problem solving and memory

Scott H Young

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How do you keep everything in mind when solving tough problems? When you read a book, listen to a podcast or have a conversation–how does your brain hold onto all the information?

The answer is something psychologists call working memory .

Unlike long-term memory, which I’ve covered in-depth here , working memory isn’t about remembering the past. Instead, it’s about holding together the present in your mind so you can learn, make decisions and solve problems.

Working memory is essentially your mental bandwidth. If you have a good working memory, or can use yours more effectively, you can think and learn better. Thus, understanding this important facet of your mind is essential for anyone who wants to perform better in work, school and life.

To give you that understanding, I’ve collaborated again with Jakub Jilek , who has his masters in cognitive science and is currently studying for his PhD. We’ve put together a full guide to explaining what your working memory is, how it works, and most of all–how you can apply simple methods to think and learn better.

Side note: Like our last guide, this one is substantial. If you’d like to go over it as a PDF instead of just reading along here (either to print or to save for later) you can join my newsletter and I’ll send you a free copy of the PDF:

Just want the advice? Jump ahead to Summary of Key Methods and Techniques !

Table of Contents

• Working Memory

How Working Memory Underpins Your Ability to Learn

How can you measure your working memory, are all sounds equally harmful to learning, does music affect everyone the same way, how to use sound to boost your learning, strategies for improving your visuospatial working memory, how to use visualization and drawing to improve learning, the hidden costs of multi-tasking, who is affected by multi-tasking, how badly designed textbooks split your attention, how to use chunking as a mnemonic technique, chunking works by reducing memory load, how experts use chunks, build chunks with pre-training, reduce intrinsic load with segmenting and worked-examples, reduce extrinsic load with visually simple textbooks and a goal-free approach, how to optimize cognitive load, why does anxiety burden our working memory, how you can overcome anxiety, summary and conclusion, citations and references, what is working memory the four components underlying your ability to think and learn.

What is working memory? The easiest way to understand working memory is by visualizing it as a carpenter’s workbench: [ 1 ] The carpenter temporarily places tools and materials on the workbench as she builds new products. The workbench has a small size – only a few items can be placed on it at once.

Similarly, you temporarily store information in your working memory when you’re solving a problem or making a decision. Working memory also has a small capacity – it can only hold a few items at once.

However, the workbench is not just for keeping materials in one place. It’s a workspace – the carpenter uses it to combine different materials to create new products. Similarly, working memory is not just a simple storage. Working memory enables you to generate new thoughts, change them, combine them, search them, apply different rules and strategies to them, or do anything else that helps you navigate your life.

By enabling all of these functions, working memory underpins your thinking, planning, learning and decision-making.

Scientists have developed various models of working memory. In this guide, we will draw on the most popular model, which has been developed by Alan Baddeley . [ 2 ] According to this model, working memory can be divided into four components:

The first component is called the phonological loop. It’s essentially a storage of sounds – it allows you to temporarily memorize digits, words and sentences (by the way they sound).

The second component is called the visuospatial sketchpad. As the name suggests, the sketchpad stores two- and three-dimensional images of objects.

The third component is the central executive. Its main responsibility is directing attention and manipulating information.

Using our workbench analogy, you could think of the the phonological loop and the visuospatial sketchpad as two different vises that hold materials in one position. Each vise can hold a different kind of material (such as wood or metal). Similarly, the phonological loop can hold sounds and the visuospatial sketchpad can hold images.

You could think of the central executive as the carpenter herself. The carpenter decides which tools and materials to use in the same way as the central executive decides which things to pay attention to. She shapes metal and wood by using chisels, saws and drills to create a new product such as a chair. Similarly, the central executive re-arranges ideas and applies the rules of grammar, logic or algebra to come up with a solution to a problem or make a decision.

Baddeley’s model also has a fourth component (“episodic buffer”) which we won’t cover here because it’s not so well researched as the other three components.

You may have also heard of the term “short-term memory”. Scientists currently use this term when they talk about a simple temporary storage (but not manipulation) of information, [ 3 ] which can be of any kind (visual or auditory). The term “working memory” is used to talk about the whole storage and manipulation system.

To give you a quick recap, here’s the three main parts of working memory:

• Phonological loop – stores sounds including words, digits, sentences
• Visuospatial sketchpad – stores images of objects
• Central executive – directs attention and manipulates information

In this guide we’ll look at all these three components and see how they impact on your learning. In addition, we’ll cover another three important topics, which are closely connected to working memory:

• Chunking – the compression of information
• Cognitive load – the processing demands placed on working memory
• Anxiety – the culprit behind problems with working memory

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Why Working Memory Matters

Working memory is a key aspect of intelligence. [ 4 ] Much of your learning depends on your working memory.

Think of the last time you followed a hard class. In the beginning, you might have kept up fine. But eventually it became harder and harder to understand what the professor was saying. Even though you tried your best to pay attention, you left feeling confused and frustrated.

It turns out that the culprit is likely an overloaded working memory [ 5 ] (read Summary and Conclusion for other possibilities). The study material required your working memory to process too much new information at the same time. As a result, the system became overwhelmed and broke down.

Even if you don’t regularly attend confusing lectures, understanding how your working memory functions is essential for learning better.

In order to learn, you first must comprehend. [ 6 ] [ 7 ] To do this, your working memory is always involved:

Your phonological loop must keep track of the sounds of the words you read or hear. Your central executive must constantly update these sequences as you go along. Finally, these meanings need to be integrated so you can understand everything. If any of these processes fail, you’ll get lost and confused.

Solving problems is also essential to learning. [ 8 ] Once again, your working memory is working hard.

Consider trying to solve the problem of adding two numbers:

87 + 65 = ?

Most of us learn how to add numbers like these in grade school (the solution is 152). Despite the simplicity, however, there’s a lot of complicated cognition to pull off this calculation. [ 9 ] [ 10 ]

Your visuospatial sketchpad first has to store a visual representation of the symbols. Your central executive has to apply the rules of addition and store the intermediate steps (e.g. 80 + 60). Finally, your phonological loop has to maintain the subvocal instructions to control the operation (“add eighty and sixty” etc.). [ 11 ] If any of these problems fail the result is, again, confusion and getting lost.

Besides comprehension and problem-solving, working memory underpins many other learning skills. Note-taking [ 12 ] requires you to quickly store and process what is has just been said while simultaneously processing what is being said right now.

It shouldn’t surprise you now that working memory capacity has been found to be significantly connected to reading comprehension [ 13 ][ 14 ] , maths [ 15 ] and problem-solving. [ 16 ] Students who have a better working memory enjoy better grades. [ 17 ] Most importantly, higher working memory capacity predicts better learning outcomes and achievement. [ 18 ][ 19 ][ 20 ]

Can You Improve Your Working Memory

You’ve probably heard of memory experts who can remember astonishingly long sequences of random digits or words. For example, Rajan Mahadevan is able to correctly retrieve a staggering 31,811 digits of the mathematical constant pi (long-term memory). He can also remember up to 63 randomly presented digits or words (working memory). [ 21 ] Another mnemonist, Suresh Kumar Sharma, holds the Guinness world record for managing to recite pi to 70,030 digits without making any mistakes. [ 22 ]

You may be thinking that it’s impossible to achieve such amazing feats unless you’re born naturally gifted.

Although both of these mnemonists have likely had an above-average working memory since childhood, genetic predispositions are by no means the whole story. If these champions were naturally blessed with a fantastic working memory, then we would expect them to excel in all tasks requiring working memory, right?

Researchers decided to test this idea. [ 23 ] Instead of digits or words, they gave Rajan Mahadevan series of symbols (such as !, @, *, +, etc.). Can you guess how many symbols Rajan managed to remember?

To everyone’s surprise, Rajan could only keep 6 of these symbols in his working memory – the same as an average university student.

When interviewing these and other mnemonists, scientists found that they had devoted extensive time of practice to hone their memory. What’s more important, they use highly sophisticated and refined versions of mnemonic techniques such as the method of loci or the story method. [ 24 ]

All these results suggest that working memory is (to some degree) a skill like any other – if you practice it, you can improve it.

While the jury is still out whether and to what degree it’s possible to improve the core processes of working memory, [ 25 ] scientists have discovered many techniques that help you make your working memory more efficient and effective. In the following sections we’ll describe how you can apply these techniques to boost your comprehension and problem-solving skills.

If you set out to improve your working memory, it can be useful to know how you can measure it. Scientists distinguish between short-term memory capacity and working memory capacity. [ 26 ]

Short-term capacity is simply your ability to temporarily store of small amounts of information. [ 27 ] This information can be digits, letters, words, symbols, pictures, scenes, or anything else. Short-term memory span is the number of items that one can store in their short-term memory.

Would you like to know your digit span?  Try this online test . Scroll down the webpage, uncheck “sound enabled”, set the starting sequence length to 3 and click start. Do this at least three times and then compute the average, which will be your digit span. You can also click “repeat” if you want to repeat a sequence with the same number of digits.

The average human span is 4 items, [ 28 ] although the exact number depends on the type of items. People can typically remember more letters than words and more digits than letters. The average digit span is 7 digits.

Working memory capacity is your combined ability to store and manipulate information. It’s traditionally measured with complex span tasks (such as the operation span) and the famous n-back. These tests can’t be taken online, but you can download them here .

Phonological Loop: How Music Disrupts Your Studies

Phonological loop is the first kind of short-term memory storage which stores sounds. Being able to have a conversation, listen to music and understand a lecture all depend on your phonological loop.

As you read these lines, your phonological loop is working at every moment. It uses subvocalisation (your internal voice) to translate visual information (digits, letters, words and sentences) into auditory information, which is then processed to extract meaning. [ 29 ]

If the subvocalisation process is disrupted, it will be hard to maintain information in your phonological loop. As a consequence, your comprehension will suffer. To see this on yourself, try the following experiment:

If you haven’t already done so, measure your digit span . After you’ve done that, measure your digit span again. This time, however, firstly start playing a favorite song of yours that contains lyrics (it shouldn’t be a purely instrumental piece). Set the volume to a comfortable level (not too quiet but not too loud). What is your digit span now?

It’s likely that your digit span is now one or more digits lower. [ 30 ] This is because the music interfered with the subvocalisation process, which was thus less effective at encoding information in your phonological loop.

Many studies have shown that listening to many kinds of sounds and music can have a profoundly negative impact on your working memory, reading comprehension and mathematical problem-solving. [ 31 ] For instance, one study has shown that students who revise in a quiet environment later perform 60% better in an SAT comprehension test than their peers who listen to music (with lyrics). [ 32 ]

However, different kinds of sounds have different effects. Firstly, the detrimental effect is much stronger with vocal music compared to instrumental music. One study showed that students who revised without music were 10% better than students who revised while listening to instrumental music. [ 33 ]

Secondly, it doesn’t matter if you don’t understand the language. Foreign language also impairs working memory. [ 34 ] Thirdly, although even pure tones can disrupt performance, the tones have to fluctuate. If the pure tone has a constant pitch, it doesn’t have a harmful effect on memory. [ 35 ]

Listening to music doesn’t affect everyone in the same way. In general, individuals with a high working memory capacity are more resistant to the harmful effects of music. [ 36 ]

However, students are very bad at predicting what effect music has on their performance. Interestingly enough, the students who prefer listening to music while studying are also those whose reading comprehension is most likely to suffer due to interference from music. [ 37 ]

Why do so many students listen to music although it impairs their learning? Why do they even feel that they benefit from this? We believe that the reason for this might be twofold:

Firstly, music could help reduce anxiety and help one calm down, which may be beneficial for studying. [ 38 ] Secondly, music could drown out even more disrupting external noise, which might actually help to protect working memory.

Interestingly, although white noise seems to worsen the performance of students with normal attention, it can actually improve the performance of students with attention problems. [ 39 ]

In general, we would recommend that you avoid listening to music while studying (especially vocal music). It’s important that you study in a quiet environment where no-body is speaking or making any other noise. The exception to this rule is when you’re preparing for an exam that will take place in a noisy environment. In this case, it’s beneficial to spend some time revising in a noisy environment (to see why, check our Complete Guide on Memory, section “ Context-dependence ” ).

If you cannot revise in a quiet environment, the best way to reduce noise is by using earplugs. Alternatively, a not too harmful option is to listen to white noise (check out the plethora of white-noise nature sounds on YouTube). If you do have to listen to music, go for instrumental music.

The first strategy to improve your learning is by protecting your phonological loop from interfering sounds. Scientists have found yet another strategy that significantly boosts learning and that also makes use of sound.

In an intriguing study, students had to memorize lists of words. [ 40 ] The first group read the words aloud, the second listened to a recording of their own voice reading the words, the third group listened to someone else, while the fourth group studied the words in silence. Interestingly, the first group showed the best performance (20% better than the fourth group), followed by the second, third and fourth group.

The advantage of reading aloud over reading silently for subsequent memory performance is called the “production effect”. [ 41 ]

Scientists believe that producing words makes them more distinctive than reading them silently because you additionally use your vocal cords and facial muscles. [ 42 ]

To harness the production effect, however, you shouldn’t read aloud all of your study material. Distinctiveness is relative – a word read aloud will stand out in the context of silently-read words but it won’t stand out if all other words are also read aloud. [ 43 ] Therefore, to get the most benefit, we recommend that you use the production effect only for a selection of the most important information.

In summary, we recommend the following:

• Ideally, avoid noise during learning and don’t listen to any kind of music
• The best way to down out noise is by using earplugs (or listening to white noise)
• If you do have to listen to music (because it helps you calm down for instance), choose instrumental music with no lyrics
• Only apply this to a selection of the most important concepts / information
• If you read aloud everything, it won’t work

Visuospatial Sketchpad: Upgrade Your Imagination

Visuospatial sketchpad is the second kind of short-term memory storage. It stores two- or three-dimensional objects and their positions in space.

The visuospatial sketchpad is essential for understanding mathematical, science, technology and engineering subjects. Visuospatial working memory capacity in childhood reliably predicts mathematical achievements in adolescence even when other factors such as intelligence are accounted for. [ 44 ]

In a stunning study, researchers from Berkley examined the visuospatial skills of engineering students. [ 45 ] They found that the men performed on average nearly 10% better than women in various tasks such as mental rotation of objects. The researchers later interviewed experienced engineers and asked them to share their strategies for solving visuospatial problems.

On the basis of these strategies, they designed a visuospatial training program. All women who had low scores were invited to attend the program. Interestingly, after only 3 hours of training, there were no longer any significant differences between men and women.

This study demonstrates how the use of appropriate strategies can substantially (and quickly) help your visuospatial sketchpad. Which strategies are the best? In the study mentioned above, the researchers found that different engineers used different strategies that achieved the same result.

Therefore, there seems to be no single “right” strategy for approaching visuospatial problems. However, you can develop your own strategy. We’re going to show you how to do it on the following task: Have a look at the picture below and try to find the folded cube which cannot be made from the unfolded cube (there’s only one).

Before we give you the correct answer, think of the strategy that you used. There are two broad strategies for these kinds of problems. A holistic strategy consists of firstly folding the cube, then rotating it mentally as a whole and comparing it with the folded cubes. This is the most working-memory demanding strategy. In contrast, an analytic strategy consists of noticing the relationships between the patterns in a step-by-step way. Let’s walk through an analytic strategy:

If you look at the first folded cube, you can ask yourself: If the white cross is above the black x , can the five dots be on the right?

Then look at the unfolded cube. Visualize the unfolded cube in such a way that the white cross is above the black x .

From this position you can see easily that the first folded cube is the same as the unfolded cube.

As an alternative, you could “unfold” the cubes first, possibly even draw them unfolded. Then rotate and compare the unfolded cubes to see if they fit.

If you apply one of these strategies to the remaining three cubes, you’ll see that it’s the fourth cube that doesn’t fit.

If you can use the holistic approach straightaway then it’s likely that your visuospatial sketchpad has a high capacity. If not, then you can benefit from using a more piecemeal approach. The whole idea is to offload information from your working memory – to break down the task into smaller, more manageable pieces and to store intermediate steps on paper. This way you can achieve the same result as someone with a high working memory capacity, albeit perhaps more slowly.

The visuospatial sketchpad is useful not only for visuospatial problems. The phonological loop and the visuospatial sketchpad are largely independent of each other. [ 46 ] Therefore, you can use your visuospatial sketchpad to help your phonological loop and vice versa.

A beautiful demonstration of how the visuospatial sketchpad can help the phonological loop was carried out by scientists who examined Japanese experts on mental calculation. [ 47 ] These experts have a very high digit span (16 number) and they can quickly subtract and add up numbers having up to 9 digits. Where does their miraculous ability come from? Through practice, these experts have learnt to construct a “virtual” abacus in their minds that they use to make calculations.

While a mental abacus is probably no longer needed in the age of computers, you can use visualization in other ways: If you’re going shopping and you want to remember shopping list, you can chunk it into one picture. For instance, you could imagine peppers, milk, chicken and mustard as mustard-covered chicken, swimming in a bowl of cereal and surrounded by peppers.

Visualization strategies can be beneficial for your reading comprehension as well. In an interesting study, researchers asked students to read a scientific text from chemistry. [ 48 ] One group of students was given no strategy, one group was asked to focus on the text (summarize and find the main points), whereas the last group was asked to use the drawing-construction strategy (draw molecules and their bonds). At the end of the study session, students were assessed with a test.

One would expect that focusing on the text, finding its main points and being able to summarize it, should be the key ingredients of reading comprehension. However, the results showed the exact opposite. The drawing students outperformed the no-strategy students by 30%. What’s more, summarizing actually worsened the performance of the text-focused group compared to the control group.

Although the drawing-construction strategy improves students’ comprehension of particular scientific texts, [ 49 ] research has yet to show whether it generalizes to all subjects and all kinds of texts. You need to experiment with yourself to find out how when drawing is useful and when it isn’t.

Moreover, the quality of drawings is essential for the technique to be effective. [ 50 ] This means that your drawings need to be a faithful representation of the text’s contents, correctly capturing the relationships between different concepts.

Therefore, it undoubtedly takes some practice to master the skill of visualization. Nevertheless, although drawing is not an out-of-the-box strategy, if done well, it can become a powerful technique in you learning arsenal.

• Don’t worry if you have problems with visuospatial tasks – it’s mostly a matter of choosing the right strategy.
• Break down complex tasks into small components.
• Offload the results of intermediate steps onto paper.
• This strategy can make you process information more deeply.

Central Executive: How to Concentrate Your Mind Easily

The central executive is the third component of working memory. The central executive has many functions. Here we’ll focus on allocation of attention and manipulation of information.

Selective attention is the ability to direct cognitive resources to things which are relevant to the task at hand and to filter out everything else. [ 51 ]

Trying to pay attention to multiple things at the same time (multi-tasking) is generally harmful to performance. Using our workbench analogy from the beginning, imagine that we asked our carpenter to chisel, saw and drill several different pieces of wood at the same time. The result of such effort would likely be a shoddy product. Unsurprisingly, a wealth of studies have shown the detrimental effects of multi-tasking on comprehension, learning and students’ grades. [ 52 ]

As a matter of fact, “multi-tasking” is a bit of a misnomer. [ 53 ] True multi-tasking is quite rare because it is very difficult to pay attention to two things at the same time. Multi-tasking typically consist of switching back and forth between multiple tasks, rather than simultaneously focusing on several tasks.

Multi-tasking is inefficient because each switch that you make incurs a cost. [ 54 ] If you’re oscillating between reading your notes and checking your phone, for instance, each switch takes some time and energy – you have shift your goals (“Now I want to do this instead of that”) and re-activate the rules for the activity you’re switching to (read a paragraph – type a response).

Although one task switch may only take a few seconds (and seem insignificant), all the myriad switching done within one day can add up to a substantial amount of time and eat away at your productivity.

The negative effect of multi-tasking can be quite insidious. In a series of studies, [ 55 ] researchers had students read a text passage and assessed their comprehension with tests. Some students also carried out an interruption task (solving a math problem between each paragraph).

Researchers found that the interruption had no effect on students’ knowledge (they could correctly answer questions despite the interruption). However, when global comprehension was assessed (the text’s theme and tone, the author’s goals and morale), the interruption worsened performance by as much as 30%.

This study nicely demonstrates that you might feel that multi-tasking is not affecting your performance based on the fact that you remember everything from the text easily. However, your comprehension, which requires synthesizing information from different parts of the text, could still suffer.

It may come as a surprise, but multi-tasking may not always harmful. What matters is whether the two tasks employ the same cognitive processes. [ 56 ] This happens, for instance, when you’re watching television while reading your notes. Doing these two activities simultaneously is going to interfere with your comprehension as both of these activities compete for access to your phonological loop.

However, reading a book while sitting on the train or practicing flashcards while commuting, will likely not substantially impair your comprehension. (Scott: I was listening to music while drawing the images for this post, but I never listen to music while writing.)

Research has also shown that individuals with a high working memory capacity are more resistant to the negative effects of multi-tasking (especially if the secondary task is not too demanding). [ 57 ] Therefore, if you have a high working memory capacity, you might be able to do multi-tasking without substantially hurting your performance.

Multi-tasking is a form of dividing your attention. Besides different activities (like watching TV and reading notes), attention can also be divided among different study materials. If you have multiple source materials which you have to look at while studying, then your comprehension will suffer. This is called the split-attention effect. [ 58 ]

As a demonstration, we’ve prepared two tasks from geometry. You don’t need to solve the tasks, just have a look at them. Both tasks ask you to do exactly the same thing (calculate two angles), however, each task is presented differently. Which of the two tasks seems easier?

The correct answers are 60°and 120° degrees, respectively. Did you find the second task easier to understand?

Whereas the first task was presented with separate textual and graphical information, the second task featured information integrated into a coherent whole.

The first task placed an unnecessary load on the central executive, which had to shift attention between the text and the picture and combine it together to enable understanding. This was essentially extra manipulation of information that had nothing to do with solving the actual task. In contrast, the second task freed up cognitive resources that could be instead devoted to solving task.

Researchers have found that if study material is presented in an integrated format, then comprehension improves dramatically (one study has reported a 30% improvement compared to split-attention format [ 59 ] ). This effect has been found for all kinds of subjects, including geometry, programming, geography and engineering. [ 60 ]

Consider another example. The simple arrangement and distance of words on vocabulary flashcards can make a significant difference to your retention:

Compared to the second example, the first example places a demand on your central executive, which has to figure out the way from the Chinese character to its phonic equivalent. Indeed, presenting flashcards like the second example substantially improves later recall. [ 61 ]

You may not be able to select your study material or perhaps there are no textbooks / lecture notes available which present material in an integrative way. However, you need not depend on the particular way your study material is structured. When taking notes, make sure that you have all information in one place. Stick to the rule “one concept must fit on one page”. If you can’t fit one concept on one page then you need to break it down into smaller concepts.

Pay attention to how your study material is structured. If you have to study from multiple sources (several textbooks / notebooks), it might be a good idea to combine the information and put it all into one place (by re-writing or photocopying for instance). If this is too cumbersome, then drawing a structure, a concept map or an outline of what you’re studying should also help.

If you have difficulty understanding a concept, re-draw graphs and re-write your notes so that everything is integrated in one place. This way you will free up precious working memory resources, which you’ll be able to devote to comprehension.

• Avoid multi-tasking and interruptions even if you feel that it’s not affecting you – the negative effect can be well hidden from your sight
• Multi-tasking will not affect your learning and performance only if the two or more activities that you do simultaneously don’t share the same working memory resources (e.g. practicing flashcards while commuting)
• When studying, put all information relevant to one concept into one place to prevent divided attention
• Try to find study materials which feature integrated information (graphs and text combined together rather than presented separately)
• If necessary, re-draw or photo-copy different parts of your notes/textbooks/lecture notes so that everything is integrated
• Design your own study materials (like flashcards) in an integrative way to boost your memory

Chunking – the secret to expertise

For two years, researchers followed a single student of average intelligence and short-term memory capacity. [ 62 ] Every day, the student had to listen to sequences of digits. While at the start, he could only recall 4 digits, by the end of the study, he managed to correctly remember a series of 80 digits.

When interviewing the student, the researchers found that the he was a competitive runner. When hearing the sequences of digits, the student transformed every 4 digits into a running time (e.g. 3492 was transformed to 3 minutes and 49.2 seconds). In this way, he effectively compressed 4 units of information into 1 unit of information.

The process of compressing information is called “chunking”. To see how chunking works, you can try the following little experiment: [ 63 ]

1) Look at these letters for 10 seconds and try to memorize as many of them as possible, while covering the rest of the page:

2) Now do the same thing with these letters:

The chances are that you probably couldn’t recall all of the letters from the first list, but you could easily recall all of the letters from the second list. What’s going on here?

You may have noticed that the letters in both lists are the same, only arranged differently. However, while in the first list you had to memorize 12 letters (which is way above the average short-term memory span), in the second list you were not memorizing letters at all. Instead, you memorized 4 syllables (FRAC-TO-LIS-TIC).

The key idea behind chunking is that you group the underlying items by some sort of meaning or structure. The group then becomes a single unit (=chunk). Although our short-term memory can only hold 4 chunks at a time, these chunks can be fairly complex.

You can easily use chunking to memorize phone numbers, passwords or PIN codes. Simply divide the given sequence into chunks containing the maximum of 4 items each. For instance, to remember the phone number 743293045, you could split the number with dashes like this: 743-293-045. This way, you effectively have to remember only 3 chunks of information, instead of 9 separate digits. If you’re interested in more advanced chunking methods for long sequences of numbers, have a look at the phonetic-number system .

You can also use chunking to boost your learning. A useful chunking technique is organization. Organization is when you categorize unstructured study material into meaningful groups. For example, you can group foreign language vocabulary based on topics, similar meanings (synonyms) or similar pronunciation.

The structure can also be more complex (hierarchical). For instance, you can study chemical elements grouped by their various properties. Research shows that people can memorize up to twice as many hierarchically organized items than unorganized items. [ 64 ]

Chunking reduces the load on working memory because it replaces the items in your working memory with items from your long-term memory. [ 65 ] To see how it works, try the following experiment:

Memorize the following list of 5 words (while covering the rest of the page). You have 5 seconds:

large, run, tremble, believe, fish, series

How many words did you remember?

Now memorize another list of 5 words. You have 5 seconds:

besar, berlari, gemetar, percaya, ikan, siri

How many words did you remember now? Although the second list contained the same number of words (which had the same meaning and almost the same number of letters in total), you probably remembered fewer words from the second list than from the first list. How is this possible?

As an English speaker, you probably knew all the words from the first list. However, unless you speak Malay, you didn’t know any of the words from the second list. The first list was easier precisely because you could use your pre-existing knowledge of English vocabulary stored in your long-term memory. You simply “downloaded” each word from your long-term memory as a chunk.

In contrast, since you couldn’t retrieve the Malay words from your long-term memory, you could only “download” smaller chunks from your long-term memory – syllables or letters. As a result, there were many more pieces of information that had to be stored in your working memory from the second list.

Researchers have found that although humans have a very limited working memory capacity, their long-term memory capacity can be astonishingly high. In one study, [ 66 ] scientists asked subjects to look at 2500 pictures for three seconds each. After that, they asked them about the details of selected pictures such as the positions of objects, their shape and color. Surprisingly, subjects were 90% accurate at remembering the details of the pictures.

Therefore, the most powerful way that you can free up your working memory capacity is by drawing on your long-term memory resources. The more knowledge you have stored in your long-term memory, the less information you need to process with your working memory and the easier will it be to understand your study material and solve problems.

Chunking is the secret behind acquiring mastery in any subject [ 67 ] (alternative explanations have also been proposed – see Ericsson’s long-term working-memory hypothesis). [ 68 ] This is because any kind of complex skill is essentially a huge chunk containing a large number of nested chunks.

Consider playing the piano: Playing the piano consists of many skills, such as sight reading, finger techniques, understanding of rhythm, pushing the pedals, and many others. Each of these skills also consists of further sub-skills. For example, sight reading requires the knowledge of keys, notes, scales and various musical symbols denoting rhythm and volume. For a novice player, doing all of these things at the same time is an impossible task. And yet expert musicians can play complex pieces with little effort, even by sight-reading only.

Expert musicians can play the piano with little effort precisely because they do not have to retrieve each individual skill separately. This would overload their working memory and make performance impossible. Instead, they retrieve one large chunk from their long-term memory that contains all of these sub-skills “compressed” within it. This saves precious working memory resources which can be devoted to processing other information such as sight-reading.

Therefore, to master any subject, you need to firstly build solid foundations of the basics (the elementary chunks). Only then can you attempt to form increasingly complex chunks.

Understanding chunking can help you with your comprehension and problem-solving skills. If you’re experiencing difficulty understanding your study material or cannot solve a problem, then it’s likely that your working memory is overloaded. [ 69 ] Working memory becomes overloaded if it has to process too much information at the same time. This typically happens when you don’t have sufficient knowledge of the prerequisites.

If this is the case, practicing your target skill (e.g. solving many differential equations) likely won’t be of much help or it will be inefficient. A far superior strategy is to firstly identify the underlying sub-skills (arithmetic, algebra) that you may be lacking and master these first. This way you can save yourself substantial amounts of time and effort.

If you have difficulty understanding something, firstly identify the underlying chunks and store them into your long-term memory. This technique is called pre-training. [ 70 ] Pre-training is very effective for all kinds of subjects. As an illustration, consider the following study: [ 71 ]

Students were taught about the car-braking system. One group was firstly introduced to the names of each component (the pedals, the piston, the master cylinder) and their locations. Only once they had mastered the individual components were they taught about their behavior and how they worked together to achieve braking. In contrast, the second group of students was taught all information at once.

Although both groups were exposed to identical material, the pre-training procedure led to substantially better comprehension and recall (up to 30%) than presenting all information at the same time.

You can use pre-training to approach any study material. Firstly, identify the key concepts and vocabulary. Secondly, use the internet or any other resource to find simple definitions. Thirdly, begin to explore how the concepts relate to one another.

In all courses and textbooks it’s often the case that each new lecture (or chapter) requires some knowledge of the previous chapters. If you’re having difficulty understanding a lecture, you might be missing something from the previous lectures and you need to re-study it.

If you have trouble solving mathematical problems, it’s likely that you don’t have properly formed chunks for the underlying operations. For instance, it’s difficult to solve a differential equation without the knowledge of algebra (re-arranging equations) and arithmetic (addition, subtraction, multiplication and division). If you master the underlying sub-skills first, then mathematics will be much easier.

Our general recommendations are the following:

• Use chunking to compress information so that you can remember more.
• For instance, you can group foreign language vocabulary by topics, similar meanings, or similar pronunciation.
• You can do this with pre-training (pre-studying the definitions and meanings of concepts before your lecture or before you read a textbook)
• If you don’t understand something, try to identify what exactly you’re having a problem with and study this first
• Firstly master the underlying sub-skills and then practice your target skill to save time and energy

Cognitive load: the culprit behind learning difficulties

So far we’ve talked about various ways how you can reduce the load placed on your working memory in order to boost your comprehension and problem-solving skills. Scientists have developed a theory of cognitive load which explores in detail the different kinds of load that can be placed on working memory. [ 72 ]

Cognitive load is defined as the effort used by the working memory system to process information. The main idea of the cognitive load theory is that working memory capacity is limited. If the working memory resources that are needed to process information are greater than your capacity, then you will fail to understand the information. Using our workbench analogy, this would be comparable to our carpenter trying work with too many tools and materials at the same time, which would start falling off the workbench as a result.

There are three types of cognitive load: Intrinsic, extrinsic and germane. All types of load are additive – their sum makes up the overall load on your working memory.

Intrinsic load is associated with the task, it’s basically the level of difficulty of the subject. As an illustration, compare the obvious differences in difficulty between solving a simple calculation (2 + 2 = ?) and a complicated equation like the one below:

Intrinsic load is fixed for a particular kind of task and for each individual (given their current level of abilities). High intrinsic load can be beneficial as it stimulates effective learning. However, if it exceeds your working memory resources, it can impair your learning.

One way you can reduce intrinsic load is by gaining more knowledge of the underlying chunks (we covered this in the previous section). Another way is to reduce the complexity of the material.

You can reduce complexity by segmenting and sequencing. [ 73 ] Instead of reading a textbook chapter all at once, split it up into bite-sized chunks. Separate long passages of text graphically (e.g. draw a line to create new paragraphs if necessary). When you’ve done this, study the information step by step. If you come across a graph or a passage that you cannot understand, cover up parts of it and focus on smaller elements. The less information you need to process at one time, the easier it will be to understand it.

Another great way to reduce complexity is by going through worked-example problems. [ 74 ] Worked examples guide you through each step of problem-solving and teach you the model that you can then apply on new problems. Worked examples are especially useful during early stages of learning. Many textbooks now have worked examples.

However, be careful – badly designed worked examples are useless. Good worked-examples have clear language and graphics and are easy to follow. If your worked example is difficult to understand – it causes high cognitive load – then you need to find a different one.

In contrast with intrinsic load, extrinsic load is associated with the way the study material is presented. If you’re experiencing difficulty understanding something, maybe it’s because of high extrinsic load.

Perhaps your lecturer is difficult to understand. Maybe your textbook / lecture notes are not well written and understandable. Do not feel that you are stuck with whatever your course offers to you. Devoting some time before you start learning something to find high-quality materials is definitely a worthwhile investment.

One reason why study materials may impose a high cognitive load is because they contain a lot of redundant information. Authors of textbooks often try to make them visually appealing by including lots of unnecessary decorations, photos and graphics. The rule of thumb is that the more visually appealing a textbook is, the higher extrinsic load it will impose. Unless they are used for explanation of study material, graphics only burden the visuospatial sketchpad.

Another way that you can reduce extrinsic load is by approaching problems in a goal-free way. In the geometrical example that we presented in section “visuospatial sketchpad”, the goal was to compute the angles alpha and beta. A goal-free approach to this problem would be to calculate any kind of angle and as many angles as possible in any order. [ 75 ]

If you have a given goal, then you have to process the goal, the problem givens and the difference between the two simultaneously. In a goal-free approach, you focus only on the current state and how to get to the next state. As a result, the extrinsic load on your working memory is decreased.

The goal-free approach is particularly suitable for math and programming. [ 76 ] For instance, if you have a programming assignment, instead of trying to solve it straight-away, firstly explore its components. Play with different functions – see what kind of inputs they take and what outputs they produce. Similarly, if you’re solving a math or geometry problem such as the one above, don’t try to reach the goal immediately. Instead, explore the problem and calculate different things in a step-by-step way.

The third type of cognitive load is called germane. Germane load is the effort that you have to make to construct integrated chunks of information (called schemas) from the concepts in your study material. To successfully learn something, you need to devote some of your working-memory resources to germane load. To achieve this, you need to minimize the level of extrinsic load and optimize the level of intrinsic load (i.e. find the right level of difficulty).

How do you know which type of cognitive load is causing you problems? Researchers have developed a simple questionnaire that reliably tells apart between different types of cognitive load. [ 77 ]

In essence, if you feel that the activity, the covered concepts, formulas or definitions are complex, then high intrinsic load is likely the culprit. However, if you feel that the instructions/explanations are unclear or ineffective, or full of unclear language, then the problem lies with high extrinsic load.

• If your study material feels too complex, then you need to reduce your intrinsic load
• If your study material feels unclear or confusing, then you need to reduce your extrinsic load
• To reduce intrinsic load, use segmenting and sequencing or find some worked examples
• To reduce extrinsic load, find study materials with clear language and modest graphics, and approach solving problems in a goal-free way

Anxiety: how to turn it into excitement

So far we have covered various things that can place a load on your working memory and impair your comprehension and problem-solving skills. It turns out that one of the major causes of cognitive load is anxiety.

Try to imagine how well our carpenter would perform if she felt anxious. Her hands would probably tremble and she would have difficulty concentrating. In fact, she might even drill a hole in the wrong place or saw off an important part, spoiling the final product.

Anxiety is especially harmful to mathematics, [ 78 ] but it can also worsen performance in other subjects, such as biology. [ 79 ] One would expect that individuals with an already low working memory capacity would be most affected by anxiety. However, the opposite is true. High working memory capacity individuals use high-demand strategies for solving problems. Performance pressure takes away the resources that these individuals need to solve problems.

Scientists believe that when you are anxious, your working memory is preoccupied with anxious thoughts. [ 80 ] So instead of the task at hand, your short-term storage is filled with irrelevant information. In particular, verbal rumination (sub-vocally repeating anxious thoughts) interferes with the phonological loop. Anxious thoughts can be associated with images, which occupy the visuospatial sketchpad. Moreover, if you pay attention to these anxious thoughts, this also places demands on the central executive.

Math anxiety could be a learned phenomenon. Researchers believe that we learn anxiety from our parents when they help us with homework. [ 81 ] They give out verbal and non-verbal signals that math is something difficult and anxiety-provoking.

Unfortunately, math anxiety is also caused directly by teachers. Teachers who are themselves insecure about their mathematical ability (it’s surprising how many of them are!) [ 82 ] tend to give harsh feedback, use defective teaching methods and spread the toxic belief that some people can never become good at math. All of these factors have a severe impact on students’ mathematical abilities and self-confidence.

It may be impossible to change your school or university teacher. However, in the age of internet you’re not bound to one incompetent teacher. For math in particular, you can check online courses and websites (the best one is the Khan Academy) which have excellent teachers who will guide you through the whole curriculum step-by-step, with a calm reassuring voice and completely for free. Don’t let your teacher spoil your experience with math – ignore them, take the initiative and make a switch to someone better.

In addition, you can take steps to effectively address your own anxiety. It turns out that the effect that anxiety has on your performance largely depends on the beliefs you have about it. If you believe that math anxiety will harm you, then you will perform worse. On the other hand, if you believe that math anxiety will help you perform better, then it won’t impact on you. [ 83 ]

One way to overcome anxiety is therefore through a technique called “cognitive reappraisal”. [ 84 ] Try to think of anxiety not as anxiety, but as excitement. These two emotions are both arousing and seem to be quite similar physiologically. Researchers have found that although such a simple reframing of your emotions does nothing to change your anxiety level or bodily response (heart rate, etc.), it improves your performance.

You can reframe your mindset by using subvocalization or speaking aloud to yourself. In particular, you can override the anxious thoughts by repeating excitement-promoting mantras (“I’m excited”, “Get excited”). Often it’s as simple as that. Even reading an article about the benefits of short-term stress can help.

Another techniques that has been found to be effective is expressive writing (or journaling ). [ 85 ] If you are anxious about a test or an exam, write about your thoughts and your worries. By writing these down, you can effectively offload them from your working memory. Expressive writing is especially effective if you elaborate in detail on your deep feelings and what in particular is causing you to feel anxious (which aspects of math or math tests you’re most afraid).

• If your teacher is math-anxious, ignore them and find a better teacher online (e.g. the Khan academy)
• Use cognitive reappraisal and subvocalization to transform anxiety into excitement (“I’m excited”)
• Use expressive writing to offload your worries from memory onto paper

Let’s recap what we’ve learned!

Your working memory is the workbench of your mind. It keeps track of what you’re seeing, hearing, thinking and imagining while allowing you to work with that to produce long-term memories and solutions.

The most popular scientific model has four components of which we reviewed the most well-studied three:

• Phonological Loop . Keeps track of what you’ve just heard. Also used to subvocalize thoughts, while reading, speaking or thinking.
• Visuospatial Sketchpad . Keeps track of pictures and spatial information.
• Central Executive . Allocates attention and manipulates information, just like a carpenter on the workbench.

The most important finding about working memories is that they are limited. The average person can only hold 4-7 pieces of information at a time .

The flip-side of this is that we can chunk information. By combining complex information into recognizable chunks, even super complicated things can fit onto your mental workbench.

To make best use of your working memory:

• Avoid music and distracting sounds while doing mentally demanding work and studying.
• Emphasize the most important information by speaking it aloud.
• Use visual mnemonics to keep track of more ideas at once.
• Visualization can improve studying over merely summarizing for some subjects. Try to apply your imagination more when you study.
• If you struggle with a problem, break it into simpler parts.
• Mastery comes from chunking–building up stored patterns so complex things become simple.

