My last post looked at a picture I’d made of a timeline of adolescent cognitive development, and the somewhat pessimistic conclusions that I drew from it regarding the way we conduct developmental research. Discussions continue, and I agree that larger scale online (possibly game-based) research tools may be a way of addressing some of the concerns I raised.
In discussing the timeline when I first sent it out on Twitter, Lucia Magis Weinberg, a developmental cognitive neuroscientist at UC Berkeley, sent me another picture that is similarly, perhaps even more, haunting!
It’s taken from this review paper by van Duijvenvoorde et al (2016) on adolescent responses to rewards. It simply shows how different studies have chosen to define ‘adolescence’ (and well as ‘children’ and ‘adults’ in some cases). Light blue represents children (as defined in the studies). Blue represents adolescence (as defined in the studies), and dark blue represents adults (as defined in the studies).
The picture caption concludes:
The graph shows that there are large differences between studies in how adolescence is defined in ages in years. Moreover, there is overlap between studies in the ages that refer to as children and adolescents. What can be concluded from this figure is that here is a great need for more detailed measurement of adolescence in terms of age in years, but also in terms of pubertal development
In less measured terms, how on earth can we have any idea what ‘adolescents’ can and can’t do if no one can agree on how to define the period in the first place?
Recently I made the picture below (larger version can be viewed and downloaded here).
It’s a timeline of the maturation of adolescent cognitive control, in other words when different studies have found that adolescents reach ‘adult’ performance levels on various tasks that require some sort of effortful control of behaviour (such as resisting distraction, inhibiting responses etc.) Above the arrow are ‘hot’ tasks (with an emotional/affective component). Below the arrow are ‘cool’ tasks (with no affective component). Blue arrows between studies = same test/task used in between different studies. Red arrow = different tasks used by the same study/sample (or an arrow to a picture of what the task looks like).
Caveats as follows:
I only included studies which gave some comparison to ‘normal’ adult performance. Unless otherwise stated, the age given is the age at which adult performance is reached…
… any other performance level is described, e.g. “still developing” “more than adults” etc
I doing this I ignored lots of research comparing different developmental age groups to each other (but not to adults)
I will also have undoubtedly missed many other useful studies. If you think I have then please get in touch and I will be only too happy to collaborate on an updated version.
Being at an ‘adult level’ by a particular age could of course mean that the stage has been reached earlier – the ability to draw these conclusions is dependent on the age groups chosen by each study
Some of these studies, especially some of the older ones, have fairly small sample sizes (though I did try to police this, and other methodological issues, to an extent). If you know of any specific problems with cited studies then please let me know.
I have definitely made mistakes
I made this picture as I hoped that by creating a visual depiction of adolescent cognitive development, I would find it easier to synthesise the research into some general themes or points of agreement. In actual fact, however, I think the picture tells us less about adolescent development, and more about how we do research (both specifically research on adolescents, but also research in general). Specifically, I think it sheds some light on why it is often so difficult to draw any firm conclusions from parts of the academic literature. To summarise them into two primary concerns:
We have too many, construct-led experimental tasks and procedures.
Too many different tasks/paradigms
The huge variety in experimental tasks used in developmental research makes comparison very hard. I have no idea what half of the tasks in these papers actually involve without looking them up. Often I have even less idea about how performance on one of them can help us understand or predict performance on a different task. This is partly the result of a novelty bias in research where people are encouraged to create ‘novel’ research tests and designs, (which may merely muddy the water rather than extend our knowledge of how skills develop). The relatively small number of blue arrows on the picture (showing the same task being used across studies) bears testament to this, and sometimes these are just the same research group reusing the task that they invented! Of course, this is part of a larger issue which can be tracked all the way back to the replication crisis in Psychology and the perverse academic incentives which encourage bad science. Many others have written about this far better than I can. However, the confusion is not entirely due to this. It is also partially the result of…
Too much construct-led research
To make matters worse, our tests are often construct-led, which means that they are designed to investigate a specific construct, however poorly-defined and arbitrarily demarcated. For example, we have a huge variety of overlapping terms and constructs describing various aspects of how we control our thoughts and behaviour. Inhibitory control is sometimes used to describe just inhibiting specific movements or behaviours (also sometimes called response-inhibition, or just self-control). Sometimes, however, ‘inhibitory control’ is used to also encompass inhibiting attention (variously called selective attention, attention control, executive attention or distraction control), and inhibiting thoughts or memories (also sometimes called cognitive inhibition). I have also seen cognitive and attentional inhibition grouped as ‘interference control’ as distinct from response inhibition.
These constructs also dictate the design of the tasks that we use in experiments, and the conclusions that we draw from them. For example, the ‘Flanker Task’ (below) is generally used as a test of executive/selective attention, but is also sometimes described as a test of general inhibitory control.
Similarly, the famous ‘Stroop’ task is sometimes defined as a test of purely response competition, but I have also seen it used as a ‘test of’ general inhibitory control and even of just attention control.
This multiplicity of tests, domains and definitions creates a very muddled picture1 (see the very muddled picture at the top of the page for a prime example!) How useful is this? What does this allow us to really conclude about when the average adolescent can display a certain skills? More importantly, how much does allowing constructs to guide our research aims actually tell us about the real world? Does the distinction between, for example, cognitive inhibition and selective attention actually help us to predict real behaviour in adolescents? Can we dissociate them and their effects on everyday behaviour? Remarkably little research has been done which comes close to answering these sorts of questions.
