comprehension

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.

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.

Reading Scientific Text

There are many memory strategies that can be effective in improving your recall of text. However, recent research shows that it is simplistic to think that you can improve your remembering by applying any of these strategies to any text. Different strategies are effective with different types of text.

One basic classification of text structure would distinguish between narrative text and expository text. We are all familiar with narrative text (story-telling), and are skilled in using this type of structure. Perhaps for this reason, narrative text tends to be much easier for us to understand and remember. Most study texts, however, are expository texts.

Unfortunately, many students (perhaps most) tend to be blind to the more subtle distinctions between different types of expository structure, and tend to treat all expository text as a list of facts. Building an effective mental model of the text (and thus improving your understanding and recall) is easier, however, if you understand the type of structure you're dealing with, and what strategy is best suited to deal with it.

Identifying structure

Five common types of structure used in scientific texts are:

  • Generalization: the extension or clarification of main ideas through explanations or examples
  • Enumeration: listing of facts
  • Sequence: a connecting series of events or steps
  • Classification: grouping items into classes
  • Comparison / contrast: examining the relationships between two or more things

Let's look at these in a little more detail.

Generalization

In generalization, a paragraph always has a main idea. Other sentences in the paragraph either clarify the main idea by giving examples or illustrations, or extend the main idea by explaining it in more detail. Here's an example:

Enumeration

Enumeration passages may be a bulleted or numbered list, or a list of items in paragraph form, for example:

Sequence

A sequence describes a series of steps in a process. For example:

Classification

In classification, items are grouped into categories. For example:

Comparison / contrast

This type of text looks at relationships between items. In comparison, both similarities and differences are studied. In contrast, only the differences are noted. For example:

[examples taken from Cook & Mayer 1988]

A study [1] involving undergraduate students inexperienced in reading science texts (although skilled readers otherwise) found that even a small amount of training substantially improved the students' ability to classify the type of structure and use it appropriately.

Let's look briefly at the training procedures used:

Training for generalization

This involved the following steps:

  • identify the main idea
  • list and define the key words
  • restate the main idea in your own words
  • look for evidence to support the main idea
    • what kind of support is there for the main idea?
    • are there examples, illustrations?
    • do they extend or clarify the main idea?

Training for enumeration

This involved the following steps:

  • name the topic
  • identify the subtopics
  • organize and list the details within each subtopic, in your own words

Training for sequence

This involved the following steps:

  • identify the topic
  • name each step and outline the details within each
  • briefly discuss what's different from one step to another

[Only these three structures were covered in training]

Most effective text structures

Obviously, the type of structure is constrained by the material covered. We can, however, make the general statement that text that encourages the student to make connections is most helpful in terms of both understanding and memory.

In light of this, compare/contrast would seem to be the most helpful type of text. Another text structure that is clearly of a similar type has also been found to be particularly effective: refutational text. In a refutational text, a common misconception is directly addressed (and refuted). Obviously, this is only effective when there is a common misconception that stands in the way of the reader's understanding -- but it's surprising how often this is the case! Incompatible knowledge is at least as bad as a lack of knowledge in hindering the learning of new information, and it really does need to be directly addressed.

Refutational text is however, not usually enough on its own. While helpful, it is more effective if combined with other, supportive, strategies. One such strategy is elaborative interrogation, which involves (basically) the student asking herself why such a fact is true.

Unfortunately, however, text structures that encourage connection building are not the most common type of structure in scientific texts. Indeed, it has been argued that "the presentation of information in science textbooks is more likely to resemble that of a series of facts [and thus] presents an additional challenge that may thwart readers' efforts to organize text ideas relative to each other".

Most effective strategies

The fundamental rule (that memory and understanding are facilitated by any making of connections) also points to the strategies that are most effective.

As a general rule, strategies that involve elaborating the connections between concepts in a text are the most effective, but it is also true that the specifics of such strategies vary according to the text structure (and other variables, such as the level of difficulty).

Let's look at how such a linking strategy might be expressed in the context of our five structures.

