I Work Out!: Brain Training

Editorial Note: I'm really excited about this week's topic because we are going to hear from a very good friend of mine, Dr. Jason Chein. In today's post, our guest writer is going to discuss a highly controversial and extremely interesting topic: brain training. Dr. Chein has conducted research in this area [1-3], which is why I'm so excited that he agreed to write this week's post. Take it away, Jason!

Is it time to hit the gym…for your brain? 

With so many advertisements and pop-culture books claiming that you can achieve a “smarter you” in just a few minutes a day of “brain training,” you might be thinking about hitting the cognitive gym. But don’t start strapping on your brain workout gear just yet. In today’s post I’ll take you through a brief history of some brain training research, and tell you about where the field stands today. (Hint: it’s not ready for primetime.)

If you were going by what had been the conventional wisdom in experimental psychology for the last several decades, then brain training — engaging in regular mental exercises that are intended to enhance your general cognitive functioning — would seem like a pretty silly idea. Just about all of the research from the mid-1960’s up through the turn of the millennium indicated that, while you could get incredibly good at just about any task (even really demanding ones) with enough practice, the benefits would be observed only for that specific task, and wouldn't transfer to other mentally challenging activities. Take for example the seminal work of Chase and Simon (1973) exploring the amazing memory of chess experts [4]. With just a few seconds to glance at the arrangement of pieces on the chessboard, advanced chess players can reconstruct the position of nearly every piece. Pretty impressive stuff! But, that’s true only if the pieces are in positions that “make sense” in the course of actual game play. If you change things up so that the pieces are placed randomly on the board (not in positions that would occur in a real game), then the experts’ memory drops to near novice levels (see for yourself in this video posted by psychologist Daniel Simons). And, it turns out that playing all that chess doesn't make someone generally smarter than others, or any better at problem solving in other situations. All those thousands of hours of practice and all it’s good for is beating someone at chess? Yep.

Core Strength

In the ensuing years, many psychologists have tried to find a mentally engaging activity that would leave a bigger footprint on the landscape of cognitive functioning, but time and time again the results suggested that practice with a given skill just doesn't transfer to other skills. So, you'd think everyone would have given up on the idea of brain training long ago (and many had). But in the early 2000's a new(ish) idea started to gain some traction. What if, just as performance with many physical activities can be enhanced by focusing exercises on "core" musculature, intellectual functioning could be generally improved by focusing mental exercises on "core" cognitive systems. Makes sense, right? And, based on a large body of prior behavioral experiments, and corroborating neuroimaging studies, researchers had a pretty good idea what some of those “core” cognitive abilities might be. One that seemed especially promising was working memory; the topic of an earlier post from Dr. Bob. Working memory is supposed to serve as a general workspace for the mind, and a slew of studies show that individual differences in working memory capacity can explain why some people excel while others lag behind on a very wide range of cognitively demanding tasks. If the capacity of this general mental workspace could somehow be expanded, perhaps through repeated exercises that target working memory, this could have a profound impact on overall intellectual functioning!

With this basic idea in mind, a few pioneering researchers decided to throw caution to the wind and to try their hand once again at the brain training enterprise. And, to many scientists great surprise (especially those who were pretty settled on the conclusion that practice just doesn’t transfer), the early results looked really promising. First came a pair of studies showing that training focused on working memory and other executive processes was effective in improving cognitive performance among kids diagnosed with ADHD, and it turned out, even among the healthy kids and college students who had been included as the comparison groups in those studies [5, 6]. Those exciting early results inspired another study [7] that really captured the imagination of the field, showing that scores on a test of general fluid intelligence (the closest thing we have to an index of someone’s general intellectual ability) were improved by working memory training, and in a dose dependent fashion (more training = more improvement). At the time that paper was published, I was myself already engaged in another working memory training study [1], which ultimately showed that a month of training could enhance both attention control and reading comprehension in college students (we looked, but didn’t find any evidence of improved fluid intelligence in this group).

Drinking From the Firehose

What started as a trickle of papers on working memory training soon turned into a deluge. Study after study seemed to be finding the same basic thing: that mental exercises targeting core functions of the mind (not just working memory, but also other “executive” and attentional functions) could produce meaningful transfer to important intellectual abilities. Yay, brain training works!…right?

Well, that depends on who you ask and what you mean by “works.” This is where the story gets interesting (and complicated, but don’t worry, I’ll keep it simple). After some of the initial excitement wore off, reports of failed replication attempts and null results (studies showing no benefits of training) started to come in. Others trying to reproduce the most impressive findings, like the gains in fluid intelligence and improvements in ADHD symptoms, weren’t always meeting with as much success. It seemed like the field was dividing into camps: let’s call them the ‘believers’ and the ‘doubters’. The doubters were understandably worried about failed replications, and raised some really important concerns about the methods used in earlier studies (like whether the groups that completed training and those that didn’t just had different expectations about how they should perform, similar to the placebo effect that can arise in drug studies). The believers kept at it, improved their studies to address the doubters’ concerns, and, even with these more careful measures in place (e.g., better control groups), many of their studies continued to produce exciting results.