I would suggest that research on adolescents (and in other areas) would be greatly enhanced by moving more from being a construct-led process, to being a function-led process. By this I mean that research aims and hypotheses should be initially guided by real world abilities and behaviours produced by adolescents, and the known outcomes of these2. Examples of these might be reports of distraction in schools classrooms, peer influence on driving safety, sleep patterns, drug taking, social media use, depression and so on3.
If we know, for example, that distracted children do worse at school (they do), then we can start by trying to design a test which might mimic the ability to control attention in a real classroom, see if this test can quantify and then predict the levels of distraction of that child in the classroom. We can use this same test repeatedly to see how this ability develops within individuals over time and then relate this to classroom reports again to see if the same patterns of development are present in behaviour. Finally we can see if our test (or other measures derived from the test) can be of use in reducing distraction in students, for example by testing the effectiveness of a number of simple environmental adjustments to reduce external distractions. This research program (which, as I have unfortunately discovered, is rather closer to a career’s worth than a PhD’s worth!) makes no reference to a pre-existing research construct regarding the control of behaviour. It would be an example of applied, function-led cognitive neuroscience research and, I think, it would tell us much more about what real world skills adolescents are capable of, and when.
Looking at the muddle of my timeline, I rather hope it happens soon!
P.S. If any readers draw any other interpretations from the timeline, or read any patterns in the tea leaves that I can’t spot, then I’d love to hear from you! Please do get in touch.
… and I haven’t even touched here on research which looks at one of these constructs in conjunction with another one, for example inhibitory control in emotional settings or under working memory load!
Construct-led, basic scientific research on developmental abilities is still important and should continue (see for example this interesting recent paper which dissociated brain activity associated with various types of inhibition), I would just like to see the balance shifted to, at the very least, parity between projects which are primarily guided by real world applications and those which are guided by more theoretical concerns.
This is not to say that there are no programs researching these areas. In some of these examples, such as peer influence on driving safety, the link between the lab and real world behaviour has been made very successfully and convincingly. They are in a minority though.
We frequently urge our students (and ourselves!) to “pay attention”, but what do we really mean by this phrase? The notion of ‘paying attention to’ an object creates an impression of attention as a resource which we have at our disposal, ready to be deployed (or not) at our command. This is reinforced by the numerous metaphors we have for the concept (a filter, a spotlight, a zoom lens, even a glue). In all of these metaphors attention is cast in the role of a ‘tool’ for us to use. Going a level deeper, such metaphors all take for granted a reified concept of attention, i.e. that attention is a real, measurable ‘thing’. Increasingly in the academic study of attention, however, there is some opposition to these traditional notions of what attention is. These can generally be summarised as an effort to recast attention as an effect rather than a cause (e.g. here, here and here, amongst others).
I think that this seemingly niche academic debate actually has some interesting implications for educators. Thinking of attention as an effect rather than a cause can throw a new light on the understudied problem of attention in schools, and what teachers can do about it.
What’s the problem?
There are two main problems with the metaphors which reify attention as a resource, one logical and one practical. The logical one (which is of less relevance perhaps to educators, but still useful for fans of logical validity), is that the evidence for these models of memory often rest on circular arguments. For example, if we take the metaphor of attention as a flashlight, imagine the following dialogue, taken from this paper by Vince Di Lollo
Person A: a stimulus flashed at a location just ahead of a moving object is perceived more promptly and more accurately.
Person B: why is that?
A: because the attentional spotlight is deployed to that location, and stimuli presented at an attended location are processed more promptly and more accurately.
B: and how do we know that attention has been deployed to that location?
A: we know it because stimuli presented at that location are perceived more promptly and more accurately.
The practical problem is that metaphors such as this place the burden for the control of attention firmly on the student themselves, to deploy as their preferences or abilities allow. Now I am in no way arguing that students are not capable of exercising control over their attention, nor that teachers should be held responsible when a student’s attention wanes; indeed I would strongly repudiate this. I do think, however, that a model which casts attention as a resource of the student is unhelpful to teachers. Teachers looking to improve student performance using this model are left with few options, other than perhaps brain training (so far unimpressive) or vague appeals to a student’s better nature (“direct your attention towards this pleeeease”).
Attention as an effect not a cause
Far more productive for educators would be the discussions arising from seeing attention as an effect, rather than a cause. This reconceptualisation naturally invites the consideration of
which conditions are most likely to engender the effect of focused attention?
which information should we create these conditions for?
Again, this suggestion in no way denies that students are causal agents in their own behaviour, merely that teachers will be more empowered by a focus on the conditions that they can create whereby attention emerges as an effect.
I will look at each of these two questions in turn
Which conditions are most likely to engender the effect of focused attention?
Decades of careful psychological work in dark laboratories has helped to confirm a lot of common sense notions about how attention can be captured (e.g. by bright colours, unexpected shapes or other features which stand out, stimuli which move, or loom, motivation, meaning, reward and so on.) If these features are present in the task, we are generally more able to focus on the task. If they are present in a stimulus which is not part of the task, then we are more likely to be distracted.
So far so good; as teachers we need to try to make our stimuli as salient as possible and reduce other distractions. However this apparent simplicity leads us on to the second, less commonly considered implication of considering attention as an effect; if we can create the conditions to direct student attention, what information exactly do we want to create these conditions for? In other words (returning to the old metaphor for simplicity’s sake), what do we want student attention to be focused on?