In addition to the components of working memory, we talked about three other issues. Chunking, cognitive load and anxiety.

Cognitive load determines a lot of what makes something confusing or difficult. (Attention and specific learning disabilities, can also be factors, however.) In particular there are three types of cognitive loads:

• Intrinsic load. The difficulty of the idea itself.
• Extrinsic load. Difficulties due to poor presentation/instruction.
• Germane load. The effort required to make new chunks and remember.

You can mitigate intrinsic load by pre-training . Breaking down a complex subject into simple parts, which you master first before moving on.

You can ease extrinsic load by finding good resources for learning, or reorganizing confusing ones .

Finally anxiety has a big impact on working memory. By crowding out the information you need to process, distracting thoughts can make it very hard to perform. Try reframing your anxiety as excitement, seeking confident instructors and journaling your thoughts to make it easier.

Scott Young

I’m a writer, programmer, traveler and avid reader of interesting things. For the last ten years I’ve been experimenting to find out how to learn and think better. More About Scott

Jakub Jílek

Jakub recently graduated from Cognitive and Decision Sciences at University College London and he’s currently starting a PhD in Cognitive Neuroscience. More About Jakub

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What Is Cognitive Psychology?

The Science of How We Think

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Steven Gans, MD is board-certified in psychiatry and is an active supervisor, teacher, and mentor at Massachusetts General Hospital.

Topics in Cognitive Psychology

• Current Research
• Cognitive Approach in Practice

Careers in Cognitive Psychology

How cognitive psychology differs from other branches of psychology, frequently asked questions.

Cognitive psychology involves the study of internal mental processes—all of the workings inside your brain, including perception, thinking, memory, attention, language, problem-solving, and learning.

Cognitive psychology--the study of how people think and process information--helps researchers understand the human brain. It also allows psychologists to help people deal with psychological difficulties.

This article discusses what cognitive psychology is, the history of this field, and current directions for research. It also covers some of the practical applications for cognitive psychology research and related career options you might consider.

Findings from cognitive psychology help us understand how people think, including how they acquire and store memories. By knowing more about how these processes work, psychologists can develop new ways of helping people with cognitive problems.

Cognitive psychologists explore a wide variety of topics related to thinking processes. Some of these include: 

• Attention --our ability to process information in the environment while tuning out irrelevant details
• Choice-based behavior --actions driven by a choice among other possibilities
• Decision-making
• Information processing
• Language acquisition --how we learn to read, write, and express ourselves
• Problem-solving
• Speech perception -how we process what others are saying
• Visual perception --how we see the physical world around us

History of Cognitive Psychology

Although it is a relatively young branch of psychology , it has quickly grown to become one of the most popular subfields. Cognitive psychology grew into prominence between the 1950s and 1970s.

Prior to this time, behaviorism was the dominant perspective in psychology. This theory holds that we learn all our behaviors from interacting with our environment. It focuses strictly on observable behavior, not thought and emotion. Then, researchers became more interested in the internal processes that affect behavior instead of just the behavior itself. 

This shift is often referred to as the cognitive revolution in psychology. During this time, a great deal of research on topics including memory, attention, and language acquisition began to emerge. 

In 1967, the psychologist Ulric Neisser introduced the term cognitive psychology, which he defined as the study of the processes behind the perception, transformation, storage, and recovery of information.

Cognitive psychology became more prominent after the 1950s as a result of the cognitive revolution.

Current Research in Cognitive Psychology

The field of cognitive psychology is both broad and diverse. It touches on many aspects of daily life. There are numerous practical applications for this research, such as providing help coping with memory disorders, making better decisions , recovering from brain injury, treating learning disorders, and structuring educational curricula to enhance learning.

Current research on cognitive psychology helps play a role in how professionals approach the treatment of mental illness, traumatic brain injury, and degenerative brain diseases.

Thanks to the work of cognitive psychologists, we can better pinpoint ways to measure human intellectual abilities, develop new strategies to combat memory problems, and decode the workings of the human brain—all of which ultimately have a powerful impact on how we treat cognitive disorders.

The field of cognitive psychology is a rapidly growing area that continues to add to our understanding of the many influences that mental processes have on our health and daily lives.

From understanding how cognitive processes change as a child develops to looking at how the brain transforms sensory inputs into perceptions, cognitive psychology has helped us gain a deeper and richer understanding of the many mental events that contribute to our daily existence and overall well-being.

The Cognitive Approach in Practice

In addition to adding to our understanding of how the human mind works, the field of cognitive psychology has also had an impact on approaches to mental health. Before the 1970s, many mental health treatments were focused more on psychoanalytic , behavioral , and humanistic approaches.

The so-called "cognitive revolution" put a greater emphasis on understanding the way people process information and how thinking patterns might contribute to psychological distress. Thanks to research in this area, new approaches to treatment were developed to help treat depression, anxiety, phobias, and other psychological disorders .

Cognitive behavioral therapy and rational emotive behavior therapy are two methods in which clients and therapists focus on the underlying cognitions, or thoughts, that contribute to psychological distress.

What Is Cognitive Behavioral Therapy?

Cognitive behavioral therapy (CBT) is an approach that helps clients identify irrational beliefs and other cognitive distortions that are in conflict with reality and then aid them in replacing such thoughts with more realistic, healthy beliefs.

If you are experiencing symptoms of a psychological disorder that would benefit from the use of cognitive approaches, you might see a psychologist who has specific training in these cognitive treatment methods.

These professionals frequently go by titles other than cognitive psychologists, such as psychiatrists, clinical psychologists , or counseling psychologists , but many of the strategies they use are rooted in the cognitive tradition.

Many cognitive psychologists specialize in research with universities or government agencies. Others take a clinical focus and work directly with people who are experiencing challenges related to mental processes. They work in hospitals, mental health clinics, and private practices.

Research psychologists in this area often concentrate on a particular topic, such as memory. Others work directly on health concerns related to cognition, such as degenerative brain disorders and brain injuries.

Treatments rooted in cognitive research focus on helping people replace negative thought patterns with more positive, realistic ones. With the help of cognitive psychologists, people are often able to find ways to cope and even overcome such difficulties.

Reasons to Consult a Cognitive Psychologist

• Alzheimer's disease, dementia, or memory loss
• Brain trauma treatment
• Cognitive therapy for a mental health condition
• Interventions for learning disabilities
• Perceptual or sensory issues
• Therapy for a speech or language disorder

Whereas behavioral and some other realms of psychology focus on actions--which are external and observable--cognitive psychology is instead concerned with the thought processes behind the behavior. Cognitive psychologists see the mind as if it were a computer, taking in and processing information, and seek to understand the various factors involved.

A Word From Verywell

Cognitive psychology plays an important role in understanding the processes of memory, attention, and learning. It can also provide insights into cognitive conditions that may affect how people function.

Being diagnosed with a brain or cognitive health problem can be daunting, but it is important to remember that you are not alone. Together with a healthcare provider, you can come up with an effective treatment plan to help address brain health and cognitive problems.

Your treatment may involve consulting with a cognitive psychologist who has a background in the specific area of concern that you are facing, or you may be referred to another mental health professional that has training and experience with your particular condition.

Ulric Neisser is considered the founder of cognitive psychology. He was the first to introduce the term and to define the field of cognitive psychology. His primary interests were in the areas of perception and memory, but he suggested that all aspects of human thought and behavior were relevant to the study of cognition.

A cognitive map refers to a mental representation of an environment. Such maps can be formed through observation as well as through trial and error. These cognitive maps allow people to orient themselves in their environment.

While they share some similarities, there are some important differences between cognitive neuroscience and cognitive psychology. While cognitive psychology focuses on thinking processes, cognitive neuroscience is focused on finding connections between thinking and specific brain activity. Cognitive neuroscience also looks at the underlying biology that influences how information is processed.

Cognitive psychology is a form of experimental psychology. Cognitive psychologists use experimental methods to study the internal mental processes that play a role in behavior.

Sternberg RJ, Sternberg K. Cognitive Psychology . Wadsworth/Cengage Learning. 

Krapfl JE. Behaviorism and society . Behav Anal. 2016;39(1):123-9. doi:10.1007/s40614-016-0063-8

Cutting JE. Ulric Neisser (1928-2012) . Am Psychol . 2012;67(6):492. doi:10.1037/a0029351

Ruggiero GM, Spada MM, Caselli G, Sassaroli S. A historical and theoretical review of cognitive behavioral therapies: from structural self-knowledge to functional processes .  J Ration Emot Cogn Behav Ther . 2018;36(4):378-403. doi:10.1007/s10942-018-0292-8

Parvin P. Ulric Neisser, cognitive psychology pioneer, dies . Emory News Center.

APA Dictionary of Psychology. Cognitive map . American Psychological Association.

Forstmann BU, Wagenmakers EJ, Eichele T, Brown S, Serences JT. Reciprocal relations between cognitive neuroscience and formal cognitive models: opposites attract? . Trends Cogn Sci . 2011;15(6):272-279. doi:10.1016/j.tics.2011.04.002

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

ORIGINAL RESEARCH article

How working memory provides representational change during insight problem solving.

• 1 Department of Psychology, Yaroslavl State University, Yaroslavl, Russia
• 2 Laboratory for Cognitive Studies, The Russian Presidential Academy of National Economy and Public Administration, Moscow, Russia

Numerous studies of insight problem solving are focused on both the control and storage systems of working memory. We obtained contradictory data about how working memory systems are involved in insight problem solving process. We argue that measuring the dynamics of the control system and storage systems through the course of problem solving can provide a more refined view on the processes involved, as a whole, and explain the existing controversies. We theorize that specific insight mechanisms require varying working memory capacities at different stages of the problem solving process. Our study employed a dual task paradigm to track the dynamics of working memory systems load during problem solving by measuring the reaction time in a secondary probe-task during different stages of problem solving. We varied the modality (verbal, visual) and the complexity of the probe-task during insight and non-insight problem solving. The results indicated that the dynamics of working memory load in insight problems differs from those in non-insight problems. Our first experiment shows that the complexity of the probe-task affects overall probe-task reaction times in both insight and non-insight problem solving. Our second experiment demonstrates that the solution of a non-insight problem is primarily associated with the working memory control system, while insight problems rely on relevant storage systems. Our results confirm that insight process requires access to various systems of working memory throughout the solution. We found that working memory load in non-insight problems increases from stage to stage due to allocation of the attentional control resources to interim calculations. The nature of the dynamics of working memory load in insight problems remains debatable. We claim that insight problem solving demands working memory storage during the entire problem solving process and that control system plays an important role just prior to the solution.

Introduction

For a long time, the problem of working memory role in problem solving, particularly in insight problems, was (and still is) a focus of numerous studies in the field. A number of reviews and original research articles have been devoted to working memory in problem solving ( Hambrick and Engle, 2003 ; Wiley and Jarosz, 2012 ). An interest in the role of working memory during insight problem solving stems from the information processing theories viewing insight as a representational change that can possibly occur within working memory ( Ohlsson, 1992 , 2011 ; Öllinger et al., 2013 ). Baddeley’s working memory model describes both the storage systems (visuo-spatial sketchpad, phonological loop and episodic buffer) required to hold representations and the control system (central executive) enabling the restructuring process ( Baddeley, 2002 ). Investigating the processes involved in working memory during problem solving can provide a unique perspective into its internal structure. The conclusions drawn from the working memory studies can be useful for answering the vital question: “Are there any specific mechanisms dedicated to insight solutions?”

Information processing theories seek to determine whether there is something special in insight phenomenon that makes it uniquely different from analytical problem solving; whether insight is a metacognitive epiphenomenon accompanying a broad range of unrelated processes, or whether it involves specific cognitive mechanisms. At first sight, comparing the information processing occurring in different types of problems is a good way to approach this question. Although this widespread approach seems encouraging, studies that employ the traditional experimental designs and paradigms commonly used in working memory research (e.g., distractors in the dual task paradigm, working memory span studies) often report controversial results.

Contradictions in Working Memory Effects

A number of studies have revealed contradictory results regarding the role of working memory in insight problem solving process ( DeCaro et al., 2016 , 2017 ; Chuderski and Jastrzȩbski, 2017 ). The discussion on the role of working memory in insight primarily focuses on the working memory control system in problem solving. Some studies claim that working memory is a crucial component of both insight and non-insight problem solving processes. Working memory capacity has a strong positive correlation with insight problem solving performance and creativity ( Cinan and Doğan, 2013 ; Chuderski, 2014 ; Chuderski and Jastrzȩbski, 2018 ). De Dreu et al. (2012) demonstrated that creative task performance suffers under working memory load. DeYoung et al. (2008) showed that insight problems are as related to working memory as non-insight problems, but only insight problem solving is related to divergent thinking and breaking the frame. Murray and Byrne (2005) found that accuracy in insight problem solving is positively correlated with working memory storage as well as with attention switching processes, but not with selective and sustained attention. However, some studies revealed different effects of working memory control and storage systems on insight problems. Nęcka et al. (2016) claimed that insight problem solving positively correlates with the recognition of the already presented items in working memory (updating processing in working memory storage) rather than with the substitution of old items with new ones (executive control).

Other studies revealed that working memory affects insight problems less than non-insight problems. Concurrent counting during the problem solving process shows a greater negative effect on non-insight than insight problems, and these findings were supported by ERP data via P300 amplitude analysis ( Lavric et al., 2000 ). Ash and Wiley (2006) demonstrated that insight problems with reduced initial phase are not as related to working memory. Fleck (2008) found that insight problem solving correlates only with verbal working memory, but not with control system or spatial working memory. Verbal working memory may affect only the initial phases of problem comprehension without affecting specific insight processes.

Some studies clearly demonstrated that working memory deficits can be beneficial to insight problem solvers. For example, lateral frontal lobe damage patients solve matchstick problems better compared to healthy participants ( Reverberi et al., 2005 ). Participants with mild alcohol intoxication perform remote associate tests better, faster, and experience more insight solutions ( Jarosz et al., 2012 ). Higher working memory capacity is associated with lower matchstick problem accuracy due to inhibited constraint relaxation ( DeCaro et al., 2016 ). Additionally, higher working memory also leads participants to employ complex ineffective strategies in water jar tasks despite the availability of simpler strategies ( Beilock and DeCaro, 2007 ).

Moreover, there is different data regarding the role of storage systems of working memory in insight problem solving. Performance in insight problem solving is not linked to the control system but is associated with the verbal and visuo-spatial components of working memory ( Gilhooly and Fioratou, 2009 ). Gilhooly and Murphy (2005) claimed that verbal insight problem solving rates are positively related to verbal working memory (vocabulary scores) and spatial insight problem solving rates are positively related to spatial working memory (spatial flexibility). Performance on the nine-dot problem is related to spatial but not verbal working memory ( Chein et al., 2010 ). However, the storage systems of working memory are not involved in insight problem processing independently of the control system. Performance in Compound Remote Associate problems can be predicted by both verbal working memory and attention switching ( Chein and Weisberg, 2014 ). On the other hand, verbal working memory distraction via articulatory suppression enhances insight problem solving because it reduces the verbal-based problem processing ( Ball et al., 2015 ). Surprisingly, the preliminary load of spatial working memory enhances the solution rate in the T-puzzle insight problem ( Suzuki et al., 2014 ).

Some controversies can be accounted for by the differences in the procedures and task materials used in these studies. However, the main source of these controversies might stem from two other major factors: heterogeneity of the problem solving process and the complex nature of the working memory model.

Heterogeneity refers to the idea that insight problem solving process consists of several phases (problem comprehension, impasse, and representation restructuring) that are not equally related to working memory. For example, the selective forgetting hypothesis claims that forgetting and memory clearing occurs during the impasse phase ( Simon, 1977 ; Ohlsson, 1992 ). According to this hypothesis, reduced attention control should be less demanding on the control system of working memory during the impasse phase compared to other phases. The relationship between working memory and insight problem solving can change from phase to phase during this process ( DeCaro et al., 2017 ). The dynamics of insight problem solving processes are infrequently discussed within the working memory studies ( Ash and Wiley, 2006 ; Korovkin et al., 2014 ; Yeh et al., 2014 ; Lv, 2015 ). At the same time, heterogeneity of the phases in insight problem solving was demonstrated in eye-movement studies ( Knoblich et al., 2001 ; Ellis et al., 2011 ; Yeh et al., 2014 ). Thus, we propose that the role of working memory in problem solving should be discussed in regards to each phase separately.

The working memory model itself is a challenging theoretical framework featuring certain ambiguity in terms of relevant components and parameters. This challenge is aggravated by the lack of unity between theoretical models of working memory ( Engle et al., 1999 ; Baddeley, 2002 ; Cowan, 2010 ). Two main approaches to working memory studies in problem solving are experimental and individual differences approaches ( Hambrick and Engle, 2003 ). These approaches differ not only in their methodology but also in their theoretical basis. The experimental approach typically incorporates the distraction paradigm and is based on Baddeley’s (2002) working memory model. Distractors selectively target one of the storage systems of working memory to isolate the modal-specific effects within the problem solving process. The individual differences approach is based on the concept of working memory capacity and focuses on the quantity of stored items. We consider it necessary to take all characteristics of working memory into account to shed light on the processes that make up insight. Understanding the control system is crucial to describing overcoming of the impasse. Additionally, understanding the modal-specific storage systems is necessary to reveal the mechanisms of representation restructuring. Finally, understanding the overall capacity is essential for assessing the information processing aspects of problem solving.

Conventional methods used in working memory studies do not capture the dynamics of working memory load over time. We propose a technique that can accomplish this goal. This technique relies on the assumptions drawn from Kahneman’s (1973) resource model. According to this model, cognitive resources are limited and distributed in concordance with subjective importance. Therefore, if two tasks are performed at the same time continuously, the performance drop in one of them, indicating that available resources have been allocated to the second task instead. If participants should engage in problem solving, while performing a monotonous secondary probe-task, the reaction time in the probe-task should increase whenever the primary problem solving process becomes particularly resource demanding, and vice versa.

Wieth and Burns (2014) clearly showed that both insight and non-insight problem solving processes suffer under multitasking conditions. This fact is in line with our assumptions that the problem solving process competes with the secondary task for resources. Moreover, the interference which occurs due to the competition does not appear to be very damaging to the problem solving process. The surprising result is that providing an incentive does not allow participants to overcome the difficulties associated with multitasking. This may be due to limited attentional resource which cannot be significantly increased. Instead, the authors assume that high motivation leads to surface processing. This means that in the multitasking condition participants shift their attention to the simpler task, essentially making the secondary task the main task. This fact could be a limitation when only using reaction times as the only dependent variable in a dual-task paradigm. Thus, we used reaction times as a main dependent variable and solution rates, solution times, and probe-task accuracy as additional indicators.

The overall problem-solving trial time can be divided into several equal time stages. For example, if the problem was solved in 300 s, the data obtained within the first 100 s, middle 100 s, and last 100 s would represent three stages and corresponding dynamics. Splitting this process into three stages allows us to trace the temporal dynamics of working memory.

Based on the assumption that working memory resources are not unified, we can also vary the content of the secondary probe-task in such a way that it should compete with only some of the systems, but not others. For example, by varying the overall complexity of the probe-task we can investigate the overall working memory capacity demands in problem solving, while, by altering the content of the probe-task (e.g., modality of stimuli) we can isolate the effect of specific storage systems availability.

This technique allows us to answer the following questions on the role of working memory during the insight problem solving process:

(1) Is working memory necessary for insight problem solving process? Does working memory load vary across insight and non-insight problems? Does the insight problem solving process add to working memory load in addition to single probe-task performance?

(2) Are working memory storage systems, the control system, and their overall capacities that are involved in insight problems drastically different compared to non-insight problem solving?

(3) Is there a specific pattern of the temporal dynamics of working memory load during the insight problem solving process? Do capacity, storage, and control systems demands differ across various phases of problem solving?

The study described below was designed to answer these questions regarding the role of working memory and its components in insight problem solving. It was operated under the aforementioned assumptions associated with the dual-task paradigm. This allowed us to operationalize the level of working memory load (low/high) caused by the problem solving process via the reaction time in the simultaneously performed probe task; the slower the reaction time, the higher the working memory load.

Experiment 1

Experiment 1 was conducted to test hypotheses about the role of working memory in insight problem solving. First, we hypothesized that working memory is necessary for insight problem solving; although not to the same degree as for non-insight problem solving. We predicted that working memory load in insight problem solving will be significantly greater than baseline yet significantly lower than in non-insight problem solving. Second, we expected the probe-tasks to take up the working memory capacity proportionally to their complexity. Third, we predicted that different stages of the problem solving would require different amounts of working memory; more specifically, working memory load should be higher toward the end of problem solving in both problem types due to the accumulation of problem-related information.

To test these hypotheses, we employed a 2 (problem type) × 2 (probe type) × 3 (problem stage) full factorial within-subject design with the reaction time in the probe task serving as a dependent variable. The problem type variable consisted of two levels: insight problems and non-insight problems. The probe type variable featured two levels varying in the number of items held in working memory: a simple probe-task (two possible choices) and a complex probe-task (six possible choices). The problem stage acted as a grouping variable with three levels: the average reaction time in the probe task during the first, the middle, and the last part of overall problem solving time course. Full factorial design was incorporated leading to four (2 × 2) conditions that were later split into three stages each.

Participants

Participants in the experimental group were 32 people (25 women), aged 18–34 ( M = 22.16; SD = 3.18). Participants in the control group were 32 people (22 women), aged 18–28 ( M = 21.66; SD = 2.61). The majority of the sample consisted of undergraduate and graduate students at Yaroslavl State University. All participants were tested individually, took part voluntarily, and were not paid for their participation.

We had two types of probe-tasks:

The Simple Probe-Task

Participants were shown the pictures of two alternatives: a circle and a square. Participants were instructed to respond by pressing the left key if they saw a circle and the right key button if they saw a square. The participants’ goal was to perform the task as quickly and accurately as possible.

The Complex Probe-Task

Participants performed the same task, but had six alternatives choices instead. The alternatives were: a square, a circle, a triangle, a cross, a pentagon, and a hexagon. Participants were instructed to press the left key if they saw a circle, a triangle or a pentagon, and the right key in all the other cases.

All probe-tasks were presented in the center of the screen. All figures were black; the background was white. All trials were preceded by a brief (100 ms) blank screen. These probe-tasks were designed to be demanding, yet realistically possible to be performed simultaneously with the primary problem.

We used two types of problems as a primary task:

Non-insight Problems

These problems have clear conditions, a solution algorithm and a logical answer. Participants know all important operators for finding a correct solution and have the right representation of conditions. An example of a non-insight problem: “Given four coins of identical look and feel, two of which are slightly heavier and two are slightly lighter, how could one identify all of them when only allowed to use the balance scale twice?”

Insight Problems

These problems require a change of operators or representation, wherein the participant does not know a new system of operators. The solution occurs suddenly and is often associated with an emotional response. An example of an insight problem: “If you have black socks and brown socks in your drawer, mixed in a ratio of 4–5, how many socks will you have to take out to make sure that you have a pair the same color?”

We selected problems with average solution time between 60 and 150 s. In this experiment we used verbal problems only. Participants were not allowed to use notes and write any information down because this would conflict with the probe-task performance. The problems were solved aloud, and participants answered verbally. All the problems are presented in the Supplementary Materials . The control group (no probe-task) was included in this study to verify whether or not problem solving was substantially altered by the dual-task itself and whether probe-task performance is affected by the problem solving process in the first place. Participants in the control group solved the same set of problems as in the experimental group but without any secondary task (4 insight and 4 non-insight problems).

The experiment was performed with PsychoPy2 scripts (Version 1.81.02; Peirce, 2008 ) on the HP Envy x360 15-ar001ur computer with a 15.6″ screen.

Each participant completed two parts of the experiment: practice trials and experimental trials. The purpose of the practice trials was to familiarize participants with the secondary probe-tasks. During the practice trials participants completed 30 trials of both types of probe-tasks – one at a time, not engaged in the problem solving process. There were 30 trials of each type of probe-tasks presented in random order. Average reaction time of the probe-tasks was calculated and served as a baseline for future comparisons. The scheme of the procedure is presented in Figure 1 .

FIGURE 1. The scheme of the experimental procedure.

When participants finished the practice trials, they proceeded to the experimental trials. Each participant solved two insight and two non-insight problems per each of two probe-task levels in random order (eight problems total). The probe-task trials repeated indefinitely for as long as it took to finish the primary problem. Participants had up to 5 min to solve each problem and were instructed to report the proposed solution verbally. Unsolved trials were not included in the data analysis. Participants were provided with a short break (up to 1 min) after each problem trial.

Preliminary Analysis

Each of the 32 participants in the experimental group attempted to solve 8 problems (256 problems in total). Trials in which participants solved the problem in under 30 s were excluded from the analysis, since such a short thinking time might be indicative of participants’ exposure to a given problem in the past. Trials that took more than 5 min were considered unsolved and were excluded as well. Besides those exclusions, extreme values of the probe-task reaction times above 3 IQR were considered indicative of participant’s low engagement in the task and, therefore, were excluded from the analysis. Overall, 15 non-insight trials and 50 insight trials were excluded from the analysis. The rest of the trials constituted the obtained data set. The control group data was pre-processed the same way: 9 non-insight trials and 51 insight trials were excluded.

Each problem solving trial was split into three equal time intervals similar to the approach previously used by Knoblich et al. (2001) . After that, we averaged the probe-task reaction time within each of those stages, resulting in three probe-task reaction time observations per problem trial. Data obtained from problems in the same condition were averaged across participants, giving us a single data point per each condition for each participant.

The decision to split the overall solution time into three stages was the result of a compromise: while having only two stages would insufficiently represent the course of the problem solving process since it would leave the middle stage of the problem solving unobserved; having more stages can lead to over-conservative statistical estimations due to the aggressive multiple comparison correction, making it hardly possible to reach significance even with a profound effect. We consider the division into three stages theoretically plausible as well: the first stage represents the familiarization with a problem, the middle stage is representative of an impasse, and the final stage is related to overcoming the impasse as well as solution verification.

The preliminary analysis revealed that participants typically successfully solve the majority of the problems (the average solution rate is 77.9%). Participants were successfully performing the probe-tasks as well (95.7% accuracy). This data suggests that participants were adequately focused on both the primary problem and secondary probe-tasks. We found that there are no significant differences between the control and experimental groups in solution times, F (1,62) = 0.004, P = 0.952, η p 2 < 0.001; there is no main effect of problem type, F (1,62) = 0.565, P = 0.455, η p 2 < 0.009; as well as no interaction between the group and problem type factors, F (1,62) = 0.163, P = 0.687, η p 2 = 0.003. We, therefore, argue that the probe-task does not substantially alter the problem solving process itself. Despite the difference between the solution rates of insight and non-insight problems, we suggest that the difficulty of problems has no major effect on reaction time because for both problem types, only trials of the approximately same duration (30–300 s) were analyzed. A brief overview of these results can be found in Table 1 . For a detailed analysis refer to the Supplementary Table S4 .

TABLE 1. The descriptive statistics of solution time and solution rate of the problems in Experiment 1.

A 3 × 2 × 3 repeated measures ANOVA with Greenhouse–Geisser correction was performed to test our hypotheses. The results are shown in Figures 2 , 3 . A main effect of the probe-task type was found for reaction time, F (1.94,40.72) = 184.18, P < 0.001, η p 2 = 0.898. Post hoc pairwise comparisons with the Bonferroni adjustment revealed that reaction time in all three groups were significantly different. The fastest condition was the practice trials with a single probe-task without parallel problem solving ( M = 0.79; SD = 0.15); the slowest condition was non-insight problem solving with a parallel probe-task ( M = 1.93; SD = 0.43). The difference between the practice trial and non-insight problem conditions was found to be significant [ t (27) = -14.83, p < 0.001, r = -0.874]. The probe reaction time in the insight problem condition ( M = 1.67; SD = 0.42) was significantly greater than in practice trials [ t (28) = 12.97, p < 0.001, r = 0.828] and significantly less than in non-insight problems [ t (28) = -4.32, p < 0.001, r = -0.319]. Thus we may conclude that insight problem processing competes with the probe-task for resources of working memory. This means that working memory is necessary for insight problem solving, but is not as crucial for non-insight problem solving.

FIGURE 2. Dynamics of working memory load via the simple probe-task. Vertical bars denote standard errors.

FIGURE 3. Dynamics of working memory load via the complex probe-task. Vertical bars denote standard errors.

A main effect of probe type was revealed [ F (1,21) = 32.65, P < 0.001, η p 2 = 0.609]. The results are shown in Figures 4 , 5 . Post hoc analysis of the probe-tasks in practice trials showed that the simple probe-task was performed faster ( M = 0.57; SD = 0.06) than the complex probe-task ( M = 0.99; SD = 0.26), t (29) = -9.25, p < 0.001, r = -0.736. Moreover, the simple probe-tasks were significantly faster than the complex probe-tasks both in the insight [ t (24) = -2.53, p = 0.018, r = -0.247] and non-insight problems [ t (28) = -2.93, p = 0.007, r = -0.253].

FIGURE 4. Dynamics of working memory load in the insight problems. Vertical bars denote standard errors.

FIGURE 5. Dynamics of working memory load in the non-insight problems. Vertical bars denote standard errors.

As we expected, the analysis did not reveal any interaction between the probe type and the stage factor [ F (1.77,37.21) = 0.5, P = 0.59, η p 2 = 0.023], between task type and probe type [ F (1.7,35.8) = 0.47, P = 0.601, η p 2 = 0.022], nor between probe type, task type, and the stage factors [ F (3.04,63.76) = 0.9, P = 0.447, η p 2 = 0.041].

There was a significant main effect of the stage factor [ F (2,41.95) = 76.04, P < 0.001, η p 2 = 0.784] and an interaction between the task type and stage factors [ F (3.13,65.81) = 31.69, P < 0.001, η p 2 = 0.601]. Various task conditions of the probe-task performance revealed different dynamics. The reaction time decreased in the practice trial over time [the first and second stages: t (30) = 3.21, p = 0.003, r = 0.278; the first and third stages: t (30) = 4.55, p < 0.001, r = 0.356], representing a typical learning curve. At the same time, the reaction time increased over time in both insight and non-insight problems [the first and second stages of insight problems: t (28) = -3.74, p < 0.001, r = -0.322; the first and third stages of insight problems: t (28) = -6.5, p < 0.001, r = -0.51; the first and second stages of non-insight problems: t (29) = -6.04, p < 0.001, r = -0.535; the first and third stages of non-insight problems: t (29) = -13.22, p < 0.001, r = -0.764].

Post hoc pairwise comparisons with the Holm–Bonferroni adjustment revealed a gradual increase in reaction time values in all conditions. There were significant differences in non-insight problems when performing the simple probe-task between the first and second stages [ t (29) = -5.46, p < 0.001, r = -0.454], the first and third stages [ t (29) = -9.28, p < 0.001, r = -0.681], and the second and third stages [ t (29) = -5.26, p < 0.001, r = -0.416]. The same effect was observed for the complex probe-task in non-insight problems between the first and second stages [ t (30) = -4.37, p < 0.001, r = -0.401] and the first and third stages [ t (30) = -7.2, p < 0.001, r = -0.587]. Reaction times for both simple and complex probes increased from stage to stage during non-insight problem solving. This may be due to a gradual increase of working memory load by analytical processes and the accumulation of problem-related information over time.

Surprisingly, we observed a stage-to-stage increase of the reaction time for insight problems as well. The reaction time for the simple probe in the first stage of insight problems was smaller than in the second stage [ t (27) = -4.64, p < 0.001, r = -0.272] and the third stage [ t (27) = -4.18, p < 0.001, r = -0.351]. Similarly, the reaction time for the complex probe in the first stage of insight problems was smaller than in the second stage [ t (26) = -2.56, p = 0.017, r = -0.304] and the third stage [ t (26) = -3.99, p < 0.001, r = -0.466]. Nevertheless, the reaction times (presumably indicative of working memory load) were generally higher in non-insight problems. However, pairwise comparisons revealed that insight and non-insight problems differ at the second stage [ t (26) = -2.4, p = 0.024, r = -0.274] and the third stage [ t (26) = -5.1, p < 0.001, r = -0.465] in the simple probe condition and at the second stage [ t (26) = -2.55, p = 0.017, r = -0.296] and the third stage [ t (26) = -3.06, p = 0.005, r = -0.356] in the complex probe condition. The reaction time for the same probe types in the first stage is equal for the insight and non-insight problems.

The complex probe-task was performed slower both in both insight and non-insight problems but not at the third stage. The reaction times in non-insight problems were different between the probes at the first stage [ t (28) = -3.68, p < 0.001, r = -0.344] and second stage [ t (28) = -2.5, p = 0.019, r = -0.267]. The same results may be observed in insight problems where the probes were different at the first stage [ t (24) = -2.82, p = 0.009, r = -0.277] and second stage [ t (24) = -2.48, p = 0.021, r = -0.241]. We argue that simple probes become harder during the later stages of the problem solving process because of the concurrent problem solving processes in the final stage of a solution.

The obtained results generally confirmed our hypotheses. Hypothesis 1, that working memory is necessary for insight problem solving although not to the same degree as for non-insight problem solving, was completely confirmed. We found that working memory load in insight problem solving is higher than the baseline reaction time in practice trials. This leads to a conclusion that while insight problem solving is demanding in terms of working memory, non-insight problem solving is notably more so. While non-insight problem processing includes planning, holding interim calculations in memory, and control; solving insight problems may involve posing and testing hypotheses, problem comprehension, restructuring of a representation, and verification of solutions. These processes are cognitively demanding but are relatively rare, impermanent, and eventual.

Hypothesis 2 was confirmed by the main effect of probe-task type. Probe-task processing occupies a part of working memory capacity during the problem solving process proportionally to task complexity. Comparison of the probe-tasks in the practice trials revealed that these tasks initially differ by their complexity. The complex probe performance during the main problem solving process is slower than the simple probe performance in all problem types. On the one hand, this shows that the probes are performed well and do not crucially distract from the main problem solving process. On the other hand, it can be described as a modality-independent increase in working memory load under the complex condition because we used different modalities in the main problem (the problems were presented textually) and probe-tasks (the probes were presented visually).

Hypothesis 3 was confirmed by the main effect of the stage factor and an interaction of stage and task factors. We found that the patterns of reaction time dynamics are different in various conditions. We observe a clear learning curve in the practice trials for both probes where reaction times decrease from stage to stage. In contrast, working memory load in the insight and non-insight problems prominently increases. The notable difference between the first and third stages in both types of problems demonstrates that cognitively demanding processing accumulates during the problem solving process. Working memory load in the first stage is similar in insight and non-insight problems and is significantly higher than baseline. We theorize that the same processes related to problem comprehension and building a mental model of the problem are implemented at this stage. The further increases to reaction time in non-insight problem solving may be explained by the increasing processing. As mentioned earlier, the same pattern of working memory load is observed in insight problem solving; the closer one gets to insight solution, the more important of a role working memory plays in insight problem solving. Nevertheless, working memory load does not increase to the same degree in non-insight problems.

Unexpectedly, we found that the probe-tasks of different types are performed similarly at the third stage both in the insight and non-insight problems. Based on the qualitative analysis of the experimental sessions, we speculate that participants might have distracted themselves from the probe-tasks to continue engaging in the problem solving process during the later stages of the trial. This distraction might have obscured the difference between the probe-task types. It also means that parallel competition between the two tasks becomes impossible and turns into switching between the tasks. This also indicates the heavy load of working memory during the last stage of the insight solution.

There were some limitations in this experiment. First, increase in reaction time during the last stage could have been confounded by the process of the verbalization required to report the solution. Second, the obtained results do not allow us to draw any definitive conclusions regarding the role of working memory modal-specific systems. Some of such effects were reported to be found in previous studies ( Gilhooly and Fioratou, 2009 ; Chein et al., 2010 ). We designed and conducted Experiment 2 to overcome the limitations of Experiment 1.

Experiment 2

To overcome the limitations of the first experiment, we modified the procedure and attempted to isolate the effect of solution verbalization and verification by separating it from the dual task performance. When a participant found a solution for a problem, they were instructed to press a pause button to report the solution and get the experimenter’s response. If the participant’s solution was incorrect, they resumed the dual task performance. Additionally, we attempted to identify the modality of the representational processing in insight problem solving. To do so, we introduced the variable of congruence – whether the problem and the probe-task were of the same modality or not. Representational change in insight problem solving can occur within the modal-specific storage systems while being relatively unaffected by the control system. Visual representational change in insight problems can be processed in the visuo-spatial sketchpad, while verbal restructuring – in the phonological loop. In other words, if the problem and the probe-task are both visual or both verbal – the competition occurs on the storage system level (congruent condition), while if the problem and the probe-task are presented in different format – they do not compete in the same storage systems, only for non-specific control system (non-congruent condition).

The general hypotheses of Experiment 2 were as follows:

(1) Working memory storage systems are involved in both types of problem solving.

(2) There is a modal specificity of working memory storage system load in insight problem solving. Insight problem solving is expected to be more demanding in terms of working memory storage systems, while non-insight problem solving was expected to heavily rely on the control system.

(3) Working memory load varies across different stages of the problem solving process. We expected an increased control system load in non-insight problem solving and an increased storage systems load during the last stages of insight problem solving.

To test these hypotheses, we employed the 2 × 2 × 3 factorial within-subject design. The first factor was primary problem-type with two levels: insight and non-insight. The second factor was a congruence of the primary problem format and the probe-task with two levels: congruent and non-congruent. The stage acted as a grouping variable with three levels: first, middle and last stage of the trial. The response time in the probe-task was measured.

Participants in the experimental group were 32 volunteers (22 women; age M = 21.03; SD = 3.01). Participants in the control group were another 32 volunteers (21 women), aged 18–34 ( M = 21.5; SD = 4.86). The majority of the sample consisted of undergraduate and graduate students at Yaroslavl State University. All participants were tested individually; participation was not monetarily compensated.

We modified the materials used in the original experiment, introducing two formats of the primary problem – involving visual images and text, as well as two formats of the probe-tasks: visual and text versions as well. These versions were meant to load the corresponding working memory storage system. The congruent condition always featured the problem and the probe-task of the same format (both visual or both text), while the opposite was true for the non-congruent condition.

The two types of the probe-tasks were as follows:

The Text Task

Participants were presented with two alternatives: open or closed syllables. They were instructed to respond with the right key every time they saw a closed syllable (e.g., “LON”) and with the left key every time they saw an open syllable (e.g., “PLE”). They were also instructed to perform the task as quickly and accurately as possible.

The Visual Task

Participants were presented with two alternatives: obtuse or acute angles. They were instructed to respond with the left key every time they saw an obtuse angle and with the right key every time they saw an acute angle. The instructions were to perform the task as quickly and accurately as possible.

Non-insight Text Problems

These problems have clear conditions, solution algorithms and logical answers. Participants know all important operators necessary to find the correct solution and to build the right condition representation. The problem solution is mainly based on the text code. An example of a non-insight text problem: “Three couples went to a party together. One woman was dressed in red, another one – in green and the third one – in blue. The men were also dressed in one of these colors. When all three couples danced, a man in red danced with the woman in blue. “Christina, it is funny, isn’t it? None of us danced with a partner dressed in the same color.” Think about the man dancing with the woman in red. What color is he wearing?”

Non-insight Visual Problems

These problems are similar to non-insight text problems, but the solution is mainly based on the visual code. An example of a non-insight visual problem is the following matchstick problem: “Turn inequality into equality by moving one match: 8 + 3 - 4 = 0” (Figure 6 ).

FIGURE 6. An example of a non-insight visual problem: “Turn inequality into equality by moving one match”.

