Education and Neuroscience

Evidence, Theory and Practical Application

Paul Howard-Jones

• Publisher: Routledge
• Year: 2013
• eBook ISBN: 9781317987680
• Topic: Education
• Subtopic: Education General

This book brings together contributions from scientists and educators at the forefront of interdisciplinary research efforts involving neuroscience and education. It includes consideration of what we know about brain function that may be relevant to educational areas including reading, mathematics, music and creativity. The increasing interest of educators in neuroscience also brings dangers with it, as evidenced by the proliferation of neuromyths within schools and colleges. For this reason, it also reviews some of the more prominent misconceptions, as well as exploring how educational understanding can be constructed in the future that includes concepts from neuroscience more judiciously.

This book will be of interest to educators, policymakers and scientists seeking fresh perspectives on how we learn.

This book was published as a special issue in Educational Research, a journal of the National Foundation for Educational Research (NFER).

Neuromythologies in education

John Geake
Oxford Brookes University, Oxford, UK

Background: Many popular educational programmes claim to be ‘brain-based’, despite pleas from the neuroscience community that these neuromyths do not have a basis in scientific evidence about the brain.
Purpose: The main aim of this paper is to examine several of the most popular neuromyths in the light of the relevant neuroscientific and educational evidence. Examples of neuromyths include: 10% brain usage, left- and right-brained thinking, VAK learning styles and multiple intelligences
Sources of evidence: The basis for the argument put forward includes a literature review of relevant cognitive neuroscientific studies, often involving neuroimaging, together with several comprehensive education reviews of the brain-based approaches under scrutiny.
Main argument The main elements of the argument are as follows. We use most of our brains most of the time, not some restricted 10% brain usage. This is because our brains are densely interconnected, and we exploit this interconnectivity to enable our primitively evolved primate brains to live in our complex modern human world. Although brain imaging delineates areas of higher (and lower) activation in response to particular tasks, thinking involves coordinated interconnectivity from both sides of the brain, not separate left- and right-brained thinking. High intelligence requires higher levels of inter-hemispheric and other connected activity. The brain’s interconnectivity includes the senses, especially vision and hearing. We do not learn by one sense alone, hence VAK learning styles do not reflect how our brains actually learn, nor the individual differences we observe in classrooms. Neuroimaging studies do not support multiple intelligences; in fact, the opposite is true. Through the activity of its frontal cortices,among other areas,the human brain seems to operate with general intelligence, applied to multiple areas of endeavour. Studies of educational effectiveness of applying any of these ideas in the classroom have failed to find any educational benefits.,
Conclusions: The main conclusions arising from the argument are that teachers should seek independent scientific validation before adopting brain-based products in their classrooms. A more sceptical approach to educational panaceas could contribute to an enhanced professionalism of the field.

Introduction

Neuromythologies are those popular accounts of brain functioning, which often appear within so-called ‘brain-based’ educational applications. They could be categorised into neuromyths where more is better: ‘If we can get more of the brain to "light up", then learning will improve...’, and neuromyths where specificity is better: ‘If we concentrate teaching on the "lit-up" brain areas then learning will improve....Prominent examples of neuromythologies of the former include: the 10% myth, that we only use 10% of our brain; multiple intelligences; and Brain Gym. Prominent examples of neuromytholgies of the latter include: left- and right-brained thinking; VAK (visual, auditory and kinaesthetic) learning styles; and water as brain food. Characteristically, the evidential basis of these schemes does not lie in cognitive neuroscience, but rather with the various enthusiastic promoters; in fact, sometimes the scientific evidence flatly contradicts the brain-based claims.

The assumption here is that educational practices which claim to be concomitant with the workings of the branin should, in factbe soat least to the extent that the scientific jury can ever be conclusive (Blakemore and Frith 2005). A counter-argument might be posed that the ultimate criterion is pragmatic, not evidential, and if it works in the classroom who cares if it seems scientifically untenable. For this author, basing education on scientific evidence is the hallmark of sound professional practice, and should be encouraged within the educational profession wherever possible. The counter-argument only serves to undermine the professionalism of teachers, and so should be resisted.

