By: Paul Howard-Jones
We can all see the extent to which video games engage young people. Teachers often notice how willingly students respond to learning when it’s in the form of a game, and I noticed this myself when I was a teacher.
So ‘gamification’ of learning is certainly an area where insights are needed. And if we’re to take games seriously, we need to know what the cognitive processes are that bring about this engagement, and how we can tap into them for greater learning.
Learning in the real world is complex, and I think we need many different types of evidence to help understand it, including classroom data, teacher observation and brain imaging. Together, I believe these sources of data can provide a more comprehensive and thorough basis for improved teaching and learning.
One potential insight about learning games involves uncertain reward. The mantra of ‘consistent reward’ has often been popular amongst educators (and Ofsted) but neuroscience suggests we may need to turn that on its head.
When we anticipate rewards, such as food and praise, there is an associated increase of the neurotransmitter dopamine in the midbrain. Increased dopamine uptake in this area is associated with a faster rate of learning, and this response appears magnified when an element of uncertainty is associated with receiving the reward.
This provides a simple theoretical basis for learning games: instead of a point for a correct answer, offer the chance to win either 0 points or 2 points, depending on the spin of a wheel. Using such a strategy, we (along with other research groups) have shown that uncertain reward can boost emotional response, motivation and learning.
Studies suggest that dopamine ramps up between an uncertain reward being anticipated and finding out the outcome. This heightens the emotional response to the learning and increases motivation, and so should be good for academic attainment as well.
Introducing uncertain reward into teaching practice in this way departs radically from the traditional emphasis on reward consistency and may seem counter-intuitive, but it draws on our scientific understanding of how games ‘hook’ the attention of their players. The type of chance element or luck involved is a feature of many games, including those played by much younger children such as “snakes and ladders”.
That’s all well and good, of course, but there is still the question of how such an approach can become part of an everyday lesson. There are lots of questions like this that can only be answered by working with teachers. This is one of several reasons why neuroscience may only be able to help inform education if there is authentic collaboration between those who are versed in the neuroscience and those have expertise in the classroom.
The ‘Sci-napse project
Sci-napse stands for “Neuroscience–informed Approaches to Science Education”. This project, which is funded by the Wellcome Trust and the Education Endowment Foundation as part of the Education and Neuroscience initiative, aims to examine the effect of uncertain reward on Year 8 attainment in Science. It is being coordinated by the University of Bristol in partnership Manchester Metropolitan University.
Sci-napse aims to test if uncertain reward can improve students’ attainment by applying our new and growing understanding about how the brain learns. Since the games involve continuous questioning, which itself may raise achievement, we will also compare the game-based approach against a standard quizzing or test-based approach. Both approaches involve all students answering and receiving feedback on questions throughout their lessons. These questions encourage the remembering, understanding and application of new concepts.
In the test-based approach, students accumulate a fixed number of points for a correct answer. Findings from the sciences of mind and brain emphasise the importance of such testing as a means to encourage learning. In the game-based approach, the points available will escalate throughout the lesson and students can choose to game their points (double or nothing) according to chance. Insights from neuroscience emphasise how this type of game-based approach can increase stimulation of the brain’s reward system, accelerating the rate at which learning occurs.
At the same time, in other work at the University of Bristol, we are pursuing further neuroimaging research with adult learners to understand more about the underlying brain processes involved with uncertain reward and educational learning. Insights from this research may also feed into the development of the classroom approaches as they emerge.
Personally, I’m really excited about the Sci-napse project – it is the culmination of many years of classroom experiments and lab-based studies. If this excites you too, and you work in a school in the South West or North West of England who might be interested in participating (2016-2017) please do get in touch. Along with the other Wellcome Trust-EEF projects, I believe this research is going to help the UK lead the way in a fascinating new field that may benefit every student in the future.
For more information contact: Dr Katie Blakemore (firstname.lastname@example.org)
Educational neuroscience (also ‘Neuroeducation’ and ‘Mind, Brain and Education’) are growing disciplines which bring together neuroscience, psychology and education with the aim to produce powerful school learning experiences.
As professionals facilitating learning on a daily basis, it is clear why teachers want to understand more about the brain and learning. However the reality is that not all ‘brain-based’ resources and programmes available are based on research.
