Todd's Review of Range by David Epstein

Range: Why Generalists Triumph in a Specialized World
by David Epstein
New York, Riverhead Books, 2019

Review by Todd I. Stark

This is a beautifully written and well justified discussion of the various specific things that are not taken into account by the widespread cultural emphasis on early specialization for success and our popular model of performance in terms of domain-specific expertise.

This takes the form of a single conclusion which I would paraphrase as: "we need to be able to play and explore widely and to color outside the lines for a while in order to become very good at solving the difficult problems we later encounter.  But our cultural obsession with specialization pushes counter to that." 

There is a constant tension of the author's confidence in his conclusion that generalists are uniquely valuable and desperately needed and his recognition that he is fighting an almost Quixotic uphill battle against powerful cultural trends and incentives for specialization. 

What he means by specialization and the factors closely tied to it:

1. Head Start: Encouraging children from an early age to narrowly pursue things they seem talented at or have an interest in.
    
2. Domain-Specificity: Training with heavy emphasis on the specific narrow range skills we know we will need in the target environment and assuming far transfer of skills from other activities will be limited or non-existent.
    
3. Disciplinary Focus: Viewing learning as consisting of accumulating facts and theories specific to a particular field or subfield of study in order to become highly skilled at working in that narrow field.
    
4. Persistence:  The idea that we should identify a passion early and stick with it no matter what because it’s what we’re good at and enjoy and so can become successful at it if we manage to persist.
    
5. Fast and Efficient Short-Term Learning:  The assumption that we are learning better when we feel familiar with the material quickly and that we are then learning more efficiently. 
   
   
    
   Against those powerful and popular specialization factors, Epstein presents several compelling lines of evidence:
    

1. Domain-Specificity varies with Kind vs. Wicked Learning:  The argument for early specialization and domain-specificity is based on the observation that we need a long period of deliberate practice to accumulate the patterns and skills specific to performing in that specific activity and that practicing or exploring other activities is unlikely to do anything helpful for our performance in our specialty.  Epstein counters that on closer inspection we find a crucial distinction between different kinds of domains and learning environments, where in some of them deliberate practice reliably makes us better but in others deliberate practice either helps much less or can even make us perform more poorly under some conditions.  So not all domains or learning environments are equally specific and the head start is not equally helpful in all activities. 
2. Creative Performance comes from early exploration and interdisciplinary learning:  Given the domain-specific view of expertise we tend to assume that in order for someone to perform at a high level in any activity, since they need expertise, they need to specialize in that activity.  Epstein counters that when we focus specifically on creative performance, we find that deep expertise can be invaluable but is not enough.  In order to come up with truly novel solutions to problems we need to make use of analogies that cross different domains while sharing deep structural similarities.  That means being familiar with a wider range of ideas and ways of thinking than just those in our specialty, and so Epstein says creative performance is found more in people with broader backgrounds.  Epstein argues that outstanding creative performance also tends to be associated with early exploration of different activities more than with early specialization. 
3. The Efficiency We Perceive from Narrow Immersion is Very Often Illusory: We tend to assume that when we feel more familiar with the activity or material that we are learning it.  That’s part of the strong intuitive appeal for immersion in an activity comes from, it feels like we are learning more when we are more immersed.  Epstein argues that the evidence from learning research show quite often exactly the opposite, that the learning we think we are doing under conditions of immersion is either much less or much shorter lived than we assume.  Robert Bjork’s concept of “desirable difficulty” in learning and the evidence base behind it plays a central role in this argument.   This, Epstein argues, tells us that “slow learning” which helps us make new connections between a wider range of experiences is much more conducive to learning in the long run than fast, efficient learning from immersion in a narrow subject matter.
4. Match Quality is Not Necessarily the Same as Early Passion: Part of the argument for early specialization is based on the assumption that people have certain interests and talents from early on and if they can find something that matches them well and start early, they can align their passion with a successful career in that activity.   Epstein argues that what we know about lifespan development tells us that people’s passions are not so fixed or narrow and finds a number of cases of exceptionally successful people who spent their lives exploring and trying different things before finding a match that was truly satisfying and successful for them.   

Range is an appeal to encourage exploration in our lives from early on and for experimenting and experiencing broadly in our learning, even though it may seem to be inefficient or slow.  Epstein does not deny the immense value of long deliberate specialized practice in “kind” domains or the value of having deep specialized experience in some areas, but he has also made a passionate and well-argued case for making better use of a completely different dimension of performance.  A dimension rooted in longer term developmental outcomes, more exploratory or playful learning, and an ongoing search for ever better matches between our interests and abilities and our activities. 

Review on Amazon:
https://www.amazon.com/review/R3I57PTPZXSYU4/ref=cm_cr_srp_d_rdp_perm?ie=UTF8

Book Review:  Ungifted by Scott Barry Kaufman

Todd I. Stark  5/29/2013

Link to review on Amazon

Intelligence turns out to be a difficult topic, for reasons that aren’t at all obvious at first. Our understanding of mental ability has been captured in several independent threads of research that are surprisingly oblivious of each other for the most part.  Our stereotypes of the gifted and the ungifted often miss the details of what is going on.  The study of individual differences in general, while useful, doesn’t just de-emphasize, but actually systematically misses some of the most important things going on when people become exceptionally successful contributors.  

The author of Ungifted is well situated to make an important contribution to our understanding of intelligence.  He has made a deep academic study of a wide span of existing research programs, he has worked directly in collaboration with many of the leading researchers in several related fields and he has passionately engaged these ideas since childhood when he became  painfully aware of the academic sorting process for giftedness, and he himself is a wonderful example of many of the principles that emerge in his new book.  This is not another book that just starts out with a vague progressive vision of education and ability that everyone is a potential “genius” and then fills it in with wishful thinking.  No, this is a book that dives very deeply and realistically into the literature of psychometrics, heritability, cognitive neuroscience, and expertise.  It looks closely at patterns from the span of phenomena of human differences, including savantism, prodigy, autism, schizophrenia, personality, g factor, motivation, and creativity.

Ungifted is so compelling, rich, and significant a book for me that most of it was well worn by the end of the first day it arrived. What makes this book so rich is the unique combination of personal passion, strong but not intrusive scholarship, deep domain expertise, clear analysis, and all tied together with an original new and constructive perspective. Hard to ask for more than that from a non-fiction book. Every chapter is a stimulating lesson in an important topic that brings together a broad range of data, ever heading toward the book’s conclusion, nothing less than a complete rethinking of human intellectual ability, consistent with considerable evidence gathered along the way.

The most impressive and distinctive aspect of the author's thinking is his consistent and effective use of perspective-taking. In each case where a controversy is identified, each side is explored with great depth and sympathy to understand what its advocates understand that those on the other side seem to miss. It isn't difficult to do that for the side of an argument we agree with but it is an impressive achievement to do it for different sides and then to synthesize the perspectives into an overlapping understanding. This ability is particularly relevant to topics like IQ, giftedness, and learning disabilities, where sharp controversy shapes conversations at every turn.

Ungifted is about how we conceive of mental ability in general. Some people manage to accomplish much more with their mind than others do. There’s no escaping that basic observation, nor the fact that it has immense significance for political and educational thinking. What is the difference that makes a difference? The answers we get depend on the kinds of questions we ask.

