working memory

In October I reported on a study that found older adults did better than younger adults on a decision-making task that reflected real-world situations more closely than most tasks used in such studies. It was concluded that, while (as previous research has shown) younger adults may do better on simple decision-making tasks, older adults have the edge when it comes to more complex scenarios. Unsurprisingly, this is where experience tells.

Last year I reported on another study, showing that poorer decisions by older adults reflected specific attributes, rather than age per se. Specifically, processing speed and memory are behind individual differences in decision-making performance. Both of these processes, of course, often get worse with age.

What these two studies suggest is that your ability to make good decisions depends a lot on whether

• you have sufficient time to process the information you need,
• your working memory is up to the job of processing all the necessary information, and
• your long-term memory is able to provide any information you need from your own experience.

One particular problem for older adults, for example, that I have discussed on many occasions, is source memory — knowing the context in which you acquired the information. This can have serious consequences for decision-making, when something or someone is remembered positively when it should not, because the original negative context has been forgotten.

But the trick to dealing with memory problems is to find compensation strategies that play to your strengths. One thing that improves with age is emotion regulation. As we get older, most of us get better at controlling our emotions, and using them in ways that make us happier. Moreover, it appears that working memory for emotional information (in contrast to other types of information) is unaffected by age. Given new research suggesting that decision-making is not simply a product of analytic reasoning processes, but also involves an affective/experiential process that may operate in parallel and be of equal importance, the question arises: would older adults be better relying on emotion (their ‘gut’) for decisions?

In Scientific American I ran across a study looking into this question. 60 younger (aged 18-30) and 60 older adults (65-85) were presented with health care choices that required them to hold in mind and consider multiple pieces of information. The choices were among pairs of health-care plans, physicians, treatments, and homecare aides. Working memory load increased across trials from one to four attributes per option. On each trial, one option had a higher proportion of positive to negative attributes. Each attribute had a positive and negative variant (e.g., “dental care is fully covered” vs “dental care is not covered”).

In the emotion-focus condition participants were asked to focus on their emotional reactions to the options and report their feelings about the options before making a choice. In the information-focus condition, participants were told to focus instead on the specific attributes and report the details about the options. There were no such instructions in the control condition.

As expected, working memory load had a significant effect on performance, but what’s interesting is the different effects in the various conditions. In the control condition, for both age groups, there was a dramatic decrease in performance when the cognitive load increased from 2 items to 4, but no difference between those in which the load was 4, 6, or 8 items. In the information-focus condition, the younger group showed a linear (but not steep) decrease in decision-making performance with each increase in load, except at the last — there was no difference between 6 and 8 items. The older group showed a dramatic drop when load was increased from 2 to 4, no difference between 4 and 6, and a slight drop when items increased to 8. In the emotion-focus condition, both groups showed the same pattern they had shown in the information-focus condition, except that, for the younger group, there was a dramatic drop when items increased to 8.

So that’s one point: that the effect of cognitive load is modified by instructional condition, and varies by age.

The other point, of course, concerns how level of performance varies. Interestingly, in the control condition, the two age groups performed at a similar level. In the information-focus condition, the slight superiority of the younger group when the load was lightest expanded significantly as soon as the number of items increased to four, and was greatest at the highest load. In the emotion-focus condition, however, the very slight superiority of the younger group at two items did not increase as the load increased, and indeed reversed when the load increased to eight.

Here’s what I think are the most interesting results of this study:

There was no significant difference in performance between the age groups when no instruction was given.

Younger adults were better off being given some instruction, but when the cognitive load was not too great (2, 4, 6 items), there was no difference for them in focusing on emotions or details. The difference — and it was a significant one — came when the load was highest. At this point, they were much better to concentrate on the details and apply their reasoning abilities.

Older adults, on the other hand, were better off, always but especially when the load was highest, in focusing on their feelings.

Performance on a digit-symbol coding task (a measure of processing speed) correlated significantly with performance in the information-focus condition for both age groups. When processing speed was taken into account, the difference between the age groups in that condition disappeared. In other words, younger adults' superior performance in the information-focus condition was entirely due to their higher processing speed. However, age differences in the emotion-focus condition were unaffected.

Younger adults performed significantly better in the information-focus condition compared to the control condition, indicating that specific instructions are helpful. However, there was no significant difference between the emotion-focus condition and the control for the older adults, suggesting perhaps that such processing is their ‘default’ approach.

The findings add weight to the idea that there is a separate working memory system for emotion-based information.

It should be noted that, somewhat unusually, the information was presented to participants sequentially rather than simultaneously. It may well be that these results do not apply to the situation in which you have all the necessary information presented to you in a document and can consider it at your leisure. On the other hand, in the real world we often amass information over time, or acquire it by listening rather than seeing it all nicely arrayed in front of us.

The findings suggest that the current emphasis on providing patients with all available information in order to make an “informed choice” may be misplaced. Many older patients may be better served by a greater emphasis on emotional information, rather than being encouraged to focus on myriad details.

But I'd like to see this experiment replicated using a simultaneous presentation. It may be that these findings should principally be taken as support for always seeking written documentation to back up spoken advice, or, if you're gathering information over time and from multiple sources, making sure you have written notes for each instance. Personally, I dislike making any decisions based solely on information given in conversation, and this is a reluctance I have found increasing steadily with age (and I'm not that old yet!).

The limitations of working memory have implications for all of us. The challenges that come from having a low working memory capacity are not only relevant for particular individuals, but also for almost all of us at some points of our lives. Because working memory capacity has a natural cycle — in childhood it grows with age; in old age it begins to shrink. So the problems that come with a low working memory capacity, and strategies for dealing with it, are ones that all of us need to be aware of.

Today, I want to talk a little about the effect of low working memory capacity on reading comprehension.

A recent study involving 400 University of Alberta students found that 5% of them had reading comprehension difficulties. Now the interesting thing about this is that these were not conventionally poor readers. They could read perfectly well. Their problem lay in making sense of what they were reading. Not because they didn’t understand the words or the meaning of the text. Because they had trouble remembering what they had read earlier.

Now these were good students — they had at least managed to get through high school sufficiently well to go to university — and many of them had developed useful strategies for helping them with this task: highlighting, making annotations in the margins of the text, and so on. But it was still very difficult for them to get hold of the big picture — seeing and understanding the text as a whole.

This is more precisely demonstrated in a very recent study that required 62 undergraduates to read a website on the taxonomy of plants. Now this represents a situation that is much more like a real-world study scenario, and one that has, as far as I know, been little studied: namely, drawing together information from multiple documents.

In this experiment, the multiple documents were represented by 24 web pages. Each page discussed a different part of the plant taxonomy. The website as a whole was organized according to a four-level hierarchical tree structure, where the highest level covered the broadest classes of plants (“Plants”), and the lowest, individual species. However — and this is the important point — there was no explicit mention of this organization, and you could navigate only one link up or down the tree, not sideways. Participants entered the site at the top level.

After pretesting, to assess WMC and prior plant knowledge, the students were given 18 search questions. Participants were asked both to read the site and answer the questions. They were given 25 minutes to do so, after which they completed a post-test similar to their pre-test of prior knowledge: (1) placing the eight terms found in the first three levels on the hierarchical tree (tree construction task); (2) selecting the correct two items from a list of five, that were subordinates to a given item (matching task).

Neither WMC nor prior knowledge affected performance on the search task. Neither WMC nor prior knowledge (nor indeed performance on the search task) directly affected performance on the post-test matching task, indicating that learning simple factual knowledge is not affected by your working memory capacity or how much relevant knowledge you have (remember though, that this was a very simple and limited amount of new knowledge).

But, WMC did significantly affect understanding of the hierarchical structure (assessed by the tree construction task). Prior knowledge did not.

These findings don’t only tell us about the importance of WMC for seeing the big picture, they also provide some evidence of what underlies that, or at least what doesn’t. The findings that WMC didn’t affect the other tasks argues against the idea that high WMC individuals may be benefiting from a faster reading speed, or that they are better at making local connections, or that they can cope better at doing multiple tasks. WMC didn’t affect performance on the search questions, and it didn’t affect performance on the matching task, which tested understanding of local connections. No, the only benefit of a high WMC was in seeing global connections that had not been made explicitly.

