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)

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.

Book Review - Apollo Root Cause Analysis: A New Way of Thinking (?)

Book Review - Apollo Root Cause Analyis: A New Way of Thinking Book by Dean Gano, Apollonian Publications, 2008. Review by Todd I. Stark Link to Amazon review.

Very useful principles for preventing bad things from happening, April 11, 2011 By
Todd I. Stark "Cellular Wetware plus Books"

This review is from: Apollo Root Cause Analysis: A New Way of Thinking (Paperback)

How can we prevent bad things from happening, and how can a formal method help us in this quest? That's the topic of this book.

"By understanding the cause and effect principle and creating a RealityChart, your understanding of what constitutes reality will be changed forever ... allow you to see a reality that was previously beyond your comprehension." (Apollo Root Cause Analysis, p. 3)

"... the problem of a linear language in a nonlinear world while accommodating the simple human mind has been a challenge for the ages. I believe this challenge has been met with Apollo Root Cause Analysis." (ARCA, p. 176)

The importance of the topic covered by this book is immense, so for my purposes, I'll forgive the author his amusing enthusiasm for this own method as seen in the quotes above and try to determine what may actually be truly useful in it rather than dwell on what may or may not be unique about it. I will be referring to this book and to the Apollo method in this review. The method is also implemented with associated RealityCharting(tm) software which is outside the scope of this review, except where explicitly mentioned.

Problem solving well is one of if not the most critical factor for human success across a wide range of activities. One big part of problem solving is explaining *why* something unexpected happened. I you think about it, anytime something unexpected happens, we generally want to know why. We do this naturally, automatically, and effortlessly. We can't stop ourselves from doing it to at least some extent. Sometimes the explanation seems obvious and sometimes it seems very elusive. Even when the explanation seems obvious, there is often more going on of importance than we realized. We do this seemingly because especially if it's a bad thing that happened, understanding why it happened potentially helps us predict whether it will happen again and perhaps prevent it from happening again. That's important to us as individuals as well as to organizations. It's so important that we already do it all the time. We also tend to assume that we're very good at it, I think. My experience is in agreement with the author of this book, people are in general not nearly as good at solving problems as we think we are, at least when the problem become complex and involve multiple people and organizations.

We have a powerful natural ability to make sense of events that happen around us by identifying people, places, things, and weaving a story around how they interact with each other. (1) The power of storytelling to make sense of events is also its tragic downfall when we need to understand how things happen in great detail. Our temptation to tell stories actually gets in the way of understanding what specific things need to happen in order for other things to happen, and that's the sort of understanding that is needed in root cause analysis. Getting past our natural but distracting abilities to use the right tools effectively is the greatest value of an effective formal method. We often look to formal methods to systematize our thinking in general, but problem solving outside the academic realm of worked problems is often not amendable to being constrained by formal methods. Instead, formal methods provide their real benefit in forcing us to look past our own blind spots at key points during problem solving. To his credit, the author of this book seems to have grasped this important point very well and applied it effectively.

The most problematic blind spots in our thinking are those that aren't avoided by intelligence, education, or domain-specific expertise, skills or knowledge. The most problematic blind spots are actually natural abilities that serve us well most of the time. One of the most important examples is that we tend to use storytelling to construct a plausible sequence of events leading to an outcome, putting people into the center of the action and making the events meaningful to us. There are a number of reasons why this natural and compelling process is a bad idea in problem solving.

First, since a story starts with the putative "root cause" and then proceeds to its effects, it tends to assume a single cause. Events don't have a single cause. If our goal is to prevent bad things from happening, we'd rather identify as many relevant causes as possible, and potentially address each one as makes sense.

Second, storytelling tends to center around characters, their motives, and the things they do. The motives and behaviors of people are often the things we have the least reliable control over in problem solving. We'd prefer to find things we can control better if at all possible, and save human choices and our interpretation of human motives as a last resort.

Third, we can usually tell multiple different stories about the same events, depending on what information we emphasize and how we emphasize it, which in turn depends on our motivations and perspective. This means that the choice of which "root cause" we focus on tends to be more political and subjective than a result of careful analysis.

Our storytelling is natural and compelling but it tends to be more to make sense of events than to understand the details of what causes what.

So effective Root Cause Analysis tools and methods should discourage storytelling and instead search the prerequisite conditions and sequences of events that lead to the results we want to prevent. That's the message of this book and its method. So how well does it accomplish this?

Why a formal process?

Causal analysis (or what is sometimes more specifically referred to as "root cause analysis") is the more formal process of doing what we tend to do naturally, try to figure out why events diverged from what we expected. Why do we need a formal process for doing this, if we already do it naturally? Because often we don't do it very well. The very fact that we do it naturally means that we will tend to make *confident guesses* about why things happened. Often more confident than accurate. This far, the author is on solid ground. The author doesn't refer to it explicitly, but there is a very large literature on the cognitive science of decision making that supports the author's claim that people tend to jump confidently to unwarranted conclusions when they first try to explain causation. (2, 3, 6)

One very important reason for a formal process is to systematize the data gathering and analysis process in order to help compensate for our natural biases that lead us to make confident but inaccurate guesses. The value of formal vs. informal processes is debated widely in the decision science literature, since much of our innate intelligence is the result of automatic non-conscious processes that are opaque to us in our own thinking. (5) However in general I think it is a pretty safe conclusion that a good formal process often helps us focus attention on things that we would not otherwise have seen to see past some of our own blind spots. That's the first reasonable rationale for causal analysis methods like Apollo Root Cause Analysis (ARCA) and its primary tool, RealityCharting(tm). Simply having a process to focus us on causes rather than stories is useful in itself.

