The Multidisciplinary Gardner

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.

References:

Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching. Educational Psychologist 41 (2): 75, 41(2), 75–86. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Work+:+An+Analysis+of+the+Failure+of+Constructivist+,+Discovery+,+Problem-Based+,+Experiential+,+and+Inquiry-Based+Teaching#8

Footnotes:

  1. 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.
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The Multidisciplinary Gardner

Pay attention! Why I think it is important to study attention in school children

cell_phoneAlthough 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:

  1. Attention is the gateway to cognition
  2. Attention directly impacts school attainment across the whole spectrum – not just at the lowest end
  3. Attention may mediate other key variables which contribute to school success
  4. 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.

as-multi
The Multi-Store Model of Memory

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.

Screen Shot 2017-10-05 at 15.53.36
Box and whisker plot showing relatively linear decreases in attainment at age 11 by number of criteria met relating to inattention at age 5. Taken from Merrell et al. (2016)

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.

 

References

  • Bornstein, M. H., & Sigman, M. D. (1986). Continuity in mental development from infancy. Child development, 251-274.
  • Caldwell, J. E. (2007). Clickers in the large classroom: Current research and best-practice tips. CBE Life Sciences Education, 6, 9-120.
  • Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: potential of the concept, state of the evidence. Review of Educational Research, 74, 59–109.
  • Hidi, S. (1995). A re-examination of the role of attention in learning from text. Educational Psychology Review, 7, 323–350
  • Posner, M. I., & Rothbart, M. K. (2005). Influencing brain networks: implications for education. Trends in Cognitive Sciences, 9, 99–103
  • Breslau, J., Miller, E., Breslau, N., Bohnert, K., Lucia, V. C., & Schweitzer, J. (2009). The Impact of Early Behavior Disturbances on Academic Achievement in High School. Pediatrics, 123(6), 1472–1476. http://doi.org/10.1542/peds.2008-1406 
  • Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C., Klebanov, P., … Zill, N. (2007). School Readiness and Later Achievement. Developmental Psychology, 43(6), 1428–1446. Retrieved from http://dx.doi.org/10.1037/[0012-1649.43.6.1428].supp
  • Halin, N., Marsh, J. E., Hellman, A., Hellström, I., & Sörqvist, P. (2014). A shield against distraction. Journal of Applied Research in Memory and Cognition, 3(1), 31–36. http://doi.org/10.1016/j.jarmac.2014.01.003
  • Mann, S., & Robinson, A. (2009). Boredom in the lecture theatre: an investigation into the contributors, moderators and outcomes of boredom amongst university students. British Educational Research Journal, 35(2), 243–258. http://doi.org/10.1080/01411920802042911
  • Merrell, C., Sayal, K., Tymms, P., & Kasim, A. (2016). A longitudinal study of the association between inattention, hyperactivity and impulsivity and children’s academic attainment at age 11. Learning and Individual Differences. http://doi.org/10.1016/j.lindif.2016.04.003
  • Merrell, C., & Tymms, P. B. (2001). Inattention, hyperactivity and impulsiveness: their impact on academic achievement and progress. British Journal of Educational Psychology, 71(1), 43-56.
  • Barriga, A. Q., Doran, J. W., Newell, S. B., Morrison, E. M., Barbetti, V., & Robbins, B. D. (2002). Relationships Between Problem Behaviors and Academic Achievement in Adolescents The Unique Role of Attention Problems. Journal of Emotional and Behavioral Disorders10 (4), 233-240. 
  • Gaspar, J. M., Christie, G. J., Prime, D. J., Jolicœur, P., & McDonald, J. J. (2016). Inability to suppress salient distractors predicts low visual working memory capacity. Proceedings of the National Academy of Sciences. http://doi.org/10.1073/pnas.1523471113
  • Gathercole, S. E., Alloway, T. P., Kirkwood, H. J., Elliott, J. G., Holmes, J., & Hilton, K. A. (2008). Attentional and executive function behaviours in children with poor working memory. Learning and Individual Differences, 18(2), 214–223. http://doi.org/10.1016/j.lindif.2007.10.003
  • Gazzaley, A. (2011). Influence of early attentional modulation on working memory. Neuropsychologia, 49(6), 1410–24. http://doi.org/10.1016/j.neuropsychologia.2010.12.022
  • Brooks, S. (2015). Does personal social media usage affect efficiency and well-being? Computers in Human Behavior, 46, 26–37. http://doi.org/10.1016/j.chb.2014.12.053
  • Kross, E., Verduyn, P., Demiralp, E., Park, J., Lee, D. S., Lin, N., … Ybarra, O. (2013). Facebook Use Predicts Declines in Subjective Well-Being in Young Adults. PLoS ONE, 8(8), 1–6. http://doi.org/10.1371/journal.pone.0069841
  • Seli, P., Risko, E. F., & Smilek, D. (2016). On the Necessity of Distinguishing Between Unintentional and Intentional Mind Wandering. Psychological Science. http://doi.org/10.1177/0956797616634068
  • Smallwood, J., Fitzgerald, A., Miles, L. K., & Phillips, L. H. (2009). Shifting moods, wandering minds: negative moods lead the mind to wander. Emotion (Washington, D.C.), 9(2), 271–6. http://doi.org/10.1037/a0014855
  • Killingsworth, M. A., & Gilbert, D. T. (2010). A wandering mind is an unhappy mind. Science (New York, N.Y.), 330, 932. http://doi.org/10.1126/science.1192439
  • McCall, R. B., & Carriger, M. S. (1993). A meta‐analysis of infant habituation and recognition memory performance as predictors of later IQ. Child development, 64(1), 57-79.
  • Mialet, J.-P., Pope, H. G., & Yurgelun-Todd, D. (1996). Impaired attention in depressive states: a non-specific deficit? Psychological Medicine, 26(05), 1009. http://doi.org/10.1017/S0033291700035339
  • Pacheco-Unguetti, A. P., & Parmentier, F. B. R. (2014). Sadness Increases Distraction by Auditory Deviant Stimuli. Emotion, 14(1), 203–213.  http://doi.org/10.1037/a0034289
  • Risko, E. F., Anderson, N., Sarwal, A., Engelhardt, M., & Kingstone, A. (2012). Everyday Attention: Variation in Mind Wandering and Memory in a Lecture. Applied Cognitive Psychology, 26(2), 234–242. http://doi.org/10.1002/acp.1814 
  • Sana, F., Weston, T., & Cepeda, N. J. (2013). Laptop multitasking hinders classroom learning for both users and nearby peers. Computers & Education, 62, 24–31. http://doi.org/10.1016/j.compedu.2012.10.003 
  • Sigman, M., Cohen, S. E., & Beckwith, L. (1997). Why does infant attention predict adolescent intelligence? Infant Behavior and Development, 20(2), 133–140. http://doi.org/10.1016/S0163-6383(97)90016-3
  • Szpunar, K. K., Khan, N. Y., & Schacter, D. L. (2013). Interpolated memory tests reduce mind wandering and improve learning of online lectures. Proceedings of the National Academy of Sciences, 110(16), 6313–6317. http://doi.org/10.1073/pnas.1221764110
  •  Zanto, T. P., & Gazzaley, A. (2009). Neural Suppression of Irrelevant Information Underlies Optimal Working Memory Performance. Journal of Neuroscience, 29(10), 3059–3066. Retrieved from http://gazzaleylab.ucsf.edu/wp-content/uploads/2014/09/Zanto2009-Neural-suppression-of-irrelevant-information-underlies-optimal-working-memory-performance.pdf
  • Zeamer, C., & Fox Tree, J. E. (2013). The process of auditory distraction: Disrupted attention and impaired recall in a simulated lecture environment. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(5), 1463–1472. http://doi.org/10.1037/a0032190
Pay attention! Why I think it is important to study attention in school children