This is not to say that there is not a glimmer of truth embedded within various neuromyths. Usually their origins do lie in valid scientific research; it is just that the extrapolations go well beyond the data, especially in transfer out of the laboratory and into the classroom (Howard-Jones 2007). For example, there is plenty of evidence that cognitive function benefits from cardiovascular fitness; hence, general exercise is good for the brain in general (Blakemore and Frith 2005). But this does not mean that pressing particular spots on one’s body, as per Brain Gym, will enhance the activation of particular areas in the brain. As another example, there are undoubtedly individual differences in perceptual acuities which are modality based, and include visual, auditory and kinaesthetic sensations (although smell and taste are more notable), but this does not mean that learning is restricted to, or even necessarily associated with, one’s superior sense. All of us have areas of ability in which we perform better than others, especially as we grow older and spend more time on one rather than another. Consequently, a school curriculum which offers multiple opportunities is commendable, but this does not necessarily depend on there being multiple intelligences within each child which fortuitously map on to the various areas of curriculum. General cognitive ability could just as well play an important role in learning outcomes across the board.

The generation of such neuromythologies and possible reasons for their widespread acceptance has become a matter for investigation itself. In particular, the phenomenon of their widespread and largely uncritical acceptance in education raises several questions: why has this happened?; what might this suggest about the capacity for the education profession to engage in professional reflection on complex scientific evidence? And one cannot help but wonder about the extent to which political pressure for endless improvement in standardised test scores, publicised via school league tables, drives teachers to adopt a one-size-fits-all, brain-based life-raft when their daily classroom experience is replete with children’s individual differences.

To gather some data about these issues, Pickering and Howard-Jones (2007) surveyed nearly 200 teachers either attending an education and brain conference in the UK (one brain based, the other academic) or contributing to an OECD website internationally. All respondents were enthusiastic about the prospects of neuroscience informing teaching practice, particularly for pedagogy, but less so for curriculum design. Moreover, despite a prevailinge thos of pragmatism (notably with the brain-based conference attendees),it was generally conceded that the role of neuroscientists was to be professionally informative rather than prescriptive. This, in turn, points to the critical necessity for a mutually comprehensible language with which neuroscientists and educators can engage in a genuine interdisciplinary dialogue.

The American Nobel Laureate physicist Richard Feynman, in one of his more famous graduation addresses at Caltech, warned his audience of young science graduates about ‘cargo cult science’ (Feynman 1974). His point was that, while it might accord with ‘human nature’ to engage in wishful thinking, good scientists have to learn not to fool themselves. Feynman’s warning could well be applied to the myriad ‘brain-based’ strategies that pervade current educational thinking. Whereas it is commonly stated in such schemes that the brain is the most complex object in the universe (although how this could possibly be verified remains unexplained), this assumption is then completely ignored in proposin gapedagogy based on the simplest of analyses–e.g.,in the brain there are two hemispheres, left and right, therefore there are two kinds of thinking: of-the-left-brain and of-the-right-brain, and therefore there are only two kinds of teaching necessary: for-the-left-brain and for-the-right-brain. Not a very exciting universe where the most complex object has only two states! And not, fortunately, the universe in which we exist, where the complexity of the human brain has been the focus of intense investigation for over a century, but particularly over the past two decades, thanks to the invention of neuroimaging technologies.

The resulting neuroimages – brains with brightly coloured areas – are disarmingly simple, and seem to fit with a commonsense view of the brain as having localised specialist functions which enable us to do the various things we do. But such apparent simplicity is generated out of considerable complexity. In functional magnetic resonance imaging (fMRI), for example, the images are the end-result of many years’ work on understanding the quantum mechanics of nuclear magnetic resonance phenomena, the development of the engineering of superconducting magnets, the application of inverse fast Fourier transforms to large data sets and the refinement of high-speed computing hardware and software to analyse large data sets across multiple parameters. The neuroimaging picture is undoubtedly worth the proverbial thousand words, but the scientist’s words can be quite different from those of the layperson.

