comprehension

Working memory, expertise & retrieval structures

In a 1987 experiment (1), readers were presented with a text that included one or other of these sentences:

After doing a few warm-up exercises, John put on his sweatshirt and began jogging.

or

After doing a few warm-up exercises, John took off his sweatshirt and began jogging.

Both texts went on to say: John jogged halfway around the lake.

After reading the text, readers were asked if the word sweatshirt had appeared in the story. Now here is the fascinating and highly significant result: those who read that John had put on a sweatshirt responded “yes” more quickly than those who had read that he had taken off his sweatshirt.

Why is this so significant? Because it tells us something important about the reading process, at least in the minds of skilled readers. They construct mental models. If it was just a matter of the mechanical lower-order processing of letters and words, why would there be a difference in responses? Neither text was odd — John could as well have put on a sweatshirt before going out for a jog as taken it off — so there shouldn’t be a surprise effect. So what is it? Why is the word sweatshirt not as tightly / strongly linked in the second case as it is in the first? If they were purely textbase links (links generated by the textbase itself), the links should be equivalent. The difference in responses implies that the readers are making links with something outside the textbase, with a mental model.

Mental models, or as they are sometimes called in this context, situation models, are sometimes represented as lists of propositions, but in most cases it seems likely that they are actually analogue in nature. Thus the real world should be better represented by the situation model than by the text. Moreover, a spatial situation model will be similar in many ways to an image, with all the advantages that that entails.

All of this has relevance to two very important concepts: working memory and expertise.

Now, I’m always talking about working memory. This time I want to discuss not so much the limited attentional capacity that is what we chiefly mean by working memory, but another, more theoretical concept: the idea of long-term working memory.

Think about reading. To make sense of the text you need to remember what’s gone before — this is why working memory is so important for the reading process. But we know how limited working memory is; it can only hold a very small amount — is it really possible to hold all the information we need to make sense of what we’re reading? Shouldn’t there be constant delays as we access needed information from long-term memory? But there aren’t.

It’s suggested that the answer lies in the use of long-term working memory, a retrieval structure that keeps a network of linked propositions readily available.

Think about when you are studying / reading a difficult text in a subject you know well. Compare this to studying a difficult text in a subject you don’t know well. In the latter case, you may have to painfully backtrack, checking earlier statements, trying to remember what was said before, trying to relate what you are reading to things you already know. In the former case, you seem to have a vastly expanded amount of readily accessible relevant information, from the text itself and from your long-term memory.

The connection between long-term working memory and expertise is obvious. And expertise has already been conceptualised in terms of retrieval structures (see for example my article on expertise). In other words, you can increase your working memory in a particular domain by developing expertise, and the shortest route to developing expertise is to concentrate on building effective retrieval structures.

One of the areas where this is particularly crucial is that of reading scientific texts. Now we all know that scientific texts are much harder to process than, for example, stories. And there are several reasons for that. One is the issue of language: any science has its own technical vocabulary and you won't get far without knowing it. But another reason, far less obvious to the untutored, concerns the differences in structure — what may be termed differences of genre.

Now it might seem self-evident that stories are far simpler than science, than any non-fiction texts, and indeed a major distinction is usually made between narrative texts and expository texts, but it’s rather like the issue of faces and other objects. Are we specially good at faces because we're 'designed' to be (i.e., we have special 'expert' modules for processing faces)? Or is it simply that we have an awful lot of practice at it, because we are programmed to focus on human faces almost as soon as we are born?

In the same way, we are programmed for stories: right from infancy, we are told stories, we pay attention to stories, we enjoy stories. Stories have a particular structure (and within the broad structure, a set of sub-structures), and we have a lot of practice in that structure. Expository texts, on the other hand, don't get nearly the same level of practice, to the extent that many college students do not know how to handle them — and more importantly, don't even realize that that is what they're missing: a retrieval structure for the type of text they're studying.

References: 

Glenberg, A.M., Meyer, M. & Lindem, K. 1987. Mental models contribute to foregrounding during text comprehension. Journal of Memory and Language, 26, 69-83.

