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Published: 01 October 2021

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Dr Kerry Hempenstall, Senior Industry Fellow, School of Education, RMIT University, Melbourne, Australia. 2021

Each of my articles is available as a PDF at https://tinyurl.com/y6vat4ut

It is worthwhile separating the terms neuroscience from brain-based teaching/learning. The first involves science; whereas, the second varies from the useful to the fanciful. Some writers are sceptical about the second field, and others are more optimistic about the future if not so impressed by its current status. One caveat is that it is true that neuroscience studies can show something of what is happening in the brain when individuals engage in reading. However, they don’t show how the two groups arrived at their skilled or unskilled status, nor how the unskilled might become skilled readers. We continue to need cognitive and behavioural research to do that. It’s also important to appreciate the possibility that the noted brain processes in unskilled readers are possibly a consequence rather than a cause of their struggles with reading. Of course, if there are atypical brain processes discernible among children prior to reading instruction, then the neuroscience techniques, such as brain imaging, could become a potential means of early identification and hence earlier intervention that is currently performed. Intervening directly on the brain, that is, other than by environmental intervention such as reading instruction, is in its infancy.

One such (somewhat scary) area is neurotechnology:

“‘Neurotechnology’ is a broad field of brain-centred research and development dedicated to opening up the brain to computational analysis, modification, simulation and control. It includes advanced neural imaging systems for real-time brain monitoring; brain-inspired ‘neural networks’ and bio-mimetic ‘cognitive computing’; synthetic neurobiology; brain-computer interfaces and wearable neuroheadsets; brain simulation platforms; neurostimulator systems; personal neuroinformatics; and other forms of brain-machine integration (Nuffield Council on Bioethics, 2013; Rose et al. 2016; Yuste et al. 2017). … A vast range of techniques has been developed ‘aimed at cognitive modification and enhancement’, such as ‘brain-machine interfaces, … electric stimulators, and brain mapping technologies’, which ‘now target the brain for modification and rewiring’ (Pitts-Taylor 2016: 18).” (p.66)

Williamson, B. (2019). Brain data: scanning, scraping and sculpting the plastic learning brain through neurotechnology. Postdigital Science and Education, 1, 65–86. https://doi.org/10.1007/s42438-018-0008-5

Back to the here and now - neuroscience and education: What’s the potential link?

“Education is about enhancing learning, and neuroscience is about understanding the mental processes involved in learning. This common ground suggests a future in which educational practice can be transformed by science, just as medical practice was transformed by science about a century ago.” (Royal Society, 2011, p. v)

Royal Society. (2011). Brain waves module 2: Neuroscience: Implications for education and lifelong learning. https://royalsociety.org/topics-policy/projects/brain-waves/education-lifelong-learning/

“Educational neuroscience is an interdisciplinary research field that seeks to translate research findings on neural mechanisms of learning to educational practice and policy. There are equivalent fields that seek to translate neuroscience findings to law (e.g. Royal Society, 2011a), economics (e.g. Glimcher & Fehr, 2013) and social policy (e.g. Royal Society, 2011b), drawing on research in behavioural regulation, decision-making, reward, empathy and moral reasoning. The field is also a basic science that studies how education changes the brain, and what the mechanisms are that lead to behavioural change (or the absences thereof) through education. The relevance of neurobiology to education was recognised throughout the 20th century (e.g. Thorndike, 1926), but it was not until the 1990s and the “Decade of the Brain” (Jones & Mendell, 1999) that technological advances in in vivo imaging of brain function led to the theoretical advances that made educational neuroscience viable as a field (Varma, McCandliss, & Schwartz, 2008).

Despite strong critics (Bishop, 2014; Bowers,2016a; Bruer, 1997) and vigorous ongoing debate about the merits of bringing knowledge from neuro-scientific research to bear on educational problems(Gabrieli, 2016; Howard-Jones et al., 2016), the potential connections between neuroscience and education are being actively explored across the globe. Different labels have been used to describe such efforts, such as Neuroeducation, Educational Neuroscience and Mind, Brain and Education. The growth of the field has led to the establishment of new societies and groups: the International Mind, Brain and Education Society (IMBES; www.imbes.org) was founded in 2004; in 2009, the European Association for Research on Learning and Instruction (EARLI) founded a Special Interest Group called ‘Neuroscience and Education’ which has been holding biannual meetings since 2010. New journals have been established, such as ‘Trends in Neuro-science and Education’, ‘Mind, Brain and Education and ‘Educational Neuroscience’, which attract theoretical and empirical work that explores the inter-sections of neuroscience, psychology and education.” (p. 477)

Thomas, M. S. C., Ansari, D., & Knowland, V. C. P. (2019). Annual research review: Educational neuroscience: Progress and prospects. Journal of Child Psychology and Psychiatry, 60, 477–492. doi:10.1111/jcpp.12973

“It is important to stress from the outset that the “neuroscience” in EN refers almost exclusively to cognitive neuroscience. In other words, it is concerned with making links between the neural substrates of mental processes and behaviors, especially those related to learning. Observed correlations between brain imaging data and behavioral change only reflect a small part of this enterprise, with many methodologies shedding light on the mechanisms by which brain function—and in the current context, cognition—is realized. At its core, then, is the established brain-mind-behavior model of explanation that frames cognitive neuroscience (Morton & Frith, 1995), where the behavior is explicitly learning in the context of (formal) education. Therefore, although it may be concerned with biological processes and classroom behavior, it also has psychology, quite literally, at the center of its theorizing (Bruer, 1997). It is for this reason we welcome this exchange in the pages of Psychological Review.

EN does not favor solely neural levels of explanation, and certainly does not suggest that educational efficacy should be evaluated solely on the basis of neural function. Rather, EN claims that studies of brain function can contribute, alongside behavioral data, to an understanding of underlying learning processes, and that understanding underlying learning processes is relevant to education and can lead to improved teaching and learning. As far as we are aware, there are no established EN research groups who claim that neuroscience, in isolation from psychology or other disciplines, holds any value whatsoever for education. Instead, the exploitation of data from neuroscience is part of a wider perspective on the sphere of causal influences operating on educational outcomes that, for example, now includes a focus on factors such as sleep, diet, stress, and exercise.”(p. 620-621)

Howard-Jones, P., Varma, S., Ansari, D., Butterworth, B., DeSmedt, B., Goswami, U., ... & Thomas, M.S.C. (2016). The principles and practices of educational neuroscience: Commentary on Bowers. Psychological Review, 123, 620–627.

What might such a link achieve?

“The overall goal of brain-based education is to attempt to bring insights from brain research into the arena of education to enhance teaching and learning. The area of science often referred to as brain research typically includes neuroscience studies that probe the patterns of cellular development in various brain areas; and brain imaging techniques, with the latter including functional MRI (fMRI) scans and positron-emission tomography (PET) scans that allow scientists to examine patterns of activity in the awake, thinking, human brain. These brain imaging techniques allow scientists to examine activity within various areas of the brain as a person engages in mental actions such as attending, learning, and remembering.

Proponents of brain-based education espouse a diverse group of educational practices and approaches, and they generally attempt to ground claims about effective practice in recently discovered facts about the human brain. They argue that there has been an unprecedented explosion of new findings related to the development and organization of the human brain and that the current state of this work can inform educational practice in meaningful ways… . Other brain-based education literature that makes closer ties with brain research focuses on brain imaging of particular learning disabilities.”

McCandliss, B. (2021). Brain-based education: Summary principles of brain-based research, critiques of brain-based education. https://education.stateuniversity.com/pages/1799/Brain-Based-Education.html

“The reader’s brain contains a complicated set of mechanisms admirably attuned to reading. For a great many centuries, this talent remained a mystery. Today, the brain’s black box is cracked open and a true science of reading is coming into being. Advances in psychology and neuroscience over the last twenty years have begun to unravel the principles underlying the brain’s reading circuits. Modern brain imaging methods now reveal, in just a matter of minutes, the brain areas that activate when we decipher written words. Scientists can track a printed word as it progresses from the retina through a chain of processing stages, each of which is marked by an elementary question: Are these letters? What do they look like? Are they a word? What does it sound like? How is it pronounced? What does it mean?

On this empirical ground, a theory of reading is materializing. It postulates that the brain circuitry inherited from our primate evolution can be co-opted to the task of recognizing printed words. According to this approach, our neuronal networks are literally “recycled” for reading. The insight into how literacy changes the brain is profoundly transforming our vision of education and learning disabilities. New remediation programs are being conceived that should, in time, cope with the debilitating incapacity to decipher words known as dyslexia.”

Dehaene, S. (2009). Reading in the brain: The new science of how we read. New York: Penguin.

