Applications of Cognitive Neuroscience in Educational Research
Applications of Cognitive Neuroscience in Educational Research
- Bert De SmedtBert De SmedtKU Leuven, University of Leuven
The application of neuroscience to educational research remains an area of much debate. While some scholars have argued that such applications are not possible (and will never be possible), others have been more optimistic and suggest that these are possible, albeit under certain conditions, for example when one aims to understand very basic cognitive processes. Concrete examples of these applications are increasing in the emerging interdisciplinary field of mind, brain, and education or educational neuroscience, which posits itself at the intersection of cognitive neuroscience, psychology, and educational research. From a methodological point of view, cognitive neuroscience can be applied to (some types of) educational research, as it offers a toolbox to investigate specific types of educational research questions. Promising applications of cognitive neuroscience to educational research include comprehending the origins of atypical development, understanding the biological processes that play a role when learning school-relevant skills, predicting educational outcomes, generating predictions to be tested in educational research, and undertaking biological interventions. The challenges of applying cognitive neuroscience deal with ecological validity, the scope of a biological explanation, and the potential emergence of neuromyths.
- Cognition, Emotion, and Learning
Connections between neuroscience and educational research have gained traction since the late 1990s, as is exemplified in a systematic increase in the number of academic publications, with a specifically steep acceleration of publications from 2005 (Howard-Jones, 2014a). There is now an increasing number of academic journals (e.g., Mind, Brain, and Education, Trends in Neuroscience and Education, Educational Neuroscience), textbooks (Mareschal, Butterworth, & Tolmie, 2014), master programs in education departments (e.g., Harvard, Bristol, London, Leiden), funding initiatives (e.g., Welcome Trust Education and Neuroscience funding scheme), and scientific societies, such as the International Mind Brain and Education Society (IMBES) and special interest groups on neuroscience and education of the European Association for Research on Learning and Instruction (EARLI) and American Educational Research Association (AERA), oriented at this new interdisciplinary research field that is termed as “Mind, Brain and Education” (e.g., Fischer, 2009), “Educational Neuroscience” (e.g., McCandliss, 2010), or “Neuro-education” (e.g., Howard-Jones, 2010). These terms are used interchangeably and they all represent “a collaborative attempt to build methodological and theoretical bridges between cognitive neuroscience, cognitive psychology and educational practice without imposing a knowledge hierarchy” (Howard-Jones et al., 2016, p. 625).
Despite this enthusiasm, the application of neuroscience to educational research remains an area of much debate. In the late nineties, Bruer (1997) posited that this application was “a bridge too far,” that the distance between education and neuroscience was too wide to be bridged, because there was (at that time) not enough knowledge to apply neuroscience to education. Instead, he argued, (cognitive) psychology should act as a scaffold to bridge the gap between neuroscience and education, an issue that is largely acknowledged by researchers who apply (cognitive) neuroscience to education. More recently, this skepticism was revisited and even culminated in the claim that neuroscience cannot affect instructional design and that it is unlikely to improve teaching in the future, hence that it is redundant to education and educational research (e.g., Bowers, 2016; Smeyers, 2016). While this latter group of scholars has argued that such applications are not possible (and will never be possible), the educational neuroscience field (Howard-Jones et al., 2016) suggests that connections between (cognitive) neuroscience and education are possible, albeit under certain conditions, for example when one aims to understand very basic cognitive processes.
It is important to clarify that neuroscience spans a very wide range of sub-disciplines, ranging from molecular and cellular science, which tries to understand the chemistry of neuronal function, to cognitive neuroscience, which studies the brain mechanisms underlying human behavior and cognition (Squire et al., 2013). Not all these branches of neuroscience have (and can have) connections with educational research. The application is possible from only one sub-discipline, cognitive neuroscience (see Ward, 2006, for an introduction), and the “neuroscience” in Educational Neuroscience or Mind, Brain, and Education exclusively refers to cognitive neuroscience (Howard-Jones et al., 2016). Likewise, it is important to point out that the application to education does not cover all areas of educational research, but is limited to certain subfields, particularly those that align with a (post)positivist research paradigm.
A brief primer is presented on contemporary brain imaging methods in cognitive neuroscience that are the most relevant to educational research and when and how these methods should be applied. Promising applications, which include creating causal models of atypical development, predicting educational outcomes via biological data, understanding learning at the biological level, generating predictions for educational research, and determining effects of biological interventions, are discussed. The challenges that are attached to the application of cognitive neuroscience to educational research are explored.
A Brief Primer on Cognitive Neuroscience Methods
The most widely used noninvasive brain imaging methods that are relevant for educational research are (functional) Magnetic Resonance Imaging or (f)MRI and electroencephalography or EEG (Dick, Lloyd-Fox, Blasi, Elwell, & Mills, 2014 for a general introduction on cognitive neuroscience methods that are relevant for educational research; see also Ward, 2006); these methods have also been very popular in the field of psychology (e.g., Cacioppo, Berntson, & Nusbaum, 2008). These methods are noninvasive as they are not dependent on radioactive agents and they do not induce or depend on brain lesions. Sometimes, psychophysiological methods, such as eye movement data, heart rate, or skin conductance are referred to as neuroscience methods. These methods indeed provide measures of the nervous system, yet they do not directly tap into the brain and therefore are not considered here.
Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) relies on the magnetic properties of hydrogen atoms in brain tissue to visualize brain structure and in blood to investigate brain function (Ward, 2006). These properties are investigated by applying very powerful magnetic fields through the MRI-scanner. This requires participants to lie very still—they are not allowed to move more than a few millimeters—in a noisy environment, which is quite different from what is happening in the classroom. This method can investigate the structure of the brain—that is, the gray matter (cell bodies and synapses of the neurons), by means of voxel-based morphometry, or white matter (myelinated axons, also called tracts or fibers, which conduct information from cell bodies of a neuron away to another neuron), by means of diffusion tensor imaging or DTI. Common research questions include how the size of particular brain structures or white matter connections are related to individual differences in performance (e.g., in language see Richardson & Price, 2009), how brain structures differ between individuals with and without neurodevelopmental disorders (e.g., in ADHD, Norman et al., 2016), and how brain structures change as a result of education or specific interventions. For example, Keller and Just (2009) demonstrated by means of DTI that 100 hours of intensive remedial instruction in reading changed white matter tracts in the brains of poor readers. Supekar et al. (2013) showed that the size of the grey matter of the hippocampus, a structure that is relevant for the consolidation of facts in memory, predicted the learning gains of a one-on-one math tutoring intervention.