Generalization

Restatement in your own words -- paraphrasing -- is a useful strategy not simply because it requires you to actively engage with the material, but also because it encourages you to connect the information to be learned with the information you already have in your head. We can, however, take this further in the last stage, when we look for the evidence supporting the main idea, if we don't simply restrict ourselves to the material before us, but actively search our minds for our own supporting evidence.

Enumeration

This text structure is probably the hardest to engage with. You may be able to find a connective thread running through the listed items, or be able to group the listed items in some manner, but this structure is the one most likely to require mnemonic assistance (see verbal mnemonics and list-learning mnemonics).

Sequence

With this text structure, items are listed, but there is a connecting thread — a very powerful one. Causal connections are ones we are particularly disposed to pay attention to and remember; they are the backbone of narrative text. So, sequence has a strong factor going for it.

Illustrations particularly lend themselves to this type of structure, and research has shown that memory and comprehension is greatly helped when pictures portraying a series of steps, in a cause-and-effect chain, are closely integrated with explanatory text. The closeness is vital — a study that used computerized instruction found dramatic improvement in memory when the narration was synchronous with the animation, for example, but there was no improvement when the narration was presented either before or after the text. If you are presented with an illustration that is provided with companion text, but is not closely integrated with it, you will probably find it helpful to integrate it with the text yourself.

Classification

Classification is frequently as simple as grouping items. However, while this is in itself a useful strategy that helps memory, it will be more effective if the connections between and within groups are strong and clear. Connections within groups generally emphasize similarities, while connections between groups emphasize both similarities (between closely connected groups) and differences. Ordering groups in a hierarchical system is probably the type of arrangement most familiar to students, but don't restrict yourself to it. Remember, the important thing is that the arrangement has meaning for you, and that the connections emphasize the similarities and differences.

Compare / contrast

This type of structure lends itself, of course, to making connections. Your main strategy is probably therefore to simply organize the material in such a way as to make those connections clear and explicit.

References: 
  1. Cook, L.K. & Mayer, R.E. 1988. Teaching readers about the structure of scientific text. Journal of Educational Psychology, 80, 448-54.
  2. Castaneda, S., Lopez, M. & Romero, M. 1987. The role of five induced learning strategies in scientific text comprehension. The Journal of Experimental Education, 55(3), 125–131.
  3. Diakidoy, I.N., Kendeou, P. & Ioannides, C. 2002. Reading about energy: The effects of text structure in science learning and conceptual change. http://www.edmeasurement.net/aera/papers/KENDEOU.PDF

Asking better questions

Questions — especially why questions — help us make connections to existing anchor points — facts we know well. But some questions are better than others.

To decide whether a question is effective, ask:

  • does it make the information more meaningful?
  • does it make the information more comprehensible?
  • does it increase the number of meaningful connections?

Consider our facts about blood:

  • arteries are thick and elastic and carry blood that is rich in oxygen from the heart.
  • veins are thinner, less elastic, and carry blood rich in carbon dioxide back to the heart.

We could, as is often advised, simply turn these into why questions. And we can answer these on the basis of the connections we’ve already made:

Why are arteries elastic?

Because they need to accommodate changes in pressure

Why are arteries thick?

Because they need to accommodate high pressure

Why do arteries carry blood away from the heart?

Because blood coming from the heart comes out at high pressure and in spurts of variable pressure

Why do arteries carry blood that is rich in oxygen?

Because the blood coming from the heart is rich in oxygen

Why are veins less elastic?

Because the blood flows continuously and evenly

Why are veins less thick?

Because the blood flows at a lower pressure

Why do veins carry blood to the heart?

Because blood going to the heart flows continuously and evenly

Why do veins carry blood that is rich in CO2?

Because the blood going to the heart is rich in CO2

What’s missing? Connections between these facts. The facts have become more meaningful, but to be really understood you need to make the connections between the facts explicit.

