follow in the coming years. The Educational Forum • Volume 73 • 2009 • 339

Willis Another intriguing possibility is how activation of the cerebellum might influence these visual, auditory, thalamus-to-frontal-cortex networks. In fact, the greatest number of connections of nerve networks traveling to the frontal lobes comes from the cerebellum, the center for balance and coordination (Doyon et al. 1998). Interventions to increase cerebellar activity—such as balancing exercises—are used in a number of programs to treat attention deficit hyperactivity disorder based on the theory that increasing cerebellar-to-frontallobe stimulation could promote frontal lobe plasticity in areas that are less developed in some children with attention disorders. The stimulation of cerebellar–prefrontal cortex networks is also the theoretical basis of some visual tracking exercises. For example, the use of specially designed video games might strengthen and build up the neural networks (neuroplasticity) for gaze stabilization and visual tracking, as well as an inferior prefrontal cortex, and potentially increase neural activity in word fluency networks.

Word Recognition Research and Strategies If word recognition is facilitated through strategies compatible with the brain’s patterning systems, modeling and practicing activities that increase the recognition of patterns could build students’ patterning and categorizing skills. This approach is, in the author’s opinion, compatible with the way the brain appears to recognize, transport, and store information. Patterning words and letters into categories could increase students’ abilities to recognize and sort new data into categories more quickly and accurately. The assumption is that words taught in relation to existing categories will be more efficiently recognized in the specific regions where the brain stores related data. Patterning activities such as segmenting, blending, and categorization build upon the brain’s proposed tendency to construct meaning from new input by the recognition and organization of information as patterns. Presenting material in an organized, well-sequenced manner and drawing attention to patterns in art, nature, math, or with graphic organizers could help students build pattern recognition skills. Recognizing that a particular stimulus fits into an established category appears to build the brain’s efficiency for recognizing, coding, patterning, and storing new information. Neural scans in one study had subjects name people, animals, and tools as PET scans were taken. It turned out that during the naming of items in each of the three categories, there was specific activation in three different, specific parts of the temporal lobe. For example, the same small region showed increased metabolism each time a tool was named and a different region consistently “lit up” each time an animal was named. There was also regional metabolic activity in the prefrontal and hippocampal regions when the brain responded to (recognized) new words as belonging to a previously created category. When the word subjects saw did not stimulate any associated memory or category link, their brain scans failed to show the activation in these relational memory processing regions (Grossen 1997). A study of pattern-image classification processing in monkeys measured the electrical activity of specific groups of neurons in the visual input response region of their lower temporal lobes. Researchers followed the path of the electrical activity in 340 • The Educational Forum • Volume 73 • 2009

Essays neurons after the monkey was shown a visual image from one of three categories (faces, toys, or vehicles). The visual input excited neurons first in the retina and then in the lower temporal lobe. Neuronal regions in the inferior temporal lobe were determined by computer analysis to respond to images in specific categories, regardless of the size or color of the image. The correlation was such that researchers could look at the computer output and, without seeing the image, know what image the monkey was viewing. When they were presented with new input that the monkeys had not seen before, there was less localized activation. Instead there was activation in multiple recognition systems. An interpretation is that with no pre-existing recognition category, their brains were scanning through greater territory (like a computer scanning its hard drive to find a file in which to fit new data) to recognize or evaluate the new visual input (Hung et al. 2005).

Phonemes and the Brain’s Patterning Systems Patterning and pattern recognition are integral throughout the development of reading skills. The alphabetic principle can describe identification of patterns in printed words. Words may be identified by linking the abstract representations in letters (graphemes) to the sounds of the words (phoneme to grapheme correspondence). The memory making pathways described earlier involve the visual, auditory, or other sensory input reception traveling from specialized sensory receptor regions to the hippocampus for connection with related information activated in storage regions. Relational patterns are then transmitted to sensory association cortexes in the occipital, temporal, and frontal lobe to be sorted into existing categories. It is proposed that in these association areas, abstract orthographic representations are linked with phonological codes and meanings that are associated with words. For example, in a study of word-pattern processing, subjects were asked to remember words, either by their meaning (semantics or contexts, such as the word “EXIT,” as seen on doors leading out of buildings) or by their visual appearance (upper- or lowercase spelling or the word structure itself; e.g., “pillow” has two tall lines in the middle). Activity levels in what have been called “patterning centers” in the prefrontal cortex and hippocampus were predictive of which words were remembered or forgotten in subsequent tests. In this study, word memory was increased when the subjects concentrated on either the appearance of the words or the word’s meaning, and recall was greater when the patterning was through semantics—that is, learning words in context-related phrases and sentences that give cues to their definitions (Wagner et al. 1997).

