Which part of ‘two way street’ did you not understand? Redressing the balance of neuroscience and education

Educational Research Review 6 (2011) 223–231

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Position paper

Which part of ‘two way street’ did you not understand? Redressing the balance of neuroscience and education David A. Turner Faculty of Business and Society, University of Glamorgan, UK

1. Executive summary It has become a common view in the interdisciplinary area of neuroscience and education, or mind, brain and education, that there must be a balanced dialogue, or ‘two way street’, between the contributing disciplines. It is argued in this paper that a formal commitment to such balance is, in practice, frequently combined with implicit assumptions that ensure imbalance, giving dominance to the concepts and methods of neuroscience. In this paper it is argued that such an approach will not only hinder the development of the interdisciplinary field; if it is left unchallenged, long-term damage will be done to research in education as a whole. This de facto imbalance in the presumed relationship between neuroscience and education research is traced in only a small number of papers, so that it is possible to drill down to sources, and show that there is a systematic tendency to give credit to neuroscience for results that actually derive from behavioural studies, to ignore important weaknesses in the methods currently employed in neuroscience, and to overlook such areas as interpersonal differences and differences in strategy which are the strengths that can be provided by educational research. 2. Introduction De Smedt et al. (2010) state (repeatedly) that the relationship between neuroscience and education should be conceived as a two way street. My contention in this paper is that what they then go on to describe is not a two way street at all, but a very unbalanced exchange between neuroscience and education. In the interaction that they propose between neuroscience and education, neuroscience identifies the methods, and provides concrete explanations, while education is permitted to provide some background information about variables that might be included in experiments, and point up some of the complexities that neuroscience has yet to deal with. Most importantly, while neuroscience is presumed to have the tools and methods to illuminate educational issues, there is no reciprocity in allowing educationists to question neuroscientific methods and results. The findings of neuroscientific studies are taken as given, and no question of whether the methods of neuroscience contribute to the difficulty that neuroscience has in handling the complexities of learning environments is admitted. This is not a sentiment that is unique to De Smedt et al. (2010) and can be found in the works of authors who approach the link between neuroscience and education from a number of different angles (e.g., Bruer, 1997, 2008; Fischer et al., 2007; Fischer, 2009; Howard-Jones, Winfield, & Crimmins, 2008). There are differences in emphasis even in the writings of a single author. Bruer (1997) described the link between neuroscience and education as a ‘bridge too far’, and therefore may be seen as skeptical of the direct link between neuroscience and education. However, in subsequent writing he is more sanguine about the possibility of making connections: ‘‘We must build a better bridge between developmental neurobiology and the learning sciences’’ (Bruer, 2008, pp. 55–56). Most of the papers written in the field of neuroscience and education, whether written by neuroscientists or educationists, are broadly sympathetic to the approach provided by neuroscience. Insofar as it is possible to generalize about a whole field, the paper by De Smedt et al. (2010) is typical of much that is written on the relationship between neuroscience and DOI of original article: 10.1016/j.edurev.2011.10.003 E-mail address: [email protected] 1747-938X/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.edurev.2011.10.002