Why a formal group process?

There are various reasons for using a formal process in organizations. Unexpected events often affect more than one person, often more than one person is part of the problem, and often more than one person has information or perspective needed to figure out what happened. So causal analysis very often becomes a group process. In addition to the reasons for using a formal process in general (to get around our individual blind spots), it is also important in groups in order to help avoid "group blind spots" such as the principles of behavior in groups studied by psychologists. We are often tempted us to go along with the consensus, to protect our own ideas, to react initially negatively to new ideas, and other tendencies that can negatively impact the problem solving process in groups. (6) A good formal group process can help get around our group blind spots just as it can help us get around our individual blind spots. In spite of the well-established problems created by "groupthink" and other group dynamics studied in social psychological, under optimal conditions, groups can often perform significantly better than individuals on some kinds of problems. (7)

The author makes a key point about group processes that he says defies conventional wisdom (p. 9). He implies essentially that the principles of good problem solving are simple and domain-general so should be taught to everyone, whereas domain expertise is deeper and necessarily differs more between people, so "subject matter experts" should also be at hand. The conventional wisdom he says is that problem solving is entirely "inherent to the subject at hand," or what I would call domain-specific, ignoring the value of domain-general principles in problem solving. I don't know how much this really defies conventional wisdom, but I agree with him completely and I think this is an important principle. It is a major part of the rationale for involving more people in a collaborative group problem solving process rather than just pulling in a few experts. If the process is good, and this principle is valid, then it has deep implications for improving problem solving in organizations by broadening involvement and investment in the process itself. That brings us to the real question at hand, whether the author's method accomplishes this objective.

Why Apollo?

So far my description here is I think pretty much in line with the author's rationale. Now we come to the real meat. How good is this particular method at getting around our individual and group blind spots, compared to other methods? That's the significance the author claims for this book and for his method, so that's what we really need to know to evaluate this book. The book covers a lot of ideas that are very important to know, but many are common knowledge to experienced problem solvers, so I am not going to focus on those. What I will focus on is what is supposed to be special about ARCA and RealityCharting(tm) in particular.

According to the author, the key distinction between his formal method and all others is that all others are ways of categorizing causes and schemes for voting on the best way to categorize them, while his method discerns (and RealityCharting displays) the actual relationship between different causes, along with the evidence for each cause. (p. 193)

All viable methods of "root cause analysis" in groups involve taking chains of events that would be too numerous and related in too complex a manner to envision in a common way by everyone involved in the process of they were simply described in words. Visual representations of trees of causes play a key role in all formal methods of "root cause analysis," including ARCA.

The trick to understanding the underlying message of this book and grasping the uniqueness claimed for the method is to understand exactly what he means by the *relationships between causes* and how the method forces you to think about evidence for causes. I don't personally favor the author's explanation for why his method is unique. His rules of causality seem awkward to me and I think there is a slightly different and clearer way to think of how his method works.

First, every effect is also a cause. There is no distinction between causes and effects except at the endpoints. Feedback loops of causes that have effects that ultimately lead back to the original cause are common in nature as well as human design and this method has no problem handling them. We start with the effect we are trying to explain and end with causes that either do not need to be explained or cannot yet be explained. This seems fairly straightforward to me for a causal chain, I'm not sure why the author feels it is unique to his method. I suppose it may be that commonly used methods tend to de-emphasize this aspect of causality.

Second, the method forces you to identify both actions and conditions that had to be in place for the action to cause the effect. This isn't useful for causal modeling as much as it is a useful trick for helping to identify all of the causes that might be relevant to solving the problem. Breaking the "Why?" question into an action and a set of conditions is seemingly somewhat unique to this method and helps avoid thinking solely in terms of actions or solely in terms of things that were in place at the time or characteristics of the situation. The theory is that it is usually *easier* to see actions that happened and less obvious what conditions had to be in place as well, but it is often *more useful* to identify the conditions because they tend to be more predictable and more controllable.

Third, the method forces you to have evidence for each cause, and takes an unusual and perhaps mildly questionable empiricist slant here. It distinguishes "sensed" from "inferred" evidence. "Sensed" of course refers to direct observation by someone, with as little interpretation as possible. "Inferred" refers to anything else. Assumptions and opinions represent doubts and must be investigated further, and just about anything that someone doesn't observe directly themselves has some doubt associated with it, whereas direct observation is automatically given very high credibility by the rules of the methodology.

This is a slightly odd sort of slant from my perspective because the credibility of eyewitness testimony in general is often in doubt (8), the reliability of inference is often not obvious, and because in my experience, hypothesis testing is particularly useful and important problem solving tool. Storytelling, much maligned and minimized in the ARCA method for good reason, is built on inferences from observations. However so do scientific theories and mathematical models arise from inferences. (9) The main difference is that science and math involve systematic linking and pragmatic testing of inferences rather than just weaving meaningful narratives. The author frequently asserts in different ways that storytelling negatively impacts problem solving, and defers to direct observation. I would instead argue that both are in doubt, and that their relative strength is not absolute based on source, but depends upon how inferences and observations support each other. One contemporary philosopher usefully compares the relationship to that of a crossword puzzle. (10) It is notable that the ARCA method and reality charting tool have no trouble accommodating different conceptions of the reliability of evidence.

Strengths

1. The philosophy behind the method is extremely inclusive organizationally. The idea is not to find the putative "true causes" but to gather enough information to produce effective solutions. So the method encourages as many people as possible to participate as fully as possible, rather than to argue over the right causes to focus on. That is perhaps the greatest strength of this method, its formal incorporation of diverse perspectives, allowing everyone to be heard and every perspective considered while discouraging the usual drama of competing narratives. Different definitions of the problem are managed by starting from different primary causes and creating separate charts. Since the framing of the problem often guides or constrains the search for a solution, I consider this a strength of this method.