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Why good readers might have reading comprehension difficulties and how to deal with them

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

References: 

Press release on the first study: http://www.physorg.com/news/2012-01-high-school-whiz-kids-comprehension.html; see also http://rrl.educ.ualberta.ca/research.html

Second study: Banas, S., & Sanchez, C. a. (2012). Working Memory Capacity and Learning Underlying Conceptual Relationships Across Multiple Documents. Applied Cognitive Psychology, n/a-n/a. doi:10.1002/acp.2834

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Finding the right strategy through perception and physical movement

I talk a lot about how working memory constrains what we can process and remember, but there’s another side to this — long-term memory acts on working memory. That is, indeed, the best way of ‘improving’ your working memory — by organizing and strengthening your long-term memory codes in such a way that large networks of relevant material are readily accessible.

Oddly enough, one of the best ways of watching the effect of long-term memory on working memory is through perception.

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Should learning facts by rote be central to education?

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’.

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Concept maps

Broadly speaking, a concept map is a graphic display that attempts to show how concepts are connected to each other. A concept map is a diagram in which labeled nodes represent concepts, and lines connecting them show the relationships between concepts.

There is one type of concept map you’re probably all aware of — mind maps. Mind maps are a specialized form of concept map popularized very successfully by Tony Buzan.

A mind map has four essential characteristics:

  • the subject is crystallized in a central image
  • main themes radiate from it as branches
  • the branches comprise a key image or key word
  • the branches form a connected nodal structure

The essential difference between a mind map and the more general concept map is that in a mind map the main themes are connected only to this single central image — not to each other. In a concept map, there are no restrictions on the links between concepts.

Also, the connections between concepts in a concept map are labeled — they have meaning; they’re a particular kind of connection. In a mind map, connections are simply links; they could mean anything.

Mind maps are also supposed to be very pictorial. In Buzan’s own words:

“The full power of the Mind Map is realised by having a central image instead of a central word, and by using images wherever appropriate rather than words.”

Concepts in a concept map, on the other hand, can be (and usually are) entirely verbal. But the degree to which you use words or pictures is entirely up to the user.

In fact, this insistence on images is one of the things I don’t like about mind maps (I hasten to add that there are many things I do like about mind maps). While images are certainly powerful memory aids, they are not for everyone, nor for all circumstances.

Mind maps and concept maps are really aimed at different purposes, and perhaps, different personalities.

The chief usefulness of mind mapping, I believe, is when you’re still trying to come to grips with an idea. Mindmapping is good for brainstorming, for outlining a problem or topic, for helping you sort out the main ideas.

Concept maps, on the other hand, are particularly useful further down the track, when you’re ready to work out the details, to help you work out or demonstrate all the multitudinous ways in which different concepts (and a “concept” can be anything) are connected.

Concept maps are more formal than mind maps, and are better suited to situations where the concept is to be shared with others. Mind maps are considerably more personal, and are often not readily understood by others.

Both mind maps and concept maps are good at clarifying your thoughts, but because of the greater formality of the concept map — the need to be more precise in your connections — concept maps are better at showing you exactly what you don’t understand properly.

Which is why concept maps take a while to get right!

This is a very important point that I should emphasize — hardly anyone ever gets their map (mind or concept) right the first time. In fact, if you did, you probably didn’t need to construct it! It’s the redesigning that is important.

But concept maps can come in different flavors — from the more formal, to a visual display which simply use the basic idea of nodes and links. You can see a whole bunch of proper concept maps, constructed using cmap, at http://cmex.ihmc.us/cmex/table.html . And if you’re interested in becoming a cmapper yourself, check out http://cmap.ihmc.us/ .

And here’s a couple more links to help you learn more about concept maps:
http://www.fed.cuhk.edu.hk/~johnson/misconceptions/concept_map/concept_maps.html
http://cmc.ihmc.us/CMC2004Programa.html (this one has a number of conference papers available in pdf format).

This article first appeared in the Memory Key Newsletter for October 2006

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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.

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Understanding scientific text

In the last part I talked about retrieval structures and their role in understanding what you’re reading. As promised, this month I’m going to focus on understanding scientific text in particular, and how it differs from narrative text.