Look again at our original questions. See how they relate the facts to each other? They don’t ask: why are arteries elastic? They ask: Why do arteries need to be more elastic than veins? They don’t ask: why do arteries carry blood that is rich in oxygen? They ask: why do vessels carrying blood from the heart need to be rich in oxygen?

By answering these questions, we have built up an understanding of the facts that ties them together in a multi-connected cluster:

pictorial representation of this information

For simplicity, I’ve just focused on the arteries. See how the four facts about arteries are connected together. Meaningfully connected. In a perfect world we’d be able to close the circle with a direct connection between the facts “Arteries carry blood rich in oxygen” and “Arteries are thick”, but as far as I know, the only connection between them is indirect, through the fact that “Arteries carry blood from the heart”.

So … the world isn’t perfect, and information doesn’t come in neatly wrapped bundles where every fact connects directly to every other fact. But the more connections you can make between related facts — the stronger a cluster you can make — the more deeply you will understand the information, and the more accessible it will be. That is, you will remember it more easily and for longer.

If it’s well enough connected

If it’s connected to strong anchor points

You will simply 'know' it.

You’re never going to forget that you breathe in oxygen and that your heart pumps out blood. These are strong anchor points. If the facts about arteries are strongly connected to these anchor points, you will never forget them either.

Asking questions is one of the best ways of making connections,

but

Bad questions can be worse than no questions at all.

Rote questions that direct your attention to unimportant details are better not asked.

Effective questions prepare you to pay attention to the important details in the text.

The best questions not only direct your attention appropriately, but also require you to integrate the details in the text. Ask yourself:

  • Is this helping me to select the important information?
  • Is it helping me make connections?

When the subject is new to you

When you don’t have enough prior knowledge about a subject to ask effective questions, you are better off forming connections using mnemonics — either through verbal elaboration, as in our sentence about “Art (ery) being thick around the middle so he wore trousers with an elastic waistband” or by creating interactive images.

However, mnemonics such as these — while perfectly effective — are only good for rote learning. Sometimes that’s all you want, of course. But if you’re going to be learning more information that relates to these facts, then you’re making a rod for your own back.

When you learn something by rote, it never gets easier. When you learn by building connections, every new fact is acquired more easily. And it’s progressive. An expert on a subject can hear a new fact in her area of expertise, and it’s there. Remembered. Without effort. Because she’s an expert. And what makes her an expert? Simply the fact that she’s built up a network of information that is so tightly connected, and that has so many strong anchor points, that the information is always retrievable.

Why questions, like any questions, are only effective to the extent that they direct attention to appropriate information.

Research confirms that it is better to search for consistent relations than inconsistent ones. In many cases your background knowledge may include information that is consistent with the new information, and information that is inconsistent.

By asking “Why is this true?” you focus on the consistent information.

References: 
  • Woloshyn, V.E., Willoughby, T., Wood, E., & Pressley, M. 1990. Elaborative interrogation facilitates adult learning of factual paragraphs. Journal of Educational Psychology, 82, 513-524.
  • Pressley, M. & El-Dinary, P.B. 1992. Memory strategy instruction that promotes good information processing. In D. Herrmann, H. Weingartner, A. Searleman & C. McEvoy (eds.) Memory Improvement: Implications for Memory Theory. New York: Springer-Verlag.

The Science and Art of the Diagram

Carbon cycle diagram

Following the (historically) brief period when we became fixated on text as the sole reliable source of information and means of communication, we are now clearly returning to an appreciation of the value of images, both in encapsulating and expressing information. Accordingly, I was intrigued to see this three-part series on the history, science and art of the diagram.

Clarissa Ai Ling Lee, guest blogger at Scientific American:

Effects of diagram orientation on comprehension

The most popular format of the most common type of diagram in biology textbooks is more difficult to understand than formats that use different orientations.

A study into how well students understand specific diagrams reminds us that, while pictures may be worth 1000 words, even small details can make a significant difference to how informative they are.