Insight Text Problems

These problems are based on a representational change, but the participant is not aware of the new system of operators. Finding an answer occurs suddenly for solvers and is often accompanied by an emotional response. The solution is mainly based on the text code. An example of an insight text problem: “Sally Lu likes eucalyptus more than pine. She likes electric lighting and does not like to sit by candlelight. Eccentric people evoke more sympathy from her than balanced ones. What do you think is Sally’s profession - an economist or an accountant?”

Insight Visual Problems

These problems are similar to insight text problems, but the solution is mainly based on the visual code. An example of an insight visual problem: “Organize 6 identical pencils to get 4 equiangular triangles.”

The problems with an average solution time between 70 to 185 s were selected for the experiment. Participants were not allowed to use notes or write any information down because this would conflict with the probe-task performance. The problems were solved aloud, and participants answered verbally. All the problems are presented in the Supplementary Materials .

The control group was included in this study to compare the solution times and solution rates of the problems solved in the dual-task conditions vs. the problems solved without any secondary task. Participants in the control group solved the same set of problems as in the experimental group but without any secondary task (4 insight and 4 non-insight problems).

The experiment was conducted using PsychoPy2 scripts (Version 1.81.02; Peirce, 2008 ) on the ASUS K55VD computer with a 15.6″ screen.

The procedure used in Experiment 2 was identical to the procedure of the Experiment 1. Each participant solved 8 problems total – one problem trial in each condition presented in random order. The problems were presented at the upper part of the screen; the probe-task stimuli were presented at its center.

The participants were solving problems while performing the probe-tasks continuously the whole time, except for when they were verbally reporting the solution to a problem they were solving. If their proposed solution was incorrect – they resumed performing the secondary probe-task as well as thinking about the problem solution. After the solution to the problem was found, participants had an option to take up to a 1 min break before proceeding to the next problem.

As in Experiment 1, the average response time for the probe-task served as a dependent variable of interest.

The data analysis was identical to that from Experiment 1. Thus, each of the 32 participants attempted to solve 8 problems (256 problems in total), but some problem solving trials were excluded: we excluded unsolved problems (took more than 5 min to solve) and problems that were solved in less than 30 s (due to possibility that participant already knew the answer). Besides this, extreme values for the probe-task reaction time above 3 IQR were identified as outliers. Trials with these outliers were excluded from further analysis. Overall, eleven insight problem trials and eighteen non-insight problem trials were excluded from the analysis for those reasons.

Identical to the experimental group, each of the 32 participants in the control group solved 8 problems – one trial in each condition. We used the same criteria for data exclusion. Overall, 51 insight problem trials and 25 non-insight problem trials were excluded from the analysis.

Each problem solving trial was preprocessed and its solution time was split into three equal time intervals as in the Experiment 1. The average reaction time for the probe-task in each of three stages was calculated.

Obtained results indicated that participants typically solved the majority of the problems (the average solution rate is 70.3%). Similarly, the participants were successfully performing the probe-tasks (87.6% accuracy). This arguably shows that participants were actively engaged in the process and paid sufficient attention and effort to both the primary and secondary tasks.

The average probe-task reaction time in non-insight ( M = 1.55; SD = 0.33) problem solving was greater than in insight problem solving ( M = 1.35; SD = 0.27), t (31) = 5.16, p < 0.001, r = 0.304. Besides, the average probe-task reaction time in insight problems was significantly greater than when the probe-tasks were performed without problem solving ( M = 0.86; SD = 0.11), t (31) = 9.08, p < 0.001, r = 0.748 (Figure 7 ).

FIGURE 7. Average dynamics of working memory load in all probe-tasks. Vertical bars denote standard errors.

We found that solution times in the experimental condition were greater both in insight [ t (62) = 2.61, p = 0.011, r = 0.315] and non-insight [ t (62) = 4.51, p < 0.001, r = 0.497] problems compared to the control condition. This supports the notion that modally specific probe-tasks affect the problem solving process, however, the probe-tasks were not destructive enough to meaningfully alter the solving process. The solution times of insight problems were significantly greater than that of non-insight problems [ t (31) = 2.29, p = 0.029, r = 0.269] in the control group. However, there was no significant difference between insight and non-insight problems solution times in the experimental group [ t (31) = 1.97, p = 0.058, r = 0.185]. These results revealed that insight problems were harder than we expected in the control condition, but probe-tasks involvement removed the difference between insight and non-insight problems. The solution rate data showed that insight problems were solved less often. A brief overview of these results can be found in Table 2 . For a detailed analysis refer to the Supplementary Table S4 .

TABLE 2. The descriptive statistics of the solution time and the solution rate of the problems in Experiment 2.

Problem Type

A repeated measures ANOVA revealed a significant main effect of problem type. The probe-task was performed significantly slower during non-insight problem solving compared to insight problem solving, F (1,30) = 37.75, p < 0.001, η p 2 = 0.557.

Modality Congruence

No significant main effect of modality congruence was revealed. The probe-task average reaction times were equal both in cases when the probe-task was of the same modality as the primary problem and in cases where they were different (e.g., visual problem and a text probe-task), F (1,30) = 0.24, p = 0.631, η p 2 = 0.008.

Problem Stage

A repeated measures ANOVA with Greenhouse–Geisser correction revealed a significant main effect of problem stage, F (1.68,50.26) = 19.59, p < 0.001, η p 2 = 0.395. A Holm–Bonferroni post hoc comparison revealed that the probe-task reaction time was significantly smaller in the first stage ( M = 1.34, SD = 0.04) compared to the middle stage ( M = 1.42, SD = 0.05), while the last stage featured the highest probe-task reaction time ( M = 1.59, SD = 0.07).

Problem Type × Modality Congruence Interaction

An interaction effect of problem type and modality congruence was found, F (1,30) = 8.63, p = 0.006, η p 2 = 0.223. A post hoc comparison revealed that if the probe-task modality was congruent to the problem modality, its performance became slower for insight problem solving, while it made no difference during non-insight problem solving. It is also notable that probe-task reaction time was significantly slower during non-insight problem solving, compared to insight problem solving only when the probe-task modality was non-congruent to the primary problem (Figure 8 ).

FIGURE 8. Working memory load in congruent and non-congruent conditions. Vertical bars denote standard errors.

Modality Congruence × Problem Stage Interaction

No significant interaction of modality congruence × problem stage was found, F (1.88,56.25) = 0.4, p = 0.657, η p 2 = 0.01. The probe-task temporal dynamic was approximately the same in both cases, when the problem modality was congruent to the probe-task modality, and when it was not.

Problem Stage × Problem Type

A significant interaction effect of problem stage × problem type was found, F (2,60) = 33.09, p < 0.001, η p 2 = 0.524. A post hoc comparison revealed that the probe-task reaction time was initially the same during the first stage for both insight and non-insight problems. However, in the middle stage the probe-task reaction time became significantly slower in non-insight problem solving. The magnitude of change further increased in the last stage. Each consecutive stage in non-insight (but not insight) problem solving featured a significant increase in probe-task reaction time (Figure 7 ).

No significant three-way interaction effect was found, F (1.86,55.64) = 1.34, p = 0.269, η p 2 = 0.043.

The results of the second experiment indicate that working memory systems are involved in insight and non-insight problem solving processes unequally. Whenever the probe-task and the primary problem were of the same modality, the resource demands were approximately the same (reflected by the same probe-task reaction time) in insight and non-insight problem solving processes. However, in cases when the probe-task and the primary problem were of different modalities – the probe-task during insight problem solving was performed faster than in non-insight problem solving. This leads to a conclusion that non-insight problem solving competes for general resources of working memory – the control system, since competing with the probe-task within the same storage system (phonological loop or visuo-spatial sketchpad) made no difference compared to when the primary problem and the probe-task were processed within separate storage systems. However, it made a substantial difference for insight problem solving – not having both the primary problem and the probe-task processed within the same system at the same time – significantly decreased the average reaction time, and, therefore, reflects better availability of resources in such cases. In other words, the general availability of the control system is more important for non-insight problem solving, while the availability of specific storage systems is more important for insight problem solving. The results suggest that the processing involved in a representation change in insight problem solving occurs on a level as low as the manipulations with the perceptual image of the visual information within the modal-specific storage systems. This falls in line with Duncker’s (1945) ideas regarding insight mechanisms: the solver has to “re-see” the solution (to view the problem from a different angle). Similar findings regarding the importance of modal-specific components can be found in a number of studies which showed that insight problem solving relies on congruency with problem representation storage systems. For example, the nine-dots problem solving performance is positively associated with visual working memory capacity ( Chein et al., 2010 ); heavy visuo-spatial sketchpad load hinders the chess matches problem solving ( Robbins et al., 1996 ); verbal insight problems are solved worse under the phonological loop load ( Gilhooly and Murphy, 2005 ).

Within modality competition and cross-modality competition did not reveal different temporal dynamics over the course of the three stages of problem solving. It seems that although insight and non-insight problem solving processes are different in terms of what working memory components are more crucial for their processing; this difference is equally present during all the stages of the problem solving process. However, the stage-to-stage dynamics without regards to probe-task modality was different for insight and non-insight problem solving processes, replicating the results found in Experiment 1. We observed a gradual increase in the control system load in non-insight problem solving. This might represent the need to keep the results of the intermediate calculations in working memory, as well as the monitoring of the problem solving progress, and the necessity to hold rules and operators in memory. These factors are especially prevalent in non-insight problem solving, but are not as prominently present in insight problem solving because insight solutions mainly require a problem representation shift, which might be less working memory intensive because it does not require the accumulation of explicitly held pieces of information.

The temporal dynamics of working memory load across various stages of insight and non-insight problem solving processes were not affected by whether the probe-task and the primary problem were of the same modality or not. The first reason why this was the case lies in the homogeneity of the initial and final representations of the problem. The problems we used did not require participants to build a problem representation of a different modality in order to achieve the solution. The visual problems required participants to manipulate the visual problem space, while verbal problems revolved around the semantics and the relation between the problem elements. Arguably, if in order to achieve the solution, participants had to switch the modality of the initial problem representation (e.g., verbal to visual), this would have been represented in the results; for example, the visual probe-task reaction time would increase after the initial verbal representation was changed to visual and vice versa. This hypothesis can be tested in future studies. For example, “symmetric problems” ( Vladimirov et al., 2016 ) can be used to investigate this topic, since solving them requires participants to realize that the problem they are facing only appears to be a visual picture reconfiguration, while in reality the problem space represents signs and numbers. The methodological approach we developed (division of the problem into three equal time stages) would likely not be suitable to identify a singular event of the representation change since it is based on averaging a rather large portion of the problem solving session. We plan to supplement this approach by event-related measurements/grouping criteria as well. An impasse and an “aha” moment can serve as markers guiding our data analysis in the future. In particular, Jones (2003) proposed an eye-tracking procedure for identifying the impasse phase. They argue that the moment of the impasse gives way to a more than twofold increase in the fixation duration on certain elements of the problem compared to the average fixation duration prior to that. Identifying the moment of impasse would allow us to test whether the probe-task methodology is consistent with the eye-tracking data.

General Discussion

In conclusion, we would like to note the technique we used to assess the dynamics of the solution. Despite the popular idea that an insight solution can be divided into various phases, empirical verification of this statement is hard to obtain. Our proposed technique allows one to uncover and probe different phases of the solution separate from each other. This approach lacks disadvantages commonly associated with participant self-reports or an individual differences approach such as: an inability to investigate the micro-dynamics of problem solving; invasiveness – alteration of the natural course of the problem solving process; as well as confound effects of metacognition and memory processes. The main disadvantages are the impossibility of recording the micro-dynamics of problem solving; invasiveness, i.e., influence on the course of the solving process; the low possibility of reflection; the general mechanics of the process; and the influence of metacognitive skills and memory processes in cases of self-reports. The probe-task can act as either a facilitator or a distractor of the problem solving process based on the experimental needs. Besides this, reaction time measurements typically provide a more robust and reliable effect that can benefit the research of working memory during the problem solving process.

It is worth noticing that the probe-task itself in Experiment 1 did not substantially increase the problem (both types) solution times. However, this was the case for Experiment 2 – both insight and non-insight problems were solved slower when performing a dual-task. It is possible that this happened for the very same reason the effects obtained in Experiment 2 were more robust: the combined difficulty level of the problem and the probe-task were likely more appropriate (higher) in Experiment 2.

All in all, both experiments supported the notion that working memory is involved in insight problem solving. Every type of the probe-task used as the secondary task in insight problem solving revealed an increase of reaction time in the dual task condition compared to the single task performance, suggesting a fluctuating impact of the problem solving process on probe-task performance. Working memory in general is involved in both types of problem solving because they share some of the general activities involved in the solving process such as text comprehension, storage of problem elements, holding the interim calculations, attentional control of strategies, and heuristics. Both the control system and storage systems are involved in those general processes. However, the emphasis on either control system or storage systems is different in insight and non-insight problems. While non-insight problem solving is more demanding on the control system, insight problem solving seems to rely on the processing within the modal-specific storage systems to a greater extent. While working memory is typically viewed as a system involved in explicit processing, the fact that working memory (especially the storage systems) plays a role in insight problem solving (that features rather limited conscious self-awareness), supports the idea that working memory is crucial for implicit processing as well ( Reber and Kotovsky, 1997 ; Baars and Franklin, 2003 ; Soto et al., 2011 ; Lebed and Korovkin, 2017 ). Overall, insight problem solving appears to be less demanding on working memory compared to non-insight problem solving, especially if the distinction between control system load and storage systems load is not accounted for.

In terms of the unique contribution of working memory systems, the results indicate that non-insight problems are more demanding on the control system. This could be the case because these problems typically involve more explicit processing, such as progress monitoring, implementation of heuristics, and operations within the problem space. Insight problem solving, on the contrary, involves rejection of the incorrect representations and ineffective rule-sets, which occurs only occasionally and does not require constant monitoring maintained by the control system. This differentiation between the working memory systems involvement was supported by the fact that the probe-task was performed more efficiently if it did not compete for same modality processing as the primary problem – but this was the case only for insight problem solving, not non-insight. Arguably, this notion supports the idea that insight restructuring relies on rather low-level processing that occurs within the working memory storage systems.

All the data regarding the temporal dynamics feature a similar pattern: gradual increase of working memory load in the non-insight problem solving process, but not in the insight problem solving process. This result is in line with our prediction that the solver exerts more and more effort associated with the control system as they progress toward the solution in non-insight problems. The insight problem solving dynamics results were somewhat ambiguous. Results obtained in Experiment 1 revealed a significant increase in working memory load from phase to phase. The results on Experiment 2, however, reveal no such dynamics. Since the procedure in Experiment 2 was modified and participants were not required to perform the probe-task as they were verbally reporting their proposed solution is what might have caused these differences in the results. If this is the case, then the verbalization of the solution in insight problem solving might cooccur with some of the relevant processes contributing to the dynamics in Experiment 1. Such as when the verification of the proposed solution is pronounced verbally.

The lack of observable dynamics in insight problem solving does not speak in favor of the selective forgetting hypothesis ( Simon, 1977 ; Ohlsson, 1992 ), according to which insight solution involves mere forgetting of the incorrect solutions; if that was the case, one might expect a decrease of working memory load after the incorrect solution was forgotten.

The proposed probe-tasks technique differs from the traditional distraction paradigm commonly employed in the field. This technique relies on the secondary probe-task reaction time over the course of problem solving, not the problem solution time itself. This paradigm is more suitable for research of working memory load in problem solving.

Insight problem solving is similar to non-insight analytical processing in terms of involvement of working memory resources. However, taking specific functions within working memory into consideration can reveal unique differences between the two problem solving types. Control systems and modal-specific storage systems play a rather different role in insight and non-insight problem solving processes. Insight problems appear to be less demanding on control systems while relying on the availability of modal-specific storage systems in working memory. The working memory demands seem to increase over the problem solving course for non-insight problems, but not for insight problems since they involve less cumulative explicit knowledge acquisition.

Even though identifying the key components involved in insight problem solving can tell us more about the nature of this phenomenon, the control system is crucial for the performance of almost every intellectual activity in humans, therefore, making it rather challenging to isolate its contribution to each problem type individually. Our claim of representational change in insight problem solving occurs within the modal storage systems, should and will be further tested in the future studies.

Ethics Statement

This study was approved by the Ethics Committee of the Psychology Department of the Yaroslavl State University. All subjects gave written informed consent in accordance with the Declaration of Helsinki.

Author Contributions

All authors together designed the experiments. AS conducted the first experiment. AC conducted the second experiment. SK and IV wrote the first draft of the manuscript and analyzed the data. All authors were critically involved in the interpretation of the results and in revising the manuscript.

This work was supported by the Russian Science Foundation 18-78-10103.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors are grateful to Viktor Z. Gochiyaev for help in data processing. Alexandra Chistopolskaya is grateful to the Mikhail Prokhorov Fund for the opportunity to conduct this study in the Laboratory for Cognitive Studies at RANEPA within the postdoctoral fellowship under the Karamzin Scholarships program. Authors thank Anton Lebed for his significant help in manuscript preparation.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2018.01864/full#supplementary-material

TABLE S1 | The list of insight and non-insight problems.

TABLE S2 | The results of Experiment 1.

TABLE S3 | The results of Experiment 2.

TABLE S4 | The solution time and solution rate data for both experiments.

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Keywords : insight, working memory, representational change, probe-task, executive functions, storage and control systems

Citation: Korovkin S, Vladimirov I, Chistopolskaya A and Savinova A (2018) How Working Memory Provides Representational Change During Insight Problem Solving. Front. Psychol. 9:1864. doi: 10.3389/fpsyg.2018.01864

Received: 27 April 2018; Accepted: 12 September 2018; Published: 01 October 2018.

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Copyright © 2018 Korovkin, Vladimirov, Chistopolskaya and Savinova. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sergei Korovkin, [email protected]

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• Published: 12 November 2021

Interleaved practice enhances memory and problem-solving ability in undergraduate physics

• Joshua Samani   ORCID: orcid.org/0000-0001-8774-6646 1 &
• Steven C. Pan   ORCID: orcid.org/0000-0001-9080-5651 2  

npj Science of Learning volume  6 , Article number:  32 ( 2021 ) Cite this article

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We investigated whether continuously alternating between topics during practice, or interleaved practice, improves memory and the ability to solve problems in undergraduate physics. Over 8 weeks, students in two lecture sections of a university-level introductory physics course completed thrice-weekly homework assignments, each containing problems that were interleaved (i.e., alternating topics) or conventionally arranged (i.e., one topic practiced at a time). On two surprise criterial tests containing novel and more challenging problems, students recalled more relevant information and more frequently produced correct solutions after having engaged in interleaved practice (with observed median improvements of 50% on test 1 and 125% on test 2). Despite benefiting more from interleaved practice, students tended to rate the technique as more difficult and incorrectly believed that they learned less from it. Thus, in a domain that entails considerable amounts of problem-solving, replacing conventionally arranged with interleaved homework can (despite perceptions to the contrary) foster longer lasting and more generalizable learning.

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Introduction

In virtually all learning domains, different topics or skills need to be mastered. Examples include derivatives and integrals in calculus, body systems in physiology, and the forehand, backhand, and serve in tennis. An intuitive approach to achieving mastery in such cases is to focus on learning one topic or skill at a time, which cognitive scientists refer to as blocking or massing (e.g., given concepts A, B, and C, studying three examples of each concept according to an “A 1 A 2 A 3 B 1 B 2 B 3 C 1 C 2 C 3 ” schedule). Blocking is ubiquitous throughout education, including in mathematics, science, and language curricula 1 , 2 , 3 . Its use is consistent with the common assumptions that human beings learn best when topics are introduced in isolation 4 , the learning of concepts is facilitated by exposure to successive examples of the same concept 5 , and that repetition practice fosters the development of expertize 6 (although there are varying perspectives as to the veracity of these assumptions). In contrast, researchers have recently begun investigating an alternative approach known as interleaved practice (henceforth, interleaving ). Interleaving involves switching between topics (or skills, concepts, categories, etc.) during learning (e.g., studying concepts A, B, and C using an “A 1 B 1 C 1 A 2 B 2 C 2 A 3 B 3 C 3 ” schedule) 7 . Consequently, to-be-learned materials are learned in juxtaposition to one another, rather than one at a time. Interleaving may improve attention 8 , induce memory retrieval processes 9 , prompt mental comparison processes 10 , foster relational processing 3 , and simulate the unpredictability of real-world situations 9 , all of which may be beneficial for learning. However, the benefits of interleaving have not yet been extensively explored in authentic educational contexts 11 , and the technique is not generally well known as an effective learning technique among students or instructors 9 . Hence, interleaving is currently rarely used in pedagogical settings 1 , 2 , 3 .

To date, most research on interleaving involves laboratory studies wherein perceptual categories such as artists’ painting styles 12 , 13 , 14 , biological taxonomic classifications 15 , 16 , 17 , or artificial shapes 18 , 19 , 20 are learned. In these studies, example images of to-be-learned categories are studied in blocked or interleaved fashion, followed by a classification test wherein new images that were drawn from the previously learned categories are shown. Typically, categories that were interleaved are classified more accurately than categories that were blocked 7 , 20 . A recent meta-analysis found that the typical benefit of interleaving for perceptual category learning is Hedges’ g (effect size) = 0.67, 95% confidence interval (CI) [0.57, 0.77] for artists’ paintings and g  = 0.31, 95% CI [0.17, 0.54] for artificial shapes 8 . The largest interleaving benefits have usually been observed for groups of categories that are perceptually similar (e.g., evolutionarily-related bird families), which implies that interleaving is more effective when to-be-learned materials are confusable with one another 8 , 21 . Mechanistically, benefits of interleaving for perceptual category learning have been attributed to the temporal spacing between category exemplars that occurs during such interleaving, which constitutes a form of distributed practice (which over a century of research has established can improve memory 22 ), as well as learners’ attention being focused on differences between categories (i.e., the attention bias and discriminative contrast framework, wherein interleaving-induced focused attention may yield improvements in the ability to discriminate between perceptually similar categories) 12 , 13 , 23 , 24 .

Based on the aforementioned research, recent reviews have defined the “interleaving effect” as improved inductive learning — that is, the mental process of acquiring conceptual knowledge from the study of exemplars—that stems from interleaving exemplars of visual or other perceptual categories 8 , 11 , 25 . A question left largely unanswered, however, is whether the interleaving effect extends beyond inductive learning tasks wherein the only determination of category membership is needed. In particular, it has yet to be fully established (a) whether interleaving enhances memory for to-be-learned facts as opposed to perceptual categories, (b) whether interleaving is effective for tasks that require substantial problem-solving, and (c) whether interleaving is effective in authentic educational settings and across extended time intervals 3 , 9 , 21 . These questions pertain to many contexts wherein interleaving could be used. As one example, an instructor might choose to interleave a series of different homework problems that require factual knowledge and the execution of stepwise procedures. Initial efforts to address these questions have involved interleaving in such domains as mathematics 21 , 26 , 27 , second language instruction 2 , 28 , 29 , and other areas 30 .

Thus far, the emerging literature on such uses of interleaving has yielded promising results and especially in the domain of middle-school mathematics. For example, in a 2014 classroom study, the use of interleaved homework assignments to practice algebra and graphing problems (e.g., solving for x in an equation; graphing an equation in the form of y  =  mx  +  b ) yielded subsequent surprise test performance that was nearly double that relative to a condition using blocked homework assignments 21 . Such benefits occurred even for materials that were not necessarily confusable with one another (as featured in most studies of interleaving and perceptual category learning). Even more impressively, a recent randomized controlled trial of interleaved algebra and graphing homework assignments in 54 classrooms (constituting the largest-ever investigation of interleaving to date) reported improvements of Cohen’s d (effect size) = 0.83, 95% CI [0.68, 0.97] on surprise delayed tests 31 . These and other results 27 , 32 , 33 raise the prospect that the interleaving effect encompasses more than inductive learning, with potentially broad implications for theories of learning, skill acquisition, and curriculum design.

To further explore the different types of learning that interleaving may promote, the present study examined the effects of interleaving on factual knowledge and problem-solving ability in a previously unexplored domain, namely undergraduate physics. Physics is one of the most popular academic subjects (in the United States alone, ~350,000 undergraduate students take introductory physics courses and over 280,000 high school students take Advanced Placement Physics exams each year) 34 , 35 . Physics is required not just for physics majors, but also for aspiring professionals in such fields as engineering, medicine, and other areas. Due to the extensive problem-solving skills that are needed, physics is a difficult subject to master, and owing to that difficulty, physics test scores are often among the lowest of all science subjects 34 (which can cause students to abandon the pursuit of science, technology, engineering, and math (STEM) careers) 36 . Accordingly, there is a pressing need to develop and investigate learning techniques that can be highly effective in physics courses.

The present study addressed that need by conducting a real-world, reasonably well-controlled test of interleaving in undergraduate physics. This test took the form of a preregistered experiment in two large lecture sections of an introductory-level undergraduate physics course (“Physics for Life Science Majors”) at a major US public university. The experiment spanned the first 8 weeks of the 10-week course, during which conventionally blocked homework assignments (wherein, only one problem type is practiced at a time) were replaced with interleaved assignments (involving switching between problem types). Importantly, rather than constructing or selecting materials specifically for research purposes, only the arrangement of homework problems during the course of normal instruction was manipulated and no other aspects of the course were altered. Hence, this test of interleaving occurred in an otherwise “business-as-usual” learning environment, which should increase confidence in its generalizability to real-world settings.

Across both lecture sections, 350 students participated in a counterbalanced, within-subjects design. During weeks 1–4 (Stage 1), students in the first and second sections (henceforth, Lecture 1 and Lecture 2) received blocked and interleaved homework assignments, respectively, whereas during weeks 5–8 (Stage 2), the assignment types were reversed (see Fig. 1 ). In other words, Lecture 1 students experienced blocking during Stage 1 and interleaving during Stage 2, whereas Lecture 2 students experienced the reverse. This arrangement ensured that each student in each section ultimately experienced both practice types.

In each of the two stages of the course, students completed 84 practice problems across 10 homework assignments. Blocked assignments typically featured three successive problems for each of three topics, whereas interleaved assignments typically featured only one problem per topic. In the figure, letters represent topics and subscripts represent the problem number for a given topic (1, 2, or 3). Different topics are also assigned different colors so that it is easier to visually tell them apart. Reflecting the relative simplicity of practicing one topic at a time, topics in each row of the blocked condition correspond perfectly to the assignment subject labeling that row, but this is not the case for the interleaved condition. Topics addressed on the criterial tests are also listed. Due to course time constraints, the last two blocked assignments of each stage include only two problems per topic instead of three. Topics from these assignments were not included in criterial tests.

During the course, each of the three weekly lectures was accompanied by a homework assignment. With blocked assignments, each topic was repeatedly practiced in succession with no intervening topics, whereas with interleaved assignments, each successive problem involved a change in the topic (for a list of topics, see Table 1 ). Of the nine problems per assignment, blocked assignments had three successive isomorphic problems per topic (i.e., having the same underlying problem-solving structure with contrasting surface features), which resembles the arrangement of practice exercises that occurs in many educational contexts 1 , whereas interleaved assignments had only one problem per topic, thus requiring students to engage in switching between topics (with the second and third problems per topic appearing on subsequent assignments). Crucially, within each stage, all students completed the same 84 total problems, with only the arrangement of those problems differing.

To measure the potential effects of interleaving, we administered an in-class surprise criterial test at the conclusion of each stage. These tests followed the approach taken in recent studies of interleaving and mathematics 31 , 33 and avoided contaminating effects of cramming, study group activities, and other events that can occur with increasing frequency in the period leading up to pre-announced exams. Both tests featured three novel problems that were more difficult than those included in the homework assignments. The first two problems required integrating concepts and procedures from two separate topics, whereas the third problem required applying a single topic in a new scenario. All three problems required recall and application of factual content conveyed in formulas (see Fig. 1 ). To derive answers, students had to correctly recognize the topics involved, all of which were last encountered more than 1 week prior; recall relevant formulas, rules, and principles; and in two of three problems, integrate and apply that information to devise a new solution strategy 37 (which could be viewed as requiring higher-order reasoning, integration, and constructive thought processes as opposed to simply recalling and repeating previously learned information) 38 , 39 .

As an example, one criterial test problem required recognizing the relevance of both Faraday’s Law and torque on a current loop in a magnetic field, recalling corresponding relevant formulas, and combining them in a novel way to compute the torque on a current loop in the magnetic field of an magnetic resonance imaging machine. Importantly, this combination of problem-solving processes was not included in any of the homework assignments and had not been specifically taught in the course. This type of problem also differed from the isomorphic problems commonly used in prior research on interleaving and problem-solving skills 26 , 31 , 32 , 33 , 40 .

How did students perform on interleaved versus blocked homework assignments—and how did they perceive both practice types?

Across both lecture sections, 290 students in stage 1 (83% of the total enrolled) and 286 students in Stage 2 (82% of total enrolled) experienced the experimental manipulation in its entirety by completing and turning in all of the homework assignments. Per our preregistered inclusion criteria, only data from those students were analyzed. Although that analysis revealed disparities between interleaving and blocking in terms of student performance, judgments of difficulty, and judgments of pedagogical effectiveness, there was no advance indication of any interleaving benefit.

With respect to overall performance, students correctly solved more blocked than interleaved homework problems (Table 2 ), with a mean deficit on interleaved assignments of 0.05 and 0.09 proportion correct in Stages 1 and 2, respectively. When interpreting these results, it is important to consider that there were nine different problem types on most interleaved assignments, with each type requiring a different problem-solving strategy, whereas, with most blocked assignments, there were only three problem types. Hence, the expectation that the blocked assignments would be easier was confirmed by student performance.

When asked at the end of each assignment to make metacognitive judgments—that is, to evaluate their own process of learning—students tended to rate interleaved assignments as more challenging and yielding less mastery (Table 2 ). For both practice types, the largest proportion of students’ judgments of difficulty spanned from the “medium” to “difficult” categories, but a higher proportion of those ratings occurred at the conclusion of interleaved assignments. Correspondingly, for both practice types, the largest proportion of students’ judgments of learning spanned from “well” to “extremely well,” but a higher proportion of those ratings occurred at the conclusion of blocked assignments. Thus, on interleaved assignments, students performed more poorly, experienced greater difficulty, and perceived fewer learning benefits. On the basis of these findings, one might predict that student performance on a delayed test of the practiced topics would suffer.

How did interleaving and blocking affect learning as measured on the criterial tests?

Belying the patterns observed on the homework assignments, however, students who had completed interleaved assignments well outperformed those who had completed blocked assignments on the surprise criterial tests. Interleaving yielded higher criterial test scores than blocking in Stage 1, d  = 0.40, 95% CI [0.17, 0.65], t (288) = 3.41, p  = 0.0008, and in Stage 2, d  = 0.91, 95% CI [0.66, 1.20], t (284) = 7.68, p  < 0.0001. Thus, interleaving improved the ability to correctly recall and use prior knowledge in an attempt to generate solutions to novel problems. Inspection of the full distributions of test scores further confirms the occurrence of strong interleaving benefits (see Fig. 2 ). Specifically, interleaving improved median test scores over-blocking by 50% and 125% in Stages 1 and 2, respectively (i.e., interleaving improved learning across both halves of the course and in both counterbalanced groups). In Stage 2, when students had twice as much course content to draw upon (including topics that were arguably more difficult than those that were presented during Stage 1), the effect size of the interleaving advantage was larger.

Each histogram displays the distributions of criterial test scores in a given stage, with green representing performance in the interleaved condition and purple representing performance in the blocked condition. The median score in each condition is included as a vertical bar of the corresponding color. Histograms are normalized so that in each condition, the sums of values of all bins equals 1. Mean performance in Stages 1 and 2, respectively, was 0.43 and 0.27 in the blocked condition and 0.54 and 0.47 in the interleaved condition.

For additional insights into the effects of interleaving, we examined two distinct sub-measures of test performance: (a) whether students were able to correctly recall necessary formulas, which relies on long-term memory, and (b) whether students’ solution strategies yielded an exact match to the correct answer both in numerical value and in units, which is a more stringent measure of problem-solving ability (as it necessitated devising a multi-step problem-solving strategy and executing its associated computations without making a single error). It should be noted, however, that producing precisely correct answers is uncommon in many introductory-level physics courses due to the inherent conceptual difficulty and computational complexity of the material; in line with that expectation, the mean rate of correct answers, across both conditions, was no >0.34 proportion correct. Sub-measure analyses revealed that interleaving improved long-term memory in Stage 1, d  = 0.41, 95% CI [0.17, 0.66], t (288) = 3.49, p  = 0.006, and in Stage 2, d  = 0.96, 95% CI [0.70, 1.24], t (284) = 8.05, p  < 0.0001. Further, interleaving improved the correctness of answers in Stage 1, d  = 0.25, 95% CI [0.02, 0.48], t (288) = 2.17, p  = 0.0311, and in Stage 2, d  = 0.40, 95% CI [0.16, 0.64], t (284) = 3.32, p  = 0.0010. Thus, interleaving enhanced both memory and problem-solving accuracy.

Results at the level of individual problems (Table 3 ) also showed the advantages of interleaving. These advantages were the most consistent (i.e., across both sub-measures) for the easiest problem in each stage (which addressed one as opposed to two topics). Overall, interleaving yielded at least a numerical advantage on both sub-measures for all three problems on both criterial tests.

How did interleaving and blocking affect learning and study behaviors in the remainder of the course?

On high-stakes midterm exams occurring 3 days after each criterial test, scores did not significantly differ between the blocked and interleaved conditions (post-Stage 1 midterm, d  = 0.20, 95% CI [−0.04, 0.43], t (288) = 1.68, p  = 0.0944), and post-Stage 2 midterm, d  = 0.02, 95% CI [−0.21, 0.25], t (284) = 0.16, p  = 0.8758). Only in Stage 1 was there a hint of an interleaving benefit on the high-stakes exams (as most students did not complete the final exam due to a pandemic-induced campus closure, that exam was not analyzed). Although these patterns suggest a possible limitation on the efficacy of interleaving, there were factors that called into question the diagnosticity of the midterm exams, and these factors led us to include surprise criterial tests as our primary outcome measures. Specifically, exit surveys confirmed that most students engaged in extensive cramming prior to the midterms, but not before the criterial tests (Table 4 ). Further, the criterial tests were a potentially powerful learning event that previewed the problem format and scope on the midterms and likely influenced students’ study behaviors. These observations are consistent with the fact that the mean proportion correct on midterms (0.74) was high compared with the criterial tests (0.42). Thus, although the benefits of interleaving were not detected on midterm exams, any such benefits may have been occluded by cramming and practice testing.

With respect to the potential effects of interleaving and blocking on study behaviors, there were no significant self-reported study time differences between the two conditions (Table 3 ). Rather, the most common pattern across both conditions involved minimal studying prior to the criterial test (≤3 h over 4 weeks) and intense studying between the criterial tests and midterms (≥10 h over 3 days). Such cramming is almost universal among student study behaviors 41 . These patterns suggest that the benefits of interleaving on the criterial tests cannot be attributed to interleaving-induced changes in the volume of studying, but rather to qualitative changes in the learning that occurred during the completion of the homework assignments.

The present results reveal that interleaving can indeed enhance memory and problem-solving ability in the domain of undergraduate physics. Specifically, the use of homework assignments wherein problem types were interleaved, as opposed to conventionally blocked, generated learning improvements on two surprise criterial tests that were comprised of novel and more challenging problems. Such improvements were, in effect size terms, relatively large compared with other pedagogical techniques 42 , 43 (despite some variation across stages and across problems) and comparable to interleaving-induced improvements in such domains as middle-school mathematics 31 , 33 and second language learning 2 , 28 . Further, learning benefits were observed (a) for the case of long-term memory for factual content, (b) for the correctness of answers, (c) after retention intervals of at least one to several weeks, and (d) on surprise criterial tests but not on subsequent high-stakes exams. From the perspective of the literature on interleaving and related techniques (e.g., variability during practice) 44 , 45 , 46 , the present results bolster the conclusion that the benefits of alternating between topics or skills during learning extend well beyond the ability to classify perceptual category exemplars; these benefits can also encompass certain problem-solving skills. Moreover, the present results suggest that the avoidance of supposed preconditions for effective learning—including learning topics in isolation 4 , successive exposures to the same concept 5 , and single-session repetition practice 9 —may not be detrimental for learning. Rather, in line with pedagogical perspectives that encourage variability of practice 1 , 2 , violating those preconditions may in fact enhance learning. That tentative conclusion may validate the practices of instructors that already incorporate some form of interleaving in their homework assignments, but may not necessarily be aware of it as an evidence-supported learning technique.

Several theoretical mechanisms may account for the observed benefits of interleaving. Here, we summarize five candidates. These explanatory accounts are not necessarily mutually exclusive and have been largely drawn from the literature on interleaving, with some adaptations to problem-solving in introductory physics.

First, interleaving may have facilitated inductive learning of problem categories defined by specific physical concepts or principles. These categories, whose correct identification was necessary to solve criterial test problems, are often easily confusable to novice physics learners, who tend to base their problem representations on literal features instead of abstract principles 47 . The course progressed in a hierarchical manner whereby problems across topics commonly shared literal features, but problem classification was never explicitly discussed; hence, any inductive learning of problem categories would most likely have occurred during practice on homework sets. As has been repeatedly demonstrated in the literature (e.g., the attention bias and discriminative contrast framework), inductive learning of confusable perceptual categories is a context wherein interleaving can excel relative to blocking 12 , 13 , 23 , 24 . It is plausible that the interleaved homework sets, which provided more opportunities to compare non-isomorphic problem categories than the blocked homework sets, yielded similar benefits. However, it is important to note that the criterial tests required additional problem-solving steps, including memory retrieval of formulas. As such, inductive learning of problem categories alone might not be sufficient to explain the observed results.

Second, as previously noted, interleaving incorporates distributed practice (i.e., learning spread out over multiple sessions), which is known to improve long-term memory 10 . According to the study-phase retrieval account of the spacing effect, distributed practice during homework sets may have forced students to engage in repeated long-term memory retrieval processes, which are known to enhance the durability and accessibility of memories 3 . In contrast, with blocking, every successive set of three homework problems involved the same topic, thus allowing students to bypass memory retrieval in favor of knowledge temporarily held in working memory (i.e., repeatedly reusing the same solutions). Hence, productive memory retrieval processes may have been attenuated in the blocked condition, potentially reducing the rate of successfully recalling correct formulas on criterial tests, even in the case that the problem solver had achieved a correct conceptual classification of the problem. Other cognitive processes that distributed practice may engage, such as increased encoding of varied contextual cues, may have also had a facilitative effect on learning 48 .

Relatedly, there is evidence in the interleaving literature to support both minimal and major roles of distributed practice depending on the learning context. In the case of perceptual category learning, conditions that feature extensive amounts of distributed practice in the absence of interleaving have failed to yield similar learning benefits 13 , 15 , which suggests a minimal role, whereas, in studies involving mathematics or second language learning, interleaving schedules that incorporate substantial amounts of distributed practice have yielded larger benefits, which suggests a major role 2 , 24 . It is important to note, however, that differences in experimental and task design across studies may have also been factors.

A third explanation involves reduced lag-to-test—that is, elapsed time from practice to assessment—in the interleaved versus blocked conditions. In the present study, each interleaved topic was practiced across a 1-week period following its introduction, whereas each blocked topic was practiced only shortly after its introduction. The interleaved condition, therefore, had more recent exposure (by up to 1 week) on at least one topic per problem at the time of the criterial test, although the lags in both conditions were still at least 1–3 weeks long. It should be noted, however, that having students review to-be-tested topics shortly before a criterial test, which might be expected to attenuate differences in lag-to-test, has not eliminated the interleaving benefit in recent math learning studies 33 .