2. ARCA explicitly includes the source and specifics of evidence for each cause, helping to identify and prioritize information gathering tasks to further validate assumptions and opinions, or reproduce observations. You are strongly encouraged to provide evidence for every cause, a process that I think can lead to a far more comprehensive yet focused data gathering effort than other methods.

3. ARCA allows for the explicit representation of causal feedback loops, which can be important factors in understanding what is happening. Many causal charts make this important relationship very difficult.

4. The technique of treating each effect as a cause and subjecting each to requirements for evidence, and explicit follow-up for finding further causes, intermediate causes, information required, or explicit reasons for stopping is a very powerful aid to causal thinking and to me is the core of both the method and the associated software.

Limitations and Criticisms

1. Potential overreliance on unreliable eyewitness testimony vs. discouragement of useful inferences. The process of hypothesis generation is a particular kind of inference where we consider more alternatives than we suggest, and as a result we understand that they are explicitly hypotheses. There is experimental evidence that generating hypotheses ourselves leads to a more realistic appraisal of their strength and less false confidence than inferences provided from other people. (cf. Derek Koehler) Other research supports the notion that our causal judgments are themselves inferences that often result from mental simulation. [This is a comment regarding the method in the book, which downplays the role of "inference" vs. "direct observation." It is a minor point in practice because there is nothing in the RealityCharting tool that forces you to evaluate sources of evidence in a particular way.]

2. Overemphasis on uniqueness of the method. This mostly refers to the tone in which the book is written, which I often found distracting and annoying, wherein it often makes the book sound like a sales presentation for the method and the tool rather than a treatise on effective problem solving. This point goes along with the scarcity of credit to other sources that for me would have greatly enriched my appreciation of the rationale for the method. The author relies too heavily on himself as an authority for his rationale to rate this a five star book.

3. Restriction of causal statements to very brief verb-noun and noun-verb format rather than sentences. I think this has some value in fostering clarity if you manage to find a phrase that everyone happens to understand in the same way. Still, in practice I found that it can be time consuming to come up with these compact ways of expressing causes, and that they were easier to misinterpret than full sentences would be. [This is also a potential issue in the tool because the rule checking tries to enforce it. However, it can be disabled if desired.]

4. Does not allow you to visibly categorize causes except into actions vs. conditions. This is an attribute of both the method and the associated software. This "limitation" is the intentional result of the author's core principle that he feels distinguishes his method in particular: categorizing causes is relatively useless compared to relating them. Categorizing causes is suggested in ARCA only if you are stuck finding plausible candidate causes and want to try thinking in terms of categories in order to help find candidates. Since this is intentional, it is a limitation only in the sense that someone familiar with other methods may run into it and have to think differently in order to use the method as intended.

Given that so far every substantive criticism I've had of this method is easily accommodated by minor changes to the method (as well as in the associated tool, with the exception of categorizing causes) I found this to be a particularly versatile method. It captures a number of key principles of problem solving, especially in groups, and provides practical and effective ways of compensating for our most problematic blind spots and biases. For the most part, I think understanding the principles of ARCA and RealityCharting would probably enhance most other methods as well as the method standing on its own.

A formal method itself is no substitute for improving the reflective intelligence of each problem solver, but I think this method could go a long way in any organization toward getting people to think more effectively, and especially for helping them communicate their best thinking to each other. I found very little to disagree with and much of value in this book and found the method easy to understand and apply.

References

(1) Brian Boyd on art and storytelling as biological adaptations which derive from play.

"On the Origin of Stories: Evolution, Cognition, and Fiction," Brian Boyd, 2009, Belknap Press of Harvard University Press

(2) David Perkins discusses the significance of domain-general principles for effective thinking, and why they can't be replaced completely by intelligence or expertise. A plausible research-based discussion of why we need help getting around our built-in individual blind spots.

"Outsmarting IQ: The Emerging Science of Learnable Intelligence," David Perkins, 1995, Simon and Schuster

(3) The psychological study of how we tend to answer questions of the form "Why ... ?" is called attribution theory. Probably a result of our bias toward storytelling explanations, most attribution theory has attempted to address attributions of people in social situations and sometimes products in the case of consumer research. A much smaller amount of research has been done on the perception of causality in other ways, such as in building causal explanations, and only a tiny amount has made its way into popularly accessible books.

"Causal attribution: from cognitive processes to collective beliefs," Miles Hewstone , Wiley-Blackwell

(4) There are not many modern books that deal relatively broadly with causal modeling from both a philosophical and mathematical perspective. One of the few is Pearl's text.

"Causality: Models, reasoning, and inference," J. Pearl, 2000, Cambridge University Press

(5) The non-conscious processes in realistic problem solving is addressed by a fairly sizeable body of technical literature by Dijksterhuis, Bargh, Gollwitzer, and others, and introduced in an accessible and practical way by Gary Klein.

"Sources of Power: How People Make Decisions," 1999, Gary Klein, MIT Press

(6) The problems exacerbated by group behavior under non-optimal conditions are introduced accessibly and briefly yet very broadly in:

"The Psychology of Judgment and Decision Making," Scott Plous, 1993, Mcgraw-Hill.

(7) The positive potential of groups under optimal conditions is discussed in
"Group Problem Solving" by Patrick Laughlin, 2011, Princeton University Press.

The common belief that direct observation is inherently more reliable than inference is based on two ideas that are at best only partially true: (1) the assumption that reports based on observation does not significantly depend on memory, inference, or explanation, and so is automatically free of distortions, biases, or storytelling, and (2) the assumption that inferences are roughly equivalently reliable under a wide range of conditions.

