Principles of learning, implications

Chapter 4

Principles of learning, implications for teaching? Cognitive neuroscience and the classroom Usha Goswami

Overview

'

Educational neuroscience is a long-term enterprise, and it may be some time before education can 'cash out'the promise that it offers. Nevertheless, advances in neuroscience are extremely important for education, because education is the most powerful means we have to enhance learning and to overcome limitations in biological and environmental conditions. Neuroscience seeks to understand the processes that underpin learning, and to delineate causal factors in individual differences in learning. These insights into the mechanisms of learning will be of value for education, as they will provide information about the drivers of development and the optimal targets for educational intervention. The evidence from neuroscience is not just interesting scientifically. lt will eventually provide an evidence base for education in which mechanisms of learning will be precisely understood. This evidence base will be at a new and complementary level of enquiry the biological level. A biological approach will not replace social, emotional and cultural analyses of learning. Rather, it will provide research tools for these complementary analyses which will enrich the entire educational field. Currently many educators seem to see neuroscience as a challenge to the traditions of their discipline. ln this chapter; I will argue that it should instead be seen as an opportunity. Education enables human beings to transcend the physical limits of biological evolution. The experiences that education provides work their changes via changing the brain. Neuroscience enables us to understand these brain-changing processes at a level of detail and specificity that will be extremely valuable for education. The effects of social, emotional and cultural practices on these brain-changing processes will then be amenable to rigorous empirical study.

4.1 lntroduction As shown by some of the contributors to this volume, there is a spirit of healthy scepticism about what neuroscience can offer to education. Despite the scepticism, and also despite more trenchant criticism that neuroscience is often asking misguided questions via misconceived experiments (Bennett & Hacker, 2003),I do not believe that education can afford simplyto ignore neuroscience.

48

PRINCIPLES OF LEARNING, IMPLICATIONS FOR TEACHING? I

Of course there is'bad and'ugly' in some attempts to apply neuroscience to education. However, a deeper understanding of brain physiology could also generate advances in learning and theory that could parallel the remarkable advances made in medicine as we have gained a deeper understanding of the physiology of the body. Accordingly, here I offer some in principle examples of how current cognitive neuroscience may eve,ntually make a contribution to teaching and learning in the classroom. First, some ground-clearing points. The negative critiques by educationists of neuroscience are often based upon a particular world view. This view suggests that approaches to education based on the empirical examination of brain physiology put at stake 'questions about the nature of knowledge and how we learn, of what constitutes human being and the good life' (Cigman & Davis, 2009, p. ix). In general, such commentators view the reductionism of neuroscience as running counter to understanding the essence of being human and the good life. True education has to involve development of mind,body and spirit, hence by focusing on the cognitive, neuroscience is unnecessarily narrow. Neuroscience is thus rejected as method of enquiry for helping educators to develop the potentialities of the human mind and spirit. Medical/neuroscience approaches are seen as simply one discourse' among many, based on professional identity and power relationships rather than science and knowledge. 'On precisely what basis might neuroscience have any authority to make claims about the nature of learning?' (Cigman & Davis, 2009,p.76). In my view, the claims of neuroscience are important simply because its discoveries about learning are open to empirical test and disconfirmation. Scientific discoveries are not about power relationships. They are part of an ongoing process of the discovery of knowledge. Although reductionism is viewed by some educational researchers as mutually exclusive to discovering the potentialities of the human mind and spirit, this is an unnecessarily divisive stance. A simple example is offered by the technology of the cochlear implant. Basic research into how the brain converts speech sounds into language may not give us insight into how meaning is derived from an acoustic signal. However, it does give us insight into how changes in sound pressure on the ear when receiving this acoustic signal are converted into electrical impulses that stimulate the brain tissue involved in audition. This research eventually discovered that a small implant utilizing relatively few frequency channels could provide electrical stimulation directly to the brain tissue of those born congenitally deaf, and that those experiencing this stimulation could hear and learn spoken language.In fact, deaf children who are implanted as infants can learn spoken language skills that are equivalent to those of hearing children. The effect on the potentiality of the minds and spirits of those concerned are likely to be enormous. By offering an understanding of mechanism, neuroscience has the potential to transform our understanding of human learning and cognitive development, and, I would argue, ultimately it has the potential to transform education. In my view educational neuroscience has to begin by careful basic research into mechanisms of learning. Indeed, the longer I spend in the field, the more basic I find the essential research questions to be. My own key aim is to study how sensory systems build cognitive systems over deveiopmental time. Small initial differences in sensory function are likely to cause large differences in cognitive performance over the learning trajectory, and neuroscience will enable understanding of developmental mechanisms in fine-grained detail. For example, mechanisms of neural information coding and transmission seem particularly important to understand (as in the cochlear implant example). These mechanisms are the likely building blocks of the cognitive systems critical for education, such as attention and language. The argument from some educational quarters that it is in principle impossible to uncover general laws of learning, as the learning and capacities of individuals are inextricably linked to the contexts and situations of learning, misses the point. Neuroscientists are trying to achieve an rrnderstandinsof how learnins occurs in the brain. As this will depend on physiological processes,

