“While caffeine has numerous benefits, it appears that the drug may undermine creativity more than it stimulates it. When we drink a caffeinated beverage, the caffeine quickly crosses the blood-brain barrier—an interface of sorts between the brain and the body’s circulatory system, designed to protect the central nervous system from chemicals in the blood that might harm it—and proceeds to block the activity of a substance called adenosine.

Normally, a central function of adenosine is to inhibit the release of various chemicals into the brain, lowering energy levels and promoting sleep, among other regulatory bodily functions. When it’s blocked, we’re less likely to fall asleep on our desks or feel our focus drifting. According to a recent review of some hundred studies, caffeine has a number of distinct benefits. Chief among them are that it boosts energy and decreases fatigue; enhances physical, cognitive, and motor performance; and aids short-term memory, problem solving, decision making, and concentration.

But all of that comes at a cost. Science is only beginning to unravel the full complexity behind different forms of creative accomplishment; creativity is notoriously difficult to study in a laboratory setting, and the choice of one approach over another limits the way that creativity can be measured. Still, we do know that much of what we associate with creativity—whether writing a sonnet or a mathematical proof—has to do with the ability to link ideas, entities, and concepts in novel ways. This ability depends in part on the very thing that caffeine seeks to prevent: a wandering, unfocussed mind.

Creative insights and imaginative solutions often occur when we stop working on a particular problem and let our mind move on to something unrelated. In one recent study, participants showed marked improvements on a task requiring creative thought—thinking of alternative uses for a common object, such as a newspaper—after they had engaged in a different, undemanding task that facilitated mind wandering. The more their mind wandered when they stepped away, the better they fared at being creative. In fact, the benefit was not seen at all when the subjects engaged in an unrelated but demanding task.

In other words, a break in intense concentration may increase unconscious associative processing. That, in turn, allows us to perceive connections that we would otherwise miss. Letting our minds wander may also increase communication between the brain’s default mode network—the parts of our brain that are more active when we’re at rest—and its executive areas, which are used in so-called higher reasoning and decision-making functions. These two regions become activated right before we solve problems of insight. Caffeine prevents our focus from becoming too diffuse; it instead hones our attention in a hyper-vigilant fashion.

Caffeine also inhibits another mental process that’s necessary for creative thinking: sleep. A 2009 study showed that people who experienced REM sleep performed better on two tests of creative thinking than those who simply rested or napped without entering the REM cycle. During REM, their brains were able to integrate unassociated information so that, upon waking up, they were more adept at solving problems they had been primed with earlier. Without sound sleep, the effect dissipated. Sleep deprivation has also been linked to negative effects on other elements associated with creativity and thought clarity: it diminishes emotional intelligence, constructive thinking, and the ability to cope with stress.” (Read more here.)

Fascinating: so caffeine increases focus and prevents sleepiness—which is exactly why we use it—but these very features of the drug may also impede creativity. Perhaps we should think about how to use caffeine strategically—drinking coffee when we need to mentally home in on a task, and skipping it when we need our thoughts to be more expansive and diffuse.

Is caffeine a part of your creative process? (I’m a tea addict myself.)

Sparks reports on a symposium at the annual Association for Psychological Science research meeting held late last month, where panelists discussed how and when asking students for explanations can best enhance their learning:

“’Often students are able to say facts, but not able to understand the underlying mathematics concept, or transfer a problem in math to a similar problem in chemistry,’ said Joseph Jay Williams, a cognitive science and online education researcher at the University of California, Berkeley.

For example, a student asked to explain why 2 x 3 = 6 cannot simply memorize and parrot the answer, but must understand the underlying relationship between multiplication and addition, Williams said. Students who can verbally explain why they arrived at a particular answer have proved in prior studies to be more able to catch their own incorrect assumptions and generalize what they learn to other subjects.

‘We know generating explanations leads to better educational outcomes generally. When children explain events, they learn more than when just getting feedback about the accuracy of their predictions,’ said Cristine H. Legare, an assistant psychology professor and the director of the Cognition, Culture, and Development Laboratory at the University of Texas at Austin.

In forthcoming research with UC-Berkeley, Ms. Legare brought in 96 children ages 3 to 5 and set before them a complex toy made up of colorful, interlocking gears with a crank on one end and a propeller on the other.

With half the children, the researchers asked each one, ‘Can you explain this to me?’ With the other half, they simply said, ‘Oh look, isn’t this interesting?’