How presentation format can reduce cognitive load: an accidental case study

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:

matrix reasoning example

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. 

matrix reasoning combined

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).

matrix reasoning separated

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.

Tips on the use of CLT in the presentation of information can be found here and here, but  those with a more detailed interest may enjoy Clark, Nguyen and Sweller’s 2005 book ‘Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load‘.

Footnotes:

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.

Reference:

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

How presentation format can reduce cognitive load: an accidental case study

‘Liking’ vs ‘wanting’. A neuroscientific view on classroom motivation

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.

motivation-and-fishing

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 Needs is 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 BerridgeFor 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.

runner-in-pain-article
Many ‘hobbies’ may not in themselves be pleasurable at the time. Piano practice was far worse than this.

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?

Footnotes:

  1. 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 rewards have 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.
  2. 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.

References:

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.

Kringelbach, M. L., & Berridge, K. C. (2009). Towards a functional neuroanatomy of pleasure and happiness. Trends in Cognitive Sciences, 13(11), 479–487. http://doi.org/10.1016/j.tics.2009.08.006

Olds, J., & Milner, P. (1954). Positive reinforcement produced by electrical stimulation of septal area and other regions of rat brain. Journal of comparative and physiological psychology47(6), 419.

Ryan, R. M., and Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 55, 68–78

Smith, K. S., & Berridge, K. C. (2007). Opioid limbic circuit for reward: interaction between hedonic hotspots of nucleus accumbens and ventral pallidum. Journal of Neuroscience27(7), 1594-1605.