So, which camp is right, the believers or the doubters? In situations like this we need to take a step back and look at the overall pattern and weight of the evidence. One way to do that is through meta-analysis – pull all of the relevant studies together, account for the size of the study sample (how many people participated) by giving more weight to larger studies, and then look to see where the “truth” lies. But here too the doubters and believers come to different conclusions. That’s because the answer you get depends on which specific studies you think should count, which methods you use to gauge the size of the training effect produced by each study, and most importantly, which behavioral outcomes you decide to focus on. There isn’t much debate about the benefits of training on tasks that are really similar to those that made up the training regime (we call these “near transfer” measures). In general, training does seem to improve performance on closely related tasks. So, if by “works” you mean “makes you better able to remember lists of things” (and indeed, that might be an important skill in some scenarios), then yes, it looks like training works. But does it boost your IQ, sharpen your attention, and improve your overall cognitive acumen (does it lead to “far transfer”)? I’d say we just don’t know yet. While there is some evidence that it can do these things, the overall body of evidence isn’t unequivocally favorable. But on the flip side, there also isn’t enough evidence that it doesn’t work (getting a little technical here, Bayesian factor analysis suggests that there is neither enough evidence to accept the claim nor to reject it). So pick your favorite metaphor – the jury is still out, the dust hasn’t settled, the waters are still too muddy – and maybe wait until the next New Year before you make a brain training resolution.

About the Author

Dr. Jason M. Chein is currently a faculty member at Temple University, where he is the principle investigator of the Neurocognition Lab. I met Jason in 1998 when we were both graduate students at the Learning Research and Development Center. While in grad school, Jason became an expert in cognitive neuroscience, which included learning cool methodologies like conducting studies using fMRI. While in grad school, Jason also became quite proficient at frisbee golf.

For More Information

[1] Chein, J., & Morrison, A. (2010). Expanding the mind’s workspace: Training and trans- fer effects with a complex working memory span task. Psychonomic Bulletin & Review, 17(2), 193–199.

[2] Morrison, A. B., & Chein, J. M. (2011). Does working memory training work? The promise and challenges of enhancing cognition by training working memory. Psychonomic Bulletin & Review, 18(1), 46-60.

[3] Morrison, A. B., & Chein, J. M. (2012). The controversy over Cogmed. Journal of Applied Research in memory and Cognition, 1(3), 208-210.

[4] Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive psychology, 4(1), 55-81.

[5] Klingberg, T., Forssberg, H., & Westerberg, H. (2002). Training of working memory in children with ADHD. Journal of clinical and experimental neuropsychology, 24(6), 781-791.

[6] Klingberg, T., Fernell, E., Olesen, P. J., Johnson, M., Gustafsson, P., Dahlström, K., Gillberg, C.G., Forssberg, H., & Westerberg, H. (2005). Computerized training of working memory in children with ADHD-a randomized, controlled trial. Journal of the American Academy of Child & Adolescent Psychiatry, 44(2), 177-186.

[7] Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences, 105(19), 6829-6833.

There and Back Again: Near and Far Transfer

Imagine for a moment that you are landing in a city you have never visited before, and you have to find your way from the airport to your hotel. When you land and get off the plane, what do you do? What steps might you have to take to navigate your way from the airport terminal all the way to the comfort of your 4-star hotel room? If you have travelled before, what aspects from your prior experiences, if any, might you draw upon to get you to your hotel in this new, unfamiliar city?

Near vs. Far Transfer

Hopefully you will be able to take advantage of some of your previous travel experiences in the above scenario thanks to what is known as transfer. Transfer is when knowledge learned in one domain is applied to a different domain. A domain is the topic or the subject matter of the to-be-learned knowledge. An everyday example of transfer comes from navigating a mass transit system. Suppose you grew up in a place where the only mass transit option was the city bus. When learning how to ride the bus, you figured out that a key to getting on the right bus was the sign found at the top of each bus, which displayed the line number and the terminal destination for that bus. This information was critical because it indicated which route the bus was going to follow, and whether the bus was heading towards the stop you want or away from the stop you want. This information is useful because it tells you if the bus is heading in the direction you want to go.

Now suppose you then find yourself in a new city, such as Washington, DC, and you want to ride their Metro, which is their subway system. You would demonstrate transfer by applying what you know about riding the bus (i.e., the source domain) to riding the subway (i.e., the target domain). Subway trains also display the line number and the terminal destination at the front of each train.

We might say that transferring knowledge from riding a bus on one line to riding a bus on a completely different line isn't much of a stretch because they are both busses, and they both use a destination sign to communicate with the rider. That is an example of near transfer because it involves learning within the same domain (i.e., riding the bus). What would be an example of far transfer? Learning how to ride the subway would be an example of far transfer because the surface features vary slightly and so does the setting or context (e.g., bus stops vs. subway stations).

Why Might Transfer Fail?

Although transfer can be very useful, it can also be very hard to accomplish. Why is transfer hard, and what are some of the ways that it might fail?

Transfer might fail when there is a mismatch between the learning setting and the application setting. A lot of learning occurs in the classroom, and it is the hope of every teacher that students transfer their lessons to real life. A semi-famous counterexample is a study of Brazilian street children making change [1]. When working on the streets, these children were able to perform fairly complicated computations in their heads. But when they were asked problems that had the same deep structure, and only the surface features changed (i.e., isomorphic problems), then they failed to solve the problems. In other words, the Brazilian street merchants were not able to transfer what they knew from selling candy to the classroom environment.