Let’s go back to the first study for a moment. Many of the students having difficulties apparently did use strategies to help them deal with their problem, but their strategy use obviously wasn’t enough. I suspect part of the problem here, is that they didn’t really realize what their problem was (and you can’t employ the best strategies if you don’t properly understand the situation you’re dealing with!).

This isn’t just an issue for people who lack the cognitive knowledge and the self-knowledge (“metacognition”) to understand their intrinsic problem. It’s also an issue for adults whose working memory capacity has been reduced, either through age or potentially temporary causes such as sleep deprivation or poor health. In these cases, it’s easy to keep on believing that ways of doing things that used to work will continue to be effective, not realizing that something fundamental (WMC) has changed, necessitating new strategies.

So, let’s get to the burning question: how do you read / study effectively when your WMC is low?

The first thing is to be aware of how little you can hold in your mind at one time. This is where paragraphs are so useful, and why readability is affected by length of paragraphs. Theoretically (according to ‘best practice’), there should be no more than one idea per paragraph. The trick to successfully negotiating the hurdle of lengthy texts lies in encapsulation, and like most effective strategies, it becomes easier with practice.

Rule 1: Reduce each paragraph to as concise a label as you can.

Remember: “concise” means not simply brief, but rather, as brief as it can be while still reminding you of all the relevant information that is encompassed in the text. This is about capturing the essence.

Yes, it’s an art, and to do it well takes a lot of practice. But you don’t have to be a master of it to benefit from the strategy.

The next step is to connect your labels. This, of course, is a situation where a mind map-type strategy is very useful.

Rule 2: Connect your labels.

If you are one of those who are intimidated by mind maps, don’t be alarmed. I said, “mind map-type”. All you have to do is write your labels (I call them labels to emphasize the need for brevity, but of course they may be as long as a shortish sentence) on a sheet of paper, preferably in a loose circle so that you can easily draw lines between them. You should also try to write something by these lines, to express your idea of the connection. These labels will also provide a more condensed label for the ideas being connected. You can now make connections between these labels and the others.

The trick is to move in small steps, but not to stay small. Think of the process as a snowball, gathering ideas and facts as it goes, getting (slowly) bigger and bigger. Basically, it’s about condensing and connecting, until you have everything densely connected, and the information getting more and more condensed, until you see the whole picture, and understand the essence of it.

Another advantage of this method is that you will have greatly increased your chances of remembering it in the long-term!

In a situation similar to that of the second study — assorted web pages — you want to end up with a tight cluster of labels for each page, the whole of which is summed up by one single label.

What all this means for teachers, writers of text books, and designers of instructional environments, is that they should put greater effort into making explicit global connections — the ‘big picture’.

A final comment about background knowledge. Notwithstanding the finding of the second study that there was no particular benefit to prior knowledge, the other part of this process is to make connections with knowledge you already have. I’d remind you again that that study was only testing an extremely limited knowledge set, and this greatly limits its implications for real-world learning.

I have spoken before of how long-term memory can effectively increase our limited WMC (regardless of whether your WMC is low or high). Because long-term memory is essentially limitless. But information in it varies in its accessibility. It is only the readily accessible information that can bolster working memory.

So, there are two aspects to this when it comes to reading comprehension. The first is that you want any relevant information you have in LTM to be ‘primed’, i.e. reading and waiting. The second is that you are obviously going to do better if you actually have some relevant information, and the more the better!

This is where the educational movement to ‘dig deep not broad’ falls down. Now, I am certainly not arguing against this approach; I think it has a lot of positive aspects. But let’s not throw out the baby with the bathwater. A certain amount of breadth is necessary, and this of course is where reading truly comes into its own. Reading widely garners the wide background knowledge that we need — and those with WMC problems need in particular — to comprehend text and counteract the limitations of working memory. Because reading widely — if you choose wisely — builds a rich database in LTM.

We say: you are what you eat. Another statement is at least as true: we are what we read.

In A Prehistory of Ordinary People, anthropologist Monica Smith argues that rather than deploring multitasking, we should celebrate it as the human ability that separates us from other animals.

Her thesis that we owe our success to our ability to juggle multiple competing demands and to pick up and put down the same project until completion certainly makes a good point. Yes, memory and imagination (our ability to project into the future) enable us to remember the tasks we’re in the middle of, and allow us to switch between tasks. And this is undeniably a good thing.

I agree (and I don’t think have ever denied) that multitasking is not in itself ‘bad’. I don’t think it’s new, either. These are, I would suggest, straw men — but I’m not decrying her raising them. Reports in the media are prone to talking about multitasking as if it is evil and novel, and a symptom of all that is wrong in modern life. It is right to challenge those assumptions.

The problem with multitasking is not that it is inherently evil. The point is to know when to stop.

There are two main dangers with multitasking, which we might term the acute and the chronic. The acute danger is when we multitask while doing something that has the potential to risk our own and others’ safety. Driving a vehicle is the obvious example, and I have reported on many studies over the past few years that demonstrate the relative dangers of different tasks (such as talking on a cellphone) while driving a car. Similarly, interruptions in hospitals increase the probability of clinical errors, some of which can have dire consequences. And of course on a daily level, acute problems can arise when we fail to do one task adequately because we are trying to do other tasks at the same time.

A chronic danger of multitasking that has produced endless articles in recent years is the suggestion that all this technology-driven multitasking is making us incapable of deep thought or focused attention.

But Smith argues that we do not, in fact, engage in levels of multitasking that are that much different from those exhibited in prehistoric times. ‘That much’ is of course the get-out phrase. How much difference is too much? Is there a point at which multitasking is too much, and have we reached it?

These are the real questions, and I don’t think the answer is something we can draw a line with. Research with driver-multitasking has revealed significant differences between drivers, as a function of age, as a function of personal attributes, as a function of emotional or physical state. It has revealed differences between tasks —e.g. talking that involves emotions or decisions is more distracting than less engaging conversation; half-overheard conversations are surprisingly distracting (suggesting that having a passenger in the car talking on a phone may be more distracting than doing it yourself!). These are the sort of things we need to know — not that multitasking is bad, but when it is bad.

This approach applies to the chronic problem also, although it is much more difficult to study. But these are some of the questions we need to know the answers to:

• Does chronic multitasking affect our long-term ability to concentrate, or only our ability to concentrate while in the multitasking environment?
• If it does affect our long-term ability to concentrate, can we reverse the effect? If so, how?
• Is the effect on children and adolescents different from that of adults?
• Does chronic multitasking produce beneficial cognitive effects? If so, is this of greater benefit for some people rather than others? (For example, multitasking training may benefit older adults)
• What are the variables in multitasking that affect our cognition in these ways? (For example, the number of tasks being performed simultaneously; the length of time spent on each one before switching; the number of times switching occurs within a defined period; the complexity of the tasks; the ways in which these and other factors might interact with temporary personal variables, such as mood, fatigue, alcohol, and more durable personal variables such as age and personality)

We need to be thinking in terms of multitasking contexts rather than multitasking as one uniform (and negative) behavior. I would be interested to hear your views on multitasking contexts you find beneficial, pleasant or useful, and contexts you find difficult, unpleasant or damaging.

Humans are the animals that manipulate their cognitive environment.

I reported recently on an intriguing study involving an African people, the Himba. The study found that the Himba, while displaying an admirable amount of focus (in a visual perception task) if they were living a traditional life, showed the same, more de-focused, distractible attention, once they moved to town. On the other hand, digit span (a measure of working memory capacity) was smaller in the traditional Himba than it was in the urbanized Himba.

This is fascinating, because working memory capacity has proved remarkably resistant to training. Yes, we can improve performance on specific tasks, but it has proven more difficult to improve the general, more fundamental, working memory capacity.