A crucial point that most of the media overlook, or ignore, is that neuroimaging data are statistical. The coloured blobs on brain maps representing areas of significant activation (so-called ‘lighting up’) are like the peaks of sub-oceanic mountains which rise above sea level, in neuroimaging, how much or how little activation (sea level) to reveal being determined by the researcher in setting a suitable level of statistical threshold. In fact, the most challenging aspect of most neuroimaging experimental design is to determine suitable control conditions to highlight a particular area of experimental interest and thus avoid showing how most of the brain is involved in most cognitive tasks. So, in a classroom it would be quite silly to think that only a small portion of pupils’ brains are involved in a task, just because a small area of brain activity was reported in a neuroimaging study of a similar task (Geake 2006). Neuroscience is a laboratory-based endeavour. Even with the best of intentions, extrapolations from the lab to the classroom need to be made with considerable caution (Howard-Jones 2007). As Nobel Laureate Charles Sherrington (1938, 181) warned in Oxford some 70 years ago: ‘To suppose the roof-brain consists of point to point centres identified each with a particular item of intelligent concrete behaviour is a scheme over simplified and to be abandoned.’ In other words, we have to be very wary of oversimplifications of the neuro-level of description in seeking applications at the cogntive or behavioural levels.

The central characteristic of brain function which generates its complexity is neural functional interconnectivity. There are common brain functions for all acts of intelligence, especially those involved in school learning (Geake in press). These interconnected brain functions (and implicated brain areas) include:

• Working memory (lateral frontal cortex);
• Long-term memory (hippocampus and other cortical areas);
• Decision-making (orbitofrontal cortex);
• Emotional mediation (limbic subcortex and associated frontal areas);
• Sequencing of symbolic representation (fusiform gyrus and temporal lobes);
• Conceptual interrelationships (parietal lobe);
• Conceptual and motor rehearsal (cerebellum).

This parallel interconnected functioning is occurring all the time our brains are alive. Importantly, these neural contributions to intelligence are necessary for all school subjects, and all other aspects of cognition. Creative thinking would not be possible without our extensive neural interconnectivity (Geake and Dobson 2005). Moreover, there are no individual modules in the brain which correspond directly to thes chool curriculum (Geake 2006). Cerebral interconnectivity is necessary for all domain-specific learning, from music to maths to history to French as a second language. Neuromyths typically ignore such interconnectivity in their pursuit of simplicity. Steve Mithen (2005) argues that it was a characteristic of the Neanderthal brain that it was not well interconnected. This could explain the curious stasis of Neanderthal cultureover several hundred thousand years, and the even more curious fact that Neanderthal culture was rapidly out-competed by our physically less robust Cro-Magnon forebears, whose brains, Mithen argues, had evolved to become well interconnected.

Multiple intelligences

Highly evolved cerebral interconnectedness has implications for any brain-based justification of the widely promoted model of multiple intelligences (MI). Gardner (1993) divided human cognitive abilities into seven intelligences: logic-mathematics, verbal, interpersonal, spatial, music, movement and intrapersonal. Some 2500 years earlier, Plato recommended that a balanced curriculum have the following six subjects: logic, rhetoric, arithmetic, geometry-astronomy, music and dance-physical. For philosopher-kings, additionally, meditation was recommended. Clearly MI is nothing new: Gardner has just recycled Plato. But although such a curriculum scheme is long-standing, it doesn’t mean that our brains think about these areas completely independently from one another. Each MI requires sensory information processing, memory, language, and so on. Rather, this just demonstrates Sherrington’s point that the way the brain goes about dividing its labours is quite separate from how we see such divisions on the outside, so to speak. In other words, there are no multiple intelligences, but rather, it is argued, multiple applications of the same multifaceted intelligence.