Turban, D. B., & Yan, W. (2016). Relationship of eudaimonia and hedonia with work outcomes. Journal of Managerial Psychology31(6), 1006-1020.

‘Liking’ vs ‘wanting’. A neuroscientific view on classroom motivation

My NPJ Science of Learning Interview – ‘Educational implications of attention and distraction in teenagers’

The Nature partner journal ‘Science of Learning’ website is another useful addition to the increasing number of resources encouraging a more scientific approach to education and learning.

It’s also just gone up in my estimations greatly (!) as they’ve published an interview with me about my PhD work. Read it here. If you’re a teacher or researcher and any of this sounds interesting to you, please feel free to get in contact.

 

My NPJ Science of Learning Interview – ‘Educational implications of attention and distraction in teenagers’

The ‘transfer’ problem is not a surprise. It’s central to how the brain operates

One of my favourite storylines in series 4 of The Wire followed the initially calamitous attempts of Prez, the recently disgraced cop turned high-school teacher, to get his maths classes learning anything (it’s tragic that in a drama about drug gangs I still find the bits set in a school the most interesting, but I can’t help myself). Prez’s breakthrough comes when he realises that the probability theories his students are failing to absorb in class are the same ones required for success in the sidewalk dice games that they often play after hours. He begins to present the problems in the context of the dice games, and sees their understanding take a leap forward. The implication (though from memory it is never explicitly stated in the series) is that once the students have executed the strategies in this relevant real-world context, they will also be able to demonstrate that understanding back on the paper and textbook tasks on which they were originally so stuck. This storyline is a great dramatisation of the problem of transfer, the ability to apply knowledge learned in one context into another.

As David Didau pointed out in an excellent recent blog post, transfer is pretty much the point of education itself. Despite this, learning can often remain defiantly, and surprisingly, context-specific. Didau recounts a number of studies which clearly demonstrate that the transfer of knowledge is actually much more complicated and less efficient than we expect it to be, including an example very similar to that fictionalised in The Wire, involving children working in Brazilian markets being able to demonstrate mathematical strategies on their stalls that they could not do in the classroom.

Although there is lots of excellent coverage (both academically and in blogs, e.g. here, here, here and here) on what the problem is and where it occurs, I realised that I hadn’t ever read much on why it occurs. I thought it might be interesting for those interested in these problems to provide some small insight into the neuroscience of transfer, to explain why it is about the way that the brain operates which means that transfer is so difficult to produce.

One of the reasons why we expect knowledge to transfer between contexts lies in our natural intuition that information in our brains is stored in a way that is fairly stable, so that it can be called up as often and whenever it is needed. Unfortunately this is an illusion. In actual fact, the development of our brains and the storage of information in them is hugely context-dependent, so that even in maturity we are still only ever dealing with ‘partial representations’; representations of the world which capture some, but not all of it. Partial representations are, by their very nature, completely context-dependent; that is they reflect of the features of the world (and of the brain) which were the case when the information was originally stored. What follows, taking the theory of ‘Neuroconstructivism’ by Mareschal et al (2007) as my guide, are four different levels on which the activity of the brain is constrained by the context in which it occurs, and why this context-dependence is relevant to the transfer problem.

  1. Neural context – ‘encellment

The cellular neighbours of a neuron exert a large influence over its eventual function as a processor of information. The characteristics of its response and the way in which it connects and influences other neurons is in turn dependent on the type and amount of activity that the neuron itself receives. On a simple level this can be demonstrated by the foundational principle of neuroscience, paraphrasing Donald Hebb, that “cells that wire together fire together”. The more that cells communicate with each other, the more that their connections are strengthened, and the greater influence that a preceding cell exerts over the activity of subsequent cell. However, the context-dependence of neural activity is not limited to the simple co-operative strengthening of connections. They can also compete. Many areas of the brain show competition between different neurons within the same region. Competition between cells is thought to be crucial to creating specialisation (such as cells which respond only to particular orientations in visual cortex), but it can also have more drastic effects. A famous example comes from Hubel and Wiesel’s Nobel Prize-winning work on the cat visual cortex. They found that newborn cats who had one eye occluded for a time (and then reopened) showed reduced space dedicated to processing information from the occluded eye and increased space processing that from the uncovered eye. As other structures earlier in the visual system still functioned normally after the re-opening (e.g. retinal ganglion cells and the lateral geniculate nucleus – the relay station to the visual cortex), the conclusion was that these changes were the result of activity-based competition between neurons; with the diminished input from the eye at a competitive disadvantage to input from other sources. This disadvantage eventually leads to visual processing being outcompeted, and other functions expanding to occupy the territory.

