Showing posts by Bill Jenkins, Ph.D. Show all posts >
In an effort to understand this interplay between literacy and these faculties, Stanford University neuroscientist Jason Yeatman examined the correlation between reading ability and the growth of white matter tracts that connect different regions of the brain. Yeatman and his colleagues studied students aged 7 to 12 over the course of three years. During that time, the team used brain scans to visualize the development of these white matter tracts – specifically, the arcuate fasciculus connecting the brain’s language centers, and the inferior longitudinal fasciculus, which links these centers to the areas that process visual input.
They found that:
Yeatman and his colleagues concluded that the reason for such differences lie in two processes related to brain plasticity:
In short, their studies indicate that:
How might this understanding help us as educators? Previous studies (linked below) have shown that we can influence brain development with Fast ForWord®, improving reading, fluency and vocabulary with Fast ForWord Language and Fast ForWord Reading and Reading Assistant. Through the training and reinforcement that such tools afford learners of all skill levels, we can select and strengthen pathways through the brain. This is the true power of brain plasticity – the ability to change the physical structure of this most dynamic organ of the human body.
With Yeatman’s research, we now face the potential of being able to time such interventions for maximum benefit. If we can identify the optimal time when these processes of myelination and pruning are most in balance, such a moment might represent the perfect window for a student to experience maximum success with these interventions.
Resources and links:
Neural mechanisms of selective auditory attention are enhanced by computerized training: Electrophysiological evidence from language-impaired and typically developing children. (See a YouTube video for explanation of this study)
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What would it be like if you went to a cocktail party – or a rock concert or even your quiet corner coffee joint, for that matter – and you didn’t have the ability to filter out one voice or sound from the sea of other sounds around you? This ability is called “selective hearing” and is a computational function in your brain that enables you to focus in on your companion’s voice in the midst of the endless sound waves coming from ceiling fans, ambient music, and other people’s voices bouncing off the walls. Your ability to focus in on that single selected voice is impressive.
Doctoral candidate Bridget Queenan of Georgetown University Medical Center is figuring out how we humans are able to perform this difficult feat by studying bats. She has found that certain neurons in bats’ brains can “quiet” other neurons, allowing the bat to prioritize certain sounds over others. In short, through “turning up the volume” on certain neurons, bats can zero in on the most important sounds, such as their own echolocation sounds, and allow other sounds to fade into the background. (2010)
Researchers at UCSF recently published an article in the journal Nature that describes how they have actually seen this process take place in humans. Using a sheet of 256 electrodes placed on the brain, they can see which neurons activate at the sound of certain voices through the use of sound samples played simultaneously. They could then “decode” the data from the electrodes to find out what the patient heard without talking to the patients themselves. (2012)
When you consider that a bat must hunt, gather, and navigate through spaces populated with thousands and thousands of other bats, it’s easy to see why a brain function like selective hearing is essential to survival. Humans have depended on selective hearing throughout our history for much the same reason.
Although most modern humans are no longer engaged in hunting and gathering activities, our world would look very different were it not for selective hearing. Imagine living in a city – or even a moderately sized suburban town, for that matter – with its ambient atmosphere combining traffic, voices, weather sounds such as wind or rain, and the rest of the cacophony of daily life that we simply don’t think about from moment to moment. Were it not for selective hearing, we would drown in an overwhelming sea of noise, unable to focus on any one sound well enough to effectively evaluate its importance. Considered in that context, the neurological capability that we call selective hearing has played a significant role in defining how we function as a species.
You can also see how this ability would be important in the real-world context of the classroom. Without it, students who are already easily distracted would simply be swallowed by the noise. Independent research has shown that students’ selective auditory attention improves after they use the Fast ForWord program for as little as six weeks. (2008)
So the next time you find yourself unable to focus on someone’s voice at a party, or you encounter a student who is having a hard time paying attention in a noisy classroom, take a moment. Appreciate your ability to use your selective hearing. And have patience while that other person works to engage theirs.
Bardi, J. (2012). How Selective Hearing Works In the Brain. Retrieved from the University of California San Franciso website: http://www.ucsf.edu/news/2012/04/11868/how-selective-hearing-works-brain.