But some in education are sceptical:

“The core claim of educational neuroscience is that neuroscience can improve teaching in the classroom. Many strong claims are made about the successes and the promise of this new discipline. By contrast, I show that there are no current examples of neuroscience motivating new and effective teaching methods, and argue that neuroscience is unlikely to improve teaching in the future. The reasons are twofold. First, in practice, it is easier to characterize the cognitive capacities of children on the basis of behavioral measures than on the basis of brain measures. As a consequence, neuroscience rarely offers insights into instruction above and beyond psychology. Second, in principle, the theoretical motivations underpinning educational neuroscience are misguided, and this makes it difficult to design or assess new teaching methods on the basis of neuroscience. Regarding the design of instruction, it is widely assumed that remedial instruction should target the underlying deficits associated with learning disorders, and neuroscience is used to characterize the deficit. However, the most effective forms of instruction may often rely on developing compensatory (nonimpaired) skills. Neuroscience cannot determine whether instruction should target impaired or nonimpaired skills. More importantly, regarding the assessment of instruction, the only relevant issue is whether the child learns, as reflected in behavior. Evidence that the brain changed in response to instruction is irrelevant. At the same time, an important goal for neuroscience is to characterize how the brain changes in response to learning, and this includes learning in the classroom. Neuroscientists cannot help educators, but educators can help neuroscientists.” (p. 600)

Bowers, J. S. (2016). The practical and principled problems with educational neuroscience. Psychological Review, 123, 600–612. http://dx.doi.org/10.1037/rev0000025

“There are educational resources that tout the importance of the brain in learning and education. Carefully reviewing those sources shows that few of them draw from strong research evidence. What is more, they recommend a lot of poppycock methods, practices, techniques, and such. There can be little doubt that individual’s brains are involved in learning; the very concept of plasticity is a description of learning! But making the leap to popular practices—particularly practices for which there is little or no evidence that employing those practices improves learners’ outcomes—is a leap too far.

Is “brain-based learning” bunkum? No. Of course, not. Everyone uses her brain in learning! Is what lots of people contend is the evidence for brain-based education and recommended practices bogus? Yes.

Let’s please use sensible standards of evidence and logic before adopting teaching practices that may well waste the precious time of kids with disabilities. Learners with disabilities need the very best, the most efficient and effective instruction that we can provide. Few, if any, will benefit when we adopt popular theories that are not founded on solid evidence.”

Lloyd, J.W. (2021). Brain-based education: Is there any other kind? https://www.spedtalk.com/p/editorial-brain-based-education

“At present, though, genetic, structural and functional findings remain largely correlational and unconnected with one another. Results are provocative, but much work still is needed to move from a list of “neurophenotypes” towards a causal theory of gene-brain behavior relations in reading acquisition and RD. … Strong claims that a given program is brain-based are premature at best” (p.22).

Pugh, K., & Hagan, E.C. (2010). New directions in the cognitive neuroscience of reading development and reading disability. Perspectives on Language and Literacy, 36(1), 22.

“The idea that neuroscience research might provide guidance for teachers sounds promising. However, as with any new and aspiring research field, educational neuroscience has suffered to some extent from over-optimism and wishful thinking. A huge demand for improving educational practice has been a fertile ground for misconceptions around the question of how neuroscience can be applied to education. Speculative educational applications have emerged in the name of neuroscience (p.136)

Weigmann, K. (2013). Educating the brain. EMBO Reports, 14(2), 136-9. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3566840/

“Background: Our ability to look at structure and function of a living brain has increased exponentially since the early 1970s. Many studies of developmental disorders now routinely include a brain imaging or electrophysiological component. Amid current enthusiasm for applications of neuroscience to educational interventions, we need to pause to consider what neuroimaging data can tell us. Images of brain activity are seductive, and have been used to give credibility to commercial interventions, yet we have only a limited idea of what the brain bases of language disorders are, let alone how to alter them. Scope and findings: A review of six studies of neuroimaging correlates of language intervention found recurring methodological problems: lack of an adequate control group, inadequate power, incomplete reporting of data, no correction for multiple comparisons, data dredging and failure to analyse treatment effects appropriately. In addition, there is a tendency to regard neuroimaging data as more meaningful than behavioural data, even though it is behaviour that interventions aim to alter. Conclusion: In our current state of knowledge, it would be better to spend research funds doing well designed trials of behavioural treatment to establish which methods are effective, rather than rushing headlong into functional imaging studies of unproven treatments” (p.247).

“Our first priority should be to first develop interventions for children with language impairments and other neurodevelopmental disorders, and to produce good evidence of their efficacy using randomized controlled trials. Second, we also need to do far more methodological work to ensure our neuroimaging tools are as reliable, sensitive and standardized as our behavioural measures (Dichter et al., 2012). Third, we will need to develop multicentre collaborations to do studies with adequate statistical power to detect treatment effects. Only then will we be in a strong position to combine neuroimaging with intervention to answer questions about underlying mechanisms of effective intervention” (p.257).

Bishop, D. V. M. (2012). Neuroscientific studies of intervention for language impairment in children: interpretive and methodological problems. Research Review: Emanuel Miller Memorial Lecture 2012. Journal of Child Psychology and Psychiatry, 54(3), 247–259.

“The first point to note here is that the term ‘brain-training’ is somewhat of a tautology, since all learning happens in the brain. As one of our colleagues is known to say: “it certainly doesn’t happen in your big toe”. Any intervention that is given to any child, will, in some way, “train their brain”. So the question here is not should we train children’s brains, but how should we train their brains? … neuroplasticity tells us that the brain can adapt, but it does not tell us how the brain should be stimulated (or trained). Thus, neuroplasticity per se also does not inform us about how to treat learning difficulties.” (p.1)

Castles, A., & McArthur, G. (2013). ‘Brain-training’ … or learning as we like to call it. Learning Difficulties Australia Bulletin, 45(1), 1-2.

“Certainly our understanding of how neurons work, the role of neurotransmitters, and data showing correlations between brain activity and academic tasks has provided distinct clues into how a child learns. The problem, then, is not with the neuroscience data themselves, but how authors of these purported brain-based approaches appear to have erroneously filled in the missing research gaps. Thus, the problem is not with what neuroscientists and educators know, but with what they think they know. This ‘filling-in-the-gaps’ results from a variety of factors including misunderstanding of the research, misinterpretation or over interpretation of the data, and a belief in claims that are unsubstantiated or go beyond what the evidence supports. … Thus, given the data from neuroscience combined with evidence-based practices used in special education, special educators can be assured that they are, indeed, using brain-based educational instruction. Mastery-based programs that focus on fluency and repetition are most likely to increase both better traditional learning outcomes and produce neural circuits critical for both educational activities and transfer to daily living skills” (p. 46).

For example, the research described above on the formation of memory through long-term potentiation strongly suggests that neural connections are strengthened through repetition or practice (Freeberg, 2006; Garrett, 2008; Hardiman, 2003). Note that the importance of practice and rehearsal has been known for more than a century, long before the process of long-term potentiation was identified (Ebbinghaus, 1913; Hebb, 1949; Thorndike, 1913).” p.50).

Alferink, L. A., & Farmer-Dougan, V. (2010). Brain-(not) based education: Dangers of misunderstanding and misapplication of neuroscience research. Exceptionality, 18, 42-52.

“Many brain studies on reading and word comprehension are based on data from imaging techniques that reveal the distribution of activity in the brain during tasks such as reading single words and sentences and solving simple assessments problems (for instance by comparing watching an activity in a photo with reading about a similar activity - e.g. González, et al. 2006, Zwaan et al., 2002 but see Barsalou, 2016). However, such studies investigate the neural activity as an expression of what happens at the brain level when subjects solve tasks. The explanatory power of such studies may be challenged both methodologically and theoretically (e.g. Schilhab, 2017a), not least because current imaging techniques are too crude to identify differences between individual neurons or individual congregations of neurons (e.g. Beilock, 2010). The delicacy and complexity of what happens at the neural level become invisible, heightening the risk of producing flawed inferences (Alexander et al., 2015). Optimists might respond that such objections will become obsolete as the methods for capturing brain activity increase in subtlety. Moreover, cross-referencing different data sources, such as imaging and EEG, could compensate for the current lack of sensitive equipment. The theoretical objections, however, are more serious and less easy to dismiss.” (p.5)

Trasmundi, S.B., Kokkola, L., Schilhab, T., & Mangen, A. (2021). A distributed perspective on reading: implications for education. Language Sciences, in press. https://doi.org/10.1016/j.langsci.2021.101367

Whilst recognising current weaknesses in the neuroscience contributions to date, are there also grounds for optimism?

