expertise

About expert knowledge

Principles of expert knowledge

  • Principle 1: Experts are sensitive to patterns of meaningful information
  • Principle 2: Expert knowledge is highly organized in deeply integrated schemas.
  • Principle 3: Expert knowledge is readily accessible when needed because it contains information about when it will be useful.

Do experts simply know "more" than others, or is there something qualitatively different about an expert's knowledge compared to the knowledge of a non-expert?

While most of us are not aiming for an expert's knowledge in many of the subjects we study or learn about, it is worthwhile considering the ways in which expert knowledge is different, because it shows us how to learn, and teach, more effectively.

Experts are sensitive to patterns of meaningful information

A basic principle of perception is that it depends on the observer. What is green to you may be teal to me; a floppy disk to me may be a curious square of hard plastic to you. The observer always sees the world through her own existing knowledge.

An essential part of the difference between an expert and a novice can be seen in terms of this principle. A configuration of chess pieces on a board, seen briefly, will be bewildering and hard to remember for someone with no knowledge of chess, and even for someone with some experience of the game. But to a chess master, the configuration will be easily grasped, and easily remembered.

When chess pieces are placed randomly on a board, the chess master is no better than the novice at remembering briefly seen configurations. This is because the configuration is not meaningful. After tens of thousands of hours of playing chess, of studying the games of other masters, of memorizing patterns of moves, the master has hundreds of stored patterns in his memory. When he sees a configuration of pieces, he breaks it into meaningful elements that are related by an underlying strategy. Thus, while the novice would have to try and remember every single piece and its absolute or relative position on the board, the master only has to remember a few “chunks”.

The master can do this because he has a highly organized structure of knowledge relating to this domain. (It’s worth noting that expertise is highly specific to a domain of knowledge; a chess master will be no better than anyone at remembering, say, a shopping list.)

Expert knowledge is highly organized in deeply integrated schemas.

This sensitivity is thought to grow out of the deep conceptual schemas that experts develop in their area of expertise.

A schema is an organized body of knowledge that enables the user to understand a situation of set of facts. Schema theories include the idea of “scripts”, which help us deal with events. Thus, we are supposed to have a “restaurant script”, which we have developed from our various experiences with restaurants, and which tells us what to expect in a restaurant. Such a script would include the various activities that typically take place in a restaurant (being seated; ordering; eating; paying the bill, etc), and the various people we are likely to interact with (e.g., waiter/waitress; cashier).

Similarly, when we read or hear stories (and many aspects of our conversations with each other may be understood in terms of narratives, not simply those we read in books), we are assisted in our interpretation by “story schemas” or “story grammars”.

A number of studies have shown that memory is better for stories than other types of text; that we are inclined to remember events that didn’t happen if their happening is part of our mental script; that we find it hard to remember stories that we don’t understand, because they don’t fit into our scripts.

Schemas provide a basis for:

  • Assimilating information
  • Making inferences
  • Deciding which elements to attend to
  • Help search in an orderly sequence
  • Summarizing
  • Helping you to reconstruct a memory in which many details have been lost

(following Anderson 1984)

A schema then is a body of knowledge that provides a framework for understanding, for encoding new knowledge, for retrieving information. By having this framework, the expert can quickly understand and acquire new knowledge in her area of expertise, and can quickly find the relevant bits of knowledge when called on.

How is an expert schema different from a beginner’s one?

Building schemas is something we do naturally. How is an expert schema different from a beginner’s one?

An expert’s schema is based on deep principles; a beginner tends to organize her growing information around surface principles.

For example, in physics, when solving a problem, an expert usually looks first for the principle or law that is applicable to the problem (e.g., the first law of thermodynamics), then works out how one could apply this law to the problem. An experienced novice, on the other hand, tends to search for appropriate equations, then works out how to manipulate these equations (1). Similarly, when asked to sort problems according to the approach that could be used to solve them, experts group the problems in terms of the principles that can be used, while the novices sort them according to surface characteristics (such as “problems that contain inclined planes”) (2).