Phonemic Development Teaching Strategies Supported by Neural Patterning Strategies that emphasize phonemes and segmenting words orally, and later in writing, are consistent with the patterning theory in that students hear the component sounds and build recognition of categories of sounds. When children then blend these sounds, first by repetition and then by experimenting with new patterns themselves, they are establishing categories. When they blend new sounds with sounds they already have in memory storage, which correlates to the brain activation in the inferior temporal The Educational Forum • Volume 73 • 2009 • 341

Willis lobe (especially the hippocampus where relational connections appear to be made as described previously). This relational connection could correlate with matching the new phoneme with a category of sounds. This is again consistent with the Hung group’s monkey categorizing studies where the connecting of new data with stored memory patterns was seen as increased activity in the neurons of the inferior temporal lobe (Hung et al. 2005). Cognitive studies of reading-skill progression describe children as first attaining alphabetic knowledge followed by phoneme to grapheme correspondence and the use of phonetic cues to decode words. These early correspondences or connections appear to progress to consolidation of commonly occurring letter sequences, such as “tion” or “ate,” into clusters that may become recognizable units that help them identify unknown words by analogy to similar categories (Turkeltaub et al. 2003). To help students recognize these types of repeating patterns in letters, words (e.g., “hibernate,” “decorate,” and “collaborate”), and sentences, the patterns can be made more obvious by emphasizing the commonalities with different colors on a white or blackboard, different fonts on printed material, highlighting, or bold print. Auditory patterns can be emphasized by voice pitch, slow speed, robotic speech, or volume emphasis. When students are repeating spoken words with patterns, they can be encouraged to respond with emphasis on the commonality in the words. These oral repetition activities can be more engaging and fun by having students use a variety of voices, pitches, rates, and volumes for their responsive practice. Patterns also can be emphasized by teacher-modeled and student-copied physical movements that correspond to the phoneme or sound pattern to be learned. For example, the teacher could have students stand and rotate 90 degrees like robots each time a distinct word sound (such as “buh” “ah” and “tuh” in “bat”) is said by the teacher or when each syllable of a word is verbalized. This can be especially useful after a period of sitting still—physical movement is invigorating. Using multisensory presentations of patterns through sound, color, size, and graphic organizers can accommodate students’ multiple learning style strengths. Students who may not be particularly sensitive to differences in color or shape may grasp patterns that are isolated and grouped in other ways. Children who have trouble with written symbols may learn more readily from hearing patterns emphasized in speech. Using a variety of approaches may enable teachers to reach a greater number of students. It may sometimes seem that using different approaches to cover the same material is repetitive and carries the risk of generating behavior problems from bored students. In the author’s experience, the opposite is usually the case. Behavior problems tend to arise when students are frustrated, anxious, or confused. These are the students who may be better engaged by lessons that are taught with multiple learning-strength strategies. As with most practice activities, patterning activities likely to be more meaningful, engaging, and stimulate more of students’ cognitive function are those in which students actively manipulate the knowledge themselves. With patterning guidance, students 342 • The Educational Forum • Volume 73 • 2009

Essays can practice by speaking and writing their patterns, either on individual whiteboards with colored markers, notebooks with colors, or on worksheets while they whisper the designated word parts or phonemes as they write, underline, highlight, or circle them. Another activity where students manipulate words for phonemic awareness involves giving students index cards with words that can be combined into compound words. The children walk around the class and find classmates with cards that “work” with their own cards to form compound words. Each time they find a new compound word, the pair adds it to the growing list on the board.