Fourth, by allowing students to mentally compare different types of problems, interleaving may have fostered more relational processing 3 , potentially improving the ability to integrate concepts from superficially distinct problem categories in order to solve criterial test problems that combined non-isomorphic problem types (see Fig. 1 ). These problem types were merged through shared concepts, such as emitted radiation power, and not recognizing these connections would have rendered the problems unsolvable. Recognition of common concepts may have been more likely in the interleaved condition due to the inclusion of non-isomorphic problem types on each homework assignment, whereas in the blocked condition, students would have had to deliberately juxtapose different homework sets in order to find the relevant connections. The potential for increased relational processing in the interleaved condition might also be described as an instance of material-appropriate processing — that is, cognitive processes that match that needed to perform well on a criterial test 49 (in the present case, integrating non-isomorphic problem types via specific, connecting concepts) and are not redundant with other processes that may already be occurring.

Finally, given that every successive problem on the interleaved homework assignments involved a different topic, interleaving may have given students practice in strategy selection—that is, choosing the correct solution for a given problem from a range of possible options 3 , 21 , 50 . In contrast, the predictability of blocked assignments obviated any need to engage in strategy selection (as students could repeatedly use the same solutions with a high degree of success). Proficiency in strategy selection was crucial for all criterial test problems.

It should be reiterated, however, that none of the accounts presented here are mutually exclusive (e.g., improvements in inductive learning of problem categories and/or relational processing may have facilitated better strategy selection), nor was it the purpose of the present study to adjudicate between them. Any or all of these mechanisms may have jointly contributed to the efficacy of interleaving.

Although the present results are quite clear with respect to an interleaving benefit for memory, the results for “far” transfer of learning 37 —which in the present case involved combining information across topics in order to devise new solution strategies—are more equivocal. If such transfer is to be judged based on numerical and unit correctness, then there was, in effect size terms, a smaller benefit of interleaving relative to the recall of relevant formulas and principles. However, a high level of correct responding was not expected, and the correctness sub-measure could not fully capture the degree to which students were able to successfully transfer their learning (i.e., that measure could not account for better, but imperfect, solution strategies). In our view, further research using more fine-grained measures of problem-solving ability (e.g., having students delineate each solution step, which would have required longer test sessions, and then subjecting those steps to analysis) is needed to clarify the potential of interleaving for far transfer and whether the technique is competitive with other transfer-enhancing approaches 47 , 51 .

The disparity between homework and criterial test data—wherein interleaving initially yielded poorer performance and lower difficulty and efficacy ratings, yet better criterial test performance—illustrates a metacognitive illusion 52 that may complicate student acceptance of interleaving. That illusion reflects the tendency of human beings to be inaccurate at judging the progress of their own learning and the relative utility of contrasting pedagogical activities (with more effective techniques being judged as less beneficial and vice versa) 53 . In response, instructors might consider additional measures, such as explaining the long-term benefits of interleaving prior to administering homework assignments 54 . Fortunately, there did not seem to be an overtly hostile reception towards interleaving, at least as conveyed to the course instructor, and student evaluations of the course were also relatively unchanged versus prior iterations of the course taught by the same instructor.

From an application standpoint, it is promising that the methods used in the present study were relatively simple and could be adapted to other contexts wherein multiple topics are learned using blocked homework assignments. Simply interleaving those assignments in a similar fashion may greatly enhance their effectiveness. We wish to caution, however, that instructors and researchers will need to be careful in generalizing the present results to cases wherein assignments do not contain multiple isomorphic or nearly isomorphic problems for each topic, and it is unclear whether such interleaving benefits will be apparent on high-stakes exams after extensive cramming (especially when considering the tendency of some laboratory-developed learning interventions to “wash out” in classroom contexts) and practice exams 55 . If no such benefits reliably occur, then that would constitute a notable limitation, particularly if enhancing exam performance was the sole objective. However, it remains to be determined whether a larger interleaving benefit would be observed in cases where practice exams were more substantially different than subsequent high-stakes exams, as well as after high-stakes exams, during which any benefits of cramming may have dissipated. Finally, implementation issues 56 such as the relative predictability of interleaving schedules 28 and the point during the learning process that interleaving is introduced 2 , 21 remain to be resolved. Given the incipient state of the classroom-focused interleaving literature, real-world uses of interleaving will inevitably involve a certain amount of trial-and-error.

From the perspective of undergraduate physics education and other forms of STEM learning, the present results serve as a proof-of-concept for a relatively low-cost learning intervention (in terms of time required and necessary equipment) that has the potential to yield sizeable learning benefits. The finding that interleaving benefits learning for one of the most challenging subjects that college students have to master, and does so for the case of relatively difficult problem-solving materials, invites a reevaluation of conventional instructional approaches and a greater appreciation for the influence of practice schedules in the development of skills and expertise. Indeed, it is becoming increasingly apparent that there are a variety of educationally authentic contexts in which human learners benefit more from practicing multiple topics from a given domain at one time, rather than practicing one topic at one time.

Preregistration

The study design and analysis plan were preregistered prior to data collection at: https://osf.io/8t4e5/ . Of the analyses described in the main text, the preregistered analysis plan contains the only comparison of overall criterial test and midterm exam performance across conditions. All other analyses, including performance on course assignments, accompanying judgments of learning, and exit survey analysis, were planned after preregistration but before data collection and should be regarded as exploratory.

Participants

Participants were 350 undergraduate students enrolled in either of two back-to-back lecture sections of Physics 5 C (“Physics for Life Sciences Majors: Electricity, Magnetism, and Modern Physics”) at the University of California, Los Angeles (UCLA) in Winter 2020, which began on 6 January 2020 and ended on 20 March 2020. Per the preregistered inclusion criteria, any student that did not complete any homework assignment during Stage 1 (weeks 1–4) or Stage 2 (weeks 5–8) or that did not take the associated criterial test was removed from the data analyses for the corresponding stage of the study. Consequently, in Stage 1, analyses were performed using data from 139 students in the first lecture section and 151 students in the second lecture section (henceforth, referred to as Lecture 1 and Lecture 2, respectively). In Stage 2, 137 students in Lecture 1 and 149 students in Lecture 2 were included in the analyses. Demographic information for all students included in the data analyses is listed in Supplementary Table 1 . It should be noted that there was no significant difference in mean GPA between students in Lecture 1 and Lecture 2. Thus, despite the fact that students enrolled in the lecture section of their choice (often based on their schedule of availability and preference for time-of-day), any potential differences in academic aptitude between the students in the two lecture sections were likely to have been negligible.

The study was approved by the UCLA Human Research Protection Program as exempt from formal review. No written informed consent was required for data collected during the course of normal instruction and reported in a fully anonymous and summary fashion as occurs in this manuscript. Informed consent was obtained for any individually identifiable reporting of data, of which there are none in this manuscript.

Course description

Physics 5 C is a 10-week lower-division course that is the third in a sequence of required physics courses for life sciences majors at UCLA. The official description of the course states that it addresses: “Electrostatics in vacuum and in water. Electricity, circuits, magnetism, quantum, atomic and nuclear physics, radioactivity, with applications to biological and biochemical systems.” In Winter 2020, the course involved thrice-weekly lecture sections of 50 min each (Lecture 1 from 10 to 10:50 AM and Lecture 2 from 11 to 11:50 AM; each student was enrolled in either of those sections), a weekly discussion section with a duration of 50 min, and a weekly laboratory section with a duration of 110 min. Both lecture sections were taught by the first author of this manuscript (J.S.), a faculty member in the Department of Physics and Astronomy at UCLA, on Mondays, Wednesdays, and Fridays. The discussion and laboratory sections, of which there were multiple sections available each week, were taught by graduate teaching assistants and collaborative problem-solving therein was further facilitated by undergraduate learning assistants.

Grading in Physics 5 C during Winter 2020 was determined via participation questions administered during the lecture sections (5%), discussion section assignments (5%), thrice-weekly homework assignments (20%), laboratory activities (15%), and two midterm exams (22.5% each). Participation questions and homework assignments were completed individually, whereas the remaining graded components were completed entirely or partly in groups. A cumulative final exam was originally scheduled and intended to be the most heavily-weighted aspect of the course (30%); however, that exam was removed from the required list of graded components and was made optional due to COVID-19 pandemic-induced suspension of all in-person instruction at UCLA on 11 March 2020. Importantly, the experimental manipulation and all primary measures of interest (i.e., the criterial tests) had been completed and were unaffected by the time in-person instruction was suspended.

Study materials are archived at the Open Science Framework (OSF): https://osf.io/8t4e5/ . Course materials were drawn from the assigned textbook (University Physics for the Life Sciences by Knight, Jones and Field), which is a common textbook for undergraduate physics courses in the United States. A list of topics covered during weeks 1–8 of the course is presented in Table 1 . There were 30 topics per experimental stage. Each lecture covered topics that roughly corresponded to between 1 and 3 sections of the course textbook. Each lecture began with an outline of what was to be learned followed by explanations of key concepts, worked examples, and clicker questions that were often accompanied by peer instruction. Discussion sections consisted of a short review of relevant topics from that week followed by a group exercise involving a single, reasonably challenging corresponding problem on a worksheet. Students were given credit for attending discussion sections and for demonstrating a reasonable level of effort and completion on the weekly problem as judged by their teaching assistant, but discussion worksheets were not scored for correctness. Weekly labs gave students hands-on experience applying course concepts to real physical systems and typically involved materials that had already been covered a week or two beforehand in lecture and on homework assignments.

Both experimental stages featured 10 homework assignments each spread across 4 weeks. There were nine problems per assignment (exceptions included the last two assignments of the blocked condition as well as the first two and last three assignments of the interleaved condition), for a total of 84 homework problems (see Fig. 1 and the main text). There were three isomorphic problems for a given topic (excepting six topics per experimental stage, for which there were two isomorphic problems). It should be noted that given the constraints used to define blocking and interleaving, the interleaved condition had fewer problems on the first two assignments per cycle (given the number of topics introduced to date), and on the final week of a given cycle, the interleaved condition had one additional problem per assignment and up to two problems per topic (but not presented adjacent to one another), with the blocked condition also having fewer than nine problems each. Given the proximity to the end of each cycle and variations in assignment length, topics that appeared on the final week of assignments were not included on the criterial tests.

Each assignment took the form of a multi-page PDF uploaded to Gradescope (a web application for turning in and scoring assignments) on a Monday, Wednesday, or Friday of a given week. Each assignment contained instructions reminding students to complete each assignment on their own, avoid skipping problems, always show their work in the provided spaces (so as to receive completion credit), and clearly indicate their final answers in provided boxes. Each problem type consisted of using a concept and related formulas to compute the values of one or more physical quantities. Isomorphs for each problem type was generated by varying superficial features that left the underlying computational and conceptual structure invariant, such as by changing values for given physical quantities or changing the context in which the given information was presented.

The final page of each assignment contained three multiple-choice survey questions: (a) How difficult did you find the questions on this assignment?; (b) Over how many days did you complete this assignment?; and (c) How well do you think you have learned the concepts and procedures addressed by these problems?

There were three assignments each week except in weeks 3 and 7, during which there was no class on Monday owing to a holiday. This holiday fell on precisely the same day in the practice schedule during each stage, so the two stages had identical problem set schedules despite the holidays.

Both criterial tests were intended to be completed within a 50-min lecture period and contained three questions each. The formatting of the tests, which were administered in pen (or pencil)-and-paper form, mirrored the homework assignments in that there were provided spaces and boxes to show work and to indicate final answers. Critically, however, the criterial test problems required integrating knowledge from two separately-learned topics, or applying knowledge regarding a previously learned topic in a new way (as described in the main text). The topics addressed on the criterial tests are noted in Fig. 1 of the main text. Given the deviations in the number of problems per assignment in the final week of each cycle, as well as the proximity in time between instruction and the criterial test, all topics addressed in that week were not covered on the criterial tests.

Two midterm exams were administered (both occurring on the first Monday after the end of an experimental stage and ~72 h after the criterial test). Each midterm exam contained five problems that were of a similar type as those presented on the criterial tests.

At the end of the course, students were asked to complete an online exit survey in exchange for extra credit. The survey contained questions addressing (a) how the homework assignments were completed; (b) study activities that occurred prior to the surprise and midterm exams; (c) level of surprise in the surprise exams, and (d) prior physics courses. Questions addressing (a–c) were posed separately for Stages 1 and 2. A complete copy of the exit survey is archived at the aforementioned OSF link.

Design and procedure

A 2 × 2 counterbalanced design was used with within-subjects factors of condition (blocked vs. interleaved) and Stage (1 vs. 2). Blocking versus interleaving was manipulated by having one lecture section experience blocking and interleaving during Stages 1 and 2, respectively, whereas the other section experienced the reverse of that arrangement. The experiment was implemented as part of regular course activities as follows. On the first day of class, the instructor outlined course expectations as described in the syllabus. The substantial contribution of homework assignments to the course grade was emphasized (and to further incentivize completion of homework assignments, an additional 1% extra credit bonus was promised to all students that completed every single homework assignment). Homework assignments were then released regularly online on each Monday, Wednesday, and Friday (during weeks 1–4 and 5–8, and excepting the Friday of weeks 4 and 8). Each assignment was to be completed within 72 h of it being made available, and finished assignments were to be scanned and uploaded to Gradescope for grading. Fully worked solutions and answers for each assignment were posted each Sunday evening. Grades, rubrics, and answer keys for each assignment were posted on Gradescope within roughly 1 week of the due date. All other course activities, including the lectures, discussion sections, and lab sections, proceeded as per standard practice. The course instructor delivered identical lecture content to both sections throughout the entire course.

During the lecture sections on the Fridays of weeks 4 and 8, the surprise criterial test was administered. That lecture had been billed as a “review session” addressing the content covered over the preceding 4 weeks, with students incentivized to attend by a promise of 1% extra credit. In place of a review session, however, the test was handed out, students were told that they would get up to 1% extra credit according to their performance on the test (although during the actual assignment of grades, all students were given the full 1% extra credit), and students were then given the full 50-minute lecture period to complete the test. Aside from increasing every student’s final grade by 1%, the criterial tests did not impact student grades. The test was proctored by the course instructor and teaching assistants. Survey data revealed that the majority of students were surprised that the “review session” actually entailed a criterial test (see Table 4 ).

Performance on the blocked and interleaved homework assignments was analyzed to provide insights into the relative difficulty of the two learning schedules used. To facilitate analysis, each students’ intended answers, as indicated by entry into provided answer boxes, were transcribed into an electronic spreadsheet, and the answers were then computer-scored against a correct answer list. In all cases, the transcription of homework data was conducted by research assistants that were blind to the condition. In addition, the answers to the three multiple-choice survey questions on each assignment were also transcribed by hand.

Performance on the criterial tests was the primary outcome of interest given that the criterial tests were the purest measures of the effects of the experimental manipulation (i.e., uncontaminated by any additional study or review activities, or foreknowledge of the question types). Every problem on the criterial tests required (a) recognizing which mathematical relationships (often equations) were relevant for solving that problem, (b) writing those relationships down, and (c) appropriately combining them with given values of physical quantities to compute a single final numerical answer with a corresponding physical unit. A rubric based on that employed throughout lower-division physics courses at UCLA was used to score the criterial tests and allowed for inferring whether steps (a–c) were successfully completed. The rubric items per problem fell into two mutually exclusive, exhaustive categories: In the first, “memory” category, each item indicated whether or not one of the necessary equations was recalled and written down correctly; in the second, “correctness” category, each item indicated whether or not the final numerical answer and unit were correct. Criterial tests were each scored by at least two trained raters that were blind to the condition. Each rubric item for each problem was first scored independently by two scorers, after which a third scorer independently scored only those items on which the original two scorers differed. For each rubric item, inter-rater reliability (IRR) between the original two raters was assessed. In Stages 1 and 2, the mean IRR across all rubric items on the criterial test was Cohen’s κ  = 0.81 and 0.83, respectively.

Null hypothesis significance testing of criterial test data was conducted using t tests as per our preregistered analysis plan. All tests were two-tailed. Effect sizes were reported in terms of Cohen’s d as defined in prior work 57 . As a supplement to the t tests, permutation tests (which do not require the assumption of normality of underlying population distributions) were also conducted. The permutation tests yielded negligibly different p values relative to the t tests and are not detailed further for simplicity.

Performance on the midterm and final exams were originally to be analyzed separately. Performance on these exams would have reflected the effects of the experimental manipulation as well as review and study activities, including cramming, prior to the exams. However, as the final exam was made optional (and switched to take-home format) due to the COVID-19 pandemic, data for that exam were not available for the vast majority of students. Hence, the analysis of that exam was dropped. Per procedures that the instructor had used in prior physics courses, the midterm exams—which were completed at separate exam periods outside of normal lecture hours—were completed in individual and group stages (i.e., students first attempted the questions on their own, they were organized into groups to share ideas and revise their answers). The results reported in the main text reflect data from the individual stages. The midterm exams were scored by teaching assistants that were also blind to condition.

The exit surveys, which provided additional context for interpreting the study results, were transcribed by research assistants that were blind to condition.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

Data and materials are archived at the OSF: https://osf.io/8t4e5/ .

Code availability

Analysis code is available upon request.

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Acknowledgements

Thanks to the UCLA Teaching and Learning Lab for helpful discussions and Casey Shapiro for helpful advice. Thanks to Shirley Zhang, Quynh Tran, Chester Li, and Nam Phuong Nguyen for assisting with the scoring of criterial tests, and to Jeana Wei and other members of the UCLA Bjork Lab for assisting with transcription of homework assignments. This research was supported by the UCLA Division of Physical Sciences.

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Working Memory

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Working memory is a form of memory that allows a person to temporarily hold a limited amount of information at the ready for immediate mental use. It is considered essential for learning, problem-solving, and other mental processes.

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• How We Use Working Memory
• How Working Memory Works

Making use of working memory is like temporarily pinning up certain images or words to a board so they can be moved or manipulated in some other way. The ability to keep certain details “at hand,” including those we haven’t committed to long-term memory, supports a variety of day-to-day mental functions.

Recalling the earlier part of a sentence to understand a later part, holding a number in mind while doing a math problem in one’s head, remembering where an object was just seen, and keeping multiple concepts in mind in order to combine them have been described as examples of working memory.

Working memory is believed to support many kinds of mental abilities at a fundamental level. It allows one to retain multiple pieces of information for use in the moment, which is essential to activities from reading or having a conversation to learning new concepts to making decisions between different options.

While long-term memory can store a huge amount of information, the amount of details contained for ready usage in working memory is thought to be relatively limited. There are differing models of the working memory system. Some have argued that it includes multiple components that handle different kinds of information and are distinct from long-term memory. Others propose that working memory represents a part of long-term memory that is especially activated and a smaller part that is the focus of attention.

Though the limits are debated, some scientists have suggested that when people aren’t able to use tactics like repeating details out loud, they may be able to keep just a few items in focus at a time. Those items can be simple or complex—including individual letters or numbers to be remembered as well as larger “chunks” of information (such as acronyms like “USA” and “UK,” and even more complex concepts).

Virtually everyone seems to put working memory to work throughout the day, but the performance of this memory system (or “working memory capacity”) is stronger in some individuals than in others—with implications for a person’s ability to learn and function.

The representation of different kinds of information (such as visual or or verbal details) in working memory seems to depend on parts of the cerebral cortex that are involved in the perception and long-term memory of those kinds of information. The prefrontal cortex, a part of the brain linked to cognitive control, is thought to play a key role in managing the current contents of working memory, regardless of type.

Research shows that measures of an individual’s working memory ability are strongly related to measures of intelligence—particularly an aspect called fluid intelligence, which is involved in solving novel problems.

Measures of working memory suggests that it typically improves throughout childhood. Working memory tends to decline in older age, research suggests—though it may begin to gradually decrease after early adulthood. It has been proposed that later working-memory decline may help account for age-related declines on other kinds of cognitive tasks.

Individual differences in working memory ability can be assessed using a range of tasks. Among them are “working memory span” tasks in which a person tries to, for example, read through sentences while remembering particular words from each. Another type of measure is an “n-back” task, in which one sees or hears a sequence of items and has to indicate when the current item matches a previous one. In a 2-back version, for instance, if the letters P, S, T, H, A, F were followed by an A, one would indicate that it matched what came two letters back. N-back task performance doesn’t necessarily correlate strongly with performance on other working memory tasks, and they may measure different components of working memory.

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7.1 What is Cognition?

Learning objectives.

By the end of this section, you will be able to:

• Describe cognition
• Distinguish concepts and prototypes
• Explain the difference between natural and artificial concepts

   Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet, you don’t notice most of your brain’s activity as you move throughout your daily routine. The infinite amount of sub-routines we organize every day to make up larger behaviors such as driving, operating machinery, participating in sports or even holding conversations (all relatively new behaviors in terms of the evolution of a species) go unnoticed but together allow us to navigate our environment safely and efficiently. There are facets to the multitude of complex processes involved in human cognition and what we understand about animal thought processes. Simply put, cognition is thinking, and it encompasses the processes associated with perception, knowledge, problem-solving, judgment, language, and memory. Scientists who study cognition are searching for ways to understand how we integrate, organize, and utilize our conscious cognitive experiences without being aware of all of the unconscious work that our brains are doing (for example, Kahneman, 2011).

COGNITIVE PSYCHOLOGY – A BRIEF HISTORY

Although discussions and descriptions of thought processes date back millennia to societies such as the ancient Greeks, Egyptians and Maya, the formal scientific study of cognition is relatively new, growing out of philosophical debates including Rene Descartes’ 16th century argument suggesting humans are born with innate knowledge and the that the mind and body reflect two different entities.  This theory was known as substance dualism. From Descartes’ theories in the 16th century, major debates formed on whether human thought is created solely through the stimulation of our sense organs (empiricism) or whether we are born with innate knowledge which allows us to form language and maintain conscious experience (nativism). Supporters of empiricist views included philosophers such as George Berkeley, an Irish bishop who denied the existence of material substance, suggesting instead that the objects we interact with are only ideas in the minds of the perceivers, and John Locke, an English philosopher who founded the study of theory of mind which lead to modern conceptions of identity and the self.  Supporters of nativism included Immanuel Kant, a German philosopher who argued that the human mind creates the structure of human experience and that the world (as it is) is independent of humanity’s perception of it. These arguments in philosophy would later lead to important advancements in the 19th century by Paul Broca and Carl Wernicke. Paul Broca, a French physician, anatomist, and anthropologist treated a patient now known as “Tan”, who with the exception of some curse words, could only create the utterance “tan” when he tried to speak. After the patient died, Dr. Broca inspected his brain and discovered that a specific area of the lateral frontal cortex (now known as “ Broca’s area “) was damaged. He concluded that Broca’s areas was an important processing center for language production. Shortly after Broca’s publication documenting language deficits related to damage in the lateral frontal cortex, the German physician, psychiatrist, and anatomist, Carl Wernicke, noticed that not all language deficits were related to damage to Broca’s area. Wernicke found that damage to the left posterior and superior temporal gyrus resulted in deficits in language comprehension as opposed to language production . This area of the brain is what we now refer to as Wernicke’s area and these two findings together provided important evidence for theories related to functional localization within the brain, a theory separate from previous ideas related to the study of phrenology.

Around the turn of the 20th century, experimental research conducted in the experimental labs of Wilhelm Wundt and Ernst Weber in German, and Charles Bell in Britain, lead to the experimental study of behavior.  Edward Thorndike’s Law of Effect (1898) described how behavior can be shaped by conditions and patterns of reinforcement. Theories in behaviorism were popular until the 1920s, when Jean Piaget began studying thoughts, language, and intelligence as well as how these capabilities change over the course of human development and aging. While WWII was taking the lives of millions of humans across the globe, psychology searched for new and innovative ways of studying human performance in order to address questions such as how to best train soldiers to use new technology, and how attention might be affected by stress. This research eventually lead to Claude Shannon’s developments in information theory in 1948, which described the quantification, storage, and communication of information. Developments in computer science soon led to parallels between human thought processes and computer information processing.  Newell and Simon’s development of artificial intelligence (AI) described both advanced capabilities in computing and descriptive models of cognitive processes. In responses to behaviorists’ criticisms of analyzing and modeling thought processes, Noam Chompsky argued against B.F. Skinner’s views that language is learned through reinforcement, suggesting that Skinner ignored the human creativity found in linguistics. Within the same decade George Miller published research describing humans’ ability to maintain information while performing secondary tasks (Miller, 1956) and founded the Harvard Center for Cognitive Studies. Soon after, the first Cognitive Psychology textbook was published in 1967 by Ulrich Neisser (1967), a former student of George Miller.  Neisser was influenced by Gestalt psychologists, Wolfgang Kohler and Hans Wallach, as well as MIT computer scientist Oliver Selfridge. Neisser’s definition of the new term “cognition” illustrates the then progressive concept of cognitive processes as:

“all processes by which the sensory input is transformed, reduced, elaborated, stored, recovered, and used. It is concerned with these processes even when they operate in the absence of relevant stimulation, as in images and hallucinations. . . Given such a sweeping definition, it is apparent that cognition is involved in everything a human being might possibly do; that every psychological phenomenon is a cognitive phenomenon. But although cognitive psychology is concerned with all human activity rather than some fraction of it, the concern is from a particular point of view. Other viewpoints are equally legitimate and necessary. Dynamic psychology, which begins with motives rather than with sensory input, is a case in point. Instead of asking how a man’s actions and experiences result from what he saw, remembered, or believed, the dynamic psychologist asks how they follow from the subject’s goals, needs, or instincts.” (page 4 of Neisser’s 1967 publication of  Cognitive Psychology)

   Upon waking each morning, you begin thinking—contemplating the tasks that you must complete that day. In what order should you run your errands? Should you go to the bank, the cleaners, or the grocery store first? Can you get these things done before you head to class or will they need to wait until school is done? These thoughts are one example of cognition at work. Exceptionally complex, cognition is an essential feature of human consciousness, yet not all aspects of cognition are consciously experienced. For example, many decisions we make about choosing to do something or refraining from doing something involve cognitive processes related to weighing options and making comparisons to other events in memory. However, cognition has been argued to not be involved in all the actions we make, such as reflexes that recoil your hand after touching an extremely hot surface, which operate on automatic feedback loops between the effector and spinal cord. Cognition is described in the Oxford dictionary as the mental actions or processes involved in acquiring, maintaining and understanding knowledge through thought, experience and the senses (definition of Cognition from the English Oxford Dictionary, 2018), and is described by Licht, Hull and Ballantyne (2014) as the mental activity associated with obtaining, converting and using knowledge .  It is important to recognize that although the term Cognition is an umbrella term that encompasses many different mental processes, similarities exist between how groups define cognition by defining it as a variety of mental processes that allow us to maintain, understand and use information to create knowledge and reflect upon it. Within the pieces that make up cognition, a main component is what is commonly referred to as thinking, which Matlin (2009) has defined as coming  to a decision, reaching a solution, forming a belief, or developing an attitude. Again, we see that even a subcomponent of cognition, such as thinking, still represents somewhat of an umbrella term which can be broken up into groups of processes and procedures that make up our thinking. Definitions are not universally accepted, and some groups within psychology consider cognition and thinking as the same group of processes. However, we will use the definitions provided above for the sake of simplicity.

Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem solving, in addition to other cognitive processes. Cognitive psychologists strive to determine and measure different types of intelligence, why some people are better at problem solving than others, and how emotional intelligence affects success in the workplace, among countless other topics. They also sometimes focus on how we organize thoughts and information gathered from our environments into meaningful categories of thought, which will be discussed later. Basically, cognitive scientists work to define the smallest components of what make up broader topics in cognition in order to continue improving working definitions of how we conceptualize human cognition. Many techniques have been discovered that allow psychologists to selectively evaluate and compare different components of cognition. Modern advancements in technology have allowed psychologist to use these methods to collect various forms of cognitive data such as basic measurements of reaction times and response accuracies to more advance techniques of physiological responses, such as eye tracking, electromyography (EMG), electroencephalography (EEG), functional magnetic resonance imaging (fMRI), magnet encephalography (MEG), and positron emission tomography. Cognitive scientists work to create experimental designs using these methods, generate new findings, publish their work and add to the world-wide discussion of how various cognitive processes work and what makes our life experience similar or different from other species.

CONCEPTS AND PROTOTYPES

   The human nervous system is capable of handling endless streams of information, as emphasized in the sensation and perception chapter. The senses serve as the interface between the mind and the external environment, receiving stimuli and translating it into nervous impulses that are transmitted to the brain. The brain then processes this information and uses the relevant pieces, which are held in working memory, later expressed through language, or stored in memory for future use. To make this process more complex, the brain does not gather information from external environments only. When thoughts are formed, the brain pulls information from emotions and memories (figure below). Emotion and memory are powerful influences on both our thoughts and behaviors.

Sensations and information are received by our brains, filtered through emotions and memories, and processed to become thoughts.

   In order to organize this staggering amount of information, the brain has developed a file cabinet of sorts in the mind. The different files stored in the file cabinet are called concepts . Concepts are categories or groupings of linguistic information, images, ideas, or memories, such as life experiences. Concepts are, in many ways, big ideas that are generated by observing details, and categorizing and combining these details into cognitive structures. You use concepts to see the relationships among the different elements of your experiences and to keep the information in your mind organized and accessible.

Concepts are informed by our semantic memory (you will learn more about semantic memory in a later chapter) and are present in every aspect of our lives; however, one of the easiest places to notice concepts is inside a classroom, where they are discussed explicitly. When you study United States history, for example, you learn about more than just individual events that have happened in America’s past. You absorb a large quantity of information by listening to and participating in discussions, examining maps, and reading first-hand accounts of people’s lives. Your brain analyzes these details and develops an overall understanding of American history. In the process, your brain gathers details that inform and refine your understanding of related concepts like democracy, power, and freedom.

Concepts can be complex and abstract, like justice, or more concrete, like types of birds. In psychology, for example, Piaget’s stages of development are abstract concepts. Some concepts, like tolerance, are agreed upon by many people, because they have been used in various ways over many years. Other concepts, like the characteristics of your ideal friend or your family’s birthday traditions, are personal and individualized. In this way, concepts touch every aspect of our lives, from our many daily routines to the guiding principles behind the way governments function.

HIERARCHIES OF CONCEPTS

Concepts can be understood by considering how they can be organized into  hierarchies . At the top are superordinate concepts. This is the broadest category which encompasses all the objects belonging to a concept. The subordinate concept of “furniture” covers everything from couches to nightstands. If we were to narrow our focus to include only couches, we are considering the midlevel or basic level of the hierarchy. This is still a fairly broad category, but not quite as broad as the superordinate concept of furniture. The midlevel category is what we use most often in everyday life to identify objects. Sub-ordinate concepts are even narrower, referring to specific types. To continue with our example, this would include loveseat, a La-Z-Boy, or sectional.

Another technique used by your brain to organize information is the identification of prototypes for the concepts you have developed. A prototype is the best example or representation of a concept. For example, for the category of civil disobedience, your prototype could be Rosa Parks. Her peaceful resistance to segregation on a city bus in Montgomery, Alabama, is a recognizable example of civil disobedience. Or your prototype could be Mohandas Gandhi, sometimes called Mahatma Gandhi (“Mahatma” is an honorific title).

In 1930, Mohandas Gandhi led a group in peaceful protest against a British tax on salt in India.

Mohandas Gandhi served as a nonviolent force for independence for India while simultaneously demanding that Buddhist, Hindu, Muslim, and Christian leaders—both Indian and British—collaborate peacefully. Although he was not always successful in preventing violence around him, his life provides a steadfast example of the civil disobedience prototype (Constitutional Rights Foundation, 2013). Just as concepts can be abstract or concrete, we can make a distinction between concepts that are functions of our direct experience with the world and those that are more artificial in nature.

NATURAL AND ARTIFICIAL CONCEPTS

    In psychology, concepts can be divided into two categories, natural and artificial. Natural concepts are created “naturally” through your experiences and can be developed from either direct or indirect experiences. For example, if you live in Essex Junction, Vermont, you have probably had a lot of direct experience with snow. You’ve watched it fall from the sky, you’ve seen lightly falling snow that barely covers the windshield of your car, and you’ve shoveled out 18 inches of fluffy white snow as you’ve thought, “This is perfect for skiing.” You’ve thrown snowballs at your best friend and gone sledding down the steepest hill in town. In short, you know snow. You know what it looks like, smells like, tastes like, and feels like. If, however, you’ve lived your whole life on the island of Saint Vincent in the Caribbean, you may never have actually seen snow, much less tasted, smelled, or touched it. You know snow from the indirect experience of seeing pictures of falling snow—or from watching films that feature snow as part of the setting. Either way, snow is a natural concept because you can construct an understanding of it through direct observations or experiences of snow.

(a) Our concept of snow is an example of a natural concept—one that we understand through direct observation and experience. (b) In contrast, artificial concepts are ones that we know by a specific set of characteristics that they always exhibit, such as what defines different basic shapes. (credit a: modification of work by Maarten Takens; credit b: modification of work by “Shayan (USA)”/Flickr)

   An artificial concept, on the other hand, is a concept that is defined by a specific set of characteristics. Various properties of geometric shapes, like squares and triangles, serve as useful examples of artificial concepts. A triangle always has three angles and three sides. A square always has four equal sides and four right angles. Mathematical formulas, like the equation for area (length × width) are artificial concepts defined by specific sets of characteristics that are always the same. Artificial concepts can enhance the understanding of a topic by building on one another. For example, before learning the concept of “area of a square” (and the formula to find it), you must understand what a square is. Once the concept of “area of a square” is understood, an understanding of area for other geometric shapes can be built upon the original understanding of area. The use of artificial concepts to define an idea is crucial to communicating with others and engaging in complex thought. According to Goldstone and Kersten (2003), concepts act as building blocks and can be connected in countless combinations to create complex thoughts.

A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.

There are several types of schemata. A role schema makes assumptions about how individuals in certain roles will behave (Callero, 1994). For example, imagine you meet someone who introduces himself as a firefighter. When this happens, your brain automatically activates the “firefighter schema” and begins making assumptions that this person is brave, selfless, and community-oriented. Despite not knowing this person, already you have unknowingly made judgments about him. Schemata also help you fill in gaps in the information you receive from the world around you. While schemata allow for more efficient information processing, there can be problems with schemata, regardless of whether they are accurate: Perhaps this particular firefighter is not brave, he just works as a firefighter to pay the bills while studying to become a children’s librarian.

An event schema, also known as a cognitive script, is a set of behaviors that can feel like a routine. Think about what you do when you walk into an elevator. First, the doors open and you wait to let exiting passengers leave the elevator car. Then, you step into the elevator and turn around to face the doors, looking for the correct button to push. You never face the back of the elevator, do you? And when you’re riding in a crowded elevator and you can’t face the front, it feels uncomfortable, doesn’t it? Interestingly, event schemata can vary widely among different cultures and countries. For example, while it is quite common for people to greet one another with a handshake in the United States, in Tibet, you greet someone by sticking your tongue out at them, and in Belize, you bump fists (Cairns Regional Council, n.d.)

What event schema do you perform when riding in an elevator? (credit: “Gideon”/Flickr)

   Because event schemata are automatic, they can be difficult to change. Imagine that you are driving home from work or school. This event schema involves getting in the car, shutting the door, and buckling your seatbelt before putting the key in the ignition. You might perform this script two or three times each day. As you drive home, you hear your phone’s ring tone. Typically, the event schema that occurs when you hear your phone ringing involves locating the phone and answering it or responding to your latest text message. So without thinking, you reach for your phone, which could be in your pocket, in your bag, or on the passenger seat of the car. This powerful event schema is informed by your pattern of behavior and the pleasurable stimulation that a phone call or text message gives your brain. Because it is a schema, it is extremely challenging for us to stop reaching for the phone, even though we know that we endanger our own lives and the lives of others while we do it (Neyfakh, 2013).

Texting while driving is dangerous, but it is a difficult event schema for some people to resist.

   Remember the elevator? It feels almost impossible to walk in and not face the door. Our powerful event schema dictates our behavior in the elevator, and it is no different with our phones. Current research suggests that it is the habit, or event schema, of checking our phones in many different situations that makes refraining from checking them while driving especially difficult (Bayer & Campbell, 2012). Because texting and driving has become a dangerous epidemic in recent years, psychologists are looking at ways to help people interrupt the “phone schema” while driving. Event schemata like these are the reason why many habits are difficult to break once they have been acquired. As we continue to examine thinking, keep in mind how powerful the forces of concepts and schemata are to our understanding of the world.

   In this section, you were introduced to cognitive psychology, which is the study of cognition, or the brain’s ability to think, perceive, plan, analyze, and remember. Concepts and their corresponding prototypes help us quickly organize our thinking by creating categories into which we can sort new information. We also develop schemata, which are clusters of related concepts. Some schemata involve routines of thought and behavior, and these help us function properly in various situations without having to “think twice” about them. Schemata show up in social situations and routines of daily behavior.

References:

Openstax Psychology text by Kathryn Dumper, William Jenkins, Arlene Lacombe, Marilyn Lovett and Marion Perlmutter licensed under CC BY v4.0. https://openstax.org/details/books/psychology

Review Questions:

1. Cognitive psychology is the branch of psychology that focuses on the study of ________.

a. human development

b. human thinking

c. human behavior

d. human society

2. Which of the following is an example of a prototype for the concept of leadership on an athletic team?

a. the equipment manager

b. the scorekeeper

c. the team captain

d. the quietest member of the team

3. Which of the following is an example of an artificial concept?

b. a triangle’s area

c. gemstones

d. teachers

4. An event schema is also known as a cognitive ________.

a. stereotype

d. prototype

Critical Thinking Questions:

1. Describe an event schema that you would notice at a sporting event.

2. Explain why event schemata have so much power over human behavior.

Personal Application Question:

1. Describe a natural concept that you know fully but that would be difficult for someone else to understand and explain why it would be difficult.

artificial concept

cognitive psychology

cognitive script

event schema

natural concept

role schema

Answers to Exercises

1. Answers will vary. When attending a basketball game, it is typical to support your team by wearing the team colors and sitting behind their bench.

2. Event schemata are rooted in the social fabric of our communities. We expect people to behave in certain ways in certain types of situations, and we hold ourselves to the same social standards. It is uncomfortable to go against an event schema—it feels almost like we are breaking the rules.

artificial concept:  concept that is defined by a very specific set of characteristics

cognition:  thinking, including perception, learning, problem solving, judgment, and memory

cognitive psychology:  field of psychology dedicated to studying every aspect of how people think

concept:  category or grouping of linguistic information, objects, ideas, or life experiences

cognitive script:  set of behaviors that are performed the same way each time; also referred to as an event schema

event schema:  set of behaviors that are performed the same way each time; also referred to as a cognitive script

natural concept:  mental groupings that are created “naturally” through your experiences

prototype:  best representation of a concept

role schema:  set of expectations that define the behaviors of a person occupying a particular role

schema:  (plural = schemata) mental construct consisting of a cluster or collection of related concepts

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Cognitive Remediation Therapy: 13 Exercises & Worksheets

This can result in concentration, organizational, and planning difficulties that impact their quality of life and independent living.

Cognitive Remediation Therapy (CRT) helps by increasing awareness of intellectual difficulties and improving thinking skills. While originally designed for people with thinking problems associated with schizophrenia, it has also proven successful for those with other diagnoses (Bristol Mental Health, n.d.).

CRT works by encouraging a range of exercises and activities that challenge memory, flexible thinking, planning, and concentration problems.

This article explores CRT and its potential to help clients and includes techniques, activities, and worksheets to build effective therapy sessions.

Before you continue, we thought you might like to download our three Positive CBT Exercises for free . These science-based exercises will provide you with detailed insight into Positive CBT and give you the tools to apply it in your therapy or coaching.

This Article Contains:

What is cognitive remediation therapy (crt), how does cognitive remediation work, 8 techniques for your sessions, 7 exercises, activities, & games, 6 helpful worksheets and manuals, implementing online crt programs, 3 best software programs for helping your clients, a take-home message.

“Cognitive remediation is a behavioral treatment for people who are experiencing cognitive impairments that interfere with daily functioning” (Medalia, Revheim, & Herlands, 2009, p. 1).