The different structure of expert knowledge is also revealed through the pattern of search times. Novices retrieve information at a rate that suggests a sequential search of information, as if they are methodically going down a list. Expert knowledge appears to be organized in a more conceptual manner, with information categorized in different chunks (mini-networks) which are organized around a central “deep” idea, and which have many connections to other chunks in the larger network.

These mini-networks, and the rich interconnections between them, help the expert look in the right place. One of the characteristics that differentiates experts from novices is the speed and ease with which experts retrieve the particular knowledge that is relevant to the problem in hand. Experts’ knowledge is said to be “conditionalized”, that is, knowledge about something includes knowledge as to the contexts in which that knowledge will be useful.

Expert knowledge contains information about when it will be useful.

Conditionalized knowledge is contrasted with “inert” knowledge. This concept is best illustrated by an example.

Gick and Holyoake (1980) presented college students with the following passage, which they were instructed to memorize:

After students had demonstrated their recall of this passage, they were asked to solve the following problem:

Although the students had recently memorized the military example, only 20% of them saw its relevance to the medical problem and successfully applied its lesson. Most of the students were unable to solve the problem until given the explicit hint that the passage they had learned contained information they could use. For them, the knowledge they had acquired was inert. However, when the analogy was pointed out to them, 90% of them were able to apply the principle successfully.

Much of the information “learned” in school is inert. A compelling demonstration of this comes from studies conducted by Perfetto, Bransford and Franks (1983), in which college students were given a number of “insight” problems, such as:

Some students were given clues to help them solve these problems:

These clues were given before the students were shown the problems. Some of the students given clues were also explicitly advised that the clues would help them solve the problems. They performed very well. Other students however, were not prompted to use the clues they had been given, and they performed as poorly as those students who weren’t given clues.

The poor performance of those students who were given clues but not prompted to use them surprised the authors of the study, because the clues were so obviously relevant to the problems, but it provides a compelling demonstration of inert knowledge.

The ability of students to apply relevant knowledge in new contexts tends to be grossly over-estimated by instructors. Most assume that it will happen “naturally”, but what this research tells us is that the conditionalization of knowledge is something that happens quite a long way down the track, and if students are to be able to use the information they have learned, they need help in understanding where, when and how to use new knowledge.

Differences between experts and novices:

  • experts have more categories
  • experts have richer categories
  • experts’ categories are based on deeper principles
  • novices’ categories emphasize surface similarities3
References: 
  • Anderson, R.C. 1984. Role of reader's schema in comprehension, learning and memory. In R. Anderson, J. Osborn, & R. Tierney (eds), Learning to read in American schools: Basal readers and content texts. Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Bransford, J.D., Brown, A.L. & Cocking, R.R. (eds.) 1999. How people learn: Brain, Mind, Experience, and School. Washington, DC: National Academy Press.
  • Bransford, J.D., Stein, B.S., Shelton, T.S., & Owings, R.A. 1981. Cognition and adaptation: The importance of learning to learn. In J. Harvey (ed.), Cognition, social behavior and the environment. Hillsdale, NJ: Erlbaum.
  • Bransford, J.D., Stein, B.S., Vye, N.J., Franks, J.J., Auble, P.M., Mezynski, K.J. & Perfetto, G.A. 1982. Differences in approaches to learning: an overview. Journal of Experimental Psychology: General, 111, 390-398.
  • Gick, M.L. & Holyoake, K.J. 1980. Analogical problem solving. Cognitive Psychology, 12, 306-355.
  • Perfetto, G.A., Bransford, J.D. & Franks, J.J. 1983. Constraints on access in a problem solving context. Memory & Cognition, 11, 24-31.

1. Chi, MTH, Feltovich, PJ, & Glaser, R. 1981. Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121-152.

Larkin, JH, 1981. Enriching formal knowledge: A model for learning to solve problems in physics. In JR Anderson (ed), Cognitive skills and their acquisition. Hillsdale, NJ: Erlbaum.

1983. The role of problem representation in physics. In D. Gentner & A.L. Stevens (eds), Mental models. Hillsdale, NJ: Erlbaum.