(8) On the reconstructive nature of memory for events, see:
"Searching for Memory: The Brain, The Mind, and the Past," Daniel L. Schacter (1997), Basic Books

(9) On the nature of inference and especially scientific inference, see:

"Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science," Mayo and Spanos (2010), Cambridge University Press

(10) The useful crossword puzzle metaphor for the relationship of observation and inference is introduced in:

"Evidence and Inquiry: A Pragmatist Reconstruction of Epistemology," Susan Haack (2009), Prometheus Books

A lifetime learning approach, more than a course in "critical thinking"

Review of --> Critical Thinking: Learn the Tools the Best Thinkers Use

by Richard Paul

A lifetime learning approach, more than a course in "critical thinking."

I enjoyed this book and got a lot out of it. For such a high level book it isn't easy reading because it requires you to think about your own thinking while you are reading it in order to get full value out of it. If you skip that exercise, in my opinion, you will not appreciate the value of this book. So if you want to grow from what the authors are offering, be prepared to take a long term vision of your own abilities and to do a lot of work. In spite of the somewhat misleading title of this book, it is not really about what most people would call "critical thinking," it is a much deeper and more useful view of thinking abilities in general.

This book presents a relatively accurate (in my opinion) and very usable model of how human reasoning works in practice and how we get better at it. In addition it offers many simple but practical drills for identifying how well you are thinking and what sorts of things would be good to work on. So I do recommend this book.

Having gotten that positive impression from my reading, I also noticed the many very negative reviews here and that made me pause and think a bit. It may be helpful to expand on what I think it good about this book and what the negative reviewers are seeing.

(...)

Link to complete review on Amazon

Book Review: Zimbardo's outstanding guide to social influence for the seriously curious

Book Review: The Psychology of Attitude Change and Social Influence

Phillip G, Zimbardo and Michael R. Leippe - 1991 McGraw Hill

Link to review on Amazon .

I 've been studying social psychology for years and I've remained fascinated by it since college, and have read dozens of texts on the subject. I bought this text because I like Zimbardo's clear informal style of writing and I suspected this might be a good reference book to give to my son who is studying psychology. I was surprised that when I received the book and started reading it that I would find it so uniquely engaging. I enjoyed it so much that I bought another copy just for myself.

The thing that makes this book so outstanding to me is that it doesn't just list the principles or theories of influence, it actually organizes them into something I didn't even realize existed: a single relatively coherent framwork for persuasion and social influence. The authors choose several particularly comprehensive and robust models such as the "Theory of Reasoned Action" and the "Attitude Systems" model to help organize their book into a systematic explanation of how and why influence works. The result is that they manage to tie together a vast diversity of theories into a reasonable framework for study.

The book starts off making the critical distinction between attitudes, behaviors, thoughts, emotional evaluations, and intentions, and introduces the crucial concept that these are all linked together in various ways. The resulting general model is an enormous help in understanding the various many experimental results and theories that the authors introduce in later chapters.

Second, after introducing the general model, the book begins with how behavior changes from situational forces independently of attitudes or beliefs. We learn about the classic experiments in obedience to authority, conformity with groups, reciprocity, and committment; the various click-whirr responses we use to help guide our behavior without thinking.

Third, the book begins to apply the general model, we first see how our observation of our behavior feeds back to our own thinking and attitudes.

Fourth we enter the grander realm of persuasion: we learn the conditions under which we tend to think about the situation rather than just responding with behavior. We learn how our thinking is guided by both internal and situational factors, and the patterns by which it organizes itself to maintain consistency, and to help us achieve our goals.

Fifth, we begin to see the conditions under which the various shifts we have been seeing in people sometimes become more lasting, and when they are actually manifested in later behaviors. Yet again, we see how the general model introduced at the beginning of the book helps to understand the diverse theories in the book. This is not a small accomplishment in my opinion.

What creates resistance to influence, and what makes for exceptional vulnerability to influence? The Sixth major topic covers the fascinating question of why our attitudes and values form a stable system for thinking and why and how it can shift sometimes.

Seventh, there are various applications of the previous ideas, investigating such questions as "unconscious" or "subliminal" influence, how well it works and for what purposes, and how it relates to the cognitive model. We also look at the important application of these ideas to juries and courtrooms.

The book ends appropriately with the application of its ideas to health, survival, thriving, and happiness. This isn't "positive psychology" per se, rather it is an exploration of how the concepts of persuasion and influence detailed previously in the book might relate more directly to physical and mental health through attitudes, beliefs, and behaviors.

This is a very focused book and probably the best overview I've come across on the social psychological and social-cognitive theories of influence and persuasion (both self and other influence and persuasion) especially if you want to understand the theories and how they relate to each other.

There are other books more focused on applying the theories for those who just want strategies to improve their influence skills, but I have not found another book with a better educational discussion of the ideas. This is my favorite book on persuasion and influence for the seriously curious.

Book Review: David Perkins' must-read brilliant map of human thinking ability and its improvement

Review of David Perkins’ “Outsmarting IQ: The Emerging Science of Learnable Intelligence,” 1995, Free Press.

Link to review on Amazon --> http://www.amazon.com/review/R3AYGZV7G7AUTO/ref=cm_cr_rdp_perm

Profound Thinking By Example

This is the single best book I’ve come across on the potential for improving human thinking ability. I give it my highest recommendation; I think it should be read by everyone interested in problem solving, decision making, and human abilities in general. It is amazingly broad in its coverage of data, profoundly deep in its treatment of specific lines of relevant evidence, and ingenious in its vision of the future.