“Of course, educational neuroscience is a fledgling field, and there are legitimate criticisms that can be made of it. Here are some of them, drawn from a recent review (Thomas et al., 2019). First, educational neuroscience must amount to more than relabeling effects that are already well known from behavioral psychology with the names of brain structures (such as “executive function” with “prefrontal cortex” or “episodic memory” with “hippocampus”). It must progress psychological theory, and it must point to ways to improve brain health. Second, as Bishop (2014) argues, neuroscience methods are still limited in their sensitivity and specificity as screening or diagnostic tools for deficits. They can only complement more traditional behavioral and social markers of risk. However, some neuroscience measures may be available earlier, such as infant electroencephalographic measures of auditory processing to predict later dyslexia risk (Guttorm, Leppanen, Hamalainen, Eklund, & Lyytinen, 2009) or available-at-birth DNA measures to predict possible educational outcomes (Plomin, 2018). Early availability increases the opportunity for intervention or more targeted monitoring of traditional risk markers. A third legitimate criticism is that while educational neuroscience bears on learning, learning is only one aspect of education that influences outcomes; others include institutional, professional, curricular, political, economic, and societal aspects (Bronfenbrenner, 1992). Fourth, educational neuroscience needs to improve the quality of the dialogue between teachers, psychologists, and educators to ensure that the discussion is genuinely bidirectional, for example, through codesigning studies with teachers to improve the relevance of research and increase the chance of changing practices in the classroom. Finally, educational neuroscience’s progress has been gradual. Researchers (e.g., Howard-Jones et al., 2016; Thomas et al., 2019) have been clear on the complexity of the challenge of linking the classroom phenomenon of learning with learning in the brain, which is the interplay of perhaps eight different neural systems. Much of the groundwork in educational neuroscience will consist of understanding why the educational methods that work do indeed work (Thomas, 2013) in order to ultimately improve them? (p. 338)

Thomas, M.S.C. (2019). Response to Dougherty and Robey (2018) on neuroscience and education: Enough bridge metaphors—interdisciplinary research offers the best hope for progress. Current Directions in Psychological Science, 28(4), 337–340.

What has neuroscience contributed to our understanding of the brain processes involved in skilled and unskilled reading?

“As we learn to read, a brain region known as the 'visual word form area' (VWFA) becomes sensitive to script (letters or characters). However, some have claimed that the development of this area takes up (and thus detrimentally affects) space that is otherwise available for processing culturally relevant objects such as faces, houses or tools. … When we learn to read, we exploit the brain's capacity to form category-selective patches in visual brain areas. These arise in the same cortical territory as specialisations for other categories that are important to people, such as faces and houses. A long-standing question has been whether learning to read is detrimental to those other categories, given that there is limited space in the brain," explains Alexis Hervais-Adelman.

Reading-induced recycling did not detrimentally affect brain areas for faces, houses, or tools -- neither in location nor size. Strikingly, the brain activation for letters and faces was more similar in readers than in non-readers, particularly in the left hemisphere (the left ventral temporal lobe).

"Far from cannibalising the territory of its neighbours, the visual word form area (VWFA) is rather overlaid upon these, remaining responsive to other visual categories," explains Falk Huettig. "Thus learning to read is good for you," he concludes. "It sharpens visual brain responses beyond reading and has a general positive impact on your visual system."

Max Planck Institute for Psycholinguistics. (2019). Learning to read boosts the visual brain. ScienceDaily, 18 September 2019. www.sciencedaily.com/releases/2019/09/190918140743.htm

“Studies employing sophisticated brain imaging tools (e.g., functional magnetic resonance imaging, positron emission tomography, proton echo-planar spectroscopic imaging) have added to the knowledge about what actually occurs at the cellular level during successful intervention (Barquero, Davis, Cutting, 2014; Richards et al., 1999, 2000; Simos et al., 2007; Waldie, Haigh, Badzakova-Trajkov, & Kirk, 2013). It has been noted that struggling readers tend to have a significant amount of brain activity in Broca's area (an area important for speech) and also within the brain's right hemisphere (Waldie, Haigh, Badzakova-Trajkov, & Kirk, 2013). This is indicative of using less appropriate brain structures for the task – structures better suited to visualisation tasks. The consequence (Richards et al., 1999) is that the poorer readers may expend four to five times as much energy to complete a reading task when compared to good readers.

Facile readers display vigorous activity in both the left temporo-parietal and left temporo-occipital areas of the brain (Fletcher et al., 2000). This area enables the association of sounds to words and word parts – the phonological centre. The conversion of print to sound involves the angular gyrus (visual association) linking with the superior temporal gyrus (area for language). Pugh et al. (2002) asserted that the temporo-parietal region is initially crucial in integrating the phonological and orthographic features of text; whereas, the occipito-temporal system becomes important in enabling the effortless fluent word recognition in skilled readers. Subsequent brain research supports this view (Glezera et al., 2016). Some brain function differences are also evident in orally presented phonological tasks, prior to any contact with print, and eventually imaging (should it become simpler, quicker, and cheaper) may be employed as a means of predicting potential reading problems prior to instruction.

Importantly, when the struggling students were taught phonological processing skills (for example, over a 15 two-hour sessions), the brain energy expenditure levels and the locations of relevant brain activities came to resemble those of good readers (Richards et al., 2000). Lyon and Fletcher (2001) reported similar neuro-imaging changes when a 10-year-old student with severe reading disabilities was provided with 60 hours of intensive phonics instruction that also elevated his word-reading ability into the average range.”

Hempenstall, K. (2017). Is there an educational role for phonological processes other than phonemic awareness? https://www.nifdi.org/resources/hempenstall-blog/kerry-s-complete-list-of-blogs

Where is the reading activity occurring in the brain?

Horowitz-Kraus, T., & Hutton, J.S. (2015). From emergent literacy to reading: How learning to read changes a child’s brain. Acta Pædiatrica, 104, 648–656.

p. 274

Weiss, L. G., Saklofske, D. H., Holdnack, J. A., & Prifitera, A. (2015). WISC-V assessment and interpretation: Scientist-practitioner perspectives. San Diego, CA: Academic Press.

Apart from examining processes in the brain, there is also interest in differences in the physiology/structure of the brain. For example, might grey or white matter volume in the brain be related to reading?

“Studies have converged in their findings of relatively less gray matter volume (GMV) in developmental dyslexia in bilateral temporoparietal and left occipitotemporal cortical regions. However, the interpretation of these results has been difficult. The reported neuroanatomical differences in dyslexia may be causal to the reading problems, following from, for example, neural migration errors that occurred during early human development and before learning to read. Alternatively, less GMV may represent the consequence of an impoverished reading experience, akin to the experience-dependent GMV differences attributed to illiterate compared with literate adults. Most likely, a combination of these factors is driving these observations. Here we attempt to disambiguate these influences by using a reading level-matched design, where dyslexic children were contrasted not only with age-matched controls, but also with younger controls who read at the same level as the dyslexics. Consistent with previous reports, dyslexics showed less GMV in multiple left and right hemisphere regions, including left superior temporal sulcus when compared with age-matched controls. However, not all of these differences emerged when dyslexics were compared with controls matched on reading abilities, with only right precentral gyrus GMV surviving this second analysis. When similar analyses were performed for white matter volume, no regions emerged from both comparisons. These results indicate that the GMV differences in dyslexia reported here and in prior studies are in large part the outcome of experience (e.g., disordered reading experience) compared with controls, with only a fraction of the differences being driven by dyslexia per se.

Krafnik, A. J., Flowers, D. L., Luetje, M. M., Napoliello, E. M., & Eden, G. F. (2014). An investigation into the origin of anatomical differences in dyslexia. The Journal of Neuroscience, 34(3), 901-908.

“Neuroimaging studies of dyslexia have identified differences in structure and function that are associated with reading difficulty from childhood through adulthood. Although dyslexia is often diagnosed once reading difficulties become apparent around 7 or 8 years old, there is strong evidence that dyslexia is the consequence of differences in prereading abilities that are the building blocks of learning to read and in the brain regions that support those abilities. … Fewer studies have examined the effects of remediation on brain structure, but there is evidence of increases in gray matter volume or thickness and strengthened white matter connectivity as a result of remediation (Keller & Just, 2009; Krafnick et al., 2011; Romeo et al., 2017).” (p.798, 804)

D'Mello, A., & Gabrieli, J.D.E. (2018). Cognitive neuroscience of dyslexia. Language Speech and Hearing Services in Schools 49(4), 798-809. 10.1044/2018

“Neural specialization for reading is experientially driven and occurs through utilizing and repurposing distributed brain structures that support vision, audition, and language (Dehaene, 2009). The efficient integration across these spatially disparate brain regions is made possible by long-range white matter connections that form across development (Wandell, Rauschecker, & Yeatman, 2012). Three white matter tracts in particular have a documented association with reading and reading-related skills in adults and children as early as preschool.”