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education, and the need for ‘dialogue’. However, in order to avoid generalizations, and the temptation to set up an argument of straw, I shall focus on the arguments presented in a single paper. The paper by De Smedt et al. (2010) is not selected for special scrutiny because it is bad, or poorly constructed. On the contrary, it is an excellent paper and provides a wealth of supportive evidence for its arguments. It is precisely these qualities that make it worthy of detailed analysis in order to highlight the shortcomings of stance it adopts. The paper first outlines the development of the field of neuroscience and education, noting rapid growth, and generally welcoming those developments. The authors note that they limit their observations to the links between cognitive neuroscience and mathematics education, and the paper is then divided into three main sections. These three sections deal with the influence of cognitive neuroscience on education, the influence of education on cognitive neuroscience, and future challenges respectively. Perhaps I should start by saying, or perhaps I have no need to say, I am not an expert in neuroscience and neuroimaging. For neuroscientific research I am the educated lay audience. For this reason, I have had to rely fairly heavily on the work of Henson (2008), for guidelines on how to design and interpret an fMRI study. However, it is worth noting that a high degree of statistical sophistication is involved in arriving at neuroimaging conclusions, and there would seem to be more need for neuroscientists to explain their methods to those engaged in education than is currently being met. If, in the process of this paper, I misrepresent the neuroscience, I think that I will be only partly to blame. In setting out a critical examination of the possible connections between neuroscience and education, I will start with some general background on the interpretation of brain images in an educational context. This will be followed by an examination of the influence of neuroscience on education and the influence of education on neuroscience, following the structure used by De Smedt et al. (2010). This will give a picture of the traffic flowing in each direction on that two-way street. This paper will then end with conclusions about the current imbalance, and possible remedies. 3. Developments in neuroscience In the not very distant past, neuroscience depended upon studies of individuals who had suffered traumatic brain damage, or who exhibited extreme mental abnormalities, and the post-mortem examination of brains with lesions. One of the reasons that neuroscience and education has become a hot topic is the extraordinary rise in non-invasive technologies that make it possible to study the living brain. Because PET scanning involved the injection of radioactive tracers into the brain, it was rarely used with healthy children. It is really the development of functional magnetic resonance imaging (fMRI) since 1990 that has stimulated the growth in neuroscience, and hence in the interest in neuroscience and education. Even so, the noise and confinement of the environment necessary for fMRI, and the isolation of the subject, mean that it is impossible at the moment to model everyday learning environments very closely. De Smedt et al. (2010) start by noting that most neuroimaging studies have been conducted with adults, and there are very few studies, either with children, or tracing longitudinal development with children. There are good reasons for this, which are not mentioned, and which are intrinsic to the techniques used. Henson (2008) notes: One problem with fMRI is that there is a lot of low-frequency noise. This has various causes, from aliased biorhythms to gradual changes in physical parameters (e.g., ambient temperature). Thus any ‘‘signal’’ (induced by your experiment) that is low-frequency may be difficult to distinguish from background noise. Many analysis packages high-pass filter fMRI data (i.e, remove low-frequency components). Since contrasts between trials that are far apart in time correspond to lowfrequency effects, they can be filtered out. (Henson, 2008: Section 1.1.3) This has obvious implications for experimental designs that involve longitudinal study and long term training effects. The reason for the absence of longitudinal studies may in part be attributable to the fact that fMRI techniques are not well suited to such studies. In practice, most studies involve tasks that can be accomplished in a few seconds, and certainly within a minute. A further problem arises from the fact that most brain imaging experiments are designed to show group effects, averaged over a number of individuals. Although, as normally presented, fMRI scans look like a photograph of somebody’s brain, as Roskies (2008) makes clear, that is very far from the case. fMRI images are always averaged over several scans, and normally averaged across several individuals. In order to develop the kind of brain scan image with which we are all now familiar, some very sophisticated techniques are employed. Each subject is given two tasks, the task of interest and a comparison task. The level of activity in the task of interest is then found by a subtraction of the brain activity from that in the comparison task. The comparison task might simply be resting, or it might be a task designed to compensate for elements of the task of interest which are not the focus of the study, such as reading the questions and pressing buttons in response. Over a series of tests, the probability that a part of the brain is more active during the task of interest is calculated at some arbitrary cut-off point(s). In order to account for physiological differences between individuals, active brain areas have to be transposed onto a ‘standard brain’, because a specific brain area will not correspond to a specific physical space in people with brains of different shapes or sizes. After that mapping, the average likelihood of a particular area showing increased activity during the task of interest can be calculated. The result it then presented as though it were a photograph of a specific brain with different probabilities indicated by different colours or shades of grey. It is a mistake, however, or at least an

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over-simplification, to interpret this as meaning that an fMRI scan shows the areas of the brain that are active during the task of interest. In settings of interest in the educational context, tasks typically take more than a minute, and how a person deals with those tasks will depend upon their background knowledge, the strategies that they employ, their level of expertise and their motivation to engage with the task. In fMRI studies, or other neuroimaging studies, the process of averaging necessarily obscures such individual differences. For example, if our purpose was to compare children’s neural performance with that of adults, the question would arise as to how one might select children who were at the ‘same’ level of development. Designing a cross-sectional study of the developmental process would thus be very difficult. While these design issues may be overcome, or indeed may have been overcome, there would appear to be grounds for assuming that the kinds of experiments that would be of interest to an educationist are exactly those that are most difficult for neuroscientists to perform, because of the methodological assumptions built into neuroscience techniques. We might at least expect an exploration of these limitations in a discussion of the interface between education and neuroscience. In short, the proponents of an interdisciplinary field at the boundaries of neuroscience and education are inclined to base their arguments on a number of presumptions which are, to say the least, insecure. These include the following: (1) That certain areas of the brain are known to be associated with certain mental processes, and that, therefore, when those parts of the brain are activated it can be assumed that the particular mental function is being performed. (2) That identifying that activation of a particular part or parts of the brain is the correlate of specific mental processes is of value in its own right, even though no consequences follow. (3) That any shortcomings of the neuroscience evidence at the present time indicates the newness of the technology, and any difficulties can be overcome in time and with improved techniques; and (4) That pieces of research, which are based solely on behavioural studies, can be claimed as successes for neuroscience, even though the original research reports give no support to that claim. Putting together several of these presuppositions together, we are presented with a passage like the following: Data from cognitive neuroimaging studies indicate that reading (Pugh et al., 2001) and arithmetic (Dehaene, Piazza, Pinel & Cohen, 2003) show a neural overlap in the left temporo-parietal cortex. In reading, this area appears to be particularly active during phonological decoding or mapping graphemes onto phonemes. In arithmetic, this area is particularly related to arithmetic fact retrieval, a process that is assumed to rely on phonological codes. Thus, reading and arithmetic rely on the processing of the phonological code of language and, hence, the quality of phonological representations. (De Smedt et al., 2010, pp. 99–100) The argument suggests that neuroimaging shows that phonological decoding during reading is located in a specific part of the brain. It is therefore assumed that anything that happens in that part of the brain is phonological decoding. From which it follows that remembering arithmetical facts, which also activates this part of the brain, is dependent on phonological decoding. And this leads to a restatement of the conclusion that both reading and arithmetical recall depend on phonological decoding. Poldrack (2006) argues that such ‘reverse inference’, from the activation of a region of the brain to the use of a cognitive function is invalid, although he does go on to suggest ways of retrieving some information from reverse inferences. Some recognition of the difficulties with the argument that is being presented would be helpful. A further complication in the statistical reasoning arises from the fact prior knowledge is frequently incorporated into the analysis of brain images. Studies can be more discriminating if they are looking for an increase in activity in a particular region of the brain than if they search for increased activity somewhere in the brain. Because we ‘know’ that the pre-frontal cortex is associated with mechanisms of self-control, studies of self-control can be focused on those regions, with the result that smaller effects will be found to be statistically significant than if no prior knowledge were assumed. There is the evident risk here that studies may simply confirm the outcomes of earlier studies, especially if the balance between what is known and what is discovered in any particular study is not explicit. These various difficulties in interpreting neurological studies need to be borne in mind whenever the connections between neuroscience and education are considered. Their absence from the discussion of educational issues means that neuroscientists are never pressed to provide detailed and rigorous explanations of how their results lead to educational conclusions. There is a tendency to assume that the outcomes of neurological studies (typically brain scans) are in some way less open to critical examination than the results of studies of learner behaviour in educational settings. The twoway traffic is biased by the underlying belief that neuroscience can be a positive influence on education research even where the evidence is slim, or where the neuroscientific findings have no consequences. I will examine this bias from the two perspectives of the influence of cognitive neuroscience on education studies and the influence of education studies on cognitive neuroscience.

