encoding

When we are presented with new information, we try and connect it to information we already hold. This is automatic. Sometimes the information fits in easily; other times the fit is more difficult — perhaps because some of our old information is wrong, or perhaps because we lack some of the knowledge we need to fit them together.

When we're confronted by contradictory information, our first reaction is usually surprise. But if the surprise continues, with the contradictions perhaps increasing, or at any rate becoming no closer to being resolved, then our emotional reaction turns to confusion.

Confusion is very common in the learning process, despite most educators thinking that effective teaching is all about minimizing, if not eliminating, confusion.

But recent research has suggested that confusion is not necessarily a bad thing. Indeed, in some circumstances, it may be desirable.

I see this as an example of the broader notion of ‘desirable difficulty’, which is the subject of my current post. But let’s look first at this recent study on confusion for learning.

In the study, students engaged in ‘trialogues’ involving themselves and two animated agents. The trialogues discussed possible flaws in a scientific study, and the animated agents took the roles of a tutor and a student peer. To get the student thinking about what makes a good scientific study, the agents disagreed with each other on certain points, and the student had to decide who was right. On some occasions, the agents made incorrect or contradictory statements about the study.

In the first experiment, involving 64 students, there were four opportunities for contradictions during the discussion of each research study. Because the overall levels of student confusion were quite low, a second experiment, involving 76 students, used a delayed manipulation, where the animated agents initially agreed with each other but eventually started to express divergent views. In this condition, students were sometimes then given a text to read to help them resolve their confusion. It was thought that, given their confusion, students would read the text with particular attention, and so improve their learning.

In both experiments, on those trials which genuinely confused the students, those students who were initially confused by the contradiction between the two agents did significantly better on the test at the end.

A side-note: self-reports of confusion were not very sensitive, and students’ responses to forced-choice questions following the contradictions were more sensitive at inferring confusion. This is a reminder that students are not necessarily good judges of their own confusion!

The idea behind all this is that, when there’s a mismatch between new information and prior knowledge, we have to explore the contradictions more deeply — make an effort to explain the contradictions. Such deeper processing should result in more durable and accessible memory codes.

Such a mismatch can occur in many, quite diverse contexts — not simply in the study situation. For example, unexpected feedback, anomalous events, obstacles to goals, or interruptions of familiar action sequences, all create some sort of mismatch between incoming information and prior knowledge.

However, all instances of confusion aren’t necessarily useful for learning and memory. They need to be relevant to the activity, and of course the individual needs to have the means to resolve the confusion.

As I said, I see a relationship between this idea of the right level and type of confusion enhancing learning, and the idea of desirable difficulty. I’ve talked before about the ‘desirable difficulty’ effect (see, for example, Using 'hard to read' fonts may help you remember more). Both of these ideas, of course, connect to a much older and more fundamental idea: that of levels of processing. The idea that we can process information at varying levels, and that deeper levels of processing improve memory and learning, dates back to a paper written in 1972 by Craik and Lockhart (although it has been developed and modified over the years), and underpins (usually implicitly) much educational thinking.

But it’s not so much this fundamental notion that deeper processing helps memory and learning, and certain desirable difficulties encourage deeper processing, that interests me as much as idea of getting the level right.

Too much confusion is usually counter-productive; too much difficulty the same.

Getting the difficulty level right is something I have talked about in connection with flow. On the face of it, confusion would seem to be counterproductive for achieving flow, and yet ... it rather depends on the level of confusion, don't you think? If the student has clear paths to follow to resolve the confusion, the information flow doesn't need to stop.

This idea also, perhaps, has connections to effective practice principles — specifically, what I call the ‘Just-in-time rule’. This is the principle that the optimal spacing for your retrieval practice depends on you retrieving the information just before you would have forgotten it. (That’s not as occult as it sounds! But I’m not here to discuss that today.)

It seems to me that another way of thinking about this is that you want to find that moment when retrieval of that information is at the ‘right’ level of difficulty — neither too easy, nor too hard.

Successful teaching is about shaping the information flow so that the student experiences it — moment by moment — at the right level of difficulty. This is, of course, impossible in a factory-model classroom, but the mechanics of tailoring the information flow to the individual are now made possible by technology.

But technology isn't the answer on its own. To achieve optimal results, it helps if the individual student is aware that the success of their learning depends on (or will at least be more effective — for some will be successful regardless of the inadequacy of the instruction) managing the information flow. Which means they need to provide honest feedback, they need to be able to monitor their learning and recognize when they have ‘got’ something and when they haven’t, and they need to understand that if one approach to a subject isn’t working for them, then they need to try a different one.

Perhaps this provides a different perspective for some of you. I'd love to hear of any thoughts or experiences teachers and students have had that bear on these issues.