4.3 at the neuroscience level of analysis there are

EXAMPLE 1: NEURAL STRUCTURES FOR LEARNTNG

likelyto

be general laws of learning. These discoveries

can then be applied to studying learning in response to the various contexts and situations of learning that are of interest to educators. Neuroscience does not seek to replace understandings arising

from social science. Rather, it seeks to make

a

contribution to education at a complementary level.

4.2 ln principle: cognitive neuroscience and classroom teaching In cognitive neuroscience, researchers both measure electrochemical activity directly and model neural activity using connectionist computational models. When electrochemical activity is measured directly, patterns of activityacross large networks of neurons (called cell assemblies) are found. These activation patterns correspond to or are correlated with mental states such as remembering a telephone number. Brain imaging techniques can also reveal the time course of the electrochemical activity (e.g. which neural structures were activated in which order) and

interactions and feedback processes that may occur within these large networks. Computational modglling of these interactions and feedback processes then enables in-principle understanding of how synchronized neuronal activity within cell assemblies results in learning and development. I will argue that these kinds of information are in principle of interest to the discipline of education, even though the field of cognitive neuroscience is still in its early stages. Education is concerned with how to enhance learning, and the discipline of neuroscience aims to understand the processes of learning. Considerable brain development has already taken place when h baby is born. Most of the neurons (brain cells) that will make up the mature brain have already formed, and have migrated to the appropriate neural areas. Brain structures such as the temporal cortex (audition) and the occipital cortex (vision) are present, and will become progressively specialized as the infant and young child experiences environmental stimulation. Environmental stimulation determines specialization, as fibre connections growbetween brain cells and within and between different neural structures in response to external input (this is'synaptogenesis'). For our basic sensory systems, growth in fibre connections reflects'experience expectant'processes. Here there is abundant early fibre growth in response to types of environmental stimulation (such as light) that the brain 'expects' (via evolution) to receive. Other fibre connections are'experience dependent'. Here the brain is growing connections in order to encode unique information that is experienced by the individual. Everybabyisborn into a distinctive environment, even children growing up in the same family. Experience-dependent connections are the ones that make each brain subtly different. Experience-dependent synaptogenesis enables lifelong plasticity with respect to new learning and reflects the kind of learning mechanisms that maybe of most interest to education. The specialization of brain structures takes place within developmental trajectories, and these trajectories are constrained by both biology and environment. Neuroscientists study these developmental trajectories by asking questions about structure and function, as well as questions about information coding and transmission. Such questions include which brain structures are important for learning different educational inputs (e.g. reading versus arithmetic), which types of information coding or transmission are important for different educational inputs, what the temporal sequencing is between brain structures for a particular type of learning, and what can go wrong. Neuroscientists also try to distinguish cause from effect.

