Several reports in the last few years have shown a relationship between cognitive function and the frequency of media multitasking (e.g., Cain et al, 2014; Minear et al, 2013; Ophir et al, 2009). Studies don’t always directly replicate each measure, but overall people who report habitual media multitasking tend to be less proficient in the use of working memory, and more impulsive. 

One drawback of this work is that it has tested only college students, although they are obviously not alone in multitasking. As technology prices drop, younger children have increased access to mp3 players, smart phones, gaming platforms, etc., and with increased access comes increased use. Is the relationship between multitasking habits and cognition different for younger children?

In a recent study, Matthew Cain and colleagues (in press) tested 73 subjects (average age = 14.4 years) on a battery of cognitive and personality tests. They largely replicated previous work, and added some interesting extensions.

First, they found that frequency of media multitasking was negatively associated with working memory span measures. They did not find a relationship with the ability to filter information in working memory, which contrasts with some previous work (Ophir et al, 2009).  Cain et al speculated that Ophir et al may have used a filtering task that was also demanding of span.

Second, Cain et al wanted to use a measure that had more real-world validity, so they correlated media multitasking scores with student scores on the state tests for math in English (for Massachusetts, where the work was conducted). A significant negative correlation was observed. Scores were not related to standard lab measures of math and English ability, however, leading the researchers to suggest that scores on the state test might have been affected because test administration is longer, so scores would more likely be affected if students were prone to distraction.  In other words, students might have been equally proficient in math and English, but less able to maintain attention for a long duration.

Third, researchers wanted to show that media multitasking was not related to cognition across the board, so they administered some tests of cognitive ability they thought were unlikely to be related, and indeed found that manual dexterity, and the unconscious learning of a complex category, were not related to media multitasking.

Fourth, researchers tested whether media multitasking was related to personality characteristics and beliefs that are known to be related to academic achievement. They observed no relationship with grit or conscientiousness, but did see a robust negative relationship with growth mindset. The authors expected to see relationships of all three with media multitasking. Why is not clear to me. If you think that most teens know that multitasking while reading or studying is not conducive to good performance, you might predict a negative correlation with grit and conscientiousness, but it’s not clear that most teens are aware of the relationship. The observed relationship with growth mindset—a belief about the nature of intelligence—puzzles me, and the authors say little about it.

Perhaps the most important aspect of these results is the age of the children tested. The results show that the relationship between media multitasking and cognition that we see in college students is observable by middle adolescence. That marginally increases the probability that the direction of causality is cognition prompting media multitasking behavior, not multitasking prompting changes in cognition. The authors are appropriately cautious in discussing causality but the results are a bit easier to account for with that explanation, the idea being that younger children have had less multitasking experience, and so there has been less opportunity for multitasking to influence cognition—yet the relationship is present.

One point about this study does make me uneasy. The median self-reported estimate of media use was 18 hours/day. A good number of subjects offered weekly estimates of media use that amounted to more than 24 hours per day. The authors note that these seem like overestimates, and so they suggest that these raw figures are not really interpretable.
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But they go on to take the subject reports of frequency of media multitasking at face value. For each medium, subjects were asked how often they engaged with that medium while doing something else, choosing among four categories (Never, a little of the time, some of the time, most of the time). Why trust these estimates, if their estimates of the number of hours spent with media are not trustworthy? Both are retrospective memory reports, and the judgment of media multitasking has not been validated, as far as I can tell.
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That validation ought to be high on the priority list for researchers in this area. It would be a terrible shame if all of these effects that we are tying to media multitasking actually ought to be tied to accuracy and bias in memory reports. 

A few weeks ago I published an op-ed in the NY Daily News about how purchasing decisions are made in educational technology. In this blog I want to elaborate on a couple of points I made.