Impaired vision is common in old age and even more so in Alzheimer’s disease, and this results not only from damage in the association areas of the brain but also from problems in lower-level areas. A major factor in whether visual impairment impacts everyday function is contrast sensitivity.

Contrast sensitivity not only slows down your perceiving and encoding, it also interacts with higher-order processing, such as decision-making. These effects may be behind the established interactions between age, perceptual ability, and cognitive ability. Such interactions are not restricted to sight — they’ve been reported for several senses.

In fact, it’s been suggested that much of what we regard as ‘normal’ cognitive decline in aging is simply a consequence of having senses that don’t work as well as they used to.

The effects in Alzheimer’s disease are, I think, particularly interesting, because we tend to regard any cognitive impairment here as inevitable and a product of pathological brain damage we can’t do anything much about. But what if some of the cognitive impairment could be removed, simply by improving the perceptual input?

That’s what some recent studies have shown, and I think it’s noteworthy not only because of what it means for those with Alzheimer’s and mild cognitive impairment, but also because of the implications for any normally aging person.

So let’s look at some of this research.

Let’s start with the connection between visual and cognitive impairment.

Analysis of data from the Health and Retirement Study and Medicare files, involving 625 older adults, found that those with very good or excellent vision at baseline had a 63% reduced risk of developing dementia over a mean follow-up period of 8.5 years. Those with poorer vision who didn’t visit an ophthalmologist had a 9.5-fold increased risk of Alzheimer disease and a 5-fold increased risk of mild cognitive impairment. Poorer vision without a previous eye procedure increased the risk of Alzheimer’s 5-fold. For Americans aged 90 years or older, 78% who kept their cognitive skills had received at least one previous eye procedure compared with 52% of those with Alzheimer’s disease.

In other words, if you leave poor vision untreated, you greatly increase your risk of cognitive impairment and dementia.

Similarly, cognitive testing of nearly 3000 older adults with age-related macular degeneration found that cognitive function declined with increased macular abnormalities and reduced visual acuity. This remained true after factors such as age, education, smoking status, diabetes, hypertension, and depression, were accounted for.

And a study comparing the performance of 135 patients with probable Alzheimer’s and 97 matched normal controls on a test of perceptual organization ability (Hooper Visual Organization Test) found that the VOT was sensitive to severity of dementia in the Alzheimer’s patients.

So let’s move on to what we can do about it. Treatment for impaired vision is of course one necessary aspect, but there is also the matter of trying to improve the perceptual environment. Let’s look at this research in a bit more detail.

A 2007 study compared the performance of 35 older adults with probable Alzheimer’s, 35 healthy older adults, and 58 young adults. They were all screened to exclude those with visual disorders, such as cataracts, glaucoma, or macular degeneration. There were significant visual acuity differences between all 3 groups (median scores: 20/16 for young adults; 20/25 for healthy older adults; 20/32 for Alzheimer’s patients).

Contrast sensitivity was also significantly different between the groups, although this was moderated by spatial frequency (normal contrast sensitivity varies according to spatial frequency, so this is not unexpected). Also unsurprisingly, the young adults outperformed both older groups at every spatial frequency, except at the lowest, where it was matched by that of healthy older adults. Similarly, healthy older adults outperformed Alzheimer’s patients at every frequency bar one — the highest frequency.

For Alzheimer’s patients, there was a significant correlation between contrast sensitivity and their cognitive (MMSE) score (except at the lowest frequency of course).

Participants carried out a number of cognitive/perceptual tasks: letter identification; word reading; unfamiliar-face matching; picture naming; pattern completion. Stimuli varied in their perceptual strength (contrast with background).

Letter reading: there were no significant differences between groups in terms of accuracy, but stimulus strength affected reaction time for all participants, and this was different for the groups. In particular, older adults benefited most from having the greatest contrast, with the Alzheimer’s group benefiting more than the healthy older group. Moreover, Alzheimer’s patients seeing the letters at medium strength were not significantly different from healthy older adults seeing the letters at low strength.

Word reading: here there were significant differences between all groups in accuracy as well as reaction time. There was also a significant effect of stimulus strength, which again interacted with group. While young adults’ accuracy wasn’t affected by stimulus strength, both older groups were. Again, there were no differences between the Alzheimer’s group and healthy older adults when the former group was at high stimulus strength and the latter at medium, or at medium vs low. That was true for both accuracy and reaction time.

Picture naming: By and large all groups, even the Alzheimer’s one, found this task easy. Nevertheless, there were effects of stimulus strength, and once again, the performance of the Alzheimer’s group when the stimuli were at medium strength matched that of healthy older adults with low strength stimuli.

Raven’s Matrices and Benton Faces: Here the differences between all groups could not in general be ameliorated by manipulating stimulus strength. The exception was with the Benton Faces, where Alzheimer’s patients seeing the medium strength stimuli matched the performance of healthy older adults seeing low strength stimuli.