However, there have been two areas where more success has been found. One is the area of ADHD, where training has appeared to be more successful. The other is an area no one thinks of in this connection, because no one thinks of it in terms of training, but rather in terms of development — the increase in WMC with age. So, for example, average WMC increases from 4 chunks at age 4, to 5 at age 7, 6 at age 10, to 7 at age 16. It starts to decrease again in old age. (Readers familiar with my work will note that these numbers are higher than the numbers we now tend to quote for WMC — these numbers reflect the ‘magic number 7’, i.e. the number of chunks we can hold when we are given the opportunity to actively maintain them.)

Relatedly, there is the Flynn effect. The Flynn effect is ostensibly about IQ (specifically, the rise in average IQ over time), but IQ has a large WM component. Having said that, when you break IQ tests into their sub-components and look at their change over time, you find that the Digit Span subtest is one component that has made almost no gain since 1972.

But of course 1972 is still very modern! There is no doubt that there are severe constraints on how much WMC can increase, so it’s reasonable to assume we long since hit the ceiling (speaking of urbanized Western society as a group, not individuals).

It’s also reasonable to assume that WMC is affected by purely physiological factors involving connectivity, processing speed and white matter integrity — hence at least some of the age effect. But does it account for all of it?

What the Himba study suggests (and I do acknowledge that we need more and extended studies before taking these results as gospel), is that urbanization provides an environment that encourages us to use our working memory to its capacity. Urbanization provides a cognitively challenging environment. Our focus is diffused for that same reason — new information is the norm, rather than the exception; we cannot focus on one bit unless it is of such threat or interest that it justifies the risk.

ADHD shows us, perhaps, what can happen when this process is taken to the extreme. So we might take these three groups (traditional Himba, urbanized Himba, individuals with ADHD) as points on the same continuum. The continuum reflects degree of focus, and the groups reflect environmental effects. This is not to say that there are not physiological factors predisposing some individuals to react in such a way to the environment! But the putative effects of training on ADHD individuals points, surely, to the influence of the environment.

Age provides an intriguing paradox, because as we get older, two things tend to happen: we have a much wider knowledge base, meaning that less information is new, and we usually shrink our environment, meaning again that less information is new. All things being equal, you would think that would mean our focus could afford to draw in. However, as my attentive readers will know, declining cognitive capacity in old age is marked by increasing difficulties in ignoring distraction. In other words, it’s the urbanization effect writ larger.

How to account for this paradox?

Perhaps it simply reflects the fact that the modern environment is so cognitively demanding that these factors aren’t sufficient on their own to enable us to relax our alertness and tighten our focus, in the face of the slowdown in processing speed that typically occurs with age (there’s some evidence that it is this slowdown that makes it harder for older adults to suppress distracting information). Perhaps the problem is not simply, or even principally, the complexity of our environment, but the speed of it. You only have to compare a modern TV drama or sit-com with one from the 70s to see how much faster everything now moves!

I do wonder if, in a less cognitively demanding environment, say, a traditional Himba village, whether WMC shows the same early rise and late decline. In an environment where change is uncommon, it is natural for elders to be respected for their accumulated wisdom — experience is all — but perhaps this respect also reflects a constancy in WMC (and thus ‘intelligence’), so that elders are not disadvantaged in the way they may be in our society. Just a thought.

Here’s another thought: it’s always seemed to me (this is not in any way a research-based conclusion!) that musicians and composers, and writers and professors, often age very well. I’ve assumed this was because they are keeping mentally active, and certainly that must be part of it. But perhaps there’s another reason, possibly even a more important reason: these are areas of expertise where the proponent spends a good deal of time focused on one thing. Rather than allowing their attention to be diffused throughout the environment all the time, they deliberately shut off their awareness of the environment to concentrate on their music, their writing, their art.

Perhaps, indeed, this is the shared factor behind which activities help fight age-related cognitive decline, and which don’t.

I began by saying that humans are the animals that manipulate their cognitive environment. I think this is the key to fighting age-related cognitive decline, or ADHD if it comes to that. We need to be aware how much our brains try to operate in a way that is optimal for our environment — meaning that, by controlling our environment, we can change the way our brain operates.

If you are worried about your ‘scattiness’, or if you want to prevent or fight age-related cognitive decline, I suggest you find an activity that truly absorbs and challenges you, and engage in it regularly.

The increase in WMC in Himba who moved to town also suggests something else. Perhaps the reason that WM training programs have had such little success is because they are ‘programs’. What you do in a specific environment (the bounds of a computer and the program running on it) does not necessarily, or even usually, transfer to the wider environment. We are contextual creatures, used to behaving in different ways with different people and in different places. If we want to improve our WMC, we need to incorporate experiences that challenge and extend it into our daily life.

This, of course, emphasizes my previous advice: find something that absorbs you, something that becomes part of your life, not something you 'do' for an hour some days. Learn to look at the world in a different way, through music or art or another language or a passion (Civil War history; Caribbean stamps; whatever).

You can either let your cognitive environment shape you, or shape your cognitive environment.

Do you agree? What's your cognitive environment, and do you think it has affected your cognitive well-being?

The thing to remember about Ericsson’s famous expertise research, showing us the vital importance of deliberate practice in making an expert, is that it was challenging the long-dominant view that natural-born talent is all-important. But Gladwell’s popularizing of Ericsson’s “10,000 hours” overstates the case, and of course people are only too keen to believe that any height is achievable if you just work hard enough.

The much more believable story is that, yes, practice is vital — a great deal of the right sort of practice — but we can’t disavow “natural” abilities entirely.

Last year I reported on an experiment in which 57 pianists with a wide range of deliberate practice (from 260 to more than 31,000 hours) were compared on their ability to sight-read. Number of hours of practice did indeed predict much of the difference in performance (nearly half) — but not all. Working memory capacity also had a statistically significant impact on performance, although this impact was much smaller (accounting for only about 7% of the performance difference). Nevertheless, there’s a clear consequence: given two players who have put in the same amount of effective practice, the one with the higher WMC is likely to do better. Why should WMC affect sight-reading? Perhaps by affecting how many notes a player can look ahead as she plays — this is a factor known to affect sight-reading performance.

Interestingly, the effect of working memory capacity was quite independent of practice, and hours of practice apparently had no effect on WMC. Although it’s possible (the study was too small to tell) that a lot of practice at an early age might affect WMC. After all, music training has been shown to increase IQ in children.

So, while practice is certainly the most important factor in developing expertise, other factors, some of them less amenable to training, have a role to play too.

But do general abilities such as WMC or intelligence matter once you’ve put in the requisite hours of good practice? It may be that ability becomes less important once you achieve expertise in a domain.

The question of whether WMC interacts with domain knowledge in this way has been studied by Hambrick and his colleagues in a number of experiments. One study used a memory task in which participants listened to fictitious radio broadcasts of baseball games and tried to remember major events and information about the players. Baseball knowledge had a very strong effect on performance, and WMC had a much smaller effect, but there was no interaction between the two. Similarly, in two poker tasks, in which players had to assess the likelihood of drawing a winning card, and players had to remember hands during a game of poker, both poker knowledge and WMC affected performance, but again there was no interaction between domain knowledge and WMC.

Another study took a different tack. Participants were asked to remember the movements of spaceships flying from planet to planet in the solar system. What they didn’t know was that the spaceships flew in a pattern that matched the way baseball players run around a baseball diamond. They were then given the same task, this time with baseball players running around a diamond. Baseball knowledge only helped performance in the task in which the baseball scenario was explicit — activating baseball knowledge. But activation of domain knowledge had no effect on the influence of WMC.

Although these various studies fail to show an interaction between domain knowledge and WMC, this doesn’t mean that domain knowledge never interacts with basic abilities. The same researchers recently found such an interaction in a geological bedrock mapping task, in which geological structure of a mountainous area had to be inferred. Visuospatial ability predicted performance only at low levels of geological knowledge; geological experts were not affected by their visuospatial abilities. Unfortunately, that study is not yet published, so I don’t know the details. But I assume they mean visuospatial working memory capacity.