Using multisensory

Once patterns are recognized, the goal is to make them permanent so their future recognition will be automatic. This coincides with the neuroplasticity of building and reinforcing the brain networks of connecting neurons and dendrites established when patterns and categories are formed. Repeated stimulation offers the intended goal of increasing the permanence and speed of retrieval access from the newly stored pattern so it becomes available to process subsequent new words.

presentations of patterns through sound, color, size, and graphic organizers can accommodate students’ multiple learning style strengths. Students who may not be particularly sensitive to differences in color or shape may grasp patterns that are isolated and grouped in other ways. Children who have trouble with written symbols may learn more readily from hearing patterns emphasized in speech.

Following the presumption that restimulation of neural networks increases their durability and efficiency, graphic organizers provide another reinforcement of newly learned patterns. A flagpole with banners hanging to the left could be labeled “ate” and the banners hanging on its left side would be filled in with beginnings of words that end in “ate.” Students also could add sketches representing several action verbs ending with “ate,” such as concentrate, celebrate, and decorate.

Appropriate Research Design: Words of Caution Considering all the cognitive tasks that are required to go from connecting symbols to sounds, sounds to words, words to meaning, meaning to memory, and memory to information processing through executive function, it is not surprising that an estimated 20 to 35 percent of American students experience significant reading difficulties (Schneider and Chein 2003). The Educational Forum • Volume 73 • 2009 • 343

Willis Neuroimaging “results” are being misused and over-interpreted for commercial promotion of “brain research proven” reading therapy programs by people who prey on parents and educators. They use misleading interpretations of impressive, colorful PET scans or EEG brain maps as proof that their strategies are “supported by science” and, therefore, the best. Parents will come to their children’s classrooms and resource teachers seeking advice. There is as yet no critically analyzed, double-blind, peer-reviewed study that meets the medical standard of proof to support the claims of any commercial reading program that their intervention is the proximate cause of changes in brain anatomy (or neuroimaging changes) that correlate with cognitive testing and classroom results (including testing for the specific skill being practiced) and confirm its impact on long-term gains in reading fluency and comprehension. Evaluating the studies about what makes a good reader or what factors and strategies correlate with successful achievement of reading milestones can be tricky. Consider the faulty logic that “correlates” milk drinking with murderers because 99 percent of all murderers drank milk regularly in childhood. Similarly, the interest group that stands to gain when a curriculum or reading-intervention program is purchased or implemented can misrepresent the implications of data. These are some of the factors and controls to be resolved before findings from neuroimaging and in cognitive testing can be completely aligned to show objectively a cause–effect relationship demonstrating which reading intervention strategies are best for students. Well-controlled, single-variable studies require university research centers with unbiased researchers who have no vested interest in the outcome of the study. Through this well-scrutinized research there can be accumulation of valuable documentation of brain changes (of lack of changes) that appear to be associated with specific aspects of the reading process and how these are influenced by reading interventions.

The Future The fact that individual students develop and coordinate the many skills involved in reading at different rates and in different ways makes it challenging for teachers to structure lessons that benefit the individual needs and reading developmental levels of all students simultaneously. Fortunately, the future of brain research interpretation is likely to provide more strategies to help educators assess students’ developmental readiness and neurological strengths and challenges. It is a realistic hope that in the near future strategies will be designed to suit the variety of brain processing dynamics revealed by neurological research and will make clear how sensory input is best presented for each child’s successful patterning and the resulting development of successful reading. The increasing emergence of the neuroscience of reading will continue to provide valuable insights into how the brain becomes a successful reading organ. Neurology Department Chairman at the University of California, Los Angeles School of Medicine, Dr. John Mazziotta, contends that brain research already has and will continue to provide strategies and curriculum to benefit all learners. Mazziotta (2001, 428) stated: 344 • The Educational Forum • Volume 73 • 2009

Essays We might someday even use brain mapping to get to the bottom of the phonics versus whole language debate by scanning children who are just about to learn to read and using the scans and a battery of tasks to elucidate the strategy that each individual is using. I look forward to that day and to the next generation of school children who will have the support of neuroscience, genetic and cognitive research, and well-informed educators and curriculum developers to help them become joyful, lifelong readers.