In summary, then, for letter reading (reaction time), word reading (identification accuracy and reaction time), picture naming, and face discrimination, manipulating stimulus strength in terms of contrast was sufficient to bring the performance of individuals with Alzheimer’s to a level equal to that of their healthy age-matched counterparts.

It may be that the failure of this manipulation to affect performance on the Raven’s Matrices reflects the greater complexity of these stimuli or the greater demands of the task. However, the success of the manipulation in the case of the Benton Faces — a similar task with stimuli of apparently similar complexity — contradicts this. It may that the stimulus manipulation simply requires some more appropriate tweaking to be effective.

It might be thought that these effects are a simple product of making stimuli easier to see, but the findings are a little more complex than I’ve rendered them. The precise effect of the manipulation varied depending on the type of stimuli. For example, in some cases there was no difference between low and medium stimuli, in others no difference between medium and high; in some, the low contrast stimuli were the most difficult, in others the low and medium strength stimuli were equally difficult, and on one occasion high strength stimuli were more difficult than medium.

The finding that Alzheimer’s individuals can perform as well as healthy older adults on letter and word reading tasks when the contrast is raised suggests that the reading difficulties that are common in Alzheimer’s are not solely due to cognitive impairment, but are partly perceptual. Similarly, naming errors may not be solely due to semantic processing problems, but also to perceptual problems.

Alzheimer’s individuals have been shown to do better recognizing stimuli the closer the representation is to the real-world object. Perhaps it is this that underlies the effect of stimulus strength — the representation of the stimulus when presented at a lower strength is too weak for the compromised Alzheimer’s visual system.

All this is not to say that there are not very real semantic and cognitive problems! But they are not the sole issue.

I said before that for Alzheimer’s patients there was a significant correlation between contrast sensitivity and their MMSE score. This is consistent with several studies, which have found that dementia severity is correlated with contrast sensitivity at some spatial frequencies. This, and these experimental findings, suggests that contrast sensitivity is in itself an important variable in cognitive performance, and contrast sensitivity and dementia severity have a common substrate.

It’s also important to note that the manipulations of contrast were standard across the group. It may well be that individualized manipulations would have even greater benefits.

Another recent study comparing the performance of healthy older and younger adults and individuals with Alzheimer's disease and Parkinson's disease on the digit cancellation test (a visual search task used in the diagnosis of Alzheimer’s), found that increased contrast brought the healthy older adults and those with Parkinson’s up to the level of the younger adults, and significantly benefited Alzheimer’s individuals — without, however, overcoming all their impairment.

There were two healthy older adults control groups: one age-matched to the Alzheimer’s group, and one age-matched to the Parkinson’s group. The former were some 10.5 years older to the latter. Interestingly, the younger control group (average age 64) performed at the same level as the young adults (average age 20), while the older old control group performed significantly worse. As expected, both the Parkinson’s group and the Alzheimer’s group performed worse than their age-matched controls.

However, when contrast was individually tailored at the level at which the person correctly identified a digit appearing for 35.5 ms 80% of the time, there were no significant performance differences between any of the three control groups or the Parkinson’s group. Only the Alzheimer’s group still showed impaired performance.

The idea of this “critical contrast” comparison was to produce stimuli that would be equally challenging for all participants. It was not about finding the optimal level for each individual (and indeed, young controls and the younger old controls both performed better at higher contrast levels). The findings indicate that poorer performance by older adults and those with Parkinson’s is due largely to their weaker contrast sensitivity, but those with Alzheimer’s are also hampered by their impaired ability to conduct a visual search.

The same researchers demonstrated this in a real-world setting, using Bingo cards. Bingo is a popular activity in nursing homes, senior centers and assisted-living facilities, and has both social and cognitive benefits.

Varying cards in terms of contrast, size, and visual complexity found that all groups benefited from increasing stimulus size and decreasing complexity. Those with mild Alzheimer’s were able to perform at levels comparable to their healthy peers, although those with more severe dementia gained little benefit.

Contrast boosting has also been shown to work in everyday environments: people with dementia can navigate more safely around their homes when objects in it have more contrast (e.g. a black sofa in a white room), and eat more if they use a white plate and tableware on a dark tablecloth or are served food that contrasts the color of the plate.

There’s a third possible approach that might also be employed to some benefit, although this is more speculative. A study recently reported at the American Association for the Advancement of Science annual conference revealed that visual deficits found in individuals born with cataracts in both eyes who have had their vision corrected can be overcome through video game playing.

After playing an action video game for just 40 hours over four weeks, the patients were better at seeing small print, the direction of moving dots, and the identity of faces.

The small study (this is not, after all, a common condition) involved six people aged 19 to 31 who were born with dense cataracts in each eye. Despite these cataracts being removed early in life, such individuals still grow up with poorer vision, because normal development of the visual cortex has been disrupted.