2. Chi et al 1981

3. Taken from The Memory Key.

Acquiring expertise through deliberate practice

K. Anders Ericsson, the guru of research into expertise, makes a very convincing case for the absolutely critical importance of what he terms “deliberate practice”, and the minimal role of what is commonly termed “talent”. I have written about this question of talent and also about the principles of expertise . Here I would like to talk briefly about Ericsson’s concept of deliberate practice.

Most people, he suggests, spend very little (if any) time engaging in deliberate practice even in those areas in which they wish to achieve some level of expertise. Experts, on the other hand, only achieve their expertise after several years (at least ten, in general) of maintaining high levels of regular deliberate practice.

What distinguishes deliberate practice from less productive practice? Ericsson suggests several factors are of importance:

The acquisition of expert performance needs to be broken down into a sequence of attainable training tasks.

  • Each of these tasks requires a well-defined goal.
  • Feedback for each step must be provided.
  • Repetition is needed — but that repetition is not simple; rather the student should be provided with opportunities that gradually refine his performance.
  • Attention is absolutely necessary — it is not enough to simply mechanically “go through the motions”.
  • The aspiring expert must constantly and attentively monitor her progress, adjusting and correcting her performance as required.

For these last two reasons, deliberate practice is limited in duration. Whatever the particular field of endeavor, there seems a remarkable consistency in the habits of elite performers that suggests 4 to 5 hours of deliberate practice per day is the maximum that can be maintained. This, of course, cannot all be done at one time without resting. When the concentration flags, it is time to rest — this most probably is after about an hour. But the student must train himself up to this level; the length of time he can concentrate will increase with practice.

Higher levels of concentration are often associated with longer sleeping, in particular in the form of day-time naps.

Not all practice is, or should be, deliberate practice. Deliberate practice is effortful and rarely enjoyable. Some practice is however, what Ericsson terms “playful interaction”, and presumably provides a motivational force — it should not be despised!

In general, experts reduce the amount of time they spend on deliberate practice as they age. It seems that, once a certain level of expertise has been achieved, it is not necessary to force yourself to continue the practice at the same level in order to maintain your skill. However, as long as you wish to improve, a high level of deliberate practice is required.

This article first appeared in the Memory Key Newsletter for November 2005

References: 

Ericsson, K.A. 1996. The acquisition of expert performance: An introduction to some of the issues. In K. Anders Ericsson (ed.), The Road to Excellence: The acquisition of expert performance in the arts and sciences, sports, and games. Mahwah, NJ: Lawrence Erlbaum.

Context & the conditionalization of knowledge

Context is absolutely critical to successful communication. Think of the common experience of being a stranger at a family gathering or a meeting of close friends. Even familiar words and phrases may take on a different or additional meaning, among people who have a shared history. Many jokes and comments will be completely unintelligible, though you all speak the same language.

American anthropologist Edward Hall makes a useful distinction between ‘High context’ and ‘Low context’ communications. Your family gathering would be an example of a high context situation. In this setting, much of the meaning is carried in the speakers, their relationships, their knowledge of each other. In a low context situation, on the other hand, most of the meaning is carried in the actual words.

Part of the problem with email, as we all recognize, is that the context is so lacking, and the burden lies so heavily on the words themselves.

The importance of context for comprehension has, of course, profound implications for learning and memory.

I was reminded of this just the other day. I’m a fan of a TV program called NCIS. I only discovered it, however, at the beginning of the third season. After I’d watched it for some weeks, I purchased the DVDs of the earlier seasons. Most recently, I bought the DVD of season 3, which I had, of course, seen on TV. Watching the first episode of that season, which was the first episode of NCIS I ever saw, I was surprised to hear a line which I had no memory of, that was freighted with significance and led me to a much deeper understanding of the relationship between two of the characters — but which had meant absolutely nothing to me when I originally saw it, ignorant as I was of any of the characters and the back story.

The revelation meant nothing to me as a novice to the program, and so I didn’t remember it, but it meant everything to me as (dare I say it?) an expert.