Ungifted is in part the story of how the important questions have changed over time and why. There seem to be two tragic errors that we’ve fallen into historically. For one, we’ve often ignored the differences between us and tried to force everyone into cookie-cutter educational molds that well serve only a minority of the people they are intended to serve. Most of us appreciate this personal plight, as does the author in his "subjective" voice and personal experience throughout the book.

The other tragic error is ironically the reverse. In trying to appreciate the differences between people we’ve taken the opposite extreme of becoming obsessed with stable, predictive individual differences. We test ourselves and compare ourselves with each other and we look for the numbers that tell us who deserves what because we assume we are identifying potential. The author appreciates the motives and sometimes successes of this approach when done well, but the focus of Ungifted is on how we can do better.

It is the tragedy of our obsession with individual differences that Ungifted in the author's "objective" voice most eloquently addresses. We assume that testing people against each other will tell us how to best teach each person by identifying what makes people different. Ungifted describes in great detail and punctuated by the author’s own personal life story why that well-intended approach has failed us time and time again.

It isn’t simply as many politically motivated accounts would have it, that IQ has no meaning, or is too culture bound, or just measures test taking ability. IQ and similar kinds of tests when given and interpreted intelligently provide a useful and well-validated way of identifying the lion’s share of variation in human intellectual ability across a wide range of situations and this has some very real correlations with meaningful life outcomes. The problem is not that the tests are useless but that they have come to be misconstrued as if they measure a single stable ability that resides in each person and predicts what that person is capable of accomplishing. People who do well in IQ tests do tend to be smart people in general. But so are many people who do more poorly in IQ tests, and doing well in IQ tests doesn’t provide any guarantee that we also have persistence, motivation, or other qualities so important to making the best of our abilities.

A number of theorists have made useful additions to the body of well validated tests, but these are still tests of static abilities and they don't solve the most basic problem identified in Ungifted. The reasons the individual differences approach has failed us in the broad global sense that we have tried to apply it are that while effectively explaining variation between people in the same population, it has not taken broader environments into account, it has not taken the course of development into account, and it has not taken the dynamic differences into account that make the most difference in human lives over time.

A number of fundamental misunderstandings of heritability, genetics, development, and psychometric research have been exploited, often through politically motivated movements, to obscure the larger vision of intellectual ability. Ungifted makes a serious bid to help correct these fundamental misunderstandings.

Do some people have more of a critical trait or traits from the start, or do some people learn more from their experience, and in either case how much can we influence our abilities over the course of our life? The traditional dialectic of nature vs. nurture seems to have its own unavoidable groove in our thinking that we rarely manage to escape. Yet as long as we have been studying human ability scientifically, there has been evidence that the dichotomy is inadequate. 21st century research has given us some useful insights into the specifics. Ungifted summarizes the most important lessons from a wide range of data about human abilities, and the author is particularly careful to distinguish his subjective passion for the subject (which is often in evidence) from his more detached coverage of the data in the various fields.

Ungifted starts out with a concise summary of principles of the 21st century picture of development, setting the background for the rest of the book. The concept of traits is explored, and the patterns by which they develop.

Then we have a tour of the ways we have tried to measure human mental ability and our reasonable motivations for the various testing innovations over time. From the measurement of ability, we then see how measurement slides into the sorting of people into categories for practical purposes and the allocation of finite educational resources. We see the well-motivated practical reasons for labeling people, but we also get a sense of the often tragic real world limitations of that way of thinking.

We are introduced in an understandable but expert way to the real strengths and weaknesses of IQ testing, and to the best available model of how its scales map to specific cognitive abilities. This prepares us to begin to understand the different ways that "giftedness" has been defined and measured and why we are still so far behind where we need to be to cultivate the best in every individual. We also are given enough background to begin to appreciate the unique challenges of being different, whether perceived as higher or lower in ability than others around us. Ungifted artfully blends a sympathetic understanding of the needs of researchers and testers with those of teachers, and the diverse individuals just striving to do their best.

Then we are introduced to the core concept that distinguishes this developmental reframing of human mental ability, the concept of engagement. Engagement brings together the factors that distinguish the learnable, experiential aspects of intelligence from those that seem particularly stable. Engagement is the hinge that swings the big door to individual potential. Engagement is built on a number of factors that are particularly malleable and context-dependent, so it is of particular interest to the way education is done. Once we have a sense of what engagement is about, we take a fresh look at human abilities in terms of what is known about their development over time. The role of engagement over time in development begins to become clearer as we tour through the critical concepts of intelligence, creativity, talent, and expertise, and begin to see how they each relate to development over time. At the end, the key points learned along the way are summarized to give the outline of a new theory of intelligence.

For me the theory of intelligence introduced here is distinguished by two critical characteristics: (1) it emphasizes what happens in the individual over time rather than differences between people, and in so doing draws on different kinds of data, and (2) it is synthetic in spirit, emphasizing multiple ways of achieving the same outcomes by drawing on different resources, rather than looking for additional ways of distinguishing the abilities of different people. These two characteristics make this theory very different from most of the alternatives that are intended to address some of the same gaps in our intelligence models, alternatives such as “multiple intelligences,” “emotional intelligence,” and so on. Rather than just identifying more things that we think might be missed by IQ testing, and turning them into new sources of labeling and categorizing, the personal developmental theory of intelligence assumes that there are many different components to be identified, but places them into an overarching biological framework where ability is developed over time by identifying, selecting, modifying, and constructing niches suited to the thriving of the individual.

Several important shifts of emphasis emerge:

1.      Away from reliance on studying stable individual  differences and toward the details of person-centered development

We have focused primarily on measuring stable individual differences, whether “general intelligence” or other kinds of “intelligence” or personality in order to support research and allocate finite educational resources.  What this approach misses is the details of development within each individual over time.  It turns out that these two approaches, individual differences and person-centered, are not just different but produce incompatible results.  So this is a very important source of new information about how mental ability arises.  It is common for authors to point out that nature and nurture are an archaic dichotomy and that it is the interaction that matters, but the details are generally left vague.  There’s a need for specific research programs that focus on the development of ability over time.  The developmental approach is not just a detail, it is a separate source of crucial empirical data.

2.      Away from viewing the positive manifold of abilities on tests (“g”)  as a single ability in each person, and toward understanding its value in conveniently capturing most of the variation an array of mental abilities that collectively underlie many different kinds of tests.    

3.      Away from seeing intelligence as a number or even a trait, and toward seeing it as the adaptive fit between the individual and their environment by finding, selecting, shaping, and creating niches they can thrive in.

4.      Away from focusing on cognitive skills in isolation, and toward consideration of the motivations, strategies, and experience that turn those skills into practical abilities over time.

5.      Away from the focus on being able to measure abilities that represent potential in a brief test, and toward finding the best way to engage and cultivate each person.  Active engagement with the world and ability are inseparably intertwined in a mutual feedback process over time.

6.      Away from focus solely on controlled cognitive processes underlying reasoning, and toward better understanding of the role of both controlled and spontaneous processes in intelligence.  Being smart involves flexible use both controlled and spontaneous mental processes, and using the right resources when needed, rather than relying on one to the exclusion of the other.