First, regarding the overall point of the op-ed: most people got it, but a few took it to be a criticism of the use of technology in schools, and wrote me irate emails, tweets, or blog posts. The op-ed was a criticism of how tech purchasing decisions are made, not a blanket criticism of ed tech. "Ed tech" is such a broad category (like "explicit instruction" or "progressive pedagogy") it seems improbable you're going to be able to draw conclusions about effectiveness that will apply in most cases. Sure, you can do meta-analyses (Hattie has the effect size of tech interventions at around 0.3, I believe) but there will be so many exceptions and caveats, it may not be worth it. 

My point in the op-ed was that data to guide purchasing decisions seldom exist. Decisions must be based on intuition, and intuition has in the past been a poor guide. 

Second, op-eds do not provide space to get much into evidence, so I provide some citations here, specifically for the three reasonable-sounding intuitions that I said were wrong.

Intuition 1: reading on screen and paper will be equivalent. I claimed that reading comprehension consistently takes a small hit on screen vs. paper. I just published a piece over at Larry Felazzo's blog that cites lots of the data on this point.  

Intuition 2: information is more readily accessible, so students don't need to have as much information in their memory. I claimed that the Internet has not changed the need to have information in ones head. There's a great deal to say about this claim, and other reasons I listed ended up edited out of the piece; I've described these reasons in magazine pieces, books (here and here), blog posts, and a video. One claim that remained in the op-ed was that people can look stuff up, but they don't. Once the number of words you don't know hits two percent, the odds go way up that readers find the text difficult and will quit.

The 2% figure comes from Carver (1994), who called texts with 0% unknown words "easy," 1% unknown words "appropriate" and 2% or more "difficult." Schmitt et al (2011), studying second language learners, argued that there is (unsurprisingly) a linear relationship between known vocabulary and comprehension, and that 98% is a reasonable middle ground for what constitutes appropriate challenge.

I touched only briefly on the fact that "looking things up" is trickier than it sounds, and usually requires a fair bit of knowledge to get right, a point made forcefully by George Miller in 1987. As he points out, meaning comes from context, and dictionaries can't provide much. So children look up a word like "relegate," take away an abbreviated, context-free understanding of the definition, like "send away," and so misuse the word, e.g., "I relegated the letter to my pen pal."

It's not that "looking things up" isn't useful. It's that people not doing the looking-up underestimate the extent to which lookers-up will view it as mental work, and overestimate the likelihood that an accurate definition will be learned.

Intuition 3: kids don't need to worry about handwriting, because keyboarding is so important. Finally, I mentioned evidence that handwriting may improve cognition: recent papers include Cameron et al 2012, Grissmer et al 2010; Dinehart & Manfra, 2013. These are correlational findings, but the effect size is pretty big, and the weird thing is that what seems to be the best predictor of academic ability along these lines is how well children can copy a figure; look at a complex model, and then try to reproduce it on paper. That's pretty much exactly what handwriting practice is. The effect may not be causal, but before we jettison a year of practicing something that might aid cognition substantially, we should be sure it's disposable.

My real motivation in writing this op-ed was frequent encounters with teachers, who feel that ed-tech interventions are held to a lower standard of promise when administrators evaluate them. Teachers who are doubtful about a new intervention are brushed off as fuddy-duddies, or frightened of change.

I think there's something to this. I don't think it's that ed tech enthusiasts are dazzled by the shiny and new. But I do think this business about intuition is a problem. It often seems obvious how the human cognitive system will interact with a new situation, but these intuitions can easily mislead. 

Texting while driving is banned in 46 states.

It makes you wonder what's on the minds of the legislators in the other 4 states, like the old Trident ads that claimed "4 out 5 dentists recommend sugarless gum"--what the hell is that fifth dentist thinking? But I digress.

Compliance is a problem, however. The ad campaign implores: "It can wait." That's meant to keep us from peeking at the screen when we hear that ping.

One might take this advice as an indication that driving will not be disrupted by an incoming text message so long as you don't answer it. That might be the case if the distracting part of the message were the notification itself, but that's probably not the problem. The notification is, after all, quite brief. The bigger problem may be persistent thoughts after the notification, especially 

wondering who texted you and why, and also what psychologists call prospective memory--reminding yourself that you have an unchecked message and that you ought to check it when you stop driving.