Context is such a slippery word; so hard to define and pin down. But I think it’s fair to say that the difference between the novice and the expert rests on this concept. When an expert is confronted with a piece of information from her area of expertise, she knows what it means and where it belongs — even if the information is new to her. Because of this, she can acquire new information much more easily than a novice. But this advantage applies only in the expert’s area of expertise.

To take another example from the frivolous world of popular culture, a British study of fans of the long-running radio soap opera The Archers were given one of two imaginary scripts to read. One story was representative of the normal events in The Archers (a visit to a livestock market); the other was atypical (a visit to a boat show). These experts were able to remember many more details of the typical, market story than a group of subjects who knew little about the soap opera, but were no better at remembering details for the atypical story. Most importantly, this occurred even though the two stories shared many parallel features and most of the questions (and answers) used to assess their memory were the same. This indicates the specificity of expert knowledge.

Part of the advantage experts have is thought to rest on the ‘conditionalization’ of knowledge. That is, experts’ knowledge includes a specification of the contexts in which it is relevant.

It is surprising to many, this idea that it is not necessarily a lack of knowledge that is the problem — that people often have relevant knowledge and don’t apply it. In reading, for example, readers often don’t make inferences that they are perfectly capable of making, on the knowledge they have, unless the inferences are absolutely demanded to make sense of the text.

Another example comes from the making of analogies. I discuss this in my workbook on taking notes. Here’s a brief extract:

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Rutherford’s comparison of the atom to the solar system gave us a means to understand the atom. The story goes that Newton ‘discovered’ gravity when an apple fell on his head — because of the comparison he made, realizing that the motion of an apple falling from a tree was in some sense like the motion of the planets. These are comparisons called analogies, and analogy has been shown to be a powerful tool for learning.

But the problem with analogies is that we have trouble coming up with them.

Generally, when we make analogies, we use an example we know well to help us understand something we don’t understand very well. This means that we need to retrieve from memory an appropriate example. But this is clearly a difficult task; people frequently fail to make appropriate connections — even, surprisingly, when an appropriate connection has recently come their way. In a study where people were given a problem to solve after reading a story in which an analogous problem was solved, 80% didn’t think of using the story to solve the problem until the analogy was pointed out to them.

It’s thought that retrieving an appropriate analogy is so difficult because of the way we file information in memory. Certainly similarity is an important attribute in our filed memories, but it’s not the same sort of similarity that governs analogies. The similarity that helps us retrieve memories is a surface similarity — a similarity of features and context. But analogies run on a deeper similarity — a similarity of structure, of relations between objects. This will only be encoded if you have multiple examples (at least more than one) and make an explicit effort to note such relations.

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The conditionalization of knowledge is of course related to the problem of transfer. Transfer refers to the ability to extend (transfer) learning from one situation to another (read more about it here) . Transfer is frequently used as a measure of successful learning. It’s all very well to know that 399-(399*0.1) = 359.1, but how far can you be said to understand it — how much use is it — if you can’t work out how much a $3.99 item will cost you if you have a 10% discount? (In fact, the asymmetry generally works the other way: many people are skilled at working out such purchase calculations, but fall apart when the problem is transferred to a purely numerical problem).

Transfer is affected by the context in which the information was originally acquired — obviously transfer is particularly problematic if you learn the material in a single context — and this is partly where the experts achieve their conditionalization: because, spending so much time with their subject they are more likely to come across the same information in a variety of contexts. But the more important source is probably the level of abstraction at which experts can operate (see my article on transfer for examples of how transfer is facilitated if the information is framed at a higher level of abstraction).

In those with existing expertise, an abstract framework is already in place. When an expert is confronted by new information, they automatically try and fit it into their existing framework. Whether it is consistent or inconsistent with what is already known doesn’t really matter — either way it will be more memorable than information that makes no deep or important connections to familiar material.