Mallet, K. (2010). Bat Brains Offer Clues As to How We Focus on Some Sounds and Not Others. Retrieved from the Georgetown University Medical Center: http://explore.georgetown.edu/news/?ID=54075&PageTemplateID=295.
Stevens,C., Fanning, J., Coch, D., Sanders, L., & Neville, H. (2008). Neural mechanisms of selective audiory attention are enhanced by computerized training: Electrophysiological evidence from language-impaired and typically developing children. Brain Research. 1205, 55 – 69. doi: 10.1016/j.brainres.2007.10.108.
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Educators and psychologists like to talk about predictors--we like to know what about today can tell us about how students will achieve tomorrow. While such knowledge is academically interesting, its true value only lies in the action it inspires.
For example, a recent study published in Psychological Science outlines the early predictors of mathematics achievement (Siegler, 2012). Based on various theories of how humans naturally develop numerical concepts, the research team hypothesized that an early understanding of fractions could predict how well a student would perform in algebra and general mathematics later on. Siegler’s earlier research had revealed that a student’s understanding of the number line was critical to future mathematical success; this recent publication is an extension of those original findings.
In the 2012 study, the team did a retrospective study of data from two populations of students – one in the United States and one in England. The data indicated that their hypothesis was true: fractions knowledge indeed predicted later success. After controlling for intellectual ability, family background and existing mathematical abilities, the students who had a greater understanding of fractions early on ended up doing better in algebra and math in high school, 5 or 6 years later.
The paper’s authors say it well: “If researchers can identify specific areas of mathematics that consistently predict later mathematics proficiency…society can increase efforts to improve instruction and learning in those areas.” (2012)
So, we have a solid hypothesis, and the data support it. With the scientific method as their toolset, the research team provided us with a bit of useful, straightforward information. The real question becomes: what do we do with this knowledge?
Let’s bring this down to the level of practice: what are some simple things parents and teachers can do to help young learners develop a better understanding of fractions?
One crucial reinforcement to the above strategies comes from Siegler’s findings about the importance of the number line. It is helpful to use the strategies above, but the student must ultimately understand how that fraction is represented in relation to other numbers (e.g., knowing that 2/4 and 1/2 are the same point between 0 and 1). Use the strategies above to engage learners, but always remember to reinforce the concepts by taking the activity back to the number line.
Studies like the ones I’ve discussed can be great, enlightening tools. They show us a relatively straight road to get from here to there and a clear relationship of cause to effect. If we can help students understand fractions early, given the regular patterns of development and learning, those students will have advantages when it comes to developing deeper math skills later on.
The simple problem is that acting on this knowledge takes change. Do we have the will to take on those changes?
Seigler, R.S., Duncan, G.J., Davis-Kean, P.E., Duckworth, K., Claessens, A., Engel, M., Susperreguy, M.I., Chen, M. Early Predictors of High School Mathematics Achievement. Psychological Science. 14 June 2012.
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Have you ever wondered what structures or areas in your brain allow you to understand language? Read books? Appreciate music? At a basic level, scientists have already correlated discrete brain structures to specific human abilities. As today’s researchers take this understanding further and actually map intellectual ability in the brain, they are discovering that many abilities are not neatly confined to a single area.
Scientists have employed various techniques to delve into this “intracranial cartography.” One method used by Dr. Aron Barbey, professor of neuroscience at the University of Illinois, involved finding patients with highly localized brain injuries and comparing their cognitive abilities and executive function with other individuals who had those same structures intact. Barbey’s evidence showed that intelligence relies on localized areas of the brain working together collaboratively as opposed to residing independently in a single region or the brain as a whole. In his own words: "We found that general intelligence depends on a remarkably circumscribed neural system. Several brain regions, and the connections between them, were most important for general intelligence." (2012) Barbey’s research supports the idea that areas of the brain controlling executive function, which governs skills such as self-control and planning, overlap “to a significant extent” with areas that control general intelligence. (2012)
Another method of mapping intellectual ability involves performing brain scans while subjects carry out cognitive tasks, and then indexing the areas of the brain that are engaged in specific types of processes. Using the Wechsler Adult Intelligence Scale (WAIS), a standard index for measuring IQ, Caltech neuroscientist Ralph Adolphs was able to measure subjects’ performance in the four specific areas that the WAIS covers: verbal comprehension, perceptual organization, working memory, and processing speed. (2010)
Interestingly, Adolphs found that even though the WAIS defines verbal comprehension and working memory as separate abilities, areas responsible for each were shown to overlap, suggesting that they represent a similar type of intelligence. Also of note, the study found that processing speed seemed to be a more global function controlled by connections across different areas of the brain as opposed to localized structures.