The game required players to respond to action directly ahead of them and in the periphery of their vision, and to track objects that are sometimes faint and moving in different directions. Best results were achieved when players were engaged at the highest skill level they could manage.

Now this is quite a different circumstance to that of individuals whose visual system developed normally but is now degrading. However, if vision worsens for some time before being corrected, or if relevant activities/stimulation have been allowed to decline, it may be that some of the deficit is not due to damage as such, but more malleable effects. In the same way that we now say that cognitive abilities need to be kept in use if they are not to be lost, perceptual abilities (to the extent that they are cognitive, which is a great extent) may benefit from active use and training.

In other words, if you have perceptual deficits, whether in sight, hearing, smell, or taste, you should give some thought to dealing with them. While I don’t know of any research to do with taste, I have reported on several studies associating hearing loss with age-related cognitive impairment or dementia, and similarly olfactory impairment. Of particular interest is the research on reviving a failing sense of smell through training, which suggested that one road to olfactory impairment is through neglect, and that this could be restored through training (in an animal model). Similarly, I have reported, more than once, on the evidence that music training can help protect against hearing loss in old age. (You can find more research on perception, training, and old age, on the Perception aggregated news page.)

 

For more on the:

Bingo study: https://www.eurekalert.org/pub_releases/2012-01/cwru-gh010312.php

Video game study:

https://www.guardian.co.uk/science/2012/feb/17/videogames-eyesight-rare-eye-disorder

https://medicalxpress.com/news/2012-02-gaming-eyesight.html

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 recently read an interesting article in the Smithsonian about procrastination and why it’s good for you. Frank Partnoy, author of a new book on the subject, pointed out that procrastination only began to be regarded as a bad thing by the Puritans — earlier (among the Greeks and Romans, for example), it was regarded more as a sign of wisdom.

The examples given about the perils of deciding too quickly made me think about the assumed connection between intelligence and processing speed. We equate intelligence with quick thinking, and time to get the correct answer is part of many tests. So, regardless of the excellence of a person’s cognitive product, the time it takes for them to produce it is vital (in test).

Similarly, one of the main aspects of cognition impacted by age is processing speed, and one of the principal reasons for people to feel that they are ‘losing it’ is because their thinking is becoming noticeably slower.

But here’s the question: does it matter?

Certainly in a life-or-death, climb-the-tree-fast-or-be-eaten scenario, speed is critical. But in today’s world, the major reason for emphasizing speed is the pace of life. Too much to do and not enough time to do it in. So, naturally, we want to do everything fast.

There is certainly a place for thinking fast. I recently looked through a short book entitled “Speed Thinking” by Ken Huds. The author’s strategy for speed thinking was basically to give yourself a very brief window — 2 minutes — in which to come up with 9 thoughts (the nature of those thoughts depends on the task before you — I’m just generalizing the strategy here). The essential elements are the tight time limit and the lack of a content limit — to accomplish this feat of 9 relevant thoughts in 2 minutes, you need to lose your inner censor and accept any idea that occurs to you.

If you’ve been reading my last couple of posts on flow, it won’t surprise you that this strategy is one likely to produce that state of consciousness (at least, once you’re in the way of it).

So, I certainly think there’s a place for fast thinking. Short bouts like this can re-energize you and direct your focus. But life is a marathon, not a sprint, and of course we can’t maintain such a pace or level of concentration. Nor should we want to, because sometimes it’s better to let things simmer. But how do we decide when it’s best to think fast or best to think slow? (shades of Daniel Kahneman’s wonderful book Thinking, Fast and Slow here!)

In the same way that achieving flow depends on the match between your skill and the task demands, the best speed for processing depends on your level of expertise, the demands of the task, and the demands of the situation.

For example, Sian Beilock (whose work on math anxiety I have reported on) led a study that demonstrated that, while novice golfers putted better when they could concentrate step-by-step on the accuracy of their performance, experts did better when their attention was split between two tasks and when they were focused on speed rather than accuracy.

Another example comes from a monkey study that has just been in the news. In this study, rhesus macaques were trained to reach out to a target. To do so, their brains needed to know three things: where their hand is, where the target is, and the path for the hand to travel to reach the target. If there’s a direct path from the hand to the target, the calculation is simple. But in the experiment, an obstacle would often block the direct path to the target. In such cases, the calculation becomes a little bit more complicated.

And now we come to the interesting bit: two monkeys participated. As it turns out, one was hyperactive, the other more controlled. The hyperactive monkey would quickly reach out as soon as the target appeared, without waiting to see if an obstacle blocked the direct path. If an obstacle did indeed appear in the path (which it did on 2/3 trials), he had to correct his movement in mid-reach. The more self-controlled monkey, however, waited a little longer, to see where the obstacle appeared, then moved smoothly to the target. The hyperactive monkey had a speed advantage when the way was clear, but the other monkey had the advantage when the target was blocked.

