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 Although a little outdated now, the Multi-Store Model of Memory (Atkinson & Shiffrin, 1968) is a useful visual illustration of this attentional bottleneck into our memory systems.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
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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

What is the obsession with ‘new’ ideas in teaching?

I have written before about the tendency for people in education to fall foul of a ‘novelty bias’, drawn naturally towards new (often untested) ideas at the expense of more reliable, time-honoured ones. Until recently, I had only considered this from the perspective of those working in front-line education. This week, however, I had an interesting insight into another possible facet of this pervasive preference; the idea that a novelty bias within academic circles may lead to a research focus which is unnaturally skewed towards producing ‘novel applications’ for education, rather than simply constraining and explaining the methods and techniques we already have.

This week I attended a seminar given by Geoffrey Bowers, a prominent critic of the field of Educational Neuroscience. Bowers is an engaging speaker and, whilst I do not agree with a number of his conclusions regarding the field, I do have sympathy with a number of his premises. I won’t review the talk or the main arguments made here (Annie Brookman-Byrne has already published this reply to the talk on her blog, and some entertaining academic exchanges between Bowers and the proponents of the field can be read, in order, here, here, here and here).

One aspect of Bowers’ argument that I hadn’t fully appreciated in my previous reading of his papers, but which came through strongly in the seminar, was the insistence that contributions of neuroscience to education could only be accepted as meaningful if they led to ‘new’ methods of instruction. Bowers stated that he could not conceive of a neuroscience finding which would inform a novel instructional method for education. Whilst I accept his point highlighting the distance between the neuroscience lab and the classroom (findings about brain activity do not in themselves immediately suggest how things should be done in schools), I am at a loss as to why research into education should be expected to spawn completely new and original educational applications.

Education been around for a long time; formal education involving training in literacy and numeracy has been practised in parts of the world for at least 3500 years. Educational philosophies have waxed and waned throughout this time in line with the prevailing political and social fashions, bringing with them new methods to try. Even today – at the end of this long period of development – intelligent and highly qualified people may possess almost directly opposing views about what constitutes a ‘good’ education. The end result of all this is that a huge number of different educational techniques will have been tried at one time or another. The space between the Montessori nursery and the lecture theatre is littered with the debris of three thousand-years’ experimentation. Is it therefore realistic to expect that something totally new will be designed? Why expect a paradigm shift which makes instruction qualitatively different from anything that has been tried before?

There’s an old saying attributed to Abraham Maslow: “if the only tool you have is a hammer, you treat every problem as if it were a nail”. But if you spend all your time demanding brand new tools, then you’ll never know whether the ones you already have could actually have done the job just as well.

What is the obsession with ‘new’ ideas in teaching?

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’

Teach like a chimp! The validity-transportability paradox in teaching

validity-transportability-paradoxA paradox of attempting to apply ideas from research into the real world is that the simplified environments of scientific experiments allow for the formation of extremely complex explanations, whilst the application of those ideas into the more complicated real world often require that they are somewhat simplified. The ideal conditions required for creating validity, and those required for creating transportability (the easy transmission of an idea into the real world, to borrow a phrase from Jack Schneider’s 2014 book ‘From the ivory tower to the school house’), are almost completely opposing. This clearly creates a dilemma: how much erosion of validity do we accept in order to allow a theory to become transportable?

‘The Chimp Paradox’… Paradox

Until recently I was something of a validity purist on this matter. I remember a seminar I attended last year with Vincent Walsh, a neuroscientist at UCL who studies sporting performance and decision-making under pressure and works with various GB sports teams. At one point, mention was made of Steve Peters, the psychiatrist who has also made a name working with some of the biggest names in UK sport such as Chris Hoy, Victoria Pendleton, Steven Gerrard and Ronnie O’Sullivan, as well as a lucrative consultancy side-arm with hundreds of companies. Peters’ work, detailed in his book ‘The Chimp Paradox’ involves dividing the mind into two competing parts; a primitive “Chimp” brain (the limbic area), which deals with emotions, and a more rational “Human” brain (the frontal cortex). In Peters’ formulation the chimp brain works 5 times faster than the human brain. In some of his work with sportspeople a third region is introduced: the “Computer” brain, which is even faster (20 times as fast as the human and 4 times faster than the chimp!)