It’s possible that general intelligence or WMC are most important during the first stages of skill acquisition (when attention and working memory capacity are so critical), and become far less important once the skill has been mastered.

Similarly, Ericsson has argued that deliberate practice allows performers to circumvent limits on working memory capacity. This is, indeed, related to the point I often make about how to functionally increase your working memory capacity — if you have a great amount of well-organized and readily accessible knowledge on a particular topic, you can effectively expand how much your working memory can hold by keeping a much larger amount of information ‘on standby’ in what has been termed long-term working memory.

Proponents of deliberate practice don’t deny that ‘natural’ abilities have some role, but they restrict it to motivation and general activity levels (plus physical attributes such as height where that is relevant). But surely these would only affect number of hours. Clearly the ability to keep yourself on task, to motivate and discipline yourself, impinges on your ability to keep your practice up. And the general theory makes sense — that if you show some interest in something, such as music or chess, when you’re young, your parents or teachers usually encourage you in that direction; this encouragement and rewards lead you to spend more time and energy in that domain, and if you have enough persistence, enough dedication, then lo and behold, you’ll get better and better. And your parents will say, well, it was obvious from an early age that she was talented that way.

But is it really the case that attributes such as intelligence make no difference? Is it really as simple as “10,000 hours of deliberate practice = expert”? Is it really the case that each hour has the same effect on any one of us?

A survey of 104 chess masters found that, while all the players that became chess masters had practiced at least 3,000 hours, the amount of practice it took to achieve that mastery varied considerably. Although, consistent with the “10,000 hour rule”, average time to achieve mastery was around 11,000 hours, time ranged from 3,016 hours to 23,608 hours. The difference is even more extreme if you only consider individual practice (previous research has pointed to individual practice being of more importance than group practice): a range from 728 hours to 16,120 hours! And some people practiced more than 20,000 hours and still didn't achieve master level.

Moreover, a comparison of titled masters and untitled international players found that the two groups practiced the same amount of hours in the first three years of their serious dedication to chess, and yet there were significant differences in their ratings. Is this because of some subtle difference in the practice, making it less effective? Or is it that some people benefit more from practice?

A comparison of various degrees of expertise in terms of starting age is instructive. While the average age of starting to play seriously was around 18 for players without an international rating, it was around 14 for players with an international rating, and around 11 for masters. But the amount of variability within each group varies considerably. For players without an international rating, the age range within one standard deviation of the mean is over 11 years, but for those with an international rating, FIDE masters, and international masters, the range is only 2-3 years, and for grand masters, the range is less than a year. [These numbers are all approximate, from my eyeball estimates of a bar graph.]

It has been suggested that the younger starting age of chess masters and expert musicians is simply a reflection of the greater amount of practice achieved with a young start. But a contrary suggestion is that there might be other advantages to learning a skill at an early age, reflecting what might be termed a ‘sensitive period’. This study found that the association between skill and starting age was still significant after amount of practice had been taken account of.

Does this have to do with the greater plasticity of young brains? Expertise “grows” brains — in the brain regions involved in that specific domain. Given that younger brains are much more able to create new neurons and new connections, it would hardly be a surprise that it’s easier for them to start building up the dense structures that underlie expertise.

This is surely easier if the young brain is also a young brain that has particular characteristics that are useful for that domain. For music, that might relate to perceptual and motor abilities. In chess, it might have more to do with processing speed, visuospatial ability, and capacious memory.

Several studies have found higher cognitive ability in chess-playing children, but the evidence among adults has been less consistent. This may reflect the growing importance of deliberate practice. (Or perhaps it simply reflects the fact that chess is a difficult skill, for which children, lacking the advantages that longer education and training have given adults, need greater cognitive skills.)

Related to all this, there’s a popular idea that once you get past an IQ of around 120, ‘extra’ IQ really makes no difference. But in a study involving over 2,000 gifted young people, those who scored in the 99.9 percentile on the math SAT at age 13 were eighteen times more likely to go on to earn a doctorate in a STEM discipline (science, technology, engineering, math) compared to those who were only(!) in the 99.1 percentile.

Overall, it seems that while practice can take you a very long way, at the very top, ‘natural’ ability is going to sort the sheep from the goats. And ‘natural’ ability may be most important in the early stages of learning. But what do we mean by ‘natural ability’? Is it simply a matter of unalterable genetics?

Well, palpably not! Because if there’s one thing we now know, it’s that nature and nurture are inextricably entwined. It’s not about genes; it’s about the expression of genes. So let me remind you that aspects of the prenatal, the infant, and the child’s, environment affect that ‘natural’ ability. We know that these environments can affect IQ; the interesting question is what we can do, at each and any of these stages, to improve affect basic processes such as speed of processing, WMC, and inhibitory control. (Although I should say here that I am not a fan of the whole baby-Einstein movement! Nor is there evidence that many of those practices work.)

Bottom line:

• talent still matters
• effective practice is still the most important factor in developing expertise
• individuals vary in how much practice they need
• individual abilities do put limits on what’s achievable (but those limits are probably higher than most people realize).

A New Yorker cartoon has a man telling his glum wife, “Of course I care about how you imagined I thought you perceived I wanted you to feel.” There are a number of reasons you might find that funny, but the point here is that it is very difficult to follow all the layers. This is a sentence in which mental attributions are made to the 6^th level, and this is just about impossible for us to follow without writing it down and/or breaking it down into chunks.

According to one study, while we can comfortably follow a long sequence of events (A causes B, which leads to C, thus producing D, and so on), we can only comfortably follow four levels of intentionality (A believes that B thinks C wants D). At the 5^th level (A wants B to believe that C thinks that D wants E), error rates rose sharply to nearly 60% (compared to 5-10% for all levels below that).

Why do we have so much trouble following these nested events, as opposed to a causal chain?

Let’s talk about working memory.

Working memory (WM) has evolved over the years from a straightforward “short-term memory store” to the core of human thought. It’s become the answer to almost everything, invoked for everything related to reasoning, decision-making, and planning. And of course, it’s the first and last port of call for all things memory — to get stored in long-term memory an item first has to pass through WM, where it’s encoded; when we retrieve an item from memory, it again passes through WM, where the code is unpacked.

So, whether or not the idea of working memory has been over-worked, there is no doubt at all that it is utterly crucial for cognition.

Working memory has also been equated with attentional control, and working memory and attention are often used almost interchangeably. And working memory capacity (WMC) varies among individuals. Those with a higher WMC have an obvious advantage in reasoning, comprehension, remembering. No surprise then that WMC correlates highly with fluid intelligence.

So let’s talk about working memory capacity.

The idea that working memory can hold 7 (+/-2) items has passed into popular culture (the “magic number 7”). More recent research, however, has circled around the number 4 (+/-1). Not only that, but a number of studies suggest that in fact the true number of items we can attend to is only one. What’s the answer? (And where does it leave our high- and low-capacity individuals? There’s not a lot of room to vary there.)

Well, in one sense, 7 is still fine — that’s the practical sense. Seven items (5-9) is about what you can hold if you can rehearse them. So those who are better able to rehearse and chunk will have a higher working memory capacity (WMC). That will be affected by processing speed, among other factors.

But there is a very large body of evidence now pointing to working memory holding only four items, and a number of studies indicating that most likely we can only pay attention to one of these items at a time. So you can envision this either as a focus of attention, which can only hold one item, and a slightly larger “outer store” or area of “direct access” which can hold another three, or as a mental space holding four items of which only one can be the focus at any one time.

A further tier, which may be part of working memory or part of long-term memory, probably holds a number of items “passively”. That is, these are items you’ve put on the back burner; you don’t need them right at the moment, but you don’t want them to go too far either. (See my recent news item for more on all this.)

At present, we don’t have any idea how many items can be in this slightly higher state of activation. However, the “magic number 7” suggests that you can circulate 3 (+/-1) items from the backburner into your mental space. In this regard, it’s interesting to note that, in the case of verbal material, the amount you can hold in working memory with rehearsal has been found to more accurately equate to 2 seconds, rather than 7 items. That is, you can remember as much as you can verbalize in about 2s (so, yes, fast speakers have a distinct advantage over slower ones). You see why processing speed affects WMC.