4.3 Example 1: neural structures for learning A very busy area of research in cognitive neuroscience is the study of which brain structures are active as the brain learns different inputs or performs different tasks. The most frequent method of study is functional magnetic resonance imaging (fMRI). This technique measures changes in

I

49

so I

enrr.rctpLES oF LEARNtNG. tMpltcATtoNS FoR TEACHTNG?

blood flow in the brain. As blood flows to neural structures that are active, this kind of neuroimaging can identify which parts of the brain are most involved in certain tasks. Such research is correlational, showing relationships between structure and function. The fact that groups of cells in a particular brain structure are active when language is heard does not mean that we have found. the site of the mental lexicon. Nevertheless, if different groups of cells are active when meaning is conveyed in different ways, this tells us something useful. For example, it has been shown that reading action words like lick and kick causes activity in the parts of the brain that are also active when moving one's tongue versus moving one's legs respectively (Hauk, ]ohnsrude & Pulvermullec 2004). This suggests that the cell assemblies involved in action also contribute to our conceptual understanding of what it means to'liclC versus'kicli. Hence the neural structures underpinning learning may not always be those that intuition may suggest. The structures that underpin the ' understanding of action words are in part the same structures that are active when actions are carried out by the body. An educational example of structure-function correlations comes from the study of reading development. The brain structures that are active in novice readers as they perform tasks with print can be measured with fMRI. It turns out that the neural structures for spoken language are the most active (Turkeltaub et a1., 2003). These data make it less likelythat logographic theories of reading acquisition are correct (e.g. Frith, 1985; Seymour & Elder, 1996). Logographic theory argued that children first learn to read by going directly from the visual word form to meaning. The sounds of the words were not involved. Rather, holistic visual stimuli were associated with meanings in the same way as familiar symbols like €and $ are associated with the meanings of 'pound' and 'dollar'. But if children can really go directly from print to meaning without recoding the print into sound first, only the neural structures active when viewing text and when understanding meaning should show activation. Learning a mental dictionary of visual word forms is a slow and incremental process. Studies using fMRI show that as children are exposed to more and more printed words, a structure in the visual cortex of the brain becomes increasingly active (Cohen & Dehaene , ZOO4\. This area has been labelled the'visual word form area' (VWFA), and appears to store information about 1etter patterns for words and chunks of words and their connections with sound. It is experience dependent, storing the learning that results as children are exposed to more and more printed words and experience reading them aloud (recoding them into sound). Because this learning is experience dependent, the VWFA also responds to'nonsense words'that have never been seen before. Sub-parts of these nonsense words are familiar from prior learning experiences (e.g.'treen' is a nonsense word, but it is analogous to real words like'seerf and'tree'). So the brain is also able to recode these nonsense letter strings into sound, even though the string TREEN has never been encountered before. Experiments using electrophysiology (electroencephalography, EEG) have also been performed to study childrent reading. In EEG, the tiny electrical currents that move between neural structures during a cognitive task like reading are recorded by sensors that measure how this activity waxes and wanes over time. Experiments contrasting real words and nonsense words show that the child's brain responds differently to real versus nonsense words within one-fifth of a second (200 milliseconds). This difference implies that lexical access (contact between the visual word form and its meaning) occurs very rapidly during reading. The speed of this differentiation has been shown to be similar for both children and adults, across languages, suggesting that the time course of visual word recognition is very rapid (160-180 milliseconds, see Csepe & Szucs, 2003; Sauseng, Bergmann &Wimmer,Z}}4;this response is called the'N170'). In such experiments, the visual/spatial demands linked to processing text are kept constant, and the only factor that varies

'

4.3

EXAMPLE 1: NEURAL STRUCTURES FOR LEARNING

is whether the target is a real word.or not. The Nl70 has been replicated in many studies. Such information represents an objective fact about how the brain behaves during reading. Some neuroscientists have suggested that the amount of activity in the VWFA is the best neural correlate that we have of reading expertise (Pugh, 2006). As might be expected, the VWFA shows reduced activation in developmental dyslexia (e.g. Shaywitz et al., 2005). More recently, neuroscientists have analysed how neural activity in the WVFA 'tunes itself'to print, namely how activity in these particular brain cells becomes specialized for the letter strings that are real words. Maurer, Brandeis and their colleagues (e.g. Maurer et al., 2005,2007) have followed a sample of German-