First of all, a reminder about situation models. A situation, or mental, model is a retrieval structure you construct from a text, integrating the information in the text with your existing knowledge. Your understanding of a text depends on its coherence; it’s generally agreed that for a text to be coherent it must be possible for a single situation model to be constructed from it (which is not to say a text that is coherent is necessarily coherent for you —that will depend on whether or not you can construct a single mental model from it).

There are important differences in the situation models constructed for narrative and expository text. A situation model for a narrative is likely to refer to the characters in it and their emotional states, the setting, the action and sequence of events. A situation model for a scientific text, on the other hand, is likely to concentrate on the components of a system and their relationships, the events and processes that occur during the working of the system, and the uses of the system.

Moreover, scientific discourse is rooted in an understanding of cause-and-effect that differs from our everyday understanding. Our everyday understanding, which is reflected in narrative text, sees cause-and-effect in terms of goal structures. This is indeed the root of our superstitious behavior — we (not necessarily consciously) attribute purposefulness to almost everything! But this approach is something we have to learn not to apply to scientific problems (and it requires a lot of learning!).

This is worth emphasizing: science texts assume a different way of explaining events from the way we are accustomed to use — a way that must be learned.

In general, then, narrative text (and ‘ordinary’ thinking) is associated with goal structures, and scientific text with logical structures. However, it’s not quite as clear-cut a distinction as all that. While the physical sciences certainly focus on logical structure, both the biological sciences and technology often use goal structures to frame their discussions. Nevertheless, as a generalization we may say that logical thinking informs experts in these areas, while goal structures are what novices focus on.

This is consistent with another intriguing finding. In a comparison of two types of text —ones discussing human technology, and ones discussing forces of nature — it was found that technological texts were more easily processed and remembered. Indications were that different situation models were constructed — a goal-oriented representation for the technological text, and a causal chain representation for the force of nature text. The evidence also suggested that people found it much easier to make inferences (whether about agents or objects) when human agents were involved. Having objects as the grammatical subject was clearly more difficult to process.

Construction of the situation model is thus not solely determined by comprehension difficulty (which was the same for both types of text), but is also affected by genre and surface characteristics of the text.

There are several reasons why goal-oriented, human-focused discourse might be more easily processed (understood; remembered) than texts describing inanimate objects linked in a cause-effect chain, and they come down to the degree of similarity to narrative. As a rule of thumb, we may say that to the degree that scientific text resembles a story, the more easily it will be processed.

Whether that is solely a function of familiarity, or reflects something deeper, is still a matter of debate.

Inference making is crucial to comprehension and the construction of a situation, because a text never explains every single word and detail, every logical or causal connection. In the same way that narrative and expository text have different situation models, they also involve a different pattern of inference making. For example, narratives involve a lot of predictive inferences; expository texts typically involve a lot of backward inferences. The number of inferences required may also vary.

One study found that readers made nine times as many inferences in stories as they did in expository texts. This may be because there are more inferences required in narratives — narratives involve the richly complex world of human beings, as opposed to some rigidly specified aspect of it, described according to a strict protocol. But it may also reflect the fact that readers don’t make all (or indeed, anywhere near) the inferences needed in expository text. And indeed, the evidence indicates that students are poor at noticing coherence gaps (which require inferences).

In particular, readers frequently don’t notice that something they’re reading is inconsistent with something they already believe. Moreover, because of the limitations of working memory, only some of the text can be evaluated for coherence at one time (clearly, the greater the expertise in the topic, the more information that can be evaluated at one time — see the previous newsletter’s discussion of long-term working memory). Less skilled (and younger) readers in particular have trouble noticing inconsistencies within the text if they’re not very close to each other.

Let’s return for a moment to this idea of coherence gaps. Such gaps, it’s been theorized, stimulate readers to seek out the necessary connections and inferences. But clearly there’s a particular level that is effective for readers, if they often miss them. This relates to a counter-intuitive finding — that it’s not necessarily always good for the reader if the text is highly coherent. It appears that when the student has high knowledge, and when the task involves deep comprehension, then low coherence is actually better. It seems likely that knowledgeable students reading a highly coherent text will have an “illusion of competence” that keeps them from processing the text properly. This implies that there will be an optimal level of coherence gaps in a text, and this will vary depending on the skills and knowledge base of the reader.