What does this mean for transfer?

How any neuron responds to an input is constrained by a number of different factors: the ever-changing strengths of connections to potentially thousands of other inputs (both excitatory and inhibitory), competition (or co-operation) between neighbouring cells, or a progressive specialisation of the cell’s function. This means that a signal from a neuron can only be interpreted as representing that cell’s response to a particular set of circumstances at that specific time; the neural context, if you will.

Another consequence of the reliance of each neuron’s activity on so many of its neighbours is that this means that any information that is encoded by the neuron is likely to be done so in a distributed fashion, across large groups of neurons. Such ‘distributed representations’, whilst more robust on the face of damage and brain changes, are also far more likely to be ‘partial representations’, relying as they do on numerous small contributions from different neural sources. They will never capture a concept or an idea in its entirety. Instead, they record a blurred snapshot of some of the key details approximating the concept, a partial representation.

2. Network context – ‘embrainment’

Just as individual neurons can be affected by the context in which they find themselves, so entire brain areas can co-operate, compete and change function as a result of their context within the brain as a whole. On a larger scale than that noticed by Hubel and Wiesel, Cohen et al (1997) found that in people who have been blind from an early age, visual cortex begins to take over other functions entirely, such as touch when reading braille. Similarly, if you re-route visual information into a ferret auditory cortex, the area will begin to respond to different orientation patterns from the visual scene outside (Sur and Leamey, 2001), as normally happens in visual cortex. In less drastic fashion, maturation in the brain involves the progressive specialisation of many different brain areas, which gradually take over sole control of functions which initially call upon wider networks of regions. Again this process can be categorised by competition, with one area gradually coming to exert a dominant influence over a particular kind of processing. Good examples of these sorts of processes have been found in the pre-frontal cortex (PFC) during adolescence, such as the inferior frontal gyrus for response inhibition or the rostrolateral PFC for relational reasoning (see Dumontheil, 2016 for a review of these and others).

What does this mean for transfer?

Most formal education is taking place during periods of rapid brain development and maturation. Brain areas are progressively specialising and refining their functions, dependent on their relationship to other brain areas and input from the outside world. In this context, the distributed and partial representations that we build of the world are likely to be highly context-dependent, not only on the particular pattern of inputs, but also on the time and stage of development in which the information was learned.

3. Bodily context – ‘embodiment

The brain does not sit in glorious isolation from the rest of the body. Some hard-wired nervous behaviours, such as reflexes, can in fact form the basis for the beginnings of brain development. Infants make spontaneous reaching movements from an early age and even new-born infants will move their limbs to block a light beam (Van der Meer et al, 1995). These kinds of behaviour initiate the beginnings of feedback mechanisms between the visual and motor areas of the brain and eventually allow for the development of complex visually-guided behaviour. Of equal importance, the design of some parts of the body can constrain brain development by ensuring that it does not need to develop certain skills; cricket ears are designed to respond preferentially to male phonotaxis (a sound made by rubbing one wing against the other). The cricket brain has no such specialisation for making this distinction, because the job has already been done (Thelen et al., 1996). In human cognition, examples of ‘embodiment’ might include state-dependent memory; the finding that we recall information more successfully in a similar ‘state’ to when we learned it, for example after exercise (Miles and Hardman, 1984) or even when drunk (Goodwin et al., 1969).

What does this mean for transfer?

The development of our brains is constrained and uniquely differentiated by our nervous systems and by the body in which we find ourselves. Again, this is not just the case between individuals but also within individuals as they develop over time, and as they pass through the myriad different internal states which characterise our existence. The representations that we have of the world will reflect these changing embodiments, and will be ‘partial representations’ in that they are formed, and linked to, this embodied context. This therefore provides further scope for learning to be constrained by the situation (in the widest possible sense) in which the information was initially encountered. The Goodwin et al. paper is particularly relevant here, as it tested two outcomes; recognition and transfer. They found that, whilst recognition memory was not significantly affected by the a change in states between learning and recall, the ability to transfer the information was. Transfer, as a more complicated cognitive procedure than simple recall, is as a result even more susceptible to being restricted by the context in which it occurs.

4. Social context – ‘ensocialment

The concept of ensocialment, the idea that the social context for any act of learning is crucial to shaping the learning that takes place, will be the most familiar of these four levels of analysis to educators. Vygotsky’s social constructivist theories are probably the most famous educational application of this sort of idea. People learn from others with more skills than them; with the more knowledgeable mentors using language and guidance to ‘scaffold’ the learner’s interactions with the world in the most productive manner. The concept of scaffolding; a supportive structure which is gradually removed as the learner gains in ability, is used to one degree or another by almost every major educational approach.