speaking children who either were at genetic risk for developmental dyslexia or who had no genetic risk for dyslexia, from the age of 5 years. They have used EEG to measure millisecondlevel changes in the electrical actMty associated with the recognition of word forms from before reading instruction began. The children were asked to detect the repetition of either real words or of meaningless symbol strings in a stream of consecutively-presented items. Before any reading instruction had commenced, none of the children showed an Nl70 to printed words, despite having considerable knowledge about individual letters. After approximately 1.5 years of reading instruction, the typically-developing children did show a reliable N170 to words. The children at risk for dyslexia showed a significantly reduced N170 to word forms, even though they had not yet been diagnosed as having reading difficulties. This is still correlational data. However, this finding raises the possibility of using a brain response like the Nl70, which is not under conscious control, to identify children who are at risk for reading difficulties before these difficulties are manifest in behaviour. Eventually, neuroscience will be able to offer education'neural markers'of this t1pe. These markers will be evidence of a processing difficulty rather than, say, evidence of 'being dyslexic'. Nevertheless, they will be useful to education, for example, in counteracting arguments that the child is being lary, stupid or is not trying. Clearly, the brain did not evolve for reading. Nevertheless, as this brief survey shows, neuroscience is revealing how fibre connections to support reading develop and encode print experience into the nervous system. This is partly achieved by recruiting neural structures that by evolution already performed similar functions, such as the neural tissue active during object recognition which is in and near to the VWFA. One prominent educational neuroscientist calls this feature of experience-dependent plasticity the 'neuronal recycling hypothesis' (Dehaene, 2008). Dehaene argues that our evolutionary history and geneti c organization constrain new cultural acquisitions to some extent, as new learning must be encoded by a brain architecfure that evolved to encode at least partially similar functions over primate evolution. Nevertheless, studies using fMRI have revealed that unexpected neural structures can be involved in educational performance. A good example comes from studies of arithmetic and number processing. Early studies with adults found that an area in parietal cortex was particularly active whenever numerical magnitude had to be accessed (Dehaene,1997). Nearby areas of parietal cortex were activated when judgements about size or weight were required. This led Dehaene and others to argue that the parietal cortex could be the location of an approximate, analogue magnitude representation in the human brain. In particular, they argued that activity in this brain structure in the horizontal intraparietal sulcus (IPS) enabled an intuitive understanding of quantities and their relations (Dehaene, 1997). It was surprising then to discover that brain activation in an exact addition task (e.g. 4 + S 9) was = not particularly high in this parietal structure. The IPS was only preferentially activated if the task involved approximate addition (e.g.a + 5 = 8) (Dehaene, Spelke, Pinel, Stanescu & Tsivkin, Lggg). In the exact calculation task, the highest relative activation was in a left-lateralizedarea in the inferior frontal lobe, an area usually most active in language tasks. Dehaene et al. (1999) argued

lu,

s2

PRINCIPLES OF LEARNING. IMPLICATIONS FOR TEACHING? I

that this could be because exact arithmetic requires the retrieval of over-learned 'number facts', which are thought to be stored in the language areas. Again, this correlational insight would seem to be of interest to educators. It implies that part of school mathematical learning is linguistic. The multiplication tables and'number facts'that children are taught may be learned as verbal routines, like the months of the year. Admittedly, this supposition is based on the finding that the highest brain activity found when reciting the months of the year is in the same neural structure as the highest brain activity found when doing mental arithmetic. Again, these are correlations. But a priori, one might not have expected the brain to develop fibre connections in the cell assemblies most active during language processing in order to encode classroom activities assumed to develop neural structures for mathematics.

4.4 Example 2: brain mechanisms of learning extract structure from input As we have seen, different neural strucfures are specializedto encode different kinds of

information, and one important mechanism for this is the growth of fibre connections that record experiential input (such as the incremental learning of printed word forms that is encoded primarily by fibre growth in the visual cortex). Most environmental experiences are multisensory, and therefore fibre connections between modalities are ubiquitous. But this mechanism of ever-growing and branching fibre connections between structures carries with it an important corollary for our understanding of learning. Because learning is encoded cumulatively by large networks of neurons, a whole network of cells that have been connected because of prior experiences will still be activated even when a particular aspect of sensory information in a particular experience is absent. This is the documented ability of the brain to respond to abstracted dependencies of particular sensory constellations of stimuli. This fact about learning enables, for example, the brain to 'fill in' a missed word when someone coughs across another speaker. In sensory terms, the word was drowned out by the louder cough. Yet even though our brain received the sensory information about the cough rather than the sensory input comprising the phonemes in the missed word, prior learning of the statistical regularities between words in connected discourse has enabled the brain to 'fill in' the missing information and 'hear' the absent word (e.g. Pitt & McQueen, 1998, for a related example).