With a move towards education becoming more evidence informed, there is great interest to discover if this knowledge can improve education, but also in helping us to understand why things that work are so successful.
The Wellcome Trust has teamed up with ‘I’m A Scientist’ and launched an online platform providing an opportunity for teachers to have conversations with scientists about the research on how young people learn.
On this site we have neuroscientists and psychologists who carry out research on a broad range of topics, from maths and anxiety to memory and language.
To find out more: http://learning.imascientist.org.uk
To meet the scientists: http://learning.imascientist.org.uk/meet-the-scientists/
Give us a shout on Twitter (@WTeducation), and share with any teachers who you think might be interested. And remember if you’re talking about the event, use the #edneuro hashtag, to take part in the big discussion.
Find out more about the Wellcome Trust’s ‘Education and Neuroscience Initiative’.
By: Kathryn Atherton
Academic performance depends on the condition of the brain
All our experiences change and shape our brains. Teaching is no exception. Teachers provide experiences that cause changes in the brain; indeed, this is the mechanism by which school-based education works. Therefore, it makes sense that academic performance would depend on the condition of the brain.
Academic performance is complex – many different cognitive functions play a part in how well we perform, and these are supported by different brain networks.
Learning and memory are critical to academic success. The hippocampus, a structure located within the medial temporal lobes of the brain, plays a major role in the formation of long-term memories. The more effectively this structure works, the easier it may be for a person to learn and remember new things.
In addition to long-term memory, another mental ability that plays an important role in academic success is cognitive control. Cognitive control (also known as executive function) refers to our ability to control our thoughts and actions, which allows us to make decisions and to plan in accordance with our goals. These processes are thought to be largely supported by the prefrontal cortex, which lies in the frontal lobes of the brain. When this brain area functions well, we are proficient at holding and manipulating information in working memory, formulating plans, making decisions and controlling our behaviour.
Therefore, if we want to maximise the positive impact of education on academic performance we should strive to optimise the functioning of the brain, in particular the hippocampus and prefrontal cortex.
At its simplest, sleep is driven by two broad systems working symbiotically: sleep/wake homeostasis and biological circadian rhythms. Sleep/wake homeostasis tells us that the need for sleep is accumulating as we progress throughout the day, and helps us to maintain sleep throughout the night to make sure that the ‘sleep debt’ we have built up throughout the day is repaid.
If this homeostatic system existed alone then we would all start the day feeling fresh and progressively get drowsier as the day went on. This is not the case: we have peaks and troughs in our tiredness/alertness throughout the day. This rhythm is driven by our internal circadian biology: our biological clock. In adults we naturally feel more tired between 13:00-15:00 and our strongest sleep-drive is at around 02:00-04:00. The intensity of tiredness during the circadian-lows will vary depending on how much sleep we have had the night before, as will the intensity of the alertness: if we are sleep deprived we will feel more tired during the natural dips and less alert during the natural highs. In this way sleep homeostasis informs circadian rhythms. Of course, as with anything in nature, there is natural variation in these rhythms from person to person but as a general rule these patterns are pretty consistent.
The circadian rhythm is driven by the suprachiasmatic nucleus (SCN), a part of our brains, which receives light signals from the optic nerve. In the morning, when it begins to get light, the SCN tells us it is time to be awake. The SCN then signals the onset of various processes, such as increasing body temperature and the production of certain hormones, such as cortisol which, amongst other functions, increases blood sugar, to provide an increase in energy levels. Other hormones, such as melatonin, are suppressed. Melatonin is involved in the onset of sleep and sleep maintenance. Melatonin levels typically remain low during the day and high during the night, increasing as we approach sleep.
With new findings from neuroscience catching the headlines every day, surely we can tap into these results to improve our education system? The Education and Neuroscience Initiative hopes to address this question – this joint programme of work between the Wellcome Trust and the Education Endowment Foundation (EEF) aims to: build research and expertise at the interface between neuroscience and education; support the responsible transfer of technologies, resources and practices based upon neuroscience into education; and help teachers to be able to make informed choices based upon the best available evidence. In this post we explain why we are embarking on this work, share some of the learning we’ve gained in the process, and we invite a wider conversation on this topic.