Ozernov-Palchik, O., Norton, E.S., Wang, Y., Beach, S.D., Zuk, J., Wolf, M., Gabrieli, J.D.E., & Gaab, N. (2018). The relationship between socioeconomic status and white matter microstructure in pre-reading children: A longitudinal investigation. Human Brain Mapping, 1–14. https://www.researchgate.net/publication/327944343_The_relationship_between_socioeconomic_status_and_white_matter_microstructure_in_pre-reading_children_A_longitudinal_investigation

The examples below show how neuroscience findings can be supportive of, rather than supplanting, previous basic educational research:

“Likewise, the data suggest that formation of memories through neural consolidation works best if students have a number of short learning sessions separated over time, not single long sessions. Again, the advantages of spaced or distributed practice over massed practice have also been known for many decades (see Olson & Hergenhahn, 2009; Ebbinghaus, 1913). Neuroscience, in this case, reinforced these best practices by providing the data at the neural level that supported these methods” (p.50).

Alferink, L.A., & Farmer-Dougan, V. (2010): Brain-(not) based education: Dangers of misunderstanding and misapplication of neuroscience research. Exceptionality, 18(1), 42-52.

“It should be clear that I am advocating here a strong ‘phonics’ approach to teaching, and against a whole-word or whole-language approach. Several converging elements support this conclusion (for a longer development, see Dehaene, 2009). First, analysis of how reading operates at the brain level provides no support for the notion that words are recognized globally by their overall shape or contour. Rather, letters and groups of letters such as bigrams and morphemes are the units of recognition. Second, experiments with adults taught to read the same novel script with a whole-word versus grapheme-phoneme approach show dramatic differences (Yoncheva, Blau, et al., 2010): only the grapheme-phoneme group generalizes to novel word and trains the left-hemispheric VWFA. Adults whose attention was drawn to the global shape of words, by whole-word training, showed brain changes in the homolog region of the right hemisphere, clearly not the normal circuit for expert reading.” (p.28)

Dehaene, S (2011). The massive impact of literacy on the brain and its consequences for education. Human neuroplasticity and education. Pontifical Academy of Sciences, 117, 19-32.

“It simply is not true that there are hundreds of ways to learn to read […] when it comes to reading we all have roughly the same brain that imposes the same constraints and the same learning sequence” (p. 218).

“We now know that the whole-language approach is inefficient; all children regardless of their socioeconomic backgrounds benefit from explicit and early teaching of the correspondences between letters and speech sounds. This is a well-established fact, corroborated by a great many classroom experiments. Furthermore, it is coherent with our present understanding of how the reader’s brain works” (p. 326).

“Every child is unique…but when it comes to reading, all have roughly the same brain that imposes the same constraints and the same learning sequence. Thus we cannot avoid a careful examination of the conclusions – not prescriptions – that cognitive neuroscience can bring to the field of education” (p. 218).

Dehaene, S. (2009). Reading in the brain: The science and evolution of a human invention. New York: Viking/Penguin.

“Neurocognitive processes: Interleaving topics can increase the efficiency with which learned material is remembered and also the effectiveness of some other learning processes. Interleaving may operate by reducing the suppression of neural activity in memory regions that occurs when similar stimuli are repeatedly presented.” (p. 31)

Howard-Jones, P. (2014). Neuroscience and education: A review of educational interventions and approaches informed by neuroscience. London: Education Endowment Foundation (p. 1-62). https://educationendowmentfoundation.org.uk/public/files/Publications/EEF_Lit_Review_NeuroscienceAndEducation.pdf

Sometimes the neuroscience research can explain phenomena previously noted, but sometimes unexplained or even misunderstood, in conventional educational research. For example:

“Recently, our growing understanding of how the brain is recycled for reading has led to a clarification of another mysterious phenomenon that occurs during childhood: mirror reading and mirror writing. Many young readers confuse mirror letters such as p and q or b and d. Furthermore, they occasionally write in mirror form, from right to left, quite competently and without seemingly noticing their error. This peculiar behavior can be explained by considering that the function of the ventral visual cortex, prior to reading, is the invariant recognition of objects, faces and scenes. In the natural world, very few objects have a distinct identity for left and right views. In most cases, the left and right views of a natural object are mirror images of each other, and it is useful to generalize across them and treat them as the same object. Single-cell recordings in monkeys show that this principle is deeply embedded in the visual system: many neurons in the occipito- temporal visual cortex fire identically to the left and right views of the same object or face (Freiwald & Tsao, 2010; Logothetis, Pauls, & Poggio, 1995; Rollenhagen & Olson, 2000).

Using neuroimaging, my colleagues and I have shown that, in the human brain, it is precisely the VWFA which is the dominant site for this mirror-image invariance (Dehaene, Nakamura, et al., 2010; Pegado, Nakamura, Cohen, & Dehaene, 2011). No wonder, then, that young children confuse b and d: they are trying to learn to read with precisely the brain area that confuses left and right of images! Mirror confusion is a normal property of the visual system, which is seen in all children and illiterate subjects, and which disappears for letters and geometric symbols when literacy sets in (Cornell, 1985; Kolinsky, et al., 2010). Only its prolongation in late childhood is a sign of dyslexia (Lachmann & van Leeuwen, 2007; Schneps, Rose, & Fischer, 2007). Teachers should therefore be aware of the specific difficulty posed by mirror letters, and should take the time to explain why b and d are distinct letters corresponding to distinct phonemes (it is particularly unfortunate that these phonemes are quite similar and easily confused). Interestingly, teaching the gestures of writing can improve reading, perhaps because it helps store view-specific memories of the letters and their corresponding phonemes (Fredembach, de Boisferon, & Gentaz, 2009; Gentaz, Colé, & Bara, 2003).” (p.27-28)

Dehaene, S. (2011). The massive impact of literacy on the brain and its consequences for education. Human neuroplasticity and education. Pontifical Academy of Sciences, 117, 19-32.

“We “hear” written words in our head. Sound may have been the original vehicle for language, but writing allows us to create and understand words without it. Yet new research shows that sound remains a critical element of reading. When people listen to speech, neural activity is correlated with each word's “sound envelope”—the fluctuation of the audio signal over time corresponds to the fluctuation of neural activity over time. In the new study, Lorenzo Magrassi, a neurosurgeon at the University of Pavia in Italy, and his colleagues made electrocorticographic (ECoG) recordings from 16 individuals. The researchers measured neural activity directly from the surface of the language-generating structure known as Broca's area as subjects read text silently or aloud. (This measurement was made possible by the fact that participants were undergoing brain surgery while awake.). Their neural activity was correlated with the sound envelope of the text they read, which was generated well before they spoke and even when they were not planning to speak, according to the report published in February in the Proceedings of the National Academy of Sciences USA. In other words, Broca's area responded to silent reading much in the same way auditory neurons respond to text spoken aloud—as if Broca's area was generating the sound of the words so the readers heard them internally. The finding speaks to a debate about whether words are encoded in the brain by a neural pattern symbolic of their meaning or if they are encoded via simpler attributes, such as how they sound. The results add to mounting evidence that words are fundamentally processed and catalogued by their basic sounds and shapes.

Sutherland, S. (2015). When we read, we recognize words as pictures and hear them spoken aloud. Scientific American: Mind, July 1, 2015. Retrieved from http://www.scientificamerican.com/article/when-we-read-we-recognize-words-as-pictures-and-hear-them-spoken-aloud/

Original study by Glezer, L.S., Kim, J., Rule, J., Jiang, X., & Riesenhuber, M. (2015). Adding words to the brain's visual dictionary: Novel word learning selectively sharpens orthographic representations in the VWFA. Journal of Neuroscience. 35(12), 4965-72.

“Three complementary sources of evidence suggest that words are the units of reading. First, eye movements during fluent reading are made mostly by making saccades from one word to the next. Second, the reading time of a single word is relatively independent of the number of letters. Third, a single letter may be more easily detected in brief presentations when embedded in a word. A possible inference of these findings is that education should be organized to teach children to read entire words instead of focusing in letter-by-letter identification. This procedure, usually termed holistic reading, led to concrete implementations that turned out to be a major pedagogical fiasco. As it turns out, the neuroscience of visual learning could actually have predicted this failure. The development of literacy is a case of pop-out learning, a process by which, after extensive practice, one can identify a specific set of shapes in cluttered fields very rapidly and with a subjective feeling of automaticity and lack of effort. For non-readers, reading is a slow, effortful and serial process that becomes automatic after many hours of training.”

Sigman, M., Peña, M., Goldin, A.P., & Ribeiro, S. (2014). Neuroscience and education: Prime time to build the bridge. Nature Neuroscience, 17(4), 497-502.

“We know that the activity and organization of the brain changes in response to experience. Memories and learning are reflected in the number and strength of connections between nerve cells. We also know that the brain is genetically mosaic, but a new study makes a remarkable connection between experience and the genetic diversity of the brain. It suggests that experience can change the DNA sequence of the genome contained in brain cells. This is a fundamentally new and unexplored way in which experience can alter the brain. It is of great scientific interest because it reveals the brain to be pliable, to its genetic core, in response to the world. … Linking early experience to the genomic variability of nerves suggests that early experience leaves an irreversible genomic imprint in the brain. This is an intriguing new twist on a debate that has been raging for centuries concerning the importance of nature versus nurture in behavior. This study implies that nature and nurture are not as independent as may have been imagined, and that nature is not as immutable as once thought.”