What impressed me most about this book is that the author, David Perkins, demonstrates the power of deep reflective thinking by his own example in the organization and treatment of evidence throughout this book, in his critical treatment of his own evidence and ideas, in his creative original ideas, and in his effective consolidation and filtering of massive amounts of research. Showing how asking the right questions can help us understand seemingly contradictory data about intelligence, Perkins gives an engaging plausibility proof for the kind of reflective intelligence he argues for in this book.

The Concept of Realms of Thinking

To give away the ending, the book culminates in a model of problem solving ability based on the metaphor of a map. Human thinking ability results from learning our way around. Navigation is fundamental to all sorts of human thinking. Perkins suggests that all intelligent human thinking results from navigation of various kinds, which can be thought of in terms of levels of realms. Perkins organizes the realms in an overall map or “mindscape” from the lowest level of specific contexts of thinking to the highest level dealing with thinking itself.

In learning to solve problems we not only learn our way around physical realms geographically, but we learn our way around specific contexts we find ourselves in such as the realm of buying a house or the realm of choosing a career. We learn our way around different situations like resolving conflicts or making purchases in general. We learn our way around professional fields like law, physics, and mathematics, and areas of technical expertise such as probability and statistics, game theory, and business. We learn our way around the use of tools. We learn our way around various basic kinds of challenges like problem solving, decision making, planning, and learning. Finally, at Perkins’ top level, which he calls thinking dispositions, and we learn our way around thinking itself in terms of the qualities and attitudes that make it more or less effective.

Perhaps the central thrust of this book is that in organizing human problem solving areas into navigational realms, Perkins is not just providing a training map for learning problem solving skills a million different areas, he is also making a case for the learning the critical skills of navigation itself.

Perkins’ realms are very similar to the traditional concept of domains of expertise, but different in one critically important way: realms emphasize the central skills of navigation rather than just the use of repetition or rote memorization or even just the use of deliberate practice. The concept of realms makes it more explicit that all areas of ability that we learn share some commonality in terms of key skills and attitudes we need for navigation itself.

It is learning to be a better navigator; in all realms of human thinking and not just certain subset of them; that is the central message of Perkins’ book. This is encapsulated in his concept of “reflective intelligence.” Reflective intelligence is the aspect of intelligence that can be most improved for the greatest effect across the range of all realms of thinking. Perkins reviews a number of different attempts to improve human thinking and makes various suggestions based on their results regarding specific kinds of changes that can be made to educational curricula in order to teach children to be better navigators in all areas.

Getting Perspective on Intelligence through 3 Dimensions

In giving away Perkins’ final model, I’ve skipped over two very important and interesting aspects: his argument for the model he uses and for the prospect of learnable intelligence through better navigation, and his predictions for important areas of the evolution of learnable intelligence.

The bulk of Outsmarting Intelligence deals tightly with the subject of the title, the legacy of how intelligence has been envisioned and researched so far. Perkins deals in equally deep, reflective, careful, and often fascinating manner with: (1) the evidence for a single common problem solving ability from psychometric data, (2) the evidence showing us how novices think differently from experts, and (3) the evidence showing us what happens when we try to learn general skills and rules for solving problems in general and how computers solve problems.

From these three bodies of evidence, Perkins derives three corresponding dimensions of human intelligence: (1) a neural intelligence dimension which respects what psychometric data gets right and is most closely associated with what we typically assume IQ tests are measuring, (2) an experiential intelligence dimension which respects what expertise research data gets right, and (3) a reflective intelligence dimension which respects what we have learned about metacognition and from the various programs that have tried to teach thinking skills in general.

Neural intelligence, Perkins concludes, is a real dimension of human ability and very important in some situations especially, but it is simply the wrong target for attempts at improvement for various reasons.

Experiential intelligence represents most of our actual problem solving abilities in practice.
Faced with novel and complex situations where we have no relevant experience, our neural intelligence gives us our best chance at solving the challenges presented. But once we have been acquiring experience in an area, a difference in expertise will make people better problem solvers in that area than will a difference in general intelligence.

So experiential intelligence and neural intelligence work together to make us the generally good problem solvers that we are in most situations: neural intelligence helps us deal with novelty and complexity, and experiential intelligence helps us acquire the knowledge and skills we need to deal with specific domains.

So the obvious question is: what role does reflective intelligence play and why does Perkins consider it so important?

The Significance of Reflective Intelligence

Perkins reviews various lines of research into the wide variety of situations where otherwise powerful problem solving abilities seem to fail us in systematic ways. He looks at social psychological effects, cognitive shortcuts, and so on, similar to other reviews of blind spots in human thinking by many other authors except that Perkins attempts to characterize these foibles specifically in terms of side effects of our experiential intelligence.

Perkins suggests that the human mind is mostly akin to a pattern seeker and pattern-driven problem solving engine and as a result its weaknesses are also those we would expect from a pattern-driven process. The human mind often tends to be hasty, narrow, fuzzy, and sprawling.
HASTY. The goal of a pattern seeking intelligence is to find the right response that most closely matches the current situation rather than making an exhaustive search. As a result, our experiential intelligence tends to mislead us to jump to hasty conclusions when the situation is an unusual variation of a known situation.

NARROW. As a result of efficiently seeking patterns we have already seen, the domain-specificity of expertise tends to make us think in narrow ways when we think we have grasped the situation rather than to broaden our thinking.

FUZZY. Part of the power of pattern-matching is that we can so often generalize the lessons from one situation to another similar one. In situations where the appearance is very similar but the underlying principles are different, again our pattern matching effectiveness leads to mistakes: we overgenerallize from our experience.