Barbey’s results support that same finding. “In fact,” he says, “the particular regions and connections we found support an emerging body of neuroscience evidence indicating that intelligence depends on the brain’s ability to integrate information from verbal, visual, spatial and executive processes.” (2012) The implications are intriguing, and support our evolving understanding of human intelligence as a network that can be developed by simultaneously cross-training those regions in the brain that most effectively work together.
Research performed in the past few decades has demonstrated that we can improve reading skills by teaching students “metacognitive strategies.” By metacognition, we refer to enhancing one’s awareness of “what one believes and how one knows.” (Kuhn, 2000). In other words, the more we can teach students to be actively thinking about thinking as they learn, the more effective their learning will be.
In fact, we can teach students to become what Marcia Lovett of Carnegie Mellon University calls “expert learners.” According to Lovett (2008), teaching metacognition involves three specific processes:
According to Lovett’s research, an experimental group of students who used metacognitive strategies more strongly believed themselves to be effective learners, demonstrated greater motivation to learn, and achieved better academic performance than the control group. (2008)
What exactly do such metacognitive learning strategies look like in the classroom? Diane Dahl, in her blog post at The Educator’s PLN, shows how these ideas can be implemented in any number of ways, many times by simply tweaking existing instructional strategies. Here are a few recommendations based on her list.
While it might be easiest to imagine implementing these kinds of strategies in reading instruction, they can be adapted for teaching any subject. The idea is simply to get students to be consciously aware of, and take charge of, their own learning. The more we can do that, the more effective we will be as teachers.
Dyslexia is thought to affect a high percentage of people. The condition can be caused by biological changes during brain development (known as developmental dyslexia) or by environmental effects such as illness or injury (known as acquired dyslexia). In their recent article published in the Proceedings of the National Academy of Sciences, Nora Maria Raschle, Jennifer Zuk and Nadine Gaab cite estimates that developmental dyslexia affects between 5 and 17% of all children. (2012) They further outline how it can have detrimental effects on a child’s life both inside the classroom as well as beyond.
For these reasons, educators and researchers have placed intervention strategies for developmental dyslexia very high on their priority list.
While much progress on such interventions has occurred in the area of helping individuals with developmental dyslexia once they have been diagnosed, other research is delving into identifying the neurological and physiological differences between brains that develop the condition and those that do not.
To find out if there are identifiable predictors of developmental dyslexia, Raschle, Zuk and Gaab examined the functional brain networks during phonologic processing in 36 pre-reading children with an average age of 5.5 years. That is they were looking for brain differences even before any of the children had learned to read since previous brain studies of dyslexia have been conducted on individuals after they have begun to read, albeit poorly. All of the subjects were of a similar socioeconomic status; most came from homes with relatively high SES and strong language skills. These are the type of home environments that typically result in the development of good language and reading skills.
The only substantive difference between the groups in this study was that half of the subjects had a family history of developmental dyslexia, while the other half did not.
Interestingly, the 18 children with a family history of dyslexia scored significantly lower than those who had no family history on a number of standardized assessments, including:
Not only did the research team examine the two groups’ performance on these evaluations, but they also used functional magnetic resonance imaging (fMRI) scanning to identify what was happening in the brains of each learner during the examinations.
Brain activity in the left lingual gyrus as well as the temporoparietal brain areas correlated with phonological processing skills. Interestingly, the team discovered that members of the group with a family history of dyslexia showed a reduced activation in these areas even before learning to read. Their discoveries suggest that the left temporoparietal region of the brain in this group reflect an inability to map phonemes to graphemes. In other words, their brains simply were not adequately developed to match language sounds with their written counterparts. In addition, this same region of the brain – also known as the “visual word form area” – seems to be involved in processing words during reading in both children and adults.