7.      Away from fixed rules about how long it should take to become good at something or what special levels of ability provide thresholds, and toward seeing our “readiness for engagement” as a better indicator of potential.

8.  Away from seeing intelligence and expertise as independent (and one inborn and the other learned), and toward seeing the overlaps between them as cognitive abilities,personality, and motivation support the acquisition of expertise, and cognitive expertise is part of what shows up in testing for ability.  One of the most remarkable findings in the book that links intelligence and expertise is that chunking in memory, a key aspect of organizing knowledge in memory involved in expertise, activates the brain structures involved in fluid reasoning, a central component measured by tests.  

The resulting view of intelligence doesn’t see everyone as equal by any means, it seems unavoidable that some people will not be able to excel at some things relative to other people.  Stable individual differences do not somehow disappear because we shift emphasis to the person and their development.  However we do begin to see the real value of changing the nature of education to focus on the fit between people and niches rather than selecting people for special treatment via brief tests and subjective judgments of merit.  Several practical working examples of programs that successfully accomplish this change are described in the book.

I place this book alongside two others in a trilogy that for me represents a broad understanding of the nature of mental ability as it is best envisioned at the current time.

1. "Surpassing Ourselves" is about how outstanding performers learn differently.  It shows expertise as a distinctive way of learning rather than just an endpoint of domain specialization. By comparing the same person over time as they develop, rather than just comparing novices with experts, we get an understanding of the process by which people become smarter.  This is very similar to the shift made with intelligence in Ungifted, but applied specifically to expertise and shows again how the shift to a process perspective captures additional important information.  Surpassing ourselves also introduces  the reinvestment perspective, which looks at the growth of ability in terms of becoming more efficient over time and then reinvesting the saved time and energy in new learning, which seems to be a big part of how people who ultimately become exceptional learn differently from those who do not.
http://www.amazon.com/Surpassing-Ourselves-Inquiry-Implications-Expertise/dp/0812692055/ref=sr_1_1?s=books&ie=UTF8&qid=1369837497&sr=1-1&keywords=surpassing+ourselves

2. "Outsmarting IQ" is about learnable intelligence.  It shows how experience, stable cognitive abilities, and strategies work together to navigate realms of knowledge and let us apply our knowledge.  It gives a good account of the role of strategies, including learnable strategies and cognitive expertise, in trading off between cognitive abilities in order to make the best of our existing stable cognitive abilities. 
http://www.amazon.com/Outsmarting-IQ-Learnable-Intelligence-ebook/dp/B001D1Y8Z8/ref=sr_1_1?s=books&ie=UTF8&qid=1369837528&sr=1-1&keywords=outsmarting+iq

3. "Ungifted" combines the intrapersonal process perspective with a developmental model and an overall hierarchical model of cognitive abilities, consistent with the other two books in this list and yet going way beyond them to explain in much greater detail where “IQ” and other tests scores come from and what they tell us, and how stable cognitive abilities, expertise, creativity, and personality all interact to produce intelligence. 
http://www.amazon.com/Ungifted-Intelligence-Redefined-ebook/dp/B00B3M3UME/ref=sr_1_1?s=books&ie=UTF8&qid=1369837581&sr=1-1&keywords=ungifted+intelligence+redefined

Update 5/29/13 1PM:  Link to abbreviated GoodReads version of review:

Ungifted: Intelligence Redefined by Scott Barry Kaufman
My rating: 5 of 5 stars




View all my reviews

Review of "Surpassing Ourselves" - A profound vision of a problem solving culture

My recent review of Carl Berieter and Marlene Scardamalia's wonderful 1993 book "Surpassing Ourselves" on Amazon. 

It is about how the underlying concept of expertise needs to be rescued from our inaccurate commonsense epistemology and  from the negative connotations of speciallization and elitism and instead applied more broadly to processes and groups to make our education and culture in general become more supportive of better thinking and problem solving.  It builds from the individual psychology of ability, creativity, and wisdom to the application of the same principles to groups and culture.

http://www.amazon.com/review/R35GNAAFEFWRF1/ref=cm_cr_rdp_perm

This is a remarkable book: deep in its insights, prophetic in its foresight, and profound in its implications. The most amazing thing is that this book was published in 1993, the same year the first commercial browser became available. That was long before science writers had popularized the research on expertise and long before most people had any idea what sort of thing a "social network" might be. Yet this book captures some of the most promising ideas of the modern information age, such as the use of technology to facilitate knowledge-building communities and the possibility of a wider problem solving culture. Having only the relatively limited technology examples of the time to draw from by today's standards, such as desktop computers with local databases, the authors express their vision in terms of concepts and processes rather than getting caught up in the details of technology. That turns out to be what makes this book most useful I think, because it makes the arguments very general.

A lot of my enjoyment of this book is admittedly because I had come to many of the same conclusions independently and felt reassured to see the same ideas expressed so eloquently. I do think they get a lot right here and that their core concepts have only become more plausible and more important over time since the book was written.

The driving themes of this book all revolve around a single idea: an expanded concept of expertise. I think this is a bigger leap of faith for most people than is at first obvious. Expertise has come to have very strong connotations in popular culture with specialization and elitism, which is exactly the opposite of the message in this book. The book is about how everyone can benefit from what we know about expertise, and how necessary it is to have schools and a broader culture suited to solving increasingly complex and difficult problems. It is not at all about specialization or elitism.

The disconnect between the expanded concept of expertise and the popular is not just about the politics and economics of specialization. It is also about how the research on expertise is interpreted. For practical reasons, most expertise research has focused on the difference between novices and high performers in a given domain and how much time and deliberate practice is required to make that journey. The authors recognize that body of work and draw from it, but they also recognize, critically, that there are other ways to look at expertise. We can also compare experienced high performers with experienced low performers. And we can compare novices who later become high performers with novices who later become experienced low performers.

The authors expand and elaborate on the concept of expertise in several ways:

1. Think of expertise not as an end state of ability but as an ongoing process of knowledge acquisition at increasingly high levels of performance rather than as an end state of high performing relative to other people.

2. Think of the expertise process as being a cycle of learning at one level until it requires less cognitive effort, and then reinvesting the surplus cognitive effort in reformulating the questions and addressing them at a higher level of difficulty and complexity, always working at the edge of our current ability.

3. Think of experts as people who actively engage in the process of expertise rather than as high performers, so we have expert (or "expert-like") learners even when they are novices.

4. Think of expertise as something that is done by groups as well as individuals.

5. Think of creativity as a particular kind of expertise, where we take greater leaps and risks in working at the edge of our competence and in building and drawing on our knowledge of what options are most promising.

6. Think of wisdom as another particular kind of expertise, drawing on our knowledge of human beings and what sorts of things are most promising in human lives.

The result is a vision of individual ability, education, and culture that is profoundly progressive in what it takes on, nothing less than creating the conditions for every student and citizen to either become or participate constructively with expert learners, to everyone's benefit, resulting in a broader culture of problem solving rather than one of stagnant political and philosophical stances. This is a 1993 vision for the challenges that beset us today, asking us to make better use of technology we already have available at this point, even though we did not at the time the book was written.