Whereas undoubtedly there are large individual differences in subject-specific abilities, the evidence which conflicts with a multiple in telligences interpretation of brain function is that these subject-specific abilities are positively correlated, as shown by Carroll (1993) in his large meta-analysis. Such a pervasive correlation between different abilities is conceptualised as general intelligence, g. The existence of g not only suggests that the same brain modules are likely to be involved in many different abilities, but that their functional connectivity is of paramount importance. In fact, the main thrust of research in cognitive neuroscience in the next decade will be the mapping of functional connectivity, that is how functional modules transfer information, anatomically, bio-chemically, bioelectrically, rhythmically, synchronistically, and so on. A recent study along these lines sought evidence for neural correlates of general intelligence – i.e., where and how does the brain generate measures of general intelligence? Duncan et al. (2000) found a common brain involvement, in the frontal cortex of adult subjects, on both spatial and verbal IQ tests. A further meta-analysis of 20 neuroimaging studies involving language, logic, mathematics and memory showed that the same frontal cortical areas were involved (Duncan 2001). It seems unlikely that these intelligences are independent if the same part of the brain is common to all. This point is elaborated in a recent critique of MI (Waterhouse 2006, 213).

The human brain is unlikely to function via Gardner’s multiple intelligences. Taken together the evidence for the intercorrelations of subskills of IQ measures, the evidence for a shared set of genes associated with mathematics, reading, and g, and the evidence for shared and overlapping ‘what is it?’ and ‘where is it?’ neural processing pathways, and shared neural pathways for language, music, motor skills, and emotions suggest that it is unlikely that each of Gardner’s intelligences could operate ‘via a different set of neural mechanisms’ [as Gardner claims].

To explain how those same pathways support high-level general intelligence across so many different cognitive areas, Duncan (2001, 824) suggested that: ‘neurons in selected frontal regions adapt their properties to code information of relevance to current behaviour, pruning away ... all that is currently task-irrelevant.’ So, underlying our specific abilities is adaptive brain functioning. In support of this idea of an adapting brain, Dehaene and his colleagues have proposed a dynamic model of brain functioning in which these frontal adaptive neurons coordinate the myriad inputs from our perceptual modules from all over the brain, and continually assess the relative importance of these inputs such that from time to time, a thought becomes conscious; it literally ‘comes to mind’ (Dehaene, Kerszberg, and Changeux 1998). It could be predicted, then, that deliberate attempts to restrict intelligence within classrooms according to MI theory would not promote children’s learning, and it could be noted in passing that one of the ‘independent consultants’ who advocates brain-based learning strategies acknowledges teachers’ frustration with the lack of long-term impact of applying MI theory (Beere 2006).

10% usage

None of the above implies that g is all that there is to intelligence – quite the opposite. With its population age-norming, IQ might be a convenient surrogate for intelligence in the laboratory, but not even the most resolute empiricist would claim that IQ captures all of the variance in cognitive abilities. Rather, intelligence in all its manifestations illustrates the underlying dynamic complexity of its generative neural processes, with emphasis on ‘dynamic’. There is overwhelming evidence that the brain is perpetually busy, and that even when any of our brain cell sare not involved in processing some information, they still fire randomly. As an organ which has evolved not to know what is going to happen next, such constant activity keeps our brain in a state of readiness. Consequently, the neuromyth that ...

Table of contents

• Cover
• Title
• Copyright
• Contents
• Introduction
• 1. Neuromythologies in education
• 2. Reading, dyslexia and the brain
• 3. How should educational neuroscience conceptualise the relation between cognition and brain function? Mathematical reasoning as a network process
• 4. Dyscalculia: neuroscience and education
• 5. What are the implications of neuroscience for musical education?
• 6. Co-constructing an understanding of creativity in drama education that draws on neuropsychological concepts
• Index