Another way in which transfer might fail is when the surface features of the problem change. One of my favorite examples of transfer failure comes from geometry [2]. In this example, children learn how to calculate the area of a parallelogram. When learning this particular skill, the problem is accompanied by the following diagram (see Fig. 1).

Figure 1. To calculate the area, drop two perpendicular bisectors.

The student is shown that when you drop two perpendicular bisectors (lines 1 & 2), which form two triangles of equal size that essentially equate the parallelogram with a rectangle. Since students already know how to compute the area of a rectangle, they can easily solve this problem. However, when the students are asked to compute the area of the following parallelogram (see Fig. 2), the children claim that they never learned how to solve this type of problem!

Figure 2. How do you calculate the area of this parallelogram?

This is a rather tragic example because it means that the students cannot see the applicability of their knowledge in the two different situations. This is an example of the failure of near transfer.

The STEM Connection

To reiterate, transfer is hard and can fail for multiple reasons. It can fail when the learning and application settings do not match. It can also fail when the learner does not recognize the connection between the surface features of what they learned (e.g., a parallelogram resting on its base) and a slightly different case (e.g., a parallelogram that is rotated and resting on its side). 

Why does the setting and surface features matter? In a previous post, we learned about procedural knowledge, which can be modeled using what is called a production rule. Production rules have two parts: a condition and an action. When I see this (condition), then I should do that (action).

   Production Rule: [ Condition ] => [ Action ]

We can model problem solving in geometry as a series of production rules. We might, for example, say: 

  Condition: If I see a parallelogram,
    AND: My goal is to compute the area;
  Action: Then, calculate the product of the base and the height. 

One potential explanation why transfer fails is because the condition (i.e., the left side of the production rule) is not general enough for the learner to see when his or her knowledge applies. The goal of education is to help students generalize their knowledge to the point where they can see how it applies across settings and across seemingly disparate situations.


Share and Enjoy!

Dr. Bob

For More Information

[1] Carraher, T. N., Carraber, D., & Schliemann, A. D. (1985). Mathematics in the streets and in schools. British Journal of Developmental Psychology, 3, 21-29.

[2] Wertheimer, M. (1945). Productive thinking. New York, NY: Harper.

They Call Me the Working Man: Working Memory (Part 2)

Editorial Note: This is a continuation of a previous post where we discussed a  model of a short-term memory buffer we called "working memory." In this post, we explore the underlying mechanisms for how working memory-capacity can change over the course of a lifetime. 

Here is a fun game called the dual n-back task. For the first task, you have to remember the spatial location of a series of squares. For the second task, you need to hold a few numbers in working memory. It becomes a dual task when you combine the tasks and do them at the same time. Sounds hard, right? It is. Try it for yourself.

Working Memory Expansion Pack: Adding Capacity

How did you do? Did you feel like your working memory was being taxed as you added the second task? What if you tried the n-back task when you were a kid? Would your adult self beat your younger self? In other words, does working-memory capacity change as we grow older?

There are roughly three different influences that can change working-memory capacity: neural development, knowledge, and recall strategies.
  

Neural Development. At age five, children can remember about four random digits or letters. By the time they are 20, they can remember upwards of seven or eight arbitrary digits or letters. It seems, therefore, that our brain adds capacity as it naturally develops.

Knowledge. Age, however, is hopelessly confounded with knowledge and experience. As we grow older, our brain undergoes massive changes and we file away volumes of new experiences and skills. So what would happen if we could somehow dissociate age and knowledge/experience? To do so, we would need to find areas in which kids, despite their young age, know more or have more experience than adults.

As it turns out, there are indeed areas in which kids know more than adults. Chess is just such a domain, as it is easy to find kids who have vastly more chess-related knowledge than adults. What if we pitted kids who are young, but highly knowledgeable about chess, against adults who are older, but less experienced when it comes to chess? Who would be able to remember more positions of chess pieces on a chess board? Who would be able to remember more numbers from a list of random digits? It turns out that researchers have investigated this and the results are plotted below [1].

As you can see, children were able to recall about nine chess positions, but only about six numbers. The results for adults were completely flipped. Namely, they remembered fewer chess positions than the children, but recalled more numbers. This suggests that children don't necessarily have a lower working-memory capacity. Instead, it indicates that they have less experience to help structure their recall.

Recall Strategies. You might be skeptical of that last statement. Why would experience increase your capacity to recall a list of digits? A perfect example of how experience can enhance the recall of random digits was demonstrated in a previous post in which we used the years of significant historical events in American history to form four-digit chunks. Also, we encountered a person who underwent deliberate training to increase his working memory capacity to a startling 79 items. Finally, we learned strategies for memorizing arbitrary lists of words, numbers, and phrases. Because adults have more experience temporarily storing information, we have come to develop our own strategies. Kids, on the other hand, have had fewer opportunities to figure out ways to hack their own memories.