Why are we doing this?
A vital reason to get this research off the ground is because teachers are already adapting their practice due to their interest in neuroscience, but have a weak evidence base to do so. A survey of teachers and parents we carried out in 2013 was consistent with previous research showing educators’ enthusiasm and appetite for increasing their understanding of how the brain learns and changing their teaching methods in response. Unfortunately a plethora of ‘brain-based’ programmes and publications, many of which are not based on real science let alone systematically tested, are pretty much all that is readily available to meet this demand. In many cases teachers were potentially wasting time and effort on programmes and pedagogies which were unsubstantiated. At its worst, teachers can be vulnerable to unscrupulous entrepreneurs who used pseudo-science to promote unevaluated, and sometimes expensive, educational approaches.
Many neuroscientists have emphasised the potential of their research to improve education, yet it is rare for their findings to be translated into testable and practical interventions. We wanted to help stimulate exploration of how neuroscience research could be translated into beneficial interventions and to test these ideas so that we know what does and does not work.
We do recognize that a lack of common language and different expertise can hinder neuroscientists and educators working together, but we are hopeful that bringing in other collaborators, such as psychologists and cognitive or sports scientists, will help bridge the divide. One of the aims of this Initiative is to help grow this interdisciplinary area of research and perhaps stimulate further funding in this area – watch this space!
By Lisa M. Saksida, Reader in Cognitive Neuroscience, University of Cambridge.
Nearly everything that we do has an impact on our brains. Changes in our behaviour and in our environment can lead to structural and functional alterations in our brains. These changes can happen at a number of different levels, from molecular and cellular changes that happen as a result of learning, up to the reorganization of entire cortical areas as a result of injury. This process is sometimes called experience-dependent plasticity, and it occurs at all ages, although the degree of plasticity is relatively high in childhood and decreases over the course of our lifetime. Neuroplasticity is what allows us to learn, to remember, to adapt and to modify our actions on the basis of experience.
One specific aspect of neuroplasticity that has received much attention over the past two decades is adult neurogenesis – the notion that new neurons can be produced in an adult brain. Until the mid-1960s it was firmly believed that neurogenesis in mammals ends in the period just after birth. Technological developments in the 1990s led to an ongoing period of intensive research in this area, and it is now well-established that every day thousands of new neurons are produced in the adult mammalian brain (Cameron and McKay, 2001; Spalding et al., 2013). Many of these new neurons are produced within a region of the brain called the hippocampus, which has long been established as being critical for learning and memory processes.
Although the process of neurogenesis has been well-studied, it is only very recently that the specific functional or behavioural consequences of neurogenesis have been considered. Increased neurogenesis generally correlates with better memory, as might be expected when the part of the brain associated with learning and memory is increased in volume. But what is the specific role of these new neurons in learning and memory?
There are several theories, but the largest body of evidence so far (although it is still very preliminary) supports the idea that new neurons in the hippocampus are important for a memory process known as “pattern separation” (Clelland et al., 2009) . In contrast to our usual notion of memory as the ability to retain information over time, pattern separation at the behavioural level refers to the ability to keep memories distinct and resistant to confusion. Imagine you are asked to remember where you parked your car this morning, yesterday morning and the day before. This task is difficult not because you need to remember something that happened a long time ago– it is easy to remember much of what happened three days ago – but because the similar memories of parking your car in the same car park over three consecutive mornings are so easily mixed up.
One very interesting aspect of neurogenesis is that it is highly responsive to environmental influences, some of which are described below. A number of simple factors have been shown to enhance neurogenesis. Less research has been done on the specific knock-on effects of increased neurogenesis on cognition, but some promising initial studies have been performed.
By Mairéad MacSweeney, Wellcome Trust Senior Research Fellow at the UCL Institute of Cognitive Neuroscience.