The authors recognize that even in 1993, most of these ideas were known and accepted by many educators, but they were very rarely put into practice effectively. The authors theorize that this is because of how self-sustaining the usual educational systems and in comparison how much constant vigilance and effort is required on the part of teachers and administrators to maintain a different sort of environment, what they call a "second order environment" that sustains the process of expertise for both individuals and groups.

One valuable prototype for second order environments is the research community, in which shared objectives and various intrinsic social rewards drive the team members to work together to increasingly more knowledge of their subject and tacking increasingly more difficult problems.

Compare this self-sustaining, actively learning expertise process with the simple use of technology to connect people to share knowledge. The difference is profound. Simply using technology to connect more people has some limited value, but it is a long way from the expertise process. The authors predicted this long before the "Personal Learning Network" became a promising but so often unimpressive reality. The gap between connecting people and knowledge bases, and people actually striving to make increasingly better use of available resources can be huge, and that is what the authors of "Surpassing Ourselves" identified in 1993, the way we use the technology and the way we communicate is critical as well, we can't just connect ourselves and our databases and expect to use all that potential effectively without facilitating and maintaining the process of expertise.

Does the term "expertise" itself limit our ability to make better use of these ideas because of its negative connotations? The authors point out how terms like "excellence" and "quality" have often been used for essentially the same idea, but those have their own misleading or easily abused connotations and limitations as well, and they lack the depth of individual psychological research found in the expertise literature. In the end, what we call it doesn't matter so much as whether we understand how it happens and how to create the conditions for supporting it.

Further Reading:

The books of David N. Perkins offer various proposals with a very similar and consistent vision to the one in "Surpassing Ourselves." "Outsmarting IQ" for example offers an expanded vision of individual expertise that in some ways builds on that of "Surpassing Ourselves" by providing a model of expertise as the process of navigating intersecting realms of knowledge, using the principles of far transfer to generalize expertise among domains and identifying some of the conditions for acquiring it. "Smart Schools" suggests a way to transform education along these lines. "Making Learning Whole" looks in more detail at the common process of expert learning at all levels of ability.
Outsmarting IQ: The Emerging Science of Learnable Intelligence
Smart Schools
Making Learning Whole: How Seven Principles of Teaching Can Transform Education

In "Rethinking Expertise," Harry Collins looks more specifically at the social dimension of knowledge underlying the group process of expertise.
Rethinking Expertise

In "Dialogue Gap," Peter Nixon elaborates on what is distinct about the sort of communication that brings out the different knowledge of each individual and in so doing facilitates what "Surpassing Ourselves" considers the group process of expertise, as opposed to simply arguing or conversing on a less constructive level.
Dialogue Gap: Why Communication Isnt Enough and What We Can Do About It, Fast

Maria Konnikova's "Mastermind" explores the role of a specific critical factor in individual process of expertise allowing us to work continually at the edge of our current ability. This is the ability to focus attention in a particular way in order to actively engage our thinking rather than just passively observe the same things in the same way. "Surpassing Ourselves" describes this only in the abstract as actively engaging higher levels of problems, but the concept of mindfulness offers a possible way of modeling the process in more detail.
Mastermind: How to Think Like Sherlock Holmes

The Transfer Challenge to Expertise

Consider this relatively commonsensical view of problem solving:

“We absorb information in some common generic way and then apply our individual talents to using that information to solve problems.”

As straightforward and intuitive as this description sounds, it contains a counter-productive assumption about knowledge, confusing it with information. We tend to assume that the things we experience are experienced in the same way by other people. We might all look at the same world, but we make different observations and draw different conclusions from it. The knowledge base we each build over our lifetime is not just a straightforward result of the information presented to us; it is also in part a result of how we organize that information, which can be very different from one person to another. This is the foundation of the expertise model.

Organization Matters

It is not enough to have the right information to solve a problem, the information must also be organized in a way that lets us think about it in the right way.

Expertise is a key factor in effective problem solving because it permits us to organize information about a domain, recognize patterns in that domain, apply domain knowledge to new kinds of problems, and incorporate new information about that domain.

The expertise model gives us several key insights into the problem solving process that the commonsensical view above misses:

It tells us that the way information is organized is critical to how that information can be used for problem solving. Many problem solving principles will deal with how information is best organized to solve problems.
It tells us that in certain key areas, deep accurate understanding is important and not just superficial familiarity. Both intuitive decision making and more formal methods rely on deep accurate understanding acquired from experience.
It tells us that a great deal of deliberate practice with good feedback is needed to acquire deep understanding.

It tells us that more skillful problem solving emphasizes principles, while less skillful problem solving relies on procedures.
In spite of the tremendous power of the expertise model, it has its own limitations as well. Expertise lets us detect meaningful patterns of information in particular domains because of the way we organize our own knowledge.

The organization of domain-specific knowledge in our mind has important implications. It means that experts have differently organized knowledge for thinking in different domains. The fact that the expertise model is so thoroughly domain-specific forces us to now confront the most serious challenge of all to the expertise model: the challenge of transfer. If it takes so much deliberate practice to become good in a given domain, how does anyone manage to become good at more than a very narrow range of activities? How do our narrowly cultivated abilities support other activities? Or do we have important abilities that are not domain-specific as well? What does it take to apply our hard-won expertise to problems different than the ones we specifically practiced for?

The Failure of Mental Exercise

At one time, it was widely believed that people could develop their mind by doing mental work such as solving logic puzzles, learning mathematics, reading classics of literature, and learning to speak Latin. A long history of sometimes large scale research on this approach to mental ability revealed it to apparently have very little promise.[1] Literacy in general, while valuable for its own sake, simply does not have much effect on other thinking abilities.[2] Our ability to solve puzzles doesn’t tend to generalize very well unless the training specifically teaches the underlying patterns and provides us with a way of remembering them.[3] We don’t automatically apply the lessons of solving one problem to solving a structurally similar but different problem. The different appearance of problems tends to throw us off. When we learn strategies for solving problems, we tend to learn them in a way that is tied to the specific kinds of problems that we used for learning them. Expertise, the research confirms, tends to be very context specific.

How Transfer Does Happen: Two Roads

The problem with this result is that while it seems consistent with the expertise model, it doesn’t quite make sense in terms of our everyday experience. All of us routinely do apply what we know to new kinds of problems. We aren’t equally incompetent in dealing with any sort of novel problem; our existing expertise clearly does sometimes give us an advantage in another domain. We also see negative influence of expertise when our existing abilities interfere with our attempts to perform in a similar domain. Expertise does seem to transfer between domains under some conditions. The question is what those conditions might be.