4. Cognitive neuroscience studies influence education studies De Smedt et al. (2010) give several examples which they claim illustrate the fact that cognitive neuroscience can generate findings that could not be anticipated from behavioural data alone.

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For example, Stavy and Babai (2009) examined the nature of intuitive interference in geometry. They investigated brain activation while participants had to compare the perimeters of two geometrical shapes under two conditions: one that was in line with intuitive reasoning and one in which the correct answer was counterintuitive (see also Stavy, Goel, Critchley, & Dolan, 2006). Results revealed that the correct answers to counterintuitive items were accompanied by activations in those areas of brain that are important for inhibitory control, such as the prefrontal cortices (e.g., Stuss & Knight, 2002). Stavy and Babai (2009) concluded that this highlights the importance of control mechanisms in overcoming intuitive interferences. (De Smedt et al., 2010, p. 100) The paper by Stavy et al. (2006) gives full details of the experimental method. The study involves subjects comparing shapes like those shown in Fig. 1. The rectangle on the left and the shape on the right have the same perimeter, but different areas. The subjects are asked to judge which of the shapes has the larger perimeter. Various conditions were used in the comparisons, and in particular, there were cases where the change in perimeter matched the change in area (which Stavy et al., 2006, described as ‘intuitive’) and cases where it did not (which they described as ‘counterintuitive’). Their results indicated that subjects were more likely to get the wrong answer in the counterintuitive case, and that response times were longer for the counterintuitive case. After training, response times for both the intuitive and counterintuitive case increased, but the subjects improved in their ability to get the right answers. In this case the activity in the pre-frontal cortex, an area associated with voluntary inhibitory control, showed increased activity. These are insights that might as easily have been gained from Vygotsky’s account of higher mental functions as processes of self-mastery, or even from the age-old advice from teachers to check the answers before handing them in. The only novel element is information about the part of the brain activated. Knowing that a particular part of the brain is involved in a specific mental function may be very interesting in its own right, but unless it has some further consequences for educational studies, it has no greater claim to influence educational research than other areas of knowledge that are interesting in their own right, such a astronomy or nuclear physics. Besides, in this case, interpretation of the results depends upon prior knowledge of the differentiated functions of different parts of the brain, and as noted above this raises the question of how much new has really been added. The paper cited in the passage above by Stavy and Babai (2009) is an example in microcosm of this tendency to assume a link between neuroscience and education where one does not necessarily exist. The paper reports three studies. The first study is a behavioural study of subjects making judgments about areas and perimeters. The focus is on the different reaction times between people faced with the intuitive case and the counterintuitive case. The third study describes an intervention to train people, and see how their performance changes, again focusing on behaviour, and in particular reaction time. If these are not the same studies described in Stavy et al. (2006), they are very similar. In between those two studies is a study that is based on neuroimaging, and involves ‘looking’ at the areas of the brain that are activated when the subjects react instantly or take longer to arrive at a judgment in response to the stimulus. Perhaps unsurprisingly, areas of the brain that have commonly been associated with higher mental functions such as selecting appropriate behaviour were more active when the reaction time was longer (Stavy and Babai (2009). While that may be interesting in its own right, knowing which parts of the brain are activated actually added little or nothing to the understanding that the behavioural studies had provided, and contributed nothing to the design and evaluation of the intervention in the subsequent experiment. What we see then, in the paper by De Smedt et al. (2010), but in many others like it, is a presentation of the evidence by people who are eager to believe that neuroscience has something positive to contribute to educational studies. Because of that enthusiasm, they are inclined to give more credit to neuroscience than is its due, and undervalue educational and behavioural studies to the same extent. An example of the willingness to conclude that neuroscience has played a more positive role than is in fact the case can be seen in the following example: This knowledge [of association between abnormal functioning of brain circuitry and specific mathematical skills at the behavioural level] may guide appropriate educational intervention. For example, remediation tools may then be focused on children’s acquiring of representations of numerical magnitudes, in particular on the mappings between number symbols and the quantities they represent. Recent evidence suggests that these types of interventions improve children’s numerical understanding, not only in children with dyscalculia (Wilson, Revkin, Cohen, Cohen, & Dehaene, 2006) but also in children from low income backgrounds (Griffin, 2004; Ramani & Siegler, 2008; De Smedt et al., 2010, p. 99). The implication of this passage is that cognitive neuroscience studies of children with dyscalculia can lead to the development of principles that can help in the design of interventions that will improve the performance of all children, not only those with dyscalculia. And this is supported by three references. Two of those references make no mention of neuroscience or brain imaging at all, while the third makes only a passing mention.

