,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.

EDIT: I published a review in Education Next. 

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. ​​