Research done in the late 1980’s and early 1990’s confirmed that transfer of ability between domains does occur consistently under certain conditions. One cognitive psychologist working with preschool children on simple tasks discovered that the 3 and 4 year olds could use lessons they learned under one set of conditions in a completely different set of conditions, but especially if they were shown how the different problems resembled each other and how the goals were similar, they were familiar with the problem areas, the examples also had rules associated that the children figured out for themselves, and if the learning took place in a social context that specifically encouraged them to spell out the principles, explanations, and justifications to use.[4]

Research such as this led to a general two-pronged theory of transfer, proposed by David Perkins and Gavriel Salomon. The theory is based on the finding that transfer sometimes takes place between similar domains, and sometimes takes place between very dissimilar domains, and that these seem to happen under different conditions.[5]

What the Perkins and Salomon theory calls “low road transfer” happens when situations appear to us to be similar according to simple perceptual cues rather than any deep structural pattern. This seems to be a matter of stimulus triggers. Specific elements in the situation help us recognize and apply skills and knowledge from our memory based on recognizing those elements from our practice. Since low road transfer is pattern-bound, it doesn’t generally lead to transfer to different situations. Practicing under a variety of different conditions however can help is gradually stretch our skills from one context to a similar one to generalize our skills further. Low road transfer is a result of the variety of conditions under which we practice rather than any specific cognitive skills or strategies aside from those specific to the domain. Low road transfer is a perceptual-memory phenomenon.

When a situation bears a superficial similarity to one we’ve trained for, we recognize stimulus patterns and our expertise is evoked via low road transfer. This is how many people manage to drive a truck reasonably well after having learned to drive a car for example. Even though the mechanics are very different, the steering, pedals, and so on are all familiar enough to trigger our learned skills for driving. That is, until we find ourselves in a situation where the fit isn’t so good between our skills and the ones that are needed.

What the Perkins and Salomon theory calls “high road transfer” seems to be a completely different matter. High road transfer involves the deliberate and mindful abstraction of principles during practice and using more general cognitive skills and strategies to apply them to completely different situations. In high road transfer, the learner actively seeks connections between different situations in which to apply the principles they’ve learned. High road transfer is a cognitive phenomenon.

We see that the similarity mechanism of transfer is limited. It only works for relatively similar situations and it works in a very automatic and unthinking way. To apply expertise to a very differently appearing situation with underlying structural similarity (such as we might need for more abstract problem solving) we need high road transfer and we need to use abilities we associate with conscious reflection. This allows us to transfer expertise from deliberate abstraction of principles to entirely different kinds of problems.

The lesson of the transfer challenge to expertise is that expertise does not automatically apply outside of its domain. We have to very deliberately either: (1) work on practicing in widening ranges of situations to facilitate generalization or (2) work on abstracting and applying general principles mindfully from our practice, or both.

Conclusion: Transfer and Expertise

We’ve seen that expertise is a very powerful model that explains in some detail how we organize tacit knowledge for recognizing patterns and solving a particular domain of problems. This appears to explain the lion’s share of differences in human abilities in problem solving. We’ve also seen that expertise can be acquired in such a way that it can be generalized to an increasingly wider range of conditions and in a way that makes it less likely to fail catastrophically under extreme conditions, making expertise a potentially very robust resource for problem solving.

We’ve also seen that the expertise model misses a small but critical aspect of problem solving; it does not tell us how people manage to deal with surprises or with domains that are characterized by surprises. The expertise research consistently shows strong dependence on specific contexts. We do not automatically generalize our skills or strategies to new kinds of problems just by acquiring deep expertise in a domain.

Novelty offers our most serious challenge to the power of expertise. The very concept of expertise implies domain-specificity, and domain-specificity implies that expertise is honed to deal effectively with a particular range of situations. Novelty, both within a domain and outside that domain, creates problems for the standard expertise model that need to be addressed.

Novel but superficially similar situations can be handled through expertise, but only if we specifically widen our practice to deal with a broader range of conditions.

Completely novel situations in other domains that don’t resemble the ones we practice for except in terms of their underlying deep structure can be handled through expertise as well, but only by deliberate attention to learning and applying general principles as well as acquiring domain expertise.

The Story So Far: Going Beyond Expertise

The expertise model tells us how we acquire useful patterns of tacit knowledge from experience through deliberate practice with good feedback. The expertise model explains how we deal effectively with the sorts of situations where we have accumulated extensive practice. Expertise thus acquired becomes part of our intuitive understanding of situations, enhancing, modifying, and extending our existing commonsense intuitions.

The expertise model also challenges us to explain how it is that we are able to deal with extreme yet realistic conditions and novel problems even though expertise tends to be very context-specific. Applying expertise to very different situations requires deliberate mindful work at abstracting principles and applying them through our capacity for reflective thinking. This kind of reflective thinking is not adequately captured by the expertise model. Either we need to expand the expertise model to handle the challenges we have identified, or else we need a more expansive concept to describe our abilities.



[1] (Thorndike & Woodworth, 1901), (Thorndike, The influence of first year Latin upoin the ability to read English, 1923)

[2] (Scribner & Cole, 1981)

[3] (Simon & Hayes, Psychological differences among problem isomorphs, 1977)

[4] (Brown, 1989)

[5] (Perkins & Salomon, 1987), (Perkins & Salomon, Teaching for transfer, 1988), (Salomon & Perkins, 1989)

The Unpredictability Challenge to Expertise

We almost universally recognize the legitimacy of experts in a number of different domains. In many academic fields such as mathematics, sciences, history, literature, and other academic areas, some people know much more and consistently perform much better than others in tests of ability. Similarly for many professional fields and various sports and games, we recognize that there are experts who outperform the majority of us.

A key finding in modern learning and human performance research has been the discovery of how expertise is acquired.[1] This discovery became possible with the advent of cognitive science, allowing us to model the human brain as an organized collection of information rather than just a collection of behavioral patterns. As we learned about the specific differences between novices and experts[2] in each field, we discovered certain general principles that apply to a very wide range of different fields.

A formidable body of this type of research has overturned the intuitive view that novices and experts differ because some people are simply more naturally talented than others. Experts seem to perform so effortlessly that we tend to attribute great natural ability to them rather than a different kind and degree of experience. Contrary to this intuition, expertise via deliberate practice is our best model so far of individual differences in ability in a wide range of activities. Expertise is a result of experience, and not just any experience, but deliberate practice where we meet challenges in that domain, are immersed in purposeful practice, gain knowledge from other people who are already good at it, have good coaches, and benefit from quality feedback for our performance.[3]

At the same time as verifying the legitimacy of expertise in many fields and establishing the central role of deliberate practice, social environment, coaching, and feedback, we have also discovered that there are some fields where our performance doesn’t benefit from these factors.

In spite of the tremendous power of the expertise model, it has its own limitations as well. Expertise lets us detect meaningful patterns of information in particular domains because of the way we organize our own knowledge. This assumes that there are meaningful patterns to detect that human beings are capable of using effectively. This is not always the case.


Where the Experts Fail

In the 1950’s, research into medical diagnosis and prognosis revealed something shocking: presumed experts didn’t seem to predict medical outcomes any better than novices. This line of research continued over time to demonstrate that prognosis and diagnosis in clinical work in medicine was often not improved by experience when experts relied upon informal gathering of data and their trained intuitions.[4]

Professional experience and presumed expertise also seem to make no difference in predicting the outcome of psychotherapy by psychologists, who it turns out also fare no better than less trained individuals.[5]

If there is a skill to predicting medical and psychotherapeutic outcomes in general, it doesn’t seem to be acquired from the standard professional training, or typically through experience with patients, and it isn’t obvious how else it might be acquired. There is perhaps good reason why some experienced doctors seem very reluctant to make predictions about outcomes, and maybe more of them should heed this lesson.

