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 Limitations of Expertise

The Limitations of Expertise

Although it covers a very wide range of activities, the large body of expertise research takes place in areas where we can easily identify how good people are based on standards of performance within the field itself, and where, to put it bluntly, skill matters. What about performance in real world, where things are a lot messier, and the more skillful exponent doesn’t always come out on top?

This indeed turns out to be a very real issue. While having a certain amount of skill is always valuable, it isn’t always the case that being more skillful means that we perform even better. A little skill might be good, but more skill might not be better. How can this be true?

Consider these possibilities for why being more skillful might not make us perform better:

1. Extremes of Arousal: The general state of our nervous system in response to a situation can in turn affect the performance of our trained skills, although the reason for this is surprisingly poorly understood theoretically. Picture trying to drive a challenging obstacle course while very sleepy, anxious, or terrified. Extremes of arousal may plausibly affect expertise, and perhaps even negate large differences in expertise, although the effects would probably depend on some interaction of the type or activity and whether it was low or high arousal. And it turns out that the way we interpret the situation can be an important factor as well.

2. Transfer Failure: The situation at hand may resemble the situation we practiced for, but be different enough that our skills matter less. If I learn to drive a car and then manage to drive a truck, I’m transferring my skills. If I crash the truck because I can’t figure out how to operate the different controls properly or because the different response of vehicle confuses me, then we have transfer failure. My expertise doesn’t help me if it doesn’t transfer to the situation I’m in.

3. Domain Unpredictability: Some things seem to be intrinsically difficult to predict, so no amount of experience makes us better at predicting things in those domains. I don’t necessarily get better at predicting earthquakes by living through a few earthquakes, and I don’t necessarily get better at predicting slot machine payoffs by playing more, although I might learn other valuable lessons.

Despite the power of expertise across such a wide range of activities, it’s entirely possible that our performance may depend more on something other than expertise under some conditions. I’m going to examine these challenges to the power of expertise one at a time.

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.

The Dilemma and Challenges of Exceptional Thinking Abilities

What Makes Some People Better Problem Solvers Than Others?

The Dilemma and Challenges of Exceptional Thinking Abilities

Simply getting the right answer isn’t always the best way to think about solving difficult problems. For some problems outside of the classroom and aside from questions of knowledge from within well established domains, there may not be a single right answer.

There may be additional alternatives to be considered that aren’t known yet or which don’t seem right at first but can be turned into better solutions. Needing to be right (getting the answer that others seem to think is right), or needing to think we’re right, or needing to be seen by others as being right, may mislead our thinking and blind us to better answers.

Thinking Too Much: Defying Common Sense


Wanting to be seen as clever or wanting to be seen as an expert often similarly restricts our thinking. We very often settle for the first guess that seems right or the way other people seem to be thinking. There are sometimes good reasons to stop thinking about a problem and settle for an answer, but most of the time we use shortcuts rather than stopping when we truly have the best answer available to us.

Shortcuts are natural to us and they are an important part of what makes us good thinkers. Shortcuts in thinking are part of our common sense. It doesn’t seem right to sit and reflect on something that has an obvious answer. It can seem like a peculiarity or a symptom of subscribing to some bizarre overly complex view of reality, or maybe even a character flaw.

The trouble is that our common sense that serves us so legitimately well in so many everyday situations turns out ot be poorly suited to many other kinds of complex and counter-intuitive situations. Our natural instincts for reasoning are significantly better adapted to some kinds of problems than others.

The shortcuts that serve us for biological needs like feeding ourselves and mating and getting along with other people in small groups tend to fail us when we think about things like cultures, corporations, markets, and nations or when we’re presented with a completely different kind of problem.

Importantly, the way we learn is not well suited to automatically recognizing which kinds of situations we are thinking poorly in. The shortcuts in our thinking work so well because we rely on them so naturally. We don’t necessarily get an alarm bell in our mind that we are thinking in the wrong way about a problem. We instead get responses from our natural learning systems that we experience as compelling feelings and intuitions that guide our thinking.

It is only by learning about the thinking process itself and how our own mind works that we begin to learn how to make best use of our natural learning systems to think clearly about problems that our natural abilities are not well optimized to solve, situations where our common sense and our intutions fail us. This learning also helps us reason through situations where we have to think across different domains of expertise without a sense of how well we have captured the meaningful patterns in each of those domains.

Sure, when there’s a right answer, we want to be able to figure out what it is. More generally though we want to think clearly about the problem. This means thinking in a way that leads to, if not an ultimate perfect answer, the best solution available, even if that means bringing more expertise and different perspectives into play and challenging our own intuitions.

How do we know when our shortcuts and intuitions are failing us and that the situation requires a different kind of thinking? I think it comes down to making it a priority to learn about our own thinking while we are learning other things. This means being strategic about thinking: knowing as much as possible about our own tools and resources, both their strengths and their weaknesses.

Identifying Our Strengths and Identifying Our Weaknesses


I’ve been a professional problem solver for decades and from time to time I have worked alongside other problem solvers whose abilities truly amazed me. Some people are able to look at a situation and see opportunities and possibilities that others cannot seem to appreciate until after they become real solutions, and sometimes not even then.