The STEM Connection

Working memory capacity is obviously important for education. Research on this topic suggest a couple of conclusions. First, individuals have different working-memory capacities. We refer to this as an individual difference (like height or eye color). We also learned that working memory can change as a function of normal development. Some estimate that kids between the ages of 5-7 can remember four words and about the same number of digits. College-age students, on the other hand, can store upward of six words and approximately eight digits. 

We can't do anything to expedite normal development, but we can help students gain familiarity with the symbols and structures of information from a particular domain. That is where the real growth can happen. As we saw in the chess example, if students put in the time, they can expand their working-memory capacity to a point where they can outperform adults.

In conclusion, working-memory capacity seems to change over the course of a lifetime, and there are three potential explanations. First, we add capacity naturally as a consequence of natural brain maturation. Second, we learn new techniques and strategies for remembering, like repeating the same items over and over. Finally, we acquire new knowledge, which we can then use to help structure the to-be-remembered information. Kids who are chess experts can form larger chunks than adults who are novice chess players. Hopefully, this will supply you with the tools you need to go out and get a mental upgrade!


Share and Enjoy!

Dr. Bob

For More Information

[1] Chi, M. T. H. (1978). Knowledge structures and memory development. In R. Siegler (Ed.), Children's thinking: What develops? (pp. 73-96). Hillsdale, NJ: Erlbaum.

They Call Me the Working Man: Working Memory (Part 1)

Editorial Note: For the next two weeks, I want to discuss the distinction between short-term memory and working memory. Once we've sorted out the differences, then we will dive into the connection between working memory and intelligence. First, let's talk about how to model what's going through your mind...right now.

Short-term vs. Long-term Memory

In a previous post, we talked about the distinction between short-term and long-term memory. The evidence for proposing that there are two distinct systems came from a study that demonstrated enhanced memory for items that were early in a list of words, as well as superior recall for items later in the list. To make sense of this type of U-shaped curve, the authors theorized that the items early in the list made it into a permanent memory buffer, whereas the items that occurred later in the list were still hanging around in short-term memory.

In addition to behavioral evidence, there is also neuro-scientific evidence for the two memory systems. Using a methodology called a double-dissociation, neuroscientists demonstrated that some patients have damage to their long-term memory, but their short-term memory works just fine. The double-dissociation was established when they also found patients with the opposite problem. Namely, patients' long-term memory was intact; however, they had difficulty remembering information for a short period of time.

Working Memory & The Three Sub-components

Although short- versus long-term memory was successful in explaining some of the empirical findings, it became clear that it couldn't explain all of the behavioral results. Here is an example. Consider the following list of words: pit, day, cow, pen, rig. According to the research on the limitations of short-term memory, these five items should fit comfortably in short-term memory. But consider a different list of words: man, cap, can, map, mad. Does it seem harder to remember these words? According to the model of short-term memory, this list should be neither easier nor harder than the previous list of words because, again, there are only five items. How do we reconcile these observations?

Because the concept of "short-term memory" was unable to explain these findings, the concept of a temporary memory buffer had to be extended. To do so, a cognitive scientist named Alan Baddeley proposed a revision to short-term memory that he called working memory [1, 2]. It is similar to short-term memory in the sense that it is a temporary storage facility, but it had to be elaborated to help explain why phonetically similar words, such as cap/map and man/mad were easily to confuse when trying to remember them. The new model of memory included three distinct sub-components: the central executive, the phonological loop, and the visuo-spatial sketch-pad. To see how these components interact, Baddeley provided the following diagram (see Fig. 1).

Figure 1. A schematic representation of the working memory components.

Central Executive

The first component is called the central executive. It is responsible for focusing your attention on relevant information and to switch attentional focus when needed. In other words, it is the central executive's job to coordinate the flow of information to and from the subsystems to accomplish a task. An example of coordinating information occurs when you are attempting to navigate with a map. You have to hold spatial information from the map in mind while looking up at the real world. The central executive has to synthesize the spatial information from the map with the verbal information located on the street signs.

Phonological Loop

The next component is the articulatory or phonological loop. The best way to visualize the phonological loop is to imagine an extremely short cassette tape. When I say "extremely short," I mean it only can hold about two seconds of audio or phonological information. It's also called an "articulatory" mechanism is because it replays the audio over and over. This makes intuitive sense because when people have a list of numbers or words they have to remember for a short period of time, they repeat it to themselves over and over. The purpose of rehearsing the list is to hold that information until it can be recalled. After which time, it can be dumped from the phonological loop.

Visuo-Spatial Sketchpad

Finally, the visuo-spatial sketch-pad is meant to track and momentarily retain spatial information. For example, when driving on the highway, it is necessary to keep track of the arrangement of cars behind you so that you don't unintentionally cut someone off when changing lanes. A quick glance in your rearview mirror quickly updates the spatial information found in the visuo-spatial sketch-pad.

"Are you sure we're not getting some interference?"

Occam's razor posits that the simplest explanation is best. Do we really need three different sub-components? In the case of a momentary memory storage, I think it is completely warranted [3]. The concept of working memory, which includes a central executive aided by two sub-systems, can explain behavioral findings that a unitary concept of short-term memory could not. Probably the best example of a finding that working memory can explain, but short-term memory cannot, is the concept of interference. 