Peters’ clients are taught to recognise their mental states and to govern nerves, pressures and insecurities according to these three brain labels. Now, from a purely neuroscientific perspective, Peters’ ideas are at best a severe oversimplification, and at worst outright inaccurate. I won’t spend time here covering the reasons for this (though this page is a decent introduction to the difficulties of dividing the brain into regions according to their ‘development’). However (and this was the point made to me when I voiced these concerns), when you have Chris Hoy crediting Peters with his gold medals, you can’t really argue with his results. Sometimes an idea can be a ‘useful simplification’ (or even a ‘useful fiction’, depending on how stringent your criteria are), containing enough accurate information to help people whilst remaining widely transportable. The success of ‘The Chimp Paradox’, then, is precisely because the complicated science behind Peters’ claims have been simplified enough to have broad accessibility and appeal to people’s everyday lives – ‘The Chimp Paradox’ Paradox, if you will.

Validity vs transportability in teaching

Whilst I’m sure this observation is far from new, it has struck an interesting chord with me recently in thinking about applications of research to teaching. I was reminded of it last week when I inevitably stumbled across yet another ‘Edu-Twitter’ debate about the chosen methods of a particular North-West London school. I know I shouldn’t, but, like a fire in a carpet warehouse, it’s hard not to slow down and watch the carnage unfold. This particular debate centred on the school’s use of certain principles of cognitive psychology (notably interleaving and spacing) as a justification for some of their methods. Some comments on the thread accused the school of oversimplifying complex theories (and implied that this therefore made them worthless). I might previously have agreed with this position, but as we have seen it is clear that some simplified scientific ideas, properly packaged, can be enormously useful to some people. If we are to embed evidence-based practice in school, then the first step is surely to embrace it in any form initially, and work out the finer details from there.

The difficulty is that there are no clear indicators as to where to draw the line between validity and transportability. Indeed, the ‘Goldilocks zone’ may be different for each idea anyway, depending, for example, on the transportability of the original idea and the extent to which it can be simplified whilst still retaining a coherent message. The downside of this process is that more complex theories (which may well resist simplification for very good reasons) will lack transportability, limiting the extent to which they are able to be widely adopted. As an example from another Twitter feed last week:
screen-shot-2017-01-30-at-16-11-17

Whilst the post was widely appreciated in certain circles, I was struck by how Cognitive Load Theory, an idea that is so central to much educational scholarship (and which is potentially an extremely helpful concept for educators) has, in my experience at least, never really caught on in classrooms. I would argue that this might be because its rather nuanced division of cognitive load into three different types is not the sort of thing that is easily transported in 140 characters or casual break-time conversation. The validity/transportability balance for CLT is an interesting story in itself; this essay from Michael Pershan charts Sweller’s attempts to balance the complexity and validity of his theory with its transportability. It is hard to see how CLT could be further simplified without losing its essential essence, but equally in its current form I’m not convinced it’s that transportable.

Teach Like a Chimp

teach-like-a-champion
With apologies to Doug Lemov…

So what are the ‘useful simplifications’ in teaching? Which academic ideas can be easily simplified into transportable forms without losing their validity? I might suggest the following:

– Applied memory strategies (e.g. spacing, interleaving, testing etc)
– Elaboration (linking to previous knowledge)
– Encouraging metacognition
I would previously have suggested feedback, but following the EEF review into marking it’s clear that this issue is rather more complicated (or less well researched) than we might imagine.

What other ideas would people suggest?

Teach like a chimp! The validity-transportability paradox in teaching

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