As a result of the difficulty of prediction in areas like this, novices using simple formal statistical methods have often outperformed the experts in tests in spite of the greater experience and training of the experts (or perhaps in some cases partly because of it).

This is not by any means to imply that statistical methods are always superior to expertise, even in a particular field where clinical experience has proven less than optimal. However, it does give us good reason to pause and reflect on the meaning of this finding for the expertise model. In some domains, the best we can do for prediction is provided by a simple statistical method; and the value of expertise in particular reaches a limit fairly early on in the training for those fields.

What Makes the Value of Expertise Vary So Much?

Research into the value of expertise in different domains shows it to vary[6] with:

1.the level of inference[7] required (moderate levels of inference are more conducive to using expertise than high levels of inference),
2.whether experience or training available is adequate to confer expertise,
3.whether the conditions and instruments available allow for the expression of expertise

What this tells us is that even though expertise helps us make sense of complex situations by recognizing patterns, there is also a limit to how well acquired expertise can help us make better judgments in very complex situations. The more specialized knowledge we need in a field just to understand what is going on, the more likely it is that expertise will fail us when the situation requires a great deal of challenging inference. In the most complex fields at least, it may be that intelligence can also play an important role alongside expertise.[8]

Both intelligence and expertise play some role in every field, but each is more important to some fields than others and at different points in the development and expression of ability. The relevance of intelligence in a field seems to depend to a large degree on the role that abstract reasoning plays in success in that field. The relevance of expertise is more general. The role of expertise in a field depends on how well the situation is made comprehensible to the expert through specialized tools, the quality of their training and experience, and the kinds of conditions in which they have to perform.

Even allowing for a role for intelligence in particularly difficult technical fields requiring very high inference levels, there are fields where neither expertise nor intelligence nor any combination of the two seems to predict performance any better than simple methods.

We’ve discovered that in some areas, experts perform significantly better than non-experts and consistently outperform computer models of various kinds because of their rich background of task-relevant skills and knowledge. In .other areas, simple computer models, statistical indexes, and non-experts consistently outperform experts.

Intelligence may play more of a role in ability in highly technical domains where a high level of inference is often required in addition to recognizing important patterns. Domains are apparently not all equal with regard to what it takes to be good at them.

How Surprises Can Negate Expertise

The difference has to do with the varying role of understanding the situation for solving problems in different fields, and the role that surprise plays in each field. Fields involving things that move freely and things that scale wildly rather than behaving according to standard statistical methods[9] tend to produce surprises that can’t be managed primarily by either intelligence or expertise or both. In these areas the requisite intelligence is relatively low and the practical role of expertise is relatively marginal because reasoning doesn’t help much and it is particularly difficult to get the necessary skills even if you can identify them. So in these fields, simple statistical rules can sometimes perform as well as any expert, regardless of their IQ.

For examples of fields more or less dominated by surprises think of stockbrokers, risk management advisors, clinical psychologists, counselors, psychiatrists, admissions officers, court judges, economists, financial advisors, and intelligence analysts. Think in general of all the fields where experts fare poorly compared to non-experts, where overconfidence cancels out the benefits of expertise, or where time spent in formal practice has relatively little impact on effective outcomes.

In these fields, formal domain-specific expertise and general intelligence provide relatively little advantage in producing good outcomes compared to simple algorithms, direct local observation, direct experience, practical skills, and domain general problem solving skills. Formal expertise and intelligence in these fields especially tends to produce overconfidence more than real predictive ability. It’s not impossible that there may be some real experts in these fields who fare better than others, but they are particularly difficult to identify and train with formal methods.

Not all fields are dominated by surprises regardless of intelligence and expertise. Think of fields involving things that stay put or else move within strictly defined ranges according to physical laws or arithmetic or statistical relationships. These are much better suited to intelligence and domain-specific expertise because in those fields a better understanding of identifiable patterns and potentially complex information does tend to lead to better prediction of outcomes. Think of theoretical mathematicians and physicists, astronomers, test pilots, firefighters, livestock, grain, and soil judges, accountants, chess masters, insurance analysts (who deal with Gaussian topics like mortality), competitive athletes, and surgeons. Think in general of the many fields studied by expertise researchers where deliberate formal practice yields measureable improvements in results, and where the critical skills can be identified and trained.

We Don’t Learn Well from History

Part of the problem with expertise in fields where surprise plays an important role is that we don’t learn well from history in general. One of our consistent biases is that we systematically overweight the likelihood of events that actually happened, relative to ones that didn’t happen (but could have). This means that we have a very strong predisposition to describe events that happened as if they were fated to happen that way. This also means that we tend to think of our descriptive stories as if they were also explanations, not just descriptions. The remarkable power of stories becomes a disadvantage for explanation because the narrative content tends to replace our ability to analyze cause and effect.[10]

We become experts by being exposed to similar conditions over and over again and learning from consistent patterns in our experience. When a domain is characterized by events that are relatively uncommon yet influential, our confidence in our ability to predict events in that domain tends to grow way out of proportion to our actual ability to predict or explain the course of events. Our hindsight bias (“I knew it all along”) often kicks in to replace our missing explanatory ability.[11]

The human mind is particularly well suited to remembering and making sense of events after the fact by weaving facts into a plausible narrative, and particularly poorly suited to capturing actual frequencies of events in order to use that information in other judgments. Our common sense excels at generating plausible stories for what happens, our expertise then generates trained intuitions that add to our confidence in our explanations, but in some cases does not also add to our explanatory ability. Then history leaves us with only a single chain of events to explain, the one that actually happened. We infer from all of this that we are explaining why a sequence of events took place in a particular situation, whereas we have often only described the events, not explained them.[12]

Conclusion: Surprises and Expertise

We saw in the previous section that extremes of arousal can negate some kinds of expertise, especially expertise relying on fine motor skills. We also saw that our mindset can determine whether expert performance is retained during high arousal or fails catastrophically. In addition we saw that our ability to flexibly adapt our responses to novelty in the situation is hampered by high arousal.

Now we see that novelty offers a more general and more serious kind of challenge than just our tendency to lock in to central stimuli under high arousal. When relatively uncommon events tend to be influential in a domain, the power of expertise to help us predict and explain events is severely compromised and often even negated entirely. In these cases we have a compelling natural tendency to tell plausible stories and rely on them as explanations, and additional expertise only serves to increase our overconfidence.


[1] A June 2008 review of major trends in expertise research: (Charness & Tuffiash, 2008)

[2] An expert is typically defined for research purposes as someone who consistently performs more than two standard deviations above the mean average performance on representative tasks for their domain, assuming that ability in the field can be represented by measureable tasks and that this ability is normally distributed (Ericsson & Charness, 1994). Experts defined in this way are assumed to be roughly the top 5% of the performers in a field.

[3] The body of research has been variously summarized in popular books by journalists but a far better source for reviewing the evidence directly is the edited technical article collection: The Cambridge Handbook of Expertise and Expert Performance (Ericsson, Charness, Feltovich, & Hoffman, 2006)

[4] Classic early research showing the limits of human judgment from experience was done by Paul E. Meehl. Meehl demonstrated the limits of informal aggregation of data and prognostication by presumed experts in clinical situations such as diagnosing patients and predicting medical outcomes (Meehl, 1954). An influential review of research showing the superiority of actuarial vs. clinical judgment appeared in the journal Science in 1989: (Dawes, Faust, & Meehl, 1989)

[5] (Dawes, 1994)

[6] (Westen & Weinberger, 2005)

[7] The level of inference means the amount of specialized individual knowledge needed to understand what is going on. Situations with low levels of inference are understandable by most people, those with high levels of inference are only accurately understood by experts. Even experts utilize their abilities better in situations of lower levels of inference.