Fig. 1. Shapes for comparison in Stavy et al. (2006).

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Griffin (2004) reports a behavioural study of children working with material that gives them experience of numbers in a variety of formats. She does list five principles of good mathematics teaching, including, ‘‘Build upon children’s current knowledge’’ and ‘‘Provide plenty of opportunity for hands-on exploration, problem-solving, and communication’’, but she might more easily have derived those from the work of John Dewey than from studies in neuroscience. Similarly, Ramani and Siegler (2008) report a behavioural study using board games, and their use in developing a concept of a number line. Neither paper can be seen as contributing anything towards the idea that neuroscience can contribute to the design of effective educational interventions. The paper referred to by Wilson, Revkin et al. (2006) reports the behavioural test of an adaptive computer program to support the development of skills in judging the difference between two magnitudes. The paper describes a study which measures the performance of children on a behavioural task before and after use of the computer program to provide exercise. The study involved no neuroimaging. Indeed, the article made no reference to cognitive neuroscience, except to refer to another article which described the design of the adaptive computer program (Wilson, Dehaene et al., 2006). The latter paper (Wilson, Dehaene et al., 2006) explains that an adaptive computer program is one which provides exercise tasks for the person using it, and where the difficulty of tasks is adjusted by the program in response to the performance of that individual. In two consecutive paragraphs this particular feature of the design is related firstly to fMRI studies and secondly to Vygotsky’s notion of the ‘‘zone of proximal development’’. The mention of Vygotsky is of interest, partly because of his pre-eminence in designing behavioural studies that could inform both educational understanding and interventions, but also because he had been dead for fifty years before the first effective fMRI study was conducted. The suggestion that this study demonstrates how neuroscience can inform educational interventions automatically raises the question as to why brain imaging studies are being valued ahead of behavioural studies, and how this can be seen as an example of the influence of cognitive neuroscience on educational research. On balance, we would have to conclude that what the evidence actually indicates is that educational principles, based on behavioural studies, can support the development of educational programmes that help the development of number skills. Neuroscience may, or may not, provide evidence that improvement of number skill is associated with changes in brain structure and/or function, but that is of no practical consequence. So, coming back to the original claim, there is no evidence at all in the references cited that knowledge of how the brain works is contributing to educational interventions; on the contrary, the evidence provided emphasises the importance of behavioural studies in improving basic mathematics skills. But that is not the impression which is given by the original claim, which rather suggests that neuroscience can help in the development of remedial programmes. A positive, twoway dialogue can hardly be encouraged on the basis of attributing the advances made by one of the parties to the other. The claims for neuroscience might be scaled back a little on the basis of this analysis so that they are more realistic. We might conclude, as the evidence suggests, that behavioural studies are always of greater value, and of greater practical significance, than neuroimaging studies. In coming to that conclusion, we would have also to conclude that the results of neuroscience were being consistently overstated in an unhelpful way. It might, nevertheless, be the case that cognitive neuroscience coincidentally provides some interesting insights into the neural correlates of certain cognitive skills, and that might form a basis for some future insights. How should we evaluate that more circumspect claim? We might take, for example, De Smedt et al. (2010, p. 99): For example, studies in children with dyscalculia have shown structural (Rotzer et al., 2008) and functional abnormalities (Kaufmann et al., 2009; Mussolin et al., 2010; Price, Holloway, Rasanen, Vesterinen, & Ansari, 2007) in those areas of the brain that are dedicated to the processing of numerical magnitudes. There are two claims here that are compounded in a single sentence, and it would perhaps be well to separate them. The claims are that there are (i) structural abnormalities and (ii) functional abnormalities in the areas dedicated to processing numerical magnitudes in children with dyscalculia. In support of the first claim the authors cite Rotzer et al. (2008). Rotzer et al. (2008) claim to have found reduced volumes of white and grey matter in certain areas of the brains of children with dyscalculia. Roskies (2008) has argued that there is a complicated inferential process to arrive at a conclusion as to what a brain image actually indicates, and I think that this paper may be a case in point. I think it would take a specialist statistician to be able to understand from the paper how large the reduction in volume actually is. It is certainly statistically significant, but what that might mean in terms of the percentage differences between children exhibiting or not exhibiting dyscalculia is unclear. However, even if we take it as established that there are important differences in brain structure, Rotzer et al. (2008) do not claim that those differences are in areas of the brain dedicated to the processing of numerical magnitudes, or at least not exclusively. They appear to be at something of a loss over the interpretation of their findings, because they go to some lengths to explain how differences in structure in areas of the brain normally associated with working memory and attention can affect ability in arithmetic. And that certainly is a difficulty, since dyscalculia is a specific shortcoming in arithmetic not associated with other disabilities, and it is unclear why differences in structure in working memory and attention, if important, do not affect all mental functions. In support of the second claim, of functional abnormalities in the brains of children with dyscalculia, the evidence is harder to evaluate. The papers by Kaufmann et al. (2009) and Price et al. (2007) deal with the comparison of numbers that are not represented as symbols. In both studies, subjects are asked to look at hands with fingers extended, and judge in which of two cases the number of fingers is the larger. It is argued that this ‘non-symbolic number magnitude’ task demands a basic skill