Suppose we play a game similar to the old electronic game Simon. We will play two rounds. In the first round, just play as usual. For the second round, however, you have to repeat the word the. How did you do? If you're like most people, repeating the doesn't really interfere with your ability to play the game because the information is held in a spatial buffer.

However, suppose I ask you to memorize the following list of words, but after you read through the list, you have to repeat the.

• Butterfly
• Airport
• Kitchen
• Church
• School
• Knife
• Solid

Now how did you do? If you're like me, it is impossibly hard. Why? Because the articulatory loop can't do its job refreshing the contents of the list that you want to remember.

That concludes Part 1 of our discussion of working memory. Check back next week for the link between working memory and intelligence, plus the connection to education!

Share and Enjoy!

Dr. Bob

For More Information

[1] Baddeley, A. D., & Hitch, G. J. (1974). Working memory. The psychology of learning and motivation, 8, 47-89.

[2] Baddeley, A. (2000). The episodic buffer: a new component of working memory? Trends in cognitive sciences, 4(11), 417-423.

[3] There have been further refinements to the model of working memory. For example, Baddeley proposed that an additional set of components are needed to bind episodic information held in long-term memory to the contents of working memory. Here is a schematic of those components (see Fig. 2). 

Figure 2. A further elaboration of the working memory model.

Baddeley, A. (2003). Working memory: looking back and looking forward. Nature reviews Neuroscience, 4(10), 829-839.

Getting Off the Couch: Motivation

In the movie Office Space, the main character is struggling to figure out what he wants to be when he grows up. He recalls a procedure from high school for determining what his profession should be:

Our high school guidance counselor used to ask us what you'd do if you had a million dollars and you didn't have to work. And invariably what you'd say was supposed to be your career. So, if you wanted to fix old cars then you're supposed to be an auto mechanic.

Suppose you didn't have to get up and go to work tomorrow. How would you spend your time? What would motivate you to get out of bed?

"It's a problem of motivation, all right?" -Peter Gibbons

Motivation is a slippery subject. To help clarify our discussion, let's start with a definition. I am using the word motivation to describe your own private rationale for engaging in some activity. In other words, motivation is your internal mechanism for figuring out how to spend your time. You might be motivated to engage in an activity because it will result in some concrete output (e.g., earning a paycheck, painting a picture, or writing a poem), builds a new memory or skill (e.g., going sight-seeing, attending a top-rope course, or skateboarding), or just passes the time (e.g., watching television or playing a game).

What are the sources of motivation? Off the top of my head, I can think of a few:

• Power
• Money
• Prestige
• Fame
• Impressing a potential mate
• Demonstrating mastery
• Contributing to something bigger than yourself (meaning)
• The promise of a better future
• Someone in power tells you what you must do (authority)
• Your or someone’s life depends on you completing a task (survival)

Some of these sources are better motivators that others [1]. For example, seeing a grizzly bear is a powerful motivator to leave the situation (i.e., survival). On the other hand, some sources are more nuanced, and they might ebb and flow. On some days you might feel like practicing the piano, while other days you just can't bring yourself to sit down in front of the keys and practice your scales (i.e., demonstrate mastery).

Thanks for all your hard work...bzzz!

Now that we have a working definition, let's talk about what the data say about motivation. One of my favorite motivation studies was led by the behavioral economist Dan Ariely [2]. The study included two experiments. The first of which asked undergraduate participants (who we will call "laborers") to find duplicate letters (e.g., "ss") on a sheet of paper filled with hundreds of letters. There were three different experimental conditions. In the Acknowledged condition, the laborers turned in their work to the experimenter. The experimenter then checked the work to see if the laborer found all the duplicate letters. In the Ignored condition, the experimenter took the sheet and, without checking the accuracy of the completed work, placed it on a very tall ream of paper. In the Shredded condition, the experimenter took the laborer's sheet and promptly ran it through a paper shredder. Bzzt!

After completing the first sheet, the laborer had to make a choice. Does she want to fill out another sheet or stop? Of course, there was a catch. The first sheet paid a "salary" of $0.55; but for each sheet thereafter, the salary decreased by $0.05. There was a diminishing return on the laborer's time. The experimenters were interested to see if manipulating the meaning of the work had any impact on how many sheets the laborers completed in the three experimental conditions. What would you predict? 

If you mentally placed yourself in the participant's shoes, you may have predicted that the Acknowledged condition completed far more sheets. And you would be correct. On average, they completed about 9 sheets, which was many more than the participants in the Ignored (~7 sheets) and Shredded (~6 sheets) conditions. By the way, there was no statistical difference between the Ignored and Shredded conditions.

In the second experiment, participants were asked to assemble Bionicle Lego robots. What could be more fun than getting paid to play with Legos!? In the Meaningful condition, the robots were placed on the experimenter's table, so the laborer could see the fruits of her labor. In the Sisyphus condition, when the laborer turned in a robot, the experimenter promptly disassembled it. Like the previous study, the laborer could decide to stop at any time. Laborers in the Meaningful condition assembled an average of 10.6 robots, while the Sisyphus condition only assembled an average of 7.2 robots.