The authors unequivocally state, “Developmental Dyslexia can have severe psychological and social consequences, potentially negatively impacting a child’s life.” All too often, we identify learning disabilities much too late. In the case of dyslexia, we might make such a diagnosis and begin interventions halfway through elementary school, but by then we have much catching up to do. If these students’ vocabulary skills and motivation to read have already been compromised, the climb back may be much more difficult than if had the situation been identified earlier.
Research like that of Raschle, Zuk and Gaab will help us begin to address learning disabilities at the neurological and physiological levels much earlier in life. Through very early diagnosis and intervention, we may one day be able to more effectively ameliorate – and maybe even eliminate – the distressing experience of developmental dyslexia.
Read this study to learn how Fast ForWord helped significantly improve reading skills in children with dyslexia.
Gaab et al., 2007, "Neural correlates of rapid auditory processing are disrupted in children with developmental dyslexia and ameliorated with training: An fMRI study,"; Restorative Neurology and Neuroscience 25, 295-310.
Raschle, N. M., Zuk, J. and Gaab, N., 2012, "Functional characteristics of developmental dyslexia in left-hemispheric posterior brain regions predate reading onset." PNAS, v. 109, p. 2156–2161.
Many a study has laid out the innate physiological differences between the male and female brain. Michael D. De Bellis and his team of researchers, for example, clearly showed how the maturing brain differs between boys and girls, and how those differences vary over the course of regular development.
Based on the work of De Bellis et al., we know, for example, that the proportions of white matter to grey matter predictably vary between the genders. We also know that the volume of the corpus callosum area is proportionally different between males and females. And of course, we know that the varying levels of testosterone and estrogen create behavioral differences, especially during pre-adolescence and adolescence. (2001)
With these findings in mind, the question arises: Can such information help us better educate our young people? And maybe more importantly, should it be used to differentiate instruction based on gender?
Caryl Rivers and Rosalind C. Barnett, authors of The Truth About Girls and Boys: Challenging Toxic Stereotypes About Our Children (Columbia University Press, 2011), argue that boys’ and girls’ brains and ways of thinking are actually much more the same than they are different, and that “the differences that do exist are trivial."
Nevertheless, there is a current trend of well-meaning educators and parents citing these brain differences to support gender stereotypes—a trend that is damaging to learners as individuals and to our society as a whole, says Catherine A. Cardno in her recent EdWeek review of the book. The following are a few of the stereotypes often expounded:
She cites a caution the authors make in their introduction, that "Today, parents and educators are being fed a diet of junk science that is at best a misunderstanding of the research and at worst what amounts to a deliberate fraud on the American public."
In her book Pink Brain, Blue Brain, Lise Eliot, associate professor of neuroscience at the Chicago Medical School, discusses her conclusions after comprehensively reviewing the research on the child through adolescent brain. Her conclusion is that there is “surprisingly little evidence of sex differences in children’s brains.” (2009) The real differences, she says, arise from the neuroplastic nature of the brain and how children’s ways of thinking differentiate along gender lines over time as a result of the input they receive via parents, friends, relatives and educators – NOT because of any innate physiological variations between the sexes.
It is thus our role and responsibility as educators to be aware of the pitfalls of gender-based – and all – stereotyping in our classrooms that we may be perpetuating. Only through completely supporting each learner – regardless of their skin color, SES, gender or any other difference – can we ensure that they will reach their greatest potential.
Rosalind C. Barnett and Caryl Rivers. Why Science Doesn't Support Single-Sex Classes. May 20, 2012. http://www.edweek.org.
As educators, we are constantly faced with the question of how we can best present material so that it is optimally “learnable” for the different students we are trying to reach.
There is considerable evidence both for and against self-directed and exploratory learning, so there is a great opportunity for neuroscience to examine the ground-level differences between these and more traditional methods of instruction and how the brain reacts to each. One of those differences is the subject of current investigation: the divide between explicit and implicit instruction.
By explicit instruction, we mean teaching where the instructor clearly outlines what the learning goals are for the student, and offers clear, unambiguous explanations of the skills and information structures they are presenting.
By implicit instruction, we refer to teaching where the instructor does not outline such goals or make such explanations overtly, but rather simply presents the information or problem to the student and allows the student to make their own conclusions and create their own conceptual structures and assimilate the information in the way that makes the most sense to them.