Functional MRI, one of the most often used techniques in cognitive neuroscience, is a specific type of MRI that can be used to investigate brain function. More specifically, it indirectly assesses brain activity through the measurement of changes in the oxygen level in the cerebral blood while participants are typically doing a specific cognitive task in the scanner. The technique rests on the observation that neuronal activity and cerebral blood flow are tightly coupled, such that increases in the oxygen level in cerebral blood are the result of the vascular system’s response to increases in brain activity. This allows one to investigate where in the brain a particular (cognitive) process is taking place. For example, these studies have revealed the brain regions that are implicated in language and reading (e.g., Price, 2012), mathematics (e.g., Menon, 2015), and more general processes, such as executive functions (e.g., Bunge & Souza, 2009). While many of the earlier work limited its focus to localize brain activity in one or a few brain areas, it has become increasingly clear that the brain constitutes a mixture of highly functionally interconnected systems or large-scale networks, which cooperate to bring on complex cognitive abilities (Bressler & Menon, 2010), and these abilities are central in educational research. Functional brain activity studies have therefore expanded their attention to understand how brain regions interact over time, and for this reason, network approaches have become increasingly useful in fMRI research (e.g., Bressler & Menon, 2010). The practical constraints of the MRI environment (e.g., the fact that no movement is possible as well as the presence of loud noise) obviously limit the type of tasks that participants can perform in the scanner, even though a number of studies with complex tasks, such as video game play (Anderson et al., 2011) and even face-to-face interactions (Redcay et al., 2010), are now possible.
This method directly measures the electrical activity of the brain, which constitutes the information transfer from one neuron to the other. It is important to point out that EEG is not measuring activity in a single neuron—this can be done only via single-cell recording techniques, which cannot be applied to human participants. Instead, EEG measures the synchronized activity of thousands of neurons at the same time. The acquisition of EEG data involves mounting a cap of electrodes on the head of a participant. The participant has to perform a very basic cognitive task, during which these electrodes measure brain activity. Most often, the brain activity in large ensembles of neurons in response to a particular stimulus, the so-called event-related activity or event-related potential (ERP), is measured. This is registered on a very accurate temporal scale, which makes this method particularly useful to investigate when a particular type of process takes place (high temporal resolution). As this activity is measured on the surface of the skull, it is, however, difficult to precisely know where this brain activity is originating from (low spatial resolution). In order to reliably estimate the brain response to a stimulus, researchers typically need a large number of stimuli of a particular type; thus, these ERP studies typically include more than a few dozen stimuli per type. ERPs are mainly used to study very fast cognitive processes, such as attention or perception, which are difficult to capture by behavioral data alone. For example, Morgan-Short, Steinhauer, Sanz, and Ullman (2012) used ERPs to investigate the differential effects of explicit language education (grammar-focused classroom setting) versus implicit education (immersion setting) on how the brain (quickly) processes syntactic information. As has been the case in MRI research, there is also increasing interest in using EEG methods to understand how functional brain networks are formed and interact with each other during complex cognitive tasks. Therefore, the study of brain oscillations (i.e., oscillations in various frequencies of the continuous EEG signal, also known as brain waves or rhythms, which are assumed to indicate knowledge representation as well as knowledge transfer between different brain areas), as well as the synchronicity in these oscillations in different brain regions, has become increasingly popular (Klimesch, Schack, & Sauseng, 2005). For example, these EEG oscillations have been related to cognitive load, and these EEG measures can be used in educational research as online continuous measures to assess cognitive load during learning (Antonenko, Paas, Grabner, & van Gog, 2010). Technological advances in EEG equipment have resulted in the availability of wireless EEG systems, which are particularly promising for educational research as this allows one to collect data in more ecologically valid settings and in multiple participants at the same time. For example, Dikker et al. (2017) recently investigated group interactions in the classroom and showed that the synchronization in brain activity patterns between different students predicted their classroom engagement, showing that more synchrony in brain activity patterns between students was related to higher class engagement.
What is common to these two categories of imaging methods is that they both require a solid cognitive theory of the skill under investigation. Indeed, the signals that indicate brain structure or function can be meaningfully interpreted only if they are linked to cognitive theories (e.g. Cacioppo, Berntson, & Nusbaum, 2008; De Smedt et al., 2011), and this represents a necessary step in studies in cognitive neuroscience. In order for these methods to be used in educational research, a well-developed detailed cognitive theory of the phenomenon under investigation is required.
Cognitive Neuroscience Methods as Tools in Educational Research
When are these cognitive neuroscience methods applicable to educational research? This should depend on the specific research question at hand (De Smedt, 2014). A nice analogy to understand when cognitive neuroscience methods might be employed in educational research was provided by Stern and Schneider (2010). They compared the application of these methods to the use of a digital road map. These maps allow one to adjust the zoom level or resolution, depending on the level of detail (macro or highways vs. micro or alleys) the map viewer is looking for. Some types of educational research focus on very broad large-scale phenomena (macro-level), as is the case in research on educational systems, which are at a low level of resolution, as the map needs to contain the broader environment. Other questions aim to discover very specific cognitive processes (micro-level) that underlie the learning of specific skills, for example the types of representations that are used when executing certain arithmetic strategies. These cognitive processes can be difficult to measure via behavior, through tests, questionnaires, and observations, and require measurement at a high level of resolution. It is at this high level of resolution that cognitive neuroscience methods can be applied to educational research. It is important to note that not all educational research is at the same level of resolution and, as a result, cognitive neuroscience methods are not relevant for all educational research. It is only when a micro-level of understanding is required that cognitive neuroscience methods can be applied to educational research. This is particularly true when educational research adopts a positivist paradigm, as this paradigm is also key to cognitive neuroscience. In all, these cognitive neuroscience methods can be applied to some but not all types of educational research.
The application of cognitive neuroscience methods to educational research is particularly relevant when these micro-level research questions are difficult to answer with behavioral data alone; and for similar reasons such methods have been useful to the field of psychology too (Cacioppo, Berntson, & Nusbaum, 2008). One example comes from research on cognitive load in which EEG data have been used to continuously measure cognitive load during specific instructional interventions, for example the learning from hypertext and multimedia (Antonenko, Paas, Grabner, & van Gog, 2010). These EEG measures allowed these researchers to detect subtle changes in cognitive load during the instruction, whereas such differences would have been difficult to capture with standard behavioral measures of cognitive load. More recently, Anderson, Pyke, and Fincham (2016) used fMRI to identify different cognitive stages (i.e., encoding, planning, solving, and responding) and their duration during mathematical problem-solving at the level of an individual trial. This approach offers exciting opportunities to investigate different stages of problem-solving and their time course, which are difficult (or sometimes impossible) to detect by analyzing errors, reaction times, or verbal protocol data.