So perhaps we should start thinking of processing speed as a personality, rather than cognitive, variable!

[An aside: it’s worth noting that the discovery that the two monkeys had different strategies, undergirded by different neural activity, only came about because the researcher was baffled by the inconsistencies in the data he was analyzing. As I’ve said before, our focus on group data often conceals many fascinating individual differences.]

The Beilock study indicates that the ‘correct’ speed — for thinking, for decision-making, for solving problems, for creating — will vary as a function of expertise and attentional demands (are you trying to do two things at once? Is something in your environment or your own thoughts distracting you?). In which regard, I want to mention another article I recently read — a blog post on EdWeek, on procedural fluency in math learning. That post referenced an article on timed tests and math anxiety (which I’m afraid is only available if you’re registered on the EdWeek site). This article makes the excellent point that timed tests are a major factor in developing math anxiety in young children. Which is a point I think we can generalize.

Thinking fast, for short periods of time, can produce effective results, and the rewarding mental state of flow. Being forced to try and think fast, when you lack the necessary skills, is stressful and non-productive. If you want to practice thinking fast, stick with skills or topics that you know well. If you want to think fast in areas in which you lack sufficient expertise, work on slowly and steadily building up that expertise first.

I was listening to a podcast the other day. Two psychologists (Andrew Wilson and Sabrina Galonka) were being interviewed about embodied cognition, a topic I find particularly interesting. As an example of what they meant by embodied cognition (something rather more specific than the fun and quirky little studies that are so popular nowadays — e.g., making smaller estimations of quantities when leaning to the left; squeezing a soft ball making it more likely that people will see gender neutral faces as female while squeezing a hard ball influences them to see the faces as male; holding a heavier clipboard making people more likely to judge currencies as more valuable and their opinions and leaders as more important), they mentioned the outfielder problem. Without getting into the details (if you’re interested, the psychologists have written a good article on it on their blog), here’s what I took away from the discussion:

We used to think that, in order to catch a ball, our brain was doing all these complex math- and physics-related calculations — try programming a robot to do this, and you’ll see just how complex the calculations need to be! And of course this is that much more complicated when the ball isn’t aimed at you and is traveling some distance (the outfielder problem).

Now we realize it’s not that complicated — our outfielder is moving, and this is the crucial point. Apparently (according to my understanding), if he moves at the right speed to make his perception of the ball’s speed uniform (the ball decelerates as it goes up, and accelerates as it comes down, so the catcher does the inverse: running faster as the ball rises and slower as it falls), then — if he times it just right — the ball will appear to be traveling a straight line, and the mental calculation of where it will be is simple.

(This, by the way, is what these psychologists regard as ‘true’ embodied cognition — cognition that is the product of a system that includes the body and the environment as well as the brain.)

This idea suggests two important concepts that are relevant to those wishing to improve their memory:

We (like all animals) have been shaped by evolution to follow the doctrine of least effort. Mental processing doesn’t come cheap! If we can offload some of the work to other parts of the system, then it’s sensible to do so.

In other words, there’s no great moral virtue in insisting on doing everything mentally. Back in the day (2,500 odd years ago), it was said that writing things down would cause people to lose their ability to remember (in Plato’s Phaedrus, Socrates has the Egyptian god-pharaoh say to Thoth, the god who invented writing, “this discovery of yours will create forgetfulness in the learners' souls, because they will not use their memories; they will trust to the external written characters and not remember of themselves.”)

This idea has lingered. Many people believe that writing reminders to oneself, or using technology to remember for us, ‘rots our brains’ and makes us incapable of remembering for ourselves.

But here’s the thing: the world is full of information. And it is of varying quality and importance. You might feel that someone should be remembering certain information ‘for themselves’, but this is a value judgment, not (as you might believe) a helpful warning that their brain is in danger of atrophying itself into terminal dysfunction. The fact is, we all choose what to remember and what to forget — we just might not have made a deliberate and conscious choice. Improving your memory begins with this: actually thinking about what you want to remember, and practicing the strategies that will help you do just that.

However, there’s an exception to the doctrine of least effort, and it’s evident among all the animals with sufficient cognitive power — fun. All of us who have enough brain power to spare, engage in play. Play, we are told, has a serious purpose. Young animals play to learn about the world and their own capabilities. It’s a form, you might say, of trial-&-error — but a form with enjoyability built into the system. This enjoyability is vital, because it motivates the organism to persist. And persistence is how we discover what works, and how we get the practice to do it well.

What distinguishes a good outfielder from someone who’s never tried to catch a ball before? Practice. To judge the timing, to get the movement just right — movement which will vary with every ball — you need a lot of practice. You can’t just read about what to do. And that’s true of every physical skill. Less obviously, it’s true of cognitive skills also.