What does this mean for transfer?

The type of scaffolding that is used may become inextricably linked to the solution that is produced, to the point where the ‘partial representation’ that we have of the solution is not accessed when the problem is framed differently. Think of Prez’s dice-rollers on the street corner or the Brazilian market-children, still struggling when trying to solve the same problems back in the classroom.

It might be a problem… but it shouldn’t be a surprise

It seems eminently sensible that if we know how to do something, we should be able to reproduce that skill regardless of the changing context. No doubt it seemed obvious to the writers of The Wire that the dice-rolling students would be able to solve the probability exam questions in their next test. What I have tried to show here is that actually there is good reason for suspecting that this natural intuition is flawed. Context-specificity is built into even the most basic levels of our brain function, and it operates right through from the cellular level to the societal. It is therefore hardly surprising that we also see it occurring at the higher levels of cognition focused on by education, given that it occurs pretty much everywhere else. Even the most seemingly simplistic acts such as learning a sequence of movements does not transfer into improved learning of a sequence of the same movements in a different order (Karni et al., 1995), so the idea that we might be able to teach a problem solving technique in Geography and expect it to be used in Biology suddenly looks very optimistic indeed. Even strategies normally taken to be clearly domain general, such as some kinds of study skills, may actually be quite context-dependent (although there is evidence that some other skills, such as metacognition, may improve performance across domains). In fact the potential scale of this problem is something that I think many in education are simply in denial of, as to consider just how ‘partial’ are our representations of the world can seem to be the first step on a slippery slope into educational nihilism.

What the transfer problem means for education

Not that I think such pessimism is justified. None of this is to say that transfer is not possible or does not happen. Barnett and Ceci (2002) provide examples of how transfer can be made more likely. Indeed, as Didau points out, if the conditions are right then transfer could indeed become the norm. This would be especially true if we focus on problems of ‘near’ transfer with more modest goals, such as transferring strategies between different exam questions, or different classrooms etc. I agree with Didau’s prescription that explicit teaching of knowledge plus practice in applying the knowledge to different contexts is the approach most likely to bear fruit in educational terms. From the perspective of ‘partial representations’ this strategy is likely to lead to multiple, overlapping partial representations which are strengthened through repeated access, increasing the likelihood of them being more easily accessed subsequently. To take a contrasting educational perspective, such as discovery learning, this would only lead to the (time-consuming) creation of a single partial representation, which would be far more susceptible to context-dependency. From this perspective, it is not the discovery of the strategy which is important for subsequent success, but the practice of accessing the strategy multiple times and in multiple different ways. Perhaps ironically, then, the theory of neuroconstructivism can shine a light on why many ‘constructivist’ approaches in education fail. Constructivist learning theories tend to emphasise the importance of the construction of knowledge and the placement of knowledge into a concrete context from the very start. However, this prioritises the discovery of the strategy in a single context over the practice of a strategy in multiple contexts. It argues for the formation of a single representation over multiple representations. What I have tried to show above is that our representations of the world, by their very nature, are only ever ‘partial’ representations. Given that, it makes sense for educators to work to create as many of them, and to strengthen them, wherever possible.

References:

Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn?: A taxonomy for far transfer. Psychological bulletin128(4), 612.

Cohen, L. G., Celnik, P., Pascual-Leone, A., Corwell, B., Faiz, L., Dambrosia, J., … & Hallett, M. (1997). Functional relevance of cross-modal plasticity in blind humans. Nature, 389(6647), 180-183.

Dumontheil, I. (2016). Adolescent brain development. Current Opinion in Behavioral Sciences, 10, 39–44. http://doi.org/10.1016/j.cobeha.2016.04.012

Goodwin, D. W., Powell, B., Bremer, D., Hoine, H., & Stern, J. (1969). Alcohol and recall: State-dependent effects in man. Science, 163(3873), 1358-1360.

Karni, A., Meyer, G., Rey-Hipolito, C., Jezzard, P., Adams, M. M., Turner, R., & Ungerleider, L. G. (1998). The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex. Proceedings of the National Academy of Sciences, 95(3), 861-868.

Mareschal, D., Johnson, M. H., Sirois, S., Spratling, M., Thomas, M. S. C., & Westerman, G. (2007). Neuroconstructivism. Oxford University Press.

Miles, C., & Hardman, E. (1998). State-dependent memory produced by aerobic exercise. Ergonomics, 41(1), 20-28.

Sur, M., & Leamey, C. A. (2001). Development and plasticity of cortical areas and networks. Nature Reviews Neuroscience, 2(4), 251-262.

Thelen, E., Corbetta, D., & Spencer, J. P. (1996). Development of reaching during the first year: role of movement speed. Journal of experimental psychology: human perception and performance, 22(5), 1059.