Successful cognitive functions, including memory, attention, visual-spatial analysis, and abstract reasoning, are vital for engaging with tasks, the environment, and healthy relationships.

CRT improves cognitive processing and psychosocial functioning through behavioral training and increasing individual confidence in people with mental health disorders (Corbo & Abreu, 2018). Training interventions focus on the skills and supports required to “improve the success and satisfaction people experience in their chosen living, learning, working, and social environments” (Medalia et al., 2009, p. 2).

Exercises typically focus on specific cognitive functions, where tasks are repeated (often on a computer) at increasing degrees of difficulty. For example:

• Paying attention
• Remembering
• Being organized
• Planning skills
• Problem-solving
• Processing information

Based on the principles of errorless learning and targeted reinforcement exercises , interventions involve memory, motor dexterity, and visual reading tasks. Along with improving confidence in personal abilities, repetition encourages thinking about solving tasks in multiple ways (Corbo & Abreu, 2018).

While initially targeted for patients with schizophrenia, CRT is an effective treatment for other mental health conditions , including mood and eating disorders (Corbo & Abreu, 2018).

CRT is particularly effective when the cognitive skills and support interventions reflect the individual’s self-selected rehabilitation goals. As a result, cognitive remediation relies on collaboration, assessing client needs, and identifying appropriate opportunities for intervention (Medalia et al., 2009).

Cognitive remediation vs cognitive rehabilitation

CRT is one of several skill-training psychiatric rehabilitation interventions. And yet, cognitive remediation is not the same as cognitive rehabilitation (Tchanturia, 2015).

Cognitive rehabilitation typically targets neurocognitive processes damaged because of injury or illness and involves a series of interventions designed to retrain previously learned cognitive skills along with compensatory strategies (Tsaousides & Gordon, 2009).

While initially done in person, they can subsequently be performed remotely as required (Corbo & Abreu, 2018; Bristol Mental Health, n.d.).

Well-thought-out educational software provides multisensory feedback and positive reinforcement while supporting success, choice, and control of the learning process. Its design can target either specific cognitive functions or non-specific learning skills and mechanisms (Medalia et al., 2009).

CRT successfully uses the brain’s neuroplasticity and is often more effective in younger age groups who haven’t experienced the effects of long-term psychosis. It works by increasing activation and connectivity patterns within and across several brain regions involved in working memory and high-order executive functioning (Corbo & Abreu, 2018).

The Neuropsychological Educational Approach to Cognitive Remediation (NEAR) is one of several approaches that provide highly individualized learning opportunities. It allows each client to proceed at their own pace on tasks selected and designed to engage them and address their cognitive needs (Medalia et al., 2009).

NEAR and other CRT techniques are influenced by learning theory and make use of the following (Medalia et al., 2009):

• Errorless learning Encouraging the client to learn progressively, creating a positive experience without relying on trial and error.
• Shaping and positive feedback Reinforcing behaviors that approximate target behaviors (such as good timekeeping) and offering rewards (for example, monthly certificates for attendance).
• Prompting Using open-ended questions that guide the client toward the correct response.
• Modeling Demonstrating how to solve a problem.
• Generalizing Learning how to generalize learned skills to other situations.
• Bridging Understanding how to apply skills learned inside a session outside  in everyday life.

Encouraging intrinsic motivation (doing the tasks for the satisfaction of doing them rather than for external rewards) and task engagement are also essential aspects of successful CRT programs (Medalia et al., 2009).

Therapy is most effective when it successfully supports clients as they transfer learning skills into the real world.

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Cognitive remediation techniques must be selected according to the skills and needs of the client and typically fall into one of three major intervention categories (Medalia et al., 2009):

• Planning exercises, such as planning a trip to the beach to practice cognitive strategies
• Cueing and sequencing , such as adding signs or placing reminder notes at home to encourage completing everyday tasks (for example, brushing teeth)

Such techniques rely on several key principles, including “(1) teaching new, efficient, information processing strategies; (2) aiding the transfer of cognitive gains to the real world; and (3) modifying the local environment” (Medalia et al., 2009, p. 5).

• Restorative approaches Directly target cognitive deficits by repeating task practices and gradually increasing difficulty and complexity; along with regular feedback, they encourage accurate and high levels of performance.

Practice is often organized hierarchically, as follows:

• Elementary aspects of sensory processing (for example, improving auditory processing speed and accuracy)
• High-order memory and problem-solving skills (including executive functioning and verbal skills)

This technique assumes a degree of neuroplasticity that, with training, results in a greater degree of accuracy in sensory representations, improved cognitive strategies for grouping stimuli into more meaningful groups, and better recall.

• Repetition and reaching for increasing levels of task difficulty
• Modeling other people’s positive behavior
• Role-play  to re-enact experienced or imagined behavior from different perspectives
• Corrective feedback to improve and correct unwanted or unhelpful behavior

Complex social cognitive processes are typically broken down into elemental skills for repetitive practice, role-play, and corrective feedback.

Professor Dame Til Wykes: cognitive remediation therapy

It is vital that activities within CRT are interesting and engaging for clients. They must foster the motivation required to persevere to the end of the task or game.

The following three games and puzzles are particularly valuable for children and adolescents (modified from Tchanturia, 2015):

SET is a widely available card game that practices matching based on color, shape, shading, etc.

Clients must shift their thinking to identify multiple ways of categorizing and grouping cards, then physically sort them based on their understanding.

It may be helpful to begin with a limited set of cards to reduce the likelihood of the clients becoming overwhelmed by the game or finding it less enjoyable.

2. Rush Hour

Rush Hour is another fun game that balances problem-solving skills with speed.

Puzzles start simple and increase in complexity, with additional elements involved. Skills developed include problem-solving and abstract thinking, and the game requires a degree of perseverance.

Other activities require no specialist equipment and yet can be highly engaging and support clients in learning transferable skills (modified from Tchanturia, 2015).

• Bigger picture thinking This involves the client picturing a shape in their minds or looking at one out of sight of the therapist. They then describe the shape (without naming it), while the therapist attempts to draw it according to the instructions. This practice is helpful with clients who get overwhelmed by detail and cannot see the bigger picture.
• Word searches Word searches encourage the client to focus on relevant information and ignore everything else – an essential factor in central coherence. Such puzzles also challenge memory, concentration, and attention.
• Last word response Last word response is a challenging verbal game promoting cognitive flexibility. The first player makes up and says a sentence out loud. Each subsequent player makes up a new sentence, starting with the last word of the previous player’s sentence. For example, ‘ I like cheese’ may be followed by the next player saying, ‘ Cheese is my favorite sandwich ingredient ,’ etc.
• Dexterity Using your non-dominant hand once a week (for example, combing your hair or brushing your teeth) stimulates different parts of your brain, creating alternative patterns of neuron firing and strengthening cognitive functions.

The following therapy worksheets help structure Cognitive Remediation Therapy sessions and ensure that the needs of clients are met using appropriately targeted CRT interventions (modified from Medalia et al., 2009; Medalia & Bowie, 2016):

Client referral to CRT

The Cognitive Remediation Therapy Referral Form captures valuable information when a client is referred from another agency or therapist so that the new therapist can identify and introduce the most appropriate CRT interventions. The form includes information such as:

Primary reasons

Secondary reasons

• Self-confidence
• Working with others
• Time management
• Goal-directed activities

Cognitive Appraisal for CRT

The Cognitive Appraisal for CRT form is helpful for identifying and recording areas of cognitive processing that cause difficulty for the client and require focus during Cognitive Remediation Therapy sessions.

Clients are scored on their degree of difficulty with the following:

• Paying attention during conversation
• Maintaining concentration in meetings
• Completing tasks once started
• Starting tasks
• Planning and organizing tasks and projects
• Reasoning and solving problems

Software Appraisal for CRT

The Software Appraisal for CRT form helps assess which software would be most helpful in a specific Cognitive Remediation Therapy session. It provides valuable input for tailoring treatment to the needs of the client.

For example:

• Level of reading ability required
• Cognitive deficits addressed by the software
• What is the multimedia experience like?
• How much input is required by the therapist?

Appraisal records become increasingly important as more software is acquired for clients with various cognitive deficits from multiple backgrounds.

Software Usage for CRT

The Software Usage for CRT form helps keep track of the software clients have tried and how effectively it supports them as they learn, develop, and overcome cognitive deficits.

The client considers the software they use and whether they practiced the following areas of cognition:

• Concentration
• Processing speed
• Multitasking
• Logic and reasoning
• Organization
• Fast responses
• Working memory

Thought Tracking During Cognitive Remediation Therapy

Thought Tracking During Cognitive Remediation Therapy is valuable for identifying and recording the client’s goals for that day’s Cognitive Remediation Therapy session and understanding how it relates to their overall treatment goals.

Planning to Meet Goals in CRT

The Planning to Meet Goals in CRT worksheet is for clients requiring support and practice in planning, goal-setting, and goal achievement.

Working with the client, answer the following prompts:

• What goal or project are you working toward?
• What date should it be completed by?
• Are there any obstacles to overcome to complete the goal?
• Are there any additional resources required?
• Then consider the steps needed to achieve the goal.

Other free resources

Happy Neuron provides several other free resources that are available for download .

Consider the five Cs when selecting online CRT programs (modified from Medalia et al., 2009):

• Cognitive – What target deficits are being addressed?
• Client – What interests and level of functioning does the client have?
• Computer – What computing requirements and compatibility factors need to be considered?
• Context – Does the software use real-world or fantasy activities and environments? Are they age and cognitive ability appropriate?
• Choice – Is the learner given choice and options to adapt the activity to their preferences?

Once you’ve ordered the software, give it a thorough review to understand when it is most appropriate to use and with whom.

For online CRT programs to be effective as teaching tools and activities, they should include the following features (modified from Medalia et al., 2009, p. 53):

• Intrinsically motivating
• Active use of information
• Multisensory strategies
• Frequent feedback
• Control over the learning process
• Positive reinforcement
• Application of newly acquired skills in appropriate contexts
• Errorless learning – challenging yet not frustrating

Therapists must become familiar with each program’s content and processes so that targeted deficits are fully understood and clients are engaged without confusion or risk of failure.

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These 17 Positive CBT & Cognitive Therapy Exercises [PDF] include our top-rated, ready-made templates for helping others develop more helpful thoughts and behaviors in response to challenges, while broadening the scope of traditional CBT.

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A great deal of software “targets different skills and offers a variety of opportunities for contextualization and personalization” (Medalia et al., 2009, p. 43).

We focus on three suppliers of extensive CRT software resources below (recommended by Medalia et al., 2009).

1. Happy Neuron

Happy Neuron provides a wide variety of online brain training exercises and activities to stimulate cognitive functioning in the following areas:

• Visual-spatial

When you’re performing well, the exercises become increasingly difficult.

The exercises are grouped into the following areas:

• Brain speed
• People skills
• Intelligence

3. Games for the Brain

Cognitive difficulties, such as challenges with paying attention, planning, remembering, and problem-solving, can further compound and exacerbate mental health issues

While initially created for schizophrenia, CRT is also valuable for other mental health problems, including eating and mood disorders. Treatments are effective in one-to-one and group sessions, and lessons can be transferred to the outside world, providing crucial gains for a client’s mental wellbeing and social interaction.

Through repeated and increasingly challenging skill-based interventions, CRT benefits cognitive functioning and provides confidence gains to its users. The treatment adheres to learning theory principles and targets specific brain processing areas such as motor dexterity, memory, and visual-spatial perception, along with higher-order functioning.

Involving clients in treatment choices increases the likelihood of ongoing perseverance, engagement, and motivation as activities repeat with increasing degrees of difficulty.

This article offers a valuable starting point for exploring CRT and its benefits, with several worksheets and forms to encourage effective treatment.

We hope you enjoyed reading this article. For more information, don’t forget to download our three Positive CBT Exercises for free .

• Bristol Mental Health. (n.d.). Cognitive remediation therapy: Improving thinking skills . Retrieved December 15, 2021, from http://www.awp.nhs.uk/media/424704/cognitive-remediation-therapy-022019.pdf
• Corbo, M., & Abreu, T. (2018). Cognitive remediation therapy: EFPT psychotherapy guidebook . Retrieved December 15, 2021, from https://epg.pubpub.org/pub/05-cognitive-remediation-therapy/release/3
• Medalia, A., & Bowie, C. R. (2016). Cognitive remediation to improve functional outcomes . Oxford University Press.
• Medalia, A., Revheim, N., & Herlands, T. (2009). Cognitive remediation for psychological disorders: Therapist guide . Oxford University Press.
• Tchanturia, K. (2015). Cognitive remediation therapy (CRT) for eating and weight disorders . Routledge.
• Tsaousides, T., & Gordon, W. A. (2009). Cognitive rehabilitation following traumatic brain injury: Assessment to treatment. Mount Sinai Journal of Medicine: A Journal of Translational and Personalized Medicine , 76 (2), 173-181.

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What our readers think.

To my surprise this is a treatment that has not been discussed in the area I live and work. I just stumbled upon this when I was researching cognitive impairments with schizophrenia. I currently work on a team with multiple mental health professionals that go out into the community, to work with people diagnosed with Schizophrenia. It seems like most of what we do is manage and monitor symptoms. Are you aware of anyone or any agency in Buffalo, NY that uses this method of treatment? I am trying to figure out how to get trained and use it in practice, if that is possible. Any help will be greatly appreciated.

This looks like the treatment my daughter needs. She has struggled for years with the cognitive problems associated with depression. How do we find a therapist near us who can use these techniques?

I’m sorry to read that your daughter is struggling. You can find a directory of licensed therapists here (and note that you can change the country setting in the top-right corner). You’ll also find that there are a range of filters to help you drill down to the type of support you need: https://www.psychologytoday.com/us/therapists

I hope you find the help you need.

– Nicole | Community Manager

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7 Module 7: Thinking, Reasoning, and Problem-Solving

This module is about how a solid working knowledge of psychological principles can help you to think more effectively, so you can succeed in school and life. You might be inclined to believe that—because you have been thinking for as long as you can remember, because you are able to figure out the solution to many problems, because you feel capable of using logic to argue a point, because you can evaluate whether the things you read and hear make sense—you do not need any special training in thinking. But this, of course, is one of the key barriers to helping people think better. If you do not believe that there is anything wrong, why try to fix it?

The human brain is indeed a remarkable thinking machine, capable of amazing, complex, creative, logical thoughts. Why, then, are we telling you that you need to learn how to think? Mainly because one major lesson from cognitive psychology is that these capabilities of the human brain are relatively infrequently realized. Many psychologists believe that people are essentially “cognitive misers.” It is not that we are lazy, but that we have a tendency to expend the least amount of mental effort necessary. Although you may not realize it, it actually takes a great deal of energy to think. Careful, deliberative reasoning and critical thinking are very difficult. Because we seem to be successful without going to the trouble of using these skills well, it feels unnecessary to develop them. As you shall see, however, there are many pitfalls in the cognitive processes described in this module. When people do not devote extra effort to learning and improving reasoning, problem solving, and critical thinking skills, they make many errors.

As is true for memory, if you develop the cognitive skills presented in this module, you will be more successful in school. It is important that you realize, however, that these skills will help you far beyond school, even more so than a good memory will. Although it is somewhat useful to have a good memory, ten years from now no potential employer will care how many questions you got right on multiple choice exams during college. All of them will, however, recognize whether you are a logical, analytical, critical thinker. With these thinking skills, you will be an effective, persuasive communicator and an excellent problem solver.

The module begins by describing different kinds of thought and knowledge, especially conceptual knowledge and critical thinking. An understanding of these differences will be valuable as you progress through school and encounter different assignments that require you to tap into different kinds of knowledge. The second section covers deductive and inductive reasoning, which are processes we use to construct and evaluate strong arguments. They are essential skills to have whenever you are trying to persuade someone (including yourself) of some point, or to respond to someone’s efforts to persuade you. The module ends with a section about problem solving. A solid understanding of the key processes involved in problem solving will help you to handle many daily challenges.

7.1. Different kinds of thought

7.2. Reasoning and Judgment

7.3. Problem Solving

READING WITH PURPOSE

Remember and understand.

By reading and studying Module 7, you should be able to remember and describe:

• Concepts and inferences (7.1)
• Procedural knowledge (7.1)
• Metacognition (7.1)
• Characteristics of critical thinking:  skepticism; identify biases, distortions, omissions, and assumptions; reasoning and problem solving skills  (7.1)
• Reasoning:  deductive reasoning, deductively valid argument, inductive reasoning, inductively strong argument, availability heuristic, representativeness heuristic  (7.2)
• Fixation:  functional fixedness, mental set  (7.3)
• Algorithms, heuristics, and the role of confirmation bias (7.3)
• Effective problem solving sequence (7.3)

By reading and thinking about how the concepts in Module 6 apply to real life, you should be able to:

• Identify which type of knowledge a piece of information is (7.1)
• Recognize examples of deductive and inductive reasoning (7.2)
• Recognize judgments that have probably been influenced by the availability heuristic (7.2)
• Recognize examples of problem solving heuristics and algorithms (7.3)

Analyze, Evaluate, and Create

By reading and thinking about Module 6, participating in classroom activities, and completing out-of-class assignments, you should be able to:

• Use the principles of critical thinking to evaluate information (7.1)
• Explain whether examples of reasoning arguments are deductively valid or inductively strong (7.2)
• Outline how you could try to solve a problem from your life using the effective problem solving sequence (7.3)

7.1. Different kinds of thought and knowledge

• Take a few minutes to write down everything that you know about dogs.
• Do you believe that:
• Psychic ability exists?
• Hypnosis is an altered state of consciousness?
• Magnet therapy is effective for relieving pain?
• Aerobic exercise is an effective treatment for depression?
• UFO’s from outer space have visited earth?

On what do you base your belief or disbelief for the questions above?

Of course, we all know what is meant by the words  think  and  knowledge . You probably also realize that they are not unitary concepts; there are different kinds of thought and knowledge. In this section, let us look at some of these differences. If you are familiar with these different kinds of thought and pay attention to them in your classes, it will help you to focus on the right goals, learn more effectively, and succeed in school. Different assignments and requirements in school call on you to use different kinds of knowledge or thought, so it will be very helpful for you to learn to recognize them (Anderson, et al. 2001).

Factual and conceptual knowledge

Module 5 introduced the idea of declarative memory, which is composed of facts and episodes. If you have ever played a trivia game or watched Jeopardy on TV, you realize that the human brain is able to hold an extraordinary number of facts. Likewise, you realize that each of us has an enormous store of episodes, essentially facts about events that happened in our own lives. It may be difficult to keep that in mind when we are struggling to retrieve one of those facts while taking an exam, however. Part of the problem is that, in contradiction to the advice from Module 5, many students continue to try to memorize course material as a series of unrelated facts (picture a history student simply trying to memorize history as a set of unrelated dates without any coherent story tying them together). Facts in the real world are not random and unorganized, however. It is the way that they are organized that constitutes a second key kind of knowledge, conceptual.

Concepts are nothing more than our mental representations of categories of things in the world. For example, think about dogs. When you do this, you might remember specific facts about dogs, such as they have fur and they bark. You may also recall dogs that you have encountered and picture them in your mind. All of this information (and more) makes up your concept of dog. You can have concepts of simple categories (e.g., triangle), complex categories (e.g., small dogs that sleep all day, eat out of the garbage, and bark at leaves), kinds of people (e.g., psychology professors), events (e.g., birthday parties), and abstract ideas (e.g., justice). Gregory Murphy (2002) refers to concepts as the “glue that holds our mental life together” (p. 1). Very simply, summarizing the world by using concepts is one of the most important cognitive tasks that we do. Our conceptual knowledge  is  our knowledge about the world. Individual concepts are related to each other to form a rich interconnected network of knowledge. For example, think about how the following concepts might be related to each other: dog, pet, play, Frisbee, chew toy, shoe. Or, of more obvious use to you now, how these concepts are related: working memory, long-term memory, declarative memory, procedural memory, and rehearsal? Because our minds have a natural tendency to organize information conceptually, when students try to remember course material as isolated facts, they are working against their strengths.

One last important point about concepts is that they allow you to instantly know a great deal of information about something. For example, if someone hands you a small red object and says, “here is an apple,” they do not have to tell you, “it is something you can eat.” You already know that you can eat it because it is true by virtue of the fact that the object is an apple; this is called drawing an  inference , assuming that something is true on the basis of your previous knowledge (for example, of category membership or of how the world works) or logical reasoning.

Procedural knowledge

Physical skills, such as tying your shoes, doing a cartwheel, and driving a car (or doing all three at the same time, but don’t try this at home) are certainly a kind of knowledge. They are procedural knowledge, the same idea as procedural memory that you saw in Module 5. Mental skills, such as reading, debating, and planning a psychology experiment, are procedural knowledge, as well. In short, procedural knowledge is the knowledge how to do something (Cohen & Eichenbaum, 1993).

Metacognitive knowledge

Floyd used to think that he had a great memory. Now, he has a better memory. Why? Because he finally realized that his memory was not as great as he once thought it was. Because Floyd eventually learned that he often forgets where he put things, he finally developed the habit of putting things in the same place. (Unfortunately, he did not learn this lesson before losing at least 5 watches and a wedding ring.) Because he finally realized that he often forgets to do things, he finally started using the To Do list app on his phone. And so on. Floyd’s insights about the real limitations of his memory have allowed him to remember things that he used to forget.

All of us have knowledge about the way our own minds work. You may know that you have a good memory for people’s names and a poor memory for math formulas. Someone else might realize that they have difficulty remembering to do things, like stopping at the store on the way home. Others still know that they tend to overlook details. This knowledge about our own thinking is actually quite important; it is called metacognitive knowledge, or  metacognition . Like other kinds of thinking skills, it is subject to error. For example, in unpublished research, one of the authors surveyed about 120 General Psychology students on the first day of the term. Among other questions, the students were asked them to predict their grade in the class and report their current Grade Point Average. Two-thirds of the students predicted that their grade in the course would be higher than their GPA. (The reality is that at our college, students tend to earn lower grades in psychology than their overall GPA.) Another example: Students routinely report that they thought they had done well on an exam, only to discover, to their dismay, that they were wrong (more on that important problem in a moment). Both errors reveal a breakdown in metacognition.

The Dunning-Kruger Effect

In general, most college students probably do not study enough. For example, using data from the National Survey of Student Engagement, Fosnacht, McCormack, and Lerma (2018) reported that first-year students at 4-year colleges in the U.S. averaged less than 14 hours per week preparing for classes. The typical suggestion is that you should spend two hours outside of class for every hour in class, or 24 – 30 hours per week for a full-time student. Clearly, students in general are nowhere near that recommended mark. Many observers, including some faculty, believe that this shortfall is a result of students being too busy or lazy. Now, it may be true that many students are too busy, with work and family obligations, for example. Others, are not particularly motivated in school, and therefore might correctly be labeled lazy. A third possible explanation, however, is that some students might not think they need to spend this much time. And this is a matter of metacognition. Consider the scenario that we mentioned above, students thinking they had done well on an exam only to discover that they did not. Justin Kruger and David Dunning examined scenarios very much like this in 1999. Kruger and Dunning gave research participants tests measuring humor, logic, and grammar. Then, they asked the participants to assess their own abilities and test performance in these areas. They found that participants in general tended to overestimate their abilities, already a problem with metacognition. Importantly, the participants who scored the lowest overestimated their abilities the most. Specifically, students who scored in the bottom quarter (averaging in the 12th percentile) thought they had scored in the 62nd percentile. This has become known as the  Dunning-Kruger effect . Many individual faculty members have replicated these results with their own student on their course exams, including the authors of this book. Think about it. Some students who just took an exam and performed poorly believe that they did well before seeing their score. It seems very likely that these are the very same students who stopped studying the night before because they thought they were “done.” Quite simply, it is not just that they did not know the material. They did not know that they did not know the material. That is poor metacognition.

In order to develop good metacognitive skills, you should continually monitor your thinking and seek frequent feedback on the accuracy of your thinking (Medina, Castleberry, & Persky 2017). For example, in classes get in the habit of predicting your exam grades. As soon as possible after taking an exam, try to find out which questions you missed and try to figure out why. If you do this soon enough, you may be able to recall the way it felt when you originally answered the question. Did you feel confident that you had answered the question correctly? Then you have just discovered an opportunity to improve your metacognition. Be on the lookout for that feeling and respond with caution.

concept :  a mental representation of a category of things in the world

Dunning-Kruger effect : individuals who are less competent tend to overestimate their abilities more than individuals who are more competent do

inference : an assumption about the truth of something that is not stated. Inferences come from our prior knowledge and experience, and from logical reasoning

metacognition :  knowledge about one’s own cognitive processes; thinking about your thinking

Critical thinking

One particular kind of knowledge or thinking skill that is related to metacognition is  critical thinking (Chew, 2020). You may have noticed that critical thinking is an objective in many college courses, and thus it could be a legitimate topic to cover in nearly any college course. It is particularly appropriate in psychology, however. As the science of (behavior and) mental processes, psychology is obviously well suited to be the discipline through which you should be introduced to this important way of thinking.

More importantly, there is a particular need to use critical thinking in psychology. We are all, in a way, experts in human behavior and mental processes, having engaged in them literally since birth. Thus, perhaps more than in any other class, students typically approach psychology with very clear ideas and opinions about its subject matter. That is, students already “know” a lot about psychology. The problem is, “it ain’t so much the things we don’t know that get us into trouble. It’s the things we know that just ain’t so” (Ward, quoted in Gilovich 1991). Indeed, many of students’ preconceptions about psychology are just plain wrong. Randolph Smith (2002) wrote a book about critical thinking in psychology called  Challenging Your Preconceptions,  highlighting this fact. On the other hand, many of students’ preconceptions about psychology are just plain right! But wait, how do you know which of your preconceptions are right and which are wrong? And when you come across a research finding or theory in this class that contradicts your preconceptions, what will you do? Will you stick to your original idea, discounting the information from the class? Will you immediately change your mind? Critical thinking can help us sort through this confusing mess.

But what is critical thinking? The goal of critical thinking is simple to state (but extraordinarily difficult to achieve): it is to be right, to draw the correct conclusions, to believe in things that are true and to disbelieve things that are false. We will provide two definitions of critical thinking (or, if you like, one large definition with two distinct parts). First, a more conceptual one: Critical thinking is thinking like a scientist in your everyday life (Schmaltz, Jansen, & Wenckowski, 2017).  Our second definition is more operational; it is simply a list of skills that are essential to be a critical thinker. Critical thinking entails solid reasoning and problem solving skills; skepticism; and an ability to identify biases, distortions, omissions, and assumptions. Excellent deductive and inductive reasoning, and problem solving skills contribute to critical thinking. So, you can consider the subject matter of sections 7.2 and 7.3 to be part of critical thinking. Because we will be devoting considerable time to these concepts in the rest of the module, let us begin with a discussion about the other aspects of critical thinking.

Let’s address that first part of the definition. Scientists form hypotheses, or predictions about some possible future observations. Then, they collect data, or information (think of this as making those future observations). They do their best to make unbiased observations using reliable techniques that have been verified by others. Then, and only then, they draw a conclusion about what those observations mean. Oh, and do not forget the most important part. “Conclusion” is probably not the most appropriate word because this conclusion is only tentative. A scientist is always prepared that someone else might come along and produce new observations that would require a new conclusion be drawn. Wow! If you like to be right, you could do a lot worse than using a process like this.

A Critical Thinker’s Toolkit 

Now for the second part of the definition. Good critical thinkers (and scientists) rely on a variety of tools to evaluate information. Perhaps the most recognizable tool for critical thinking is  skepticism (and this term provides the clearest link to the thinking like a scientist definition, as you are about to see). Some people intend it as an insult when they call someone a skeptic. But if someone calls you a skeptic, if they are using the term correctly, you should consider it a great compliment. Simply put, skepticism is a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided. People from Missouri should recognize this principle, as Missouri is known as the Show-Me State. As a skeptic, you are not inclined to believe something just because someone said so, because someone else believes it, or because it sounds reasonable. You must be persuaded by high quality evidence.

Of course, if that evidence is produced, you have a responsibility as a skeptic to change your belief. Failure to change a belief in the face of good evidence is not skepticism; skepticism has open mindedness at its core. M. Neil Browne and Stuart Keeley (2018) use the term weak sense critical thinking to describe critical thinking behaviors that are used only to strengthen a prior belief. Strong sense critical thinking, on the other hand, has as its goal reaching the best conclusion. Sometimes that means strengthening your prior belief, but sometimes it means changing your belief to accommodate the better evidence.

Many times, a failure to think critically or weak sense critical thinking is related to a  bias , an inclination, tendency, leaning, or prejudice. Everybody has biases, but many people are unaware of them. Awareness of your own biases gives you the opportunity to control or counteract them. Unfortunately, however, many people are happy to let their biases creep into their attempts to persuade others; indeed, it is a key part of their persuasive strategy. To see how these biases influence messages, just look at the different descriptions and explanations of the same events given by people of different ages or income brackets, or conservative versus liberal commentators, or by commentators from different parts of the world. Of course, to be successful, these people who are consciously using their biases must disguise them. Even undisguised biases can be difficult to identify, so disguised ones can be nearly impossible.

Here are some common sources of biases:

• Personal values and beliefs.  Some people believe that human beings are basically driven to seek power and that they are typically in competition with one another over scarce resources. These beliefs are similar to the world-view that political scientists call “realism.” Other people believe that human beings prefer to cooperate and that, given the chance, they will do so. These beliefs are similar to the world-view known as “idealism.” For many people, these deeply held beliefs can influence, or bias, their interpretations of such wide ranging situations as the behavior of nations and their leaders or the behavior of the driver in the car ahead of you. For example, if your worldview is that people are typically in competition and someone cuts you off on the highway, you may assume that the driver did it purposely to get ahead of you. Other types of beliefs about the way the world is or the way the world should be, for example, political beliefs, can similarly become a significant source of bias.
• Racism, sexism, ageism and other forms of prejudice and bigotry.  These are, sadly, a common source of bias in many people. They are essentially a special kind of “belief about the way the world is.” These beliefs—for example, that women do not make effective leaders—lead people to ignore contradictory evidence (examples of effective women leaders, or research that disputes the belief) and to interpret ambiguous evidence in a way consistent with the belief.
• Self-interest.  When particular people benefit from things turning out a certain way, they can sometimes be very susceptible to letting that interest bias them. For example, a company that will earn a profit if they sell their product may have a bias in the way that they give information about their product. A union that will benefit if its members get a generous contract might have a bias in the way it presents information about salaries at competing organizations. (Note that our inclusion of examples describing both companies and unions is an explicit attempt to control for our own personal biases). Home buyers are often dismayed to discover that they purchased their dream house from someone whose self-interest led them to lie about flooding problems in the basement or back yard. This principle, the biasing power of self-interest, is likely what led to the famous phrase  Caveat Emptor  (let the buyer beware) .  

Knowing that these types of biases exist will help you evaluate evidence more critically. Do not forget, though, that people are not always keen to let you discover the sources of biases in their arguments. For example, companies or political organizations can sometimes disguise their support of a research study by contracting with a university professor, who comes complete with a seemingly unbiased institutional affiliation, to conduct the study.

People’s biases, conscious or unconscious, can lead them to make omissions, distortions, and assumptions that undermine our ability to correctly evaluate evidence. It is essential that you look for these elements. Always ask, what is missing, what is not as it appears, and what is being assumed here? For example, consider this (fictional) chart from an ad reporting customer satisfaction at 4 local health clubs.

Clearly, from the results of the chart, one would be tempted to give Club C a try, as customer satisfaction is much higher than for the other 3 clubs.

There are so many distortions and omissions in this chart, however, that it is actually quite meaningless. First, how was satisfaction measured? Do the bars represent responses to a survey? If so, how were the questions asked? Most importantly, where is the missing scale for the chart? Although the differences look quite large, are they really?

Well, here is the same chart, with a different scale, this time labeled:

Club C is not so impressive any more, is it? In fact, all of the health clubs have customer satisfaction ratings (whatever that means) between 85% and 88%. In the first chart, the entire scale of the graph included only the percentages between 83 and 89. This “judicious” choice of scale—some would call it a distortion—and omission of that scale from the chart make the tiny differences among the clubs seem important, however.

Also, in order to be a critical thinker, you need to learn to pay attention to the assumptions that underlie a message. Let us briefly illustrate the role of assumptions by touching on some people’s beliefs about the criminal justice system in the US. Some believe that a major problem with our judicial system is that many criminals go free because of legal technicalities. Others believe that a major problem is that many innocent people are convicted of crimes. The simple fact is, both types of errors occur. A person’s conclusion about which flaw in our judicial system is the greater tragedy is based on an assumption about which of these is the more serious error (letting the guilty go free or convicting the innocent). This type of assumption is called a value assumption (Browne and Keeley, 2018). It reflects the differences in values that people develop, differences that may lead us to disregard valid evidence that does not fit in with our particular values.

Oh, by the way, some students probably noticed this, but the seven tips for evaluating information that we shared in Module 1 are related to this. Actually, they are part of this section. The tips are, to a very large degree, set of ideas you can use to help you identify biases, distortions, omissions, and assumptions. If you do not remember this section, we strongly recommend you take a few minutes to review it.

skepticism :  a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided

bias : an inclination, tendency, leaning, or prejudice

• Which of your beliefs (or disbeliefs) from the Activate exercise for this section were derived from a process of critical thinking? If some of your beliefs were not based on critical thinking, are you willing to reassess these beliefs? If the answer is no, why do you think that is? If the answer is yes, what concrete steps will you take?

7.2 Reasoning and Judgment

• What percentage of kidnappings are committed by strangers?
• Which area of the house is riskiest: kitchen, bathroom, or stairs?
• What is the most common cancer in the US?
• What percentage of workplace homicides are committed by co-workers?

An essential set of procedural thinking skills is  reasoning , the ability to generate and evaluate solid conclusions from a set of statements or evidence. You should note that these conclusions (when they are generated instead of being evaluated) are one key type of inference that we described in Section 7.1. There are two main types of reasoning, deductive and inductive.

Deductive reasoning

Suppose your teacher tells you that if you get an A on the final exam in a course, you will get an A for the whole course. Then, you get an A on the final exam. What will your final course grade be? Most people can see instantly that you can conclude with certainty that you will get an A for the course. This is a type of reasoning called  deductive reasoning , which is defined as reasoning in which a conclusion is guaranteed to be true as long as the statements leading to it are true. The three statements can be listed as an  argument , with two beginning statements and a conclusion:

Statement 1: If you get an A on the final exam, you will get an A for the course

Statement 2: You get an A on the final exam

Conclusion: You will get an A for the course

This particular arrangement, in which true beginning statements lead to a guaranteed true conclusion, is known as a  deductively valid argument . Although deductive reasoning is often the subject of abstract, brain-teasing, puzzle-like word problems, it is actually an extremely important type of everyday reasoning. It is just hard to recognize sometimes. For example, imagine that you are looking for your car keys and you realize that they are either in the kitchen drawer or in your book bag. After looking in the kitchen drawer, you instantly know that they must be in your book bag. That conclusion results from a simple deductive reasoning argument. In addition, solid deductive reasoning skills are necessary for you to succeed in the sciences, philosophy, math, computer programming, and any endeavor involving the use of logic to persuade others to your point of view or to evaluate others’ arguments.

Cognitive psychologists, and before them philosophers, have been quite interested in deductive reasoning, not so much for its practical applications, but for the insights it can offer them about the ways that human beings think. One of the early ideas to emerge from the examination of deductive reasoning is that people learn (or develop) mental versions of rules that allow them to solve these types of reasoning problems (Braine, 1978; Braine, Reiser, & Rumain, 1984). The best way to see this point of view is to realize that there are different possible rules, and some of them are very simple. For example, consider this rule of logic:

therefore q

Logical rules are often presented abstractly, as letters, in order to imply that they can be used in very many specific situations. Here is a concrete version of the of the same rule:

I’ll either have pizza or a hamburger for dinner tonight (p or q)

I won’t have pizza (not p)

Therefore, I’ll have a hamburger (therefore q)

This kind of reasoning seems so natural, so easy, that it is quite plausible that we would use a version of this rule in our daily lives. At least, it seems more plausible than some of the alternative possibilities—for example, that we need to have experience with the specific situation (pizza or hamburger, in this case) in order to solve this type of problem easily. So perhaps there is a form of natural logic (Rips, 1990) that contains very simple versions of logical rules. When we are faced with a reasoning problem that maps onto one of these rules, we use the rule.

But be very careful; things are not always as easy as they seem. Even these simple rules are not so simple. For example, consider the following rule. Many people fail to realize that this rule is just as valid as the pizza or hamburger rule above.

if p, then q

therefore, not p

Concrete version:

If I eat dinner, then I will have dessert

I did not have dessert

Therefore, I did not eat dinner

The simple fact is, it can be very difficult for people to apply rules of deductive logic correctly; as a result, they make many errors when trying to do so. Is this a deductively valid argument or not?

Students who like school study a lot

Students who study a lot get good grades

Jane does not like school

Therefore, Jane does not get good grades

Many people are surprised to discover that this is not a logically valid argument; the conclusion is not guaranteed to be true from the beginning statements. Although the first statement says that students who like school study a lot, it does NOT say that students who do not like school do not study a lot. In other words, it may very well be possible to study a lot without liking school. Even people who sometimes get problems like this right might not be using the rules of deductive reasoning. Instead, they might just be making judgments for examples they know, in this case, remembering instances of people who get good grades despite not liking school.

Making deductive reasoning even more difficult is the fact that there are two important properties that an argument may have. One, it can be valid or invalid (meaning that the conclusion does or does not follow logically from the statements leading up to it). Two, an argument (or more correctly, its conclusion) can be true or false. Here is an example of an argument that is logically valid, but has a false conclusion (at least we think it is false).

Either you are eleven feet tall or the Grand Canyon was created by a spaceship crashing into the earth.

You are not eleven feet tall

Therefore the Grand Canyon was created by a spaceship crashing into the earth

This argument has the exact same form as the pizza or hamburger argument above, making it is deductively valid. The conclusion is so false, however, that it is absurd (of course, the reason the conclusion is false is that the first statement is false). When people are judging arguments, they tend to not observe the difference between deductive validity and the empirical truth of statements or conclusions. If the elements of an argument happen to be true, people are likely to judge the argument logically valid; if the elements are false, they will very likely judge it invalid (Markovits & Bouffard-Bouchard, 1992; Moshman & Franks, 1986). Thus, it seems a stretch to say that people are using these logical rules to judge the validity of arguments. Many psychologists believe that most people actually have very limited deductive reasoning skills (Johnson-Laird, 1999). They argue that when faced with a problem for which deductive logic is required, people resort to some simpler technique, such as matching terms that appear in the statements and the conclusion (Evans, 1982). This might not seem like a problem, but what if reasoners believe that the elements are true and they happen to be wrong; they will would believe that they are using a form of reasoning that guarantees they are correct and yet be wrong.

deductive reasoning :  a type of reasoning in which the conclusion is guaranteed to be true any time the statements leading up to it are true

argument :  a set of statements in which the beginning statements lead to a conclusion

deductively valid argument :  an argument for which true beginning statements guarantee that the conclusion is true

Inductive reasoning and judgment

Every day, you make many judgments about the likelihood of one thing or another. Whether you realize it or not, you are practicing  inductive reasoning   on a daily basis. In inductive reasoning arguments, a conclusion is likely whenever the statements preceding it are true. The first thing to notice about inductive reasoning is that, by definition, you can never be sure about your conclusion; you can only estimate how likely the conclusion is. Inductive reasoning may lead you to focus on Memory Encoding and Recoding when you study for the exam, but it is possible the instructor will ask more questions about Memory Retrieval instead. Unlike deductive reasoning, the conclusions you reach through inductive reasoning are only probable, not certain. That is why scientists consider inductive reasoning weaker than deductive reasoning. But imagine how hard it would be for us to function if we could not act unless we were certain about the outcome.