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that is fundamental to the development of arithmetic ability. And it is further argued that, on the basis of brain images of adults, this skill is firmly associated with particular areas of the brain. Examination of those areas of the brain when children are performing this task indicates differences in function between those children who do, and those who do not, exhibit features of dyscalculia. The paper by Mussolin et al. (2010) is very similar in terms of experimental design, but uses number symbols to represent magnitude, not fingers. Taken together, these papers provide clear evidence that there are functional abnormalities in the operation of brain areas associated with number processing, so long as one can accept that the areas of the brain dedicated to number processing have been accurately identified, and so long as the tasks set relate to relevant aspects of number processing. Again, Roskies’ (2008) warning about the inferential distance from brain images to their interpretation is relevant. We are in danger of getting into an infinite regress, with each brain study relying on the results of earlier brain studies. However, it is worth commenting on the differences in the use of language between the scientific studies and the summary provided by De Smedt et al. (2010): ‘‘This raises the possibility of. . .’’, or ‘‘First direct evidence of. . .’’ (Price et al., 2007) or ‘‘Overall, findings are suggestive of. . .’’ (Kaufmann et al., 2009), compared with ‘‘Studies. . . have shown. . .’’ (De Smedt et al., 2010). There is one claim, however, that stands out among all the others as truly remarkable. It is that cognitive neuroscience can identify children who will develop learning disabilities years before they can demonstrate any symptoms. As De Smedt et al. (2010, p. 99) explain it, If these children can be identified before or at the very beginning of formal mathematics instruction, it might be possible to minimize or even eliminate their difficulties with mathematics by remediating the foundations upon which higher level-skills are built, such as numerical magnitude processing (e.g., Booth & Siegler, 2008; Ramani & Siegler, 2008). An analogous approach has been successfully applied in the far more developed field of dyslexia, where longitudinal studies have demonstrated that brain measures, i.e. event-related potentials, collected in infants and young children (i.e. in the absence of symptoms) predict future language and reading development (e.g., Molfese, 2000). The paper by Ramani and Siegler (2008) has already been mentioned, and is a behavioural study that makes no reference to neuroscience, and the same is the case for the paper by Booth and Siegler (2008). So they are not directly relevant to the case being made. The paper by Molfese (2000) is extraordinary, and if its claims are supported would certainly indicate a role for cognitive neuroscience in underpinning educational practice. For that very reason it is surprising that it is more or less ignored in the analysis, although there is a possible explanation in that it relates to reading rather than mathematical ability. Molfese (2000) claims that EEG results from babies tested within 36 h of birth can be used to predict which of them will exhibit characteristics of dyslexia at the age of eight, with a reliability that is well above chance. He further speculates that, by identifying children at risk of dyslexia at such an early age, remedial action could be taken. However, it is not quite clear how both propositions could be true at the same time, since, if the relationship is stable enough to persist over eight years when no attempt is made to control environmental effects, it is not clear how changing the environment could be thought to disrupt the relationship. But, importantly, the study of Molfese (2000) establishes that there is a very early developmental period where responses to language are learned, and that if those skills have not been learned by the end of that period, the dysfunctions will last throughout life. That is to say, Molfese makes it quite clear that there are critical periods, or critical windows, in which certain abilities have to be learned. Which is unproblematic, except for the fact that other neuroscientists have been as some pains to explain that the existence of such critical periods is a ‘neuromyth’ (OECD, 2007). Some discussion of how the nonneuroscientist should interpret these results would appear to be in order at that point. Moreover, many of the studies that take up the ideas put forward by Molfese are behavioural studies that examine whether the development of reading difficulties can be traced to a phonological deficit in language processing.(e.g., Tsao, Liu, & Kuhl, 2004) This again underlines the important role of behavioural studies in interpreting neurological studies. 5. Educational research influences cognitive neuroscience studies When it comes to the influence of education on neuroscience, there is an interesting but subtle asymmetry in the way the relationship is described. The relationship is between ‘neuroscience studies’ and ‘educational research’. This asymmetry is perhaps clearer if we go to the work of De Jong et al. (2009) which is another important source on the two way street between neuroscience and education, and is cited by De Smedt et al. (2010) as a model that they follow. By drawing on empirical findings from both disciplines, the following general questions will be addressed: (1) Which principles, mechanisms and theories studied in educational research could be further extended or refined based on findings from cognitive neuroscience? (2) Which principles, mechanisms and theories studied in cognitive neuroscience may have implications for educational research? (3) What are these implications and which (interdisciplinary or transdisciplinary) research questions can be drawn from them? (4) What form could an interdisciplinary or transdisciplinary research program take based on research questions generated from the above questions? (De Jong et al., 2009, p. 2)