I work with people who are born severely or profoundly deaf in order to inform our understanding of how the brain processes language. People born deaf can provide a unique insight into how the brain processes language. This is because the vast majority of the population are born hearing, and hear spoken language even before they are born, while still in the womb. They are always surrounded by it. But for a child who is born profoundly deaf, the situation is very different. By definition they have incomplete access to spoken language. In addition some deaf children may access a purely visual language – in this country – British Sign Language. We can look at language development and brain development in these children to gain unique insights into how language is processed in the absence of sound. I look at sign language processing and also spoken language processing in the form of lip-reading and reading.
Reading forms a major part of my current research. Deaf children find it incredibly difficult to learn to read, because when we read we read a spoken language written on the page and deaf children have impoverished access to spoken language. Although deafness is not a learning disability – most deaf children have normal non-verbal IQs – a typical deaf child will have a reading age of 10-11 years old when they leave school aged 16. This has major consequences for their educational and vocational attainment.
A major skill in developing reading accuracy is learning how to map the written letters onto the correct sounds. This is what phonics training is all about – mapping sounds to letters or letter combinations. Broader language skills are also very important to reading comprehension, such as semantics (the meaning of words) and syntactic structure (how words are put together to form sentences).
Some deaf children do become very good readers. If we can understand how they achieve this, despite the fact that they can’t hear the language that they are reading, then this can give us insights into how we might be able to better teach all deaf children. More broadly, an understanding of how important hearing spoken language is for learning to read has the potential to inform our understanding of reading in hearing children too, not just deaf children.
For example, one of the core deficits in hearing children with dyslexia is that they have poor phonological awareness skills, such as knowing that “chair” rhymes with “bear,” or that “split” without the “l” is “spit.” One of the main theories of dyslexia in hearing children is that they have some kind of low-level auditory processing deficit. With deaf children we can look at a group who have very little or no auditory input, and look at the impact of that on their reading.
Cognitive Neuroscience of Attention and Motivation
By Masud Husain, Wellcome Trust Principal Fellow, University of Oxford.
Recent developments in the neuroscience of attention and motivation have moved forward at a rapid pace. We now understand a great deal about the brain systems, networks and neurotransmitters that underpin the deployment of attention and human motivation, two fundamental processes that are widely considered to play key roles in learning and educational outcome. However, application of these findings to education and improved performance is still in its infancy.
Neurofeedback is perhaps the technique which is closest to being used in educational settings. In this relatively new approach students are given real-time feedback on their own neurological state. This is typically achieved by visualising brainwaves using electroencephalography (EEG; Gruzelier, 2013), although neurofeedback has also been attempted with real-time functional magnetic resonance imaging (Weiskopf, 2012) in adults. It is proposed that by monitoring their brain activity through the use of neurofeedback, students are able to train their brains to produce specific patterns of activity that are optimal for learning (Enriquez-Geppert et al., 2013).
Several studies and, more recently, randomized trials have been conducted using EEG in children with attention deficit hyperactivity disorder (ADHD) (Loo, et al, 2012; Lofthouse, et al, 2012; Moriyama, et al, 2012; Gevensleben, et al, 2012). One recent six month study has even reported that neurofeedback outperformed standard drug treatment (methylphenidate) for ADHD in terms of academic outcome (Meisel, et al, 2013).
Other potential interventions include transcranial magnetic stimulation (TMS) (Demirtas‐Tatlidede, et al, 2013), transcranial direct current stimulation (tDCS) (Kuo, et al, 2013) and cognitive enhancement using drugs (Husain & Mehta, 2011). The use of these techniques to enhance cognitive function has been explored, to varying extents, in adults. For example, tDCS has been reported to improve numerical abilities in adults (Cohen Kadosh, et al, 2010) but the use of such techniques in children, in particular, raises both safety and ethical issues (Cohen Kadosh, et al, 2012). Similarly, studies of cognitive enhancement using drugs in adults have shown signs that the individuals most likely to benefit are those who have the lowest performance (Husain & Mehta, 2011), but these studies have not been systematically tested. For most drugs, the long-term safety profile and effects on cognition have not been established in children.
In order for techniques to be translated effectively into the classroom, to become useful and safe, they need much further testing, considering both ethical and safety issues. Obtaining regulatory and ethics committee permissions for such studies would not be straightforward, but a case could be made, particularly if there is an unmet need, for instance, for students with different types of learning disability.