Inductive reasoning can be represented as logical arguments consisting of statements and a conclusion, just as deductive reasoning can be. In an inductive argument, you are given some statements and a conclusion (or you are given some statements and must draw a conclusion). An argument is  inductively strong   if the conclusion would be very probable whenever the statements are true. So, for example, here is an inductively strong argument:

• Statement #1: The forecaster on Channel 2 said it is going to rain today.
• Statement #2: The forecaster on Channel 5 said it is going to rain today.
• Statement #3: It is very cloudy and humid.
• Statement #4: You just heard thunder.
• Conclusion (or judgment): It is going to rain today.

Think of the statements as evidence, on the basis of which you will draw a conclusion. So, based on the evidence presented in the four statements, it is very likely that it will rain today. Will it definitely rain today? Certainly not. We can all think of times that the weather forecaster was wrong.

A true story: Some years ago psychology student was watching a baseball playoff game between the St. Louis Cardinals and the Los Angeles Dodgers. A graphic on the screen had just informed the audience that the Cardinal at bat, (Hall of Fame shortstop) Ozzie Smith, a switch hitter batting left-handed for this plate appearance, had never, in nearly 3000 career at-bats, hit a home run left-handed. The student, who had just learned about inductive reasoning in his psychology class, turned to his companion (a Cardinals fan) and smugly said, “It is an inductively strong argument that Ozzie Smith will not hit a home run.” He turned back to face the television just in time to watch the ball sail over the right field fence for a home run. Although the student felt foolish at the time, he was not wrong. It was an inductively strong argument; 3000 at-bats is an awful lot of evidence suggesting that the Wizard of Ozz (as he was known) would not be hitting one out of the park (think of each at-bat without a home run as a statement in an inductive argument). Sadly (for the die-hard Cubs fan and Cardinals-hating student), despite the strength of the argument, the conclusion was wrong.

Given the possibility that we might draw an incorrect conclusion even with an inductively strong argument, we really want to be sure that we do, in fact, make inductively strong arguments. If we judge something probable, it had better be probable. If we judge something nearly impossible, it had better not happen. Think of inductive reasoning, then, as making reasonably accurate judgments of the probability of some conclusion given a set of evidence.

We base many decisions in our lives on inductive reasoning. For example:

Statement #1: Psychology is not my best subject

Statement #2: My psychology instructor has a reputation for giving difficult exams

Statement #3: My first psychology exam was much harder than I expected

Judgment: The next exam will probably be very difficult.

Decision: I will study tonight instead of watching Netflix.

Some other examples of judgments that people commonly make in a school context include judgments of the likelihood that:

• A particular class will be interesting/useful/difficult
• You will be able to finish writing a paper by next week if you go out tonight
• Your laptop’s battery will last through the next trip to the library
• You will not miss anything important if you skip class tomorrow
• Your instructor will not notice if you skip class tomorrow
• You will be able to find a book that you will need for a paper
• There will be an essay question about Memory Encoding on the next exam

Tversky and Kahneman (1983) recognized that there are two general ways that we might make these judgments; they termed them extensional (i.e., following the laws of probability) and intuitive (i.e., using shortcuts or heuristics, see below). We will use a similar distinction between Type 1 and Type 2 thinking, as described by Keith Stanovich and his colleagues (Evans and Stanovich, 2013; Stanovich and West, 2000). Type 1 thinking is fast, automatic, effortful, and emotional. In fact, it is hardly fair to call it reasoning at all, as judgments just seem to pop into one’s head. Type 2 thinking , on the other hand, is slow, effortful, and logical. So obviously, it is more likely to lead to a correct judgment, or an optimal decision. The problem is, we tend to over-rely on Type 1. Now, we are not saying that Type 2 is the right way to go for every decision or judgment we make. It seems a bit much, for example, to engage in a step-by-step logical reasoning procedure to decide whether we will have chicken or fish for dinner tonight.

Many bad decisions in some very important contexts, however, can be traced back to poor judgments of the likelihood of certain risks or outcomes that result from the use of Type 1 when a more logical reasoning process would have been more appropriate. For example:

Statement #1: It is late at night.

Statement #2: Albert has been drinking beer for the past five hours at a party.

Statement #3: Albert is not exactly sure where he is or how far away home is.

Judgment: Albert will have no difficulty walking home.

Decision: He walks home alone.

As you can see in this example, the three statements backing up the judgment do not really support it. In other words, this argument is not inductively strong because it is based on judgments that ignore the laws of probability. What are the chances that someone facing these conditions will be able to walk home alone easily? And one need not be drunk to make poor decisions based on judgments that just pop into our heads.

The truth is that many of our probability judgments do not come very close to what the laws of probability say they should be. Think about it. In order for us to reason in accordance with these laws, we would need to know the laws of probability, which would allow us to calculate the relationship between particular pieces of evidence and the probability of some outcome (i.e., how much likelihood should change given a piece of evidence), and we would have to do these heavy math calculations in our heads. After all, that is what Type 2 requires. Needless to say, even if we were motivated, we often do not even know how to apply Type 2 reasoning in many cases.

So what do we do when we don’t have the knowledge, skills, or time required to make the correct mathematical judgment? Do we hold off and wait until we can get better evidence? Do we read up on probability and fire up our calculator app so we can compute the correct probability? Of course not. We rely on Type 1 thinking. We “wing it.” That is, we come up with a likelihood estimate using some means at our disposal. Psychologists use the term heuristic to describe the type of “winging it” we are talking about. A  heuristic   is a shortcut strategy that we use to make some judgment or solve some problem (see Section 7.3). Heuristics are easy and quick, think of them as the basic procedures that are characteristic of Type 1.  They can absolutely lead to reasonably good judgments and decisions in some situations (like choosing between chicken and fish for dinner). They are, however, far from foolproof. There are, in fact, quite a lot of situations in which heuristics can lead us to make incorrect judgments, and in many cases the decisions based on those judgments can have serious consequences.

Let us return to the activity that begins this section. You were asked to judge the likelihood (or frequency) of certain events and risks. You were free to come up with your own evidence (or statements) to make these judgments. This is where a heuristic crops up. As a judgment shortcut, we tend to generate specific examples of those very events to help us decide their likelihood or frequency. For example, if we are asked to judge how common, frequent, or likely a particular type of cancer is, many of our statements would be examples of specific cancer cases:

Statement #1: Andy Kaufman (comedian) had lung cancer.

Statement #2: Colin Powell (US Secretary of State) had prostate cancer.

Statement #3: Bob Marley (musician) had skin and brain cancer

Statement #4: Sandra Day O’Connor (Supreme Court Justice) had breast cancer.

Statement #5: Fred Rogers (children’s entertainer) had stomach cancer.

Statement #6: Robin Roberts (news anchor) had breast cancer.

Statement #7: Bette Davis (actress) had breast cancer.

Judgment: Breast cancer is the most common type.

Your own experience or memory may also tell you that breast cancer is the most common type. But it is not (although it is common). Actually, skin cancer is the most common type in the US. We make the same types of misjudgments all the time because we do not generate the examples or evidence according to their actual frequencies or probabilities. Instead, we have a tendency (or bias) to search for the examples in memory; if they are easy to retrieve, we assume that they are common. To rephrase this in the language of the heuristic, events seem more likely to the extent that they are available to memory. This bias has been termed the  availability heuristic   (Kahneman and Tversky, 1974).

The fact that we use the availability heuristic does not automatically mean that our judgment is wrong. The reason we use heuristics in the first place is that they work fairly well in many cases (and, of course that they are easy to use). So, the easiest examples to think of sometimes are the most common ones. Is it more likely that a member of the U.S. Senate is a man or a woman? Most people have a much easier time generating examples of male senators. And as it turns out, the U.S. Senate has many more men than women (74 to 26 in 2020). In this case, then, the availability heuristic would lead you to make the correct judgment; it is far more likely that a senator would be a man.

In many other cases, however, the availability heuristic will lead us astray. This is because events can be memorable for many reasons other than their frequency. Section 5.2, Encoding Meaning, suggested that one good way to encode the meaning of some information is to form a mental image of it. Thus, information that has been pictured mentally will be more available to memory. Indeed, an event that is vivid and easily pictured will trick many people into supposing that type of event is more common than it actually is. Repetition of information will also make it more memorable. So, if the same event is described to you in a magazine, on the evening news, on a podcast that you listen to, and in your Facebook feed; it will be very available to memory. Again, the availability heuristic will cause you to misperceive the frequency of these types of events.

Most interestingly, information that is unusual is more memorable. Suppose we give you the following list of words to remember: box, flower, letter, platypus, oven, boat, newspaper, purse, drum, car. Very likely, the easiest word to remember would be platypus, the unusual one. The same thing occurs with memories of events. An event may be available to memory because it is unusual, yet the availability heuristic leads us to judge that the event is common. Did you catch that? In these cases, the availability heuristic makes us think the exact opposite of the true frequency. We end up thinking something is common because it is unusual (and therefore memorable). Yikes.

The misapplication of the availability heuristic sometimes has unfortunate results. For example, if you went to K-12 school in the US over the past 10 years, it is extremely likely that you have participated in lockdown and active shooter drills. Of course, everyone is trying to prevent the tragedy of another school shooting. And believe us, we are not trying to minimize how terrible the tragedy is. But the truth of the matter is, school shootings are extremely rare. Because the federal government does not keep a database of school shootings, the Washington Post has maintained their own running tally. Between 1999 and January 2020 (the date of the most recent school shooting with a death in the US at of the time this paragraph was written), the Post reported a total of 254 people died in school shootings in the US. Not 254 per year, 254 total. That is an average of 12 per year. Of course, that is 254 people who should not have died (particularly because many were children), but in a country with approximately 60,000,000 students and teachers, this is a very small risk.

But many students and teachers are terrified that they will be victims of school shootings because of the availability heuristic. It is so easy to think of examples (they are very available to memory) that people believe the event is very common. It is not. And there is a downside to this. We happen to believe that there is an enormous gun violence problem in the United States. According the the Centers for Disease Control and Prevention, there were 39,773 firearm deaths in the US in 2017. Fifteen of those deaths were in school shootings, according to the Post. 60% of those deaths were suicides. When people pay attention to the school shooting risk (low), they often fail to notice the much larger risk.

And examples like this are by no means unique. The authors of this book have been teaching psychology since the 1990’s. We have been able to make the exact same arguments about the misapplication of the availability heuristics and keep them current by simply swapping out for the “fear of the day.” In the 1990’s it was children being kidnapped by strangers (it was known as “stranger danger”) despite the facts that kidnappings accounted for only 2% of the violent crimes committed against children, and only 24% of kidnappings are committed by strangers (US Department of Justice, 2007). This fear overlapped with the fear of terrorism that gripped the country after the 2001 terrorist attacks on the World Trade Center and US Pentagon and still plagues the population of the US somewhat in 2020. After a well-publicized, sensational act of violence, people are extremely likely to increase their estimates of the chances that they, too, will be victims of terror. Think about the reality, however. In October of 2001, a terrorist mailed anthrax spores to members of the US government and a number of media companies. A total of five people died as a result of this attack. The nation was nearly paralyzed by the fear of dying from the attack; in reality the probability of an individual person dying was 0.00000002.

The availability heuristic can lead you to make incorrect judgments in a school setting as well. For example, suppose you are trying to decide if you should take a class from a particular math professor. You might try to make a judgment of how good a teacher she is by recalling instances of friends and acquaintances making comments about her teaching skill. You may have some examples that suggest that she is a poor teacher very available to memory, so on the basis of the availability heuristic you judge her a poor teacher and decide to take the class from someone else. What if, however, the instances you recalled were all from the same person, and this person happens to be a very colorful storyteller? The subsequent ease of remembering the instances might not indicate that the professor is a poor teacher after all.

Although the availability heuristic is obviously important, it is not the only judgment heuristic we use. Amos Tversky and Daniel Kahneman examined the role of heuristics in inductive reasoning in a long series of studies. Kahneman received a Nobel Prize in Economics for this research in 2002, and Tversky would have certainly received one as well if he had not died of melanoma at age 59 in 1996 (Nobel Prizes are not awarded posthumously). Kahneman and Tversky demonstrated repeatedly that people do not reason in ways that are consistent with the laws of probability. They identified several heuristic strategies that people use instead to make judgments about likelihood. The importance of this work for economics (and the reason that Kahneman was awarded the Nobel Prize) is that earlier economic theories had assumed that people do make judgments rationally, that is, in agreement with the laws of probability.

Another common heuristic that people use for making judgments is the  representativeness heuristic (Kahneman & Tversky 1973). Suppose we describe a person to you. He is quiet and shy, has an unassuming personality, and likes to work with numbers. Is this person more likely to be an accountant or an attorney? If you said accountant, you were probably using the representativeness heuristic. Our imaginary person is judged likely to be an accountant because he resembles, or is representative of the concept of, an accountant. When research participants are asked to make judgments such as these, the only thing that seems to matter is the representativeness of the description. For example, if told that the person described is in a room that contains 70 attorneys and 30 accountants, participants will still assume that he is an accountant.

inductive reasoning :  a type of reasoning in which we make judgments about likelihood from sets of evidence

inductively strong argument :  an inductive argument in which the beginning statements lead to a conclusion that is probably true

heuristic :  a shortcut strategy that we use to make judgments and solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

availability heuristic :  judging the frequency or likelihood of some event type according to how easily examples of the event can be called to mind (i.e., how available they are to memory)

representativeness heuristic:   judging the likelihood that something is a member of a category on the basis of how much it resembles a typical category member (i.e., how representative it is of the category)

Type 1 thinking : fast, automatic, and emotional thinking.

Type 2 thinking : slow, effortful, and logical thinking.

• What percentage of workplace homicides are co-worker violence?

Many people get these questions wrong. The answers are 10%; stairs; skin; 6%. How close were your answers? Explain how the availability heuristic might have led you to make the incorrect judgments.

• Can you think of some other judgments that you have made (or beliefs that you have) that might have been influenced by the availability heuristic?

7.3 Problem Solving

• Please take a few minutes to list a number of problems that you are facing right now.
• Now write about a problem that you recently solved.
• What is your definition of a problem?

Mary has a problem. Her daughter, ordinarily quite eager to please, appears to delight in being the last person to do anything. Whether getting ready for school, going to piano lessons or karate class, or even going out with her friends, she seems unwilling or unable to get ready on time. Other people have different kinds of problems. For example, many students work at jobs, have numerous family commitments, and are facing a course schedule full of difficult exams, assignments, papers, and speeches. How can they find enough time to devote to their studies and still fulfill their other obligations? Speaking of students and their problems: Show that a ball thrown vertically upward with initial velocity v0 takes twice as much time to return as to reach the highest point (from Spiegel, 1981).

These are three very different situations, but we have called them all problems. What makes them all the same, despite the differences? A psychologist might define a  problem   as a situation with an initial state, a goal state, and a set of possible intermediate states. Somewhat more meaningfully, we might consider a problem a situation in which you are in here one state (e.g., daughter is always late), you want to be there in another state (e.g., daughter is not always late), and with no obvious way to get from here to there. Defined this way, each of the three situations we outlined can now be seen as an example of the same general concept, a problem. At this point, you might begin to wonder what is not a problem, given such a general definition. It seems that nearly every non-routine task we engage in could qualify as a problem. As long as you realize that problems are not necessarily bad (it can be quite fun and satisfying to rise to the challenge and solve a problem), this may be a useful way to think about it.

Can we identify a set of problem-solving skills that would apply to these very different kinds of situations? That task, in a nutshell, is a major goal of this section. Let us try to begin to make sense of the wide variety of ways that problems can be solved with an important observation: the process of solving problems can be divided into two key parts. First, people have to notice, comprehend, and represent the problem properly in their minds (called  problem representation ). Second, they have to apply some kind of solution strategy to the problem. Psychologists have studied both of these key parts of the process in detail.

When you first think about the problem-solving process, you might guess that most of our difficulties would occur because we are failing in the second step, the application of strategies. Although this can be a significant difficulty much of the time, the more important source of difficulty is probably problem representation. In short, we often fail to solve a problem because we are looking at it, or thinking about it, the wrong way.

problem :  a situation in which we are in an initial state, have a desired goal state, and there is a number of possible intermediate states (i.e., there is no obvious way to get from the initial to the goal state)

problem representation :  noticing, comprehending and forming a mental conception of a problem

Defining and Mentally Representing Problems in Order to Solve Them

So, the main obstacle to solving a problem is that we do not clearly understand exactly what the problem is. Recall the problem with Mary’s daughter always being late. One way to represent, or to think about, this problem is that she is being defiant. She refuses to get ready in time. This type of representation or definition suggests a particular type of solution. Another way to think about the problem, however, is to consider the possibility that she is simply being sidetracked by interesting diversions. This different conception of what the problem is (i.e., different representation) suggests a very different solution strategy. For example, if Mary defines the problem as defiance, she may be tempted to solve the problem using some kind of coercive tactics, that is, to assert her authority as her mother and force her to listen. On the other hand, if Mary defines the problem as distraction, she may try to solve it by simply removing the distracting objects.

As you might guess, when a problem is represented one way, the solution may seem very difficult, or even impossible. Seen another way, the solution might be very easy. For example, consider the following problem (from Nasar, 1998):

Two bicyclists start 20 miles apart and head toward each other, each going at a steady rate of 10 miles per hour. At the same time, a fly that travels at a steady 15 miles per hour starts from the front wheel of the southbound bicycle and flies to the front wheel of the northbound one, then turns around and flies to the front wheel of the southbound one again, and continues in this manner until he is crushed between the two front wheels. Question: what total distance did the fly cover?

Please take a few minutes to try to solve this problem.

Most people represent this problem as a question about a fly because, well, that is how the question is asked. The solution, using this representation, is to figure out how far the fly travels on the first leg of its journey, then add this total to how far it travels on the second leg of its journey (when it turns around and returns to the first bicycle), then continue to add the smaller distance from each leg of the journey until you converge on the correct answer. You would have to be quite skilled at math to solve this problem, and you would probably need some time and pencil and paper to do it.

If you consider a different representation, however, you can solve this problem in your head. Instead of thinking about it as a question about a fly, think about it as a question about the bicycles. They are 20 miles apart, and each is traveling 10 miles per hour. How long will it take for the bicycles to reach each other? Right, one hour. The fly is traveling 15 miles per hour; therefore, it will travel a total of 15 miles back and forth in the hour before the bicycles meet. Represented one way (as a problem about a fly), the problem is quite difficult. Represented another way (as a problem about two bicycles), it is easy. Changing your representation of a problem is sometimes the best—sometimes the only—way to solve it.

Unfortunately, however, changing a problem’s representation is not the easiest thing in the world to do. Often, problem solvers get stuck looking at a problem one way. This is called  fixation . Most people who represent the preceding problem as a problem about a fly probably do not pause to reconsider, and consequently change, their representation. A parent who thinks her daughter is being defiant is unlikely to consider the possibility that her behavior is far less purposeful.

Problem-solving fixation was examined by a group of German psychologists called Gestalt psychologists during the 1930’s and 1940’s. Karl Dunker, for example, discovered an important type of failure to take a different perspective called  functional fixedness . Imagine being a participant in one of his experiments. You are asked to figure out how to mount two candles on a door and are given an assortment of odds and ends, including a small empty cardboard box and some thumbtacks. Perhaps you have already figured out a solution: tack the box to the door so it forms a platform, then put the candles on top of the box. Most people are able to arrive at this solution. Imagine a slight variation of the procedure, however. What if, instead of being empty, the box had matches in it? Most people given this version of the problem do not arrive at the solution given above. Why? Because it seems to people that when the box contains matches, it already has a function; it is a matchbox. People are unlikely to consider a new function for an object that already has a function. This is functional fixedness.

Mental set is a type of fixation in which the problem solver gets stuck using the same solution strategy that has been successful in the past, even though the solution may no longer be useful. It is commonly seen when students do math problems for homework. Often, several problems in a row require the reapplication of the same solution strategy. Then, without warning, the next problem in the set requires a new strategy. Many students attempt to apply the formerly successful strategy on the new problem and therefore cannot come up with a correct answer.

The thing to remember is that you cannot solve a problem unless you correctly identify what it is to begin with (initial state) and what you want the end result to be (goal state). That may mean looking at the problem from a different angle and representing it in a new way. The correct representation does not guarantee a successful solution, but it certainly puts you on the right track.

A bit more optimistically, the Gestalt psychologists discovered what may be considered the opposite of fixation, namely  insight . Sometimes the solution to a problem just seems to pop into your head. Wolfgang Kohler examined insight by posing many different problems to chimpanzees, principally problems pertaining to their acquisition of out-of-reach food. In one version, a banana was placed outside of a chimpanzee’s cage and a short stick inside the cage. The stick was too short to retrieve the banana, but was long enough to retrieve a longer stick also located outside of the cage. This second stick was long enough to retrieve the banana. After trying, and failing, to reach the banana with the shorter stick, the chimpanzee would try a couple of random-seeming attempts, react with some apparent frustration or anger, then suddenly rush to the longer stick, the correct solution fully realized at this point. This sudden appearance of the solution, observed many times with many different problems, was termed insight by Kohler.

Lest you think it pertains to chimpanzees only, Karl Dunker demonstrated that children also solve problems through insight in the 1930s. More importantly, you have probably experienced insight yourself. Think back to a time when you were trying to solve a difficult problem. After struggling for a while, you gave up. Hours later, the solution just popped into your head, perhaps when you were taking a walk, eating dinner, or lying in bed.

fixation :  when a problem solver gets stuck looking at a problem a particular way and cannot change his or her representation of it (or his or her intended solution strategy)

functional fixedness :  a specific type of fixation in which a problem solver cannot think of a new use for an object that already has a function

mental set :  a specific type of fixation in which a problem solver gets stuck using the same solution strategy that has been successful in the past

insight :  a sudden realization of a solution to a problem

Solving Problems by Trial and Error

Correctly identifying the problem and your goal for a solution is a good start, but recall the psychologist’s definition of a problem: it includes a set of possible intermediate states. Viewed this way, a problem can be solved satisfactorily only if one can find a path through some of these intermediate states to the goal. Imagine a fairly routine problem, finding a new route to school when your ordinary route is blocked (by road construction, for example). At each intersection, you may turn left, turn right, or go straight. A satisfactory solution to the problem (of getting to school) is a sequence of selections at each intersection that allows you to wind up at school.

If you had all the time in the world to get to school, you might try choosing intermediate states randomly. At one corner you turn left, the next you go straight, then you go left again, then right, then right, then straight. Unfortunately, trial and error will not necessarily get you where you want to go, and even if it does, it is not the fastest way to get there. For example, when a friend of ours was in college, he got lost on the way to a concert and attempted to find the venue by choosing streets to turn onto randomly (this was long before the use of GPS). Amazingly enough, the strategy worked, although he did end up missing two out of the three bands who played that night.

Trial and error is not all bad, however. B.F. Skinner, a prominent behaviorist psychologist, suggested that people often behave randomly in order to see what effect the behavior has on the environment and what subsequent effect this environmental change has on them. This seems particularly true for the very young person. Picture a child filling a household’s fish tank with toilet paper, for example. To a child trying to develop a repertoire of creative problem-solving strategies, an odd and random behavior might be just the ticket. Eventually, the exasperated parent hopes, the child will discover that many of these random behaviors do not successfully solve problems; in fact, in many cases they create problems. Thus, one would expect a decrease in this random behavior as a child matures. You should realize, however, that the opposite extreme is equally counterproductive. If the children become too rigid, never trying something unexpected and new, their problem solving skills can become too limited.

Effective problem solving seems to call for a happy medium that strikes a balance between using well-founded old strategies and trying new ground and territory. The individual who recognizes a situation in which an old problem-solving strategy would work best, and who can also recognize a situation in which a new untested strategy is necessary is halfway to success.

Solving Problems with Algorithms and Heuristics

For many problems there is a possible strategy available that will guarantee a correct solution. For example, think about math problems. Math lessons often consist of step-by-step procedures that can be used to solve the problems. If you apply the strategy without error, you are guaranteed to arrive at the correct solution to the problem. This approach is called using an  algorithm , a term that denotes the step-by-step procedure that guarantees a correct solution. Because algorithms are sometimes available and come with a guarantee, you might think that most people use them frequently. Unfortunately, however, they do not. As the experience of many students who have struggled through math classes can attest, algorithms can be extremely difficult to use, even when the problem solver knows which algorithm is supposed to work in solving the problem. In problems outside of math class, we often do not even know if an algorithm is available. It is probably fair to say, then, that algorithms are rarely used when people try to solve problems.

Because algorithms are so difficult to use, people often pass up the opportunity to guarantee a correct solution in favor of a strategy that is much easier to use and yields a reasonable chance of coming up with a correct solution. These strategies are called  problem solving heuristics . Similar to what you saw in section 6.2 with reasoning heuristics, a problem solving heuristic is a shortcut strategy that people use when trying to solve problems. It usually works pretty well, but does not guarantee a correct solution to the problem. For example, one problem solving heuristic might be “always move toward the goal” (so when trying to get to school when your regular route is blocked, you would always turn in the direction you think the school is). A heuristic that people might use when doing math homework is “use the same solution strategy that you just used for the previous problem.”

By the way, we hope these last two paragraphs feel familiar to you. They seem to parallel a distinction that you recently learned. Indeed, algorithms and problem-solving heuristics are another example of the distinction between Type 1 thinking and Type 2 thinking.

Although it is probably not worth describing a large number of specific heuristics, two observations about heuristics are worth mentioning. First, heuristics can be very general or they can be very specific, pertaining to a particular type of problem only. For example, “always move toward the goal” is a general strategy that you can apply to countless problem situations. On the other hand, “when you are lost without a functioning gps, pick the most expensive car you can see and follow it” is specific to the problem of being lost. Second, all heuristics are not equally useful. One heuristic that many students know is “when in doubt, choose c for a question on a multiple-choice exam.” This is a dreadful strategy because many instructors intentionally randomize the order of answer choices. Another test-taking heuristic, somewhat more useful, is “look for the answer to one question somewhere else on the exam.”

You really should pay attention to the application of heuristics to test taking. Imagine that while reviewing your answers for a multiple-choice exam before turning it in, you come across a question for which you originally thought the answer was c. Upon reflection, you now think that the answer might be b. Should you change the answer to b, or should you stick with your first impression? Most people will apply the heuristic strategy to “stick with your first impression.” What they do not realize, of course, is that this is a very poor strategy (Lilienfeld et al, 2009). Most of the errors on exams come on questions that were answered wrong originally and were not changed (so they remain wrong). There are many fewer errors where we change a correct answer to an incorrect answer. And, of course, sometimes we change an incorrect answer to a correct answer. In fact, research has shown that it is more common to change a wrong answer to a right answer than vice versa (Bruno, 2001).

The belief in this poor test-taking strategy (stick with your first impression) is based on the  confirmation bias   (Nickerson, 1998; Wason, 1960). You first saw the confirmation bias in Module 1, but because it is so important, we will repeat the information here. People have a bias, or tendency, to notice information that confirms what they already believe. Somebody at one time told you to stick with your first impression, so when you look at the results of an exam you have taken, you will tend to notice the cases that are consistent with that belief. That is, you will notice the cases in which you originally had an answer correct and changed it to the wrong answer. You tend not to notice the other two important (and more common) cases, changing an answer from wrong to right, and leaving a wrong answer unchanged.

Because heuristics by definition do not guarantee a correct solution to a problem, mistakes are bound to occur when we employ them. A poor choice of a specific heuristic will lead to an even higher likelihood of making an error.

algorithm :  a step-by-step procedure that guarantees a correct solution to a problem

problem solving heuristic :  a shortcut strategy that we use to solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

confirmation bias :  people’s tendency to notice information that confirms what they already believe

An Effective Problem-Solving Sequence

You may be left with a big question: If algorithms are hard to use and heuristics often don’t work, how am I supposed to solve problems? Robert Sternberg (1996), as part of his theory of what makes people successfully intelligent (Module 8) described a problem-solving sequence that has been shown to work rather well:

• Identify the existence of a problem.  In school, problem identification is often easy; problems that you encounter in math classes, for example, are conveniently labeled as problems for you. Outside of school, however, realizing that you have a problem is a key difficulty that you must get past in order to begin solving it. You must be very sensitive to the symptoms that indicate a problem.
• Define the problem.  Suppose you realize that you have been having many headaches recently. Very likely, you would identify this as a problem. If you define the problem as “headaches,” the solution would probably be to take aspirin or ibuprofen or some other anti-inflammatory medication. If the headaches keep returning, however, you have not really solved the problem—likely because you have mistaken a symptom for the problem itself. Instead, you must find the root cause of the headaches. Stress might be the real problem. For you to successfully solve many problems it may be necessary for you to overcome your fixations and represent the problems differently. One specific strategy that you might find useful is to try to define the problem from someone else’s perspective. How would your parents, spouse, significant other, doctor, etc. define the problem? Somewhere in these different perspectives may lurk the key definition that will allow you to find an easier and permanent solution.
• Formulate strategy.  Now it is time to begin planning exactly how the problem will be solved. Is there an algorithm or heuristic available for you to use? Remember, heuristics by their very nature guarantee that occasionally you will not be able to solve the problem. One point to keep in mind is that you should look for long-range solutions, which are more likely to address the root cause of a problem than short-range solutions.
• Represent and organize information.  Similar to the way that the problem itself can be defined, or represented in multiple ways, information within the problem is open to different interpretations. Suppose you are studying for a big exam. You have chapters from a textbook and from a supplemental reader, along with lecture notes that all need to be studied. How should you (represent and) organize these materials? Should you separate them by type of material (text versus reader versus lecture notes), or should you separate them by topic? To solve problems effectively, you must learn to find the most useful representation and organization of information.
• Allocate resources.  This is perhaps the simplest principle of the problem solving sequence, but it is extremely difficult for many people. First, you must decide whether time, money, skills, effort, goodwill, or some other resource would help to solve the problem Then, you must make the hard choice of deciding which resources to use, realizing that you cannot devote maximum resources to every problem. Very often, the solution to problem is simply to change how resources are allocated (for example, spending more time studying in order to improve grades).
• Monitor and evaluate solutions.  Pay attention to the solution strategy while you are applying it. If it is not working, you may be able to select another strategy. Another fact you should realize about problem solving is that it never does end. Solving one problem frequently brings up new ones. Good monitoring and evaluation of your problem solutions can help you to anticipate and get a jump on solving the inevitable new problems that will arise.

Please note that this as  an  effective problem-solving sequence, not  the  effective problem solving sequence. Just as you can become fixated and end up representing the problem incorrectly or trying an inefficient solution, you can become stuck applying the problem-solving sequence in an inflexible way. Clearly there are problem situations that can be solved without using these skills in this order.

Additionally, many real-world problems may require that you go back and redefine a problem several times as the situation changes (Sternberg et al. 2000). For example, consider the problem with Mary’s daughter one last time. At first, Mary did represent the problem as one of defiance. When her early strategy of pleading and threatening punishment was unsuccessful, Mary began to observe her daughter more carefully. She noticed that, indeed, her daughter’s attention would be drawn by an irresistible distraction or book. Fresh with a re-representation of the problem, she began a new solution strategy. She began to remind her daughter every few minutes to stay on task and remind her that if she is ready before it is time to leave, she may return to the book or other distracting object at that time. Fortunately, this strategy was successful, so Mary did not have to go back and redefine the problem again.

Pick one or two of the problems that you listed when you first started studying this section and try to work out the steps of Sternberg’s problem solving sequence for each one.

a mental representation of a category of things in the world

an assumption about the truth of something that is not stated. Inferences come from our prior knowledge and experience, and from logical reasoning

knowledge about one’s own cognitive processes; thinking about your thinking

individuals who are less competent tend to overestimate their abilities more than individuals who are more competent do

Thinking like a scientist in your everyday life for the purpose of drawing correct conclusions. It entails skepticism; an ability to identify biases, distortions, omissions, and assumptions; and excellent deductive and inductive reasoning, and problem solving skills.

a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided

an inclination, tendency, leaning, or prejudice

a type of reasoning in which the conclusion is guaranteed to be true any time the statements leading up to it are true

a set of statements in which the beginning statements lead to a conclusion

an argument for which true beginning statements guarantee that the conclusion is true

a type of reasoning in which we make judgments about likelihood from sets of evidence

an inductive argument in which the beginning statements lead to a conclusion that is probably true

fast, automatic, and emotional thinking

slow, effortful, and logical thinking

a shortcut strategy that we use to make judgments and solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

udging the frequency or likelihood of some event type according to how easily examples of the event can be called to mind (i.e., how available they are to memory)

judging the likelihood that something is a member of a category on the basis of how much it resembles a typical category member (i.e., how representative it is of the category)

a situation in which we are in an initial state, have a desired goal state, and there is a number of possible intermediate states (i.e., there is no obvious way to get from the initial to the goal state)

noticing, comprehending and forming a mental conception of a problem

when a problem solver gets stuck looking at a problem a particular way and cannot change his or her representation of it (or his or her intended solution strategy)

a specific type of fixation in which a problem solver cannot think of a new use for an object that already has a function

a specific type of fixation in which a problem solver gets stuck using the same solution strategy that has been successful in the past

a sudden realization of a solution to a problem

a step-by-step procedure that guarantees a correct solution to a problem

The tendency to notice and pay attention to information that confirms your prior beliefs and to ignore information that disconfirms them.

a shortcut strategy that we use to solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

Introduction to Psychology Copyright © 2020 by Ken Gray; Elizabeth Arnott-Hill; and Or'Shaundra Benson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Do You Understand the Problem You’re Trying to Solve?

To solve tough problems at work, first ask these questions.

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Problem solving skills are invaluable in any job. But all too often, we jump to find solutions to a problem without taking time to really understand the dilemma we face, according to Thomas Wedell-Wedellsborg , an expert in innovation and the author of the book, What’s Your Problem?: To Solve Your Toughest Problems, Change the Problems You Solve .

In this episode, you’ll learn how to reframe tough problems by asking questions that reveal all the factors and assumptions that contribute to the situation. You’ll also learn why searching for just one root cause can be misleading.

Key episode topics include: leadership, decision making and problem solving, power and influence, business management.

HBR On Leadership curates the best case studies and conversations with the world’s top business and management experts, to help you unlock the best in those around you. New episodes every week.

• Listen to the original HBR IdeaCast episode: The Secret to Better Problem Solving (2016)
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Problem solving skills are invaluable in any job. But even the most experienced among us can fall into the trap of solving the wrong problem.

Thomas Wedell-Wedellsborg says that all too often, we jump to find solutions to a problem – without taking time to really understand what we’re facing.

He’s an expert in innovation, and he’s the author of the book, What’s Your Problem?: To Solve Your Toughest Problems, Change the Problems You Solve .

  In this episode, you’ll learn how to reframe tough problems, by asking questions that reveal all the factors and assumptions that contribute to the situation. You’ll also learn why searching for one root cause can be misleading. And you’ll learn how to use experimentation and rapid prototyping as problem-solving tools.

This episode originally aired on HBR IdeaCast in December 2016. Here it is.

SARAH GREEN CARMICHAEL: Welcome to the HBR IdeaCast from Harvard Business Review. I’m Sarah Green Carmichael.

Problem solving is popular. People put it on their resumes. Managers believe they excel at it. Companies count it as a key proficiency. We solve customers’ problems.

The problem is we often solve the wrong problems. Albert Einstein and Peter Drucker alike have discussed the difficulty of effective diagnosis. There are great frameworks for getting teams to attack true problems, but they’re often hard to do daily and on the fly. That’s where our guest comes in.

Thomas Wedell-Wedellsborg is a consultant who helps companies and managers reframe their problems so they can come up with an effective solution faster. He asks the question “Are You Solving The Right Problems?” in the January-February 2017 issue of Harvard Business Review. Thomas, thank you so much for coming on the HBR IdeaCast .

THOMAS WEDELL-WEDELLSBORG: Thanks for inviting me.

SARAH GREEN CARMICHAEL: So, I thought maybe we could start by talking about the problem of talking about problem reframing. What is that exactly?

THOMAS WEDELL-WEDELLSBORG: Basically, when people face a problem, they tend to jump into solution mode to rapidly, and very often that means that they don’t really understand, necessarily, the problem they’re trying to solve. And so, reframing is really a– at heart, it’s a method that helps you avoid that by taking a second to go in and ask two questions, basically saying, first of all, wait. What is the problem we’re trying to solve? And then crucially asking, is there a different way to think about what the problem actually is?

SARAH GREEN CARMICHAEL: So, I feel like so often when this comes up in meetings, you know, someone says that, and maybe they throw out the Einstein quote about you spend an hour of problem solving, you spend 55 minutes to find the problem. And then everyone else in the room kind of gets irritated. So, maybe just give us an example of maybe how this would work in practice in a way that would not, sort of, set people’s teeth on edge, like oh, here Sarah goes again, reframing the whole problem instead of just solving it.

THOMAS WEDELL-WEDELLSBORG: I mean, you’re bringing up something that’s, I think is crucial, which is to create legitimacy for the method. So, one of the reasons why I put out the article is to give people a tool to say actually, this thing is still important, and we need to do it. But I think the really critical thing in order to make this work in a meeting is actually to learn how to do it fast, because if you have the idea that you need to spend 30 minutes in a meeting delving deeply into the problem, I mean, that’s going to be uphill for most problems. So, the critical thing here is really to try to make it a practice you can implement very, very rapidly.

There’s an example that I would suggest memorizing. This is the example that I use to explain very rapidly what it is. And it’s basically, I call it the slow elevator problem. You imagine that you are the owner of an office building, and that your tenants are complaining that the elevator’s slow.

Now, if you take that problem framing for granted, you’re going to start thinking creatively around how do we make the elevator faster. Do we install a new motor? Do we have to buy a new lift somewhere?

The thing is, though, if you ask people who actually work with facilities management, well, they’re going to have a different solution for you, which is put up a mirror next to the elevator. That’s what happens is, of course, that people go oh, I’m busy. I’m busy. I’m– oh, a mirror. Oh, that’s beautiful.

And then they forget time. What’s interesting about that example is that the idea with a mirror is actually a solution to a different problem than the one you first proposed. And so, the whole idea here is once you get good at using reframing, you can quickly identify other aspects of the problem that might be much better to try to solve than the original one you found. It’s not necessarily that the first one is wrong. It’s just that there might be better problems out there to attack that we can, means we can do things much faster, cheaper, or better.

SARAH GREEN CARMICHAEL: So, in that example, I can understand how A, it’s probably expensive to make the elevator faster, so it’s much cheaper just to put up a mirror. And B, maybe the real problem people are actually feeling, even though they’re not articulating it right, is like, I hate waiting for the elevator. But if you let them sort of fix their hair or check their teeth, they’re suddenly distracted and don’t notice.

But if you have, this is sort of a pedestrian example, but say you have a roommate or a spouse who doesn’t clean up the kitchen. Facing that problem and not having your elegant solution already there to highlight the contrast between the perceived problem and the real problem, how would you take a problem like that and attack it using this method so that you can see what some of the other options might be?

THOMAS WEDELL-WEDELLSBORG: Right. So, I mean, let’s say it’s you who have that problem. I would go in and say, first of all, what would you say the problem is? Like, if you were to describe your view of the problem, what would that be?

SARAH GREEN CARMICHAEL: I hate cleaning the kitchen, and I want someone else to clean it up.

THOMAS WEDELL-WEDELLSBORG: OK. So, my first observation, you know, that somebody else might not necessarily be your spouse. So, already there, there’s an inbuilt assumption in your question around oh, it has to be my husband who does the cleaning. So, it might actually be worth, already there to say, is that really the only problem you have? That you hate cleaning the kitchen, and you want to avoid it? Or might there be something around, as well, getting a better relationship in terms of how you solve problems in general or establishing a better way to handle small problems when dealing with your spouse?

SARAH GREEN CARMICHAEL: Or maybe, now that I’m thinking that, maybe the problem is that you just can’t find the stuff in the kitchen when you need to find it.