Which is more effective?
One study out of Vanderbilt University recently looked at this question as it applies to word learning. In this study, principal investigator Laurie Cutting and her team examined 34 adult readers, from 21 to 36 years of age.
The subjects were taught pseudowords—words that are similar to real words but that have no meaning, such as “skoat” or “chote.” Then, through both explicit and implicit instruction, subjects were taught meanings for these words. (In the study, both of these pseudowords were associated with the picture of a dog.)
The goal was to gain a clearer understanding of how people with different skills and capabilities processed short-term instruction, how effectively they learned, and how those differences looked physiologically in the brain.
In the end, the subjects were all able to learn the pseudowords. But, through functional magnetic resonance imaging (fMRI), the researchers learned that something deeper was actually taking place: subjects previously identified as excellent readers showed little difference between how they processed explicit vs. implicit instruction. Average readers, on the other hand, showed through their fMRIs that they had to work harder to learn through implicit instruction; for them, explicit instruction was the more effective method.
Granted, the study did focus on a group of adults, not school-age learners. Still the Vanderbilt team’s preliminary results support the idea that, even in group situations where all students have roughly the same degree of previous experience, prior reading ability might be an important element to consider when choosing an instructional approach.
For further reading:
Amy M. Clements-Stephens, April D. Materek, Sarah H. Eason, Hollis S. Scarborough, Kenneth R. Pugh, Sheryl Rimrodth, James J. Pekar, Laurie E. Cutting. Neural circuitry associated with two different approaches to novel word learning. Developmental Cognitive Science. Volume 2, Supplement 1. 15 February 2012. pp. S99-S113.
We generally don’t consider the development of manual dexterity like hand-eye coordination in babies to be an essential element of cognitive development. In fact, the scientific terminology itself – “motor skills” for movement and “cognitive skills” for mental processing – draws a clear and definite separation between these two types of functions.
As it turns out, such thinking may be holding us back from innovations in education that might truly be able to make a difference for a great many young learners.
Recent research has demonstrated a clear connection between the development of fine motor skills in early life and later success in math, science and reading. Such skills – those as simple as how an infant can use her eyes to track her mother’s face and then reach her hand out and touch her mother’s nose – may just help us understand how ready that child will be for kindergarten, as well as what kind of achiever she’ll be over the next few years.
The Motor-Cognition Connection
To arrive at such a conclusion, we first need to understand the connection between the motor and cognitive centers of the brain. Through neuroimaging and neuroanatomical analysis, Adele Diamond (2000) uncovered “significant evidence” for a number of motor-cognition links in the brain. Prior to such analysis, these abilities were attributed to separate areas of the brain: motor skills were centered in the cerebellum and basal ganglia, and cognition in the prefrontal cortex. But Diamond’s research showed that both could be activated during certain motor or cognitive tasks. Further research also showed that “individuals with brain damage to either the primary motor or primary cognitive areas often show impairment in both skill areas.” (p. 1013)
In fact, Karen Adolph (2005, 2008; Adolph & Berger, 2006) suggested that a complex relationship exists between cognitive and motor skills development in infants. Since infants are learning to process a complex and changing world at the same time that they are learning gross and fine motor skills, they are in a state of constant adaptation. Their bodies are changing simultaneously as the world around them is presenting new information. Thus, their physical existence in the world – and their movement through it – is one that requires constant cognitive problem solving. In short, infants spend the vast majority of their existence, when they are not sleeping, learning how to learn.
Motor Skills as a Predictor
Talk about factors that predict future achievement in reading, math and science most often includes discussions of early math skills, early reading skills, social skills, attention skills, and attention-related measures like curiosity, interest and a desire to learn. Note that none of the aforementioned abilities has a motor physical component.
Yet, from the motor-cognition connection, researchers like David Grissmer, Sophie Aiyer, William Murrah, Kevin Grimm and Joel Steele (2010) have brought the issue of motor skills development to the fore. They went back and analyzed data from six data sets, and found that, indeed, fine motor skills were a strong predictor of later achievement. In fact, they conclude that taken together, “attention, fine motor skills and general knowledge are much stronger overall predictors of later math, reading and science scores than early math and reading scores alone.” (p. 1008)
Toward Better Interventions
According to this team of researchers (Grissimer, et al, 2010), “There are few interventions directly testing whether strengthening early attention, fine motor skills, or knowledge of the world would improve later math and reading achievement.” That said, some facts are quite clear:
Ultimately, with that understanding in hand, we clearly have a research opportunity to more comprehensively pursue an understanding of these connections. Findings from such research could put us in a position to create more novel, more effective interventions that strategically integrate motor and cognitive skill building, and continue to hone how we help our youngest learners prepare for future success.