Martone, R. (2018). Early life experience: It’s in your DNA. Scientific American, July 10. https://www.scientificamerican.com/article/early-life-experience-its-in-your-dna/

“Our results show that adults with dyslexia are slightly right lateralized overall for language, a profile that differed significantly from the left-lateralized activation observed in typical readers. Though there was also left hemisphere activation observed during reading tasks in the dyslexic participants, the right hemisphere activity was more diverse and primarily occurring in OT regions during pseudoword reading. Right hemisphere compensation in dyslexia may increase as phonological demands increase

Our findings are consistent with earlier work with dyslexic children, suggesting that the activation in the right hemisphere is likely to be a cause rather than a consequence of reading impairment. Right hemisphere findings should be given more consideration in the literature, particularly as they may have important implications for early intervention, reading remediation and theories of neural plasticity. In 2003 Elise Temple and colleagues showed that auditory processing and oral language training can activate the left posterior reading network in reading disabled children but produces additional compensatory activation in other brain regions [60].

Our findings tentatively support the possibility that right OT compensation might also respond to intensive phonics/phonological processing training. Future designs would need to correlate behavioural measures of reading fluency/accuracy with the right compensatory activity throughout the remediation process to determine how best to accomplish this and whether the right hemisphere participation is helping or hindering the remediation. Such calculations might also address the possibility that the right hemisphere activity is inhibitory rather than compensatory as traditionally assumed.

It is still an open question whether right hemisphere activation acts in a compensatory or inhibitory role during single word reading in impaired readers. Taken together, in addition to an impaired left hemisphere posterior network, right posterior overactivity may be an important biological marker of dyslexia if our results are replicated. Dyslexic adults appear to compensate for their reading impairment by an increased recruitment of these areas to assist with visual coding. The possibility that the right hemisphere neural mechanisms are inhibitory rather than compensatory should be investigated in further studies.” (p.1071)

Waldie, K.E., Haigh, C.E., Badzakova-Trajkov, G., Buckley, J., & Kirk, I.J. (2013). Reading the wrong way with the right hemisphere. Brain Sciences, 3(3), 1060-1075. https://doi.org/10.3390/brainsci3031060

Earlier prediction of reading difficulties?

“But there is evidence that structural differences in the brains of children who will later have trouble learning to read are present before reading onset. (Raschle, Chang & Gaab, 2011; Raschle, Zuk & Gaab, 2012). dyslexia has a neural basis present before reading instruction begins, might you be able to identify children who will very likely have significant trouble with reading before instruction ever begins?

A number of laboratories have been working on this problem, and progress is being made. These researchers are not looking to toss out behavioral measures--they are looking to supplement them. The more successful of these efforts (e.g., Hoeft et al., 2007) show that behavioral measures predict reading problems, neuroscientific measures predict reading problems, and using both types of data provides better prediction than either measure alone. In other words, the neuroscientific data is capturing information not captured by the behavioral measures, and vice versa.”

Willingham, D. (2012). Neuroscience & Education:5 Days, 5 Ways. Day 5: Predicting Trouble. http://www.danielwillingham.com/daniel-willingham-science-and-education-blog/neurosci-educ-5-days-5-ways-day-5-predicting-trouble

“A clinical and educational goal of reading research is to improve the accuracy with which children at risk for dyslexia are identified so that they can receive early, preventive intervention rather than intervention that follows years of reading failure (Strickland, 2002). Although behavioral measures of phonological awareness, RAN, and letter knowledge in kindergartners predict reading ability years later (Catts et al., 2001; Schatschneider et al., 2004), the sensitivity and specificity of these behavioral measures is modest (Pennington and Lefly, 2001). There is some evidence that brain measures substantially enhance the accuracy of predicting reading ability across a school year (Hoeft et al., 2007; Rezaie et al., 2011) or across multiple years (Maurer et al., 2009; Hoeft et al., 2011). The present study indicates that DWI measures of white matter organization reveal a specific structural risk factor for reading difficulty that, in combination with behavioral and other brain measures, may improve the identification of prereaders at risk for dyslexia” (p.13256).

Saygin, Z.M., Norton, E.S., Osher, D.E., Beach, S.D., Cyr, A.B., Ozernov-Palchik, O., Yendiki, A., Fischl, B., Gaab, N., & Gabrieli, J.D.E. (2013). Tracking the roots of reading ability: White matter volume and integrity correlate with phonological awareness in prereading and early-reading kindergarten children. The Journal of Neuroscience 33(33), 13251-13258. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3742917/

“There is a considerable debate about how a deepening understanding of the brain basis of dyslexia may or may not be useful in light of current educational practices and policies (Bowers, 2016; Gabrieli, 2016). Although neuroimaging studies have revealed that dyslexia reflects functional and structural brain differences that diverge from typical brain development starting in infancy, reading instruction is not targeted at particular neural systems. Rather, instruction targets behavioral abilities, such as phonological awareness as a component of single-word decoding. One area in which neuroimaging studies may contribute particular educational value is in the prediction of response to instruction that would promote personalized or individualized instruction (Gabrieli, Ghosh, & Whitfield-Gabrieli, 2015). In one study of children with dyslexia around the age of 14 years, none of 17 conventional tests of reading and reading-related abilities predicted which particular children would or would not show gains in reading over the next 2.5 years (Hoeft et al., 2011). Neuroimaging methods, however, could predict with considerable accuracy which individual child would or would not make gains in reading over that same period (Hoeft et al., 2011). Similarly, brain measures in kindergarteners correlated better with reading level than did behavioral measures of those same children in fifth grade (Maurer et al., 2009). These studies suggest that brain differences among children may make them more or less likely to benefit from particular kinds of instruction.” (p. 805)

D'Mello, A., & Gabrieli, J.D.E. (2018). Cognitive neuroscience of dyslexia. Language Speech and Hearing Services in Schools 49(4), 798-809. 10.1044/2018

“Challenges to identification for both younger and older children may be best met with frameworks that recognize the multifactorial casual basis of reading problems (Pennington et al., 2012). Newer models of identification that combine across multiple indicators of risk derived from current skill, and that augment these indicators with other metrics of potential risk, may yield improved identification and interventions (e.g., Erbeli et al., 2018; Spencer et al., 2011). In particular, future research will need to consider and combine, while considering both additive and interactive effects, a wide array of measures, which may include genetic, neurological, and biopsychosocial indicators (Wagner et al., 2019). Furthermore, more evaluation is needed of some new models of identification that integrate both risk and protective, or resiliency, factors, to see if these models increase the likelihood of correctly identifying those children most in need of additional instructional support (e.g., Catts & Petscher, 2020; Haft et al., 2016). Even if beneficial, it is likely that for early identification to be maximally effective, early risk assessments will need to be combined with progress monitoring of response to instruction (Miciak & Fletcher, 2020).” (p. 12)

Petscher, Y., Cabell, S. Q., Catts, H. W., Compton, D. L., Foorman, B. R., Hart, S. A., Lonigan, C. J., Phillips, B. M., Schatschneider, C., Steacy, L. M., Terry, N. P., & Wagner, R. K. (2020). How the science of reading informs 21st-century education. Reading Research Quarterly, 55 (Suppl 1), S267–S282. https://doi.org/10.1002/rrq.352

“Longitudinal studies tracking brain development with child-friendly neuroimaging techniques during the first years of reading acquisition are critical to characterize variations in the developmental trajectories of brain networks and to relate such variations to children’s reading predisposition and attained reading level. Neuroscientific studies of reading hold the promise of identifying and characterizing early risk factors that cannot be detected by cognitive assessments in pre-reading stages4. To advance the field of dyslexia research and clinical practice, studies recruiting pre-readers or beginning readers and tracking them until the age when dyslexia is diagnosed are especially valuable. Such longitudinal designs enable observation of neuronal alterations at or before reading onset, not affected by the limited reading experience, to reveal early markers of dyslexia. Only then is it possible to disentangle causes from consequences of dyslexia and their neurobiological basis5. Compared with a cross-sectional design, which currently dominates in the field, the longitudinal approach reduces the confounding effect of between-subject variability6, enables assessment of the predictive value of different measures, and reveals how specific neural changes are related to age and changes in reading performance7.” (p. 1)

Chyl, K., Fraga-González, G., Brem, S. et al. (2021). Brain dynamics of (a)typical reading development—a review of longitudinal studies. npj Sci. Learn. 6(4), https://doi.org/10.1038/s41539-020-00081-5

Perhaps further benefits may accrue when interdisciplinary research becomes more common.