Which information should we create these conditions for? Selecting the target of focused attention
Where attention is discussed in education, it tends to be focused on the ideas above, in terms of strategies for capturing attention. Indeed, the goal of my teacher training on this topic was entirely this, the creation of an attentive, engaged class. What exactly they should be engaged by seemed less of a concern. If attention could be attracted by the teacher, then learning was assumed to be an inevitability.
Sadly this position is mistaken. I have written before of the limited capacity ‘bottleneck’ of attention. This limited capacity means that only a tiny fraction of information arriving into our perceptual systems will ever be processed to a meaningful degree. Therefore whilst an attentive class can clearly be a step in the right direction, they will only be learning efficiently if we as educators ensure that their attention is focused precisely on the stimuli that we want it to be. Just as becoming distracted by low level disruption from other students may inhibit learning, so will engaging with superfluous material presented by the teacher. If attention is the result of creating the right conditions, then we need to be very clear about exactly what is worth creating those conditions for.
Take the case of powerpoint slides. I spent many hours early in my teaching career crafting aesthetically pleasing powerpoint slides. My backgrounds were salient, full of nice bright colours. Some of the words zoomed in from the side of the screen. Text was usually accompanied with a picture (or even a gif if I was feeling particularly creative), usually humorous and tangentially related to the main information. Looking back, especially through the lens of considering attention as an effect and questioning where I was encouraging that effect to occur, is sobering. My salient features were either surface level features (the movement of the text rather than its content) or entirely irrelevant to what I wanted the students to know (the background and the pictures). I was actively inviting attention to be directed away from that which I thought was most important. This is why important and potentially impactful strategies such as dual coding, which combine visual and verbal materials (and which has been valuably popularised recently by figures such as Oliver Caviglioli), need to be treated with caution.Bad dual coding is not just ineffective, it leads to split attention or outright distraction.
Deciding on the right targets for our students’ attention is a challenge that cuts right across education, from the pedagogical issue of how best to deliver information to the curricular decisions required to identify precisely what it is that we want students to know in the first place. I have been delighted to see an increased focus on curriculum amongst teacher networks as a result (e.g. here, here and many great posts here for example, though to my knowledge the specific link between the importance of curriculum design and attention has not be explored either in blogs or research).
A new way to view attention in the classroom
Viewing attention as an effect makes us value it (and the contribution that we can make to it) more. It makes us consider more carefully how to attract attention, but crucially also what we want to attract attention to. Capturing attention is not in itself the aim. The goal is to provide the optimal conditions so that attention is captured by the exact stimuli that we have identified as most valuable. I have tried to argue here that this process may be assisted if we define attention less as a cause of student behaviour and more as an effect of the conditions that we put in place.
We all need to be multidisciplinary these days. University students are confidently told by their tutors that the future lies away from narrow specialisation; the true twenty-first century student will be able to traverse the arbitrary boundaries of specific subject categories with ease. This is all fine, but our enthusiasm for the polymathic can, I worry, have some serious dangers in a world where even achieving specialisation in one discipline is so challenging.
The application of psychology and neuroscience findings to education seems in many ways an archetypal example of such a modern multidisciplinary project, one to which increasing numbers of teachers and researchers are contributing. This interest is naturally welcome, given that I am a firm believer in the potential gains for education of taking a more scientifically informed approach to learning. However, the increasing numbers of academic researchers looking to make connections between their work and educational practice does raise some concerns about what it truly means to be ‘multidisciplinary’ in such a new and expanding field. Specifically, I would suggest that the following maxim would be a useful one for both researchers considering their own results, and also teachers looking to evaluate the potential contribution of research to their practice:
Multi-disciplinarity doesn’t mean simply applying from your discipline into another one
Whilst this is an issue that has niggled at me for some time, the straw which broke the camel’s back this week was reading ‘The Gardener and the Carpenter’ by developmental psychologist Alison Gopnik. The book is an attempt to show why developmental psychology evidence demonstrates that the modern fashion for ‘parenting’ (the ‘carpenter’ of the title, in which parents desperately try to shape their child into a particular pre-defined adult form), is misguided. I really wanted to like the book, and given that I can’t stand ‘how to’ parenting books, and find developmental psychology fascinating, I thought I would be onto a pretty sure thing. And in parts I did enjoy it. The parts describing the developmental psychology research were superb; delightfully, almost whimsically, creative experiments which produce fascinating results. Gopnik is a world-leading expert in the development of young children’s (e.g. under 7 or so) reasoning skills and I read these sections with the confidence and excitement that comes from receiving a guided tour from a true specialist. It would have been a much shorter book had it stopped there, but I would have been a happier reader.
The basic message of the evidence presented is that young children are remarkably adept and efficient at learning from the adults around them, but also from their environment. This combination allows them to both absorb the wisdom and traditions of previous generations, but also to make new discoveries themselves. Too much of one of these two strands can be detrimental to the other; too much didactic adult instruction can impede natural curiosity and discovery. So far so good, for experiments on children who are mostly of pre-school age or just above.