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Note that it is the principles and theories of educational research that will be refined and extended by the encounter with neuroscience, but the principles and theories of cognitive neuroscience that will be found to have implications. Should we not wonder why the two way street will not lead to the theories and principles of neuroscience being refined? Or the principles and theories of educational research finding applications? There is an asymmetry here in the assumed relationship between education research and neuroscience research, and it is based on the notion that it is educational research that will have to adapt. For example, De Smedt et al. (2010) summarise some of the challenges for the future: While most studies in the field of cognitive neuroscience and mathematics education have focused on the representation of numbers and on arithmetic, only few attempts have been made to study more complex and higher order mathematical skills. Several contributions at the ASC showed that researchers are starting to address this issue more systematically. . .. A particular challenge of this research is that it requires educational and psychological theories, which specify cognitive processes that are detailed enough to be examined by neuroimaging. This will be of crucial importance not only for interdisciplinary research in neuroscience and education, but also for educational research in itself in order to fully understand complex mathematical skills. (De Smedt et al., 2010, p. 102) We need to be quite clear what is being claimed here: it is of crucial importance for the future of educational research that it should provide theories which address complex and high order skills by breaking them down into component processes which are appropriate to be studied by neuroimaging. More than seventy years ago Vygotsky was arguing the exact opposite, that the reason that the psychology of education was failing was that it insisted on treating higher mental functions as nothing more than a collection of simple processes that could be understood in purely physical terms.(Rieber, 1997, p. 48) Now that we have the tools and language of complexity theory available to us, which Vygotsky did not, there is an alternative path, which is to treat higher mental functions as complex emergent properties that cannot be reduced to simple operations. Oversimplifying the educational or behavioural side of the interpretation can lead to weaknesses in the way brain images are interpreted. Again, an example is instructive. Butterworth (2001) describes the results of a study that compared the pattern of brain activation when an autistic savant (Rüdiger Gamm) calculated multiplications with the patterns of ‘normal’ adults. In Butterworth’s words, The authors found that Gamm’s calculation processes recruited a system of brain areas implicated in episodic memory, including right medial frontal and parahippocampal gyri, whereas those of control subjects did not. They suggest that experts develop a way of exploiting the unlimited storage capacity of long-term memory to maintain task relevant information, such as the sequence of steps and intermediate results needed for complex calculation, whereas the rest of us rely on the very limited span of working memory. (Butterworth, 2001, p. 11) There is an alternative interpretation, suggested by other parts of Butterworth’s report, but not explicitly mentioned. The alternative explanation is that Gamm uses long term memory, because he has remembered a lot of facts about numbers in the course of his fascination with numbers. When set the same multiplication calculation, some people will simply retrieve a result from memory, others will have to perform the multiplication, while yet others have to perform a sequence of additions. If Gamm was remembering a result previously stored, while the comparison group had to perform a multiplication, we would hardly be surprised that they used different parts of their brain. We might hesitate, however, to conclude that Gamm could recruit parts of his brain which were not normally used for multiplying. This raises important questions about when we can claim to be looking at people do the ‘same’ thing. As noted above, brain images result in the subtraction of the patterns of brain activation on two tasks. It is assumed that when the same two tasks are set, the same mental functions must be undertaken, although, as this example makes clear, what counts as ‘performing a multiplication’ might be one of a range of activities, depending on the strategy adopted by the individual. In the case described by Butterworth (2001) the situation is still more complicated, although one has to drill down to the original research to find the details (Pesenti et al., 2001). In fact, Gamm was not given the same calculations as the control group; his facility with mathematical calculations was such that any problem that obliged him to make a calculation would be beyond the capacity of the other individuals. In practice, Gamm was set two digit multiplications while the control group was set single digit multiplications. Quite explicitly, but in a way that is not discussed in Butterworth (2001), this raises the question of what is the ‘same’ problem for different people. When De Smedt et al. (2010, p. 101) state that, ‘‘Cognitive neuroscience studies typically collapse performance across trials, thereby disregarding the issue of strategic variability’’, they are not talking about an accidental difference in focus between education and neuroscience; they are pointing to an inherent feature of neuroscience imaging. As it happens, an analogous problem has exercised teachers and educational researchers for decades: does producing the right answer mean that the respondent has understood the principles involved correctly, or have the found a short-cut or ‘cheat’, that will not help, and indeed may hinder, in future problems. Roskies (2008) describes this as the ‘functional chain’, by which the experimenter infers from the problem set what mental operations the subject must be making. The functional chain is hardly mentioned in reports of neuroscientific research and is an area where education researchers could contribute significantly to extending and refining the principles of cognitive neuroscience.