Causal Models of Atypical Development
One of the most-cited applications of neuroscience to educational research has been that it contributes to our understanding of atypical development (Butterworth, Varma, & Laurillard, 2011; Gabrieli, 2009; Goswami, 2004; Royal Society, 2011). More specifically, disorders in the acquisition of school-relevant skills, such as dyslexia and dyscalculia, have been grouped under the term neurodevelopmental disorders, a category that also includes autism spectrum disorder, ADHD, and intellectual disability, all of which have serious consequences when the child enters school and for which educational interventions have been developed. The neuro in neurodevelopmental disorders refers to the idea that the origin of these disorders is biological and that they result from aberrant brain structure and/or function. The precise biological causes of these disorders remain unknown, but neuroimaging research has made considerable progress in understanding brain structure and function in these conditions. For example, research on dyslexia, a neurodevelopmental disorder that is characterized by specific and persistent deficits in learning to read, has revealed abnormalities in the brain networks that are used for the processing of phonemes (Eden, Olulade, Evans, Krafnick, & Alkire, 2016; Gabrieli, 2009), which is an important prerequisite for learning to read (Melby-Lervag, Lyster, & Hulme, 2012). Similarly, it has been shown that the brain networks that support the processing of numerical magnitudes, a skill that is very important for learning to calculate (De Smedt, Noël, Gilmore, & Ansari, 2013; Schneider et al., 2017), are impaired in individuals with dyscalculia, who experience serious and life-long difficulties in basic calculations (Butterworth et al., 2011). These neuroimaging data then also suggested that the cognitive skills that are subserved by these brain networks should be the particular focus of educational remedial interventions (Butterworth et al., 2011; Gabrieli, 2009; McCandliss, 2010; Shaywitz, Morris, & Shaywitz, 2009). Effective interventions that target specific deficits in phonological processing (e.g., Snowling & Hulme, 2011) and numerical magnitude understanding (e.g., Dyson, Jordan, & Glutting, 2013) in children with learning disabilities have been developed, and even their effects on brain structure and function have been studied (e.g., Barquero, Davis, & Cutting, 2014; Fraga González et al., 2016; Kucian et al., 2011).
On the other hand, it needs to be empirically verified whether the abnormalities in these brain networks are truly the cause of these learning disabilities or whether these brain abnormalities are the consequence of poor academic achievement. This remains unknown, as most of the existing body of data are correlational as well as cross-sectional, and they simply report associations between a particular disorder and brain abnormalities, which do not allow one to determine the direction of associations and their causality (Goswami, 2008). Interestingly, recent studies are now beginning to show that the brain abnormalities in phonological processing areas in dyslexia are already present before children learn to read, and that they predict later reading acquisition. These studies depart from the genetic nature of dyslexia (Snowling & Melby-Lervag, 2016) and compare children with a family risk for dyslexia (i.e., first-degree relative with dyslexia) to those without such a risk. Children with a family risk for dyslexia can be identified before the onset of formal education (e.g., in preschool), and their brain development can be characterized. As soon as it is possible to clinically diagnose dyslexia (i.e., in second grade), data can retrospectively be analyzed by comparing children with and without dyslexia before and during the early years of schooling. This allows one to disentangle causes (abnormalities that are already present before children learn to read) from consequences (abnormalities that emerge after schooling started, which may be the result of less reading experience). Family risk studies in dyslexia suggest that these brain abnormalities are likely, at least in part, to be the origin of their reading difficulties (Vandermosten, Hoeft, & Norton, 2016). In all, if these neurobiological causes of atypical development can be identified and can be detected at an early age, they can be used as markers to predict atypical development and, even further, educational outcome.
Neuro-prediction (De Smedt & Grabner, 2015), or neuroprognosis, refers to the application that brain imaging measures can be used as biomarkers to predict educational outcomes (e.g., Black, Myers, & Hoeft, 2015; Hoeft et al., 2007) and in particular to early identify children at risk for learning difficulties (Diamond & Amso, 2008; Gabrieli, 2009; Goswami, 2008). More specifically, brain imaging data can be collected before children possess skills that are necessary for traditional behavioral assessment, such as language. This allows the identification of at-risk children before the start of formal education and opens opportunities for early intervention, which may have preventive effects. Attempts to discover such biomarkers have been made in the early detection of dyslexia. Molfese (2000) showed that ERP responses to speech sounds recorded in newborns discriminated with 81% accuracy those infants who would develop dyslexia at the age of 8. Even though this classification accuracy is significantly beyond chance, it should be interpreted with great caution as it indicates that this biomarker incorrectly diagnoses approximately 20% of the cases (i.e., 20% false positives: incorrectly identifying children without dyslexia as having dyslexia; as well as 20% false negatives: failing to identify children with dyslexia as having dyslexia).
More recently, studies have started to investigate whether brain imaging measures can predict subsequent learning gains (Hoeft et al., 2011) or whether they even can predict response to educational interventions (Supekar et al., 2013). Supekar et al. (2013) investigated which brain measures, in addition to behavioral outcomes, predicted the gains of a one-on-one math tutoring intervention. Their data revealed that only volume and connectivity of the hippocampus, and not the behavioral data, predicted the learning gains: the larger the hippocampus before the start of the intervention, the larger the learning gains. This association of learning gains with the size of the hippocampus is not unsurprising, given that this area of the brain is particularly relevant to the consolidation of facts in memory, and that the specific educational intervention under study involved the automatization of arithmetic facts.
These are just the very early steps in trying to predict outcomes of educational interventions on the basis of neuroscientific data. The success of this approach will stand or fall on the quality of the educational interventions that are being investigated. This requires the involvement of educational researchers in these kinds of cognitive neuroscientific studies. Without this, there is a serious risk that such predictive studies are meaningless to both cognitive neuroscience and educational research, due to the lack of theoretical grounding of such studies. And even though brain imaging measures can predict later (reading) achievement, it will be important to determine the value added of these—cost intensive—measures on top of traditional behavioral assessments. There are preliminary data that indeed suggest that neuroimaging data can explain additional variance in academic achievement beyond what is predicted from behavioral measures (Hoeft et al., 2007), but this again depends on the careful selection of behavioral measures to predict a given behavior.
Learning at the Biological Level
Neuroimaging studies also allow us to understand learning at the biological level, which adds a new level of analysis to educational theory, for example in models on the acquisition of school-taught skills, such as reading and mathematics. This has the potential to complement as well as extend the existing knowledge that has been obtained on the basis of psychological educational research, and this new level of analysis might lead to a more complete understanding of learning (see also Lieberman, Schreiber, & Ochsner, 2003, for an application in political science).
One example is the componential understanding of complex cognitive skills taught via education (Dowker, 2005). Specifically, Dowker (2005) argued, against the background of cognitive neuropsychological studies with brain damaged patients (e.g., Dehaene & Cohen, 1997), that mathematics is not a unitary skill but instead should be conceived of as consisting of multiple components to which interventions should be tailored. Specifically, these neuropsychological studies showed that brain damaged patients can be selectively impaired in different yet specific areas of mathematics. This fractionation of mathematical skill has then be used to investigate individual differences in these components of mathematical skill and to develop educational interventions that take these components as starting points to tailor interventions to the specific strengths and weaknesses of children in mathematics (Dowker, 2005).
Neuroimaging data can also provide construct validity of educational theories. For example, the IQ-discrepancy criterion—the observation that in children with specific learning disorders there should be a clear discrepancy between their IQ and their academic performance—has been criticized for many years in psychological and educational research. This critique has been validated in behavioral studies by showing that individuals with and without a discrepancy between their IQ and poor academic performance do not differ in their cognitive profile and in their response to educational interventions (Fletcher, Lyon, Fuchs, & Barnes, 2007). Tanaka et al. (2011) tested the validity of this observation at the biological level. They compared the brain activity during reading in children with reading difficulties with and without a discrepancy with their IQ. Their findings revealed that the brain activity patterns in the two groups of children did not differ, which converges with the behavioral data that reading difficulties (and their neural correlates) are independent of IQ. These observations also validated the removal of the IQ-discrepancy criterion in the definition of specific learning disorders in the latest version of DSM-V (Diagnostic and Statistical Manual, American Psychiatric Association, 2013), a classification of mental disorders widely used by mental health providers.