The two groups of children focused on different things, researchers found. Children who were asked to observe noticed the colors of the toy, while those asked to explain focused on the chain of gears working on each other to eventually turn the propeller when the child turned the crank at the other end.

Children who had explained the toy were better at re-creating it and not being distracted by ornamental gears, and they were better able to transfer what they had learned about how gears work to new tasks.

The children who had observed the toy outperformed the children in the explanation group on a memory task focused on the toy’s colors.” (Read more here.)

Fascinating: asking children to explain rather than observe leads them to focus differently, act differently, and ultimately learn differently. Of course, there are situations in which observing is the skill we want to cultivate—but this research is a good reminder that being asked to explain (as opposed to listening to someone else’s explanation) is often a great way to promote deep learning.

How did the artists differ from the bank officers? Kaufman explains:

“Bank officers were about as good at divergent thinking as the general population, whereas artists were amazingly good at flexibly generating original pictures and words. In fact, they were almost at ceiling! What about temperament? This is where things get really interesting. On the whole, artists didn’t substantially differ from bank tellers in their temperament. To get to the bottom of this finding, the researchers looked at the relationships between the various measures within each group.

Surprisingly, consistent relationships between divergent thinking and temperament were found only in the sample of artists. Among bank tellers, temperament was not related to divergent thinking. But among the artists, those scoring higher on the tests of divergent thinking tended to display higher levels of the following:

• Briskness (“quick responding to stimuli, high tempo of activity, and the ability to switch between actions”)
• Endurance (“an ability to behave efficiently and appropriately in spite of intense external stimulation or regardless of the necessity to pay attention during prolonged periods of time”)
• Activity (“the generalized tendency to initiate numerous activities that lead to, or provoke, rich external stimulation; it is conceived as the basic regulator of the need for stimulation”)

What’s more, artists who scored higher in divergent thinking also scored lower in emotional reactivity. This might not be surprising, considering the ability to do well on a decontextualized, timed test requires a cool head. When all of the temperamental factors were considered at the same time, activity remained the best positive predictor of divergent thinking, and emotional reactivity remained the best negative predictor of divergent thinking.

I think these results highlight a more general point about creativity: the interconnectedness of temperament and creative production. As the researchers speculate:

‘ . . . temperament [is] the foundation for development and expression of one’s creative potential. People scoring high on activity tend to have many diverse experiences that may be used as a substrate for divergent thinking and creative activity.’”

Kaufman concludes that “a tolerance for ambiguity, complexity, engagement, openness to experience, and self-expression are all essential to creative production in any field of human endeavor.” (Read more here.)

Let’s go back for a moment to the trait that best predicted creativity in the artists: “the generalized tendency to initiate numerous activities that lead to, or provoke, rich external stimulation.” I wonder if this is where the stereotype of the “flaky” artist comes from: they’ve got a hand in a lot of different projects at once, instead of focusing in a linear fashion on one single task.

We often think that this latter approach is the only way to get things done, but it turns out that if you want to be creative—if you want to come up with new and different ideas—it’s a good idea to be a little scattered.

My own take: technology can make us smarter or stupider, and we need to develop a set of principles to guide our everyday behavior, making sure that tech is improving and not impeding our mental processes. Today I want to propose one such principle, in response to the important question: What kind of information do we need to have stored in our heads, and what kind can we leave “in the cloud,” to be accessed as necessary?

The answer will determine what we teach our students, what we expect our employees to know, and how we manage our own mental resources. But before I get to that answer, I want to tell you about the octopus who lives in a tree.

In 2005, researchers at the University of Connecticut asked a group of seventh graders to read a website full of information about the Pacific Northwest Tree Octopus, or Octopus paxarbolis. The Web page described the creature’s mating rituals, preferred diet, and leafy habitat in precise detail. Applying an analytical model they’d learned, the students evaluated the trustworthiness of the site and the information it offered.

Their judgment? The tree octopus was legit. All but one of the pupils rated the website as “very credible.” The headline of the university’s press release read, “Researchers Find Kids Need Better Online Academic Skills,” and it quoted Don Leu, professor of education at UConn and co-director of its New Literacies Research Lab, lamenting that classroom instruction in online reading is “woefully lacking.”