“An integrative approach not only allows educational and psychological protocols to be designed with a view to the neurophysiological variables of interest, but also allows neuroscientific experiments to be designed in the light of relevant psychological and educational behavioural parameters. For example, the psychological spacing effect, described above, may not only help to design educational programs to enhance rates of classroom learning but may also help to inform the design of experiments investigating the role of LTP in the brain. Another example is the testing effect, a learning phenomenon derived from educational research, in which memory retention is enhanced by multiple testing sessions during learning (Karpicke & Roediger, 2008; Rawson & Dunlosky, 2012; Roediger & Butler, 2011). The testing effect has a striking parallel in the psychological and neurophysiological phenomenon of reconsolidation, in which associative memory can be enhanced (or degraded) by the unpredictable presentation of a cue or conditioned stimulus without reinforcement, which appears to return the memory trace to a labile state (Lee, 2008; Pedreira, Pe´rez-Cuesta, & Maldonado, 2004). Optimizing the testing effect in the classroom could depend on a better understanding of the neural reconsolidation process occurring as a result of repeated testing; on the other hand, understanding more accurately the conditions and timing that produce the behavioural testing effect may help to design experiments that reveal more detail about reconsolidation in the brain.” (p.151)

Morris, J., & Sah, P. (2016). Neuroscience and education: Mind the gap. Australian Journal of Education, 60(2), 146–156.

“The overall goal of brain-based education is to attempt to bring insights from brain research into the arena of education to enhance teaching and learning. The area of science often referred to as brain research typically includes neuroscience studies that probe the patterns of cellular development in various brain areas; and brain imaging techniques, with the latter including functional MRI (fMRI) scans and positron-emission tomography (PET) scans that allow scientists to examine patterns of activity in the awake, thinking, human brain. These brain imaging techniques allow scientists to examine activity within various areas of the brain as a person engages in mental actions such as attending, learning, and remembering. Proponents of brain-based education espouse a diverse group of educational practices and approaches, and they generally attempt to ground claims about effective practice in recently discovered facts about the human brain. They argue that there has been an unprecedented explosion of new findings related to the development and organization of the human brain and that the current state of this work can inform educational practice in meaningful ways… . Other brain-based education literature that makes closer ties with brain research focuses on brain imaging of particular learning disabilities. Sousa wrote "we are gaining a deeper understanding of learning disabilities, such as autism and dyslexia. Scanning technology is revealing which parts of the brain are involved in these problems, giving hope that new therapies … will stimulate their brains and help them learn" (p. 54). Such direct ties between investigations of brain mechanisms associated with learning problems and intervention attempts provide a promising direction for brain-based educational research. Understanding how brain mechanisms of basic visual and language processes work together in typically and atypically developing readers is of central interest to many brain scientists and educators. Several studies centered on these issues were underway in fMRI laboratories in the early twenty-first century, with many of the studies involving brain scans collected before a particular educational intervention. Such direct interplay between educational intervention and brain-based measurements provides a means of assessing the degree to which a particular educational program impacts brain mechanisms associated with learning within a particular domain, such as reading. … Perhaps by explicitly combining evidence-based investigations of specific educational practices with brain imaging and psychological studies of learning, future research might take a step closer toward the goals of brain-based education and provide empirically validated contributions to enhancing education based on scientific insights into learning.”

McCandliss, B. (2021). Brain-based education: Summary principles of brain-based research, critiques of brain-based education. https://education.stateuniversity.com/pages/1799/Brain-Based-Education.html

"So I remain skeptical about the implications of neuroscience for education currently and into the near future. Maybe I should say the direct implications of neuroscience for education. I do believe that eventually we will be able to bridge neuroscience at its various levels of analysis with education, but I am convinced that all of these bridges will have a least one pier on the island of psychology.”

Bruer, J.T. (2006). Points of view: On the implications of neuroscience research for science teaching and learning: Are there any? CBE Life Science Education, 5(2), 111-7. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1618519/

“Scholarly treatments have been positive about the prospects, but more sober, and most have taken a position that is broadly consistent with ours. They argue that neuroscience has been and will continue to be helpful to education — indeed, recent reviews show beyond doubt that this is true (e.g., Katzir & Paré-Blagoev, 2006 ) — but they argue that data from neuroscience must be funnelled through a behavioral level of analysis (e.g., Bruer, 1997, 1998; Hirsh-Pasek & Bruer, 2007) or that neuroscience should be part of a broader approach to research in education, not the sole saviour (e.g., Ansari & Coch, 2006; Byrnes & Fox, 1998; Fischer et al., 2007; Geake & Cooper, 2003 ) (p. 147).

Willingham, D.T., & Lloyd, J.W. (2007). How educational theories can use neuroscientific data. Mind, Brain, and Education, 1(3), 140-149. http://www.danielwillingham.com/uploads/5/0/0/7/5007325/willingham__lloyd_2007.pdf

“Over the past decade, researchers have identified lacking professional development on dyslexia as stemming from “The Peter Effect” (i.e., “One cannot teach what they do not know”, Applegate and Applegate, 2004, as cited in Binks-Cantrell et al., 2012). Reportedly, the dyslexia myth is prevalent among higher education instructors at similar rates as preservice and in-service educators (Betts et al., 2019). Research on improving school professionals’ dyslexia knowledge has identified this pervasive misconception among pre-service and school professionals (Washburn et al., 2011, Washburn et al., 2014, Washburn et al., 2017; White et al., 2020). White et al. (2020) found no significant differences in dyslexia knowledge within education majors (e.g., elementary v. special education v. school psychology), or between education and non-education majors. This research underscores the expressed need for explicit, intensive professional development to address the persistent misconception of dyslexia among teacher candidates and school professionals. The question remains in how to change the persistent misconception of dyslexia, as the effect of knowledge transmission approaches remains an open question since “debunking” messages are not always effective in countering misinformation (Chan et al., 2017).

Researchers have identified this gap in school professional development, calling for a neuroscience primer that addresses the neurobiology of dyslexia (Anderson et al., 2020; Kearns et al., 2019). Neuroscience in education research has the potential to address this need. For example, the neuroscience concept of synaptic plasticity establishes why individuals with dyslexia require intensive practice to learn to read since axonal pathways differ from typically developing readers and individuals with dyslexia require more synapses trained to successfully associate phonemes with graphemes, to recognize words, and to associate meanings with words (Gabrieli, 2009; Klingberg et al., 2000).

While the neuroscience of reading has been identified as candidate knowledge for filling the conceptual gap in teacher-education related to dyslexia (e.g., Kearns et al., 2019; Seidenberg, 2013), little research exists on teacher education programs or standalone professional development models that provide such training. A few research studies have been aimed at improving school professionals’ knowledge of dyslexia through educational neuroscience training programs or interventions (Anderson et al., 2020). A recent study using conceptual change theory found that preservice teachers’ dyslexia knowledge could improve through reading refutation text as compared to control text on dyslexia (Peltier et al., 2020); however, it is unknown whether the researchers grounded their text explanations in the neuroscience of dyslexia.” (p. 317)

Alida, A. (2021). Advancing school professionals’ dyslexia knowledge through neuroscience: Bridging the science-education gap through developmental psychology. Frontiers in Education, 5, 316- 320. https://www.frontiersin.org/article/10.3389/feduc.2020.615791

A final topic- Neuromyths: A little knowledge … !

“Neuromyth is not a new concept. The word was first coined during the 1980s when the neurosurgeon Alan Crockard used it to describe a misleading concept about the brain function in the discipline of medicine (Howard-Jones, 2014; Fuentes and Risso, 2015). From an educational approach, a neuromyth was described as “a misconception generated by a misunderstanding, a misreading, or a misquoting of facts scientifically established (by brain research) to make a case for the use of brain research in education and other contexts” (OECD, 2002). Since that definition appeared, previous studies have emphasized the widespread presence of the neuromyths and their persistence, especially among individuals in contact with education (Howard-Jones et al., 2009; Dekker et al., 2012; Howard-Jones, 2014; Ferrero et al., 2016; Düvel et al., 2017; among others).

In 2002, the UK's OECD launched the Brain and Learning project (Howard-Jones, 2014), and Herculano-Houzel (2002) published the first survey about knowledge of the brain. She included 95 multiple-choice assertions, 83 related to the information that the general public has about brain research (Herculano-Houzel, 2002) and several neuromyths.

Five years later, the OECD wrote about the proliferation of the neuromyths around (a) critical periods, (b) the age of three as the time when everything important is decided, (c) multilingualism, (d) left vs. right brain people, and (e) the 10% of the use of our brain, as the most widely spread neuromyths. Most neuromyths are built in the base of a kernel of truth (Grospietsch and Mayer, 2018, 2019), i.e., valid scientific findings support them (Dekker et al., 2012), but they were adulterated because of misinterpretations, oversimplifications (Howard-Jones, 2014), and even due to a flawed interpretation of scientific results (Pasquinelli, 2012; Howard-Jones, 2014).