It also ties back to what I was saying about trying to achieve flow. If you’re not enjoying what you’re doing, it’s probably either too easy or too hard for you. If it’s too easy, try and introduce some challenge into it. If it’s too hard, break it down into simpler components and practice them until you have achieved a higher level of competence on them.

Enjoyability is vital for learning well. So don’t knock fun. Don’t think play is morally inferior. Instead, try and incorporate a playful element into your work and study (there’s a balance, obviously!). If you have hobbies you enjoy, think about elements you can carry across to other activities (if you don’t have a hobby you enjoy, perhaps you should start by finding one!).

So the message for today is: the holy grail in memory and learning is NOT to remember everything; the superior approach to work / study / life is NOT total mastery and serious dedication. An effective memory is one that remembers what you want/need it to remember. Learning occurs through failure. Enjoyability greases the path to the best learning and the most effective activity.

Let focused fun be your mantra.

On a number of occasions I have reported on studies showing that people with expertise in a specific area show larger gray matter volume in relevant areas of the brain. Thus London taxi drivers (who are required to master “The Knowledge” — all the ways and byways of London) have been found to have an increased volume of gray matter in the anterior hippocampus (involved in spatial navigation). Musicians have greater gray matter volume in Broca’s area.

Other research has found that gray matter increases in specific areas can develop surprisingly quickly. For example, when 19 adults learned to match made-up names against four similar shades of green and blue in five 20-minute sessions over three days, the areas of the brain involved in color vision and perception increased significantly.

This is unusually fast, mind you. Previous research has pointed to the need for training to extend over several weeks. The speed with which these changes were achieved may be because of the type of learning — that of new categories — or because of the training method used. In the first two sessions, participants heard each new word as they regarded the relevant color; had to give the name on seeing the color; had to respond appropriately when a color and name were presented together. In the next three sessions, they continued with the naming and matching tasks. In both cases, immediate feedback was always given.

But how quickly brain regions may re-organize themselves to optimize learning of a specific skill is not the point I want to make here. Some new research suggests our ideas of cortical plasticity need to be tweaked.

In my book on note-taking, I commented on how emphasis of some details (for example by highlighting) improves memory for those details but reduces memory of other details. In the same way, increase of one small region of the brain is at the expense of others. If we have to grow an area for each new skill, how do we keep up our old skills, whose areas might be shrinking to make up for it?

A rat study suggests the answer. While substantial expertise (such as our London cab-drivers and our professional musicians) is apparently underpinned by permanent regional increase, the mere learning of a new skill does not, it seems, require the increase to endure. When rats were trained on an auditory discrimination task, relevant sub-areas of the auditory cortex grew in response to the new discrimination. However, after 35 days the changes had disappeared — but the rats retained their new perceptual abilities.

What’s particularly interesting about this is what the finding tells us about the process of learning. It appears that the expansion of bits of the cortex is not the point of the process; rather it is a means of generating a large and varied set of neurons that are responsive to newly relevant stimuli, from which the most effective circuit can be selected.

It’s a culling process.

This is the same as what happens with children. When they’re young, neurons grow with dizzying profligacy. As they get older, these are pruned. Gone are the neurons that would allow them to speak French with a perfect accent (assuming French isn’t a language in their environment); gone are the neurons that would allow them to finely discriminate the faces of races other than those around them. They’ve had their chance. The environment has been tested; the needs have been winnowed; the paths have been chosen.

In other words, the answer’s not: “more” (neurons/connections); the answer is “best” (neurons/connections). What’s most relevant; what’s needed; what’s the most efficient use of resources.

This process of throwing out lots of trials and seeing what wins, echoes other findings related to successful learning. We learn a skill best by varying our practice in many small ways. We learn best from our failures, not our successes — after all, a success is a stopper. If you succeed without sufficient failure, how will you properly understand why you succeeded? How will you know there aren’t better ways of succeeding? How will you cope with changes in the situation and task?

Mathematics is an area in which this process is perhaps particularly evident. As a student or teacher, you have almost certainly come across a problem that you or the student couldn’t understand when expressed in one way, and maybe several different ways. Until, at some point, for no clear reason, understanding ‘clicks’. And it’s not necessarily that this last way of expressing / representing it is the ‘right’ one — if it had been presented first, it may not have had that effect. The effect is cumulative — the result of trying several different paths and picking something useful from each of them.

In a recent news item I reported on a finding that people who learned new sequences more quickly in later sessions were those whose brains had displayed more 'flexibility' in the earlier sessions — that is, different areas of the brain linked with different regions at different times. And most recently, I reported on a finding that training on a task that challenged working memory increased fluid intelligence in those who improved at the working memory task. But not everyone did. Those who improved were those who found the task challenging but not overwhelming.