Van der Meer, A. L. H., Van der Weel, F. R., & Lee, D. N. (1995). The functional significance of arm movements in neonates. Science, 267(5198), 693.

Veenman, M. V., & Verheij, J. (2001). Technical students’ metacognitive skills: Relating general vs. specific metacognitive skills to study success. Learning and Individual differences, 13(3), 259-272.

Wiesel, T. N., & Hubel, D. H. (1963). Single-Cell Responses in Striate Cortex of Kittens Deprived of Vision in One Eye. Journal of Neurophysiology, 26, 1003–1017. http://doi.org/citeulike-article-id:7746240

The ‘transfer’ problem is not a surprise. It’s central to how the brain operates

This confused ‘neuro-educationalist’ claptrap won’t help educational neuroscience. 

The notion of a ‘brain-friendly’ education always reminds me of ‘pet-friendly’ hotels, and I think the comparison actually bears some scrutiny. Arriving in even the most ‘pet-friendly’ of hotels, for example, you would not be surprised to find out that you were still expected to exercise some degree of control over your pet. Pet-friendly hotels do not expect the dog to be in charge; they don’t lay sheep carcasses around the lobby for them (and you) to roll in, and the decision to be ‘pet-friendly’ wouldn’t necessitate a complete redesign of the hotel layout and operating system. It is still a hotel, with some allowances made. ‘Brain-friendly education’, on the other hand, seems to often suggest that educational systems need to be subjected to a root and branch overhaul in order to accommodate learners who are entirely subjugated by a ‘developing brain’ which is in-charge, capricious, and requiring of special allowances. It seems less about being ‘friendly’ towards a brain, as much as desperately trying to appease its dictatorial authority.

If you will allow me to coin a few terms here, this ‘despotic-brain’ vision of adolescence assumes what we might call “neuro-determinism” – the primacy of a neuroscientific level of explanation over and above other levels of explanation such as the cognitive and the behavioural. This assumption is central to the claims of an increasing number of ‘neuro-educationalists’, who advocate educational reforms based on recent insights into the developing adolescent brain. It was at the heart of an article in the Washington Post today, which seemed to encapsulate many of the claims and argument techniques of those in the field: ‘Brain-hostile’ education: how schools are failing adolescents. As someone working in the intersection of neuroscience, psychology and education, what irritates me most about articles such as this is that potentially very good ideas are being badly used, with a handful of sensible propositions being swept along in a flood of premature, excessive or unwarranted extrapolations into real life. Some suggestions are deeply impractical, others ignore effective insights from other psychological disciplines which can be far more easily integrated into educational settings. Here is my reaction to some of the points made in the article, and why I think dealing with this sort of misinformation is crucial for the emerging academic field of educational neuroscience.

  1. Brain-friendly”, “brain-hostile”, “brain-ignorant

As I have said any phrase like this sets alarm bells ringing, hinting as it does at an unwarranted primacy of neural level explanations over other levels. This creates a false impression that we are nothing more than a slave to our neural wiring, which is simply untrue. See here for a good summary of the recent trend towards excessive neuro-hype.

2. “ A large-scale national survey of middle and high school students revealed that more than half of all 10th grade students were bored in class and less than half enjoyed being at school… “If we were doing right by our students and our future,” says Brandon Busteed, executive director of Gallup Education, “these numbers would be the absolute opposite. For each year a student progresses in school, they should be more engaged, not less.’’ 

Why? And why does this support anything ‘brain’ related? If teenagers are often bored in school, this is not automatic evidence of a’brain-hostile’ curriculum. If I find a topic boring, I don’t automatically conclude that the topic was at fault for not being sufficiently ‘friendly’ to my brain. Is it realistic that, as they get closer to the major summative exams which will end their school careers, students should also be expected to be enjoying themselves more and more? On a separate note, as people such as Greg Ashman have pointed out, engagement is a poor proxy for learning, so reduced enthusiasm does not necessarily translate into reduced learning.

3. “At a time when adolescents’ emotional brains are jacked up to the max, the middle and high school curriculum suddenly “gets down to business” and becomes emotionally flat in tone.”

It’s true that emotional processing may exercise disproportionate influence in the adolescent brain (see previous post here), but it doesn’t follow from this that we need some sort of tempestuous, emotionally-charged curriculum for them. Admittedly, I don’t know what an ’emotionally-charged curriculum’ would look like, but then I don’t know what an “emotionally-flat” one looks like either.

4. “At a time when the adolescent’s brain increasingly craves stimulation from peers, education becomes more teacher-centered, offering less small-group interaction and cooperative learning than elementary classrooms.” 