THOMAS WEDELL-WEDELLSBORG: Right, and so that’s an example of a reframing, that actually why is it a problem that the kitchen is not clean? Is it only because you hate the act of cleaning, or does it actually mean that it just takes you a lot longer and gets a lot messier to actually use the kitchen, which is a different problem. The way you describe this problem now, is there anything that’s missing from that description?

SARAH GREEN CARMICHAEL: That is a really good question.

THOMAS WEDELL-WEDELLSBORG: Other, basically asking other factors that we are not talking about right now, and I say those because people tend to, when given a problem, they tend to delve deeper into the detail. What often is missing is actually an element outside of the initial description of the problem that might be really relevant to what’s going on. Like, why does the kitchen get messy in the first place? Is it something about the way you use it or your cooking habits? Is it because the neighbor’s kids, kind of, use it all the time?

There might, very often, there might be issues that you’re not really thinking about when you first describe the problem that actually has a big effect on it.

SARAH GREEN CARMICHAEL: I think at this point it would be helpful to maybe get another business example, and I’m wondering if you could tell us the story of the dog adoption problem.

THOMAS WEDELL-WEDELLSBORG: Yeah. This is a big problem in the US. If you work in the shelter industry, basically because dogs are so popular, more than 3 million dogs every year enter a shelter, and currently only about half of those actually find a new home and get adopted. And so, this is a problem that has persisted. It’s been, like, a structural problem for decades in this space. In the last three years, where people found new ways to address it.

So a woman called Lori Weise who runs a rescue organization in South LA, and she actually went in and challenged the very idea of what we were trying to do. She said, no, no. The problem we’re trying to solve is not about how to get more people to adopt dogs. It is about keeping the dogs with their first family so they never enter the shelter system in the first place.

In 2013, she started what’s called a Shelter Intervention Program that basically works like this. If a family comes and wants to hand over their dog, these are called owner surrenders. It’s about 30% of all dogs that come into a shelter. All they would do is go up and ask, if you could, would you like to keep your animal? And if they said yes, they would try to fix whatever helped them fix the problem, but that made them turn over this.

And sometimes that might be that they moved into a new building. The landlord required a deposit, and they simply didn’t have the money to put down a deposit. Or the dog might need a $10 rabies shot, but they didn’t know how to get access to a vet.

And so, by instigating that program, just in the first year, she took her, basically the amount of dollars they spent per animal they helped went from something like $85 down to around $60. Just an immediate impact, and her program now is being rolled out, is being supported by the ASPCA, which is one of the big animal welfare stations, and it’s being rolled out to various other places.

And I think what really struck me with that example was this was not dependent on having the internet. This was not, oh, we needed to have everybody mobile before we could come up with this. This, conceivably, we could have done 20 years ago. Only, it only happened when somebody, like in this case Lori, went in and actually rethought what the problem they were trying to solve was in the first place.

SARAH GREEN CARMICHAEL: So, what I also think is so interesting about that example is that when you talk about it, it doesn’t sound like the kind of thing that would have been thought of through other kinds of problem solving methods. There wasn’t necessarily an After Action Review or a 5 Whys exercise or a Six Sigma type intervention. I don’t want to throw those other methods under the bus, but how can you get such powerful results with such a very simple way of thinking about something?

THOMAS WEDELL-WEDELLSBORG: That was something that struck me as well. This, in a way, reframing and the idea of the problem diagnosis is important is something we’ve known for a long, long time. And we’ve actually have built some tools to help out. If you worked with us professionally, you are familiar with, like, Six Sigma, TRIZ, and so on. You mentioned 5 Whys. A root cause analysis is another one that a lot of people are familiar with.

Those are our good tools, and they’re definitely better than nothing. But what I notice when I work with the companies applying those was those tools tend to make you dig deeper into the first understanding of the problem we have. If it’s the elevator example, people start asking, well, is that the cable strength, or is the capacity of the elevator? That they kind of get caught by the details.

That, in a way, is a bad way to work on problems because it really assumes that there’s like a, you can almost hear it, a root cause. That you have to dig down and find the one true problem, and everything else was just symptoms. That’s a bad way to think about problems because problems tend to be multicausal.

There tend to be lots of causes or levers you can potentially press to address a problem. And if you think there’s only one, if that’s the right problem, that’s actually a dangerous way. And so I think that’s why, that this is a method I’ve worked with over the last five years, trying to basically refine how to make people better at this, and the key tends to be this thing about shifting out and saying, is there a totally different way of thinking about the problem versus getting too caught up in the mechanistic details of what happens.

SARAH GREEN CARMICHAEL: What about experimentation? Because that’s another method that’s become really popular with the rise of Lean Startup and lots of other innovation methodologies. Why wouldn’t it have worked to, say, experiment with many different types of fixing the dog adoption problem, and then just pick the one that works the best?

THOMAS WEDELL-WEDELLSBORG: You could say in the dog space, that’s what’s been going on. I mean, there is, in this industry and a lot of, it’s largely volunteer driven. People have experimented, and they found different ways of trying to cope. And that has definitely made the problem better. So, I wouldn’t say that experimentation is bad, quite the contrary. Rapid prototyping, quickly putting something out into the world and learning from it, that’s a fantastic way to learn more and to move forward.

My point is, though, that I feel we’ve come to rely too much on that. There’s like, if you look at the start up space, the wisdom is now just to put something quickly into the market, and then if it doesn’t work, pivot and just do more stuff. What reframing really is, I think of it as the cognitive counterpoint to prototyping. So, this is really a way of seeing very quickly, like not just working on the solution, but also working on our understanding of the problem and trying to see is there a different way to think about that.

If you only stick with experimentation, again, you tend to sometimes stay too much in the same space trying minute variations of something instead of taking a step back and saying, wait a minute. What is this telling us about what the real issue is?

SARAH GREEN CARMICHAEL: So, to go back to something that we touched on earlier, when we were talking about the completely hypothetical example of a spouse who does not clean the kitchen–

THOMAS WEDELL-WEDELLSBORG: Completely, completely hypothetical.

SARAH GREEN CARMICHAEL: Yes. For the record, my husband is a great kitchen cleaner.

You started asking me some questions that I could see immediately were helping me rethink that problem. Is that kind of the key, just having a checklist of questions to ask yourself? How do you really start to put this into practice?

THOMAS WEDELL-WEDELLSBORG: I think there are two steps in that. The first one is just to make yourself better at the method. Yes, you should kind of work with a checklist. In the article, I kind of outlined seven practices that you can use to do this.

But importantly, I would say you have to consider that as, basically, a set of training wheels. I think there’s a big, big danger in getting caught in a checklist. This is something I work with.

My co-author Paddy Miller, it’s one of his insights. That if you start giving people a checklist for things like this, they start following it. And that’s actually a problem, because what you really want them to do is start challenging their thinking.

So the way to handle this is to get some practice using it. Do use the checklist initially, but then try to step away from it and try to see if you can organically make– it’s almost a habit of mind. When you run into a colleague in the hallway and she has a problem and you have five minutes, like, delving in and just starting asking some of those questions and using your intuition to say, wait, how is she talking about this problem? And is there a question or two I can ask her about the problem that can help her rethink it?

SARAH GREEN CARMICHAEL: Well, that is also just a very different approach, because I think in that situation, most of us can’t go 30 seconds without jumping in and offering solutions.

THOMAS WEDELL-WEDELLSBORG: Very true. The drive toward solutions is very strong. And to be clear, I mean, there’s nothing wrong with that if the solutions work. So, many problems are just solved by oh, you know, oh, here’s the way to do that. Great.

But this is really a powerful method for those problems where either it’s something we’ve been banging our heads against tons of times without making progress, or when you need to come up with a really creative solution. When you’re facing a competitor with a much bigger budget, and you know, if you solve the same problem later, you’re not going to win. So, that basic idea of taking that approach to problems can often help you move forward in a different way than just like, oh, I have a solution.

I would say there’s also, there’s some interesting psychological stuff going on, right? Where you may have tried this, but if somebody tries to serve up a solution to a problem I have, I’m often resistant towards them. Kind if like, no, no, no, no, no, no. That solution is not going to work in my world. Whereas if you get them to discuss and analyze what the problem really is, you might actually dig something up.

Let’s go back to the kitchen example. One powerful question is just to say, what’s your own part in creating this problem? It’s very often, like, people, they describe problems as if it’s something that’s inflicted upon them from the external world, and they are innocent bystanders in that.

SARAH GREEN CARMICHAEL: Right, or crazy customers with unreasonable demands.

THOMAS WEDELL-WEDELLSBORG: Exactly, right. I don’t think I’ve ever met an agency or consultancy that didn’t, like, gossip about their customers. Oh, my god, they’re horrible. That, you know, classic thing, why don’t they want to take more risk? Well, risk is bad.

It’s their business that’s on the line, not the consultancy’s, right? So, absolutely, that’s one of the things when you step into a different mindset and kind of, wait. Oh yeah, maybe I actually am part of creating this problem in a sense, as well. That tends to open some new doors for you to move forward, in a way, with stuff that you may have been struggling with for years.

SARAH GREEN CARMICHAEL: So, we’ve surfaced a couple of questions that are useful. I’m curious to know, what are some of the other questions that you find yourself asking in these situations, given that you have made this sort of mental habit that you do? What are the questions that people seem to find really useful?

THOMAS WEDELL-WEDELLSBORG: One easy one is just to ask if there are any positive exceptions to the problem. So, was there day where your kitchen was actually spotlessly clean? And then asking, what was different about that day? Like, what happened there that didn’t happen the other days? That can very often point people towards a factor that they hadn’t considered previously.

SARAH GREEN CARMICHAEL: We got take-out.

THOMAS WEDELL-WEDELLSBORG: S,o that is your solution. Take-out from [INAUDIBLE]. That might have other problems.

Another good question, and this is a little bit more high level. It’s actually more making an observation about labeling how that person thinks about the problem. And what I mean with that is, we have problem categories in our head. So, if I say, let’s say that you describe a problem to me and say, well, we have a really great product and are, it’s much better than our previous product, but people aren’t buying it. I think we need to put more marketing dollars into this.

Now you can go in and say, that’s interesting. This sounds like you’re thinking of this as a communications problem. Is there a different way of thinking about that? Because you can almost tell how, when the second you say communications, there are some ideas about how do you solve a communications problem. Typically with more communication.

And what you might do is go in and suggest, well, have you considered that it might be, say, an incentive problem? Are there incentives on behalf of the purchasing manager at your clients that are obstructing you? Might there be incentive issues with your own sales force that makes them want to sell the old product instead of the new one?

So literally, just identifying what type of problem does this person think about, and is there different potential way of thinking about it? Might it be an emotional problem, a timing problem, an expectations management problem? Thinking about what label of what type of problem that person is kind of thinking as it of.

SARAH GREEN CARMICHAEL: That’s really interesting, too, because I think so many of us get requests for advice that we’re really not qualified to give. So, maybe the next time that happens, instead of muddying my way through, I will just ask some of those questions that we talked about instead.

THOMAS WEDELL-WEDELLSBORG: That sounds like a good idea.

SARAH GREEN CARMICHAEL: So, Thomas, this has really helped me reframe the way I think about a couple of problems in my own life, and I’m just wondering. I know you do this professionally, but is there a problem in your life that thinking this way has helped you solve?

THOMAS WEDELL-WEDELLSBORG: I’ve, of course, I’ve been swallowing my own medicine on this, too, and I think I have, well, maybe two different examples, and in one case somebody else did the reframing for me. But in one case, when I was younger, I often kind of struggled a little bit. I mean, this is my teenage years, kind of hanging out with my parents. I thought they were pretty annoying people. That’s not really fair, because they’re quite wonderful, but that’s what life is when you’re a teenager.

And one of the things that struck me, suddenly, and this was kind of the positive exception was, there was actually an evening where we really had a good time, and there wasn’t a conflict. And the core thing was, I wasn’t just seeing them in their old house where I grew up. It was, actually, we were at a restaurant. And it suddenly struck me that so much of the sometimes, kind of, a little bit, you love them but they’re annoying kind of dynamic, is tied to the place, is tied to the setting you are in.

And of course, if– you know, I live abroad now, if I visit my parents and I stay in my old bedroom, you know, my mother comes in and wants to wake me up in the morning. Stuff like that, right? And it just struck me so, so clearly that it’s– when I change this setting, if I go out and have dinner with them at a different place, that the dynamic, just that dynamic disappears.

SARAH GREEN CARMICHAEL: Well, Thomas, this has been really, really helpful. Thank you for talking with me today.

THOMAS WEDELL-WEDELLSBORG: Thank you, Sarah.  

HANNAH BATES: That was Thomas Wedell-Wedellsborg in conversation with Sarah Green Carmichael on the HBR IdeaCast. He’s an expert in problem solving and innovation, and he’s the author of the book, What’s Your Problem?: To Solve Your Toughest Problems, Change the Problems You Solve .

We’ll be back next Wednesday with another hand-picked conversation about leadership from the Harvard Business Review. If you found this episode helpful, share it with your friends and colleagues, and follow our show on Apple Podcasts, Spotify, or wherever you get your podcasts. While you’re there, be sure to leave us a review.

We’re a production of Harvard Business Review. If you want more podcasts, articles, case studies, books, and videos like this, find it all at HBR dot org.

This episode was produced by Anne Saini, and me, Hannah Bates. Ian Fox is our editor. Music by Coma Media. Special thanks to Maureen Hoch, Adi Ignatius, Karen Player, Ramsey Khabbaz, Nicole Smith, Anne Bartholomew, and you – our listener.

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Explore cognitive maps as higher-order learning activity to assess learning Calculus

Cognitive maps are regarded as 'internally represented schemas or mental models for particular problem-solving domains that are learned and encoded as a result of an individual's interaction with their environment' (Swan, 1997, p. 188). Cognitive maps can be viewed as an externalization of a schema encoded in a learner’s long-term memory. They are often used as media for constructive learning activities and as communication aids in lectures, study materials, and collaborative learning (Cafias et al.,2003).  This learning tool has become popular in various educational settings. However, the existing research has not fully explored the effectiveness of cognitive mapping as a learning tool nor analyzed its utility as an assessment tool in mathematics-particularly calculus.This study will report on the implementation and evaluation of a novel assessment, cognitive mapping, in a university calculus course (N = 40). We will investigate relationships between cognitive mapping performance and two major outcome variables: academic achievement and assessment self-efficacy.

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Making adequate every-day decisions often demands recollecting past experiences from memory, integrating these experiences with novel information, and identifying which aspects are important for the decision problem at hand. Decision science portrays this process as a selection between two kinds of judgment or decision strategies: a capacity-limited abstraction of knowledge and a similarity-based retrieval of past instances. Disentangling those two strategies, past research has converged towards the view that knowledge abstraction relies more upon integrating information in working memory, whereas similarity-based retrieval draws more heavily upon episodic long-term memory. However, it remains an open question how individuals combine decisions from both strategies. Specifically, individuals may either integrate (or blend) knowledge abstraction with previously stored memories for each judgment they make, or they may select among different strategies. Drawing upon a learning approach, I aim to disentangle these strategy selection mechanisms. Evidence based on judgments and familiarity-based choices points towards the view that people rather integrate knowledge from several strategies than switch among the strategies, using memory as a guide to adjust their relative contribution.

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The Influences of Emotion on Learning and Memory

Emotion has a substantial influence on the cognitive processes in humans, including perception, attention, learning, memory, reasoning, and problem solving. Emotion has a particularly strong influence on attention, especially modulating the selectivity of attention as well as motivating action and behavior. This attentional and executive control is intimately linked to learning processes, as intrinsically limited attentional capacities are better focused on relevant information. Emotion also facilitates encoding and helps retrieval of information efficiently. However, the effects of emotion on learning and memory are not always univalent, as studies have reported that emotion either enhances or impairs learning and long-term memory (LTM) retention, depending on a range of factors. Recent neuroimaging findings have indicated that the amygdala and prefrontal cortex cooperate with the medial temporal lobe in an integrated manner that affords (i) the amygdala modulating memory consolidation; (ii) the prefrontal cortex mediating memory encoding and formation; and (iii) the hippocampus for successful learning and LTM retention. We also review the nested hierarchies of circular emotional control and cognitive regulation (bottom-up and top-down influences) within the brain to achieve optimal integration of emotional and cognitive processing. This review highlights a basic evolutionary approach to emotion to understand the effects of emotion on learning and memory and the functional roles played by various brain regions and their mutual interactions in relation to emotional processing. We also summarize the current state of knowledge on the impact of emotion on memory and map implications for educational settings. In addition to elucidating the memory-enhancing effects of emotion, neuroimaging findings extend our understanding of emotional influences on learning and memory processes; this knowledge may be useful for the design of effective educational curricula to provide a conducive learning environment for both traditional “live” learning in classrooms and “virtual” learning through online-based educational technologies.

Introduction

Emotional experiences are ubiquitous in nature and important and perhaps even critical in academic settings, as emotion modulates virtually every aspect of cognition. Tests, examinations, homework, and deadlines are associated with different emotional states that encompass frustration, anxiety, and boredom. Even subject matter influences emotions that affect one’s ability to learn and remember. The usage of computer-based multimedia educational technologies, such as intelligent tutoring systems (ITSs) and massive open online courses (MOOCs), which are gradually replacing traditional face-to-face learning environments, is increasing. This may induce various emotional experiences in learners. Hence, emotional influences should be carefully considered in educational courses design to maximize learner engagement as well as improve learning and long-term retention of the material ( Shen et al., 2009 ). Numerous studies have reported that human cognitive processes are affected by emotions, including attention ( Vuilleumier, 2005 ), learning and memory ( Phelps, 2004 ; Um et al., 2012 ), reasoning ( Jung et al., 2014 ), and problem-solving ( Isen et al., 1987 ). These factors are critical in educational domains because when students face such difficulties, it defeats the purpose of schooling and can potentially render it meaningless. Most importantly, emotional stimuli appear to consume more attentional resources than non-emotional stimuli ( Schupp et al., 2007 ). Moreover, attentional and motivational components of emotion have been linked to heightened learning and memory ( Pekrun, 1992 ; Seli et al., 2016 ). Hence, emotional experiences/stimuli appear to be remembered vividly and accurately, with great resilience over time.

Recent studies using functional neuroimaging techniques detect and recognize human emotional states and have become a topic of increasing research in cognitive neuroscience, affective neuroscience, and educational psychology to optimize learning and memory outcomes ( Carew and Magsamen, 2010 ; Um et al., 2012 ). Human emotions comprise complex interactions of subjective feelings as well as physiological and behavioral responses that are especially triggered by external stimuli, which are subjectively perceived as “personally significant.” Three different approaches are used to monitor the changes in emotional states: (1) subjective approaches that assess subjective feelings and experiences; (2) behavioral investigations of facial expressions ( Jack and Schyns, 2015 ), vocal expressions ( Russell et al., 2003 ), and gestural changes ( Dael et al., 2012 ); and (3) objective approaches via physiological responses that include electrical and hemodynamic of the central nervous system (CNS) activities ( Vytal and Hamann, 2010 ) in addition to autonomic nervous system (ANS) responses such as heart rate, respiratory volume/rate, skin temperature, skin conductance and blood volume pulses ( Li and Chen, 2006 ). The CNS and ANS physiological responses (brain vs. body organs) can be objectively measured via neuroimaging and biosensors and are more difficult to consciously conceal or manipulate compared to subjective and behavioral responses. Although functional neuroimaging enables us to identify brain regions of interest for cognitive and emotional processing, it is difficult to comprehend emotional influences on learning and memory retrieval without a fundamental understanding of the brain’s inherent emotional operating systems.

The aim of this current article was to highlight an evolutionary approach to emotion, which may facilitate understanding of the effects of emotion on learning and memory. We initially present the terminology used in affective neuroscience studies, describe the roles of emotion and motivation in learning and memory, and outline the evolutionary framework and the seven primary emotional system. This is followed by the emotional-cognitive interactions in the various brain regions that are intimately involved in emotion and memory systems. This is performed to define the congruent interactions in these regions are associated with long-term memory (LTM) retention. We then discuss the emerging studies that further our understanding of emotional effects deriving from different modalities of emotional content. This is followed by a discussion of four major functional neuroimaging techniques, including functional magnetic resonance imaging (fMRI), positron emission tomography (PET), electroencephalography (EEG), and functional near-infrared spectroscopy (fNIRS). We then present the important factors for consideration in experimental design, followed by a description of psychiatric disorders, such as depression and anxiety, which are emotionally charged dysfunctions that are strongly detrimental to cognitive performance. Our review ends with concluding remarks on the current issues and future research possibilities with respect to the efficient enhancement of educational practices and technologies.

Emotions, Moods, Feelings, Affects and Drives

Subjective terms used in affective neuroscience include emotions, moods, feelings, affects and drives. Although emotion has long been studied, it bears no single definition. A review of 92 putative definitions and nine skeptical statements ( Kleinginna and Kleinginna, 1981 ) suggests a definition with a rather broad consensus:

• simple  Emotions describe a complex set of interactions between subjective and objective variables that are mediated by neural and hormonal systems, which can (a) give rise to affective experiences of emotional valence (pleasure-displeasure) and emotional arousal (high-low activation/calming-arousing); (b) generate cognitive processes such as emotionally relevant perceptual affect, appraisals, labeling processes; (c) activate widespread psychological and physiological changes to the arousing conditions; and (d) motivate behavior that is often but not always expressive, goal-directed and adaptive.

Although this definition may be adequate for everyday purposes, it does not encompass some important aspects of emotional systems such as how emotions operate to create subjectively experienced feelings and how they control personality dimensions. Accordingly, Panksepp (1998) suggested the following:

• simple  Emotions are the psychoneural processes that are influential in controlling the vigor and patterning of actions in the dynamic flow of intense behavioral interchanges between animals as well as with certain objects that are important for survival. Hence, each emotion has a characteristic “feeling tone” that is especially important in encoding the intrinsic values of these interactions, depending on their likelihood of either promoting or hindering survival (both in the immediate “personal” and long-term “reproductive” sense). Subjective experiential-feelings arise from the interactions of various emotional systems with the fundamental brain substrates of “the self,” that is important in encoding new information as well as retrieving information on subsequent events and allowing individuals efficiently to generalize new events and make decisions.

He went further to propose seven primary emotional systems/prototype emotional states, namely SEEKING, RAGE, FEAR, LUST, CARE, PANIC/GRIEF, and PLAY that represent basic foundations for living and learning.

Moods last longer than emotions, which are also characterized by positive and negative moods. In contrast, feelings refer to mental experiences that are necessarily valence, either good or bad as well as accompanied by internal physiological changes in the body, specifically the viscera, including the heart, lungs, and gut, for maintaining or restoring homeostatic balances. Feelings are not commonly caused emotions. Because the generation of emotional feelings requires a neural re-mapping of different features of the body state in the CNS, resulting from cognitive “appraisal” where the anterior insular cortex plays a key integrative role ( Craig and Craig, 2009 ; Damasio and Carvalho, 2013 ). Nonetheless, Panksepp (2005) has defended the view that emotional operating systems (caudal and medial subcortical brain regions) appeared to generate emotional experiences via localized electrical stimulation of the brain stimulation (ESB) rather dependent on changes of the external environment or bodily states. Affects are subjective experienced emotional feelings that are difficult to describe, but have been linked to bodily states such as homeostatic drives (hunger and thirst) and external stimuli (visual, auditory, taste, touch, smell) ( Panksepp, 2005 ). The latter are sometimes called “core affect,” which refers to consciously accessible elemental processes involving pleasure and arousal that span bipolar dimensions ( Russell and Barrett, 1999 ). In addition, a “drive” is an inherent action program that is responsible for the satisfaction of basic and instinctual (biologically pre-set) physiological needs, e.g., hunger, thirst, libido, exploration, play, and attachment to mates ( Panksepp, 1998 ); this is sometimes called “homeostatic drive.” In brief, a crucial characteristic shared by emotion, mood, feeling, affect and drive is their intrinsic valence, which lies on the spectrum of positive and negative valence (pleasure-displeasure/goodness-badness). The term emotion exemplifies the “umbrella” concept that includes affective, cognitive, behavioral, expressive and physiological changes; emotion is triggered by external stimuli and associated with the combination of feeling and motivation.

Recent Evidence Regarding the Role of Emotion in Learning and Memory

The impact of emotion on learning processes is the focus of many current studies. Although it is well established that emotions influence memory retention and recall, in terms of learning, the question of emotional impacts remains questionable. Some studies report that positive emotions facilitate learning and contribute to academic achievement, being mediated by the levels of self-motivation and satisfaction with learning materials ( Um et al., 2012 ). Conversely, a recent study reported that negative learning-centered state (confusion) improve learning because of an increased focus of attention on learning material that leads to higher performances on post tests and transfer tests ( D’Mello et al., 2014 ). Confusion is not an emotion but a cognitive disequilibrium state induced by contradictory data. A confused student might be frustrated with their poor understanding of subject matter, and this is related to both the SEEKING and RAGE systems, with a low-level of activation of rage or irritation, and amplification of SEEKING. Hence, motivated students who respond to their confusion seek new understanding by doing additional cognitive work. Further clarification of this enhances learning. Moreover, stress, a negative emotional state, has also been reported to facilitate and/or impair both learning and memory, depending on intensity and duration ( Vogel and Schwabe, 2016 ). More specifically, mild and acute stress facilitates learning and cognitive performance, while excess and chronic stress impairs learning and is detrimental to memory performance. Many other negative consequences attend owing to overactivity of the hypothalamic-pituitary-adrenal (HPA) axis, which results in both impaired synaptic plasticity and learning ability ( Joëls et al., 2004 ). Nonetheless, confounding influences of emotions on learning and memory can be explained in terms of attentional and motivational components. Attentional components enhance perceptual processing, which then helps to select and organize salient information via a “bottom-up” approach to higher brain functions and awareness ( Vuilleumier, 2005 ). Motivational components induce curiosity, which is a state associated with psychological interest in novel and/or surprising activities (stimuli). A curiosity state encourages further exploration and apparently prepares the brain to learn and remember in both children and adults ( Oudeyer et al., 2016 ). The term “surprising” might be conceptualized as an incongruous situation (expectancy violation) refers to a discrepancy between prior expectations and the new information; it may drive a cognitive reset for “learned content” that draws one’s attention.

Similarly, emotionally enhanced memory functions have been reported in relation to selective attention elicited by emotionally salient stimuli ( Vuilleumier, 2005 ; Schupp et al., 2007 ). During the initial perceptual stage, attention is biased toward emotionally salient information that supports detection by the salient input. Thus, stimulating selective attention increases the likelihood for emotional information to become encoded in LTM storage associated with a top-down control in sensory pathways that are modulated by the frontal and parietal cortices. This is an example of an indirect influence on perception and attention that regulates selective sensory processing and behavioral determination ( Vuilleumier, 2005 ). Because the human sensory systems have no capacity to simultaneously process everything at once, which necessitates attentional mechanisms. Top-down attentional processing obtains adequate attentional resource allocation to process emotional valence information for encoding and retrieval via cooperation with the brain regions such as the ventromedial prefrontal cortex and superior temporal sulcus, along with the primary visual cortex (helps to realize both emotion and conceptualization). Similarly, experimental studies have examined the phenomenon by using various attentional tasks, including filtering (dichotic listening and Stroop task), search (visual search), cuing (attentional probe, spatial cuing) and attentional blink [rapid serial visual presentation (RSVP)] paradigms ( Yiend, 2010 ). These investigations demonstrated biased attentional processing toward emotionally stimulating material content attended by increased sensory responses. One study reported that emotional stimuli induce a “pop-out” effect that leads to the attentional capture and privileged processing ( Öhman et al., 2001 ). Moreover, a study using the RSVP paradigm compared healthy subjects with a group of patients with bilateral amygdala damage. The results revealed that healthy subjects exhibited increased perception and attention toward emotional words compared to patients, indicating that the amygdala plays a crucial role in emotional processing ( Anderson and Phelps, 2001 ). In addition, functional neuroimaging showed that the insular cortex, the secondary somatosensory cortex, the cingulate cortex and nuclei in the tegmentum and hypothalamus are the brain regions that regulate attentional focus by integrating external and internal inputs to create emotional feeling states, thus modulating a motivational state that obtains homeostasis ( Damasio et al., 2000 ). All emotional systems associated with strong motivational components such as psychological salient bodily need states operate through the SEEKING system that motivates appetitive/exploratory behavior to acquire resources needed for survival ( Montag and Panksepp, 2017 ).

The distinction between emotion and homeostasis, is the process of regulation for continuously changing internal states via appropriate corrective responses that respond to both internal and external environmental conditions to maintain an optimal physiological state in the body. Homeostatic affects , such as hunger and thirst, are not considered prototype emotional states. Because homeostatic affects have never been mapped using ESB that arouse basic emotional responses ( Panksepp, 2005 , 2007 ). However, emotional prototypes can be thought of as evolutionary extensions/predictions of impending homeostatic threats; for example, SEEKING might be an evolutionary extension of intense hunger and thirst (the major sources of suffering that signal energy depletion to search for food and water intake) ( Watt, 2012 ). Homeostatic imbalances engage the mesolimbic motivational system via hypothalamic interactions with the extended trajectory of the SEEKING system [centrally including the lateral hypothalamus, ventral basal ganglia, and ventral tegmental area (VTA)]. It is the distributed functional network that serves the general function of finding resources for survival that gets hungry animals to food, thirsty animals to water, cold animals to warmer environments, etc. ( Panksepp, 1998 ). To summarize, both emotion and motivation are crucial for the maintenance of psychological and physiological homeostasis, while emotional roles are particularly important in the process of encoding new information containing emotional components. The latter increases attention toward salient new information by selectively enhancing detection, evaluation, and extraction of data for memorization. In addition, motivational components promote learning and enhance subsequent memory retrieval while generalizing new events consequent to adaptive physiological changes.

The Evolutionary Framework of Emotion and The Seven Primary Emotional Systems

Evolution built our higher minds (the faculty of consciousness and thoughts) on a foundation of primary-process of emotional mechanism that preprogrammed executive action systems (the prototype emotions) rely on cognitive processing (interpretation) and appraisal in the organisms attempt to decipher the type of situation they might be in; in other words, how to deal with emotionally challenging situations, whether it is a play situation or a threat situation (where RAGE and FEAR might be the appropriate system to recruit). Emotion offers preprogrammed but partially modifiable (under the secondary process of learning and memory) behavioral routines in the service of the solution of prototypical adaptive challenges, particularly in dealing with friend vs. foe; these routines are evolutionary extensions of homeostasis and embed a prediction beyond the current situation to a potentially future homeostatic benefit or threat. Thus, evolution uses whatever sources for survival and procreative success. According to Panksepp and Solms (2012) , key CNS emotional-affective processes are (1) Primary-process emotions; (2) Secondary-process learning and memory; and (3) Tertiary-process higher cognitive functions. Fundamentally, primary emotional processes regulate unconditioned emotional actions that anticipate survival needs and consequently guide secondary process via associative learning mechanisms (classical/Pavlovian and instrumental/operant conditioning). Subsequently, learning process sends relevant information to higher brain regions such as the prefrontal cortex to perform tertiary cognition process that allows planning for future based on past experiences, stored in LTM. In other words, the brain’s neurodevelopment trajectory and “wiring up” activations show that there is a genetically coded aversion to situations that generate RAGE, FEAR and other negative states for minimizing painful things and maximizing pleasurable kinds of stimulation. These are not learned- all learning (secondary-process) is piggybacked on top of the “primary-process emotions” that are governed by “Law of Affect” (see Figure ​ Figure1 1 ). What now follows is an explanation of these CNS emotional-affective processing sub-levels and their inter-relationships.

Shows the nested hierarchies of circular emotional control and cognitive regulation for “bottom-up” influences and “top-down” regulations. The schematic shows conceptual relationships between primary processes of emotional system (lower brain function), as well as secondary processes of cognitive system and tertiary processing (higher brain function). Primary emotional processing for homeostatic, sensory and emotional affects facilitate secondary learning and memory processing via the “SEEKING” system that promotes survival and reproductive success (bottom-up instinctual influences). As secondary processes are continually integrated with primary emotional processing, they mature to higher brain cognitive faculties to generate effective solutions for living and subsequently exert top-down regulatory control over behavior. The primary emotional processing is mediated by complex unconditioned emotional responses (evolutionary “memories”) through “Law of Affect”; sometimes called “reinforcement principle” that explains how the brain emotional networks control learning. This bi-circular causation for higher brain functionality is coordinated by lower brain functions [adapted from ( Panksepp and Solms, 2012 )].

Primary-Process Emotions (Prototype Emotional States)

The emotional operating system is an inherited and genetically encoded circuitry that anticipates key survival and homeostatic needs. Thus, animals and humans share primary emotional network at the subcortical level, which includes the midbrain’s periaqueductal grey (PAG) and VTA, basal ganglia (amygdala and nucleus accumbens), and insula, as well as diencephalon (the cingulate and medial frontal cortices through the lateral and medial hypothalamus and medial thalamus). Subcortical brain regions are involved in three sub-components of affects: (1) core emotional feelings (fear, anger, joy and various forms of distress); (2) homeostatic drives/motivational experiences (hunger and thirst); and (3) sensory affects (pain, taste, temperature and disgust). Primary-process emotions are not unconscious. Strong emotion is intrinsically conscious at least in the sense that it is experienced even if we might mislabel it, or animal clearly is not able to attach a semantic label-these are simply not realistic standards for determining whether something is conscious or not conscious. Nonetheless, the emotional experiences guide behavior to promote survival and procreative success as well as mediate learning (‘ rewarding ’ and ‘ punishing ’ learning effects) and thinking at secondary and tertiary levels.

Secondary-Process Emotions (Learning and Memory)

Primary emotional systems guide associative learning and memory (classical/operant conditioning and emotional habit) processes via the mediation of emotional networks. This includes the basal ganglia (basolateral and central amygdala, nucleus accumbens, thalamus and dorsal striatum), and the medial temporal lobe (MTL) including hippocampus as well as the entorhinal cortex, perirhinal cortex, and parahippocampal cortices that responsible for declarative memories. Thus, secondary processes of learning and memory scrutinize and regulate emotional feelings in relation to environmental events that subsequently refine effective solutions to living.

Tertiary-Process Emotions (Higher Cognitive Functions)

Higher cognitive functions operate within the cortical regions, including the frontal cortex for awareness and consciousness functions such as thinking, planning, emotional regulation and free-will (intention-to-act), which mediate emotional feelings. Hence, cognition is an extension of emotion (just as emotion is an extension of homeostasis aforementioned). Tertiary processes are continually integrated with the secondary processes and reach a mature level (higher brain functions) to better anticipating key survival issues, thus yielding cognitive control of emotion via “top-down” regulation. In other words, brain-mind evolution enables human to reason but also regulate our emotions.

Psychologist Neisser (1963) suggested that cognition serves emotion and homeostatic needs where environmental information is evaluated in terms of its ability to satisfy or frustrate needs. In other words, cognition is in the service of satisfying emotional and homeostatic needs. This infers that cognition modulates, activates and inhibits emotion. Hence, emotion is not a simple linear event but rather a feedback process that autonomously restores an individual’s state of equilibrium. More specifically stated, emotion regulates the allocation of processing resources and determines our behavior by tuning us to the world in certain biased ways, thus steering us toward things that “feel good” while avoiding things that “feel bad.” This indicates that emotion guides and motivates cognition that promotes survival by guiding behavior and desires according to unique goal orientation ( Northoff et al., 2006 ). Therefore, the CNS maintains complex processes by continually monitoring internal and external environments. For example, changes in internal environments (contraction of visceral muscles, heart rate, etc.) are sensed by an interoceptive system (afferent peripheral nerves) that signals the sensory cortex (primary, secondary and somatosensory) for integration and processing. Thus, from an evolutionary perspective, human mental activity is driven by the ancient emotional and motivational brain systems shared by cross-mammalians that encode life-sustaining and life-detracting features to promote adaptive instinctual responses. Moreover, emotional and homeostasis mechanisms are characterized by intrinsic valence processing that is either a positive/pleasure or negative/displeasure bias. Homeostasis imbalance is universally experienced as negative emotional feelings and only becomes positively valenced when rectified. Hence, individuals sustain bodily changes that underlie psychological (emotional) and biological (homeostatic) influences on two sides, i.e., one side is oriented toward the survival and reproductive success that is associated with positively valenced emotional and physiologic homeostasis (anticipatory response) and the other responds to survival and reproductive failure associated with negatively valenced emotional and physiologic homeostasis (reactive response). Consequently, cognition modulates both emotional and homeostatic states by enhancing survival and maximizing rewards while minimizing risk and punishments. Thus, this evolutionary consideration suggests the brain as a ‘predictive engine’ to make it adaptive in a particular environment. Figure ​ Figure2 2 demonstrates this cyclic homeostatic regulation.

Conceptually maps the homeostatic regulation of internal and external inputs that affect cognition, emotion, feeling, and drive: Inputs → Homeostasis ↔ Emotion ∗ ↔ Cognition. This lead to the experience of one’s self via overt behavior that is biased by a specific emotion stimulated by bodily changes that underlie psychological/physiological states. ∗ Represents emotion associated with a combination of feeling and motivation/drive; ↔ indicates a bi-directional interaction; and → indicates a one-directional relationship. Adapted from Damasio and Carvalho (2013) .

Panksepp (1998) identified seven primary emotional systems that govern mammalian brains as follows: SEEKING, RAGE, FEAR, LUST, CARE, PANIC/GRIEF, and PLAY. Here, we use UPPERCASE letters to denote unconditional emotional responses (emotional primes). These primary emotional neural networks are situated in the subcortical regions; moreover, the evidence demonstrates that decortication leaves primary emotional systems intact ( Panksepp et al., 1994 ). Hence, cortical regions are non-essential for the generation of prototype emotional states but are responsible for their modulation and regulation. The present article emphasizes SEEKING because it is the most fundamental of the primary emotional systems and is crucial for learning and memory. The SEEKING system facilitates learning because when fully aroused, it fills the mind with interest that then motivates the individual to search out and learn things that they need, crave and desire. Accordingly, SEEKING generates and sustains curiosity’s engagement for a particular purpose while also promoting learning via its mediation of anticipatory eagerness ( Oudeyer et al., 2016 ). In other words, the SEEKING system has been designed to automatically learn by exploring anything that results in acquired behavioral manifestations for survival operations, all the way from the mesolimbic-mesocortical dopamine system through to the prefrontal cortex (PFC); thus, it is intimately linked with LTM formation ( Blumenfeld and Ranganath, 2007 ). Consequently, it is the foundation of secondary learning and higher cognitive processes when compared with the remaining six emotional systems. However, this system is less activated during chronic stress, sickness, and depression, all of which are likely to impair learning and various higher cognitions. On the other hand, overactivity of this system promotes excessively impulsive behaviors attended by manic thoughts and psychotic delusions. Moreover, massive lesion of SEEKING’s neural network (midline subcortical regions-the PAG, VTA, nucleus accumbens (NAc), medial forebrain and anterior cingulate) lead to consciousness disorder, specifically akinetic mutism (AKM) syndrome that the patient appears wakeful, attentive but motionless ( Schiff and Plum, 2000 ; Watt and Pincus, 2004 ). In brief, the SEEKING system holds a critical position that optimizes the performance of emotion, motivation, and cognition processes by generating positive subjective emotional states-positive expectancy, enthusiastic exploration, and hopefulness. Because the seven primary emotional systems and their associated key neuroanatomical and key neurochemical features have been reviewed elsewhere ( Panksepp, 2011a , b ), they are not covered in this review.