What is the implication for motivation? It demonstrates that when you hold money constant, people are willing to work much longer on tasks that they find even the tiniest bit meaningful. The meaning in this experiment, of course, was derived from the acknowledgement from another person. The person in charge had to acknowledge that the work had been completed. Looked at another way, the worst thing a manager can possibly do is fail to acknowledge that an employee has done a task. Instead, a manager should help employees see how their work is in some way connected to a greater purpose or project. In other words, don't run your employee's work through the proverbial paper shredder once they are done.

The STEM Connection

The danger in education is that in-class assignments and homework can feel like busy work. Unfortunately, students can't fall back on rationalizing the time they spend by thinking, "Well, at least I'm getting paid." Instead, students need to find motivation elsewhere. Teachers and guidance counselors might have to periodically remind students that they are investing their time in the promise of a better future. 

Some students, however, are more concrete and live for the moment. How do we help this type of student find motivation to study? Offering rewards won't work because studying is, by its very nature, a deferred investment. Offering an external reward can also easily undermine students' intrinsic motivation [3]. Instead, the Lego study suggests that students might be motivated by keeping track of their progress. Each completed assignment is an incremental step along a much greater path. If each assignment and exam can be quantified in some way, it is highly motivating to look back and see how far you've come.

Another source of inspiration for how to motivate people is the video-game industry. We all know how addictive video games can be. What is it about their design that draws us in and keeps us coming back? Keeping track of progress is almost universally used, and so is the idea of "leveling up." As you play the game, the user becomes more proficient. Like the idea of the flow channel, a game needs to be simultaneously accessible to beginners and challenging for expert players. Thus, games need to evolve to be commensurate with the user's improving skill. Video games allow kids to demonstrate their proficiency. Is there a way we can engineer the classroom experience so that demonstrating mastery is looked upon favorably (e.g., spelling bees, math competitions, debates)? 

In summary, motivation is fickle. Sometimes we have it; sometimes it is nowhere to be found. Probably the most reliable source of motivation is spending time on an activity that we chose for ourself, and that we find meaningful. If we can help our students find a connection to something beyond themselves, then we can tap into the same motivation that has built things like Wikipedia. 


Share and Enjoy!

Dr. Bob

For More Information

[1] Pink, D. H. (2011). Drive: The surprising truth about what motivates us. New York: Penguin.

[2] Ariely, D., Kamenica, E., & Prelec, D. (2008). Man's search for meaning: The case of Legos. Journal of Economic Behavior & Organization, 67(3), 671-677.

[3] Kohn, A. (1999). Punished by rewards: The trouble with gold stars, incentive plans, A's, praise, and other bribes. New York: Houghton Mifflin Harcourt.

Target Acquired!: Cognitive Skill Acquisition

Assuming you have your driver's license, think back to when you first learned how to drive. What were the stages that you went through? Did you take a class (e.g., Driver's Ed)? Did your parent take you to an abandoned country road and turn over the wheel? Did you learn to drive a car with a manual or automatic transmission? (Lawyers may object to the next couple of questions because I might be "leading the witness.") In the first few months behind the wheel, were you allowed to listen to the radio? Could you carry on a conversation and drive at the same time? Did you talk to yourself when trying to recall which pedal was the gas or which gear you were in?

Learning to drive is complex because it is a mixture of motor learning (e.g., when to disengage the gas, engage the clutch, and shift gears) and verbal learning (e.g., the traffic laws). After several years of practice, driving becomes second nature. How, then, did we go from a nervous teenage driver to an expert on the road? Acquiring this complex skill requires that we traverse several stages of development.

The Three Stooges, er...Stages!

Acquiring a motor task of sufficient complexity must undergo three stages [1]. An example of a motor task might be learning to serve overhand in volleyball or learning the breaststroke. These tasks are complex because the person must synchronize the timing of various muscle groups. When a novice begins to learn how to serve overhand, the first stage, called the cognitive stage, is best described as verbal. The person learning the task benefits from hearing a verbal articulation of the steps needed to complete the task. The learner might even recite the steps to themselves while practicing. Then the learner transitions to the second stage, the associative stage, where some of the motor subroutines become more fluid. The individual commits fewer errors and relies less on verbal articulation. The final stage, the autonomous stage, is when performance is nearly error free and completely fluid. You know that the learner has entered the autonomous stage when she can now do the task and carry on a conversation. This indicates that a verbal representation of the task is no longer needed and does not interfere with performance.

Given its usefulness in describing the development process for learning motor tasks, this 3-stage framework was adapted to describe how one goes about acquiring a complex cognitive skill [2]. Examples of complex cognitive skills are learning multi-column addition or learning how to balance a checkbook. Like a motor task, the acquisition of a cognitive skill is theorized to undergo three stages. The first stage is the declarative stage where information is represented as declarative chunks. Like the associative stage, the declarative stage can be articulated verbally, and the problem solver is very deliberate when attempting to practice the skill. The second stage, the knowledge compilation stage, takes place when the declarative chunks are "compiled" into procedural representations. Again, there are fewer errors when an individual reaches the second stage, and the problem solver relies less on verbally stating the rules. The final stage, the procedural stage, is similar to the autonomous stage in that all of the declarative chunks have been successfully converted over to procedural rules. Performance is smooth and much faster than the first two stages. 