Some of those same remarkable problem solvers then even more remarkably sometimes make the worst mistakes in certain situations. They apply their knowledge and skills in ways that just don’t fit the situation, and sometimes the very qualities that often serve them so well in other situations now make them overconfident in their answers. This book is about learning from both their triumphs and their failures, as well as our own triumphs and failures. It’s about learning to think better.

I have devoted many years trying to understand what it is that is different about exceptional problem solvers, and to what degree their abilities can be duplicated and perhaps even improved upon to avoid the worst mistakes that they also tend to make.

This sets out two primary challenges for me:

--> What makes some people so much better problem solvers than others, especially across different kinds of problems?

--> What causes otherwise great problem solvers to make such awful mistakes so often?

Clear Thinking: An Introduction

When a difficult problem is solved deliberately, it is generally because the right expertise was applied to good information through the right tools as part of a reliable process. These are the essential elements of effective human problem solving. The rest is in the details. We often do less than this because we are also very good at guessing well.

The human nervous system is not a logic engine, it evolved to serve human biology. This has profound implications for the way we think and what we must do to improve our thinking. Our explanations are guided by powerful intuitions that often seem to defy the theoretical ideal of rationality.

Expertise refers to the way a mind with natural learning abilities organizes its experience purposefully for action. This is where our guessing ability comes from. Expertise provides our built-in guidance for effective thinking in particular areas.

Information is the fuel for thinking, without which expertise would be an engine with a dry tank.

Tools and processes are the way we leverage our strengths and compensate for our weaknesses.

Exceptional problem solvers make better use of available resources than the rest of us and also gather more of the right resources around themselves. This isn’t magic and it isn’t something we’re born with. Nearly anyone can learn to do these things better. Nearly anyone can learn to make better guesses and also to leverage good guesses into more powerful reasoning.

This book introduces an approach which I call Clear Thinking. The basis of this approach is strategic. Through a realistic and accurate ongoing understanding of the strengths and weaknesses of our own mind, we learn to make best use of our ever changing strengths and minimize or compensate for our ever changing weaknesses. In this way we make increasingly better use of our resources and approach the ideal of clear thinking.

You should understand from the start that this is a lifetime learning process. You can’t learn to radically improve your thinking in a weekend seminar, a critical thinking course you can complete in a semester, or even a degree you can earn in a few years. To become smarter you have to learn the mindset, strategies, processes, skills, tactics, and habits of becoming smarter, and this learning is difficult, rewarding, and lifelong.

Clear Thinking is an approach that you incorporate into your daily decision making and problem solving by learning the associated tools and principles and by coming to embody the intellectual virtues shared by the best problem solvers.

Key Points:

--> Useful human knowledge, skills, and attitudes tend to break down into domains. Among other reasons, this is possibly because the human brain is organized into somewhat discrete learning systems for dealing with different kinds of biological needs.

--> Different subjects we learn have their own domain with their own domain-specific rules and methods of study. Our practical abilities tend to be organized into domains for the most part. The domain-specific elements of thinking are critical to Clear Thinking and also to education in general.

--> Most problem solving is relatively routine and involves dealing with particulars of a situation relevant to a specific domain of activity rather than dealing with abstract principles.

--> Since problem solving so often involves dealing with particulars, individual differences in problem solving ability are largely a result of specialized expertise in particular domains rather than a more generalized reasoning ability.

--> Speciallized domain expertise is the result of systematically acquired experience in which we structure our mind in a way that lets us think efficiently about a specific kind of activity in a particular way.

--> We also have important abilities that apply to multiple domains of knowledge at once or which cross domains. These domain-general elements are the ones emphasized when we try to improve problem solving and decision making through “critical thinking” and similar approaches. I have adopted the term Clear Thinking rather than “critical thinking” only because I think the emphasis on criticism can be misleading.

--> Some people are better individual problem solvers than others because they have learned to make use of their cognitive talents, domain-specific expertise, and domain-specific knowledge, by means of domain-general problem solving knowledge, skills, strategies, and attitudes.

--> One of the most critical things we can do in order to improve our thinking is to distinguish domain-specific from domain-general elements. We acquire and apply these different kinds of elements in very different ways and they have different kinds of influence on our thinking.

--> A great significance of domain-general vs. domain-specific elements is partly that more intelligent and more expert problem solvers often make even worse mistakes than less intelligent and less expert problem solvers due to negative artifacts of their abilities such as overconfidence, overspecialization, and the amplification of natural biases.

--> One way we can avoid the worst mistakes is by learning realistically about the strengths and weaknesses of human abilities in general. This becomes an important aspect of our domain-general problem solving knowledge, skills, strategies, and attitudes.

--> The domain-general emphasis of Clear Thinking is mostly intended to make the thinking process more explicit in order to make better use of our guesses. Making the thought process more explicit is the essence of the ideal of rationality.

--> The domain-general elements are also significant because they help us learn how to shift between different perspectives. Importantly, this is not because different perspectives are somehow all equally valid. It is because a perspective is much like a lens which makes some things easier to see than others. Useful bits of knowledge are sometimes obscured by our current perspective, and shifting perspectives can help additional alternatives become more visible.

--> Some groups are better collective problem solvers than others due to differences in the patterns of their interactions in making use of their individual expertise, knowledge, skills, strategies, and attitudes. This becomes another important domain-general element of human thinking.