The fact that incremental learning yields abstracted dependencies is a powerful mechanism for learning and development (Goswami, 2008). The study of abstracted dependencies actually gives us an empirical handle on top-down learning-and is therefore relevant to the contexts and situ-

ations of learning that are of interest to educators. As the child's brain is exposed to particular sensory constellations of stimuli over multiple occasions, what is common across all these learning instances will naturally be encoded more strongly by the brain than what differs. In terms of mechanism, the fibre connections that encode what is common will become stronger than the fibre connections that encode details that may differ in each learning event. This neural mechanism effectively yields our conceptual knowledge (e.g. such as our 'basic level concepts', e.g. tat', dog','tred and tar'; Rosch, 1978). After the child has experienced 100 tat'learning events, the strongest fibre connections will have encoded what has been consistent across all experienced instances, such as'four legsl'whiskers','taill and so on. In this way, the brain will have developed a generic or 'prototlryical' representation of a cat. This mechanism will apply to learning of all kinds, not just learning about perceptual objects in the real world. The same mechanism will underpin emotional reactions to frequently-experienced events, or physical reactions to these events, such as feeling nauseous.

4.4

EXAMPLE

2: BRAIN MECHANISMS OF LEARNING EXTRACT STRUCTURE FRoM INPUT

The abstraction of dependencies is not the only learning mechanism used by the brain. In addition to the generalized learning on the basis of repeated experiences, there is also bne trial' learning, when one experience is enough. This is shown, for example, by phobic reactions. Nevertheless, one effective intervention for overcoming maladaptive phobias is to'flood the brain with multiple instances of the phobic object in situations where negative consequences do not ensue. Eventually, this incremental learning becomes stronger than the learning underpinning the phobia, and the patient can function in the presence of the phobic object. Understanding more about these very different learning mechanisms in different environments seems likely to be of immense practical value to educators. One obvious example is in the education of children with emotional and behavioural disorders. In principle, therefore, the infant's brain can construct detailed conceptual frameworks about objects and events from watching and listening to the world. Active experience then transforms learning once the infant becomes capable of self-initiated movement (see Goswami, 2008, for detail). As the child learns language and attaches labels to concepts, the neural networks underpinning learning become even more complex. These networks of cell assemblies will be the physiological basis of our conceptual knowledge, and eventually will be amenable to internal manipulation. As we learn new information via language, fibre connections will form in response that encode more abstract information and therefore more abstract concepts. Via thinking and 'inner speech' we can alter these connections ourselves, without external stimulation. Understanding how this occurs at the mechanistic level is not antithetical to understanding human learning in terms of mind, body and spirit.Indeed, mechanistic understanding can lead to valuable interventions, as when participants are taught to use biofeedback to self-calm in situations that engender crippling anxiety. With respect to education, it is also important to point out that these learning mechanisms ensure that the brain will extract and represent structure that is present in the input even when it is not taught directly. This is evident from the implicit learning of many social phenomena, such as the documented effects of teacher expectations on pupil performance. An example that may be more amenable to physiological study comes from learning to read. Children will learn the higherorder consistencies in the spelling system of English without direct teaching. In research that I have previously described as showing'rhyme analogies' in reading (Goswami, 1986), we have documented sensitivity to these higher-order consistencies without overt awareness (Goswami, Ziegler, Dalton & Schneider,2003'). Spelling-to-sound relations in English are often more reliable at the larger'grain sizd of the rhyme than at the smaller'grain size'of the phoneme (Treiman et al., 1995;Zie$er & Goswami, 2005). For example, the pronunciation of a single letter like a' differs in words like'wallC and tar'from its pronunciation in words like tat'and tap'. The pronunciation in 'walk' or tar' can be described as irregular, but it is quite consistent across other rhyming words (like 'talk' and 'star'). By giving children novel 'nonsense words' to read, we explored whether the brain is sensitive to these higher-level consistencies in letter patterning. For example, we asked children to read aloud nonsense words matched for pronunciation, like daik, dake,lofi andloff r. Here the child could have learned the rhyme spelling patterns in items like dake and lffie from prior experiences with analogous real words, like take' and 'toffee'. Chunks of print like'ake' and their connections with sound should be stored in the VWFA. As there are no real English words with letter chunks lilce'aik',even though there are many words with the graphemes 'Ai' and rft', these rhyme spelling patterns should be entirely novel. Hence if children are faster and more accurate at reading items like dake than items like daik,we would have evidence for implicit higher-order learning. We (Goswami et al., 2003) indeed found that English children showed a reliable advantage for reading aloud the analogous nonsense words like