Is it too much of a leap to surmise that this response goes hand in hand with flexible processing, with strategizing? Is this what the ‘sweet spot’ in learning really reflects — a level of challenge and enjoyability that stimulates many slightly different attempts? We say ‘Variety is the spice of life’. Perhaps we should add: ‘Variety is the key to learning’.

I recently reported on a finding that memories are stronger when the pattern of brain activity is more closely matched on each repetition, a finding that might appear to challenge the long-standing belief that it’s better to learn in different contexts. Because these two theories are very important for effective learning and remembering, I want to talk more about this question of encoding variability, and how both theories can be true.

First of all, let’s quickly recap the relevant basic principles of learning and memory (I discuss these in much more detail in my books The Memory Key, now out-of-print but available from my store as a digital download, and its revised version Perfect Memory Training, available from Amazon and elsewhere):

network principle: memory consists of links between associated codes

domino principle: the activation of one code triggers connected codes

recency effect: a recently retrieved code will be more easily found

priming effect: a code will be more easily found if linked codes have just been retrieved

frequency (or repetition) effect: the more often a code has been retrieved, the easier it becomes to find

spacing effect: repetition is more effective if repetitions are separated from each other by other pieces of information, with increasing advantage at greater intervals.

matching effect: a code will be more easily found the more the retrieval cue matches the code

context effect: a code will be more easily found if the encoding and retrieval contexts match

Memory is about two processes: encoding (the way you shape the memory when you put it in your database, which includes the connections you make with other memory codes already there) and retrieving (how easy it is to find in your database). So making a ‘good’ memory (one that is easily retrieved) is about forming a code that has easily activated connections.

The recency and priming effects remind us that it’s much easier to follow a memory trace (by which I mean the path to it as well as the code itself) that has been activated recently, but that’s not a durable strength. Making a memory trace more enduringly stronger requires repetition (the frequency effect). This is about neurobiology: every time neurons fire in a particular sequence, it makes it a little easier for it to fire in that way again.

Now the spacing effect (which is well-attested in the research) seems at odds with this most recent finding, but clearly the finding is experimental evidence of the matching and context effects. Context at the time of encoding affects the memory trace in two ways, one direct and one indirect. It may be encoded with the information, thus providing additional retrieval cues, and it may influence the meaning placed on the information, thus affecting the code itself.

It is therefore not at all surprising that the closer the contexts, the closer the match between what was encoded and what you’re looking for, the more likely you are to remember. The thing to remember is that the spacing effect does not say that it makes the memory trace stronger. In fact, most of the benefit of spacing occurs with as little as two intervening items between repetitions — probably because you’re not going to benefit from repeating a pattern of activation if you don’t give the neurons time to reset themselves.

But repeating the information at increasing intervals does produce better learning, measured by your ability to easily retrieve the information after a long period of time (see my article on …), and it does this (it is thought) not because the memory trace is stronger, but because the variations in context have given you more paths to the code.

This is the important thing about retrieving: it’s not simply about having a strong path to the memory. It’s about getting to that memory any way you can.

Let’s put it this way. You’re at the edge of a jungle. From where you stand, you can see several paths into the dense undergrowth. Some of the paths are well-beaten down; others are not. Some paths are closer to you; others are not. So which path do you choose? The most heavily trodden? Or the closest?

If the closest is the most heavily trodden, then the choice is easy. But if it’s not, you have to weigh up the quality of the paths against their distance from you. You may or may not choose correctly.

I hope the analogy is clear. The strength of the memory trace is the width and smoothness of the path. The distance from you reflects the degree to which the retrieval context (where you are now) matches the encoding context (where you were when you first input the information). If they match exactly, the path will be right there at your feet, and you won’t even bother looking around at the other options. But the more time has passed since you encoded the information, the less chance there is that the contexts will match. However, if you have many different paths that lead to the same information, your chances of being close to one of them obviously increases.

In other words, yes, the closer the match between encoding and retrieval context, the easier it will be to remember (retrieve) the information. And the more different contexts you have encoded with the information, the more likely it is that one of those contexts will match your current retrieval context.

A concrete example might help. I’ve been using a spaced retrieval program to learn the basic 2200-odd Chinese characters. It’s an excellent program, and groups similar-looking characters together to help you learn to distinguish them. I am very aware that every time a character is presented, it appears after another character, which may or may not be the same one it appeared after on an earlier occasion. The character that appeared before provides part of the context for the new character. How well I remember it depends in part on how often I have seen it in that same context.

I would ‘learn’ them more easily if they always appeared in the same order, in that the memory trace would be stronger, and I would more easily and reliably recall them on each occasion. However in the long-term, the experience would be disadvantageous, because as soon as I saw a character in a different context I would be much less likely to recall it. I can observe this process as I master these characters — with each different retrieval context, my perception of the character deepens as I focus attention on different aspects of it.