[8] There is a lot of ongoing controversy about various aspects of intelligence measurement and what it can tell us, but one of the things that most theorists agree on regarding individual differences in intelligence measurements is that they seem to correspond in some sense to our capacity to handle complexity. (Neisser, et al., 1996)

[9] This was one of the main points made by Nassim Nicholas Taleb in his entertainingly and ironically sharp book exhorting the importance of epistemological humility in the face of this sort of unpredictability in important domains, The Black Swan (Taleb, 2007)

[10] For examples in technical literature making this argument more clearly, see: (Lombrozo, 2006), and (Lombrozo, 2007). The point is made even more emphatically in (Dawes R. , 1979). Formal techniques for causal analysis take the lure of stories explicitly into account by using various methods to compensate for it and force analysts to think in causal terms rather than relying on our more natural instincts for telling stories about what happened. (Gano, 2008)

[11] A good technical article introducing hindsight bias and the related idea of “creeping determinism” (what happened is what was most likely to happen) is (Fischhoff, 1982)

[12]There is a more detailed discussion of the difference between stories and explanations in the chapter History is a Fickle Teacher in (Watts, 2011, pp. 108-134)

The Power of Expertise

The Power of Expertise

Who Ya Gonna Call?

Let’s say you’re working on your computer and it starts acting strangely. You get errors that don’t understand or it crashes for no apparent reason. If you aren’t sure what to do at first, where will you look for help? You might perform a web search for the symptoms to see if it’s a known problem and other people have solved it before you. You might run some diagnostic program or an antivirus scan because those are the tools you happen to have.

If you can’t fix it easily and you aren’t confident with computers you’ll probably start looking for help from another person at some point. Who? If it were me, I probably wouldn’t head down to the local college and find the top honors student or someone in the local Mensa chapter. I probably wouldn’t look for someone with great SAT scores or someone really good at Sudoku or even a master electrician. I’d look for someone with a lot of experience with computers and a proven track record fixing them. I’d look for an expert, and an expert specifically in that area, not just a smart person or an expert in a related area.

I stacked the deck a little bit with this question, because I picked a problem that is probably going to be technical in nature. That is, it seems like it will require some specialized knowledge to solve because it involves computers which are complicated devices that are a little mysterious to the average person and far less so for someone who has worked extensively with them.

It turns out, though, that my guess is pretty accurate for a wide range of fields, not just highly technical ones. Knowledge about the job turns out to be a far better predictor of performance than how high our IQ is or any other general disposition, not just in certain kinds of jobs but across a wide range from complex technical work to manual labor.[1] Just as I’d rather have a computer expert help me rather than my friend with an astronomical IQ, in most cases I’d prefer someone who has job experience rather than someone very smart but inexperienced. And I can point to research evidence that supports my preference.

Seeing Differently vs. Seeing More

Even in many areas where we would tend to expect pure reasoning ability to play a large role, it turns out that on average experience tends to win out consistently over any more general ability or measurement we have come up with.

The research that inspired the modern study of expertise began with the game of chess. Think about chess for just a moment. Chess is an activity with a small number of relatively simple rules. Yes chess has the reputation for being a difficult game. But that’s not because chess is hard to play. Nearly anyone can learn the game. It’s because we soon discover that differences in individual ability are immense.

The difference between someone who plays chess for fun who doesn’t study the game seriously, and an average tournament player, is like night and day. It doesn’t seem like much of a competition most of the time. The difference between an average tournament player and a strong one is just as large, which is why there is a rating system.

Ratings allow people of similar ability to play relatively evenly, or to estimate handicaps as they do in golf. The difference between a strong player and a master is similarly imposing as is that between the master and a grandmaster, and between the average grandmaster and a world champion.

How can a game with a handful of simple rules end up with people playing at such astronomical differences in ability? This was the question that intrigued early researchers trying to figure out how people solve problems. The obvious answer is that the stronger players must be seeing more on the board. But what are they seeing differently?

When most of us look at the chess board we see a collection of pieces in different places that are allowed to move in particular ways. We know what we have to do to win; we have to trap the king. We also know some ways to accomplish that. For example we can capture the opponent’s pieces so we have a bigger army, and we can harass the opponent’s pieces so that they are forced into a less defensible position, allowing us to attack the king. Everyone who plays the game, even for fun, knows these things. Still most of us pretty much have to guess at how to get from some arbitrary position to that result.

If I move here, I’ll attack this piece, but how do I know that my opponent doesn’t have some better move in response that is even stronger? More insidiously, is that move by my opponent actually setting up a surprise for me later? If so, what are my options? These kinds of considerations quickly lead to the very intuitive notion that being better at chess is really about calculation, about being able to imagine a lot of different moves, and what might happen if we made them, and keeping track of all that imagining. The better player must be seeing more moves on the board, figuring out what the options are more accurately, and then predicting the outcome.

This is indeed how early chess software played the game well. It looked at the possible moves, looked at the possible responses to each move, evaluated the resulting positions, and chose the move that seemed to give the best outcome based on what the opponent was able to do. The trouble was that trying to do this more than a couple of moves ahead turned out to be a very demanding calculation. More demanding than even the most powerful computers could handle. Researchers were curious as to whether seeing more moves in their mind is really what good players were doing.

Maybe the human brain is really that much more powerful at calculation than we thought. Or maybe the brain is doing something else entirely?

In a pioneering study of chess players in the 1940’s[2] a Dutch psychologist found the surprising answer. I say his work was pioneering not just because it was early but because it led to entire fields of research based upon it and validating his basic findings. The most compelling and surprising findings:

...Weaker players examined the same number of moves as stronger players, and equally thoroughly (!)

...Stronger players could recognize an actual game position far better than weaker players.

...Stronger players were just as bad as weaker players at recognizing an arbitrary configuration of pieces.

This may not seem so earthshattering at first, but think about the implications. Experts at chess consistently beat weaker players, but without examining more moves and without examining the outcomes of those moves more thoroughly. They aren’t “looking ahead more” and they aren’t “reasoning better” and they aren’t even remembering more in general. They do remember more about chess in a sense but not because they have a better memory. And looking ahead is important, but not by keeping track of moves. Their ability is a result of their mind being better trained to remember chess configurations in particular and to use that knowledge quickly and efficiently to evaluate moves.

So what are chess experts seeing that the rest of us aren’t? They aren’t seeing more moves ahead, they are seeing the board in terms of chess configurations instead of seeing it in terms of individual pieces. Their mind has been trained to see meaningful configurations of pieces instead of individual moves. They are not seeing more per se, they are seeing differently. They are seeing in terms of larger and more meaningful groupings. Experts with extended experience acquire a larger number of more complex patterns and use these new patterns to store knowledge about which actions should be taken in similar situations.[3]

The result is profound. We have a game where a few simple rules results in an incalculably large number of possible sequences of moves. But we become good at this game of many, many moves not by thinking about more moves but by thinking in terms of larger patterns: patterns of pieces rather than movements by individual pieces.