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6. Conclusions Let me return to my starting point. I have no wish to single out the work of De Smedt et al. (2010) for particular criticism. It is a perfectly good review of recent academic work, and more importantly, from my point of view, it captures the present Zeitgeist in relation to education and neuroscience research. I think that there is a general spirit abroad that does anticipate the reorganization of educational research so that it maps on to neuroscience, and where the intellectual structures of neuroscience are dominant. I suspect that, in their enthusiasm for the undoubted opportunities that have been raised by improved neuroimaging techniques, a relatively small, but vocal, group of neuroscientists and educationists have chosen to promote interactions between the two disciplines, and in doing so have slipped into evangelism for neuroscience. However, one cannot complain about the Zeitgeist without falling into generalisation and the appearance of setting up a ‘straw man’ argument. For that reason I have chosen the paper by De Smedt et al. (2010) as an example of a line of reasoning that is commonly advanced in this area. My own prejudice is that neuroscience and education have such distinct philosophical and technical concerns as to be permanently incompatible, and that even if neuroscience identifies precise neural correlates of mental processes that will add very little to what we understand about education. I do not think that this is a technical issue, but arises from exactly those behavioural aspects of neuroscience – how we know what a person is thinking when they are scanned – that education deals with but neuroscience tries to ignore. I may, in the future, be proved wrong in that prejudice. That future cannot be hastened on, however, by accepting too readily that educational and neuroscientific research are compatible, or that a two way street between the two disciplines can be constructed on the basis currently set out by enthusiasts for the transdisciplinary field of education and neuroscience. The quality of any joint field of study can only be promoted through open and critical discussion which acknowledges the strengths of both approaches to intellectual activity. An appropriate environment for such discussion can only be created if there is appropriate and equal respect for all the traditions that are involved. It cannot be achieved if neuroscience is given credit for results that it has not contributed to, if there is a serious dislocation between academic research results presented for colleagues in neuroscience and those prepared for educational researchers or the wider media, or if inconvenient aspects of neuroscience research are hidden away in references. To date, the dialogue between neuroscience and education research has largely been managed by those who favour the dominance of neuroscience in the relationship. If the field is really to advance neuroscientists must expect to be critically interrogated by an interested community of educationists who may have something positive to contribute to all aspects of the developing relationship. Anything less will result in a field that is substantially weakened. References Booth, J. L., & Siegler, R. S. (2008). Numerical magnitude representations influence arithmetic learning. Child Development, 79, 1016–1031. Bruer, J. T. (1997). Education and the brain: A bridge too far. Educational Researcher, 26(8), 1–13. Bruer, J. T. (2008). Building Bridges in Neuroeducation. In A. M. Battro, K. W. Fischer, & P. J. Léna (Eds.), The Educated Brain: Essays in Neuroeducation. Cambridge: Cambridge University Press. Butterworth, B. (2001). What makes a prodigy? Nature Neuroscience, 4(1), 11–12. De Jong, T., van Gog, T., Jenks, K., Manlove, S., van Hell, J., Jolles, J., van Merriënboer, J., van Leeuwen, T., & Boschloo, A. (2009). Explorations in learning and the brain: On the potential of cognitive neuroscience for educational science. Berlin: Springer Verlag. For pagination and quotations I have relied on the electronic version available at http://users.edte.utwente.nl/jong/Explorations%20in%20Learning%20and%20the%20Brain%20FR%20final.pdf accessed 30.04.2010. De Smedt, B., Ansari, D., Grabner, R. H., Hannula, M. M., Schneider, M., & Verschaffel, L. (2010). Cognitive neuroscience meets mathematics education. Educational Research Review, 5(1), 97–105. Dehaene, S., Piazza, M., Pinel, P., & Cohen, L. (2003). Three parietal circuits for number processing. Cognitive Neuropsychology, 20, 487–506. Fischer, K. W. (2009). Mind, brain, and education: Building a scientific groundwork for learning and teaching. Mind, Brain, and Education, 3(1), 3–16. Fischer, K. W., Daniel, D. B., Immordino-Yang, M. H., Stern, E., Battro, A., & Koizumi, H. (2007). Why mind, brain, and education? Why now? Mind, Brain, and Education, 1(1), 1–2. Griffin, S. (2004). Building number sense with Number Worlds: A mathematics program for young children. Early Childhood Research Quarterly, 19, 173–180. Henson, R. 2008. Design efficiency in FMRI. Available at http://imaging.mrc-cbu.cam.ac.uk/imaging/DesignEfficiency accessed 28.04.2010. Howard-Jones, P. A., Winfield, M., & Crimmins, G. (2008). Co-constructing an understanding of creativity in drama education that draws on neuropsychological concepts. Educational Research, 50(2), 187–202. Kaufmann, L., Vogel, S. E., Starke, M., Kremser, C., Schocke, M., & Wood, G. (2009). Developmental dyscalculia: Compensatory mechanisms in left intraparietal regions in response to nonsymbolic magnitudes. Behavioral and Brain Functions, 5, 35. Molfese, D. L. (2000). Predicting dyslexia at 8 years of age using neonatal brain responses. Brain and Language, 72, 238–245. Mussolin, C., De Volder, A., Grandin, C., Schlögel, X., Nassogne, M. C., & Noel, M. P. (2010). Neural correlates of symbolic number comparison in developmental dyscalculia. Journal of Cognitive Neuroscience, 22(5), 860–874. OECD (2007). Understanding the Brain: The Birth of a Learning Science. Paris: OECD. Pesenti, M., Zago, L., Crivello, F., Mellet, E., Samson, D., Duroux, B., Seron, X., Mazoyer, B., & Tzourio-Mazoyer, N. (2001). Mental calculation in a prodigy is sustained by right prefrontal and medial temporal areas. Nature Neuroscience, 4, 103–107. Available at http://www.nature.com/neuro/journal/v4/n1/ full/nn0101_103.html accessed 2.04.2010. Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences, 10(2). Price, G. R., Holloway, I., Rasanen, P., Vesterinen, M., & Ansari, D. (2007). Impaired parietal magnitude processing in developmental dyscalculia. Current Biology, 17, 1042–1043. Pugh, K. R., Mencl, W. E., Jenner, A. R., Katz, L., Frost, S. J., Lee, J. R., et al. (2001). Neurobiological studies of reading and reading disability. Journal of Communication Disorders, 34, 479–492. Ramani, G. B., & Siegler, R. S. (2008). Promoting broad and stable improvements in low-income children’s numerical knowledge through playing number board games. Child Development, 79, 375–394.