This observation of converging evidence has been belittled or disparaged by critics of educational neuroscience (e.g., Bowers, 2016; Smeyers, 2016), who argue that such findings add nothing to what we already know on the basis of psychological or educational research. However, if a particular mental process has an identified biological substrate, then the theoretical understanding of this process will have more exploratory power if it is constrained by both behavioral and biological data; and consequently, a better explanatory model for a given educational phenomenon will be a better base for grounding educational interventions (Howard-Jones et al., 2016).
There are also examples of studies in which neuroimaging data reveal something different (diverging evidence) from what is observed from behavioral data. This is particularly the case when one aims to measure subtle processes that are hard to capture with behavioral data alone. Investigating optimal ways to foster foreign language learning, Morgan-Short, Steinhauer, Sanz, and Ullman (2012) used ERPs to unravel the differential effects of explicit language education (grammar-focused classroom setting) versus implicit education (immersion setting) on syntactic processing. While both approaches resulted in similar behavioral improvements, as measured by means of a cognitive task in which participants had to judge the correctness of sentences, ERP measures revealed striking differences between the two approaches. The immersion setting resulted in a brain response that was similar to what is observed in native speakers, whereas this was not the case in the grammar-focused setting, suggesting that the first approach may be more beneficial in adult foreign-language learning than the latter.
More recently, Anderson, Pyke, and Fincham (2016) demonstrated by using multivariate fMRI analyses that it is possible to parse the online problem-solving process of a particular mathematical problem into different cognitive stages (i.e., encoding, planning, solving, and responding). This offers an interesting possibility to investigate the durations of each of these stages and how they change as a function of (different) educational approaches. These processes are very difficult, if not impossible, to capture by means of behavioral data, such as systematic error analysis, the analysis of latencies, or the use of introspective verbal protocols.
Another example of neuroimaging methods that reveal something different than can be observed from behavioral data lies in the detection of compensatory processes that arise in the context of remedial interventions. Various intervention studies in the domain of reading have revealed that these evidence-based programs lead to a normalization of brain activity and structure in those networks that are typically associated with normal reading, and this is accompanied by improvements in behavioral reading performance (e.g., Hoeft et al., 2011; Keller & Just, 2009; Temple et al., 2003). On the other hand, each of these studies also revealed changes in brain circuits not typically associated with reading, such as changes in the right prefrontal cortex, an observation that is suggestive of the involvement of compensatory processes. Such compensatory mechanisms are hard to detect via behavioral data. The precise function of such compensatory processes is often unclear and needs to be better understood but offers a promising avenue for informing future interventions.
Generating Predictions for Educational Research
This study of compensatory processes is also an example of a promising application of cognitive neuroscience to educational research that is more indirect. Specifically, findings from cognitive neuroscience might have the potential to generate new hypotheses on educational phenomena that can be tested in follow-up educational research, and a similar application of neuroscience to psychological research has been described by Aue, Lavelle, and Cacioppo (2009). In this way, an iterative cycle of interdisciplinary research can be generated (see also Howard-Jones et al., 2016). One example comes from the study of the role of finger representations in numerical development (Kaufmann et al., 2008). These authors examined brain activity during the comparison of numbers, a crucial skill in mathematical development (e.g., De Smedt et al., 2013), in children and adults. While the groups did not differ in their behavioral performance, children showed more activation in those brain areas that are associated with finger movements and grasping, leading Kaufmann et al. (2008) to suggest that finger-based representations play a more important role in children’s understanding of number, and perhaps should be addressed when designing interventions. The role of finger representations in numerical development has been the focus of a series of recent behavioral studies, but the evidence is mixed to date (Long et al., 2016; Wasner et al., 2016), leaving it unresolved whether the use of fingers should be encouraged or discouraged when teaching early mathematics.
Effects of Education on Biology
One of the key findings in neuroscience is that our brains are highly plastic, which means that they are shaped by experience, a process referred to as experience-dependent plasticity (Diamond & Amso, 2008; Johnson & De Haan, 2011), and this process is present throughout life. There are massive developmental changes in brain structure and function that continue into late adolescence (Giedd & Rapoport, 2010) and beyond, and these changes are driven by environmental input. One example comes from a study of London taxi drivers (Maguire et al., 2000) in which the effect of extensive training in learning how to navigate in the city of London on brain structure was investigated. This study revealed that training navigational skills induced changes in the hippocampi, a structure that is also involved in spatial navigation, of these taxi drivers and that the amount of training correlated with the size of the observed morphological changes in the brain. Importantly, there were individual differences in the extent to which the training affected brain structure, which suggests that plasticity is not unlimited.
Because children spend a large amount of time at school, education is one of the most powerful sources that shapes the development of our brains. There is now an increasing number of studies that investigate how learning to read changes and reorganizes brain structure and function (Dehaene, Cohen, Morais, & Kolinsky, 2015; Skeide et al., 2017). Literacy acquisition not only constructs brain circuits that become associated with reading but also changes brain circuitry (as well as its connectivity) that is not typically associated with reading, such as the visual ventral stream. These changes have also been observed in formerly illiterate adults, which exemplifies that plastic brain changes can occur at different ages (Dehaene, Cohen, Morais, & Kolinsky, 2015; Skeide et al., 2017). Another example is provided by Neville et al. (2013), who designed a family-based intervention program, based on knowledge of the neuroplasticity of attention and parenting research. They showed positive effects of the intervention on preschoolers from low socioeconomic backgrounds, and these effects were observed in electrophysiological measures of brain function, cognitive measures, and parent reports on child behavior. What is currently missing are studies that investigate what precise aspects of these educational experiences affect brain development and the limits of this plasticity, and this clearly represents an area for future studies at the crossroads of cognitive neuroscience and educational research.
Special education represents another area in which the effects of education on the brain have been revealed, through the study of specific remedial interventions on brain structure and function in atypical development (e.g., McCandliss, 2010). Such studies have revealed that processes of normalization—brain function becomes more similar to a typically developing control group—and compensation—activity patterns in regions different from what is observed in typically developing children—occur. These patterns on how individuals with atypical learning compensate for their difficulties are very relevant to education, as they may provide novel ways of teaching specific compensation strategies, the effects of which should be investigated in educational research.
It is important to emphasize that studies that investigate the effects of education on the brain should carefully take into account the broader educational context (e.g., participants’ learning histories, teaching materials) in which learning takes place. These variables should not be considered as confounds that should be controlled for. Rather, they should be the focus of interest as variability in these factors will have massive effects on brain structure and function. Future studies should therefore consider how these characteristics of the learning context moderate neural data acquired via brain imaging measures.