SPRAWLING. When a pattern-seeking process does not have a single clear path to follow, as often happens in very complex situations, it will tend to follow one path after another and keep switching back and forth rather than working toward an overall goal.

Experiential intelligence, Perkins concludes, is an elegant system for long-term moderate success. When situations are new to us or complex, we get help from our neural intelligence and we have also learned various tricks for getting around our weaknesses, and these are largely accounted for in reflective intelligence. Reflective intelligence represents realms where we think about our own thinking in order to avoid settling on hasty conclusions, to broaden our thinking beyond the initial scope we assumed, to use precision to distinguish similar looking but different things, and to stay on track when notice we are sprawling.

This explains why reflective intelligence is so important to us in tricky situations where we have inadequate experience and where experience misleads us. But it also helps explain, in Perkins’ view, why reflective intelligence is so important for us to learn to be better thinkers in general. Neural intelligence does not replace experiential intelligence, it tends to reinforce it.

When we don’t have experience, neural intelligence helps us grasp the situation, but when we do have experience, we tend to use our neural intelligence to reinforce what our experience already tells us. That’s one big reason why genius is not simply high IQ. That’s why reflective intelligence is so important, it is the tool we use to remind us of the weak points in our own thinking and help us compensate for them regardless of our experience and general intelligence. The abilities and traits we need in order to overcome our blind spots are learnable. A large and crucial aspect of intelligence is learnable.

Existing Approaches: How they Compare

There are various approaches to teaching reflective intelligence, and Perkins reviews the best known and the best studied among them such as Project Intelligence, Reuven Feuerstein’s Instrumental Enrichment, Edward de Bono’s CORT, and Matthew Lipman’s Philosophy for Children, and others, reviewing their approaches and their results and comparing and contrasting them in order to get a sense of what it takes to enhance reflective intelligence.

One of the things that distinguishes Perkins as a deep reflective thinker himself is that he anticipates, researches, and deals fairly with opposition to his arguments. The very idea of learnable intelligence has in the past come under attack from several angles such as past failures of various programs which tried to teach improved thinking, the implications of expertise and psychometric research data, the apparent weakness of general methods for problem solving, and the challenge of transfer between learning domains. Perkins addresses each of these concerns in turn, resulting in a very persuasive case for the very real improvability of intelligence through changes in education.

The Future of Learnable Intelligence

Toward the end of the book, Perkins reveals the ingenuity of his vision through his discussion of several areas for the future evolution of reflective intelligence: areas which ended up being (remarkable for a book written in 1995) accurate predictions of areas that have since become central areas of interest for science and human improvement in general:

1. Intelligence can become distributed -- good thinking depends upon artifacts to offload the limitations of our attention and memory, and we can use our symbol systems and tools to help us keep track of things we could not track individually. This is a wonderful general description of how we are attempting to use computer networks to help us manage complexity (as opposed to some of the more superficial books in recent years which imply that networks somehow replace rather than enhance individual thinking).

2. Intelligence can embrace complexity -- through information visualization tools, effective use of classification, tagging, and finding things by meaning, consolidation, filtering, the mathematical tools for finding large scale patterns in complex phenomena, and by eliminating narrow information silos, we can use our intelligence to solve increasingly complex problems.

3. Intelligence can be dialectical -- this means raising the level of thinking from lower level more concrete concerns to higher order patterns by recognizing the properties specific to complex systems. Perkins offers Peter Senge’s “The Fifth Discipline” and Murray Gell-Mann’s “The Quark and the Jaguar” as exemplifying ways of understanding dialectical intelligence.

Perkins covers a massive amount of data about intelligence and problem solving, summarizes it effectively, and applies it to a practical, powerfully supported, and exceptionally understandable approach to improving human life by teaching ourselves to be more intelligent. Thinking well in general is an unnatural act but we can learn to do it. All that is left is for us to overcome the ideological and political barriers. This book would make a wonderful, gentle manifesto for that grand effort.

Are we taking knowledge and expertise for granted?

On the New York Times Opinion page, Steven Pinker weighs in on the Internet optimists vs. pessimists issue. He appreciates the value of intellectual depth but he doesn't think the brain is nearly "plastic" enough to be reshaped fundamentally by the tools we use, he doesn't believe there are general abilities that are affected by experience. "Experience does not revamp the basic information-processing capacities of the brain" he insists. He also points out that "the effects of experience are highly specific to the experiences themselves."

In making these claims, Pinker reminds us that he joined Leda Cosmides and John Tooby enthusiastically as one of the founders of the most extreme version of cognitive evolutionary psychology (CEP), the "modular brain" theory. We know that the brain has all sorts of very specific speciallizations, but the notion of opaque independent functional modules is far from universally accepted. Books that have included intelligent, scholarly critiques of CEP or stress the importance of non-modular aspects of brain function include Kenan Malik, David Buller, Merlin Donald, Terrence Deacon, Terrence Sejnowski, and Jeffrey Schwartz. The contrast between Deacon and Pinker on the role of language in the evolution of the mind is particularly interesting.

My point is not at all to "debunk" CEP by presenting people who offer other kinds of theory, since I think CEP is a viable concept that probably gets some things right regarding the evolution of the mind. My point is that it seems too radical to claim that the mind and brain are simply and entirely modular in the way they would have to be for Pinker's statements above to be completely true. Pinker wants us to believe that the brain is modular and that experience cannot affect general abilities, yet he can't help using the term "deep reflection." It is difficult to imagine how such a thing as "deep reflection" even makes sense in the modular independent architeture Pinker is insisting protects our intellectual functions from the potentially deleterious effects of experience.