lr

54

PRINCIPLES OF LEARNING. IMPLICATIONS FOR TEACHING?

dake

andlffie,

despite the fact that the indMdual letter-sound correspondences were matched across word lists (i.e. the individual graphemes in daikwere as familiar orthographically as the indMdual graphemes in dake). These data suggest that orthographic learning, presumably in the VWFA, recorded these higher-level consistencies even though'rhyme analogy'reading strategies were not taught directly to participating children (see Ziegler & Goswami,2105,for converging evidence from other paradigms). Whether learning (i.e. reading performance) would have been even stronger if rhyme analogy strategies had been taught directly to participating children remains an open question. But this example suggests that using experiments to try and understand how the brain learns is relevant to education, even when the experiments are at a mechanistic level. The'situated cognition analyses

popular in education show us that learning is 'embedded' in the experiences of the individual. Nevertheless, one goal of education is to help all individuals to extract the higher-order structure (or'principles'or'rules') that underpin a given body of knowledge. Although many in education currently question the existence of general pedagogical methods that would make education maximally and universally effective, the possible existence of such methods is open to empirical investigation. Neuroscience offers a method for posing such empirical questions, for example, by studying how different teaching regimes in initial reading actually affect the developmeni of the VWFA. Clearly, translating such research questions to education will be challenging. Deep understanding

of a given educational domain is required in order to present cumulative information in the optimal sequence for the novice learner and to contrast different teaching regimes. Nevertheless, neural modelling will enable us to investigate whether certain classroom experiences result in previously distinct parts of a network becoming connected, or whether they enabled. inefficient connections that were impeding understanding to be pruned away. In principle, this knowledge could be fed back into pedagogy, making it more effective. A deeper understanding of how the brain uses incremental experience to extract underlying structure does appear to be relevant to classroom teaching.

4.5 The dangers of seeding neuromyths Nevertheless, it is critical to remain vigilant when evaluating neuroscience research (see Section 5, this volume). Correlations are still correlations, even when they involve physiological measures. Many correlational findings that reach the popular media are given causal interpretations and this is detrimental to popular understanding of neuroscience. One example is the data sets that have been interpreted to show that fatty acids such as fish oils play a potentially causal role

in learning known to be important in brain development and in neural signal transduction. This in itself does not mean that ingesting omega-3 and omega-6 highly unsaturated fatty acids is good for the brain. In a recent paper, Cyhlaiova et al. (2007) went further, claiming that the omega-3/omega-6 balance is particularly relevant to dyslexia' (p. 116). Their study in fact measured the lipid fatty acid .orrrporition of red blood cell membranes in 52 participants, 32 dyslexic adults and 20 control adults. There were no significant differences between dyslexics and controls for any of the 21 different measures of membrane fatty acid levels taken by the researchers. However, a correlation was found between a total measure of omega-3 concentration and overall reading in the whole sample. This correlation could mean that omega-3 concentration is linked to reading efficiency, or it could reflect the influence of a third variable such as intelligence quotient. At any rate, what a correlation of this nature does not show is that fatty acids play arole in dyslexia. (see Chapter 15, this volume). Unsaturated fatty acids are

4.6

CONCLUSION

Unfortunately, when physiological variables such as changes in the brain are involved, people tend to suspend their critical faculties. Weisberg and her colleagues gave adult students'bad' explanations of psychological phenomena, either with or without accompanying neuroscience information (Weisberg et al, 2008). The neuroscientific details were completely irrelevant to the explanations given. Nevertheless, the adults rated the explanations as far more satisfying when such details were present. Weisberg et al. pointed out that our propensity to accept explanations that allude to neuroscience makes it absolutely critical for neuroscientists to think carefully about how neuroscience information is viewed and used outside the laboratory.