Again this suggests an excessive level of neuro-determinism. Also, just because of the (accepted) fact that adolescents brains respond to social interaction differently (see e.g. Chein et al., 2011 or Somerville, 2013), why does this lead to the conclusion that we must place them in these situations more? Adolescent brains also have increased sensitivity and neural responses to risk taking and rewards (e.g. Fryt & Czernecka, 2015) but I can’t imagine the author complaining that “at a time when the adolescent’s brain craves stimulation, society increasingly makes an effort to prevent them from driving too fast or taking large quantities of recreational drugs.

On a more educational note, the idea that more collaborative work would lead to improved learning in teenagers (or any age groups) is generally pretty flawed, for reasons that are well documented by Tom Bennett here.

5. “In addition, teachers promote student embarrassment by posting students’ grades and test results for everyone to see, and ban or restrict social media that could facilitate interpersonal learning in the classroom.”

When does this happen? It certainly isn’t standard practice anywhere I’ve ever worked or heard about. This seems to be a descent into simple misinformation and inaccuracy. Also, with regards to the ‘social media’ point, it should be noted that the use of technology to assist learning is often met with mixed success, especially social media (see e.g. McCoy, 2013;  Sana, Weston & Cepeda, 2013; Junco & Cotten, 2012)

6. “At a point when students’ decision-making skills are at a critical stage of development and the prefrontal cortex is going through a process of fine-tuning, zero-tolerance discipline policies run roughshod over students’ capacities to learn from their mistakes.” 

I’ve written about the adolescent brain’s ability to learn from feedback, and it is true that rewards seem to need to be more salient to produce the same level of response in adolescents as in adults (see e.g. Galvan, 2013)… but is the suggestion here that there should be some sort of brain-differentiation of discipline systems in a school? “Pupil A can have one more warning than pupil B on account of her more immature frontal cortex”? Nonsense. Adolescents are not idiots. They understand right and wrong. The fact that they may push boundaries, break rules and respond slowly to feedback will indeed have neuroscientific roots (as well as partly resulting from increased social freedoms to be able to do these things), but this in no way means that they should be exempted from our normal societal (or school) rules. If anything it makes it more important that they aren’t. Again, this is unwarranted neuro-determinism.

7. “In addition, schools heap required courses on students to prepare them for college, some actually requiring students to declare a major or course of study in ninth grade or even earlier. This approach deprives students of opportunities to take electives that are interesting to them and that might lead to a vocation in adulthood.” 

The make-up of the school curriculum is always a contentious issue. No idea what it has to do with neuroscience though.

8. “During a point when students are entering the developmental stage of formal operational thinking and are able to engage more deeply in metacognition, the curriculum begins to devote more attention to lower-order skills, such as recall of facts, formulas, and details.”

Once we’ve cut through the jargon salad here, we’re left with a point that is a) not about neuroscience, and b) incorrect, creating as it does a false dichotomy between ‘lower-order’ and ‘higher-order’ skills which does not exist. ‘Lower-order’ facts and ‘higher-order’ problem-solving are not rivals; indeed ‘lower-order’ knowledge is a necessary condition for higher-order problem-solving. They are inseparably linked. E.D. Hirsch has spent 50 years trying to get this message out.

9. “Finally, at a time when adolescents have a huge appetite for rewards, teachers start employing higher standards in judging student competence and tend to give lower grades than elementary school teachers.” 

Now I happen to think that the well-documented changes in the processing of reward stimuli in adolescents might be something which could be relevantly exploited in educational settings… but imagine if the ‘solution’ that a school came up with was to simply increase teenagers’ grades (to what? ‘A++’? ‘A****’? ‘A^^^!!@^^”’?). Quite apart from the the obvious stupidity in the idea of an arbitrary increase in teenage grades (and the small problem of looming external examinations, which presumably would not be so inflated), this again falls foul of the fallacy of neuro-determinism. It treats adolescents (or anyone) as drooling idiots, helplessly controlled by their all-powerful developing ‘brain’ and therefore unable to display any skill unless it is in a ‘brain-friendly’ setting. It also ignores the fact that rewards are context-dependent. Getting a ‘C’ grade can feel like a Nobel prize in some contexts, and an ‘A’ grade would be a kick in the teeth if everyone else was getting A****.

10. “The sensation-seeking behavior that can lead adolescents to drug abuse could alternatively be directed toward a highly engaging student-centered learning project. The reward-seeking behaviors that might lure teens into Internet addiction could be tapped through a game-based learning experience in the classroom.” 