Whether you think of WM as a focus of one and an outer store of 3, or as a direct access area with 4 boxes and a spotlight shining on one, it’s a mental space or blackboard where you can do your working out. Thinking of it this way makes it easier to conceptualize and talk about, but these items are probably not going into a special area as such. The thought now is that these items stay in long-term memory (in their relevant areas of association cortex), but they are (a) highly activated, and (b) connected to the boxes in the direct access area (which is possibly in the medial temporal lobe). This connection is vitally important, as we shall see.

Now four may not seem like much, but WM is not quite as limited as it seems, because we have different systems for verbal (includes numerical) and visuospatial information. Moreover, we can probably distinguish between the items and the processing of them, which equates to a distinction between declarative and procedural memory. So that gives us three working memory areas: verbal declarative; visuospatial declarative; procedural.

Now all of this may seem more than you needed to know, but breaking down the working memory system helps us discover two things of practical interest. First, which particular parts of the system are the parts that make a task more difficult. Second, where individual differences come from, and whether they are in aspects that are trainable.

For example, this picture of a mental space with a focus of one and a maximum of three eager-beavers waiting their turn, points to an important aspect of the working memory system: switching the focus. Experiments reveal that there is a large focus-switching cost, incurred whenever you have to switch the item in the spotlight. And the extent of this cost has been surprising — around 240ms in one study, which is about six times the length of time it takes to scan an item in a traditional memory-search paradigm.

But focus-switch costs aren’t a constant. They vary considerably depending on the difficulty of the task, and they also tend to increase with each item in the direct-access area. Indeed, just having one item in the space outside the focus causes a significant loss of efficiency in processing the focused item.

This may reflect increased difficulty in discriminating one highly activated item from other highly activated items. This brings us to competition, which, in its related aspects of interference and inhibition, is a factor probably more crucial to WMC than whether you have 3 or 4 or 5 boxes in your direct access area.

But before we discuss that, we need to look at another important aspect of working memory: updating. Updating is closely related to focus-switching, and it’s easy to get confused between them. But it’s been said that working memory updating (WMU) is the only executive function that correlates with fluid intelligence, and updating deficits have been suggested as the reason for poor comprehension (also correlated with low-WMC). So it’s worth spending a little time on.

To get the distinction clear in your mind, imagine the four boxes and the spotlight shining on one. Any time you shift the spotlight, you incur a focus-switching cost. If you don’t have to switch focus, if you simply need to update the contents of the box you’re already focusing on, then there will be an update cost, but no focus-switching cost.

Updating involves three components: retrieval; transformation; substitution. Retrieval simply involves retrieving the contents from the box. Substitution involves replacing the contents with something different. Transformation involves an operation on the contents of the box to get a new value (eg, when you have to add a certain number to an earlier number).

Clearly the difficulty in updating working memory will depend on which of these components is involved. So which of these processes is most important?

In terms of performance, the most important component is transformation. While all three components contribute to the accuracy of updating, retrieval apparently doesn’t contribute to speed of updating. For both accuracy and speed, substitution is less important than transformation.

This makes complete sense: obviously having to perform an operation on the content is going to be more difficult and time-consuming than simply replacing it. But it does help us see that the most important factor in determining the difficulty of an updating task will be the complexity of the transformation.

The finding that retrieval doesn’t affect speed of updating sounds odd, until you realize the nature of the task used to measure these components. The number of items was held constant (always three), and the focus switched from one box to another on every occasion, so focus-switching costs were constant too. What the finding says is that once you’ve shifted your focus, retrieval takes no time at all — the spotlight is shining and there the answer is. In other words, there really is no distinction between the box and its contents when the spotlight is on it — you don’t need to open the box.

However, retrieval does affect accuracy, and this implies that something is degrading or interfering in some way with the contents of the boxes. Which takes us back to the problems of competition / interference.

But before we get to that, let’s look at this issue of individual differences, because like WMC, working memory updating correlates with fluid intelligence. Is this just a reflection of WMC?

Differences in transformation accuracy correlated significantly with WMC, as did differences in retrieval accuracy. Substitution accuracy didn’t vary enough to have measurable differences. Neither transformation nor substitution speed differences correlated with WMC. This implies that the reason why people with high WMC also do better at WMU tasks is because of the transformation and retrieval components.

So what about the factors that aren’t correlated with WMC? The variance in transformation speed is argued to primarily reflect general processing speed. But what’s going on in substitution that isn’t going on in when WMC is measured?

Substitution involves two processes: removing the old contents of the box, and adding new content. In terms of the model we’ve been using, we can think of unbinding the old contents from the box, and binding new contents to it (remember that the item in the box is still in its usual place in the association cortex; it’s “in” working memory by virtue of the temporary link connecting it to the box). Or we can think of it as deleting and encoding.

Consistent with substitution not correlating with WMC, there is some evidence that high- and low-WMC individuals are equally good at encoding. Where high- and low-WMC individuals differ is in their ability to prevent irrelevant information being encoded with the item. Which brings me to my definition of intelligence (from 30 years ago — these ideas hadn’t even been invented yet. So I came at it from quite a different angle): the ability to (quickly) select what’s important.

So why do low-WMC people tend to be poorer at leaving out irrelevant information?

Well, that’s the $64,000 question, but related to that it’s been suggested that those with low working memory capacity are less able to resist capture by distracting stimuli than those with high WMC. A new study, however, provides evidence that low- and high-WMC individuals are equally easily captured by distracters. What distinguishes the two groups is the ability to disengage. High-capacity people are faster in putting aside irrelevant stimuli. They’re faster at deleting. And this, it seems, is unrelated to WMC.

This is supported by another recent finding, that when interrupted, older adults find it difficult to disengage their brain from the new task and restore the original task.

So what’s the problem with deleting / removing / putting aside items in focus? This is about inhibition, which takes us once again to competition / interference.

Now interference occurs at many different levels: during encoding, retrieval, and storage; with items, with tasks, with responses. Competition is ubiquitous in our brain.

In the case of substitution during working memory updating, it’s been argued that the contents of the box are not simply removed and replaced, but instead gradually over-written by the new contents. This fits in with a view of items as assemblies of lower-level “feature-units”. Clearly, items may share some of these units with other items (reflected in their similarity), and clearly the more they compete for these units, the greater interference there will be between the units.

You can see why it’s better to keep your codes (items) “lean and mean”, free of any irrelevant information.

Indeed, some theorists completely discard the idea of number of items as a measure of WMC, and talk instead in terms of “noise”, with processing capacity being limited by such factors as item complexity and similarity. While there seems little justification for discarding our “4+/-1”, which is much more easily quantified, this idea does help us get to grips with the concept of an “item”.

What is an item? Is it “red”? “red cow”? “red cow with blue ribbons round her neck”? “red cow with blue ribbons and the name Isabel painted on her side”? You see the problem.

An item is a fuzzy concept. We can’t say, “it’s a collection of 6 feature units” (or 4 or 14 or 42). So we have to go with a less defined description: it’s something so tightly bound that it is treated as a single unit.

Which means it’s not solely about the item. It’s also about you, and what you know, and how well you know it, and what you’re interested in.

To return to our cases of difficulty in disengaging, perhaps the problem lies in the codes being formed. If your codes aren’t tightly bound, then they’re going to start to degrade, losing some of their information, losing some of their distinctiveness. This is going to make them harder to re-instate, and it’s going to make them less distinguishable from other items.

Why should this affect disengagement?

Remember what I said about substitution being a gradual process of over-writing? What happens when your previous focus and new focus have become muddled?

This also takes us to the idea of “binding strength” — how well you can maintain the bindings between the contents and their boxes, and how well you can minimize the interference between them (which relates to how well the items are bound together). Maybe the problem with both disengagement and reinstatement has to do with poorly bound items. Indeed, it’s been suggested that the main limiting factor on WMC is in fact binding strength.