Let’s return to this idea of high and low context. Hall was talking about communications, in the context of different cultures (interestingly, he found cultures varied in the degree to which they were context-bound), but the basic concept is a useful one in other contexts. It is helpful to consider, when approaching a topic, either as student or teacher, the degree to which understanding requires implicit knowledge. A high context topic might be thought of as one that assumes a lot of prior knowledge, that assumes a knowledge of deeper structure, that is difficult to explain in words alone. A low context topic might be thought of as one that can be clearly and simply expressed, that can largely stand alone. Learning the basics of a language — how to conjugate a verb; some simple words and phrases — might be thought of as a low context topic, although clearly mastery of a language requires the complex and diverse building up of experiences that signifies a high context topic (and also clearly, some languages will be more ‘high context’ than others).

There is nothing particularly profound about this distinction, but an awareness of the ‘contextual degree’ of a topic or situation, is helpful for students, teachers, and anyone involved in trying to communicate with another human being (or indeed, computer!). It’s also helpful to be aware that high context situations require much more expertise than low context ones.

This article first appeared as "Context, communication & learning" in the Memory Key Newsletter for April 2007

References: 

Reeve, D.K. & Aggleton, J.P. 1998. On the specificity of expert knowledge about a soap opera: an everyday story of farming folk. Applied Cognitive Psychology, 12 (1), 35-42.

Novices' problems with scientific text

This is the last part in my series on understanding scientific text. In this part, as promised, I am going to talk about the difficulties novices have with scientific texts; what they or their teachers can do about it; and the problems with introductory textbooks.

The big problem for novices is of course that their lack of knowledge doesn’t allow them to make the inferences they need to repair the coherence gaps typically found in such texts. This obviously makes it difficult to construct an adequate situation model. Remember, too, that to achieve integration of two bits of information, you need to have both bits active in working memory at the same time. This, clearly, is more difficult for those for whom all the information is unfamiliar (remember what I said about long-term working memory last month).

But it’s not only a matter a matter of having knowledge of the topic itself. A good reader can compensate for their lack of relevant topic knowledge using their knowledge about the structure of the text genre. For this, the reader needs not only to have knowledge of the various kinds of expository structures, but also of the cues in the text that indicate what type of structure it is. (see my article on Reading scientific text for more on this).

One of the most effective ways of bringing different bits of information together is through the asking of appropriate questions. Searching a text in order to answer questions, for example, is an effective means of improving learning. Answering questions is also an effective means of improving comprehension monitoring (remember that one of the big problems with reading scientific texts is that students tend to be poor at judging how well they have understood what was said).

One of the reasons why children typically have pronounced deficits in their comprehension monitoring skills when dealing with expository texts, is that they have little awareness that expository texts require different explanations than narrative texts. However, these are trainable skills. One study, for example, found that children aged 10-12 could be successfully taught to use “memory questions” and “thinking questions” while studying expository texts.

Moreover, the 1994 study found that when the students were trained to ask questions intended to access prior knowledge/experience and promote connections between the lesson and that knowledge, as well as questions designed to promote connections among the ideas in the lesson, their learning and understanding was better than if they were trained only in questions aimed at promoting connections between the lesson ideas only (or if they weren’t trained in asking questions at all!). In other words, making explicit connections to existing knowledge is really important! You shouldn’t just be content to consider a topic in isolation; it needs to be fitted into your existing framework.

College students, too, demonstrate limited comprehension monitoring, with little of their self-questioning going deeply into the material. So it may be helpful to note Baker’s 7 comprehension aspects that require monitoring:

  1. Your understanding of the individual words
  2. Your understanding of the syntax of groups of words
  3. External consistency — how well the information in the text agrees with the knowledge you already have
  4. Internal consistency — how well the information in the text agrees with the other information in the text
  5. Propositional cohesiveness — making the connections between adjacent propositions
  6. Structural cohesiveness —integrating all the propositions pertaining to the main theme
  7. Information completeness — how clear and complete the information in the text is

Think of this as a checklist, for analyzing your (or your students’) understanding of the text.