There’s something wrong with this picture, and it’s not just that the arboreal octopus is, of course, a fiction, presented by Leu and his colleagues to probe their subjects’ Internet savvy. The other fable here is the notion that the main thing these kids need—what all our kids really need—is to learn online skills in school. It would seem clear that what Leu’s seventh graders really require is knowledge: some basic familiarity with the biology of sea-dwelling creatures that would have tipped them off that the website was a whopper (say, when it explained that the tree octopus’s natural predator is the sasquatch).

But that’s not how an increasingly powerful faction within education sees the matter. They are the champions of “new literacies”—or “21st century skills” or “digital literacy” or a number of other faddish-sounding concepts. In their view, skills trump knowledge, developing “literacies” is more important than learning mere content, and all facts are now Google-able and therefore unworthy of committing to memory.

There is a flaw in this popular account. Robert Pondiscio, executive director at the nonprofit organization CitizenshipFirst  (and a former fifth-grade teacher), calls it the “tree octopus problem”: even the most sophisticated digital literacy skills won’t help students and workers navigate the world if they don’t have a broad base of knowledge about how the world actually operates. “When we fill our classrooms with technology and emphasize these new ‘literacies,’ we feel like we’re reinventing schools to be more relevant,” says Pondiscio. “But if you focus on the delivery mechanism and not the content, you’re doing kids a disservice.”

Indeed, evidence from cognitive science challenges the notion that skills can exist independent of factual knowledge. Dan Willingham, a professor of psychology at the University of Virginia, is a leading expert on how students learn. “Data from the last thirty years leads to a conclusion that is not scientifically challengeable: thinking well requires knowing facts, and that’s true not only because you need something to think about,” Willingham has written. “The very processes that teachers care about most—critical thinking processes such as reasoning and problem solving—are intimately intertwined with factual knowledge that is stored in long-term memory (not just found in the environment).”

Just because you can Google the date of Black Tuesday doesn’t mean you understand why the Great Depression happened or how it compares to our recent economic slump. And sorting the wheat from the abundant online chaff requires more than simply evaluating the credibility of the source (the tree octopus material was supplied by the “Kelvinic University branch of the Wild Haggis Conservation Society,” which sounded impressive to the seventh graders in Don Leu’s experiment). It demands the knowledge of facts that can be used to independently verify or discredit the information on the screen.

There is no doubt that the students of today, and the workers of tomorrow, will need to innovate, collaborate and evaluate, to name three of the “21st century skills” so dear to digital literacy enthusiasts. But such skills can’t be separated from the knowledge that gives rise to them. To innovate, you have to know what came before. To collaborate, you have to contribute knowledge to the joint venture. And to evaluate, you have to compare new information against knowledge you’ve already mastered.

So here’s a principle for thinking in a digital world, in two parts: First, acquire a base of fact knowledge in any domain in which you want to perform well. This base supplies the essential foundation for building skills, and it can’t be outsourced to a search engine.

Second: Take advantage of computers’ invariant memory, but also the brain’s elaborative memory. Computers are great when you want to store information that shouldn’t change—say, the date and time of that appointment next week. A computer (unlike your brain, or mine) won’t misremember the time of the appointment as 3 PM instead of 2 PM. But brains are the superior choice when you want information to change, in interesting and useful ways: to connect up with other facts and ideas, to acquire successive layers of meaning, to steep for a while in your accumulated knowledge and experience and so produce a richer mental brew.

That’s one principle for thinking in a digital world; over the next few months I’ll be introducing others. Now, your turn: Have you discovered any rules for using your mind in a world full of technology? Please share!

“Lawyers intentionally [choose their words] when they describe accident scenes. The defense might call a car accident ‘contact’; the plaintiff might say one car ‘smashed’ the other. These labels really matter, as Elizabeth Loftus and John Palmer showed in a classic experiment.

After a group of students watched the same series of traffic accidents, they were asked how fast the cars were going when the accident occurred. When the cars were described as having ‘contacted’ one another, the students estimated their speed to be thirty-two miles an hour, whereas another group estimated that the cars were traveling at forty miles an hour when they were described as having ‘smashed’ one another.

In a second experiment, fourteen per cent of participants incorrectly remembered seeing shattered glass when told that the cars ‘hit’ one another, whereas thirty-two per cent of participants in a second sample made the same error when told the cars ‘smashed’ into one another.

If a single word can change how people remember an event they witnessed only minutes earlier, there isn’t much hope for eyewitnesses who recall, often months or years later, events experienced under stressful, distracted conditions.” (Read more here.)