D.A. Turner / Educational Research Review 6 (2011) 223–231

231

Rieber, R. W. (Ed.). (1997). The Collected Works of L.S. Vygotsky, Vol. 4. The History of the Development of Higher Mental Functions. New York: Plenum Press. Translated by Marie J. Hall. Roskies, A. L. (2008). Neuroimaging and inferential distance. Neuroethics, 1(1), 19–30. Rotzer, S., Kucian, K., Martin, E., Von Aster, M., Klaver, P., & Loenneker, T. (2008). Optimized voxel-based morphometry in children with developmental dyscalculia. Neuroimage, 39, 417–422. Stavy, R., & Babai, R. (2009). Problem solving in mathematics: Insights from reaction time and brain imaging studies’’ in the programme of the colloquium. Cognitive Science meets mathematics education, 26, 27. Available at http://www.earli.org/resources/ASC/Program_EARLIASC_NeuroscienceMathEducation_ Bruges2009.pdf accessed 28.04.2010. The full paper is available at http://www.springerlink.com/content/87k2230712869l18/ accessed 07.10.2010. Stavy, R., Goel, V., Critchley, H., & Dolan, R. (2006). Intuitive interference in quantitative reasoning. Brain Research, 1073, 383–388. Stuss, D. T., & Knight, R. T. (2002). Principles of frontal lobe function. Oxford: Oxford University Press. Tsao, F.-M., Liu, H.-M., & Kuhl, P. K. (2004). Speech perception in infancy predicts language development in the second year of life: A longitudinal study. Child Development, 75(4), 1067–1084. Wilson, A. J., Revkin, S. K., Cohen, D., Cohen, L., & Dehaene, S. (2006). An open trial assessment of ‘The Number Race’, an adaptive computer game for remediation of dyscalculia. Behavioral and Brain Functions, 2(20), 98. Wilson, A. J., Dehaene, S., Pinel, P., Revkin, S. K., Cohen, L., & Cohen, D. (2006). Principles underlying the design of ‘The Number Race’, an adaptive computer game for remediation of dyscalculia. Behavioral and Brain Functions, 2(19), 78.