References Amitay, S., G. Ben-Yehudah, K. Banai, and M. Ahissar. 2002. Disabled readers suffer from visual and auditory impairments but not from a specific magnocellular deficit. Brain 125(10): 2272–85. Baynes, K., J. Eliassen, H. Lutsep, and M. Gazzaniga. 1998. Modular organization of cognitive systems masked by interhemispheric integration. Science 280(5365): 902–05. Borsting, E., W. Ridder, K. Dudeck, C. Kelley, L. Matsui, and J. Motoyama. 1996. The presence of a magnocellular defect depends on the type of dyslexia. Vision Research 36(7): 1047–53. Cornelissen, P., P. Hansen, J. Hutton, V. Evangelinou, and J. Stein. 1998. Magnocellular visual function and children’s single word reading. Vision Research 38(3): 471–82. Davachi, L. and A. Wagner. 2002. Hippocampal contributions to episodic encoding: Insights from relational and item-based learning. Journal of Neurophysiology 88(2): 982–90. Devlin, T., P. Matthews, and M. Rushworth. 2003. Semantic processing in the left inferior prefrontal cortex: A combined fMRI and transcranial magnetic stimulation study. Journal of Cognitive Neuroscience 15(1): 71–84. Doyon, J., R. Laforce, G. Bouchard, D. Gaudreau, J. Roy, M. Poirier, P. Bedard, F. Bedard, and J. Bouchard. 1998. Role of the striatum, cerebellum and frontal lobes in the automatization of a repeated visuomotor sequence of movements. Neuropsychologia 36(7): 625–41. Draganski, B., C. Gaser, V. Busch, G. Schuierer, U. Bogdahn, and A. May. 2004. Neuroplasticity: Changes in grey matter induced by training. Nature 427(January): 311–12. Gabrieli, J., J. Brewer, and R. Poldrack. 1998. Images of medial temporal-lobe functions in human learning and memory. Neurobiology of Learning and Memory 70(1–2): 275–83. Grabowski, T., H. Damasio, and A. Damasio. 1998. Premotor and prefrontal correlates of category-related lexical retrieval. NeuroImage 7(3): 233–43. Grossen, B. 1997. Thirty years of research: What we now know about how children learn to read: A synthesis of research on reading from the National Institute of Child Health and Development. Santa Cruz, CA: Center for the Future of Teaching and Learning. Hung, C., G. Kreiman, T. Poggio, and J. DiCarlo. 2005. Fast readout of object identity from macaque inferior temporal cortex. Science 310(5749): 863–66. Janowsky, J., A. Shimamura, and L. Squire. 1989. Source memory impairment in patients with frontal lobe lesions. Neuropsychologia 27(8): 1043–56. Kempermann, G., G. Kuhn, and F. Gage. 1997. More hippocampal neurons in adult mice living in an enriched environment. Nature 386(April): 493–95. Kim, S., and K. Ug˘ urbil. 1997. Comparison of blood oxygenation and cerebral blood flow effects in fMRI: Estimation of relative oxygen consumption change. Magnetic Resonance in Medicine 38(1): 59–65. Lee, I., and F. Solivan. 2008. The roles of the medial prefrontal cortex and hippocampus in a spatial paired-association task. Learning and Memory 15: 357–67. Martin, S., and R. Morris. 2002. New life in an old idea: The synaptic plasticity and memory hypothesis revisited. Hippocampus 12(5): 609–36. Mazziotta, J. 2001. A four-dimensional probabilistic atlas of the human brain. Journal of the American Medical Informatics Association 8(5): 401–30. McGaugh, J., C. McIntyre, and A. Power. 2002. Amygdala modulation of memory consolidation: Interaction with other brain systems. Neurobiology of Learning and Memory 78(3): 539–52. Misra, M., T. Katzir, M. Wolf, and R. Poldrack, 2004. Neural systems for rapid automatized naming identified using fMRI. Scientific Studies of Reading 8: 241–56. Nelson, S., P. Sjöström, and G. Turrigiano. 2002. Rate and timing in cortical synaptic plasticity. Philosophical Transactions of the Royal Society B: Biological Sciences 357(1428): 1851–57. Olsen, K. 1995. Science continuum of concepts for grades K-6. Kent, WA: Center for the Future of Public of Education. Panchev, C., and S. Wermter. 2004. Spike-timing-dependent synaptic plasticity: From single spikes to spike trains. Neurocomputing 58–60 (June): 365–71. Perron, T., B. Pike, L. Richer, S. Veillette, Z. Pausova, and T. Paus. 2008. Brain size and folding of the human cerebral cortex. Cerebral Cortex 18(10): 2352–57. Perry, B., R. Pollard, T. Blakley, W. Baker, and D. Vigilante. 1995. Childhood trauma, the neurobiology of adaptation and usedependent development of the brain: How states become traits. Infant Mental Health Journal 16(4): 271–91. Phelps, E., F. Hyder, A. Blamire, and R. Shulman. 1997. FMRI of the prefrontal cortex during overt verbal fluency. NeuroReport 8(2): 561–65. Pratt, K., A. Watt, L. Griffith, S. Nelson, and G. Turrigiano. 2003. Activity dependent remodeling of presynaptic inputs by postsynaptic expression of activated CaMKII. Neuron 39(2): 269–81.