It seems especially interesting that language affects perception and memory. Reminds me of the work of UVA psychologist Timothy Wilson, who has found that simply by changing a word or two of the stories we tell to ourselves about our experiences, we can change what those stories mean for us and how they affect us in the present. He details these intriguing studies in his book Redirect; I recommend it as a fascinating and potentially very useful book.

“College students who were either non-gamers or intensive gamers were given a visual memory task that flashed a circular arrangement of eight letters for just one-tenth of a second. After a delay ranging from 13 milliseconds to 2.5 seconds, an arrow appeared, pointing to one spot on the circle where a letter had been. Participants were asked to identify which letter had been in that spot. At every time interval, intensive players of action video games outperformed non-gamers in recalling the letter.

‘Gamers see the world differently,’ said Greg Appelbaum, Ph.D., an assistant professor of psychiatry in the Duke School of Medicine. ‘They are able to extract more information from a visual scene.’

Researchers have known from earlier studies that gamers are quicker at responding to visual stimuli and can track more items than non-gamers. When playing a game, especially one of the ‘first-person shooters,’ a gamer makes ‘probabilistic inferences’ about what he’s seeing — good guy or bad guy, moving left or moving right—as rapidly as he can.

Appelbaum said that with time and experience, the gamer apparently gets better at doing this. ‘They need less information to arrive at a probabilistic conclusion, and they do it faster,’ he said.

The visual system screens information out from what the eyes are seeing, and data that isn’t used decays quite rapidly, Appelbaum said. Gamers discard the unused stuff just about as fast as everyone else, but they appear to be starting with more information to begin with.

To investigate this hypothesis, researchers examined three possible reasons for the gamers’ apparently superior ability to make probabilistic inferences. Either they see better, they retain visual memory longer or they’ve improved their decision-making.

Looking at these results, Applebaum said, it appears that prolonged memory retention isn’t the reason.

But the other two factors might both be in play; it is possible that the gamers see more immediately, and they are better able make better correct decisions from the information they have available.”

Interesting. One question, of course, is whether this ability that gamers apparently develop is of use in real-world tasks.

When Raytheon Corporation asked 1,000 middle schoolers if they’d rather eat broccoli or do a math problem, a majority said broccoli. But this lack of interest in math is no laughing matter, writes Irene Sege in the blog Eye on Early Education:

“The country has a shrinking labor pipeline and growing need for workers skilled in STEM – science, technology, engineering and math. The key to solving the problem lies in starting young, according to JD Chesloff, executive director of the Massachusetts Business Roundtable and chairman of the Massachusetts Board of Early Education and Care.

“In a globally competitive economy, with employers of all shapes and sizes increasingly seeking workers skilled in science, technology, engineering, and math,’ Chesloff writes in Education Week, ‘investing to ensure a pipeline of workers skilled in STEM competencies is a workforce issue, an economic-development issue, and a business imperative. And the best way to ensure return on these investments is to start fostering these skills in young children.’

Chesloff lays out the problem. An estimated 76 million baby boomers will retire soon, but the pipeline to replace them consists of only 51 million people. Between 2008 and 2018, STEM occupations will have grown by 17% while other occupations experience only 9.8% growth, according to U.S. Department of Commerce estimates. Overall, every job has almost four applicants; with STEM-related openings there are almost two jobs perapplicant.

The answer, Chesloff writes, lies in high-quality early education, which research shows substantially reduces grade retention and juvenile arrests and substantially increases high school graduation and college attendance. Research also finds that the young brain is particularly open to learning math and logic concepts and that early math skills strongly predict later learning.

‘Young children are natural-born scientists and engineers,’ Chesloff writes. ‘High-quality early-learning environments provide children with a structure in which to build upon their natural inclination to explore, to build, and to question.’

Our future, Chesloff concludes, depends on making math more palatable. ‘To remain competitive in the global economy, investment is needed to ensure a workforce pipeline that would rather engage in science, technology, engineering, and math than cleaning bathrooms and eating broccoli,’ he writes. ‘And the best way to shore up that pipeline is to start investing in it early.’” (Read more here.)

I’ll admit that I’m one of those people who harbors an aversion for math. But just why do so many of us dislike it so much? I don’t think it’s because we find it hard, or boring. I think it has more to do with fear.

It’s scary to feel that you don’t understand something, and to know that you will be judged on how well you can do it. My gut sense (and it’s backed up by studies showing very high rates of “math anxiety” among students) is that people dislike math because they don’t understand it, and this lack of understanding makes them feel incompetent and vulnerable. Do you agree?