4.6 Conclusion Educational neuroscience is a long-term enterprise, and there will be few immediate pay-offs (Goswami & Sztics,2011). Nevertheless, advances in neuroscience are important for education because they deliver insights into the mechanisms of learning. Eventually, neuroscience will enable componential understanding of the complex cognitive skills taught by education. Although this understanding will be at a basic ('reductionist') level, it cannot be ignored by educators. This is because neuroscience offers an empirical foundation for investigatingtheories and ideas already present in pedagogy, and for disputing others. The evidence from neuroscience is not just interesting scientifically. It enables an evidence base for education in which mechanisms of learning can be precisely understood. This understanding will only be at one level of enquiry. Neuroscience will not replace social, emotional and cultural analyses of learning. But biological, sensory and neurological influences on learning are likely to be replicable and open to falsifiability. As such, they will offer tools for applying the same empirical rigour to social, emotional and cultural analyses of learning. Again, medicine can offer us a positive analogy. Thirtyyears ago, it was acceptable to argue that autism in children was a product of 'refrigerator parentingl (Bettelheim, 1963). Autism was hypothesized to be caused by social factors, the family environments offered by professional parents who were focused on their jobs rather than their offspring. In response, the child was thought to withdraw emotionally from an environment that was cold and rejecting. No one would make that causal argument today, because the neural basis of autism is far better understood. Parenting does not cause autism. Instead, autism reflects atypical processing of cues important for social cognition, such as the information about mental states conveyed by the eyes. Further, this neural knowledge provides important clues about which features of family contexts might be most successful for children with autism, and offers measures for exploring relative benefits. For example, children with autism may prefer environments with lower demands for eye contact. They may also be better at learning about mental state information from non-biological kinds (for example, toy trains, see the Transporter videos for teaching children with autism about emotions, Golan et a1., 2010). Information about neural underpinnings does not make research on the optimal educational and cultural/social environments for teaching children with autism obsolete. Rather, it provides factual knowledge which enriches social, emotional and cultural analyses. By analogy, the truly ambitious goal for education is to cross disciplinary boundaries and embrace the scientific method. The scientific method, and neuroscience in particular, may enable surprising discoveries about the optimal way to educate mind, body andspirit.

Iru

s6

I

PRINCIPLES OF LEARNING, IMPLICATIONS FOR TEACHING?

References Bennett, M.R., & Hacket P.M.S. (2003). Philosophical foundations of neuroscience. Oxford: Blaclavell. (1963). Bettelheim, B. The Empty Fortress. Chicago, IL: Free press. Chomsky, N. (1957). Syntactic Structures.The Hague/paris: Mouton. Cigman, R., & Davis, A. (2009). New philosophies of learnfrg. Chichester: Wiley-Blackwell. Cohen, L- & Dehaene, S. (2004). Specialization within the ventral stream: the case for the visual word form area. Neurolmage, 22, 466-7 6. Csepe, V. & Szfics, D. (2003). Number word reading as a challenging task in dyslexia? An ERp study. Int ernati on al I our n al of P sy cho phy s i olo gy, 5 1, 69-83.

c-yhlarova, 8., Bell, ]. G., Dick, |. R., Mackinlay, E. E., Stein, !. F., & Richardson, A. I.eooll.Membrane fatty acids, reading and spelling in dyslexic and non-dyslexic adults. European Neuropsychopharmacology, 17,

tt6-21.

Dehaene, S. (1997). The Number

Sense:

How the Mind Creates Mathematics. New York Oxford University

Press.

Dehaene, S. (200S). Cerebral constraints in reading and arithmetic Education as a heuronal recycling' hypothesis. In A.M. Battro, K.W Fischer & P.J. Lena (Eds). The EducatedBrain: Essays in Neuroeducation (pp.232-247). New York Pontifical Academy of Sciences and Cambridge University Press.

Dehaene, S., Spelke, E., Pinel, P., Stanescu, R., & Tsivkin, S. (1999). Sources of mathematical thinking: behavioral and brain- imaging eviden ce. S cience, 294, gT 0 -7 4.