A bizarre and slightly threatening conclusion. So are teachers to blame if a student has an internet addiction, because they haven’t game-ified their lessons enough? The sad thing is that, minus the hyperbole, there are relevant and interesting things to say about some of these ideas. Take reward-seeking and game-based learning experiences. As it happens, a large-scale trial of a particular teaching approach derived from neuroscientific evidence about teenage reward processing is currently ongoing in schools. The ‘Sci-napse’ project, lead by Paul Howard-Jones in Bristol, is based on findings about neural responses to uncertain rewards, and involves a ‘gaming’ like system for points scoring within lessons. this is a really exciting and promising project, and I can’t wait to see the results… but we’ll have to wait another year at least for those. Would it really be so boring then, instead of threatening teachers with drug-abusing students if they don’t include enough group work, to have a more measured and sober conclusion? Something like “games involving uncertain rewards in the classroom may allow us to effectively exploit changes in adolescent reward processing for educational benefit, but the tests are ongoing and we’ll know more when they’re done

Ironically, it is the power of ‘neuroplasticity’ – that buzz word of popular neuro-educationalism – which is actually the reason that most of their propositions fail to hold water. Plasticity is precisely what allows us to rise above the ‘determinism’ of our developing (or regressing) cortices. It is what allows us to create behavioural and cognitive strategies which mitigate our weaknesses, be that learning the flute when we’re 80, or revising the Tudors even when we’re bored. These strategies are ‘brain-based’ in the facile sense that pretty much everything that we do is brain-based to some degree, but they are implemented at a level above the neuroscientific. The interplay between these levels is hugely complex and poorly understood, but what is clear is that any developed picture of learning (and so any coherent theory of education) requires all levels to be taken into account. Too often, neuro-educationalists ignore these non-neural levels entirely, creating a neuro-deterministic picture of us (and especially teenagers) as cerebral automatons; slaves to our circuitry without considering the programs that those circuits might be able to run.

The thing which frustrates me most about these sort of articles is that I genuinely believe that neuroscience has a lot to potentially offer to education… just not like this. Hyperbolic depictions of schools as “brain-hostile” dystopias where adolescent dreams go to die (and where the only the only saviour is neuroscience) fly in the face of reality and evidence, and create the impression that educational neuroscience is attempting to circumvent the knowledge and expertise of other established disciplines, such as developmental, cognitive and educational psychology. This is not true at all (certainly it is not true in my mind). I have referred to ‘neuro-educationalists’ in this article to try to differentiate them from ‘educational neuroscientists’, who I see as playing a collaborative role in connecting cognitive theories of learning with the underlying neuroscientific evidence and constraints. Educational neuroscience is struggling for acceptance as a discipline. I briefly covered (and linked to) some of these criticisms in my first ever blog. In a slightly hostile environment it can be tempting to grab onto any show of support, and indeed I first saw this Washington Post article when it was tweeted by a prominent ‘ed neuro’ advocate. But by promoting these articles we only strengthen the case of the detractors and increase the perception of a charlatan enterprise. With friends like these, who needs enemies?

 

References:

Sana, F., Weston, T., & Cepeda, N. J. (2013). Laptop multitasking hinders classroom learning for both users and nearby peers. Computers & Education, 62, 24–31. http://doi.org/10.1016/j.compedu.2012.10.003

Junco, R., & Cotten, S. R. (2012). No A 4 U: The relationship between multitasking and academic performance. Computers & Education, 59, 505–514. http://doi.org/10.1016/j.compedu.2011.12.023

Somerville, L. H. (2013). The Teenage Brain: Sensitivity to Social Evaluation. Current Directions in Psychological Science, 22(2), 121–127. http://doi.org/10.1177/0963721413476512

Galván, A. (2013). Current Directions in Psychological Science The Teenage Brain : Sensitivity to Rewards. Current Directions in Psychological Science, 22(2), 88–93. http://doi.org/10.1177/0963

Fryt, J., & Czernecka, K. (2015). Cognitive control, reward sensitivity and risk-taking in adolescence – research perspectives of the dual systems model. Postępy Psychiatrii I Neurologii, 24, 231–238. http://doi.org/10.1016/j.pin.2015.10.004

Chein, J., Albert, D., O’Brien, L., Uckert, K., & Steinberg, L. (2011). Peers increase adolescent risk taking by enhancing activity in the brain’s reward circuitry. Developmental Science, 14(2), F1–F10. http://doi.org/10.1111/j.1467-7687.2010.01035.x

McCoy, B. R. (2013). Digital Distractions In The Classroom: Student Classroom Use of Digital Devices for Non-Class Related Purposes. Journal of Media Education, 4(4), 5–12. Retrieved from http://en.calameo.com/read/000091789af53ca4e647f

This confused ‘neuro-educationalist’ claptrap won’t help educational neuroscience.