In addition to investigating the effects of educational interventions on brain structure and function, recent advances have made it possible to directly intervene at the biological level and to use neurophysiological interventions, that is, transcranial electrical brain stimulation or TES, to directly affect brain activity and consequently affect behavior through the change of brain function (Cohen-Kadosh, 2014). During TES, a small electrical current is non-invasively applied to the brain via electrodes fixated at the scalp. The current is thought to change the activity level of the cortical regions that are under the electrodes and is assumed to change performance or learning. Various studies show the effects of TES (Cohen-Kadosh, 2014, for a review). For example, the use of particular types of brain stimulation leads to improved performance in arithmetic (Krause & Cohen-Kadosh, 2013), although not all individuals respond in the same way to such stimulation (Krause & Cohen-Kadosh, 2014).
The field of brain stimulation is currently in its infancy, and at this point we do not fully understand the underlying mechanisms of these techniques (Schuijer, de Jong, Kupper, & van Atteveldt, 2017). It is also crucial to emphasize that TES has an effect only if it is accompanied by traditional behavioral and cognitive training. The existing studies are limited to (mainly healthy) adults and are not applicable to the developing brain, and it remains to be verified if such education-related applications are ethically possible (Schuijer et al., 2017).
One of the major issues that warrant attention in the application of cognitive neuroscience to educational research involves the adaptation of neuroscientific designs and data acquisition methods in order to obtain high ecological validity, that is, generalizability of findings in laboratory studies to educational contexts (see also De Smedt & Grabner, 2015). A first concern deals with the samples in cognitive neuroscience studies. Most of the existing studies included adults, or even more restricted, university students, as participants in their studies. These samples, which are usually rather small, are probably very homogeneous in terms of their educational and cultural backgrounds. Findings from these studies are not merely generalizable to individuals from more diverse educational and cultural backgrounds, and more diverse samples are probably needed in order to fully capture the complexity of differences in learning and educational trajectories. Furthermore, the number of studies in school-aged children, during which the effects of education on brain structure and function are the most prominent, is, although growing, rather small. This is explained, at least partially, by the adverse effects of moving during the acquisition of imaging data—moving occurs much more in children than in adults—which severely distorts the quality and even usability of imaging data. There could also be issues related to recruitment as well as considerations of local ethics committees that make research in children much more challenging. On the other hand, the number of developmental imaging studies is increasing, and guidelines are being developed in order to apply these methods to children (e.g., Vogel, Matjeko, & Ansari, 2016). It is important to point out that in view of the massive changes in brain structure and function through childhood and adolescence and in view of the large effects of the environmental context on brain plasticity, the generalization of adult findings to developmental populations is doubtful, even if these studies investigate effects of educational interventions (Ansari, 2010). Ignoring participant variability in terms of age, culture, and educational experiences will seriously limit the potential of cognitive neuroscience studies to educational research.
A second concern deals with the tasks that are being used during the acquisition of the imaging data. These tasks are very basic and quite different from what is being done in the classroom, where a much larger variety and complexity of tasks is employed. One reason to avoid complex tasks is that such tasks are solved by multistep procedures, which involve a plethora of cognitive processes that occur at various points during problem-solving. The more cognitive processes involved, the more difficult it becomes to disentangle them at the neurophysiological level. Another reason is that the signals that are being recorded during brain imaging methods are characterized by a large measurement error and require several trials of the same type of task to reliably estimate the brain response to a particular type of stimulus. fMRI and particularly ERP studies therefore require a lot of trials to be administered, a situation that is very different from assessments in the classroom. Furthermore, the response mode during the acquisition of brain imaging data is restricted to one or a few simple key presses. This is done to avoid movement artifacts. Typically, participants have to verify the correctness of an answer or select one from a few response alternatives, instead of actively producing the answer to a given problem. This again is different from the classroom, and the verification of answers probably induces different strategies, and consequently different (neuro)cognitive processes. Also, vocal responses as well as interactions between individuals are difficult to record due to either the aim to keep movement as minimal as possible to avoid distortion of the imaging data, or the noise of data acquisition methods, as is the case during MRI.
These methodological challenges could be a starting point for more constructive collaborations between neuroscientists and educational researchers that combine paradigms from both traditions (Ansari, De Smedt, & Grabner, 2012). One example involves the study of how measures of brain activity that are acquired under strictly controlled laboratory conditions correlate and predict real-world, and thus ecologically valid, measures of classroom learning. Another possibility is to study how environmental variables, such as educational history or cultural background, moderate brain activity during certain basic tasks. Importantly, this will require not only sufficiently large samples of participants who vary in their educational history, but also, and crucially, it will involve a careful analysis of the educational context and how its constituents impact on brain structure and function. This necessitates interdisciplinary projects in which cognitive neuroscientists and educational researchers are working at the same level (e.g., De Smedt et al., 2010).
The Scope of Biological Data
An important caveat in applying neuroscience to educational research is the scope of biological data or explanations. There is a belief that a biological explanation for a given psychological or educational phenomenon is considered more reliable, convincing, and deterministic compared to a nonbiological explanation (Beck, 2010). More specifically, people rated explanations, particularly incorrect ones, of psychological phenomena as more likely when these explanations referred to the brain (Weisberg, Keil, Goodstein, Rawson, & Gray, 2008; Weisberg, Taylor, & Hopkins, 2015). This may be so because neuroscientific data or explanations are perceived as causal—even though causality depends on the design of the research and not of the type of data that is being collected—and people tend to prefer information that provides evidence for the cause of an event (Weisberg et al., 2015). Nevertheless, it is of utmost importance to emphasize that the data and knowledge gleaned from cognitive neuroscience methods is at the same level, in terms of reliability, validity, and credibility, as data obtained by standard behavioral methods in educational research. There is no hierarchy of knowledge, with one type of data being more convincing than the other, but an appreciation of a variety of data collection methods to better understand educational phenomena (De Smedt et al., 2011). Relatedly, it is crucial to emphasize that biology does not mean destiny or that a particular behavior is hardwired or unchangeable. On the contrary, one of the key findings in neuroscience is that our brains are highly plastic, which means that they are shaped by (educational) experience. Furthermore, educational research has revealed that teaching adolescents about the plasticity of brain development—in particular the idea of a growth mindset or the assertion that intelligence is not fixed or hardwired in the brain but is malleable and can be developed—positively influences their attitudes toward learning and consequently their learning performance (Blackwell, Trzesniewski, & Dweck, 2007; Mangels, Butterfield, Lamb, Good, & Dweck, 2006).
Nuanced Translation Is Crucial
It is important to point out that the application of cognitive neuroscience to educational research is not a panacea or quick fix for unresolved problems in educational research. Indeed, the study of the brains of learners, or the identification of a neural correlate of a particular behavior and its dysfunction, does not readily answer questions about effective teaching and curriculum design. This direct application from highly controlled laboratory settings without translation to the complex and multi-determined realm of education would be a bridge too far (Bruer, 1997) and runs the risk of misinterpretations.