By Roi Cohen Kadosh, Wellcome Research Career Development Fellow, University of Oxford.
Harnessing neuroplasticity for education using neuromodulation
I like movies. One of my favourite scenes is in “The Matrix” where the hero, Neo (no relationship to the cortex), took a short nap while a plug was inserted into the socket at the back of his head. A few seconds later, Neo woke up and said, “I know Kung Fu!” What a lovely idea! It would be much easier to learn maths, languages, and just to upload all the articles and books that I have not yet been able to read. Unfortunately, this remains as science fiction, and depending on the subject or type of learning it can take days, months, and even years of intensive labour. In the case of those with learning difficulties, such efforts do not improve their performance as they may in other people. My aim is not to invent a machine like the one Neo used, but to make a smaller step by examining the possibility of modulating neuronal activity in the brain whilst people are learning a skill and subsequently using it. Still, to the non-expert reader, it does sound like science fiction, and one of the questions that I was asked four years ago during my interview at the Wellcome Trust was if this idea is indeed feasible. In this blog I introduce the neuroscientific principles of the technique that I use in my research and describe the progress so far. I also discuss the work that still needs to be done in our efforts to improve the speed and quality of learning, and thus, educational achievements.
Stimulating the Brain using Electricity: A shocking idea?
The first association that comes to mind when electricity and brain stimulation are mentioned is electroconvulsive therapy (ECT) in which strong electrical current is induced in anaesthetised psychiatric patients for therapeutic effect. This is not what we are doing. Rather, my lab is using a technique called transcranial electrical stimulation (tES), in which we apply a small electrical current (for example, 1milliAmp, or one thousandth of an Ampere) to the scalp to modulate neuronal activity during training in order to enhance learning and high-level cognitive functions. The current is generated from a low power source, such as two AA batteries, and is delivered to the scalp using one or more electrodes.
Our research is supported by more basic research in neuroscience, in which animal studies have shown that low electrical currents can affect neuronal excitability and make it either easier or harder for neurons to fire (Bindmann, et al., 1964). More recently, research has shown that this is safe and effective in humans (Paulus, 2013; Cohen Kadosh, 2013). By employing mostly basic tasks that have little to do with education, this early research has shown that it is possible, for instance, to aid finger movements (Nitsche & Paulus, 2000) or help the brain to detect motion more accurately (Antal, et al., 2004).
Animal studies have shown that this low level of electricity can enhance the secretion of a growth factor (brain-derived neurotrophic factor) which is crucial for synaptic learning (Fritsch, et al., 2010). Furthermore, in humans, the modulatory effect of tES affects regional levels of neurochemicals (gamma-aminobutyric acid and glutamate) that are involved in learning, memory, and neuroplasticity (the brain’s ability to change in response to new experiences or learning; Stagg, et al., 2009). In line with these findings, some studies have shown that tES in combination with training can improve motor skills acquisition (Reis, et al., 2009).
This research led to a great deal of hope for rehabilitative applications of tES, mainly in rehabilitation of those with acquired neurological damage, such as stroke patients, or degenerative illnesses (Cohen Kadosh, 2013). However, I envisage that this tool can be used to improve educational outcomes, such as learning of maths, or other functions that are critical for optimum educational outcome, such as literacy, working memory or attention (Kraus & Cohen Kadosh, 2013; Cohen Kadosh, et al., 2013).
Such an approach—to modulate neuronal excitability whilst people are learning, inducing physiological changes and harnessing neuroplasticity—is, in my view, one of the most exciting synergies between neuroscience and education. We are not only examining the neural correlates of learning or cognitive skills, but rather we are affecting the brain to increase learning outcomes in a given cognitive area. In the next section, I will describe some results from my lab, which focus mainly on one of the most sophisticated human abilities: mathematical cognition.
By Sarah-Jayne Blakemore, Royal Society Research Fellow and Professor of Cognitive Neuroscience at UCL.