Moreover, if people vary in their ability to craft good codes, if people vary in their ability to discard the irrelevant and select the pertinent, to bind the various features together, then the “size” (the information content) of an item will vary too. And maybe this is what is behind the variation in “4 +/-1”, and experiments which suggest that sometimes the focus can be increased to 2 items. Maybe some people can hold more information in working memory because they get more information into their items.

So where does this leave us?

Let’s go back to our New Yorker cartoon. The difference between a chain of events and the nested attributions is that chaining doesn’t need to be arranged in your mental space because you don’t need to keep all the predecessors in mind to understand it. On the other hand, the nested attributions can’t be understood separately or even in partitioned groups — they must all be arranged in a mental space so we can see the structure.

We can see now that “A believes that B thinks C wants D” is easy to understand because we have four boxes in which to put these items and arrange them. But our longer nesting, “A wants B to believe that C thinks that D wants E”, is difficult because it contains one more item than we have boxes. No surprise there was a dramatic drop-off in understanding.

So given that you have to fill your mental space, what is it that makes some tasks more difficult than others?

• The complexity and similarity of the items (making it harder to select the relevant information and bind it all together).
• The complexity of the operations you need to perform on each item (the longer the processing, the more tweaking you have to do to your item, and the more time and opportunity for interference to degrade the signal).
• Changing the focus (remember our high focus-switching costs).

But in our 5^th level nested statement, the error rate was 60%, not 100%, meaning a number of people managed to grasp it. So what’s their secret? What is it that makes some people better than others at these tasks?

They could have 5 boxes (making them high-WMC). They could have sufficient processing speed and binding strength to unitize two items into one chunk. Or they could have the strategic knowledge to enable them to use the other WM system (transforming verbal data into visuospatial). All these are possible answers.

This has been a very long post, but I hope some of you have struggled through it. Working memory is the heart of intelligence, the essence of attention, and the doorway to memory. It is utterly critical, and cognitive science is still trying to come to grips with it. But we’ve come a very long way, and I think we now have sufficient theoretical understanding to develop a model that’s useful for anyone wanting to understand how we think and remember, and how they can improve their skills.

There is, of course, far more that could be said about working memory (I’ve glossed over any number of points in an effort to say something useful in less than 50,000 words!), and I’m planning to write a short book on working memory, its place in so many educational and day-to-day tasks, and what we can do to improve our skills. But I hope some of you have found this enlightening.

In a recent news report, I talked about how walking through doorways creates event boundaries, requiring us to update our awareness of current events and making information about the previous location less available. I commented that we should be aware of the consequences of event boundaries for our memory, and how these contextual factors are important elements of our filing system. I want to talk a bit more about that.

One of the hardest, and most important, things to understand about memory is how various types of memory relate to each other. Of course, the biggest problem here is that we don’t really know! But we do have a much greater understanding than we used to do, so let’s see if I can pull out some salient points and draw a useful picture.

Let’s start with episodic memory. Now episodic memory is sometimes called memory for events, and that is reasonable enough, but it perhaps gives an inaccurate impression because of the common usage of the term ‘event’. The fact is, everything you experience is an event, or to put it another way, a lifetime is one long event, broken into many many episodes.

Similarly, we break continuous events into segments. This was demonstrated in a study ten years ago, that found that when people watched movies of everyday events, such as making the bed or ironing a shirt, brain activity showed that the event was automatically parsed into smaller segments. Moreover, changes in brain activity were larger at large boundaries (that is, the boundaries of large, superordinate units) and smaller at small boundaries (the boundaries of small, subordinate units).

Indeed, following research showing the same phenomenon when people merely read about everyday activities, this is thought to reflect a more general disposition to impose a segmented structure on events and activities (“event structure perception”).

Event Segmentation Theory postulates that perceptual systems segment activity as a side effect of trying to predict what’s going to happen. Changes in the activity make prediction more difficult and cause errors. So these are the points when we update our memory representations to keep them effective.

Such changes cover a wide gamut, from changes in movement to changes in goals.

If you’ve been following my blog, the term ‘updating’ will hopefully bring to mind another type of memory — working memory. In my article How working memory works: What you need to know, I talked about the updating component of working memory at some length. I mentioned that updating may be the crucial component behind the strong correlation between working memory capacity and intelligence, and that updating deficits might underlie poor comprehension. I distinguished between three components of updating (retrieval; transformation; substitution), and how transformation was the most important for deciding how accurately and how quickly you can update your contents in working memory. And I discussed how the most important element in determining your working memory ‘capacity’ seems to be your ability to keep irrelevant information out of your memory codes.

So this event segmentation research suggests that working memory updating occurs at event boundaries. This means that information before the boundary becomes less accessible (hence the findings from the walking through doorways studies). But event boundaries relate not only to working memory (keeping yourself updated to what’s going on) but also to long-term storage (we’re back to episodic memory now). This is because those boundaries are encoded particularly strongly — they are anchors.

Event boundaries are beginnings and endings, and we have always known that beginnings and endings are better remembered than middles. In psychology this is known formally as the primacy and recency effects. In a list of ten words (that favorite subject of psychology experiments), the first two or three items and the last two or three items are the best remembered. The idea of event boundaries gives us a new perspective on this well-established phenomenon.

Studies of reading have shown that readers slow down at event boundaries, when they are hypothesized to construct a new mental model. Such boundaries occur when the action moves to a new place, or a new time, or new characters enter the action, or a new causal sequence is begun. The most important of these is changes in characters and their goals, and changes in time.

As I’ve mentioned before, goals are thought to play a major role (probably the major role) in organizing our memories, particularly activities. Goals produce hierarchies — any task can be divided into progressively smaller elements. Research suggests that higher-order events (coarse-grained, to use the terminology of temporal grains) and lower-order events (fine-grained) are sensitive to different features. For example, in movie studies, coarse-grained events were found to focus on objects, using more precise nouns and less precise verbs. Finer-grained events, on the other hand, focused on actions on those objects, using more precise verbs but less precise nouns.

The idea that these are separate tasks is supported by the finding of selective impairments of coarse-grained segmentation in patients with frontal lobe lesions and patients with schizophrenia.

While we automatically organize events hierarchically (even infants seem to be sensitive to hierarchical organization of behavior), that doesn’t mean our organization is always effortlessly optimal. It’s been found that we can learn new procedures more easily if the hierarchical structure is laid out explicitly — contrariwise, we can make it harder to learn a new procedure by describing or constructing the wrong structure.

Using these hierarchical structures helps us because it helps us use knowledge we already have in memory. We can co-op chunks of other events/activities and plug them in. (You can see how this relates to transfer — the more chunks a new activity shares with a familiar one, the more quickly you can learn it.)

Support for the idea that event boundaries serve as anchors comes from several studies. For example, when people watched feature films with or without commercials, their recall of the film was better when there were no commercials or the commercials occurred at event boundaries. Similarly, when people watched movies of everyday events with or without bits removed, their recall was better if there were no deletions or the deletions occurred well within event segments, preserving the boundaries.

It’s also been found that we remember details better if we’ve segmented finely rather than coarsely, and some evidence supports the idea that people who segment effectively remember the activity better.

Segmentation, theory suggests, helps us anticipate what’s going to happen. This in turn helps us adaptively create memory codes that best reflect the structure of events, and by simplifying the event stream into a number of chunks of which many if not most are already encoded in your database, you save on processing resources while also improving your understanding of what’s going on (because those already-coded chunks have been processed).

All this emphasizes the importance of segmenting well, which means you need to be able to pinpoint the correct units of activity. One way we do that is by error monitoring. If we are monitoring our ongoing understanding of events, we will notice when that understanding begins to falter. We also need to pay attention to the ordering of events and the relationships between sequences of events.