Research has provided evidence against neuromyths. As an example, neuroimaging research has demonstrated that both hemispheres are responsible for most of the procedures and are in constant communication, even though they differ in their functions (Ansari, 2008), which runs counter to myths such as left vs. right brain people, or multiple intelligences (Geake, 2008).

The myth about only 10% of brain use seems to be the most enduring neuromyth. It has survived more than a century. In 1907, Williams James wrote about the idea that humans used mental and physical resources below their means (James, 1907). Later, physicist Albert Einstein in a radio interview in 1920 (Pallarés-Domínguez, 2016), encouraged people to think more (Geake, 2008; Dündar and Gündüz, 2016; Papadatou-Pastou et al., 2017). He invited people to enhance their possibilities, using more than 10% of their brain, but he did not intend to spread such a colossal misunderstanding. However, as reported in the previous literature, not only can excellent scientific data be behind a neuromyth but also a neuroanatomical fact. The glia-neuron rate (or white matter-gray matter) which is one for every ten (Pasquinelli, 2012) may be responsible for the myth that claims that humans only use the 10% of their brain (because of the aforementioned rates). Scientific research shows how improbable this assertion may be, just taking into consideration that no one single brain area is 100% “out of work,” even when sleeping (Centre for Educational Research and Innovation and OECD, 2007).

Closely related to education, we can find the neuromyth of the visual, auditory, and kinaesthetic (VAK) learning styles. Under this approach, every child has a dominant learning style, which should be identified to teach each of them more precisely and create lesson plans according to their preferences (Geake, 2008; Macdonald et al., 2017). In this case, the kernel of truth is found in an oversimplification (Ansari, 2008) of fundamental research that has identified different parts of the brain that process visual, auditory, or kinaesthetic information (Dekker et al., 2012), i.e., different regions of the cortex have specific roles in sensory processing (Howard-Jones, 2014). Lack of evidence in VAK/learning styles has been successfully established (Pashler et al., 2008; Riener and Willingham, 2010; Willingham et al., 2015). Nevertheless, it is one of the most deeply rooted and widely believed neuromyths (Rodrigues Rato et al., 2013; Deligiannidi and Howard-Jones, 2015; Papadatou-Pastou et al., 2017, 2018; Varas-Genestier and Ferreira, 2017; Zhang et al., 2019). This misconception is widely considered a fact, even more than that of the hemispheric preference (Tardif et al., 2015). Teachers report having been taught about VAK/learning styles during training courses organized by their schools or the educational authorities of their governments (Lethaby and Harries, 2016; Kim and Sankey, 2017; McMahon et al., 2019). Moreover, some teachers insist they intend to continue working under the VAK perspective in their classrooms, even knowing that it is a neuromyth (Newton and Miah, 2017; Tan and Amiel, 2019).” (p. 2)

The distance between neuroscience and education is still too great. We have found reasons for the lack of knowledge among educators about science and the brain. Additionally, they have difficulties in accessing to the latest findings due to the absence of scientific literature in their mother tongue or the weakness of science communication. … First, we have to solve the methodological drawbacks in the research on neuromyths. Future research is needed to define rigorous guidelines to identify a new neuromyth or debunk another. Undoubtedly, this guideline has to be built on the basis of academic criteria and science. This work has revealed the urgency of finding new ways to survey teachers about their perceptions, their cognitive bias, and their sincere beliefs. Moreover, access to knowledge could avoid widening the gap between neuroscience and education as a result of cultural conditions (Hermida et al., 2016). (p. 15)

Torrijos-Muelas, M., González-Víllora, S., & Bodoque-Osma, A.R. (2021). The persistence of neuromyths in the educational settings: A systematic review. Frontiers in Psychology, 11, article 591923.

“Alongside this craze for all things brain-based, or ‘neuro’, a smaller movement has arisen, of desperate evidence-based psychologists and educators, seeking to temper enthusiasm with reality and to dispel some of the nonsense spouted by the ‘brainiacs’, also known as ‘neuromyths’. (A less polite term that you might also encounter online is ‘neurobollocks’.) Like zombies, however, neuromyths are extremely hardy and merely providing contrary empirical evidence is rarely sufficient to kill them off. They might pause, briefly, but then they keep on coming. And they breed …

The extent of this problem is revealed in a recent article by Dekker, Lee, Howard- Jones and Jolles, published in Frontiers in Psychology (http://tinyurl.com/8wsjczw) which reports the results of a survey of 242 teachers conducted in the UK and the Netherlands. Over 90% expressed interest in ‘scientific knowledge about the brain’ and 90% were of the view that such knowledge would positively inform their teaching practice. The teachers responded to an online survey that mixed a selection of neuromyths with true statements about the brain. In addition to the collection of background information (about age, sex, level of education etc), they were also asked about their degree of interest in scientific knowledge about the brain and its influence on their teaching, any ‘brain-based’ methods they had encountered in their school, and whether they read popular science magazines or journals, among other questions.

Over 50% of the teachers indicated that they believed in seven of the 15 neuromyths included in the questionnaire. Over 80% expressed belief in the following: “Individuals learn better when they receive information in their preferred learning style (e.g., auditory, visual, kinesthetic)”; “Differences in hemispheric dominance (left brain, right brain) can help explain individual differences amongst learners”; and “Short bouts of co-ordination exercises can improve integration of left and right hemispheric brain function”. Over 80% of the British teachers had encountered Brain Gym (specifically) and (learning styles) generally (98%) in their schools.

So far, so bad; but it gets worse, much worse. When the researchers examined the results in more detail, they found that teachers who actually knew more about the brain tended to believe in more neuromyths. Yes, that’s right; the more they knew about the brain, the more neurobollocks they believed! As the authors put it: “These findings suggest that teachers who are enthusiastic about the possible application of neuroscience findings in the classroom find it difficult to distinguish pseudoscience from scientific facts. Possessing greater general knowledge about the brain does not appear to protect teachers from believing in neuromyths.”

Wheldall, K. (2012). Neuromyths: A little learning is a dangerous thing. Notes from Harefield. http://www.kevinwheldall.com/2012/10/neuromyths-little-learning-is-dangerous_26.html

There has long been concern about a gap between teacher classroom practice in children’s literacy development and the research findings about what works. A parallel concern is now being expressed about the proliferation of neuromyths within education. It appears that too high a proportion of teachers and education faculties subscribe to untenable beliefs and practices based upon these myths:

“Dyslexia’s most persistent misconception may be related to misunderstandings about the brain and learning known as “neuromyths” (Lilienfeld et al., 2010). Over the past decade, research has identified educators’ misconceptions about the brain and learning, focusing on how these myths arise and why they persist (Howard-Jones, 2014). For example, Macdonald et al. (2017) found a clustering of “classic” neuromyths (items related to learning styles, dyslexia, the Mozart effect, the impact of sugar on attention, right-brain/left-brain learners, and using 10% of the brain), such that the dyslexia myth was often endorsed by the same individuals who endorsed other neuromyths. This clustering of misconceptions raises the question of whether addressing these misconceptions through neuroscience may address the dyslexia myth, among other brain-behavior misunderstandings. Seidenberg’s (2013) two-culture hypothesis for the research to practice gap in reading is similar to Howard-Jones’ (2014), who contends the most persistent neuromyths endorsed across PK12 through higher education are due to “cultural distance” between neuroscience and education, tracing persistent myths about the brain and learning as germinating from “seeds of confusion”, “cultural conditions”, and biased distortions of scientific data (pp. 817–819). Pasquinelli (2012) identifies three processes about neuromyths’ origins as 1) distortions of scientific facts, 2) obsolete offspring of scientific hypotheses, or 3) outgrowths from misinterpretations of experimental results. In the case of the dyslexia myth, its origins can be found in obsolete ideas stemming from previously held scientific hypotheses, which have been debunked by 40 years of reading research. Unfortunately, approaches that bridge reading research and education fields featuring updated models of dyslexia with prominent contributions from neuroscience are not typically accessible to preservice educators or school professionals (Anderson et al., 2020; Riley, 2020).” (p. 317)

Alida, A. (2021). Advancing school professionals’ dyslexia knowledge through neuroscience: Bridging the science-education gap through developmental psychology. Frontiers in Education, 5, 316- 320. https://www.frontiersin.org/article/10.3389/feduc.2020.615791