This account seems especially plausible in light of the data showing that talking on a hands-free cell phone is just has distracting as talking on a hand-held device (Horrey & Wickens, 2006). The overt activity (holding the phone, checking the message) may be less important than the unobservable mental activity. A recent study offered support for this hypothesis, examining the consequences to attention of refraining from checking a message or call. 

Cary Stothart and colleagues asked subjects to perform a simple, but attention-demanding lab task. A digit appeared every 900 ms, and subjects were to press a button as quickly as possible when it did...except if the digit was a 3.

Subjects performed a few practice trials to be sure they understood the task, and then performed two blocks of 360 trials each. 

There were three conditions. Some subjects received four text messages on their phone during the second block of trials, some received phone calls, and the third group served as a control. 

A unique and clever feature of the study is that subjects were unaware of its purpose. They were asked for their cell phone number along with other information as part of the study; the program controlling the attention task contained a script that executed the calls or texts. The program also assigned subjects to condition randomly, so the experimenter did not know in advance which condition the subject would be in. 

An experimenter remained in the room, seated behind the subject, to monitor whether the subject actually checked his or her phone, and to monitor whether the phone was off or silenced. (Each was seldom the case.)

The data showed, as expected, that unanswered calls and text notifications led to performance decrements. One measure is the probability of pressing the button when a 3 appears, shown below.

It's typical to make more errors in the second block of trials--it's just hard to maintain focused attention for a long time--and control subjects show that pattern. But the increase is greater for the subjects receiving phone or text notifications. 

Another way to measure attention in this task is the frequency of very fast response times. If a subject doesn't really evaluate the digit, he or she can fall into a rhythm of pushing the button more or less in sync with the appearance of the digit, because its timing is predictable. The frequency of this sort of response increased in block 2 for the call and the text conditions. 

An important feature of the data was that the distraction effect was as great when the phone was ringing as when it was not. That supports the interpretation that the detriment is not so much the distraction of the notification, but thoughts about who might be contacting you.

The authors did not include a condition that would really help support their interpretation. They ought to have people perform this task in pairs. If my cell phone rings it ought to distract me, but have little impact on you, because you won't wonder who is trying to make contact.

How detrimental this distraction effect would be to driving is hard to say; obviously some driving conditions (a straight, empty interstate) are less demanding than others (Boston rush-hour in a snowstorm). Still, the impact on overall accident rates is predictable. Even a small distraction effect, multiplied by the number of driving hours in this and other countries where smartphones are the norm, means more accidents.

If we really want people to refrain from texting while driving, "turn it off," is better advice than "it can wait."

I doubt, however, that even well-intentioned drivers will remember to do so. The solution would be greater use of existing apps that block texting while driving. 

Humans show remarkable cultural diversity. Different groups—let’s call them tribes—use different technologies, economic organizations, political organizations, they hold different religious beliefs, and so on. Explanations of this diversity typically fall into one of two categories:

1. Humans inhabit almost every corner of the globe. Diversity of tribes’ behavior is a product of environmental diversity and the fact that humans are so good at problem-solving. Different environments prompt different behaviors, but even when environments are similar people are so ingenious they come up with different solutions to the same environmental challenges.
2. Diversity of behavior is due to cultural traditions. Sure, there are some environmental constraints on what I do—people living in the desert won’t fish—but diversity is so high because small changes are preserved with fidelity. People keep doing what the tribe has always done.

 
The first account predicts that local environmental conditions will determine tribe behaviors. Two predictions may be drawn from the second of these accounts: (1) tribes that live spatially near one another will be more behaviorally similar and; (2) behaviors will persist across generations.
 
A recent study sought to test both predictions using an enormous dataset of 172 tribes in western North America.  The dataset records 297 behavioral variables (e.g., what people eat, their religious practices, family organization, and so on) and 133 variables concerning the environment (available flora & fauna, characteristics of soil, altitude, precipitation, etc.). All data represent practices and conditions at the time the tribe first encountered Europeans.
 