For further reading:
Grissmer, D., Grimm, K., Aiyer S., Murrah, W., Steele, J. Fine Motor Skills and Early Comprehension of the World: Two New School Readiness Indicators. Developmental Psychology. 2010. Vol. 46, No. 5. 1008-1017.
Anyone who has ever conscientiously taken on the challenge of learning a skill – from playing a musical instrument to speaking a foreign language to simply improving one’s penmanship – understands the importance of practice.
As a neuroscientist, practice fascinates me because it is all about establishing pathways in the brain. The ability of the brain to form and re-form routes for specific thought patterns, and for those routes to become more deeply ingrained the more we exercise those thought patterns, makes it possible for us to learn and refine a multitude of wonderful skills throughout our lives.
The Best Practices
In her recent article “The Myth of ‘Practice Makes Perfect,’” Annie Murphy Paul reviews a book by Gary Marcus, a cognitive psychologist at New York University who studies how the brain acquires language. Marcus’ book, Guitar Zero: The New Musician and the Science of Learning, discusses how learning a new skill, such as playing the guitar, requires practice—but the right kind of practice.
Certainly practice requires a commitment of time. But more importantly, to be truly effective it requires a commitment of the mind – a deliberate intent – for optimal learning to occur.
According to Marcus, “Studies show that practice aimed at remedying weaknesses is a better predictor of expertise than raw number of hours; playing for fun and repeating what you already know is not necessarily the same as efficiently reaching a new level. Most of the practice that most people do, most of the time, be it in the pursuit of learning the guitar or improving their golf game, yields almost no effect” (2012).
In other words, the best practice demands that the learner be attentive to his or her errors, weaknesses and deficiencies, and consciously work to remedy them.
From a neuroscience perspective, this observation points to a natural conclusion. Research has shown us time and again that the more we utilize certain neural pathways for building skills – such as throwing a ball or multiplying by fives or recalling all fifty state capitals – the more effectively we ingrain those patterns in our brains and the more automatic the correct skills become.
The Hardest Work
Imagine the budding guitarist bent over her instrument. At 11 years old, she focuses on learning three more chords beyond the three she learned last week. She’s having great trouble with that F, but she’s well in control of the other five. Should she spend her hour of practice playing the music she truly enjoys and save that F for another day, preserving her positive attitude? Or should she feel her frustration, work through it and spend her time on ironing out that problematic F, again and again and again?
Which is the better practice?
Researcher Anders Ericsson of Florida State University wrote that “deliberate practice requires effort and is inherently not enjoyable” (1993). Long hours spent repeating the easy or already-mastered work is simply not enough and not as effective. The best practice requires us to dig deep and uncover our weaknesses. With a greater focus on our faults, we become better able to find them and develop solutions to remedy them.
Robert Duke of the University of Texas-Austin demonstrated this effect when he and his team videotaped piano students as they practiced a challenging concerto, and then ranked the quality of their final performance. In the end, it was not the repetitions nor the hours of practice put in. The best performers zeroed in on their errors and strove to fix them before moving on. (2009)
Behaviors for Success
The students in our everyday classrooms have an advantage over the guitar student practicing at home. She has to work independently the majority of the time, interacting with her music instructor only once or twice a week; the lion’s share of reinforcing her learning and practicing behavior is her personal responsibility.
In our day-to-day classrooms, we get – relatively speaking – much more time to help our students devise strategies and establish behaviors for success. Through helping them learn how to face the hard work, to focus on what’s difficult or wrong and make it easier or right, we can help them to establish those all-important neural pathways that will lead to success.
For further reading:
It’s Not How Much; It’s How: Characteristics of Practice Behavior and Retention of Performance Skills by Robert A. Duke, Amy L. Simmons and Carla Davis Cash