“Numerous empirical studies5 reveal that even though pre-service and in-service teachers as well as university instructors exhibit great interest in neuroscience, they are unable to differentiate neuromyths from “neurofacts”6 (Grospietsch and Mayer, 2020). Studies demonstrating endorsement of neuromyths among in-service teachers have been conducted in England (Dekker et al., 2012; Simmonds, 2014; Horvath et al., 2018), the Netherlands (Dekker et al., 2012), Switzerland (Tardif et al., 2015), Italy (Tovazzi et al., 2020), Spain (Ferrero et al., 2016), Portugal (Rato et al., 2013), Greece (Deligiannidi and Howard-Jones, 2015), Turkey (Karakus et al., 2015), Morocco (Janati Idrissi et al., 2020), China (Pei et al., 2015), Australia (Bellert and Graham, 2013; Horvath et al., 2018), Canada (Lethaby and Harries, 2016; Blanchette Sarrasin et al., 2019), United States (Lethaby and Harries, 2016; Macdonald et al., 2017; Horvath et al., 2018; van Dijk and Lane, 2018) and Latin America (Herculano-Houzel, 2002; Bartoszeck and Bartoszeck, 2012; Gleichgerrcht et al., 2015; Hermida et al., 2016; Varas-Genestier and Ferreira, 2017; Bissessar and Youssef, 2021). Studies demonstrating endorsement of neuromyths among pre-service teachers have been conducted in England (Howard-Jones et al., 2009; McMahon et al., 2019), Germany (Düvel et al., 2017; Grospietsch and Mayer, 2018; 2019), Switzerland (Tardif et al., 2015), Austria (Krammer et al., 2019; 2020), Slovenia (Škraban et al., 2018); Spain (Fuentes and Risso, 2015), Greece (Papadatou-Pastou et al., 2017), Turkey (Dündar and Gündüz, 2016; Canbulat and Kiriktas, 2017), South Korea (Im et al., 2018), Australia (Kim and Sankey, 2017), United States (Ruhaak and Cook, 2018; van Dijk and Lane, 2018) and Latin America (Herculano-Houzel, 2002; Falquez Torres and Ocampo Alvarado, 2018). The majority of such studies focus on pre-service and in-service teachers across all subjects and school types. Their findings consistently show that pre-service and in-service teachers endorse a large number of neuromyths, despite some (country-specific7) differences in the endorsement of certain individual myths (Grospietsch and Mayer, 2020). The hypothesis that cultural differences between countries influence which neuromyths gain currency where has taken hold in the research discourse (e.g., Pei et al., 2015; Ferrero et al., 2016; Hermida et al., 2016), even though this has not yet been systematically tested.

A few studies on neuromyths investigate specific groups such as post-graduate teacher trainees (Howard-Jones et al., 2009), pre-service special education teachers (Ruhaak and Cook, 2018), school principals (Zhang et al., 2019), or pre-service music (Düvel et al., 2017) and biology teachers (Grospietsch and Mayer, 2018; Grospietsch and Mayer, 2019). Comparisons of different groups are undertaken by Canbulat and Kiriktas (2017), Dündar and Gündüz (2016), Düvel et al. (2017), Gleichgerrcht et al. (2015), Herculano-Houzel (2002), Horvath et al. (2018), Macdonald et al. (2017), Simmonds (2014), Tardif et al. (2015) and van Dijk and Lane (2018). Macdonald et al. (2017) show that members of the general public endorse neuromyths more frequently than educators and persons with high neuroscience exposure. Herculano-Houzel (2002) likewise identifies a significant difference between the general public and neuroscientists. Her study finds differences between high school respondents, college respondents, graduate respondents, psychology students and neuroscientists (listed in order of decreasing endorsement of neuromyths). According to Gleichgerrcht et al. (2015) and van Dijk and Lane (2018), university professors and instructors in the field of teacher education exhibit slightly lower endorsement of neuromyths compared to (pre-service) teachers. In a study by Canbulat and Kiriktas (2017), in-service teachers endorse neuromyths slightly less frequently than pre-service teachers. These findings contradict those by Tardif et al. (2015), who found stronger endorsement of many neuromyths among in-service teachers. Zhang et al. (2019) and Horvath et al. (2018) demonstrate that even school principals and award-winning teachers endorse neuromyths with a high frequency. With the exception of the aforementioned differences, empirical findings on the prevalence of neuromyths can be considered quite consistent: Neuromyths are not sufficiently disavowed – particularly among teachers and university instructors, who are frequently assumed to be professionals in teaching and learning. Endorsement of the neuromyths on the existence of learning styles and the effectiveness of Brain Gym, which have found their way into learning guides and educational programs, is particularly high among these two groups as well as all other studied groups (Grospietsch and Mayer, 2020).

Tardif et al. (2015) demonstrate that (pre-service) teachers come into contact with neuromyths and associated practices during both their academic and practical training. A study by Howard-Jones et al. (2009) confirms that 56–83% of pre-service teachers encounter educational programs rooted in neuromyths during their first year of practical training in schools, which is associated with a high level of acceptance of these myths. Simmonds (2014) shows that many teachers use or have used unproven techniques such as Brain Gym in their instruction. Lethaby and Harries (2016) and Blanchette Sarrasin et al. (2019) provide evidence that many teachers who endorse neuromyths also employ instructional practices linked to these misconceptions in their classrooms (this is the case more frequently among preschool and elementary school teachers than secondary school teachers). Grospietsch and Mayer (2019) found a small positive association between endorsement of neuromyths and constructivist beliefs about teaching and learning. This association might indicate that highly engaged, innovative teachers are the ones who make a well-intentioned effort to incorporate ostensibly neurodidactic principles into their instruction. Conversely, Ruhaak and Cook (2018) show that teachers with accurate conceptions regarding neuromyths are more likely to employ effective instructional practices rather than ineffective ones based on neuromyths.” (p.255-256)

Grospietsch, F., & Lins I. (2021). Review on the prevalence and persistence of neuromyths in education – where we stand and what is still needed. Frontiers in Education, 6, 250-262. https://www.frontiersin.org/article/10.3389/feduc.2021.665752

And in Australian education?

“Hitherto, the contribution of philosophers to Neuroscience and Education has tended to be less than enthusiastic, though there are some notable exceptions. Meanwhile, the pervasive influence of neuromyths on education policy, curriculum design and pedagogy in schools is well documented. Indeed, philosophers have sometimes used the prevalence of neuromyths in education to bolster their opposition to neuroscience in teacher education courses. By contrast, this article views the presence of neuromyths in education as a call for remedial action, including philosophical action. The empirical basis of this article is a survey, conducted over a period of three years, involving a total of 1144 first-year pre-service student teachers, which revealed alarming levels of belief in five common neuromyths related to children and learning. This study also attempted to probe the origins of these mistaken beliefs and why they gain traction. The findings suggest an urgent need in teacher education to address the problem of neuromyths, not simply because they are mistaken, they often misdirect valuable resources and mislabel children. The article calls for a compulsory unit on neuroscience and education in all courses of teacher education. Moreover, teaching neuroscience in education cannot be left to specialist neuroscientists, philosophers must be involved.” (p. 1214)

Kim, M., & Sankey, D. (2018). Philosophy, neuroscience and pre-service teachers’ beliefs in neuromyths: a call for remedial action. Educ. Philos. Theory, 50(13), 1214–1227. doi:10.1080/00131857.2017.1395736

“Background: It is not well understood whether qualified teachers believe neuromyths, and whether this affects their practice and learner outcomes. Method: A standardised survey was administered to practising teachers (N = 228) to determine whether or not they believe fictional (neuromyth) or factual statements about the brain, the confidence in those beliefs, and their application. Results: Although factual knowledge was high, seven neuromyths were believed by >50% of the sample. Participants who endorsed neuromyths were generally more confident in their answers than those who identified the myths. Key neuromyths appear to be incorporated into classrooms. Conclusion: Australian teachers, like their overseas counterparts, have some neuroscience awareness but are susceptible to neuromyths. A stronger partnership with neuroscientists would addresss the complex problem of disentangling brain facts from fictions, and provide better support for teachers.

Hughes, B., Sullivan, K., & Gilmore, L. (2020). Why do teachers believe educational neuromyths? Trends in Neuroscience and Education, 21. 100145. https://doi.org/10.1016/j.tine.2020.100145

“The term neuromyths refers to misconceptions about learning and the brain. Educator neuromyths may result in inappropriate instruction, labelling of learners, and wasted resources. To date, little research has considered the sources of these beliefs. We surveyed 1359 Australian preservice educators (M = 22.7, SD = 5.7 years) about their sources of information for 15 neuromyth and 17 general brain knowledge statements. Consistent with previous studies, neuromyth beliefs were prevalent. Predictors of neuromyth accuracy included general brain knowledge and completion of university classes addressing neuromyths, although effects were modest. Depending on the belief, participants relied on general knowledge, academic staff, school staff, and popular media. … attempts to address widely accepted neuromyths in preservice teachers have met with inconsistent success. Developing strategies for effectively addressing neuromyths, particularly those with the potential to negatively impact on teaching and learning, stands as a priority for future research.” (p. 94)

Carter, M., Van Bergen, P., Stephenson, J., Newall, C., & Sweller, N. (2020). Prevalence, predictors and sources of information regarding neuromyths in an Australian cohort of preservice teachers. Australian Journal of Teacher Education, 45(10), 94-113. http://dx.doi.org/10.14221/ajte.2020v45n10.6

Sigh, we’re back to the role of evidence-based practice in education!