Spatial distance between tribes is simple enough to measure.  The researchers used language phylogeny as a proxy of cultural phylogeny. Analyses of the similarity of languages yields an “evolutionary tree” of languages, so the distance of any two languages on the tree can be measured with a “most recent common ancestor” approach.
 
The question of interest is which of three variables predicts whether two tribes show similar behaviors. If behavior is mostly a matter of smart individuals adapting to the local ecology, then tribes inhabiting similar terrain should behave similarly. But if learning is mostly social, then tribes that are physically and/or culturally close should be more likely to behave similarly.
 
The results showed that cultural history and ecology both affect everything…but cultural history generally has the stronger effect. Within cultural history, phylogeny mattered more than spatial distance.
 
This analysis is about group behaviors, things that most people in a tribe do. But the result showing the importance of social learning may hold a lesson for those of us who think about the education of individuals. It’s easy to be a little dazzled by the brilliance of the human mind, and to see most of cognitive development as the intrepid mind of the individual, exploring the environment like a little scientist.
 
That’s certainly the emphasis we get from many psychologists. Bandura’s social learning duly noted, the towering figure of Piaget puts the child’s individual discoveries at the center of learning.
 
When it comes to schooling, I sometimes sense a similar reverence for learning that is the product of an individual mind at work, over the mere copying of someone else’s solution. It’s true that you only get true invention/innovation from original thought. But it’s a whole lot quicker and more reliable to copy what others have done. That is probably why social learning seems to be the workhorse of cultural learning.

One of my graduate school mentors noted that if he was walking when presented with a really difficult cognitive challenge, he would stop, as though walking drew, however slightly, on his attention. Rousseau, in contrast, claimed “I can only meditate when I am walking.”
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The advent of the treadmill desk makes the question of walking and cognition more urgent. Okay, there may be health benefits, but if walking is not fully automatic, it siphons away some of your thinking capacity; it demands multitasking, so why put one in the workplace? 

Researchers have caught up to business trends, and a couple of recent studies indicate that dedicated office walkers can relax—treadmills don’t seem to compromise cognition. Probably.

In one, researchers administered well-normed measures of working memory and executive function (digit span forwards and backwards, digit-symbol coding, letter-number substitution, and others) to 45 college undergraduates. Each completed the tasks while sitting, standing, and walking (in random order), with 1 week elapsing between sessions. Participants could set the walking speed as they preferred, between 1 and 3 km/hour. Performance on the working memory/executive function tasks was statistically indistinguishable in the three conditions.

That study had people walk (or not) and measured the impact on working memory. Another approach is for researchers to tax working memory (or not) and observe the impact on walking. Other researchers used that method, having subjects either just walk (again, at a self-selected pace), or walk while performing a working memory task, or walk while reading. Researchers recorded several aspects of gait, focusing on variability. Again, they found no evidence of interference.

The neurophysiology of walking would seem consistent with these results. Humans have central pattern generators in the spinal cord—neural circuits that, even in the absence of input of the brain, can generate patterns of flexion and extension in muscles that look like walking. Thus, if the spinal cord can handle walking on its own, it’s easy to see why walking is not compromised when the brain is doing something else.

But central pattern generators set up pretty crude motor output; locomotion (like all movement) requires close monitoring of perceptual feedback (from vision, from balance) which is used to fine-tune walking movements (For a review, see Clark, 2015). We notice the need for perceptual information when we walk on ice, and for motor tuning when we pick our way through a rocky beach, but the fine-tuning goes on in a less obtrusive way in everyday situations. To get a feel for that, find yourself a nice long hallway, pick a spot about 50 feet away, and walk towards it with your eyes closed. If walking were a completely automatic program that could run without visual input from the brain, this would be no problem, yet most sighted people feel uneasy just a few steps in.

So if walking actually can’t run with total automaticity, why does treadmill walking show no attentional cost?