But questions are not always the answer. The problem for undergraduates is that although introductory texts are presumably designed for novices, the students often have to deal not only with unfamiliar content, but also an approach that is unfamiliar. Such a situation may not be the best context for effective familiar strategies such as self-explanation.

It may be that self-explanation is best for texts that in the middle-range for the reader — neither having too little relevant knowledge, or too much.

Introductory texts also are likely to provide only partial explanations of concepts, a problem made worse by the fact that the novice student is unlikely to realize the extent of the incompleteness. Introductory texts also suffer from diffuse goals, an uneasy mix of establishing a basic grounding for more advanced study, and providing the material necessary to pass immediate exams.

A study of scientific text processing by university students in a natural situation found that the students didn’t show any deep processing, but rather two kinds of shallow processing, produced by either using their (limited knowledge of) expository structures, or by representing the information in the text more precisely.

So should beginning students be told to study texts more deeply? The researchers of this study didn’t think so. Because introductory texts suffer from these problems I’ve mentioned, in particular that of incomplete explanations, they don’t lend themselves to deep processing. The researchers suggest that what introductory texts are good for is in providing the extensive practice needed for building up knowledge of expository structures (and hopefully some necessary background knowledge of the topic! Especially technical language).

To that end, they suggest students should be advised to perform a variety of activities on the text that will help them develop their awareness of the balance between schema and textbase, with the aim of developing a large repertory of general and domain-specific schemata. Such activities / strategies include taking notes, rereading, using advance organizers, and generating study questions. This will all help with their later construction of good mental models, which are so crucial for proper understanding.

References: 
  • Baker, L. 1985. Differences in the standards used by college students to evaluate their comprehension of expository prose. Reading Research Quarterly, 20 (3), 297-313.
  • Elshout-Mohr, M. & van Daalen-Kapteijns, M. 2002. Situated regulation of scientific text processing. In Otero, J., León, J.A. & Graesser, A.C. (eds). The psychology of science text comprehension. Pp 223-252. Mahwah, NJ: LEA.
  • King, A. 1994. Guiding Knowledge Construction in the Classroom: Effects of Teaching Children How to Question and How to Explain. American Educational Research Journal, 31 (2), 338-368.

Memory Champions

A study of exceptional memorizers has revealed no superior cognitive abilities, and no structural differences in their brains.
It has revealed differences in brain activity that seem to reflect the use of a spatial mnemonic; 9 of the 10 memory champions confirmed their use of the method of loci.
Despite many years of practice in mnemonics, and impressive performances in memorizing, there were no increases in gray matter, as there have been in the cases of those with expert knowledge.

In 2002, a British study scanned the brains of ten "superior memorizers" — eight leading contenders in the World Memory Championships, and two individuals previously studied for their extraordinary memory accomplishments — all people that had demonstrated truly impressive feats of memory, in terms of the ability to quickly memorize hundreds of numbers or unrelated words. The ten "memory champions" were matched with ten controls, who had no memory capabilities out of the ordinary.

Testing revealed that the memory champs scored about the same as the controls on general cognitive ability, but did, unsurprisingly, score higher on working memory and long-term verbal memory. They didn't differ in visual memory.

Participants in the study were shown three sets of images; faces, snowflakes and three-digit numbers. The numbers, being the sort of items which the memory champs excel at, were expected to show large performance differences between the two groups. Faces are a class of stimuli for which most people have a considerable expertise in, so a smaller difference was expected. And snowflakes are a very difficult visual pattern to verbalize, and it was expected that both groups would be equally poor at remembering them.

Their brains were scanned while the participants were asked them to remember which ones they had seen, and the order they were shown in. The expected differences in performance were indeed found, allowing the researchers to differentiate between brain activity that reflected the learning prowess itself from the activity reflecting the amount of information learned.

A number of brain regions were of course active in all tasks, for both groups. But there were differences between the two groups, both in terms of greater activity in some regions, and, more interestingly, in terms of the memory champs using brain regions not used by the controls. Most particularly, regardless of task and regardless of performance, the memory champs engaged the left medial superior parietal gyrus, bilateral retrosplenial cortex, and right posterior hippocampus. These areas are all known to be involved in spatial memory and navigation.