Such misinterpretations can occur on both sides. Neuroscientists can be naive and ignorant to the context in which learning takes place (e.g., curriculum, how it is taught, what pedagogical approaches are used) and can be easily tempted to simply convert an experimental task that is sensitive to individual differences in brain activity into an intervention. One example is the training of working memory, where individuals have to simply repeat easy working memory tasks that are used in scientific research (e.g., digit span) to improve academic outcomes. While practicing such tasks improves working memory, its training does not result in better academic skills (Melby-Lervag & Hulme, 2013). There are many commercially available packages that focus on the training of working memory, often advertised as brain training games, yet their effects on academic performance have not been scientifically established (Owen et al., 2010).
On the other hand, educators might over-interpret findings from brain imaging research. Such over-interpretations of brain imaging findings have been denoted as neuromyths, which may have adverse effects on educational practice (Howard-Jones, 2014b). Neuromyths are misconceptions about the brain, based on an incorrect interpretation of neuroimaging research, that are used to justify certain types of so-called brain-based educational interventions. One such example, which has been observed in a variety of countries (Howard-Jones, 2014b), deals with brain-based learning styles, in particular the so-called left-brain and right-brain learners, which are used to justify a variety of educational interventions that are equipped with very costly teaching materials (Howard-Jones, 2014b). There is no neuroimaging evidence for the existence of left-brained or right-brained thinkers (Nielsen et al., 2013), neither is there any support for the effectiveness of teaching practices that are based on this left-brained versus right-brained distinction (Lindell & Kidd, 2011).
As a consequence, there is no direct route from cognitive neuroscience to education, and cognitive neuroscience does not reveal what should be taught. Rather, it helps us to understand mechanisms that underlie teaching and learning, which then need to be translated via educational research in strategies and programs to optimize teaching and learning (Howard-Jones et al., 2016). This requires merging findings from cognitive neuroscience with educational theories and frameworks of instructional design (e.g., Van Merriënboer & Kirschner, 2007). This should result in novel educational approaches, the effects of which should be tested by means of rigorous educational research that ranges from small-scale interventions to larger randomized controlled trials (Sloane, 2008). Potentially, the neural markers of such intervention can be subsequently evaluated in laboratory studies to improve an understanding of the mechanisms underlying the intervention. This requires interdisciplinary training of researchers or translators who are versed in educational research but have a solid background in cognitive neuroscience (Ansari et al., 2012; De Smedt et al., 2010), an aim that is central to current master’s programs in educational neuroscience; mind, brain, and education; or neuro-education.
The application of cognitive neuroscience to educational research represents an interdisciplinary endeavor, which is situated at the crossroads of cognitive neuroscience, cognitive psychology, and educational research. From a methodological point of view, (cognitive) neuroscience offers a toolbox of methods that can be applied to educational research. These methods are applicable if one aims to understand very basic cognitive processes, particularly when educational research adopts a positivist paradigm. Promising applications of cognitive neuroscience to educational research, include providing causal models of atypical development, helping to understand learning at the biological level, and enabling the generation of predictions that can be tested in educational research. The development of the human brain is shaped by experience, and an increasing amount of studies investigate the effects of education on brain (re)organization. The application of cognitive neuroscience to educational research is not without challenges, however. These deal with ecological validity, the scope of a biological explanation, and the potential emergence of misunderstandings. Future cycles of translational research that merge cognitive neuroscientific findings and educational theories will contribute to a more solid knowledge base that will improve education.
- American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association.
- Anderson, J. R., Bothell, D., Fincham, J. M., Anderson, A. R., Poole, B., & Qin, Y. L. (2011). Brain regions engaged by part- and whole-task performance in a video game: A model-based test of the decomposition hypothesis. Journal of Cognitive Neuroscience, 23, 3983–3997.
- Anderson, J. R., Pyke, A. A., & Fincham, J. M. (2016). Hidden stages of cognition revealed in patterns of brain activation. Psychological Science, 27, 1215–1226.
- Ansari, D. (2010). Neurocognitive approaches to developmental disorders of numerical and mathematical cognition: The perils of neglecting the role of development. Learning and Individual Differences, 20(2), 123–129.
- Ansari, D., De Smedt, B., & Grabner, R. H. (2012). Neuroeducation—A critical overview of an emerging field. Neuroethics, 5(2), 105–117.
- Antonenko, P., Paas, F., Grabner, R., & van Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22, 425–438.
- Aue, T., Lavelle, L. A., & Cacioppo, J. T. (2009). Great expectations: What can fMRI research tell us about psychological phenomena? International Journal of Psychophysiology, 73, 10–16.
- Barquero, L. A., Davis, N., & Cutting, L. E. (2014). Neuroimaging of reading intervention: A systematic review and activation likelihood estimate meta-analysis. PLoS One, 9(1), e83668.
- Beck, D. M. (2010). The appeal of the brain in the popular press. Perspectives on Psychological Science, 5(6), 762–766.
- Black, J. A., Myers, C. A., & Hoeft, F. (2015). The utility of neuroimaging studies for informing educational practice and policy in reading disorders. New Directions in Child and Adolescent Development, 147, 49–56.
- Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78, 246–263.
- Bowers, J. S. (2016). The practical and principled problems with educational neuroscience. Psychological Review, 123(5), 600–612.
- Bressler, S. L., & Menon, V. (2010). Large-scale networks in cognition: Emerging methods and principles. Trends in Cognitive Sciences, 14, 277–290.
- Bruer, J. T. (1997). Education and the brain: A bridge too far. Educational Researcher, 26(8), 4–16.
- Bunge, S. A., & Souza, M. J. (2009). Executive function and higher-order cognition: Neuroimaging. Encyclopedia of Neuroscience, 4, 111–116.
- Butterworth, B., Varma, S., & Laurillard, D. (2011). Dyscalculia: From brain to education. Science, 332, 1049–1053.
- Cacioppo, J. T., Berntson, G. G., & Nusbaum, H. C. (2008). Neuroimaging as a new tool in the toolbox of psychological science. Current Directions in Psychological Science, 17, 62–67.
- Cohen-Kadosh, R. (2014). The stimulated brain: Cognitive enhancement using non-invasive brain stimulation. Oxford: Oxford University Press.
- De Smedt, B. (2014). Advances in the use of neuroscience methods in research on learning and instruction. Frontline Learning Research, 6, 7–14.
- 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, 97–105.
- De Smedt, B., Ansari, D., Grabner, R. H., Hannula-Sormunen, M., Schneider, M., & Verschaffel, L. (2011). Cognitive neuroscience meets mathematics education: It takes two to tango. Educational Research Review, 6, 232–237.
- De Smedt, B., & Grabner, R. (2015). Applications of neuroscience to mathematics education. In A. Dowker & R. Cohen-Kadosh (Eds.), Oxford handbook of mathematical cognition (pp. 613–636). Oxford: Oxford University Press.
- De Smedt, B., Noël, M. P., Gilmore, C., & Ansari, D. (2013). The relationship between symbolic and non-symbolic numerical magnitude processing skills and the typical and atypical development of mathematics: A review of evidence from brain and behavior. Trends in Neuroscience and Education, 2, 48–55.