It may be that there is a cost, but it’s so small that it’s not detected in these experiments. And that may mean it’s not worth worrying about. Follow-up experiments with greater statistical power to detect small effects would be needed to address that possibility. Three other caveats are worth considering before we all buy treadmill desks.

First, the studies to date have been of relatively brief duration—less than an hour. It’s possible that subjects can with some effort of concentration, walk without cognitive cost for a short period of time, but a few hours would reveal a deficit; tiredness might make walking a little sloppy, and thus more attention-demanding.

Second, at least one study has shown a movement task (key tapping) was compromised when people walk (Oblinger et al, 2011). That’s not an effect of attention, but of trying to do two motor actions at once, like rubbing your stomach while patting your head. Hence, although office activities like typing or data entry have not been tested on treadmills, I’d be willing to bet they would be compromised. 

Third, my graduate mentor and Rousseau may have been talking about different types of thought. My mentor referred to answering a question, whereas Rousseau may have meant more creative thought. Walking may not be helpful when the environment presents pressing problems in need of timely answers. But a meandering gait may promote meandering thought, which in turn promotes creativity. The latter has not be tested on office treadmills either.

,Thinking is taxing and people don’t much like it. Psychologists describe this phenomenon with the term cognitive miserliness, coined by Robyn Dawes in the ‘70s—we think when we feel we have to, and otherwise avoid it.

We use two strategies to avoid thinking. If the situation seems similar to one we’ve seen before, we rely on memory and do whatever we did last time. (Retrieving from memory incurs little cognitive cost.) My late colleague, Dan Wegner, had one dish he would order for lunch at each restaurant we frequented. He explained to me, “That way I don’t have to think.”

If the situation is unfamiliar memory won’t work, but you can often get away with heuristics—quick, cognitively inexpensive processing routines that provide an answer, often a good one. One heuristic would be association—produce an answer that is associated in memory with whatever seems to be the key term in the problem.

A classic problem to measure miserly thinking is this:
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A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost?                 cents.

The “not really thinking about it” answer is “10 cents.” You see the word “more,” figure the problem calls for subtraction, perform that operation for the two numbers in the problem, boom, 10 cents. But you don’t check your work.
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The last twenty-five years has brought a new strategy: find the answer on the internet. (For the lazy, here you go.) And in the last five, smartphones have become inexpensive enough to deeply penetrate the consumer market--64% of US adults own one. 

With this new route to miserliness, are people less likely to analyze, and more likely to use the Internet as a sort of external memory? A new study indicates the answer is “maybe, for heavy users.”

Researchers at University of Waterloo conducted three studies examining the association between self-reported smartphone use and performance on questions like the ball-and-bat problem. They separated respondents into low- medium- and high-smartphone usage groups. They found no difference between the low and medium groups, but the high usage group was less accurate on the analytic problems.

Researchers found that the effect held for total number of minutes subjects reported using their smartphones, and for search engine use, but not number of minutes spent on entertainment sites (YouTube, Reddit) or social media sites (Facebook, Snapchat). So the researchers were able to confirm a somewhat more fine-grained version of their hypothesis that people who are more cognitively miserly are more likely to search information out on their smartphone.

Researchers also reported a negative correlation between smartphone use and cognitive ability (as measured by brief numeracy and verbal intelligence tests), an association reported in previous research.  The reason is not clear. It may be that low-cognitive-ability people seek information—look up a word meaning, calculate a tip—that high-ability people have in their heads.

The authors rightly point out that their study should be thought of as just the start of what will have to be a much broader research program (or set of programs) examining how people interact with technology that can be provide cognitive support, and that is nearly always available to us. The idea of “external memory”—that we know information useful to us is available, even though it is stored in others and in recorded sources—is very old. William James discussed it Principles of Psychology over 100 years ago. Dan Wegner proposed a particular conceptualization of this idea in 1985.

New technology has made external memory complicated and the need for understanding it more urgent. Many of us spend a lot of time with our smartphones—is it making us more cognitively miserly, or are misers simply taking advantage of a new opportunity? What are the costs and benefits of either change?