On questioning, nine out of ten memory champions advised that they used the loci mnemonic for some or all of the tasks.

The researcher concluded, "Superior memory was not driven by exceptional intellectual ability or structural brain differences. Rather, we found that superior memorizers used a spatial learning strategy, engaging brain regions such as the hippocampus which are critical for memory and for spatial memory in particular."

As another researcher commented, "If you use the right technique, with a lot of application and hard work you can improve your memory. It certainly doesn't look like it's a question of neurological machinery."

The most interesting finding from this study was that, among the memory champs, there were no changes in gray matter volume, despite the fact that these people had been practicing mnemonics for an average of 11 years (a range of 3 to 38.5). Similar brain scans of musicians and London taxi drivers have found significant increases in gray matter volume as a function of number of years practice/experience. This finding would seem to support the view that practice in rote learning strategies doesn't have the benefits of strategies that develop understanding and mastery of meaningful knowledge, in terms of building new connections and growing new neurons.

On that note, you might like to read my article on photographic memory (and whether it's really as desirable as all that.)

References: 
  1. Maguire, E.A., Valentine, E.R., Wilding, J.M. &Kapur, N. 2002. Routes to remembering: the brains behind superior memory. Nature Neuroscience, 6, 90-95.
  2. Brain scan clues to 'memory marvels'. BBC article, 16 December 2002. http://news.bbc.co.uk/1/hi/health/2580867.stm
  3. Master memories are made not born. New Scientist article, 15 December 2002.
    http://www.newscientist.com/news/news.jsp?id=ns99993181

Should ‘learning facts by rote’ be central to education?

Being able to read or discuss a topic requires you to have certain concepts well-learned, so that they are readily accessible when needed.

Rote memorization is a poor tool for acquiring this base knowledge.

‘Core’ knowledge is smaller than you might think.

Building up strong concepts is best done by working through many, diverse examples.

Education is not solely or even mainly about stuffing your head with ‘facts’. Individualized knowledge, built up from personally relevant examples illuminating important concepts, needs to be matched by an equal emphasis on curating knowledge, and practice in replacing outdated knowledge.

Michael Gove is reported as saying that ‘Learning facts by rote should be a central part of the school experience’, a philosophy which apparently underpins his shakeup of school exams. Arguing that "memorisation is a necessary precondition of understanding", he believes that exams that require students to memorize quantities of material ‘promote motivation, solidify knowledge, and guarantee standards’.

How piano tuning changes the brain

In another example of how expertise in a specific area changes the brain, brain scans of piano tuners show which areas grow, and which shrink, with experience — and starting age.

I’ve reported before on how London taxi drivers increase the size of their posterior hippocampus by acquiring and practicing ‘the Knowledge’ (but perhaps at the expense of other functions). A new study in similar vein has looked at the effects of piano tuning expertise on the brain.

The study looked at the brains of 19 professional piano tuners (aged 25-78, average age 51.5 years; 3 female; 6 left-handed) and 19 age-matched controls. Piano tuning requires comparison of two notes that are close in pitch, meaning that the tuner has to accurately perceive the particular frequency difference. Exactly how that is achieved, in terms of brain function, has not been investigated until now.

The brain scans showed that piano tuners had increased grey matter in a number of brain regions. In some areas, the difference between tuners and controls was categorical — that is, tuners as a group showed increased gray matter in right hemisphere regions of the frontal operculum, the planum polare, superior frontal gyrus, and posterior cingulate gyrus, and reduced gray matter in the left hippocampus, parahippocampal gyrus, and superior temporal lobe. Differences in these areas didn’t vary systematically between individual tuners.

However, tuners also showed a marked increase in gray matter volume in several areas that was dose-dependent (that is, varied with years of tuning experience) — the anterior hippocampus, parahippocampal gyrus, right middle temporal and superior temporal gyrus, insula, precuneus, and inferior parietal lobe — as well as an increase in white matter in the posterior hippocampus.