“Because software engineering is a prosperous, growing field—and because, even beyond the tech industry, everything will soon be run on code—young people have long been counseled on the advantages of learning how to program. I’m one of the guilty parties here. In Slate and other venues, I’ve lectured youngsters to get cracking on coding. ‘You don’t need to know how a computer works in order to use it—but if you learn how computers work, you may avoid one day working for them,’ I argued last year.

I still believe that. And yet, when I visit software companies, I often notice that the most successful employees aren’t necessarily the best coders. Instead, leaders in the software business are usually pretty good coders who also happen to be fantastic communicators—they’ve got good ideas about software, but their real talent is the ability to get those ideas across to the rest of the organization.

And how, in a large software company, do top coders convey their ideas? The same way people communicate at small, spread-out software start-ups—or, for that matter, in economic endeavors far beyond tech, including medicine, finance, academia, and a million office jobs: They write.

Over the last two decades, as the Internet has edged out the phone as the world’s leading business communications platform, writing has surpassed talking as the most important skill in the modern workplace. Whatever you do in the new economy, wherever you go, you’re going to be called upon to write. And the better you write—the more succinctly and confidently you wield language on the page—the more you’ll stand out. If you want to succeed, then, write. Learn to write, and practice every single day.

It’s not only that writing helps you become a leader in organizations whose culture is defined primarily by interactions over email and instant message. Writing also gets your foot in the door: In the past, a young person just entering an industry would have had to schmooze to get to the top. Now you can start a terrific industry blog and, using social networks like this one, you can market your posts to leaders in your field. That is, you could do that—if only you knew how to write well.

But it’s not just marketing. The real advantage to writing is the way it forces you to clarify your ideas. Even if you work in an organization where few people communicate by text, writing your thoughts down helps you spot flaws in your thinking, or find solutions to problems that you can’t seem to crack . . .

Writing is really just a formalized way of thinking. Writing turns all those ideas that are flitting about your brain into a coherent picture of the world. That’s why you can’t ignore writing; in the modern economy, how well you write will often be taken as a proxy for how well you think. So if you want people to think that you know how to think, just sit down and write.” (Read more.)

Manjoo clearly follows his own advice: his writing, and his thinking, are admirably effective. I’m completely persuaded. Are you?

With apologies to Stevens, I’d like to present eight ways of looking at intelligence—eight perspectives provided by the science of learning. A few words, first, about that term: The science of learning is a relatively new discipline born of an agglomeration of fields: cognitive science, psychology, philosophy, neuroscience. Its project is to apply the methods of science to human endeavors—teaching and learning—that have for centuries been mostly treated as an art.

As with anything to do with our idiosyncratic and unpredictable species, there is still a lot of art involved in teaching and learning. But the science of learning can offer some surprising and useful perspectives on how we educate young people and how we guide our own learning. And so: Eight Ways Of Looking At Intelligence.

 The first way of looking at intelligence: Situations can make us smarter. The science of learning has demonstrated that we are powerfully shaped by the situations that we find ourselves in: situations that can either evoke or suppress our intelligence.

What are “situations”? Situations can be internal or external. They can be brief and transitory, or persistent and long-lasting. They can be as varied as the conditions under which we learn, the conditions that prevail in the classroom or the workplace, the conditions exerted by a peer group. They can be the physical conditions that learners experience by way of how much stress they’re under and how much sleep and exercise they get, and the mental conditions learners create for themselves by the levels of expertise and attention and motivation they’re able to achieve.

Situational intelligence, in other words, is the only kind of intelligence there is—because we are always doing our thinking in a particular situation, with a particular brain in a particular body.

On one level this is obvious, but on another it is quite radical. Radical, because, since its earliest beginnings, the study of intelligence has emphasized its inherent and fixed qualities. Intelligence has been conceptualized as an innate characteristic of the individual, invariant across time and place, determined mostly by genes (or before that, what was called “heredity”).

This was the view of Francis Galton, the Victorian gentleman who is the father of psychometric testing. He used the notion of inherent, fixed intelligence to show that it ran in the blood of England’s most eminent families. This was the view of Lewis Terman, the creator of the modern intelligence test. He used the notion of inherent, fixed intelligence to identify and cultivate children who were ‘gifted.’ And this was the view of Charles Murray and Richard Herrnstein, authors of the notorious 1994 book The Bell Curve. They used the notion of inherent, fixed intelligence to argue that America’s class structure was the inevitable product of the IQ levels of various racial and social groups.