The last type of memory I want to mention is semantic memory. Semantic memory refers to what we commonly think of as ‘knowledge’. This is our memory of facts, of language, of generic information that is untethered from specific events. But all this information first started out as episodic memory — before you ‘knew’ the word for cow, you had to experience it (repeatedly); before you ‘knew’ what happens when you go to the dentist, you had to (repeatedly) go to the dentist; before you ‘knew’ that the earth goes around the sun, there were a number of events in which you heard or read that fact. To get to episodic memory (your memory for specific events), you must pass through working memory (the place where you put incoming information together into some sort of meaningful chunk). To get to semantic memory, the information must pass through episodic memory.

How does information in episodic memory become information in semantic memory? Here we come to the process of reconstruction, of which I have often spoken (see my article on memory consolidation for some background on this). The crucial point here is that memories are rewritten every time they are retrieved.

Remember, too, that neurons are continually being reused — memories are held in patterns of activity, that is, networks of neurons, not individual neurons. Individual neurons may be involved in any number of networks. Here’s a new analogy for the brain: think of a manuscript, one of those old parchments, so precious that it must be re-used repeatedly. Modern technology can reveal those imperfectly erased hidden layers. So the neural networks that are memory codes may be thought of as imposed one on top of each other, none of them matching, as different patterns re-use the same individual neurons. The strongest patterns are the most accessible; patterns that are most similar (use more of the same neurons) will provide the most competition.

So, say you are told by your teacher that the earth goes around the sun. That’s the first episode, and there’ll be lots of contextual detail that relates to that particular event. Then on another occasion, you read a book showing how the earth goes around the sun. Again, lots of episodic detail, of which some will be shared with the first incident, and some will be different. Another episode, more detail, some shared, some not. And so on, again and again, until the extraneous details, irrelevant to the fact and always different, are lost, while those details that common to all the episodes will be strong, and form a new, tight chunk of information in semantic memory.

Event boundaries start off with an advantage — they are beginnings or endings, to which we are predisposed to attend (for obvious reasons). So they start off stronger than other bits of information, and by their nature are more likely to be vital elements, that will always co-occur with the event. So — if you have chosen your boundaries well (i.e., they truly are vital elements) they will become stronger with each episode, and will end up as vital parts of the chunk in semantic memory.

Let’s connect that thought back to my comment that the most important difference between those with ‘low’ working memory capacity and those with ‘high’ capacity is the ability to select the ‘right’ information and disregard the irrelevant. Segmenting your events well would seem to be another way of saying that you are good at selecting the changes that are most relevant, that will be common to any such events on other occasions.

And that, although some people are clearly ‘naturally’ better at it, is surely something that people can learn.

I’ve recently had a couple of thoughts about flow — that mental state when you lose all sense of time and whatever you’re doing (work, sport, art, whatever) seems to flow with almost magical ease. I’ve mentioned flow a couple of times more or less in passing, but today I want to have a deeper look, because learning (and perhaps especially that rewiring I was talking about in my last post) is most easily achieved if we can achieve "flow" (also known as being ‘in the zone’).

Let’s start with some background.

Mihaly Csikszentmihalyi is the man who identified and named this mental state, and he identified 9 components:

1. The skills you need to perform the task must match the challenges of the task, AND the task must exceed a certain level of difficulty (above everyday level).
2. Your concentration is such that your behavior becomes automatic and you have little conscious awareness of your self, only of what you’re doing.
3. You have a very clear sense of your goals.
4. The task provides unambiguous and immediate feedback concerning your progress toward those goals.
5. Your focus is entirely on the task and you are completely unaware of any distracting events.
6. You feel in control, but paradoxically, if you try to consciously hold onto that control, you’ll lose that sense of flow. In other words, you only feel in control as long as you don’t think about it.
7. You lose all sense of self and become one with the task.
8. You lose all sense of time.
9. You experience what Csikszentmihalyi called the ‘autotelic experience’ (from Greek auto (self) and telos (goal)), which is inherently rewarding, providing the motivation to re-experience it.

Clearly many of these components are closely related. More usefully, we can distinguish between elements of the experience, and preconditions for the experience.

The key elements of the experience are your total absorption in the task (which leads to you losing all awareness of self, of time, and any distractions in the environment), and your enjoyment of it.

The key preconditions are:

• the match between skills and task
• the amount of challenge in the task
• the clear and proximal nature of your goals (that is, at least some need to be achievable in that session)
• the presence of useful feedback.

Additionally, later research suggests:

• the task needs to be high in autonomy and meaningfulness.

Brain studies have found that this mental state is characterized by less activity in the prefrontal cortex (which provides top-down control — including that evidenced by that critical inner voice), and a small increase in alpha brainwaves (correlated with slower breathing and a lower pulse rate). This inevitably raises the question of whether meditation training can help you more readily achieve flow. Supporting this, a neurofeedback study improved performance in novice marksmen, who learned to shoot expertly in less than half the time after they had been trained to produce alpha waves. There are also indications that some forms of mild electrical stimulation to the brain (tDCS) can induce a flow state.

Some people may be more prone to falling into a flow state than others. Csikszentmihalyi referred to an ‘autotelic personality’, and suggested that such people have high levels of curiosity, persistence, and interest in performing activities for their own sake rather than to achieve some external goal. Readers of my books may be reminded of cognitive styles — those who are intrinsically motivated rather than extrinsically usually are more successful in study.

Recent research has supported the idea of the autotelic personality, and roots it particularly in the achievement motive. Those who have a strong need for achievement, and a self-determined approach, are more likely to experience flow. Such people also have a strong internal locus of control — that is, they believe that achievement rests in their own hands, in their own work and effort. I have, of course, spoken before of the importance of this factor.

There is some indication that autotelic students push themselves harder. A study of Japanese students found that autotelic students tended to put themselves in situations where the perceived challenges were higher than their perceived skills, while the reverse was true for other students.

Interestingly, a 1994 study found that college students perceived work where skills exceeded challenges to be more enjoyable than flow activities where skills matched challenges — which suggests, perhaps, that we are all inclined to underestimate our own skills, and do better when pushed a little.

In regard to occupation, research suggests that five job characteristics are positively related to flow at work. These characteristics (which come from the Job Characteristics Model) are:

• Skill variety

• Task identity (the extent to which you complete a whole and identifiable piece of work)

• Task significance

• Autonomy

• Feedback

These clearly echo the flow components.

All of this suggests that to consistently achieve a flow state, you need the right activities and the right attitude.

So, that’s the background. Now for my new thoughts. It occurred to me that flow might have something to do with working memory. I’ve suggested before that flow might have something to do with getting the processing speed just right. My new thought extends this idea.

Remember that working memory is extremely limited, and that it seems to reflect a three-tiered system, whereby you have one item in your immediate focus, with perhaps three more items hovering very closely within an inner store, able to very quickly move into immediate focus, and a further three or so items in the ‘backburner’ — and all these items have to keep moving around and around these tiers if you want to keep them all ‘alive’. Because they can’t stay very long at all in this system without being refreshed (through the focus).

Beyond this system is the huge database of your long-term memory, and that’s where all these items come from. Thus, whenever you’re working on something, you’re effectively circulating items through this whole four-tier system: long-term memory to focus to inner store to backburner and then returning to LTM or to focus. And returning to LTM is the default — if it’s to return to focus, it has to happen within a very brief period of time.

And so here’s my thesis (I don’t know if it’s original; I just had the idea this morning): flow is our mental experience of a prolonged period of balancing this circulation perfectly. Items belonging to one cohesive structure are flowing through the system at the right speed and in the right order, with no need to stop and search, and no room for any items that aren’t part of this cohesive structure (i.e., there are no slots free in which to experience any emotions or distracting thoughts).

What this requires is for the necessary information to all be sufficiently strongly connected, so that activation/retrieval occurs without delay. And what that requires is for the foundations to be laid. That is, you need to have the required action sequences or information clusters well-learned.

Here we have a mechanism for talent — initial interest and some skill produces a sense of flow; this motivating state is pursued by the individual by persevering at the same activity/subject; if they are not pushed too hard (which will not elicit flow), or held back (ditto), they will once again achieve the desired state, increasing the motivation to pursue this course. And so on.