Moreover, the comprehension strategy generally used with simple narratives focuses on referential and causal coherence, but lengthy scientific texts are likely to demand more elaborate strategies. Such strategies are often a problem for novices because they require more knowledge than can be contained in their working memory. Making notes (perhaps in the form of a concept map) while reading can help with this.

Next month I’ll continue this discussion, with more about the difficulties novices have with scientific texts and what they or their teachers can do about it, and the problems with introductory textbooks. In the meantime, the take-home message from this is:

Understanding scientific text is a skill that must be learned;

Scientific text is easier to understand the more closely it resembles narrative text, with a focus on goals and human agents;

How well the text is understood depends on the amount and extent of the coherence gaps in the text relative to the skills and domain knowledge of the reader.

References: 

Otero, J., León, J.A. & Graesser, A.C. (eds). 2002. The psychology of science text comprehension.

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Speed Reading

  • Speed-reading courses generally make extravagant claims that no independent research has justified.
  • However, speed-reading courses can improve your reading skills.
  • Speed-reading courses principally improve reading by teaching you how to efficiently skim.

Speed-reading techniques

Like many memory improvement courses, speed-reading programs tend to make inflated claims. Also like memory programs, most speed-reading programs proffer the same advice. In essence, speed-reading techniques involve the following components:

  • learning to see more in a single eye fixation
  • eliminating subvocalization ("saying" the words in your head as you read them)
  • using your index finger as a visual guide down the page
  • active reading

How reading works

The first thing you need to understand about reading is that it proceeds in jerks. Though we might think our eyes are traveling smoothly along the lines, this is an illusion. What happens is that the eyes gaze steadily for around 240 milliseconds (for a college student; less practiced readers take longer) and then jerk along (during which nothing is registered), then stop again. We "read" during the eye fixations.

Now the duration of these fixations is not hugely different between readers of different abilities - a first-grade child takes about 330 ms, which is not a vast difference when you consider the chasm between a first-grade reader and an educated adult. What does change significantly is the number of fixations. Thus, to read a 100-word passage, our first-grade reader takes some 183 fixations, while our college reader takes only 75. From this, it is calculated that the first-grade reader is taking in 0.55 of a word in each fixation (100/183), while the college reader is grasping 1.33 words in each fixation (100/75). And from this, the reading rate is calculated. [These figures are of course only indicative - different types of reading matter will obviously produce different figures; the degree to which comprehension is emphasized also makes a difference].

This is not, of course, the whole story. We also can pick up some information about letters on either side of the fixation point - about 10 to 11 letter positions right of the fixation point (or left, if you're reading in a script that goes from right to left) for specific letter information, and about 15 positions for information about word length.

It is these facts that set bounds on how fast a person can read. It has been calculated that, even being very generous with the figures (reducing the duration of fixation to 200 ms; using the upper limit of how many letters we can see at one time), the upper limit for reading speed would be about 900 wpm.

How speed-reading works

This, then, is one of the things speed-reading programs aim to tackle - to increase the span of letters you can see in one fixation, and to alter the number of fixations. It is not, however, clear that (a) you can in fact train people to increase this span, or (b) it would be useful to do so.

What research does show, is that speed readers, while they don't change the length of their fixations, do significantly differ from normal readers in the pattern of their jumps. One researcher concluded from the pattern of eye movements, that speed-readers are in fact skimming.

Now there is certainly nothing wrong with skimming. Indeed, it is an extremely valuable skill, and if you wish to improve your skill at skimming, then it may well be worthwhile for you to use a speed-reading program to do so. On the other hand, there is no particular evidence that such programs do anything more than modestly improve your skimming skills.

Testing speed-reading skills

One study compared expert speed-readers against other groups of superior readers. While the speed-readers were fastest (444 words per minute - a respectable speed (250 wpm is average) but nowhere near the claims made by many of these programs), their comprehension was relatively low (71%). [1]

Interestingly, the speed-readers' speed was about twice that when they knew their speed was being tested but their comprehension would not be. In other words, like the rest of us, they slowed down markedly when they wanted to understand what they were reading (and what otherwise is the point of reading something?)