Emotion–Cognition Interactions and its Impacts on Learning and Memory

Studies in psychology ( Metcalfe and Mischel, 1999 ) and neuroscience ( Dolcos et al., 2011 ) proposed that cognition and emotion processes are operated at two separate but interacting systems: (i) the “cool cognitive system” is hippocampus-based that is associated with emotionally neutral cognitive functions as well as cognitive controls; and (ii) the “hot emotional system” is amygdala-based that responsible for emotional processing and responses toward unconditioned emotional stimuli such as appetitive and fear-evoking conditions. In addition, an early view of a dorsal/ventral stream distinction was commonly reported between both systems. The dorsal stream encompasses the dorsolateral prefrontal cortex (DLPFC) and lateral parietal cortex, which are involved in the cool system for active maintenance of controlled processes such as cognitive performance and the pursuit of goal-relevant information in working memory (WM) amidst interference. In contrast, the hot system involves the ventral neural system, including the amygdala, ventrolateral prefrontal cortex (VLPFC) and medial prefrontal cortex (mPFC) as well as orbitofrontal (OFC) and occipito-temporal cortex (OTC), all of which encompass emotional processing systems ( Dolcos et al., 2011 ). Nonetheless, recent investigations claim that distinct cognitive and emotional neural systems are not separated but are deeply integrated and contain evidence of mediation and modulation ( Dolcos et al., 2011 ; Okon-Singer et al., 2015 ). Consequently, emotions are now thought to influence the formation of a hippocampal-dependent memory system ( Pessoa, 2008 ), exerting a long-term impact on learning and memory. In other words, although cognitive and affective processes can be independently conceptualized, it is not surprising that emotions powerfully modify cognitive appraisals and memory processes and vice versa. The innate emotional systems interact with higher brain systems and probably no an emotional state that is free of cognitive ramifications. If cortical functions were evolutionarily built upon the pre-existing subcortical foundations, it provides behavioral flexibility ( Panksepp, 1998 ).

The hippocampus is located in the MTL and is thought to be responsible for the potentiation and consolidation of declarative memory before newly formed memories are distributed and stored in cortical regions ( Squire, 1992 ). Moreover, evidence indicates that the hippocampus functions as a hub for brain network communications-a type of continuous exchange of information center that establishes LTM dominated by theta wave oscillations ( Battaglia et al., 2011 ) that are correlated with learning and memory ( Rutishauser et al., 2010 ). In other words, hippocampus plays a crucial role in hippocampal-dependent learning and declarative memories. Numerous studies have reported that the amygdala and hippocampus are synergistically activated during memory encoding to form a LTM of emotional information, that is associated with better retention ( McGaugh et al., 1996 ; Richter-Levin and Akirav, 2000 ; Richardson et al., 2004 ). More importantly, these studies (fear-related learning) strongly suggest that the amygdala’s involvement in emotional processing strengthens the memory network by modulating memory consolidation; thus, emotional content is remembered better than neutral content.

In addition to amygdala-hippocampus interactions, one study reported that the PFC participates in emotional valence (pleasant vs. unpleasant) processing during WM ( Perlstein et al., 2002 ). Simons and Spiers (2003) also reviewed studies of interactions between the PFC and MTL during the memory encoding and retrieval processes underlying successful LTM. They demonstrated that the PFC is crucial for LTM because it engages with the active maintenance of information linked to the cognitive control of selection, engagement, monitoring, and inhibition. Hence, it detects relevant data that appears worthwhile, which is then referred for encoding, thus leading to successful LTM ( Simons and Spiers, 2003 ). Consistent findings were reported for recognition tasks investigated by fMRI where the left PFC-hippocampal network appeared to support successful memory encoding for neutral and negative non-arousing words. Simultaneously, amygdala-hippocampus activation was observed during the memory encoding of negative arousing words ( Kensinger and Corkin, 2004 ). Moreover, Mega et al. (1996) proposed two divisions for the limbic system: (i) the paleocortex division (the amygdala, orbitofrontal cortex, temporal polar and anterior insula), and (ii) the archicortical division (the hippocampus and anterior cingulate cortex). The first component is responsible for the implicit integration of affects, drives and object associations; the second deals with explicit sensory processing, encoding, and attentional control. Although divided into two sub-divisions, the paleocortex and archicortical cortex remain integrated during learning. Here, the paleocortex appears to manage the internal environment for implicit learning while integrating affects, drives, and emotions. Simultaneously, the archicortical division appears to manage external environment input for explicit learning by facilitating attention selection with attendant implicit encoding. To some extent, the paleocortex system might come to exercise a supervisory role and link the ancient affective systems to the newer cognitive systems.

Amygdala–Hippocampus Interactions

The findings of previous studies suggest that the amygdala is involved in emotional arousal processing and modulation of the memory processes (encoding and storage) that contribute to the emotional enhancement of memory ( McGaugh et al., 1996 ; Richter-Levin and Akirav, 2000 ). Activation of the amygdala during the encoding of emotionally arousing information (both pleasant/unpleasant) has been reported that correlates with subsequent recall. Because of the interaction between basolateral complex of the amygdala (BLA) with other brain regions that are involved in consolidating memories, including the hippocampus, caudate nucleus, NAc, and other cortical regions. Thus, BLA activation results from emotionally arousing events, which appear to modulate memory storage-related regions that influence long-term memories ( McGaugh, 2004 ). Memory consolidation is a part of the encoding and retention processes where labile memories of newly learned information become stabilized and are strengthened to form long-lasting memories ( McGaugh, 2000 ). Moreover, the amygdala transmits direct feedback/projection along the entire rostral-caudal cortices to the visual cortex of the ventral stream system, including primary visual (V1) and temporal cortices ( Amaral et al., 2003 ); furthermore, the amygdala activates the frontal and parietal regions during negative emotion processing that are involved in attention control. Consequently, during emotional processing, direct projections from the amygdala to sensory cortices enhance attentional mechanism might also allow the parallel processing of the attentional (fronto-parietal) system ( Vuilleumier, 2005 ). This suggests that amygdala activation is associated with enhanced attention and is a part of how salience enhances information retention.

In addition to attentional biases toward emotional content during memory encoding, emotionally arousing experiences have been found to induce the release of adrenal stress hormones, followed by the activation of β-noradrenergic receptors in the BLA, which then release epinephrine and glucocorticoids in the BLA, while enhancing memory consolidation of emotional experiences ( McGaugh and Roozendaal, 2002 ). Thus, there is evidence that the consolidation of new memory that is stimulated by emotionally arousing experiences can be enhanced through the modulating effects of the release of stress hormones and stress-activated neurotransmitters associated with amygdala activation. The BLA comprises the basal amygdala (BA) and lateral amygdala (LA), which project to numerous brain regions involved in learning and memory, including the hippocampus and PFC ( Cahill and McGaugh, 1998 ; Sharot and Phelps, 2004 ; McGaugh, 2006 ). However, stress and emotion do not always induce strong memories of new information. Indeed, they have also been reported to inhibit WM and LTM under certain conditions related to mood and chronic stress ( Schwabe and Wolf, 2010 ). Consequently, understanding, managing, and regulating emotion is critical to the development of enhanced learning programs informed by the significant impacts of learning and memory under different types of stress ( Vogel and Schwabe, 2016 ).

Prefrontal Cortex–Hippocampus Interaction

The PFC is located in the foremost anterior region of the frontal lobe and is associated with higher-order cognitive functions such as prediction and planning of/for the future ( Barbey et al., 2009 ). Moreover, it is thought to act as a control center for selective attention ( Squire et al., 2013 ), and also plays a critical role in WM as well as semantic processing, cognitive control, problem-solving, reasoning and emotional processing ( Miller and Cohen, 2001 ; Yamasaki et al., 2002 ). The PFC is connected to sub-cortical regions in the limbic system, including the amygdala and various parts of the MTL ( Simons and Spiers, 2003 ). Its involvement in WM and emotional processing are intimately connected with the MTL structures that decisively affect LTM encoding and retrieval ( Blumenfeld and Ranganath, 2007 ) in addition to self-referential processing ( Northoff et al., 2006 ). Structurally, the PFC is divided into five sub-regions: anterior (BA 10), dorsolateral (BA 9 and 46), ventrolateral (BA 44, 45, and 47), medial (BA 25 and 32) and orbitofrontal (BA 11, 12, and 14) ( Simons and Spiers, 2003 ).

The mPFC has been associated with anticipatory responses that reflect cognitive expectations for pleasant/unpleasant experiences (appraising rewarding/aversive stimuli to generate emotional responses) ( Ochsner et al., 2002 ; Ochsner and Gross, 2005 ). Specifically, increased mPFC activation has been noted during reappraisal and is associated with the suppressed subjective experience of negative emotions. Furthermore, an fMRI study revealed concurrent activation levels of the dorsomedial prefrontal cortex (dmPFC) with emotional valence when processing emotional stimuli: (i) activation was associated with positive valence, and (ii) deactivation was associated with negative valence ( Heinzel et al., 2005 ). Similarly, emotional and non-emotional judgment task using the International Affective Pictures System (IAPS) demonstrated increased activation of the mPFC, specifically both ventromedial prefrontal cortex (vmPFC) and dmPFC during emotional judgment when compared with non-emotional judgment. However, an inverse relationship was observed in the lateral prefrontal cortex (VLPFC and DLPFC) during non-emotional judgment ( Northoff et al., 2004 ). These findings suggested reciprocal interactions between cognitive and emotional processing between dorsal and lateral neural systems when processing emotional and cognitive tasking demands ( Bartolic et al., 1999 ).

Other studies reported strong cognition-emotion interactions in the lateral prefrontal cortex with increased activity in the DLPFC, which plays a key role in top-down modulation of emotional processing ( Northoff et al., 2004 ; Comte et al., 2014 ). This indicates increased attentional control of regulatory mechanisms that process emotional content. For instance, one study reported that cognitive task appeared to require active retention in WM, noting that the process was influenced by emotional stimuli when subjects were instructed to remember emotional valence information over a delay period ( Perlstein et al., 2002 ). Their findings revealed increased activation in the right DLPFC in response to pleasant IAPS pictures, but with an opposite effect in response to unpleasant pictures (decreased activity in the right DLPFC). This could be interpreted as increased WM-related activity when processing positive emotional stimuli, thus leading to positive emotion maintenance of stimulus representation in WM. Furthermore, they observed that the DLPFC contributed to increased LTM performance linked to stronger item associations and greater organization of information in WM during pleasant compared to unpleasant emotion ( Blumenfeld and Ranganath, 2006 ).

Another study investigated the PFC’s role in emotional mediation, reporting that the right VLPFC provided cognitive resources for both emotional reappraisal and learning processes via two separate subcortical pathways: (i) a path through NAc appeared to greater reappraisal success (suppress negative emotion) and (ii) another path through the ventral amygdala appeared to reduced reappraisal success (boost negative experience). This result indicates the VLPFC’s role in the regulation of emotional responses (reducing negative appraisal and generating positive appraisal) by retrieving appropriate information from memory ( Wager et al., 2008 ). Certain characteristics of emotional content were found to mediate the encoding and retrieval of selective information by leading high levels of attention, distinctiveness, and information organization that enhanced recall for emotional aspects of complex events ( Talmi, 2013 ). Hence, this direction of additional attention to emotional information appears to enhance LTM with the pronounced effects deriving from positive emotions compared with negative emotions. Effects of emotion on memory was also investigated using immediate (after 20 s) and delayed (after 50 min) testing paradigm, has shown that better recall for emotionally negative stimuli during immediate test compared to delayed test because of attentional allocation for encoding while the delayed test demonstrated that the role of amygdala in modulating memory consolidation of emotional stimuli. Because selective attention drives priority assignment for emotional material ( Talmi et al., 2007 ). Meanwhile, the distinctiveness and organization of information can improve memory because unique attributes and inter-item elaboration during encoding serve as retrieval cues, which then lead to high possibilities for correct recall ( Erk et al., 2003 ). Consistent findings were also reported by ( Dolcos et al., 2004 ), who suggested an emotional mediation effect deriving from PFC activity in relation to cognitive functions such as strategic memory, semantic memory, and WM, which subsequently enhanced memory formation. Table ​ Table1 1 summarizes cognitive-emotional functions associated with each sub-region of the PFC and corresponding Brodmann areas. Taken together, these findings indicate that the PFC is a key component in both cognitive and emotional processing for successful LTM formation and retrieval.

The prefrontal cortex (PFC) sub-regions, corresponding Brodmann areas, and associated cognitive-emotional functions.

Effects Deriving From Different Modalities of Emotional Stimuli on Learning and Memory

As discussed above, evidence indicates the neural mechanisms underlying the emotional processing of valence and arousal involve the amygdala and PFC, where the amygdala responds to emotionally arousing stimuli and the PFC responds to the emotional valence of non-arousing stimuli. We have thus far primarily discussed studies examining neural mechanisms underlying the processing of emotional images. However, recent neuroimaging studies have investigated a wider range of visual emotional stimuli. These include words ( Sharot et al., 2004 ), pictures ( Dolcos et al., 2005 ; Weymar et al., 2011 ), film clips ( Cahill et al., 1996 ), and faces ( González-Roldan et al., 2011 ), to investigate neural correlates of emotional processing and the impact of emotion on subsequent memory. These studies provided useful supplemental information for future research on emotional effects of educational multimedia content (combination of words and pictures), an increasingly widespread channel for teaching and learning.

An event-related fMRI study examined the neural correlates of responses to emotional pictures and words in which both were manipulated in terms of positive and negative valence, and where neutral emotional content served as a baseline (“conditioned stimuli”/no activating emotion with valence rating of 5 that spans between 1/negative valence-9/positive valence), even though all stimuli were consistent in terms of arousal levels ( Kensinger and Schacter, 2006 ). Subjects were instructed to rate each stimulus as animate or inanimate and common or uncommon . The results revealed the activation of the amygdala in response to positive and negative valence (valence-independent) for pictures and words. A lateralization effect was observed in the amygdala when processing different emotional stimuli types. The left amygdala responded to words while either the right and/or bilateral amygdala activation regions responded to pictures. In addition, participants were more sensitive to emotional pictures than to emotional words. The mPFC responded more rigorously during the processing of positive than to that of negative stimuli, while the VLPFC responded more to negative stimuli. The researchers concluded that arousal-related responses occur in the amygdala, dmPFC, vmPFC, anterior temporal lobe and temporo-occipital junction, whereas valence-dependent responses were associated with the lateral PFC for negative stimuli and the mPFC for positive stimuli. The lateralization of the amygdala’s activation was consistent with that in other studies that also showed left-lateralized amygdala responses for words ( Hamann and Mao, 2002 ) vs. right-lateralized amygdala responses for images ( Pegna et al., 2005 ). However, a wide range of studies suggest that lateralization likely differs with sex ( Hamann, 2005 ), individual personality ( Hamann and Canli, 2004 ), mood ( Rusting, 1998 ), age ( Allard and Kensinger, 2014 ), sleep ( Walker, 2009 ), subject’s awareness of stimuli ( Morris et al., 1998 ), stress ( Payne et al., 2007 ) and other variables. Hence, these factors should be considered in future studies.

Event-related potentials (ERPs) were used to investigate the modality effects deriving from emotional words and facial expressions as stimuli in healthy, native German speakers ( Schacht and Sommer, 2009a ). German verbs or pseudo-words associated with positive, negative or neutral emotions were used, in addition to happy vs. angry faces, as well as neutral and slightly distorted faces. The results revealed that negative posterior ERPs were evoked in the temporo-parieto-occipital regions, while enhanced positive ERPs were evoked in the fronto-central regions (positive verbs and happy faces) when compared with neutral and negative stimuli. These findings were in agreement with the previous findings ( Schupp et al., 2003 ; Schacht and Sommer, 2009b ). While the same neuronal mechanisms appear to be involved in response to both emotional stimuli types, latency differences were also reported with faster responses to facial stimuli than to words, likely owing to more direct access to neural circuits-approximately 130 ms for happy faces compared to 380 ms for positive verbs ( Schacht and Sommer, 2009a ). Moreover, augmented responses observed in the later positive complex (LPP), i.e., larger late positive waves in response to emotional verbs (both positive and negative) and angry faces, all associated with the increased motivational significance of emotional stimuli ( Schupp et al., 2000 ) and increased selective attention to pictures ( Kok, 2000 ).

Khairudin et al. (2011) investigated effects of emotional content on explicit memory with two standardized stimuli: emotional words from the Affective Norms for English Words (ANEW) and emotional pictures from the IAPS. All stimuli were categorized as positive, negative or neutral, and displayed in two different trials. Results revealed that better memory for emotional images than for emotional words. Moreover, a recognition test demonstrated that positive emotional content was remembered better than negative emotional content. Researchers concluded that emotional valence significantly impacts memory and that negative valence suppressed the explicit memory. Another study by Khairudin et al. (2012) investigated the effects of emotional content on explicit verbal memory by assessing recall and recognition for emotionally positive, negative and neutral words. The results revealed that emotion substantially influences memory performance and that both positive and negative words were remembered more effectively than neutral words. Moreover, emotional words were remembered better in recognition vs. recall test.

Another group studied the impacts of emotion on memory using emotional film clips that varied in emotion with neutral, positive, negative and arousing contents ( Anderson and Shimamura, 2005 ). A subjective experiment for word recall and context recognition revealed that memory, for words associated with emotionally negative film clips, was lower than emotionally neutral, positive and arousing films. Moreover, emotionally arousing film clips were associated with enhanced context recognition memory but not during a free word recall test. Therefore, clarifying whether emotional stimuli enhance recognition memory or recall memory requires further investigation, as it appears that emotional information was better remembered for recognition compared to recall. In brief, greater attentional resource toward emotional pictures with large late positive waves of LPP in the posterior region, the amygdala responds to emotional stimuli (both words and pictures) independent on its valence, leading to enhanced memory. Table ​ Table2 2 summarizes studies on the brain regions that respond to standardized stimuli as cited above, and also for pictures of emotional facial expression or Pictures of Facial Affect (POFA), Affective Norms for English Words (ANEW) for emotional words, as well as for the International Affective Digitized Sound System (IDAS) for emotional sounds.

Comparison of different emotional stimulus categories.

Neuroimaging Techniques for the Investigation of Emotional-Cognitive Interactions

The brain regions associated with cognitive-emotional interactions can be studied with different functional neuroimaging techniques (fMRI, PET, and fNIRS) to examine hemodynamic responses (indirect measurement). EEG is used to measure brain electrical dynamics (direct measurement) associated with responses to cognitive and emotional tasks. Each technique has particular strengths and weaknesses, as described below.

Functional Magnetic Resonance Imaging (fMRI)

Functional magnetic resonance imaging is a widely used functional neuroimaging tool for mapping of brain activation as it provides a high spatial resolution (a few millimeters). fMRI is an indirect measure of hemodynamic response by measuring changes in local ratios of oxy-hemoglobin vs. deoxy-hemoglobin, typically known as a blood oxygenation level dependent (BOLD) signal ( Cabeza and Nyberg, 2000 ). Dolcos et al. (2005) examined the effects of emotional content on memory enhancement during retrieval process using event-related fMRI to measure retrieval-related activity after a retention interval of 1 year. The researchers concluded that successful retrieval of emotional pictures involved greater activation of the amygdala as well as the entorhinal cortex and hippocampus than that of neutral pictures. Both the amygdala and hippocampus were rigorously activated during recollection compared to familiarity recognition, whereas no differences were found in the entorhinal cortex for either recollection or familiarity recognition. Moreover, a study investigates motivation effect (low vs. high monetary reward) on episodic retrieval by manipulating task difficulty, fMRI data reports that increased activation in the substantia nigra/VTA, MTL, dmPFC, and DLPFC when successful memory retrieval with high difficulty than with low difficulty. Moreover, reward-related of functional connectivities between the (i) SN/VTA–MTL and (ii) SN/VTA–dmPFC appear to increases significantly with increases retrieval accuracy and subjective motivation. Thus, Shigemune et al. (2017) suggest that reward/motivation-related memory enhancement modulated by networking between the SN/VTA (reward-related), dmPFC (motivation-related) and MTL (memory-related) network as well as DLPFC (cognitive controls) with high task difficulty.

Taken together, these findings indicate that the amygdala and MTL have important roles in the recollection of emotional and motivational memory. Another fMRI study reported that greater success for emotional retrieval (emotional hits > misses ) was associated with neural activation of the bilateral amygdala, hippocampus, and parahippocampus, whereas a higher success rate for neutral retrieval is associated with a greater activity in right posterior parahippocampus regions ( Shafer and Dolcos, 2014 ). Hence, fMRI has clearly revealed interactions between cognitive and emotional neural networks during information processing, particularly in response to emotion-related content. Such interactions appear to modulate memory consolidation while also mediating encoding and retrieval processes that underlie successful LTM formation and memory recall. More specifically, it appears that amygdala activation modulates both the hippocampus and visual cortex during visual perception and enhances the selection and organization of salient information via the “bottom-up” approach to higher cognitive functions directed at awareness. Although fMRI is widely used, it poses several limitations such as poor temporal resolution, expensive setup costs, plus the difficulty of having a subject hold still during the procedure in an electromagnetically shielded room (immobility). Furthermore, fMRI is slightly more metabolically sluggish, as BOLD signal exhibits an initial dip, where the increase of subsequent signal is delayed by 2–3 s and it takes approximately 6–12 s to reach to a peak value that reflects the neural responses elicited by a stimulus ( Logothetis et al., 2001 ). This means that fMRI having a coarse temporal resolution (several seconds) when compared with electrophysiological techniques (a few milliseconds) and also not a great technique for visualizing subcortical regions (mesencephalon and brainstem) due to metabolically sluggish compared to PET.

Positron Emission Tomography (PET)

Positron emission tomography is another functional neuroimaging tool that maps CNS physiology and neural activation by measuring glucose metabolism or regional cerebral blood flow (rCBF). PET uses positron-emitting radionuclides such as 18 F-fluorodeoxyglucose (FDG) and positron-emitting-oxygen isotope tagged with water ([ 15 O] H 2 O), etc. This technique identifies different neural networks involving pleasant, unpleasant and neutral emotions ( Lane et al., 1997 ). It thus far appears that increased rCBF in the mPFC, thalamus, hypothalamus, and midbrain associated with pleasant and unpleasant emotional processing, while unpleasant emotions are more specifically associated with the bilateral OTC, cerebellum, left parahippocampal gyrus, hippocampus, and amygdala; moreover, the caudate nucleus is associated with pleasant emotions.

Using PET scanning demonstrated that emotional information enhances visual memory recognition via interactions between perception and memory systems, specifically with greater activation of the lingual gyrus for visual stimuli ( Taylor et al., 1998 ). The results also showed that strong negative emotional valence appeared to enhance the processing of early sensory input. Moreover, differences in neural activation appeared in the left amygdaloid complex (AC) during encoding, while the right PFC and mPFC responded during recognition memory. Similarly, Tataranni et al. (1999) identified CNS regions associated with appetitive states (hunger and satiation) ( Tataranni et al., 1999 ). Hunger stimulated increased rCBF uptake in multiple regions including the hypothalamus, insular cortex, limbic and paralimbic regions (anterior cingulate cortex, parahippocampal and hippocampal formation, the anterior temporal and posterior orbitofrontal cortex), as well as the thalamus, caudate, precuneus, putamen, and cerebellum. Satiation was associated with increased rCBF uptake in the bilateral vmPFC, the DLPFC, and the inferior parietal lobule. These results imply that (i) subcortical regions associated with emotion/motivation involved in hunger that signals distressing feeling (discomfort, pain and anxiety) for the regulation of food intake; and (ii) the PFC associated with inhibition of inappropriate behavioral response involved in satiation that signals excessive food consumption for a termination of meal.

In a study of emotional self-generation using PET noted that the insular cortex, secondary somatosensory cortex, and hypothalamus, as well as the cingulate cortex and nuclei in the brainstem’s tegmentum, including PAG, parabrachial nucleus, and substantia nigra maintained current homeostasis by generating regulatory signals ( Damasio et al., 2000 ). PET scanning has also been used for neuroanatomical mapping of emotions ( Davidson and Irwin, 1999 ), emotional processing ( Choudhary et al., 2015 ), and cognitive functions ( Cabeza and Nyberg, 2000 ). Although PET scanning has a relatively good spatial resolution for both the brain and bodily functions, it is costly and yields lower temporal resolution than does EEG and is invasive as opposed to fMRI. Moreover, PET tends to show better activation of more ancient brain regions in the mesencephalon and brainstem when compared to fMRI. Hence, it is generally reserved for the clinical diagnoses of cancers, neurological diseases processes (e.g., epilepsy and Alzheimer’s disease), and heart diseases.

Electroencephalography (EEG)

Electroencephalography obtains high temporal resolution in milliseconds, portable, less expensive, and non-invasive techniques by attaching scalp electrodes to record brain electrical activity. Moreover, numerous studies reported that EEG is useful in mapping CNS cognitive and emotional processing. The technique offers a comprehensive range of feature extraction and analysis methods, including power spectral analysis, EEG coherence, phase delay, and cross-power analysis. One study examined changes in EEG oscillations in the amygdala during the consolidation of emotionally aroused memory processing that exhibited theta (4–8 Hz) activity ( Paré et al., 2002 ), indicating the facilitation of memory consolidation, improved retention of emotional content, and enhanced memory recall. This finding was later supported by the revelation of increased theta activity in the right frontal ( Friese et al., 2013 ) and right temporal cortices ( Sederberg et al., 2003 ) and consequently associated with the successful encoding of new information. Another study ( Buzsáki, 2002 ) revealed that theta oscillations were positively related to the activation of the hippocampus represent the active brain state during sensory, motor and memory-related processing. The theta waves are generated through an interaction between the entorhinal cortex, the Schaffer collateral (CA3 region) and the pyramidal cell dendrites (both CA3 and CA1 regions) that result in a synaptic modification underlie learning and memory. Thus, theta oscillation is thought to be associated with the encoding of new memories.

Increased gamma oscillation in the neocortex and right amygdala have been reported in response to emotionally arousing pictures during learning and memory tasks undertaken by 148 right-handed female participants ( Headley and Paré, 2013 ). A more detailed study by Müller et al. (1999) reported increased gamma potentials in the left frontal and temporal regions in response to images having a negative valence, whereas increased gamma-bands in the right frontal regions were observed in responses to images with positive valence for 11 right-handed male participants. During an emotionally positive experience, another study reported significantly increased EEG theta-alpha coherence between prefrontal and posterior parietal regions ( Aftanas and Golocheikine, 2001 ). They concluded the change was associated with heightened attention in association with improved performance in memory and emotional processing. Thus, we have a number of EEG investigations of left and right hemispheric activity while processing positive (pleasant) and negative (unpleasant) stimuli that revealed differences in regional electrophysiological activation. Nonetheless, EEG exhibits a relatively poor spatial resolution approximately 5 to 9 cm compared with fMRI and PET ( Babiloni et al., 2001 ). Thus, scalp EEG unable to measure activation much below cortex owing to the distortion of scalp potentials where different volume conduction effects of the cortex, dura mater, skull, and scalp resulting in imprecise localization of the electromagnetic field patterns associated with neural current flow. Subsequent studies have demonstrated that the EEG spatial resolution can be improved using high-resolution EEG (high-density electrode arrays to increase spatial sampling) with surface Laplacian estimation and cortical imaging (details discussion of this area is beyond the scope of this review, see ( Nunez et al., 1994 ) for theoretical and experimental study) or integrating multiple imaging modalities that provide complement information, for instance EEG-fMRI and EEG-fNIRS ( Dale and Halgren, 2001 ).

Functional Near-Infrared Spectroscopy (fNIRS)

Functional near-infrared spectroscopy is an emerging and relatively low-cost imaging technique that is also portable and non-invasive. It can be used to map the hemodynamic responses associated with brain activation. This technology measures cerebral changes in the concentration of oxygenated hemoglobin (oxy-Hb) vs. deoxygenated hemoglobin (deoxy-Hb) using optodes (light emitters and detectors) placed on the scalp ( Villringer et al., 1993 ). It is limited to visualizations of cortical activity compared to the subcortical regions, and findings only imply increased brain activity associated with increased glucose and oxygen consumption. Elevations in cerebral blood flow and oxygen delivery exceed quo oxygen consumption, thereby enabling changes in local cerebral blood oxygenation to be measured by optic penetration.

The number of studies that have implemented this investigative technique are associated with task performance ( Villringer et al., 1993 ), including exercise ( Perrey, 2008 ), cognitive workload ( Durantin et al., 2014 ), psychiatric disorders ( Ehlis et al., 2014 ), emotional processing ( Bendall et al., 2016 ), and aging ( Hock et al., 1995 ). One study used fNIRS to examine the relationship between subjective happiness and emotional changes ( Oonishi et al., 2014 ). The results revealed that the level of subjective happiness influenced the pattern of left-right PFC activation during the emotion-related task, showing increased oxy-Hb in the left PFC when viewing pleasant pictures, and increased oxy-Hb in the right PFC when viewing unpleasant pictures. Viewing unpleasant emotional stimuli accompanied increased in oxy-Hb levels in the bilateral VLPFC while also activating several regions in both the right VLPFC (BA45/47) and left VLPFC (BA10/45/46/47). However, another fNIRS study reported that viewing pleasant emotional stimuli was associated with decreased oxy-Hb in the left DLPFC (BA46/10) when affective images were presented for 6 s ( Hoshi et al., 2011 ). Thus, this study found an opposite pattern indicating left hemisphere involvement in positive/approach processing and right hemisphere involvement in negative/withdrawal processing ( Davidson, 1992 ; Davidson and Irwin, 1999 ). This inconsistent finding of frontal hemispheric asymmetric might result from the comparison of state-related changes rather than baseline levels of asymmetric. Thus, several issues should take into consideration: (i) methodological issues to assess hemispheric asymmetry, including requires repeat measures of anterior asymmetry for at least two sessions, stimulus content should comprise both positive valence and negative valence while maintaining at a similar level of arousal and with a baseline resting condition, appropriate selection of reference electrode and individual differences, etc; and (ii) conceptual issues is related to the fact that prefrontal cortex is an anatomically and functionally heterogeneous and complex region interacts with other cortical and subcortical structures during emotional processing ( Davidson, 2004 ). Another fNIRS study examined the relationship between PFC function and cognitive control of emotion ( Ozawa et al., 2014 ). This was done by presenting emotional IAPS pictures for 5.2 s, followed by the n -back task. The results revealed a significantly greater increase in oxy-HB in the mPFC and left superior frontal gyrus in response to negative pictures compared with neutral pictures. Meanwhile, no significant hemodynamic changes were observed during image presentation and the n -back task, indicating the need for further investigation.

Factors Affecting the Effect of Emotion on Learning and Memory

The preceding section described neuroimaging techniques used to examine brain responses to emotional stimuli during WM processing leading to LTM. This section presents six key factors that are recommended for consideration in the experimental design and appropriate protocol.

Individual Differences

A number of studies have reported numerous influences in addition to a range of individual differences in emotional processing. These include personality traits ( Montag and Panksepp, 2017 ), intellectual ability ( Brackett et al., 2004 ), and sex ( Cahill, 2003 ). Moreover, sex hormones and personality traits (e.g., extraversion and neuroticism) appear to influence individual responses to emotional stimuli as well as modulate emotional processing. Appropriate screening with psychological testing as well as balancing experimental cohorts in terms of sex can help reduce spurious results owing to individual differences.

Age-Related Differences

Studies have also shown that older adults are associated with the greater familiarity with psychological stress and emotional experiences, thus causing positivity biases in emotional processing and better emotional control than in younger adults ( Urry and Gross, 2010 ; Allard and Kensinger, 2014 ). Consequently, the age of participants in a sample population should be considered for both cognitive and emotional studies.

Emotional Stimulus Selection

The selection of emotional stimuli for experimental studies is generally divided into two streams: (1) discrete emotional, and (2) dimensional emotions of valence, arousal, dominance and familiarity ( Russell, 1980 ; Barrett, 1998 ). The latter include pictures from the IAPS database and words from the ANEW database, which are both available for non-commercial research. Appropriate selection of emotional stimuli is another important consideration that ensures experimental tasks are suitable for the investigation of emotional processing in learning and memory. Furthermore, the type of stimulus determines stimulus presentation duration, especially for experimental tasks involving the induction of emotions.

Self-assessment Techniques

There are numerous self-assessment techniques used to measure individual emotional states ( Bradley and Lang, 1994 ). The most widely used techniques are the Self-Assessment Manikin (SAM), the Semantic Differential (SD) scale, and the Likert scale. The SAM is a non-verbal pictorial assessment technique directly measures emotional responses to emotional stimuli for valence, arousal, and dominance. The SD scale consists of a set of bipolar adjective pairs for the subjective rating of image stimuli. The Likert’s “ x -point” scale allows participants to rate their own emotional responses. If a study does not seek to assess distinct emotional states but rather involves the assessment of two primary dimensions of emotion (positive and negative valence), then the Positive and Negative Affect Schedule (PANAS) is a recommended method ( Watson et al., 1988 ). Thus, selection of the most appropriate self-assessment technique is an important part of the experimental design but can also become an overwhelming task.

Selection of Brain Imaging Techniques

As mentioned above, the two major types of brain imaging techniques EEG (direct) and fMRI/PET/fNIRS (indirect) have respective advantages and disadvantages. To overcome these limitations, simultaneous or combined dual-modality imaging (EEG-fMRI or EEG-fNIRS) can now be implemented for complementary data collection. Although functional neuroimaging works to identify the neural correlates of emotional states, technologies such as deep brain stimulation (DBS) and connectivity maps might provide new opportunities to seek understanding of emotions and its corresponding psychological responses.

Neurocognitive Research Design

The neuroscience of cognition and emotion requires appropriate task designs to accomplish specific study objectives ( Amin and Malik, 2013 ). Environmental factors, ethical issues, memory paradigms, cognitive task difficulty, and emotional induction task intensity must be considered for this.

Numerous neuroimaging studies cited thus far have indicated that emotions influence memory processes, to include memory encoding, memory consolidation, and memory retrieval. Emotional attentional and motivational components might explain why emotional content exhibits privileged information processing. Emotion has a “pop-out” effect that increases attention and promotes bottom-up instinctual impact that enhances awareness. Significant emotional modulation affects memory consolidation in the amygdala, and emotional content also appears to mediate memory encoding and retrieval in the PFC, leading to slow rates of memory lapse accompanied by the accurate recall. Moreover, cognitive and emotional interactions also appear to modulate additional memory-related CNS regions, such as the frontal, posterior parietal and visual cortices. The latter are involved in attentional control, association information, and the processing of visual information, respectively. Therefore, higher-level cognitive functions such as learning and memory, appear to be generally guided by emotion, as outlined in the Panksepp’s framework of brain processing ( Panksepp, 1998 ).

Neuroimaging findings also indicate the involvement of the PFC in emotional processing by indirectly influencing WM and semantic memory ( Kensinger and Corkin, 2003 ). This is reflected by the involvement of the DLPFC in WM and the role played by VLPFC in semantic processing, both of which have been found to enhance or impair semantic encoding task performance when emotion is involved. Various parts of the lateral PFC (ventrolateral, dorsolateral and medial prefrontal cortical regions) are suspected of having key roles that support memory retrieval ( Simons and Spiers, 2003 ). All of these findings suggest that PFC-MTL interactions underlie effective semantic memory encoding and thus strategically mediate information processing with increased transfer to the hippocampus, consequently enhancing memory retrieval. Accordingly, learning strategies that emphasize emotional factors are more likely to result in long-term knowledge retention. This consideration is potentially useful in the design of educational materials for academic settings and informed intelligent tutoring systems.

Based on numerous previous findings, future research might take emotional factors more seriously and more explicitly in terms of their potential impact on learning. By monitoring the emotional state of students, the utilization of scientifically derived knowledge of stimulus selection can be particularly useful in the identification of emotional states that advance learning performance and outcomes in educational settings. Moreover, functional neuroimaging investigations now include single and/or combined modalities that obtain complementary datasets that inform a more comprehensive overview of neuronal activity in its entirety. For example, curiosity and motivation promote learning, as it appears cognitive network become energized by the mesolimbic-mesocortical dopamine system (generalized motivational arousal/SEEKING system). In addition, the identification of emotional impact on learning and memory potentially has direct implications for healthy individuals as well as patients with psychiatric disorders such as depression, anxiety, schizophrenia, autism, mania, obsessive-compulsive disorder and post-traumatic stress disorder (PTSD) ( Panksepp, 2011a ). To emphasize, depression and anxiety are the two most commonly diagnosed psychiatric disorders associated with learning/memory impairment and pose negative consequences that (i) limit the total amount of information that can otherwise be learned, and (ii) inhibit immediate recall as well as memory retention and retrieval of newly learned information. Depression and anxiety are also associated with negative emotions such as hopelessness, anxiety, apathy, attention deficit, lack of motivation, and motor and mental insufficiencies. Likewise, neuroscience studies report that decreased activation of the dorsal limbic (the anterior and posterior cingulate) as well as in the prefrontal, premotor and parietal cortices causes attentional disturbance, while increased neural activation in the ventral paralimbic region (the subgenual cingulate, anterior insula, hypothalamus and caudate) is associated with emotional and motivational disorders ( Mayberg, 1997 ).

Concluding Remarks, Open Questions, and Future Directions

Substantial evidence has established that emotional events are remembered more clearly, accurately and for longer periods of time than are neutral events. Emotional memory enhancement appears to involve the integration of cognitive and emotional neural networks, in which activation of the amygdala enhances the processing of emotionally arousing stimuli while also modulating enhanced memory consolidation along with other memory-related brain regions, particularly the amygdala, hippocampus, MTL, as well as the visual, frontal and parietal cortices. Similarly, activation of the PFC enhances cognitive functions, such as strategic and semantic processing that affect WM and also promote the establishment of LTM. Previous studies have primarily used standardized emotional visual, or auditory stimuli such as pictures, words, facial expression, and film clips, often based on the IAPS, ANEW, and POFA databases for emotional pictures, words and facial expressions, respectively. Further studies have typically focused on the way individuals memorize (intentional or incidental episodic memory paradigm) emotional stimuli in controlled laboratory settings. To our knowledge, there are few objective studies that employed brain-mapping techniques to examine semantic memory of learning materials (using subject matter) in the education context. Furthermore, influences derived from emotional factors in human learning and memory remains unclear as to whether positive emotions facilitate learning or negative emotions impair learning and vice versa. Thus, several remaining questions should be addressed in future studies, including (i) the impact of emotion on semantic knowledge encoding and retrieval, (ii) psychological and physiological changes associated with semantic learning and memory, and (iii) the development of methods that incorporate emotional and motivational aspects that improve educational praxes, outcomes, and instruments. The results of studies on emotion using educational learning materials can indeed provide beneficial information for informed designs of new educational courses that obtain more effective teaching and help establish better informed learning environments. Hence, to understand how emotion influence learning and memory requires understanding of an evolutionary consideration of the nested hierarchies of CNS emotional-affective processes as well as a large-scale network, including the midbrain’s PAG and VTA, basal ganglia (amygdala and NAc), and insula, as well as diencephalon (the cingulate and medial frontal cortices through the lateral and medial hypothalamus and medial thalamus) together with the MTL, including the hippocampus as well as the entorhinal cortex, perirhinal cortex, and parahippocampal cortices that responsible for declarative memories. Moreover, the SEEKING system generates positive subjective emotional states-positive expectancy, enthusiastic exploration, and hopefulness, apparently, initiates learning and memory in the brain. All cognitive activity is motivated from ‘underneath’ by basic emotional and homeostatic needs (motivational drives) that explore environmental events for survival while facilitating secondary processes of learning and memory.

Author Contributions

CMT drafted this manuscript. CMT, HUA, MNMS, and ASM revised this draft. All authors reviewed and approved this manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank Ministry of Education (MOE), Malaysia for the financial support. We gratefully thank Frontiers in Psychology, Specialty Section Emotion Sciences reviewers and the journal Associate Editor, for their helpful input and feedback on the content of this manuscript.

Funding. This research work was supported by the HiCoE grant for CISIR (Ref No. 0153CA-002), Ministry of Education (MOE), Malaysia.

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