Unfortunately, Gopnik cannot resist extrapolating the findings of these studies into suggestions for educational policy. Not just primary schools, but even secondary schools, would apparently benefit from a redesign to allow learning to occur in a more observational manner, with greater emphasis on learning by ‘apprenticeship’, where children develop ‘mastery’ from extended interactions with experts. Inquiry and discovery learning should therefore replace a school system which only teaches children ‘to learn how to go to school’ and ‘become experts at test-taking’. Is there evidence to support these conjectures? Does Gopnik have the same level of experience in the field of educational design as in developmental reasoning? Certainly, the continual references to studies dry up entirely in these sections. Instead, statements such as,
“Many of the most effective teachers, even in modern schools, use elements of apprenticeship. Ironically, these teachers are more likely to be found in the ‘extracurricular’ classes than in the required ones. The stern but beloved baseball coach or the demanding but passionate music teacher let children learn this way”
seem to suggest that her evidence is gathered mainly from film plots. In actual fact, such ‘minimal guidance’ instruction has been found to be generally much less effective as a form of instruction, unless the students are already knowledgeable about a topic.
Gopnik is undoubtedly a multidisciplinary figure (she also writes philosophy papers), but I’m not sure that the educational system is one of those disciplines. As a philosopher, she will presumably be aware of Hume’s distinction that what ‘is’ does not imply what ‘ought’. The fact that children do have a great ability (sometimes better than adults) to learn through their own experimentation and interaction with the world, doesn’t imply that they ought to always do this to acquire their knowledge. Let’s say, for the sake of argument, that her ‘apprenticeship and inquiry’ model of education was the most effective one available. Even in this case, a range of other factors also govern judgements of ‘value’ in education, such as the time taken, resources, teacher capacity, motivation and many others1. All these need to be weighed into the mix as well. As Dylan William has written, “Big effects may not be worth pursuing if they cost too much to secure. And very small effects may be important if they are inexpensive to implement.” Goodness knows how much it would cost in terms of hiring and training new teachers if we are to redesign schools around a small group apprenticeship model. We’d need to be unbelievably confident that it was the right thing to do. As we saw above, however, there is good reason not even to think that it is the most effective way to learn in many situations.
In terms of crimes against multidisciplinarity, I do not by any means think that Gopnik is alone (or even the worst offender). Indeed I have noted before that I am often uncomfortable with many so-called ‘Educational Neuroscience’ studies being used to make unwarranted and premature applications to the classroom. To me, the role of educational neuroscience is not at all about the creation of new sparkly teaching methods, but too often this seems to be the aim (off the back of a few brain scans). Multidisciplinarity offers potentially huge benefits to learning and education, but in order to reap those rewards, and to avoid cul-de-sacs and wasted effort, we must be as confident as we can be that our ideas will stand up to scrutiny in the new field. We must all ensure that, as researchers, we understand enough about the discipline we are applying into to be able to objectively evaluate our ideas (or to work with others who can). If we don’t, then we aren’t really being multidisciplinary, and we probably aren’t making progress.
Incidentally, the is-ought distinction is also one that I think is useful for explaining why the issue of the role of genetics in learning (as ably summarised by Annie Brookman-Byrne recently) should not be as controversial as it often is. The (unarguable) fact that genetics does play a role in academic learning does nothing to imply anything normative about how people should be treated as a result of this.
Although I have written before about how attention develops throughout adolescence, it took reading this tweet this week to make me realise that I had never actually recorded why I think the study of attention in school-age children is important (and seemingly nor have many other people, given the tweet). Education is a hugely complex pursuit, with many variables contributing to any one outcome, so clearly any educational research programme needs to focus on a number of these strands concurrently. Despite this, I think that attention skills (specifically, the ability to control the focus of attention and resist distraction) are deserving of being a major strand of any such programme, for three main reasons:
Attention is the gateway to cognition
Attention directly impacts school attainment across the whole spectrum – not just at the lowest end
Attention may mediate other key variables which contribute to school success
Attention skills likely impact on our happiness
1. Attention is the gateway to cognition
Attention is a fascinating cognitive ability to study because it straddles the boundaries between low-level and higher-level brain processes. Whilst the exact relationship between attention and other cognitions such as working memory and intelligence is still being mapped out, the fact that attention can operate so early in the processing stream makes it inevitable that it will have effects on any attempt to use the information further down the line. We cannot process what we don’t let in. Although a little outdated now, the Multi-Store Model of Memory (Atkinson & Shiffrin, 1968) is a useful visual illustration of this attentional bottleneck into our memory systems.
At it’s most basic then, ‘attention’ describes the ability to select and processing of information from the surrounding environment. To illustrate how this affects real-life performance, individuals found to display good visual and working memory capacity have been shown to be more efficient at actively suppressing salient distractors (Gazzaley, 2011; Zanto & Gazzaley, 2009). In contrast, the inability to filter out such competing stimuli predict low working memory capacity (Gaspar et al., 2016). To relate this to the picture above, if we are allowing in irrelevant information through the attentional bottleneck, then our ability to process and manipulate the information in the way that we want to will be hindered by the increased presence of competing distractors. The beneficial effects of an efficient attention system can be seen very early on in development. Attention skills as an infant predict intelligence as an adolescent (e.g. Bornstein & Sigman, 1986; McCall & Carriger, 1993), creating discrepancies of up to 20 IQ points (Sigman, Cohen & Beckwith, 1997) in combination with environmental differences.
Most of the goals of education involve much ‘higher-level’ cognitive processes, such as reasoning, creativity and long-term memory integration. For this reason, it could seem reasonable to focus all of our research attention on these skills as well. However, this is to neglect the fact that if we ever want to understand or improve the functioning of a higher level process, we need to ensure that all the supporting lower-levels are working properly first.