Well, actually, there is one circumstance when you read and do not look to understand or retain what you read - which brings us back to skimming.

So, how did our speed-readers compare on skimming skills? Two tasks were used to assess these:

  • to pick the best title to passages presented at rates of 7500, 1500 and 300 wpm
  • to write summaries of 6000-word passages presented at 24000, 6000, 1500 and 375 wpm

The speed readers were in fact no better than the other groups at picking titles, and though they were best at writing summaries when the passages were presented at 1500 wpm, they were no better than the others at the other rates of presentation. In an extra test of recall of important details, the speed readers in fact did worst.

Reading for understanding

Please don't mistake me, I am not condemning speed-reading - merely their often extravagant claims. Learning to skim (if you have not developed this skill on your own, and many have) is clearly worthwhile. Learning not to subvocalize - yes, I think there's value in that too. I cannot speak to any research, but I know from my own experience that when I am reading slowly, either because the material demands the effort or because I wish to make the book last longer, I make myself 'hear' the words in my head. Subvocalization does slow you down - if you wish to read faster that you can speak, you need to discard the habit.

And lastly, active reading. Well, that deserves a whole chapter of its own. So for now, for those who don't know what it means, I shall simply define it. Active reading is about thinking when you read. It is about asking yourself (and the book) questions. It is about anticipating what is going to be said, and relating what you read to what you already know, and making inferences about what you've read. Active reading is about understanding, and thus it is an essential part of reading to remember.

So that too, is a very useful skill.

This article originally appeared in the July 2002 newsletter.

References: 

  • Underwood, G. & Batt, V. 1996. Reading and understanding. Oxford: Blackwell.
  • Crowder, R.G. & Wagner, R.K. 1992. The Psychology of Reading. 2nd ed. Oxford University Press.
  1. Carver, R.P. 1985. How good are some of the world's best readers? Reading Research Quarterly, 20, 389-419.

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Similarity

Human memory is a complex and varied phenomenon, and we could delve into its mysteries every day for a hundred years and still have plenty to talk about. But if I had to pick one factor that was absolutely crucial to the operation of memory, I would pick the deceptively simple concept of similarity. Similarity.

We all think we know what that means. An orange is similar to a mandarin; a zebra is similar to a horse; a cup is similar to a glass; my son is similar to his brother. A car is similar to an elephant.

??

Well, I might think a car was similar to an elephant. Maybe I’m imagining an elephant thundering toward me, kicking up dirt, unstoppable. Or maybe my perceptions are confused. But whether there’s a logical reason for my perception of similarity or not, whether my perception of similarity is shared with other people or not, all that is required for my brain to make the connection is ... that I perceive a connection.

Similarity — perceived similarity — is a crucial ingredient to the connections made in your head. Similarity enables us to make connections that transcend space and time, and enables us to strengthen connections made as a result of a juxtaposition of space and time.

Thus, when you meet a person and he tells you his name is Tom Brown, the first connection is made simply because the name and person are coincident in space and time. And if you leave it there, that connection will most likely be too weak to retrieve on a later occasion.

You can (and should, if you want to remember) employ another critical element to strengthen the connection: repetition (which impacts on the perceived familiarity of the information, but that’s another story). But the new information (this person is named Tom Brown) will be much more firmly lodged in your database, and much easier to find, if you make other connections, connections to information already stored in your memory. Thus, you might associate the name with the book “Tom Brown’s Schooldays”, based on the similarity of names. There might be some physical characteristic of this new person that you can link to a character in the book. If so, you are much more likely to be able to remember this name when you meet the person again. However, if you are barely familiar with the book, and have to stretch your imagination to make any further connection, such as with the characters in the book, then this similarity of names won’t greatly help you.

The important thing when connecting new information to information already existing in your database, is to ensure the existing information is itself easily retrievable, and that the connections you make are not too obscure.

So, to make new memories easily retrievable:

  • look for similarities to existing memories
  • look for similarities that are obvious to you (what other people think doesn’t matter in the slightest)
  • choose existing memories that are themselves easily retrievable

This article originally appeared in the June 2004 newsletter.

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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.

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