​The ideal design would be to examine short- and long-term changes in individual habits when people first gain access to a smartphone, but the opportunity to conduct such a study is fast disappearing.

​In a speech delivered in Las Vegas this past week, Secretary of Education John King started a brief speaking tour on a subject that appears will be one of his priorities, at least in the near term: the need to broaden school curricula.
 
The speech made many points with which educators already agree. No Child Left Behind narrowed the curriculum, as educators, concerned about standardized test scores, engaged in heavy doses of test prep. King likely knows (but figured it would not help his case to point out) that NCLB might have narrowed the curriculum further, but it was damn narrow in the late 90’s, before NCLB came on the scene (see here, here and here).
 
King offered other powerful arguments in favor of a broader curriculum, again ones that are familiar to educators: if offers a better chance for a child to find the subject that really excites them, and breadth in knowledge better comports with what we consider a real education.
 
And King pointed out that knowledge feeds academic excellence, a point I’ve harped on tirelessly (and likely, tiresomely) for years. He also tied this theme to educational justice, a point ED Hirsch has emphasized: if knowledge is the key to academic (and eventual economic) success, bear in mind that schools are the best (and probably only) hope for exposure to this knowledge for children whose access to it is limited at home.
 
The reasons behind this linkage between knowledge and successful thinking can get technical pretty quickly. I’ve written about them elsewhere  by describing experiments showing the linkage, along with some version of the cognitive theory underlying it.
 
In this blog I want to take a different turn and discuss mental representations and metaphors for thinking.
 
One of the problems in persuading people that knowledge matters to thought lies in how they think these two. They often think of them as separate: people have something like a database of stuff they know, and then they have mental processes that do the thinking. You plug the knowledge into the thinking processes. This view leads naturally to the conclusion that knowledge isn’t all that important, because you can get knowledge from the environment—by looking things up, for example.
 
Oftentimes, when I describe experiments showing that thinking processes don’t work very well in the absence of knowledge, people will respond by saying “right, I totally agree. You need something to think about.” But that’s still not really it, because it’s just another way of saying you need the knowledge database—knowledge is still separate.
 
To the extent that those of us arguing for the importance of knowledge have invoked cognitive theory, we have not done ourselves a favor by using the metaphors we have. Metaphors are enormously helpful in understand new ideas, but they can also lead you astray. That’s because the analogous situation won’t share all of the features of the new thing you’re describing. Likening atoms to our solar system leads to trouble if kids make the natural extension, thinking that electrons travel in elliptical orbits.
 
The stripped-down model of the cognitive system I’ve used is shown below.

This metaphor is really good for understanding the limitation of working memory, and that’s important. But this metaphor of the mind looks very consistent with the idea that you have a data bank of information (long term memory) and a place you think with it (working memory).
 
This is a cartoon version of one family of theories of memory, but it’s not the only one. There’s another that’s less intuitive, but that is growing in its acceptance and that does not naturally lead to the erroneous separation of knowledge & processes.
 
In the model shown above (the best known version of which is Alan Baddeley’s), working memory is likened to a place, and in order to use a long term memory representation the mind creates a copy of the representation and puts it into working memory.
 
In the other family of models (e.g., by Nelson Cowan, or Randy Engel), working memory is not a place, but a state. Long term representations can participate in different cognitive activities, some of which put that representation in a state we’d call being in working memory (e.g., associated with consciousness).
 
Another important feature that is not a part of all theories, but is growing in acceptance is that idea that all cognitive systems have memory in them. Consider your visual system. You prepare to cross a busy street—you look left, then right, then left again. In so doing you are getting a series of snapshots of the scene on each side of you, a scene that looks a little different with each view. But you don’t perceive it as different scenes. Your ability integrate these snapshot into a consistent scene requires memory at a very short scale (a second or so).
 
But your visual system also uses memory at a much longer scale. When you walk into a grocery store, you have expectations about what you’re going to see, and those expectations influence what you actually do see. These expectations are built from experiences over the course of months.
 