2. Attention directly impacts school attainment for ALL students
It’s hardly news that distracted students are a common bugbear amongst teachers, and few would dispute the general sentiment that attention is important to education. Although the terminology varies in different sources (e.g “distractibility”, “concentration”, “engagement” “cognitive control”,“executive control” etc.), the idea of students being able to resist distraction is a key component of many educational theories (e.g. Caldwell, 2007; Fredricks, Blumenfeld & Paris, 2004; Hidi, 1995; Posner & Rothbart, 2005). This makes it all the stranger, then, that serious investigation into precisely when and how attention skills impact students in the classroom has often been lacking, especially outside of children diagnosed with ADHD.
In addition to the general acknowledgement of its importance, this lack of a specific focus on attention skills is strange, because there is good evidence that attention skills (or lack of them) strongly predict academic attainment for all students, not just those with an ADHD diagnosis (Breslau et al., 2009; Duncan et al., 2007; Merrell et al., 2016), with the effects of inattention potentially becoming more detrimental the further students progress through education (Merrell and Tymms, 2001). Breslau et al. (2009), for example, used teacher ratings of attention at age six to predict reading and Maths achievement at age 17. Attention remained a significant unique predictor, even when controlling for potential confounding variables such as such as IQ, socio-economic status, parental education or other emotional or behavioural problems.
Importantly, these effects are by no means isolated to the tail of the distribution; they are felt well beyond the confines of the clinical ‘ADHD’ boundary. The plot below from Merrell et al. (2016), for example, demonstrates a linear relationship between inattention scores at age 5 and Key Stage 2 performance in Maths and English; even relatively minor decreases in attention skills have a measurable impact on school attainment.
3. Attention may mediate other key variables which contribute to school success
Although hardly surprising, the finding that individual differences in attention skills can affect educational outcomes in school age children (especially above and beyond IQ and other background variables) suggests that they are worthy of further investigation. In addition, however, there is evidence that attention may be an important mediating factor in a number of other key skills needed for school readiness. Barriga et al. (2002) examined a range of psychological and behavioural complaints in teenagers (withdrawal, somatic complaints, delinquent behaviour, and aggressive behaviour) and found that attention significantly mediated the relationship between all of these and academic achievement. In other words, attention skills (or lack thereof) played a significant role in determining whether these factors actually did end up having a detrimental effect on students: the better developed their attention skills, the less they were affected.
Also, whilst we know that ADHD will often co-occur with other executive deficits such as self-monitoring and working memory, it is less widely known that this relationship is also present in non-clinical samples (Gathercole et al., 2008), so even relatively minor problems of attention can be magnified through their relationship to other crucial skills. Admittedly the direction of causality in this case is not as clear-cut, but again this is at least indicative of the the broad importance of attention skills to general school readiness and success.
4. Attention skills impact on happiness
In addition to the direct effects of distraction on educational attainment, there are other important social and emotional consequences of everyday inattention. Emerging evidence from a number of fields suggests that the ability to control the focus of one’s attention and to resist distraction may be an important factor in people’s experience of general well-being and happiness.
Firstly, people who are distracted often report reduced happiness. For example, distraction by social media has also been found to negatively affect people’s ratings of their happiness, both under experimental conditions using questionnaires (Brooks, 2015) and in more naturalistic settings using experience sampling techniques such as by sending the participant regular text messages to assess in-the-moment changes in focus and mood (Kross et al., 2013). Being distracted by your own thoughts is also increasingly implicated in negative mood changes. ‘Spontaneous’ mind wandering (the unintentional drifting of one’s thoughts from a focal task toward other, task-unrelated thoughts; Seli, Risko & Smilek, 2016) is associated with reduced happiness – a finding that has been noted in both laboratory (Smallwood, Fitzgerald, Miles & Phillips, 2009) and real world contexts (Killingsworth & Gilbert, 2010).
Secondly, people who are sad often report increased distractibility. Distractibility is commonly recognised as a symptom in depression and other affective disorders (e.g. Mialet, Pope & Yurgelun-Todd, 1996), and artificially lowering participants’ mood during an experiment has been shown to lead to an increase in their distractibility (Pacheco-Unguetti & Parmentier, 2014).
Clearly, it is likely that these findings are at least partially related to the other two points above. If you have the requisite attention skills for educational success then it is likely that you will also be happier for lots of other reasons as well. However, lab experiments of distraction which have measured mood usually find that the effect on mood (for most participants) is relatively short lived. This suggests that, rather than simply reflecting general life circumstances, there is often something inherently unpleasant about the act of being distracted from a main focus, which therefore affects our mood accordingly1.
We know it’s important… but that’s pretty much it for studies in schools
The three points above aimed to establish why I think attention skills are worthy of greater focus from both researchers and schools. If we accept the argument so far, then a range of other questions present themselves. What are the specific differences between children with ‘good’ and ‘bad’ attention control? How do problems like distractibility manifest themselves at different ages? How long are children of different ages able to focus their attention productively in different academic situations? Are there any reliable early signs of approaching inattention that can be identified in students? What conditions make distraction more likely? And perhaps most importantly of all: what, if anything, can be done to reduce inattention in the classroom?
It is notable how few answers we have to these questions for school age children. We are closer to some answers for studies using university students, often in lecture settings, but it is not always clear how these findings should be applied to younger children and school settings. In lectures we know, for example, that attention often measurably decreases through the duration of a lecture. Students take fewer notes, fidget and look around more, and even show reduced heart rates towards the end of a lecture (see Wilson & Korn, 2007, for a discussion of all of these). Students in lectures also mind wander significantly more in the second half of lectures (Risko et al., 2012) and report becoming bored more easily (Mann & Robinson, 2009). We also know some of the conditions under which students report greater levels of distraction in lectures, including background noise (Zeamer & Fox Tree, 2013) and non-work related laptop use (not only measurably distracting for the user, but also for others in their vicinity! See Sana, Weston and Cepeda, 2013).