- Dehaene, S., & Cohen, L. (1997). Cerebral pathways for calculation: Double dissociation between rote verbal and quantitative knowledge of arithmetic. Cortex, 33, 219–250.
- Dehaene, S., Cohen, L., Morais, J., & Kolinsky, R. (2015). Illiterate to literate: Behavioural and cerebral changes induced by reading acquisition. Nature Reviews Neuroscience, 16, 234–244.
- Diamond, A., & Amso, D. (2008). Contributions of neuroscience to our understanding of cognitive development. Current Directions in Psychological Science, 17, 136–141.
- Dick, F., Lloyd-Fox, S., Blasi, A., Elwell, C., & Mills, D. (2014). Neuroimaging methods. In D. Mareschal, B. Butterworth, & A. Tolmie (Eds.), Educational neuroscience (pp. 13–45). Malden, MA: Wiley-Blackwell.
- Dikker, S., Wan, L., Davidesco, I., Kaggen, L., Oostrik, M., McClintock, J., et al. (2017). Brain-to-brain synchrony track real-world dynamic group interactions in the classroom. Current Biology, 27, 1375–1380.
- Dowker, A. (2005). Individual differences in arithmetic: Implications for psychology, neuroscience and education. New York: Psychology Press.
- Dyson, N. I., Jordan, N. C., & Glutting, J. (2013). A number sense intervention for low-income kindergartners at risk for mathematics difficulties. Journal of Learning Disabilities, 46, 166–181.
- Eden, G. F., Olulade, O. A., Evans, T. M., Krafnick, A. J., & Alkire, D. R. (2016). Developmental dyslexia. In G. Hickok & S. Small (Eds.), The neurobiology of language (pp. 815–826). Amsterdam: Elsevier.
- Fischer, K. W. (2009). Mind, brain and education: Building a scientific groundwork for teaching and learning. Mind, Brain, and Education, 3, 3–16.
- Fletcher, J. M., Lyon, G. R., Fuchs, L. S., & Barnes, M. A. (2007). Learning disabilities: From identification to intervention. New York: Guilford.
- Fraga González, G., Žarić, G., Tijms, J., Bonte, M., Blomert, L., Leppänen, P. H. T., & van der Molen, M. W. (2016) Responsivity to dyslexia training indexed by the N170 amplitude of the brain potential elicited by word reading. Brain and Cognition, 106, 42–54.
- Gabrieli, J. E. D. (2009). Dyslexia a new synergy between education and cognitive neuroscience. Science, 325, 280–283.
- Giedd, J. N., & Rapoport, J. L. (2010). Structural MRI of pediatric brain development: What have we learned and where are we going? Neuron, 67, 728–734.
- Goswami, U. (2004). Neuroscience and education. British Journal of Educational Psychology, 74, 1–14.
- Goswami, U. (2008). Principles of learning, implications for teaching: A cognitive neuroscience perspective. Journal of Philosophy of Education, 42, 381–399.
- Hoeft, F., McCandliss, B. D., Black, J. M., Gantman, A., Zakerani, N., Hulme, C., et al. (2011). Neural systems predicting long-term outcome in dyslexia. Proceedings of the National Academy of Sciences of the United States of America, 108, 361–366.
- Hoeft, F., Ueno, T., Reiss, A. L., Whitfield-Gabrieli, S., Glover, G. H. et al. (2007). Prediction of children’s reading skills using behavioral, functional, and structural neuroimaging measures. Behavioral Neuroscience, 121, 602–613.
- Howard-Jones, P. (2010). Introducing neuroeducational research: Neuroscience, education and the brain from contexts to practice. London: Routledge.
- Howard-Jones, P. (2014a). Neuroscience and education: A review of educational interventions and approaches informed by neuroscience. Bristol, UK: Education Endowment Foundation.
- Howard-Jones, P. (2014b). Neuroscience and education: Myths and messages. Nature Reviews Neuroscience, 15, 817–824.
- Howard-Jones, P., Varma, S., Ansari, D., Butterworth, B., De Smedt, B., Goswami, U., et al. (2016). The principles and practices of educational neuroscience: Commentary on Bowers (2016). Psychological Review, 123, 620–627.
- Johnson, M. H., & de Haan, M. (2011). Developmental cognitive neuroscience (3rd ed.). Malden, MA: Wiley-Blackwell.
- Kaufmann, L., Vogel, S. E., Wood, G., Kremser, C., Schocke, M., Zimmerhackl, L. B., & Koten, J. W. (2008). A developmental fMRI study of nonsymbolic numerical and spatial processing. Cortex, 44, 376–385.
- Keller, T. A., & Just, M. A. (2009). Altering cortical connectivity: Remediation-induced changes in the white matter of poor readers. Neuron, 64, 624–631.
- Klimesch, W., Schack, B., & Sauseng, P. (2005). The functional significance of theta and upper alpha oscillations. Experimental Psychology, 52, 99–108.
- Krause, B., & Cohen-Kadosh, R. (2013). Can transcranial electrical stimulation improve learning difficulties in atypical brain development? A future possibility for cognitive training. Developmental Cognitive Neuroscience, 6, 176–194.
- Krause, B., & Cohen-Kadosh, R. (2014). Not all brains are created equal: The relevance of individual differences in responsiveness to transcranial electrical stimulation. Frontiers in Systems Neuroscience, 8, 25.
- Kucian, K., Grond, U., Rotzer, S., Henzi, B., Schoenmann, C., Plangger, F., et al. (2011). Mental number line training in children with developmental dyscalculia. Neuroimage, 57, 782–795.
- Lieberman, M. D., Schreiber, D., & Ochsner, K. N. (2003). Is political cognition like riding a bicycle? How cognitive neuroscience can inform research on political thinking. Political Psychology, 24, 681–704.
- Lindell, A. K., & Kidd, E. (2011). Why right-brain teaching is half-witted: A critique of the misapplication of neuroscience to education. Mind, Brain, and Education, 5, 121–127.
- Long, I., Malone, S. A., Tolan, A., Buygoyne, K., Herron-Dulaney, M., Witteveen, K., & Hulme, C. (2016). The cognitive foundations of early arithmetic skills: It is counting and number judgment, but not finger gnosis, that count. Journal of Experimental Child Psychology, 152(2), 327–334.
- Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S. J., & Frith, C. D. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences, 97, 4398–4403.
- Mangels, J. A., Butterfield, B., Lamb, J., Good, C., & Dweck, C. S. (2006). Why do beliefs about intelligence influence learning success? A social cognitive neuroscience model. Social Cognitive and Affective Neuroscience, 1(2), 75–86.
- Mareschal, D., Butterworth, B., & Tolmie, A. (2014). Educational neuroscience. London: Wiley Blackwell.
- McCandliss, B. D. (2010). Educational neuroscience: The early years. Proceedings of the National Academy of Sciences of the United States of America, 107, 8049–8050.
- Melby-Lervag, M., & Hulme, C. (2013). Is working memory training effective? A meta-analytic review. Developmental Psychology, 49, 270–291.