This view of memory being embedded in all cognitive systems was explicit in my own theory of motor skill acquisition.
 
Once you think of all cognitive systems as having memory at different time scales , there’s not much reason to think of STM and LTM as different; rather, they are different states of the same memories. Gus Craik has made this point for many years.
 
If this is hard to wrap your mind around, here’s an alternative metaphor for you. Don’t think of memory as a databank where you store things. Think of a hill of sand—that’s your mind. You pour water on it—water is thought. The water coursing over the sand creates gullies and rivulets. That’s memory. It’s a representation of where the water (thought) has been in the past and if the water moves through those same channels they will become a little deeper. The next time you think (pour water) it will likely happen in the channels it’s followed before….but not necessarily.  The new water also has the potential to change the gullies on the hill.
 
This metaphor captures the idea that the difference between long term memory and short term memory is one of state. Long term memory is a gully. Short term memory is a gully with water in it (i.e., that is guiding thought).
 
I started this blog by saying that John King’s efforts to broaden curricula are multi-faceted, and research showing the importance of knowledge to academic skills is just part of those efforts. Then too, people’s pre-existing beliefs about knowledge and cognition are just a part of that one facet, and metaphor is just a part of pre-existing beliefs. So this metaphor business is a small point, but it may have outsize consequences for how people think about knowledge and thinking skill.
 
Spread the word.

Can young children use abstract reasoning? Or do children think concretely in the early years, and abstract reasoning comes into their repertoire only later, perhaps as late as age twelve? I’ve argued that there are good data to show that even young children use abstract reasoning. A new study provides further support.
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The researchers examined the spontaneous use by children of relational reasoning—the ability to make different sorts of comparisons of mental entities. This was a relatively small sample, about 20 children (ages five to seventeen) sorted into three groups: early (K-2), middle (fourth-eighth grade) and late (tenth and eleventh grade). To examine reasoning, each child had a one-on-one conversation with a researcher. The researcher showed the child a juice box, and prompted a conversation about its design, with questions like “Why had this product been developed,” and “What materials have been used to make the packaging?” Then the researcher did the same thing with an unfamiliar object—a vegetable cutter—and asked them “What do you think this is?” and “Why do you think this?” Conversations averaged around 5 and a half minutes for each and were recorded for later analysis.   

Subjects' discussions were coded for instances of four types of relational reasoning. In Analogy, the child offers a similarity relation between the object and something else (“It opens and shuts like a clip.”) In Anomaly, a relation of difference is noted, e.g. “[this is probably for ] people who cook at home, ‘cause there are probably commercial ones that do it differently.” In Antimony, students note incompatibility when drawing a relation, e.g. saying of the packaging “It’s probably not cardboard, but a kind of plastic.” And finally, an Antithesis draws a contrast, for example when a sixth grader observed of a good design “It has to be fast and quite easy to use instead of quite hard.”
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The figure shows the instances of relational reasoning. 

The graph shows raw numbers: to provide some perspective, 228 statements were coded as using relational reasoning, and 2,441 statements were coded as not using relational reasoning.

A couple of findings are noteworthy. Proportionally, the middle group verbalized relational reasoning more often than either the early or late group. And different types of relational reasoning were used more or less by different groups: early children were less likely to use antimony and antithesis, and late children were less likely than others to use analogy.

But also notable is that all types of relational reasoning were used by children of all groups, which included children as young as five. What’s notable to me is that children in this experiment were not presented with an instance of reasoning to see if they could understand it, nor were they presented with a problem that could only be solved by using the type of reasoning of interest. Rather, they were given a very open-ended problem to see if they would spontaneously use relational reasoning.

There are two drawbacks to this study. First, it’s possible that experimenters were, unwittingly, drawing out relational reasoning statements through their ends of the conversation. Second, the researchers reported that they sampled children to represent a spectrum of individual differences: gender, ethnicity, socio-economic status, quality of school attended, and others. But the data were not analyzed using these variables. Given the small N, it’s plausible that a few kids from particular schools carried most of the observed effect.