All of which begs the question: are autotelic personalities created or made? Because the development of people who find it easier to achieve flow may well have more to do with their good luck in childhood (experiencing the right support) than their genetic makeup.

Is flow worth pursuing? Flow helps us persist at a task, because it is an intrinsically rewarding mental state. Achieving flow, then, is likely to result in greater improvement if only because we are likely to spend more time on the activity. The interesting question is whether it also, in and of itself, means we gain more from the time we spend. At the moment, we can only speculate.

But research into the value of mental stimulation in slowing cognitive decline in older people indicates that engagement, and its correlate enjoyment, are important if benefits are to accrue. I think the experience of flow is not only intrinsically rewarding, but also intrinsically beneficial in achieving the sort of physical brain changes we need to fight age-related cognitive decline.

So I’ll leave you with the findings from a recent study of flow in older adults, that has some helpful advice for anyone wanting to achieve flow, as well as demonstrating that you're never too old to achieve this state (even if it does seem harder to achieve as you age, because of the growing difficulty in inhibiting distraction).

The study, involving 197 seniors aged 60-94, found that those with higher fluid cognitive abilities (processing speed, working memory, visual spatial processing, divergent thinking, inductive reasoning, and everyday problem-solving) experienced higher levels of flow in cognitive activities, while those with lower fluid abilities experienced lower levels of flow. However, those with lower fluid abilities experienced higher levels of flow in non-cognitive activities, while those with higher fluid abilities experienced lower levels of flow.

High cognitive demand activities included: working, art and music, taking classes and teaching, reading, puzzles and games, searching for information. Low cognitive demand activities included: social events, exercise, TV, cooking, going on vacation. Note that the frequency of these activities did not differ between those of higher fluid ability and those of lower.

These findings reinforce the importance of matching skills and activities in order to achieve flow, and also remind us that flow can be achieved in any activity.

I have spoken before, here on the website and in my books, about the importance of setting specific goals and articulating your specific needs. Improving your memory is not a single task, because memory is not a single thing. And as I have discussed when talking about the benefits of ‘brain games’ and ‘brain training’, which are so popular now, there is only a little evidence that we can achieve general across-the-board improvement in our cognitive abilities. But we can improve specific skills, and we may be able to improve a range of skills by working on a more fundamental skill they all share.

The modularity of the brain is emphasized in a recent study that found the two factors now thought to be operating in working memory capacity are completely independent of each other. Working memory capacity has long been known to be strongly correlated with intelligence, but the recent discovery that people vary not only in the number of items they can hold in short-term memory but also in how clear and precise the items are, has changed our conception of working memory capacity.

Both are measures of information; the clarity (resolution) of the items in working memory essentially reflects how much information about each item the individual can hold. So should our measures of WMC somehow encapsulate both factors? Are they related? It would seem plausible that those who can hold more items might hold less information about each of them; that those who can only hold two or three items might hold far more information on each item.

But this new study finds no evidence for that. Apparently the two factors are completely independent. Moreover, the connection between WMC and intelligence seems only to apply to the number of items, not to their clarity.

Working memory is fundamental to our cognitive abilities — to memory, to comprehension, to learning, to reasoning. And yet even this very basic process (basic in the sense of ‘at the base of everything’, not in the sense of primitive!) is now seen to break down further, into two quite separate abilities. And while clarity may have nothing to do with intelligence, it assuredly has something to do with abilities such as visual imagery, search, discrimination.

It may be clarity is more important to you than number of items. It depends on what skills are important to you. And the skills that are important to you change as your life circumstances change. When you’re young, you want as broad a base of skills as possible, but as you age, you are better to become more selective.

Many people die with brains that show all the characteristics of Alzheimer’s, and yet they showed no signs of that in life. The reason is that they had sufficient ‘cognitive reserve’ —a brain sufficiently well and strongly connected — that they could afford (for long enough) the losses the disease created in their brain. This doesn’t mean they wouldn’t have eventually succumbed to the inevitable, of course, if they had lived longer. But a long enough delay can essentially mean the disease has been prevented.

One of the best ways to fight cognitive decline and dementia is to build your brain up in the skills and domains that are, and will be, important to you. And while this can, and should, involve practicing and learning better strategies for specific skills, it is also a good idea to work on more fundamental skills. Knowing which fundamental skills underlie the specific skills you’re interested in would enable you to direct your attention appropriately.

Thus it may be that while increasing the number of items you can hold in short-term memory might help you solve mathematical problems, remember phone numbers, or understand complex prose, trying to improve your ability to visualize objects clearly might help you remember people’s faces, or where you left your car, or use mnemonic strategies.

I recently reported on a study showing how the gestures people made in describing how they solved a problem (the Tower of Hanoi) changed the way they remembered the game. These findings add to other research demonstrating that gestures make thought concrete and can help us understand and remember abstract concepts better.

For example, two experiments of children in late third and early fourth grade, who made mistakes in solving math problems, have found that children told to move their hands when explaining how they’d solve a problem were four times as likely to manually express correct new ways to solve problems as children given no instructions. Even though they didn’t give the right answer, their gestures revealed an implicit knowledge of mathematical ideas, and the second experiment showed that gesturing set them up to benefit from subsequent instruction.

And in a demonstration of improved memory, an earlier study had participants watch someone narrating three cartoons. Sometimes the narrator used hand gestures and at other times they did not. The participants were then asked to recall the story. The study found that when the narrator used gestures as well as speech the participants were more likely to accurately remember what actually happened in the story rather than change it in some way.

In another study, in which 40 children and 36 adults were asked to remember a list of letters (adults) or words (children) while explaining how they solved a math problem, both groups remembered significantly more items when they gestured during their math explanations than when they did not gesture.

It’s thought that gesturing helps memory and understanding by lightening the load on working memory while you’re thinking of what to say. Gestures use up visuospatial working memory rather than verbal memory, so essentially what you’re doing is moving part of the information in one limited working memory space into another working memory space (and brain region).

Gesturing begins at an early age, first with pointing and then with more complex gestures. It is interesting to note that several advances in cognitive abilities are displayed first in gesture before later being expressed in speech. Moreover, the early use of gesture is associated with later verbal skill.

For example, research from Susan Goldin-Meadow and her colleagues has found that toddlers (14 months), studied during an hour and a half of play with their parents, used gestures more if they were from better-educated families, and this correlated with significantly greater vocabulary at 4 ½. On average, toddlers from well-educated families used gestures to convey 24 different meanings, while those from less-educated families used gestures to convey only 13. Better-educated parents also used more gestures when interacting with their children.

Another interesting study by the same researchers showed that the number of different meanings conveyed in gesture at 18 months predicted vocabulary at 42 months, while the number of gesture+speech combinations, particularly those conveying sentence-like ideas, predicted sentence complexity.

Some months ago, I read an article in The Philadelphia Inquirer about parents communicating with their pre-verbal infants using sign language. I was greatly taken with this idea. Though it sounds, at first blush, to be part of the whole flashcards-for-babies movement, it is something quite different (I do think you need to be very judicious in the ‘hothousing’ of children; there’s more to making a person than stuffing them with knowledge like a foie gras goose). The development of verbal skills requires physical development and control that is beyond babies, but we shouldn’t assume their inability to articulate words means they don’t have the mental capacity for thought.

Nor is there any evidence that teaching them simple signs delays or impedes their verbal development. Indeed, it may help it. It may also help their social development. There’s a lot of frustration in not being able to communicate — surely eliminating, or at least reducing, that frustration is going to have positive effects.

Now this is speculation. At this point we only have anecdotal reports, no research. But we can point to the positive effects of bilingualism to tell us learning two languages is beneficial rather than a hindrance (although children growing up in a truly bilingual household may be a few weeks later in starting to speak), and we know that children’s language skills improve the more time parents spend (positively) interacting with them, and, as we have just discussed, early skill with gestures is associated with better verbal skills later on.

Caregivers of young children who are interested in this can go to: https://www.babysignlanguage.com/