We also have some early hints at ideas which might help to reduce levels of inattention, again from university student samples. For example, interpolating lecture content with regular short quizzes has been found to both improve recall and reduce mind wandering rates (Spuzner, Khan & Schachter, 2013), although to my knowledge this remains to be tested against other, external distractions. Also, there is emerging evidence that pitching the difficulty of the task correctly (i.e. challenging, but not impossible) may act as a ‘shield’ against distraction (Halin et al., 2014). Again, however, exactly how these ideas play out in the school classroom as opposed to the lecture theatre or laboratory testing room remains to be established.
In conclusion, given the widespread acknowledgement and evidence regarding the importance of attention skills to school success, it is surprising how little we know regarding the specifics of attention and distraction in the classroom. I am optimistic that a greater understanding of such processes over the next few years could lead to a number of relatively simple pedagogical or environmental strategies to improve attention in the classroom. If, that is, we pay a little more attention to them.
1. The fact that the effect of distraction on happiness is short-lived does not diminish its potential to have longer-term impacts. A short term effect that is being experienced on a very regular basis can quickly develop into something much more serious. In this way, a person experiencing very frequent distraction may begin to experience more severe and longer-lasting mood changes.
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We all know that there are some basic rules when we are presenting information: not too many words on a slide at once, try to include pictures, recap your argument from time to time and so on. I suspect, however, that most of us follow these rules more for stylistic reasons than due to any specific scientific strategy. A very neat study which came out last week, however, illustrated just how important the manner of the presentation of information really is, and how different presentation formats can lead to wildly different subsequent outcomes.
Duncan et al (2017) begin with the claim that much of the power of human cognition (and especially the facet of it known as ‘fluid intelligence’) rests heavily on the principle of compositionality – the ability to break down complex mental structures into simple parts (and vice versa, to build them back up again). They set out to test whether this ability determined performance in a commonly administered test of fluid intelligence; matrix reasoning tasks.
In a standard matrix reasoning problem (such as the one given below), to quote Duncan et al:
the task is to decide which of the four response alternatives at the bottom completes the matrix at the top. To determine the correct solution, it is necessary to take account of three varying stimulus features: whether the top part is outline or black, whether the left part is curved or angled, and whether the right part is straight or bowed. Only by considering all three features can the correct solution be determined, and reflecting the importance of complexity, if the problem has fewer varying features, it becomes progressively easier to solve.
Success in matrix reasoning tasks, then, may be due to the ability to break down the composite images into simpler constituent parts. People who do poorly on matrix reasoning tests such as these seem to struggle with keeping track of these multiple composite parts (e.g. the top, left and right sections of the example above, all of which vary independent of one another).
Duncan et al. created 20 reasoning tasks, each with three varying parts (like in the example above). Ten of the problems were presented in a traditional (or what Duncan et al call the ‘combined’) format, with the three parts varying across three pictures. However, instead of selecting their answer from four alternatives as in the standard version above, participants were required to draw the answer into a box with a common feature already provided (see below; in this case a horizontal line). In the words of Duncan et al:
By constructing matrix items from multiple parts and allowing answers for each part to be drawn in turn, we removed the requirement to store intermediate results and finally synthesise them into a single answer.
In other words, by drawing rather than selecting the answer, there is no requirement to hold onto the changes to all three parts concurrently in working memory; each can be analysed and drawn into the answer box in turn. All that is required is the cognitive insight that the problem can be segmented into three parts, and the attentional control to focus on each segmented part in isolation. Despite this apparent simplification of the procedure, however, participants’ with low fluid intelligence performed poorly on these modified matrices; success rates at solving these problems were closely related to participants’ performance on other measures of fluid intelligence. This is perhaps not surprising, given that matrix problems themselves are used as a test of fluid intelligence, but it does at least show that this modified answer format still provides a valid test of FI. However, Duncan et al’s clever innovation was to provide the other 10 reasoning problems in a different format, one which removed the compositionality demand entirely from the process by providing separate grids for each of the three varying parts of the matrix (see below for the ‘separated’ version of the same task as above).
Importantly, once the segmentation was done for them, all participants, regardless of fluid intelligence scores, achieved close to perfect scores when drawing the answers for these ten tasks. Duncan et al call the ‘separated’ versions of the tasks “trivially easy” and see them merely as a means to support their conclusion regarding the importance of compositionality for performance on FI measures. In addition to raising this question about exactly what it is that is being measured by FI tests, however, I also think that a slightly different additional conclusion can be drawn from the paper pertaining to education: presentation matters.
Here we have two ways of attempting to encourage a participant to make an identical response: the combined and separated formats of the matrix problem. Success on the combined format requires either a sufficiently developed compositional sense to segment the image into its three parts, or perhaps a sufficiently large working memory capacity to be able to hold all three parts in mind concurrently. The separated format imposes neither of these cognitive burdens, and as a result even participants who scored very poorly on the combined format are able to display high levels of accuracy. Whilst Duncan et al might label the separated format “trivially easy” it could just as well be labelled “explicitly broken down”. Were the participants to be given a series of separate matrices to do first, along with clear teacher instruction which gradually built up to attempting the combined format, it seems highly probable that even those participants who scored very poorly on the combined puzzles to start with would be more successful1.