Despite these drawbacks, I think the study is interesting because it fits with the larger pattern described elsewhere and that seem to be underappreciated: children engage a variety of reasoning strategies from an early age.
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Jablansky, S., Alexander, P. A., Dumas, D., & Compton, V. (in press) Journal of Educational Psychology. http://dx.doi.org/10.1037/edu0000070.

Have you seen the YouTube videos, People are Awesome? There’s a new one each year, featuring wild stunts (e.g., a surfer who executes a skateboarding kickflip on a wave). Most of these feats require not just daring but enormous skill, and I kept thinking of them as I read Anders Ericsson’s new book, Peak: the book teaches you what it takes to be that awesome. 

To the extent he’s known among the public, Ericsson is “Mr. 10,000 hours;” he’s the researcher who came up with idea that, if you practice something 10,000 hours, you’ll be an expert. (That’s not really what he said. More on that in a moment.) Among psychologists, he’s known as the researcher who has done the most to advance our understanding of the nature of expertise, and especially, how it’s gained.

I can’t help but think that Ericsson was motivated to write this book, in part, to correct misconceptions. Several briskly selling books on expertise have appeared in the last few years, and while they vary in their accuracy, the big-picture conclusions readers draw tend to be: (1) talent is not everything; (2) you have to practice (3) If you practice 10,000 hours, you’ll be great. Two of these are right but incomplete, and the third is wrong.

Happily, Ericsson (and his able co-author, science writer Robert Pool) don’t dwell on what other books have gotten wrong. There are a couple of pages on the errors in Malcolm Gladwell’s Outliers, but it’s about the kindest, most gentlemanly takedown imaginable. (Regarding scientific accuracy, some psychologists will doubtless sniff that Ericsson underestimates the contributions of innate talent to skill. Ericsson reckons it counts for nothing, which is definitely a minority view. There’s so much right in this book I can’t get worked up about one thing that most researchers would disagree with.)

No, the sensibility of the book is not “here, at long last, is accurate science.” That’s just a bonus. The sensibility of the book is “here’s how to become at great something.”

As you might expect, the front half of the book explains principles of deliberate practice, and how it differs from other kinds of practice:

• It’s for skills that other people have already figured out how to do and for which effective training techniques exist.
  
• You are out of your comfort zone most the time.
• The goal of a practice session is quite specific; it’s not “improve.”
• You typically work on one small aspect of the skill when you practice.
• It requires full attention; it’s not enough to just do what the coach said.
• It requires meaningful feedback, and meaningful response to the feedback.

Ericsson was wise to bring in his coauthor; this material could be plodding, but Pool deftly weaves scientific principles into stories, and Peak reads like some of the best examples of this genre.

Good science well told would be enough to make me like the book. What made me love it is the care and thought Ericsson puts into helping you apply the principles of deliberate practice to your own life.

He starts with the working world. What it would take to build a better doctor, say? Practicing medicine in the usual sense is not practicing medicine, it’s doing medicine. What’s needed is deliberate practice. For that to happen, the medical field needs to identify experts in each field, figure out what underlies their superior performance, and develop the logical steps to build those mental representations in novices. If that sounds hard, well sure. But Ericsson also describes interim steps that can be taken in medicine--or in any workplace--to boost improvement.

Okay, maybe deliberate practice can help doctors improve, help society, whatever…what about the reader who just wants to improve his short game on the golf course? Or what if you just want to be awesome?  

The recommendations for the field of medicine won’t do for the individual. The medical field has significant money and infrastructure to support a change in training. You likely have a little time, less cash, and fragile willpower. Ericsson has been working with individuals trying to improve various skills for decades, and he has practical advice to share on practical matters:  

• What to do if you find you can’t focus.
• What to do if you don’t have a teacher.
• What to do when you hit the inevitable plateau.
• How to maintain motivation.  

​Peak is among the best popular works of psychology I have ever read. It does exactly what this sort of book is supposed to do: intrigue you, educate you, make your life better. ​​