The Educational Forum • Volume 73 • 2009 • 345

Willis Ramey, C., and F. Campbell. 1991. Poverty, early childhood education, and academic competence: The Abecedarian experiment. In Children in poverty: Child development and public policy, ed. A. C. Huston, 190–221. Cambridge, England: Cambridge University Press. Schneider, W., and J. Chein. 2003. Controlled and automatic processing: Behavior, theory, and biological mechanisms. Cognitive Science 27(3): 525–59. Shadmehr, R., and H. Holcomb.1997. Neural correlates of motor memory consolidation. Science 277(5327): 821–25. Sherman, S., and R. Guillery. 2002. The role of the thalamus in the flow of information to the cortex. Philosophical Transactions of the Royal Society B: Biological Sciences 357(1428): 1695–1708. Sjöström, P., E. Rancz, A. Roth, and M. Häusser. 2008. Dendritic excitability and synaptic plasticity. Physiological Reviews 88: 769–840. Trachtenberg, J., B. Chen, G. Knott, G. Feng, J. Sanes, E. Welker, and K. Svoboda. 2002. Long-term in vivo imaging of experience-dependent synaptic plasticity in adult cortex. Nature 420 (December): 788–94. Turkeltaub, P., L. Gareau, D. Flowers, T. Zeffiro, and G. Eden. 2003. Development of neural mechanisms for reading. Nature Neuroscience 6: 767–73. Turrigiano, G., and S. Nelson. 2004. Homeostatic plasticity in the developing nervous system. Nature Reviews Neuroscience 5(2): 97–107. Wagner, A., D. Schacter, M. Rotte, W. Koutstaal, A. Maril, A. M. Dale, B. Rosen, and R. Buckner. 1998. Building memories: remembering and forgetting of verbal experiences as predicted by brain activity. Science 281(5380): 1188–91. Wagner, R., J. Torgesen, C. Rashotte, S. Hecht, A. Barker, and S. Burgess. 1997. Changing relations between phonological processing abilities and word-level reading as children develop from beginning to skilled readers: A 5-year longitudinal study. Developmental Psychology 33(3): 468–79.

Judy Willis is a neurologist and credentialed teacher at Santa Barbara Middle School, who combined her neuroscience knowledge and years of classroom experience to become an authority in the field of learning-centered brain research. She has written five books on the subject. Her most recent book, Teaching the Brain to Read: Strategies for Improving Fluency, Vocabulary and Comprehension Reading, was published by ASCD in 2008 and will be followed by How Math Adds Up in Students’ Brains in 2009. Her Web site is

346 • The Educational Forum • Volume 73 • 2009

What Brain Research Suggests  

What Brain Research Suggests

Read more
Read more
Similar to
Popular now
Just for you