Pinker even explicitly acknowledges the work it takes to develop intellectual depth:

It’s not as if habits of deep reflection, thorough research and rigorous reasoning ever came naturally to people. They must be acquired in special institutions, which we call universities, and maintained with constant upkeep, which we call analysis, criticism and debate.

If it takes so much work to develop intellectual depth, how is it reasonable to then also argue that our thinking can't be affected by experience, or by activities that detract from this developmental process?

Yes, he is right the brain has limits to how much it is reshaped by experience, but I think he significantly overstates the case. The issue is regarding specifics, not the general principle of neuroplasticity. What specific effects do specific activities have on our mind and brain over specific kinds of time period?

Pinker may very well be right that the web is not itself deteriorating our reflective thinking ability the way Nicholas Carr argues it is. Carr perhaps goes over the top when he says that his failing ability to concentrate is specifically due to his use of the web. However Pinker goes too far when he implies that the idea is simply silly in principle. It remains an empirical question, not just a conceptual one, unless we're replacing cognitive neuroscience with Pinkerist modularism.

Pinker also misses a much more important point, that our attitude toward technology affects the way it shapes our daily life. He assumes that those university activities he takes for granted will continue to be valued just because he himself takes their value for granted. The university was not always there, and there is no reason to assume it will always be there if we stop arguing for its value.

Pinker's ironic conclusion:

And to encourage intellectual depth, don’t rail at PowerPoint or Google. It’s not as if habits of deep reflection, thorough research and rigorous reasoning ever came naturally to people. They must be acquired in special institutions, which we call universities, and maintained with constant upkeep, which we call analysis, criticism and debate. They are not granted by propping a heavy encyclopedia on your lap, nor are they taken away by efficient access to information on the Internet.

The new media have caught on for a reason. Knowledge is increasing exponentially; human brainpower and waking hours are not. Fortunately, the Internet and information technologies are helping us manage, search and retrieve our collective intellectual output at different scales, from Twitter and previews to e-books and online encyclopedias. Far from making us stupid, these technologies are the only things that will keep us smart.

Is it really our knowledge that is increasing exponentially, or is it available information?

That fact that Pinker seems to conflate the two is exactly the question begging that most fundamentally ignores the most central aspect of modern culture critique, are we gaining more knowledge because we are exposed to more information?

Against the cultural critics, the smart among us have always managed to take responsibility for their own minds and organize the available information and think deeply enough to create meaningful individual knowledge from it.

Against Pinker and the others who think cultural critics are just "panicking," the fact that some smart people always manage to cultivate knowledge in spite of the challenges offered by new tools doesn't mean that everyone else will automatically inherit that ability.

If we assume that simply having access to a lot of information will make us smart (this is not an exaggeration, it is literally how many Internet optimists think), we will end up missing the real issue.

The real issue is not whether the Internet fries your brain, there is no really good evidence so far that it does. The real issue is whether we recognize and continue to appreciate the work it takes to cultivate knowledge and expertise or whether we take these things for granted.


Update: Nicholas Carr responds to Pinker on his own RoughType blog.

Update: Commentary by Nick Bilton with some useful references, argues sensibly that each form of media has its potential unique value - http://bits.blogs.nytimes.com/2010/06/11/in-defense-of-computers-the-internet-and-our-brains/?ref=technology

Update 6/15: Some of the reviewers do more thougthfully reflect on bigger picture issues and ask somewhat deeper questions than just the alarmist one of whether the Internet is "frying our brains." See this review in New Republic by Todd Gitlin: "The Uses of Half-Truths."

Also: Nicholas Carr and Douglas Rushkoff respond to Pinker on EDGE.

Book Review: Minsky's "The Emotion Machine: - Doing Strong AI Right?

Early efforts to model human-like thinking with machines using rules were interesting but failed in a number of ways to capture even simple ways that humans think. Marvin Minsky, AI pioneer at MIT, insists that we understand the mistakes and can begin to appreciate how the mind actually works in functional terms from the lessons we have learned. Learninig from our past mistakes, what a novel idea.

To put this into perspective, the question of whether a machine model can adequately describe a brain has long been considered in terms of either strong AI or weak AI. Most people find weak AI plausible: computers can solve certain kinds of problems better than humans. We mostly balk at strong AI however: machines can literally think like humans and solve the same kinds of problems just as well.

In The Emotion Machine, Marvin Minsky presents a very machine-like architecture that he claims actually represents the way real minds probably work in fundamental respects. That sounds pretty much like strong AI. So a lot of people will reject the concept of this book out of hand. I think that would be a mistake. Minsky has done a very good job identifying plausible specifics of why AI programs have failed to deliver on, where they have actually managed to deliver, and speculates on how we can fill in the gaps.

No, he doesn't spend time arguing against Searle's Chinese Room or other conundrums of AI, he just presents his case and gives examples in a clear, simple, accessible way. And I am persuaded that he probably gets a lot right. Probably more than he gets wrong. And that's a lot better than a lot of critics will give him credit for because it goes against both the mainstream disdain for strong AI and the mainstream love of flashy neuroscience images.

Minsky skips right on past the issue of connectionist networks vs. semantic networks and simply posits that we had to evolve semantic representations at some point. How is left as an exercise for neuroscientists. There is a lot of "details to be filled in later" sort of thinking here, so don't look to this book as a detailed physical model of the brain. This is a high level functional model of the mind and I like it.

So I claim that this is an important book that seems to promise a 21st century reboot of scientific naturalism as our guiding philosophy for the future. Minsky takes on nothing less than an overall architectural model for the mind in natural terms. It is brilliant. Too brilliant to be appreciated in its time because Minsky makes complex ideas so accessible that the biggest challenge for this book is that people will not appreciate its power. It reads like a simple AI model of a mind, but it is much deeper than that because of the amount of deep thought that has gone into it and the consideration of the weaknesses as well as strengths of previous AI programs.