I think this study provides a great illustration of one of the most challenging aspects of teaching – finding a manner to present complex new material in a form that initially minimises the cognitive demand for the learners – as well as clearly demonstrating the possible gains if a successful strategy is utilised. It is the sort of consideration that many teachers implicitly consider when deciding how to present new material, without always having a clear scientific framework for how to minimise the cognitive demand. Compositionality, as well as the existing principles of Cognitive Load Theory2, can help to provide a more nuanced understanding of exactly how teachers might segment and present new information, for the benefit of all.
1. Duncan et al do report that there was no separate->combined practice effect (i.e. people didn’t get better at combined puzzles if they were in the group which first tried the separate format, compared to the group which completed the experiment in the other order)… but this does not suggest how they would do if this was also paired with explicit teaching.
2. I actually think that compositionally could possibly be subsumed within the existing structure of CLT (it is a form of what CLT would call ‘element interactivity’), but my concern here is less with categorisation as with the general point about the effect of effective presentation.
Duncan, J., Chylinski, D., Mitchell, D. J., & Bhandari, A. (2017). Complexity and compositionality in fluid intelligence. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1621147114
One way in which educational neuroscience research can have an immediate and direct effect on modern education is through evaluating whether the theories and techniques which are currently used in schools are plausible, given the neuroscientific and cognitive evidence. This rather humble, constraining role is in contrast to the popular image of neuroscience as a shiny new tool with which to revolutionise classroom instruction (and as a result is far less likely to attract any funding). Still, I see it as a crucial first step in providing practical applications linking the lab and the classroom coherently. As an example, let’s take the case of motivation. Do common ideas about motivation in the classroom coherently reflect what we know about motivation in the brain?
I came across the picture below this week, with a quote attributed to educational consultants Gayle Gregory and Carolyn Chapman.
Whist I have struggled to find the specific reference given in the picture, Gregory and Chapman are a pretty prolific publishing duo, so it wasn’t difficult to find other similar material. For example, in ‘Differentiating Instruction with Style’, Gregory (2005) writes that motivation to learn was increased when students:
“chose to learn the topic, had fun learning, got a sense of personal satisfaction from the experience, were able to use the learning to enhance their lives and enjoyed working with their instructor.”
Gregory and Chapman’s fishing analogy and the quote above taps into a natural intuition that motivation and enjoyment are intrinsically linked. If we find something enjoyable, then presumably we will want to do it again. The obvious conclusion for educators to draw is that if we want motivated students, we must focus our efforts on making learning as enjoyable as possible for them.
Here, then, is an intuitive and seemingly common-sense psychological theory. It is also one that is hugely prevalent across all levels of education. Indeed, trainee teachers are taught this very concept; teacher training courses will often cover intrinsic motivation, where the satisfaction of performing the action itself provides the motivation to repeat it. Maslow’s Hierarchy of Needsis the classic example of this idea, though there have been many other adaptations since (see e.g. Csikszentmihalyi, 2000; Glasser, 1990, 1998; Ryan & Deci, 2000). All these theories assume a close, even necessary, connection between liking something and wanting to repeat it. But is this assumption supported by what we know about the neuroscience of motivation? I would argue that it is not.
Liking is not the same as wanting
Evidence emerging over the last 20 years of research into the neuroscience of motivation has begun to strongly suggest that merely finding something pleasurable may not actually be enough to generate a motivational state; in fact, liking something and wanting to repeat it may be dissociable. In an excellent review of neuroscientific models of motivation and their relevance to education, Kim (2013) writes:
This means that a state of liking for a specific object or activity cannot be understood as a motivational state and that liking is not a prerequisite for generating motivation. From this perspective, liking refers to an emotional state whereas wanting has more to do with motivation and decision utility (Berridge and Aldridge, 2008).
A good deal of the careful work unpacking the various different aspects of what makes an experience pleasurable has come from the lab of Kent Berridge. For example, whilst liking and wanting have previously both been associated with a region of the brain called the nucleus accumbens (NAcc), Berridge (2003) found that they are actually processed by distinct, anatomically separate NAcc regions which can operate independently of one another. In addition, liking and wanting may involve different neurotransmitters, as artificially suppressing dopamine release can reduce wanting behaviour towards a stimulus without reducing the degree of liking for it (Berridge and Robinson, 2003). Berridge concluded that dopamine was only important for increasing the ‘incentive salience’ ̶ the degree of wanting ̶ of a stimulus, and in turn therefore producing a motivational state to repeat it, rather than for regulating the liking of the stimulus itself.
Whilst this distinction between liking and wanting may seem initially counter-intuitive, it is actually one that we are all pretty familiar with in our everyday lives. Many of us will recognise that it is perfectly possible to be highly motivated to perform an action, without finding the action itself intrinsically pleasurable. An obvious example for many people might be our jobs, but even within the realm of activities which we freely choose to do this distinction is still surprisingly common. Take exercise, for example. Many people have strong desire to exercise (exercise has a high ‘incentive salience’) and are therefore motivated to exercise regularly. For a good proportion of these people, however, the actual process of exercise, the in-the-moment sensory experience of it, is not in itself pleasurable. Indeed, it may sometimes be actively unpleasant; the first football game after buying new boots was always an agonising ordeal, but there was no way I was actually going to stop playing. Why, then, do we continue? Because we have some higher goal (or stimulus of very high incentive salience) which motivates us, overriding the temporary experience of pain, tiredness or discomfort.