So to assert that intelligence is in large part a product of the situations we find ourselves in is a departure, not only from the way science has traditionally thought about ability, but from the way many of us think about ability today.

On to the second way of looking at intelligence: Beliefs can make us smarter. Stanford psychologist Carol Dweck distinguishes two types of mindsets: the fixed mindset, or the belief that ability is fixed and unchanging, and the growth mindset, or the belief that abilities can be developed through learning and practice.

These beliefs matter because they influence how think about our own abilities, how we perceive the world around us, and how we act when faced with a challenge or with adversity. The psychologist David Yeager, also of Stanford, notes that our mindset effectively creates the “psychological world” in which we live. Our beliefs, whether they’re oriented around limits or around growth, constitute one of these internal situations that either suppresses or evokes intelligence.

 The third way of looking at intelligence: Expertise can make us smarter. One very robust line of research within the science of learning is concerned with the psychology of expertise: what goes on in the mind of an expert. What researchers have found is that experts don’t just know more, they know differently, in ways that allow them to think and act especially intelligently within their domain of expertise.

An expert’s knowledge is deep, not shallow or superficial; it is well-organized, around a core of central principles; it is automatic, meaning that it has been streamlined into mental programs that run with very little conscious effort; it is flexible and transferable to new situations; it is self-aware, meaning that an expert can think well about his or her own thinking. Expertise takes a long time to develop, of course, but it’s never too early—or too late—go deep in a subject area that interests us.

 The fourth way of looking at intelligence: Attention can make us smarter. You’ve probably heard about the “marshmallow test,” a famous experiment conducted by psychologist Walter Mischel in the late 1960s. Mischel found that children who could resist eating a marshmallow in return for the promise of two marshmallows later on did better in school and in their careers.

Well, there’s a new marshmallow test that is faced every day, almost every minute by young people, and by the rest of us, too: it’s the ability to resist the urge to check one’s email, to respond to a text, to see what’s happening on Facebook or Twitter. We’ve all heard that ‘digital natives’ grew up multitasking and therefore excel at it, but the fact is that there are information-processing bottlenecks in the brain—everybody’s brain—that prevent us from paying attention to two things at the same time. The state of focused attention is a very important internal situation that we must cultivate in order to fully express our intelligence.

 The fifth way of looking at intelligence: Emotions can make us smarter. We sometimes give short shrift to emotions when we’re talking about academic success, but the science of learning is demonstrating that our emotional state represents a crucial internal situation that influences how intelligently we think and act.

When we’re in a positive mood, for example, we tend to think more expansively and creatively. When we feel anxious—for instance, when we’re about to take a dreaded math test—that anxiety uses up some of the working memory capacity we need to solve problems, leaving us, literally, with less intelligence to apply to the exam.

One line of investigation within the science of learning has to do with the feeling of hope. Research in this area has found that a feeling of hopefulness actually leads us to try harder and persist longer—but only if it is paired with practical plans for achieving our goals, and—this is the interesting part—specific, concrete actions we’ll take when and if (usually when) our original plans don’t work out as expected.

 The sixth way of looking at intelligence: Technology can make us smarter. There’s a fascinating line of research in philosophy and cognitive science investigating what’s called the extended mind. This is the idea that the mind doesn’t stop at the skull—that it reaches out and loops in our bodies, our tools, even other people, to use in our thinking processes.

Brain-scanning studies have found that when we use a tool, say a rake we’re using to reach an object that’s out of our grasp, our brains actually designate neurons to represent the end of the rake—as if it were the tips of our own fingers. The human mind has evolved to make our tools—including our technological devices—into extensions of itself.

The problem is that our devices so often make us dumber instead of smarter. I’ve already alluded to the way in which technology can divide our attention, producing learning that is spottier, shallower, and less flexible than learning that occurs under conditions of full concentration. Technology can also make us dumber when we allow key skills to atrophy from disuse, or fail to develop those skills in the first place.

To give you a common example: The ready availability of technology has persuaded many people that they don’t need to learn facts anymore, because they can always “just Google it.” In fact, research from cognitive science shows that the so-called “21st century skills” that we’re always hearing about—critical thinking, problem-solving, collaboration, creativity—can’t emerge in a content-free vacuum. They must develop in the context of a rich base of fact knowledge: knowledge that’s stored on the original hard drive, one’s own brain.