Through practice, chess masters have trained their mind to recognize the unique meaningful patterns that apply to their game. Further, the ability to learn to recognize new patterns (along with a huge capacity to remember them) seems to be something we all possess, not just chess masters. It is a fundamental principle of learning, at least learning to be a chess expert.

Even more interesting, we don’t recognize this as knowledge, in the sense of things we recognize that we know. I know that I know some things. I know that I know all sorts of facts like the capital of some of the U.S. states and the number of sides in a triangle and Newton’s formula relating force and mass and acceleration. These sorts of things are considered explicit knowledge.[4]

Chess masters can’t write down most of the patterns they know, both because those patterns are so vast and because they use them without thinking about them. The patterns they learn become part of their chess intuition in a manner of speaking. A common technical term for this is tacit knowledge.[5] We use tacit knowledge in our thinking without realizing that we are using it. This is why it took focused research to discover what was going on in the minds of chess masters.

Tacit knowledge becomes part of our perception. Chess masters see the board differently; for example they often immediately see positions as good or bad without having to do the kind of analysis that the rest of us would have to rely upon.[6]

Tacit knowledge is also used automatically in our thinking. When chess masters guess at the best move in a given position, their guess is informed by their vast database of tacit knowledge, so it is very different from the guess made by a weaker player. Experts make better guesses in their area of expertise. This is what I mean by their “chess intuition” above.

Trained Intuition and Better Guesses

You might be wondering at this point why I’ve spent so much time talking about chess experts. Or you may have guessed the answer. The most interesting conclusions from the research on chess masters are by no means limited to chess masters. Very similar or consistent results have been obtained across a staggeringly wide variety of fields from physical pursuits like wrestling and ballet to intellectual subjects like calculus and philosophy to artistic activities like painting and violin playing, to a wide variety of everyday jobs, to oddball activities like picking the winners at the horse races.[7] Even among scientists, where the role of abstract reasoning is particularly central and the subject matter particularly challenging, productivity doesn’t seem to be predicted on the whole by supposed general ability measures such as IQ.[8]

The chess findings are a particularly useful rhetorical device here because chess seems like it should be so dependent on reasoning and analysis. It turns out that experts analyze chess positions with the help of a vast mental database of chess configurations that apply without any recognition that they know them. The resulting perception and memory of the board just seems natural to them as a result of practice. Examined closely, in spite of its natural appearance for some people, the effortlessness of deep expertise seems to be an extreme kind of skill acquisition[9] far more than an expression of talent.

Even if you interpret all of these findings from different fields very conservatively, collectively they still tell us something of tremendous importance about how we become good at things. We modify the way we perceive the activity. In effect, we train our intuition about the activity.

In all of these activities, researchers have found that time spent in the activity lets us acquire a new way of perceiving patterns in that activity that let us transcend the limits of our working memory and sequential reasoning capacity. That’s why expertise consistently outperforms IQ or working memory capacity or other general measures as a predictor of performance in virtually every activity that has been studied so far. And expertise is not just specialized knowledge or skills; it is also more importantly an accumulation of organized tacit knowledge that lets us make better guesses.



[1] (Hunter, 1986)

[2] (de Groot, 1965)

[3] This has been the most common interpretation of the chess research findings amongst expertise researchers, based on the influential theory of Chase and Simon. (Chase & Simon, 1973), (Simon & Chase, 1973)

[4] “Explicit knowledge,” basically just means things we know that can be easily identified and written down. The descriptor declarative is sometimes used as well, meaning that we can declare it.

[5] In contrast to “explicit knowledge,” this is often referred to as “tacit knowledge,” meaning things we know but we can’t easily express, especially things that support action. Tacit knowledge is usually assumed to be useful for doing things more than for taking part in our conscious reasoning processes. The descriptor procedural is sometimes also used for tacit knowledge because we think of it as involving procedures for doing things rather than declarations about things. For this reason, a common rule of thumb is that tacit knowledge refers to “know how” whereas explicit knowledge refers to “know that” (i.e. I know that grass is green). The casual rule of thumb is troublesome because we don’t really know how we do those things we call procedural, the usage of the word “know” in “know how” is very different than the word “know” in “know that.”

[6] Following the pioneering chess research, research into other areas reinforced the same finding: expert performance depends heavily on a large accumulated memory of patterns that give us a different “intuitive perceptual orientation” to tasks. “Experts can ‘see’ what challenges and opportunities a particular situation without affords.” (and without doing any analysis) (Perkins, 1995, p. 82)

[7] One of the leading and best known figures in the study of expertise is K. Anders Ericsson, whose research encompasses a particularly wide range of fields. An excellent and accessible overview of work in diverse areas of expertise research is Ericsson’s edited collection: The Road to Excellence (Ericsson, 1996).

[8] (Taylor, 1975)

[9] (Proctor & Dutta, 1995), (VanLehn, 1996)

Getting the Right Answer: Is it the Right Answer?

What’s the most important thing about problem solving? If you paid attention in school, you probably would respond: getting the right answer!

There’s nothing wrong with wanting to get the right answer. Or is there? I want to raise four related concerns:

1. The problem structuring concern: Problems don’t always arise in a form that has an identifiable single right answer. Often there are different best answers for different sets of possible criteria, with different sets of tradeoffs.



2. The motivated thinking concern: The kind of thinking we do in order to feel we are right, to be seen by others as being right, or to advocate the right answer to others can overwhelm the kind of thinking needed to solve the problem in the best way.



3. The my-side bias concern: We have a natural tendency to look selectively for evidence in favor of the first good guess we make explicit, to ignore evidence for alternatives, and to think in ways that support our favored alternative.



4. The belief overkill concern: The my-side bias is often reinforced in such a way that that certain of our intuitions become treated as aspirations or universal facts of nature and this extends beyond things that can be verified empirically between observers. Compelling intuitions can guide our thinking into limited preferred patterns, reinforced by selective use of evidence and also by social patterns of polarized thinking.

I argue that these concerns, along with various inferences we can reasonably make about how the mind works, necessitates a certain approach to thinking, especially about more difficult problems.

These factors mean that we have to learn to adopt and leverage different perspectives in order to harvest all of the information available and the expertise needed to solve complex problems. This is why when it comes to problem solving worthy of the name, thinking clearly is more important than thinking correctly along predetermined lines.

At this point you probably have your own concerns. You might be wondering whether I am advocating some sort of fluffy relativistic “there’s no right or wrong and all perspectives are valid” sort of approach to thinking.

That’s not the case. I use the term Clear Thinking because I truly believe there is such a thing as identifiably better and worse thinking, leading to better or worse conclusions and that it very often makes a critical difference whether we get it right.

My point is just that all of us (not just other people) assume we are getting it right much more often than we really are getting it right, and that very knowledge about our own thinking processes is a key to Clear Thinking.

This means that when we need to think clearly about complex problems, we need to use our knowledge about and skill at problem solving itself to root out our own shortcuts, make our thinking more explicit, bring alternate perspectives into play, and in general consider more alternatives than would otherwise come to mind.