A less wholesome example of the same process is drug abuse. Drug addicts show a stark dissociation between liking and wanting. They may come to hate the drug itself, but the incentive salience is such that they crave it nonetheless (Berridge & Robinson, 1995). Animals too will continue to self-administer a drug long after they appear to find the experience pleasurable (Berridge & Valenstein, 1991), even to the point of complete exhaustion or death (Olds and Milner, 1954).
Explaining the difference: hedonia and eudaimonia
Identifying different components of happiness is by no means a new idea. Aristotle distinguished between hedonia (pure sensory pleasure) and eudaimonia (a life well-lived or ‘human flourishing’), and this ancient division is actually remarkably useful in helping us to interpret modern day neuroscientific findings. Hedonia represents ‘liking’, whilst eudaimonia provides the ‘wanting’ or incentive salience (as well as higher cognitive influences such as goal setting). Whilst in most conceptions of eudaimonia it is assumed to be a positive force, it is important to note the corollary, overly intense ‘wanting’ can lead to unhappiness and addiction (Kringelbach & Berridge, 2009). Whilst the brain systems governing hedonic and eudaimonic experience are complex, and extend beyond simply different areas of the NAcc mentioned above, they are again clearly distinguishable in the brain, involving different regions and neurotransmitters (Kringelbach & Berridge, 2009).
Hedonia and Eudaimonia in education
So what relevance has this neuroscientific distinction between eudaimonia and hedonia for education? I would say quite a lot. If we accept that the incentive salience of an object is not intrinsically linked to our liking of it, then suddenly the rationale behind many teaching strategies is thrown into question. As Kim (2013) concludes:
There is a need for careful reconsideration of the argument in which the school activity should be enjoyable to generate motivation because pleasure and enjoyment may not automatically lead to motivation.
When considering the happiness of students in lessons, we have a natural tendency to think in terms of hedonic experience, prioritising the immediate gratification of an enjoyable activity and assuming that this will create a motivational engagement. Instead, the component of happiness which has the strongest impact on motivational processes is eudaimonia. This raises a challenge, as it much easier to see how one might create a hedonic experience for students than a eudaimonic one. Uncovering which techniques promote a eudaimonic educational environment is a question for classroom research rather than the lab1, but the answers are likely to lie in approaches which eschew short-term emotional gratification in favour of challenge and student satisfaction over a longer time frame.
So how can neuroscience influence education?
Much of the debate around the potential impact of neuroscience on education surrounds its potential (or otherwise) to create revolutionary, novel teaching techniques. I wrote last week about why I thought that this was an unnecessarily restrictive approach. The application of the neuroscience of motivation to the classroom is a great example of how neuroscience (and cognitive psychology) research can be used to critically appraise and fine-tune what we do already, rather than re-invent the wheel. Maybe neuroscience never will revolutionise the way that information is delivered in schools (I wouldn’t be at all surprised if it didn’t). But providing teachers with a reasoned and evidence-based justification for resisting the pressure to prioritise cheap emotional gains at the expense of long-term challenge and eudaimonic satisfaction, whilst also reassuring them that this is more likely to produce motivated students, rather than less? That’s not bad for starters, is it?
An ongoing programme looking at this very issue is the Sci-Napse project run by Paul Howard-Jones from Bristol University and funded by the EEF and the Wellcome Trust. The study is based on lab findings that the dopamine responses in brain areas associated with creating incentive motivations are stronger when rewards are provided in an uncertain or inconsistent fashion. This makes sense; uncertain rewardshave been known to be highly motivating to behaviour ever since Skinner’s experiments with rats and pigeons from the 1930s. Some teachers may have ethical qualms about student learning being influenced through targeting the same circuits that were hijacked to produce the uncontrolled, addictive behaviours produced in Skinner’s pigeons, but it’s an interesting approach.
Of course, the most effective methods are likely be ones which are able to produce both hedonic and eudaemonic experiences. The interaction between the two produces stronger responses than either individual system (Smith & Berridge, 2007). A combination of eudaimonia and hedonic also more strongly predicts positive work outcomes (Turban & Yan, 2016). I focus here on the importance of eudaimonia because of its specific relationship to motivation and also because of its tendency to be neglected in the classroom.
Berridge, K. C., & Robinson, T. E. (1995). The mind of an addicted brain: neural sensitization of wanting versus liking. Current Directions in Psychological Science, 4(3), 71-75.
Berridge, K. C., and Valenstein, E. S. (1991). What psychological process mediates feeding evoked by electrical stimulation of the lateral hypothalamus? Behav. Neurosci. 105, 3–14.
Berridge, K. C., and Robinson, T. E. (2003). Parsing reward. Trends Neurosci. 26, 507–513.
Berridge, K. C., and Aldridge, J. W. (2008). Decision utility, the brain and pursuit of hedonic goals. Soc. Cogn. 26, 621–646.
Csikszentmihalyi, M. (2000). Happiness, flow, and human economic equality. Am. Psychol. 55, 1163–1164.
Gregory, G. H., & Chapman, C. (2012). Differentiated instructional strategies: One size doesn’t fit all. Corwin press.
Gregory, G. H. (Ed.). (2005). Differentiating instruction with style: Aligning teacher and learner intelligences for maximum achievement. Corwin Press.
Kim, S. I. (2013). Neuroscientific model of motivational process. Frontiers in Psychology, 4(98), 2.