The mapping between the two theoretical frameworks can be summarized as follows:

Steps or Waves? 

I've described both frameworks in terms of discrete stages that transition from one stage to the next. This is a step-wise theory of development, as represented in Figure 1. Is this an accurate depiction of how we acquire a complex skill? 

Figure 1: A step-wise, schematic representation of development.

While this looks good on paper, life is not so simple. Stages of development are rarely discrete [3]. Instead, there can be forward progress on one day, but then a regression back to the old way of doing things on a different day. This would be more like an overlapping set of functions, as represented in Figure 2.

Figure 2: A schematic representation of discontinuous development.

A good example of the contrast between the step vs. wave model of development is watching kids learn how to add. At first, their performance on this task is heavily dependent on a physical representation of the number system. In other words, kids like to add by counting their fingers. If I ask a child, "What is three plus four?", one strategy he could use is to start counting, using his fingers as placeholders:

1, 2, 3 [holds up three fingers]1, 2, 3, 4 [holds up an additional four fingers][Goes back to the beginning and counts all of the raised fingers] 1, 2, 3, 4, 5, 6, 7Three plus four is seven!

If kids do this enough, they begin to realize that they can jump start the counting by holding up three fingers and start the counting from there: 

4, 5, 6, 7 [holds up another finger for each new number]Three plus four is seven!

But when we up the ante and give the child a more difficult problem (e.g., one that goes beyond ten), he may fall back to his first strategy or come up with an entirely different strategy altogether.

A similar observation can be made in the driving example. When traffic is normal, we might be motoring along with no problems (i.e., Stage 2). But then something unexpected happens, and we suddenly find ourselves needing to turn off the radio or interrupt a conversation so we can focus on the current situation (i.e., regress back to Stage 1).

The STEM Connection

Take a minute to solve the following problem. While you are working through each step, keep track of what is currently in your working memory and where you must guide your attention. For bonus points, see if you can supply a mathematical justification for each step. 

   614

   438

 + 683  

The goal of this little exercise is twofold. First, I want to simulate what it was like to be a novice back in Stage 1. As a novice, most of your knowledge is stored declaratively, so that means you need to think about what to do in terms of the verbalizable chunks of information stored in long-term memory. Second, I also wanted interrupt your pre-compiled procedures for this task. You have been doing multi-column addition for so long that you have probably automatized the steps. That means you might not have access to those declarative representations anymore. Asking for a justification is my attempt to get you to "un-compile" your knowledge.

To teach a complex cognitive skill, it is informative to try and answer the following questions:

• What is my current goal? 
• Which pieces of information should I focus on? 
• How did I know which action to take? 
• What information can I ignore after I am done with this step? 

Once we have answers to these types of questions, we can backtrack and figure out the best way to teach the steps. In addition, knowing about the stages of development might help us figure out how to personalize our instruction. If we can figure out which stage the student is in, then we can help support the types of representations that are currently being used and offer guidance that will help the student reach the next stage.


Share and Enjoy!

Dr. Bob

For More Information

[1] Fitts, P. M. (1964). Perceptual-motor skill learning. Categories of human learning, 47, 381-391.

[2] Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological review, 89(4), 369.

[3] Siegler, R. S. (1996). Emerging minds: The process of change in children's thinking. Oxford University Press.

Mirror, Mirror: Memory As a Reflection of the Environment

Take a few minutes to reflect on these questions:

• Why is there a distinction between short-term memory and long-term memory?
• Why does forgetting happen?
• What are the environmental demands my memory?
• Why does a lot of forgetting happen initially, but then it tapers off?
• What is the optimal amount of time that I need to spend studying to remember something?

Why do we forget?

In a previous post, we graphed the forgetting curve of Hermann Ebbinghaus's study of his own memory. For very short delays, his memory was very good. But as the delays got longer, his memory for his list of trigrams (e.g., "LEK") dropped precipitously. Then the accuracy for remembering the list leveled off at around 20%.

We also drew a forgetting curve for the recall of Spanish vocabulary words across an entire life span. The shape of the graph was surprisingly similar to the forgetting curve of non-sense words used in Ebbinghaus's study. There was a large amount of forgetting initially, but then the percent of recalled words off at around 60%.

These two graphs are interesting in their own right, but you might be asking yourself the following questions: Why are these graphs shaped this way? Why are forgetting curves steep at first and then asymptote at some value? There must be a reason why memory works this way. Let's look to finches, moths, and ants for some answers.

Finches, moths, and ants...oh my!

During his trip to the Galápagos Islands, Charles Darwin noticed something peculiar about a particular family of birds. Although they were of the same family, there were several different species of finches, each with a distinctive beak. Some of the birds had a wide, stout beak; whereas, other finches had a sharp, pointed beak. It turned out that the different shapes aided the birds in consuming food for their different diets. The wide-beaked finches ate nuts and berries, while the sharp-beaked finches ate insects. In effect, the shape of the beak was optimized to the finch's environment, which included their dietary requirements [1].