- Melby-Lervag, M., Lyster, S. A., & Hulme, C. (2012). Phonological skills and their role in learning to read: A meta-analytic review. Psychological Bulletin, 138, 322–352.
- Menon, V. (2015). Arithmetic in the child and adult brain. In R. Cohen-Kadosh & A. Dowker (Eds.), Oxford handbook of numerical cognition (pp. 502–530). Oxford: Oxford University Press.
- Molfese, D. L. (2000). Predicting dyslexia at 8 years of age using neonatal brain responses. Brain and Language, 72(3), 238–245.
- Morgan-Short, K., Steinhauer, K., Sanz, C., & Ullman, M. T. (2012). Explicit and implicit second language training differentially affect the achievement of native-like brain activation patterns. Journal of Cognitive Neuroscience, 24(4), 933–947.
- Neville, H., Stevens, C., Pakulak, E., Bell, T. A., Fanning, J., Klein, S., & Isbell, E. (2013). Family-based training program improves brain function, cognition, and behavior in lower socioeconomic status preschoolers. Proceedings of the National Academy of Sciences of the United States of America, 110, 12138–12143.
- Nielsen, J. A., Zielinski, B. A., Ferguson, M. A., Lainhart, J. E., & Anderson, J. S. (2013). An evaluation of the left-brain vs. right-brain hypothesis with resting state functional connectivity magnetic resonance imaging. PLoS One, 8(8), e71275.
- Norman, L., Carlisi, C., Lukito, S., Hart, H., Mataix-Cols, D., Radua, J., & Rubia, K. (2016). A comparative meta-analysis of structural and functional brain abnormalities in ADHD and OCD. JAMA Psychiatry, 73, 815–825.
- Owen, A. M., Hampshire, A., Grahn, J., Stenton, R., Dajani, S., Burns, A. S., et al. (2010). Putting brain training to the test. Nature, 465, 775–778.
- Price, C. J. (2012). A review and synthesis of the first 20 years of PET and fMRI studies of heard speech, spoken language and reading. Neuroimage, 62, 816–847.
- Redcay, E., Dodell-Feder, D., Pearrow, M. J., Mavros, P. L., Kleiner, M., Gabrieli, J. D. E., & Saxe, R. (2010). Live face-to-face interaction during fMRI: A new tool for social cognitive neuroscience. Neuroimage, 50, 1639–1647.
- Richardson, F. M., & Price, C. J. (2009). Structural MRI studies of language function in the undamaged brain. Brain Structure and Function, 213, 511–523.
- The Royal Society (2011). Brain waves module 2: Neuroscience: Implications for education and lifelong learning. London: The Royal Society.
- Schneider, M., Beeres, K., Coban, L., Merz, S., Schmidt, S., Stricker, J., & De Smedt, B. (2017). Associations of non-symbolic and symbolic numerical magnitude processing with mathematical competence: A meta-analysis. Developmental Science, 20, e12372.
- Schuijer, J. W., de Jong, I. M., Kupper, F., & van Atteveldt, N. M. (2017) Transcranial electrical stimulation to enhance cognitive performance of healthy minors: A complex governance challenge. Frontiers in Human Neuroscience, 11, 142.
- Shaywitz, S. E., Morris, R., & Shaywitz, B. A. (2009). The education of dyslexic children from childhood to young adulthood. Annual Review of Psychology, 59, 451–475.
- Skeide, M. A., Kumar, U., Mishra, R. K., Tripathi, V. N., Guleria, A., Singh, J. P., et al. (2017). Learning to read alters cortico-subcortical cross-talk in the visual system of illiterates. Science Advances, 3, e1602612.
- Sloane, F. C. (2008). Randomized trials in mathematics education: Recalibrating the proposed high watermark. Educational Researcher, 9, 624–630.
- Smeyers, P. (2016). Neuromyths for educational research and the educational field? In P. Smeyers & M. Depaepe (Eds.), Educational research: Discourses of change and changes of discourse (pp. 71–86). Cham, Switzerland: Springer.
- Snowling, M., & Hulme, C. (2011). Evidence-based interventions for reading and language difficulties: Creating a virtuous circle. British Journal of Educational Psychology, 81, 1–23.
- Snowling, M. J., & Melby-Lervag, M. (2016). Oral language deficits in familial dyslexia: A meta-analysis and review. Psychological Bulletin, 142(5), 498–545.
- Squire, L. R., Berg, D., Bloom, F. E., Du Lac, S., Ghosh, A., & Spitzer, N. C. (2013). Fundamental neuroscience (4th ed.). Oxford: Academic Press.
- Stern, E., & Schneider, M. (2010). A digital road map analogy of the relationship between neuroscience and educational research. ZDM—The International Journal on Mathematics Education, 42, 511–514.
- Supekar, K., Swigart, A. J., Tenison, C., Jolles, D. D., Rosenberg-Lee, M., Fuchs, L., & Menon, V. (2013). Neural predictors of individual differences in response to math tutoring in primary-grade school children. Proceedings of the National Academy of Sciences, 110, 8230–8235.
- Tanaka, H., Black, J. M., Hulme, C., Stanley, L. M., Kesler, S. R., Whitfield-Gabrieli, S., et al. (2011). The brain basis of the phonological deficit in dyslexia is independent of IQ. Psychological Science, 22(11), 1442–1451.
- Temple, E., Deutsch, G. K., Poldrack, R. A., Miller, S. L., Tallal, P., Merzenich, M. M., & Gabrieli, J. D. E. (2003). Neural deficits in children with dyslexia ameliorated by behavioral remediation: Evidence from functional MRI. Proceedings of the National Academy of Sciences of the United States of America, 100, 2860–2865.
- van Merriënboer, J. J. G., & Kirschner, P. (2007). Ten steps to complex learning. A systematic approach to four-component instructional design. New York: Lawrence Erlbaum.
- Vandermosten, M., Hoeft, F., & Norton, E. (2016). Integrating MRI brain imaging studies of pre-reading children with current theories of developmental dyslexia: A review and quantitative meta-analysis. Current Opinion in Behavioral Sciences, 10, 155–161.
- Vogel, S., Matjeko, A., & Ansari, D. (2016). Imaging the developing human brain using functional and structural magnetic resonance imaging: Methodological and practical guidelines. In J. Prior & J. Van Herwegen (Eds.), Practical research with children (pp. 46–69). New York: Routledge.
- Ward, J. (2006). The student’s guide to cognitive neuroscience. New York: Psychology Press.
- Wasner, M., Nuerk, H. C., Martignon, L., Roesch, S., & Moeller, K. (2016). Finger gnosis predicts a unique but small part of variance in initial arithmetic performance. Journal of Experimental Child Psychology, 146, 1–16.
- Weisberg, D. S., Keil, F. C., Goodstein, J., Rawson, E., & Gray, J. R. (2008). The seductive allure of neuroscience explanations. Journal of Cognitive Neuroscience, 20, 470–477.
- Weisberg, D. S., Taylor, J. C. V., & Hopkins, E. J. (2015). Deconstructing the seductive allure of neuroscience explanations. Judgment and Decision Making, 10, 429–441.