Frith, U. (1985). Beneath the surface of developmental dyslexia. In K.E. Patterson, J.C. Marshall & M.Coltheart (Eds.), Surface Dyslexia (pp 301-30). London: Lawrence Erlbaum Associates. Golan, o., Ashwin, E., Granadec Y., Mcclintoch s., Kay, K., Leggett, v., & Baron-cohen, s. (2010). Enhancing emotion recognition in children with autism spectrum conditions: An intervention using animated vehicles with real emotional faces. lournal of Autism and Developmental Disorders, 40, 269-79. Goswami, U. (1986). Children's use of analogyin learning to read: A developmental study. lournal Experimental Child Psychology, 42, 7 3-83.

of

Goswami, U. (2008). Cognitive Development: The LearningBrain. Hove Psychology press, Taylor & Francis. Goswami, U., & Sztics, D., (2011). Educational neuroscience: Developmental mechanisms; towards a conceptual framework. N euro im age,

57 (3

),

65

1

- 58.

Goswami, U,Ziegler, f., Dalton, L. & Schneider, \,V (2003). Nonword reading across orthographies: How flexible is the choice of reading units? Applied psycholinguistics, z4,z35-47.

Hauh O., |ohnsrude, I. S., & Pulvermuller,F. (2004) Somatotopic representation of action words in human ' motor and premotor cortex. Neuron,4l,30l-.OT. Maurer, LL, Brem, S., Bucher, K. & Brandeis, D. (2005). Emerging neurophysiological specialization for

letter string

I ournal of Cognitiw Neuroscience, 17, lS3Z-52 Maurer, IL, Brem, s., Bucher, K., Kranz, F., Benz, R., Steinhausen, H-c., & Brandeis, D. (2007). Impaired tuning of a fast occipito-temporal response for print in dyslexic children learning fo read. Brain,l30, s.

3200-10.

Pitt' M. A., & McQueen, f. M. (1998). Is compensation for coarticulation mediated by the lexicon? lournal of Memory and Language, 39, 347 -7 0. Pugh, K. (2006). A neurocognitive overview of reading acquisition and dyslexia across languages. Dettelopmental Science, 9, 448-50. Rosch, E. (1978). Principles of categorisation. In E. Rosch & B.B. Lloyd (Eds.) Cognition and Categorisation , (pp. 27 - a$. Hillsdale, N): Erlbaum. Sauseng, P., Bergmann, f., Wimmer, H. (2004). When does the brain register deviances from standard word spellings? An ERP study. Cognitive Brain Raearch,Z0,5Zg-32.

REFERENCES

Seymour, P.H.K., & Elder, L. (1986). Beginning reading without phonology. Cognitive Neuropsychology,3, 1-36. Shaywitz, S.8., & Shaywitz, B.A. (2005). Dyslexia (specific reading disability). Biological Psychiatry,5T, 1301-9. Simos, P.G. et al. (2005). Early development of neurophysiological processes involved in normal reading and

reading disability: A magnetic source imaging study. Neuropsychology, 19 (6),787-98. Sztics, D. & Goswami, U. (2007). Educational neuroscience: Defining a new discipline for the study

mental representations. Mind, Brain and Education, l(3),

of

ll4-27.

Treiman, R., Mullennix, |., Biieljac-Babic, R., & Richmond-Welty, E. D. (1995). The special role of rimes in the description, use, and acquisition of English orthography? lournal of Experimental Psychology: GeneraL L24,107-36. Turkeltaub, P.E., Gareau, L., Flowers, D.L., 7-effiro,T.A. & Eden, G.F. (2003). Development of neural mechanisms for reading. Nature Neuroscience, 6(6), 767-73.

Vygotskf, L. (1978). Mind in Society. Cambridge, MA: Harvard University Press. Weisberg, D.S., Keil, F.C., Goodstein, E.R. & Gray, J.R. (2008). The seductive allure of neuroscience explanation s. Iournal of Cognitive Neuroscience, 2O(3), 470-77 .

Ziegler,l.C., & Goswami, U. (2005). Reading acquisition, developmental dyslexia, and skilled reading across languages: a psycholinguistic grain size theory. Psychological Bulletin,l3l(1), 3-29.

| 57