But what happens if the environment changes? Can an organism's features evolve to respond to the change? It's hard to conduct a controlled laboratory experiment to answer this question; fortunately, a natural experiment occurred at the turn of the century. During the rise of the Industrial Revolution in Great Britain, the amount of pollution escalated rapidly. Ash from the factories coated trees in the surrounding region. Trees that once had light-colored bark, now covered in soot, turned to a dark gray. Resting on the bark of these trees was a species of moth, called the peppered moth. The most prevalent pepper moth had bodies that matched the original color of the tree bark. When the trees became gray, birds could now easily spot the white moths against the gray background. Due to some natural variation in pigmentation, some moths were born a darker color, which was much more difficult for predators to see. As the lighter colored moths were eaten, the ratio of darker moths to lighter moths tipped in favor of the dark-bodied pepper moths [2].

The study of finches demonstrated that species are optimized to their environment, and the study of pepper moths showed that the range of variation within a species can tilt depending on the factors that lend themselves better to survival. So far, however, this conversation has been about the outward appearance of an organism. What about an animal's behavior? Herbert A. Simon wrote this about the complex behavior of the ant:

Imagine watching an ant on the beach. Its path looks complicated. It zigs and zags to avoid rocks and twigs. Very reminiscent of complex behavior — what an intelligent ant! 

Except an ant is just a simple machine. It wants to return to its nest, so it starts moving in a straight line. When it encounters an object, it zigs to avoid it. Repeat until the destination is reached. 

Trying to simulate the path itself would be difficult, but simulating the ant is easy. It’s maybe a half-dozen rules.

The point of this parable is to illustrate the interaction between the environment and perceived complexity. Lots of complex looking things are really the result of the territory, the shape of the beach, and not the agent, in this case, an ant. 

But, of course, with this metaphor, I’m not really talking about ants. I’m talking about people. How much of the complexity of human behavior is really the product of the environment? [3]

Memory, as we have seen, is highly complex. There appears to be at least two different storage mechanisms (i.e., short- vs. long-term memory) and several different classifications of memory types (i.e., semantic vs. episodic; procedural vs. declarative). Can we explain the complexity of memory by looking at the environment? 

"All the news that's fit to print" (in memory).

To answer that question, we need a model of the environment, and see if it matches (more or less) to the models of memory that we currently have (i.e., the forgetting curves). How would you construct a model of the information in your everyday environment? That seems like a tall order. Since we live in an information-rich environment, it might be a good idea to narrow it down. 

That's precisely what two cognitive scientists did when attempting to construct their own model of the informational environment [4]. They decided to look at all of the words that appeared in the headlines of the New York Times for a two-year period (i.e., 730 days between Jan. 1, 1986 and Dec. 31, 1987). They tracked two variables. The first was the day on which a word appeared. For example, the word Challenger occurred on days 29, 31, 34, 36, 40, 44, and 99. Then they counted how many times that word appeared in a 100-day window (n = 7 for Challenger). These two variables allowed them to construct a retention function, which is the probability that a word will appear on the 101st day given n number of days since its last occurrence. 

To make that a little more clear, let's look at some hypothetical data (see Fig. 1). Suppose we want to know, What is the probability that a word will appear on the 101st day, given it has been 20 days since the last time I saw it? According to the hypothetical retention function, I have about an 11% chance of that particular word appearing. If it's been 100 days since the last I saw it, however, then the probability drops to around 3%. In other words, it is highly unlikely that a particular word will appear in my environment as more time passes since the last time it appeared. I think this makes intuitive sense. It's unlikely that we will read about Muammar Gaddafi today, given we haven't heard anything about him in several years.

Figure 1: A hypothetical retention function for words appearing on the 101st day.

To bring this full circle, it seems that our memory is like a finch's beak, a pepper moth's coloration, or an ant's path on the beach. Memory is optimized to the environment in which it operates; thus, memory is a reflection of the environment. The forgetting curves show that memory is solid for short durations, similar to the New York Times headlines model showing that it is highly likely that a particular word will show up again given a short delay [5]. But then as time goes on, that word is less likely to show up. So why bother remembering a piece of information if it is unlikely to appear again in one's informational world?

The STEM Connection

If memory is in fact a reflection of our environment, then what does that mean for the way we structure the informational environment in our classrooms? First of all, the forgetting curves reinforce the adage: Use it or lose it. If the informational environment does not demand that I remember something, then guess what? I'm probably not going to remember it. 

However, we can systematically and intentionally structure the information environment so that important declarative chunks or procedural memories are needed and exercised in a periodic fashion. If something is important enough to remember (e.g., the slope-intercept form of a linear equation), then keep bringing it up. Keep using important information. As the demands in the informational environment escalate, then students will rise to the occasion. If learned well, then it might even make it into the part of the curve that never goes to zero (i.e., permastore).


Share and Enjoy!

Dr. Bob

For More Information

[1] Darwin's finches

[2] Peppered moth evolution

[3] Simon, H. A. (1996). The sciences of the artificial (Vol. 136). MIT press.

[4] Anderson, J. R., & Schooler, L. J. (1991). Reflections of the environment in memory. Psychological Science, 2(6), 396-408.

[5] Some might argue that editors of newspapers and magazine's are sensitive to our ability to remember and therefore might decide not to write about something that occurred long ago. While that might be the case, Anderson and Schooler (1991) also used two other databases to construct their argument. They included a database of children's speech (CHILDES) and the second author's email inbox.