Here’s an interesting study that’s just been reported: 72 seven- and eight-month-old infants watched video animations of familiar fun items being revealed from behind a set of colorful boxes (see the 3-minute YouTube video). What the researchers found is that the babies reliably lost interest when the video became too predictable – and also when the sequence of events became too unpredictable.

In other words, there’s a level of predictability/complexity that is “just right” (the researchers are calling this the ‘Goldilocks effect’) for learning.

Now it’s true that the way babies operate is not necessarily how we operate. But this finding is consistent with other research suggesting that adult learners find it easier to learn and pay attention to material that is at just the right level of complexity/difficulty.

The findings help explain why some experiments have found that infants reliably prefer familiar objects, while other experiments have found instead a preference for novel items. Because here’s the thing about the ‘right amount’ of surprise or complexity — it’s a function of the context.

And this is just as true for us adults as it is for them.

We live in a world that’s flooded with information and change. Clay Shirky says: “There’s no such thing as information overload — only filter failure.” Brian Solis re-works this as: “information overload is a symptom of our inability to focus on what’s truly important or relevant to who we are as individuals, professionals, and as human beings.”

I think this is simplistic. Maybe that’s just because I’m interested in too many things and they all tie together in different ways, and because I believe, deeply, in the need to cross boundaries. We need specialists, sure, because every subject now has too much information even for a specialist to master. But maybe that’s what computers are going to be for. More than anything else, we need people who can see outside their specialty.

Part of the problem as we get older, I think, is that we expect too much of ourselves. We expect too much of our memory, and we expect too much of our information-processing abilities. Babies know it. Children know it. You take what you can; each taking is a step; on the next step you will take some more. And eventually you will understand it all.

Perhaps it is around adolescence that we get the idea that this isn’t good enough. Taking bites is for children; a grown-up person should be able to read a text/hear a conversation/experience an event and absorb it all. Anything less is a failure. Anything less is a sign that you’re not as smart as others.

Young children drive their parents crazy wanting the same stories read over and over again, but while the stories may seem simple to us, that’s because we’ve forgotten how much we’ve learned. Probably they are learning something new each time (and quite possibly we could learn something from the repetitions too, if we weren’t convinced we already knew it all!).

We don’t talk about the information overload our babies and children suffer, and yet, surely, we should. Aren’t they overloaded with information? When you think about all they must learn … doesn’t that put our own situation in perspective?

You could say they are filtering out what they need, but I don’t think that’s accurate. Because they keep coming back to pick out more. What they’re doing is taking bites. They’re absorbing what they need in small, attainable bites. Eventually they will get through the entire meal (leaving to one side, perhaps, any bits that are gristly or unpalatable).

The researchers of the ‘Goldilocks’ study tell parents they don’t need to worry about providing this ‘just right’ environment for their baby. Just provide a reasonably stimulating environment. The baby will pick up what they need at the time, and ignore the rest.

I think we can learn from this approach. First of all, we need to cultivate an awareness of the complexity of an experience (I’m using this as an umbrella word encompassing everything from written texts to personal events), being aware that any experience must be considered in its context, and that what might appear (on present understanding) to be quite simple might become less so in the light of new knowledge. So the complexity of an event is not a fixed value, but one that reflects your relationship to it at that time. This suggests we need different information-management tools for different levels of complexity (e.g., tagging that enables you to easily pull out items that need repeated experiencing at appropriate occasions).

(Lucky) small children have an advantage (this is not the place to discuss the impact of ‘disadvantaged’ backgrounds) — the environment is set up to provide plenty of opportunities to re-experience the information they are absorbing in bites. We are not so fortunate. On the other hand, we have the huge advantage of having far more control over our environment. Babies may use instinct to control their information foraging; we must develop more deliberate skills.

We need to understand that we have different modes of information foraging. There is the wide-eyed, human-curious give-me-more mode — and I don’t think this is a mode to avoid. This wide, superficial mode is an essential part of what makes us human, and it can give us a breadth of understanding that can inform our deeper knowledge of specialist subjects. We may think of this as a recreational mode.

Other modes might include:

• Goal mode: I have a specific question I want answered
• Learning mode: I am looking for information that will help me build expertise in a specific topic
• Research mode: I have expertise in a topic and am looking for information in a specific part of that domain
• Synthesis mode: I have expertise in one topic and want information from other domains that would enrich my expertise and give me new perspectives

Perhaps you can think of more; I would love to hear other suggestions.

I think being consciously aware of what mode you are in, having specific information-seeking and information-management tools for each mode, and having the discipline to stay in the chosen mode, are what we need to navigate the information ocean successfully.

These are some first thoughts. I would welcome comments. This is a subject I would like to develop.