These differences were not affected by actual chronological age, or, interestingly, level of musicality. However, they were affected by starting age, as well as years of tuning experience.

What these findings suggest is that achieving expertise in this area requires an initial development of active listening skills that is underpinned by categorical brain changes in the auditory cortex. These superior active listening skills then set the scene for the development of further skills that involve what the researchers call “expert navigation through a complex soundscape”. This process may, it seems, involve the encoding and consolidating of precise sound “templates” — hence the development of the hippocampal network, and hence the dependence on experience.

The hippocampus, apart from its general role in encoding and consolidating, has a special role in spatial navigation (as shown, for example, in the London cab driver studies, and the ‘parahippocampal place area’). The present findings extend that navigation in physical space to the more metaphoric one of relational organization in conceptual space.

The more general message from this study, of course, is confirmation for the role of expertise in developing specific brain regions, and a reminder that this comes at the expense of other regions. So choose your area of expertise wisely!

Video gamers don’t become expert multitaskers

A comparison of skilled action gamers and non-gamers reveals that all that multitasking practice doesn’t make you any better at multitasking in general.

The research is pretty clear by this point: humans are not (with a few rare exceptions) designed to multitask. However, it has been suggested that the modern generation, with all the multitasking they do, may have been ‘re-wired’ to be more capable of this. A new study throws cold water on this idea.

The study involved 60 undergraduate students, of whom 34 were skilled action video game players (all male) and 26 did not play such games (19 men and 7 women). The students were given three visual tasks, each of which they did on its own and then again while answering Trivial Pursuit questions over a speakerphone (designed to mimic talking on a cellphone).

The tasks included a video driving game (“TrackMania”), a multiple-object tracking test (similar to a video version of a shell game), and a visual search task (hidden pictures puzzles from Highlights magazine).

While the gamers were (unsurprisingly) significantly better at the video driving game, the non-gamers were just as good as them at the other two tasks. In the dual-tasking scenarios, performance declined on all the tasks, with the driving task most affected. While the gamers were affected less by multitasking during the driving task compared to the non-gamers, there was no difference in the amount of decline between gamers and non-gamers on the other two tasks.

Clearly, the smaller effect of dual-tasking on the driving game for gamers is a product of their greater expertise at the driving game, rather than their ability to multitask better. It is well established that the more skilled you are at a task, the more automatic it becomes, and thus the less working memory capacity it will need. Working memory capacity / attention is the bottleneck that prevents us from being true multitaskers.

In other words, the oft-repeated (and somewhat depressing) conclusion remains: you can’t learn to multitask in general, you can only improve specific skills, enabling you to multitask reasonably well while doing those specific tasks.

Reference: 

[3001] Donohue, S., James B., Eslick A., & Mitroff S. (2012).  Cognitive pitfall! Videogame players are not immune to dual-task costs. Attention, Perception, & Psychophysics. 74(5), 803 - 809.

Attributes of effective practice

A large survey of a broad range of music students demonstrates which attributes of practice are associated with growing expertise.

Generalizing these to practice/study in general suggests that:

  • Amount and frequency of practice required depends on the individual.
  • The most important thing you can do is work out which strategies aren’t working for you, and stop using them.
  • While some organizational structure to your study sessions is usually helpful, the nature and extent of it will depend on the individual.
  • It’s important to identify difficult aspects/sections of your material, and work on these separately until you have mastered them sufficiently to incorporate them into the rest of your material.
  • It’s important to get some understanding of what you are about to learn/study before you begin.
  • Where possible (and especially when the material is more difficult), you should try and find expert models/examples to guide your learning.

One of my perennial themes is the importance of practice, and in the context of developing expertise, I have talked of ‘deliberate practice’ (a concept articulated by the well-known expertise researcher K. Anders Ericsson). A new paper in the journal Psychology of Music reports on an interesting study that shows how the attributes of music practice change as music students develop in expertise. Music is probably the most studied domain in expertise research, but I think we can gain some general insight from this analysis. Here’s a summary of the findings.

Choosing when to think fast & when to think slow

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.

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