1. Adolescent brain development: What have we learned in the past 15 years?
Until about 15 years ago it was assumed that the vast majority of brain development takes place in the first few years of life. Up until that point, scientists did not have the technology to look inside the living, developing human brain. In the past decade, mainly due to advances in brain imaging technologies, in particular magnetic resonance imaging (MRI), neuroscientists have started to scan the living human brain at all ages, in order to track development changes in the brain’s structure – its organisation, including how much grey matter it contains – and also how it functions, across the lifespan. Many groups around the world are working in this area, and we now have a rich and detailed picture of how the living human brain develops. This picture has significantly changed the way we think about human brain development, by revealing that development does not stop in childhood, but continues throughout adolescence and well into adulthood.
Adolescence is defined as the period of life that starts with the biological changes of puberty and ends at the point at which an individual attains a stable, independent role in society. There are clearly large cultural differences in the age range associated with adolescence, and yet there are reports of adolescent-typical behaviour, such as heightened risk-taking and peer influence, in many very different cultures. There are also similarities in descriptions of adolescents throughout history. For example, in The Winter’s Tale Shakespeare portrayed adolescents as follows:
I would there were no age between 16 and three and twenty, or that youth would sleep out the rest; for there is nothing in the between but getting wenches with child, wronging the ancientry, stealing, fighting.
Thus, almost 400 years ago, Shakespeare painted a similar picture of adolescents as we do now, and we are trying to understand this kind of adolescent-typical behaviour in terms of the underlying changes in the brain that characterise this period of life. One of the brain regions that undergoes the most striking and prolonged changes during adolescence is the prefrontal cortex. This is the part of the brain at the very front, and is involved in a wide variety of high level cognitive functions, including decision-making and planning, inhibiting inappropriate behaviour, stopping you taking risks, social interaction and self-awareness.
One of the main findings is that grey matter, which contains brain cell bodies and connections between cells in the prefrontal cortex, increases in volume during childhood, peaks in early adolescence and then starts to decrease in adolescence, and this decline continues throughout the twenties. So, the prefrontal cortex loses grey matter during adolescence. It has been proposed that this decline in grey matter volume partly reflects an important neurodevelopmental process: the loss of connections between brain cells (synapses) during development. This process, which is known as synaptic pruning, partly depends on the environment in that connections that are used are strengthened; connections that aren’t used are lost – they are pruned away. Synaptic pruning fine tunes brain tissue according partly to the environment. You can think of it as a bit like pruning a rose bush. You prune the weaker branches in order for the remaining branches to grow stronger. This is happening throughout adolescence in several cortical regions, including the prefrontal cortex.
A second line of inquiry involves scanning the brain using functional MRI (fMRI) to track changes in brain activity with age. Many fMRI studies have shown that brain activity associated with tasks such as decision-making, planning, inhibiting a response and reasoning, changes across adolescence. For example, in my research group, we are particularly interested in the social brain – that is the network of brain regions that is used to understand other people. We bring adolescents into the lab to have a brain scan, and while they are being scanned we give them tasks that involve thinking about other people’s emotions, thoughts and feelings. Studies from our lab and from other labs shows consistently higher levels of activity in a social brain region called the medial prefrontal cortex in adolescents when they carry out social tasks that require understanding irony, thinking about social emotions such as guilt or embarrassment, or thinking about someone else’s intentions, for example. The different levels of activity within regions of the social brain might be because adolescents and adults use a different cognitive strategy (mental approach) to make social decisions. This is a hypothesis currently under investigation.
In order to look at cognitive approaches to social cognition, we carry out behavioural studies with adolescents, and we and other labs are finding that the ability to understand other people, for example to take another person’s perspective to guide decisions, is still developing in adolescence. At the same time, many studies have shown that the ability to plan and delay gratification is still developing during this period of life. Another area of adolescent research is risk-taking. It is well documented that teenagers tend to take risks, especially when they are with their peers. There appears to be a drive towards seeking the approval of peers, and becoming independent from one’s parents, in adolescence. Even adolescent rats and mice take more risks immediately after puberty than before puberty or in adulthood. One proposal that attempts to explain why risk-taking peaks in adolescence is to do with the brain’s limbic system – this is the brain system that gives us a rewarding feeling when we taking a risk. There is some evidence that in adolescence the limbic system is particularly sensitive to this rewarding feeling. And at the same time, the prefrontal cortex – which stops us taking risks and acting on impulse – is still developing.