We are currently in the grip of a widespread fascination with poorly understood pop neuroscience, and most readers will be deeply disappointed that this book does not attempt to wrestle with brain science at all. I think that's a strength because it means Minsky is not falling into the weird metaphysical spins that we too often see in pop neuroscience books, especially those by non-researchers and over-enthusiastic under-trained journalists.

What Minsky is doing here is simply coming up with a logical model of what a mind has to be able to do to provide the capabilities that we observe real human minds to possess. Sounds simple, right? No, not at all. The reason Minsky has accomplished something special here is that he recognizes many of the powerful fallacies we usually fall into when we introspect about thinking and rely on traditional models. We tend to think of emotions and reasoning as separate kinds of things, and then we talk about how they are both needed and how they interact. But as Minsky points out, both neuroscience and psychology seem to provide us evidence that these are points on a continuum, not different kinds of things. Minsky takes that seriously and builds on it.

The result is something amazing that looks like a simplistic mechanical model of the mind but captures some deep insights into how minds really work.

The central implication of Minsky's model is an epistemological stance that resourcefulness in human thinking is a matter of switching between different kinds of representations, each used in a different way of thinking, each of which captures something essential about specific things in our world while neccessarily leaving out other details. A mind can't comprehend everything at once. Some decisions simply don't have an optimal answer because they look different from different angles.

The key concept underlying Minsky's model is that minds as we think of them had to start with simple rules for recognizing and responding to cues, had to be able to incorporate goals in some form in those rules as well, and then eventually had to be able to recognize kinds of problem and activate appropriate ways of thinking. It makes sense to think of this in terms of logical levels of recognizers and responders, and importantly, what Minsky calls "critics" and "selectors," where each new level provides some way to resolve conflicts that arise in the level below it.

So conflicts in our instincts can be resolved by learned rules, conflicts in learned rules can be resolved by deliberation strategies, and in turn levels with different kinds of representations of the problem and eventually the problem solver and their own ways of thinking. Once the problem solver can represent themselves and their own thinking, we have the power to shape our own thinking in meaningful ways.

I'm really not doing justice to this book in this review, because it's power is in the details of his examples and how they illustrate the architecture at work. Suffice to say that I think if you find a functional architecture of the mind of interest, I highly recommend this book. I think it gives a much more fundamental understanding of how minds most probably work than any amount of flashy recent brain scans, and certainly more than untestable holistic and quantum mechanical theories will ever tell us until we better understand the functional design. Neuroscience in the future will, I believe, be filling in the details of a framework very much like this one.

Which is Better: Conscious or Unconscious Decision Making?

Which is Better: Conscious or Unconscious Decision Making ?

http://www.sciencedaily.com/releases/2008/12/081224215542.htm

The snippet above from Science Daily reports on the superiority of unconscious decision making in the case of estimating likelihoods.

My thoughts ...

There's a fairly sizable literature on decision making, and typically the headlines hide the facts in this field. I'd estimate that about 70% of the recent experiments went the way of this article with their conclusion (and of course one of Malcolm Gladwell's popular books) and about 30% went the opposite way, saying that the work 'proves' that complex decisions should not be left to unconscious proceses. It's like the nature/nurture question, we feel compelled to slide toward the extremes.

My impression is that what the literature (collectively) has shown so far is:

1. There are perceptual tracking mechanisms and very powerful computational mechanisms that operate completely outside of awareness for the most part, yet are important in decision making (rapid cognition, etc., the "human intuition is so amazing" school)

2. Our conscious reasoning processes are built very recently (phylogenetically) on a substrate of other things, and so they have a lot of biases and blind spots and rely heavily on our limited and easily distrated working memory capacity (Kahnemann, Tversky, etc., the "human reasoning is so fallible" school)

3. The unconscious mechanisms don't work by magic, i.e. with the exception of some basic category templates for recognizing and sorting things, they still rely on an acquired store of experience built onto those templates (Kant plus Darwin)

4. Good decision making for complex problems is good largely because because it tends to use the unconscious processes effectively, and does this by focusing attention on the relevant aspects of the problem (knowing what is relevant and what is not is sometimes the meat of the problem, and sometimes a given)

5. Focusing attention on the relevant aspects of a complex problem generally requires us to draw from the skills that rely on working memory and are generally conscious and build on structural rules, sequencing, and explicit algorithms (the virtual von Neumann machine that many of us we believe we possess in our head)

So while the above is a very simplistic account of a big topic, I think it helps illustrate that "conscious" vs. "unconscious" is not really the important issue in decision making, although it is certainly useful to distinguish automatic processes. The more compelling issue is how to structure problems so we can use the powerful resources of the mind, and how to learn the skills for making best use of those abilities, and the skills for recognizing our own biases and blind spots when we structure problems.

Many natural mechanisms work best on imperfect information but fail when compared to systematic reasoning with good information. However most real and meaningful human problems in nature require responses with limited resources, limited time, and very limited information, so a lot of the research actually demonstrates that while we can do better than intuition in theory, we can't do better than intuition in practice.

That is, unless we have some way of "cheating" by having unusually good information or knowing the answer ahead of time. Our species dependence on social imitation probably reflects exactly that, a way of "cheating" the limitations of individual problem solving by copying what other people are doing. For every problem we find that legitimately shows that people screwed up by trusting systematic reasoning too far, we can find one where someone trusted an intuition that had catastrophic consequences. In my opinion, the key skill set is in learning clues for recognizing when to trust each tool. That is, rather than espousing either blind trust or mistrust in powerful automatic processes.