In order for tech to make ourselves smarter and not dumber, we need understand when to take full advantage of our devices, and when to put them away.

The seventh way of looking at intelligence: Our bodies can make us smarter. A line of inquiry related to the “extended mind” research mentioned earlier is the work now being done on what’s called “embodied cognition.”

Ever since the cognitive science revolution of the 1970s, the dominant metaphor for the brain has been the computer: a machine that processes abstract symbols. The science of learning is demonstrating that the computer metaphor is seriously flawed when it comes to describing the human brain. It might be more accurate, in fact, to compare the brain to the heart. All the things that make the heart work better—good nutrition, adequate sleep, regular exercise, moderate stress—make the brain work better too.

Take sleep, for example, since sleep is something so many of us are lacking. We often don’t recognize that sleep is actually a key part of the learning process. It’s during sleep that the brain consolidates the memories it formed during waking hours—meaning that it sorts through those memories, weakening the ones that are trivial, strengthening the ones that are important, and connecting up these new memories to the memory structures that already exist in the brain.

If we don’t get enough sleep after learning, or if that sleep is of low quality, the learning process is truncated, and we remember that information less well and less flexibly. That’s just one example of how physical state of our bodies is a key conditions under which our brain operates and under which our intelligence is evoked or suppressed.

 Lastly, the eighth way of looking at intelligence: Relationships can make us smarter. I mentioned earlier that the human mind is very adept at looping in our bodies, our tools, and even other people to use as instruments of our own thinking.

You’ve experienced this if you have a spouse or significant other: it’s likely that one of you is “in charge” of remembering when the car needs to go in for inspection, while the other is “in charge” of remembering relatives’ birthdays. This is called transactive memory, and it’s just one of the ways that relationships with others can make us smarter than we would be on our own.

There’s another kind of relationship that matters to intelligence: the relationship that we have to the institutions and organizations within which we live and work. The science of learning has demonstrated that a feeling of belonging is critical to the full expression of our ability.

The science of learning suggests that we ought to imagine our roles—as parents, as professionals, as learners. We should aim to be situation-makers—creators of circumstances that evoke intelligence in ourselves and others.

I’d love to hear from you, Brilliant readers: What makes us smarter? Commentators on Twitter have already suggested passion, curiosity, listening, empathy, discipline—and coffee.

Cognitive psychologist Scott Barry Kaufman has written a wonderful book just out this week, titled Ungifted: Intelligence Redefined. I highly recommend it for anyone interested in intelligence, learning, creativity, talent, practice . . . Scott (who is a friend of mine) provides new insights on all these important subjects.

Below is an excerpt of an interview Scott did with Sam McNerney of BigThink.com:

“McNerney: One source of anxiety for students is other students. When we compare ourselves to others —and it’s nearly impossible not to—we suffer. There is always someone getting better grades or performing better athletically or artistically.

Claude Steele and Joshua Aronson further demonstrate how racial and gender comparisons are detrimental to academic performance. You make the point that mindset plays a crucial role in engagement. I’m wondering what mindsets we can adapt that reduce the anxiety that comes from making social and academic comparisons.

Kaufman: Clearly the growth mindset (talent and ability can be improved with practice) is better than the fixed mindset (talent and ability cannot be improved). You’re absolutely right that we live in a culture of a fixed mindset, and schools are structured around that idea. Kids are given test scores and don’t have dynamic assessment—so they’re given a grade but they’re not getting a chance to revise it.

The growth mindset is key and I talk about that in the book. A lot of research shows what happens in the brain when you are put in the fixed mindset; your brain kind of shuts down in a way while the areas in the prefrontal cortex associated with self-evaluation become heightened.

The brain is a limited resource, so the more you become critical and self-evaluate (because you’re so afraid of failing and you believe that you won’t get a second shot, since it’s all fixed) the less resources you’re going to have to display your intelligence, to express yourself, to create new things. The fixed mindset is a really big problem.” (Read more here.)

The notion of mindset was first put forth by Stanford psychologist Carol Dweck; the research of Dweck and others has detailed the many ways that a fixed mindset is detrimental to learning and performance (it makes us less willing to take risks, less resilient in the face of adversity, and so on).

I hadn’t encountered this drawback before, though: the finding that a fixed mindset, because it leads to critical and self-evaluative thoughts, uses up mental resources that are then not available for solving problems, thinking up new ideas, etc. A really useful insight—thank you, Scott!