The study focused on variously formatted cladograms (also known as phylogenetic trees) that are commonly used in high school and college biology textbooks. Such diagrams are hierarchically branching, and are typically used to show the evolutionary history of taxa.

Nineteen college students (most of whom were women), who were majoring in biology, were shown cladograms in sequential pairs and asked whether the second cladogram (a diagonal one) depicted relationships that were the same or different as those depicted in the first cladogram (a rectangular one). Taxa were represented by single letters, which were either in forward or reverse alphabetical order. Each set (diagonal and rectangular) had four variants: up to the right (UR) with forward letters; UR with reverse letters; down to the right (DR), forward letters; DR, reverse. Six topologies were used, creating 24 cladograms in each set. Eye-tracking showed how the students studied the diagrams.

The order of the letters turned out not to matter, but the way the diagrams were oriented made a significant difference to how well students understood them.

In line with our training in reading (left to right), and regardless of orientation, students scanned the diagrams from left to right. The main line of the cladogram (the “backbone”) also provided a strong visual cue to the direction of scanning (upward or downward). In conjunction with the left-right bias, this meant that UR cladograms were processed from bottom to top, while DR cladograms were processed from top to bottom.

Put like that, the results are less surprising. Diagonal cladograms going up to the right were significantly harder for students to match to the rectangular format (63% correct vs 70% for cladograms going down to the right).

Moreover, this was true even for experts. Of the two biology professors included in the study, one showed the same pattern as the students in terms of accuracy, while the other managed the translations accurately enough, but took significantly longer to interpret the UR diagrams than the DR ones.

Unfortunately, the upward orientation is the more widely used (82% of diagonal cladograms in a survey of 27 high school & college biology textbooks; diagonal cladograms comprised 72% of all diagrams).

The findings suggest that teachers need to teach their students to go against their own natural inclinations, and regardless of orientation, scan the tree in a downward direction. This strategy applies to rectangular cladograms as well as diagonal ones.

It’s worth emphasizing another aspect of these findings: even the best type of diagonal cladogram was only translated at a relatively poor level of accuracy. Previous research has suggested that the diagonal cladogram is significantly harder to understand than the rectangular format. Note that the only difference between them is the orientation.

All this highlights two points:

Even apparently minor aspects of a diagram can make a significant difference to how easily it’s understood.

Teachers shouldn’t assume that students ‘naturally’ know how to read a diagram.

Reference: 

Novick, L., Stull, A. T., & Catley, K. M. (2012). Reading Phylogenetic Trees: The Effects of Tree Orientation and Text Processing on Comprehension. BioScience, 62(8), 757–764. doi:10.1525/bio.2012.62.8.8

Catley, K., & Novick, L. (2008). Seeing the wood for the trees: An analysis of evolutionary diagrams in biology textbooks. BioScience, 58(10), 976–987. Retrieved from http://www.jstor.org/stable/10.1641/B581011
 

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’.

Event boundaries and working memory capacity

The brain breaks events and activities into segments, which are hierarchically organized.

The segments begin and end at points where significant changes occur — in movement, in time, in space, in goals, or in participating actors or objects.

Working memory has to be updated at these ‘event boundaries’.

Accordingly, your memory of something that occurred before an event boundary is less than your awareness of what is occurring during the current segmented event.

Processing also slows down at event boundaries (because you’re busy updating working memory) — meaning that understanding is more difficult at these points.

However, these boundaries provide strong anchors for your long-term memory codes of these events.

This may explain why beginnings and endings are so much better remembered (the primacy and recency effects).

Finer segmentation is generally helpful for memory, but segmentation is hierarchical and ‘higher-order’ segments are useful too.

Texts are more easily understood and new skills are more quickly learned when they are explicitly and appropriately structured, segmented at the most useful boundaries and hierarchically structured.

Segments are useful as shared elements between events. Transfer (the benefits of previously learned knowledge for new learning) reflects the degree of shared segments between the familiar event and the new.

Working memory capacity may reflect, at least in part, the ability to choose the most relevant event boundaries.

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.

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.

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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