Book Review: The Blind Spot by William Byers

The Blind Spot: Science and the Crisis of Uncertainty

William Byers

Princeton University Press, 2011

Full review on Amazon.

Interesting food for thought, a reason for some humility, and an argument for perspectivism

I got some real value out of this book because it made me think more deeply about an important and fundamental philosophical problem, the problem of knowability.  How well do we directly know the universe we live in, and how well can even our best explanations really help us grasp it?  In the Western intellectual tradition, the question goes all the way back to the ancients, and we still frame it in much the same way they did.  The problem is fairly obvious to any reasonably reflective person I think, but I suspect most people tend to assume it is either an illusion or something of little consequence.
 
We have come to understand a massive number of details about our existence in various ways through science and we have discovered that with a bit of ingenuity, we can use mathematics to describe and predict the behavior of real things remarkably well.  This gives many of us the sense that we can grasp just about anything about our existence in the same way.  Afterall, from the modern humanistic point of view, what else is there besides our scientific causal explanations, our mathematical models, and our various superstitions? 

Byers seems to be playing something like the mischievous role of Ian Malcolm from Jurassic Park here in some ways. He is telling us that we shouldn't be so certain that we really understand everything the way we think we do.  Nature will always surprise us.  And he is not saying this because he thinks we should believe in miracles or because science doesn't work as well as we think.  He is saying it because science and mathematics cover the universe as we experience it rather like a lumpy carpet covers a smooth floor.  You can push down the lumps, but they always pop up again somewhere else. 

Our ability to explain our existence doesn't quite map to the totality of that existence in any uniform way.  We end up taking different perspectives on the same things in order to understand what we perceive.  This need to take different perspectives is "ambiguity." Our sense of certainty about what we understand, the perception of smooth areas of carpet, is not an illusion, but neither is it absolute, perfectly generallizable, not even entirely objective.  We tend to ignore ambiguity when we see it because it makes us feel uncomfortable.  Byers claims this discomfort creates a permanent blindspot in our perception of our own existence. 

The central theme of the book is that there are many different expressions of ambiguity in nature, and that they are all expressions of the same underlying limitation in our ability to grasp nature in terms of concepts and symbols.  It isn't just that there are some problems more difficult than others, it may be, according to Byers, that some difficulties are impossible to resolve permanently, they will always appear again in another form whenever we grasp them.  Blindspot tells us that something about the territory makes it fundamentally unmappable in any complete and consistent sense.  This, for Byers, drives us to keep trying to explain by grappling with the ambiguity in nature.

 
Byers makes some fascinating points and uses a lot of good examples from mathematics and science to make his points.  Most of the examples will be familiar to avid readers of science and math.  They include the usual suspects such as Godel's Incompleteness, Heisenberg's Uncertainty, wave/particle duality, the problem of the objective and subjective perspectives, intentionality, self-reference, and so on. Byers also draws on his own field of mathematics for some less well trodden examples such as real number theory and differential vs. integral calculus. 

While this book is definitely worthwhile in my opinion, I don't quite share the same excitement as some of the other reviewers because I also found it very repetitive and for me personally it sometimes seems to jump from point to point without really taking on important points in the detail they deserve.  I think this impression I get comes from Byers background in mathematics.  He will often start to talk about something I find really interesting, like the problem of intentionality or the problem of mental causation, but quickly turn it back into a logical question instead of exploring the scientific or philosophical questions in more detail.  I suppose my criticism is that Byers seems to be a "math" but not a "polymath" and the ambitious topic he has taken on may require someone with deep understanding of many different fields, since the thesis is that the blindspot is not just a quirk of mathematics but of our symbolic and conceptual abilities in general.

Ultimately I think the claim Byers is making is basically that the perception of clear understanding of nature is a local phenomenon not a global one because we can never get rid of the problem of incompatible but valid perspectives.  We can make the carpet perfectly smooth in an area, but we can never make the whole carpet completely smooth.  The "consilience" of nature's laws will never have a final expression in one scheme because we can't ever reconcile the different viewpoints that: (1) accurately describe situations, (2) are each self-consistent, and (3) are not only different but incompatible with each other. 

From my perspective, I think Byers has identified the problem in a legitimate way, but his logical arguments don't really convince me that the problem of perspectives is forever irreconcileable.  I agree with him that we should take the problem(s) more seriously than we do, since we usually wave this sort of problem away through various tactics.  I also agree that we should incorporate the challenge of multiple perspectives in our thinking.  It is not clear to me that the lumps are an intrinsic part of nature, or that they are all manifestations of the same blind spot, and Byers doesn't seem to me to exhibit the depth and breadth of scientific knowledge across the various relevant disciplines to make that point stick, but it does seem reasonable to think of them that way until we have actually smoothed them out and to take Byers' claim seriously as at least a sensible policy and a reason for humility.

Book Review: The Highly Hypnotizable Person

The Highly Hypnotizable Person,
Edited by David A. Oakley , Michael Heap , and Richard J. Brown

This is the most authoritative and comprehensive summary of hypnosis research compiled in over a decade, so anyone interested in the subject should take note. Noteworthy contrubutions include Helen Crawford on the genetics and neuropsychology, Steve Lynn on clinical correlates, Judith Rhue on the development of the underlying abilities, Donald Gorassini on enhancing hypnotizability, Graham Wagstaff on the sociocognitive view of high hypnotizability, and two superb chapters by Amanda Barnier and Kevin McConkey exploring what we now know about what it means when we say someone is highly hypnotizable. The book finishes up with a unique sort of perspective chapter, an invited independent viewpoint on the field from a psychologist previously unfamiliar with hypnosis research.

This information is too important and too interesting to leave buried in the hypnosis research specialty journals.

My review on Amazon is here.

Book Review: The GenoType Diet

The GenoType Diet: Change Your Genetic Destiny to live the longest, fullest and healthiest life possible

By Catherine Whitney (Author), Peter J. Dr D'Adamo (Author)

[Review based on Kindle version]

From my Amazon review:

"... is it plausible in general that metabolic traits cluster along with personality traits, and that these clusters imply different health regimens? Of course it's possible. I'm strongly disposed from my own experience to think there are personality traits that lead us to construct particular rough kinds of niches, and it makes sense that these might have distinct metabolic patterns. But does the author get it right that eating Turkey vs. Chicken or Beef vs. Pork for a particular type really is a difference that makes a difference? That part needs more research."

The question this book raises in my mind is whether the author's GeneTypes significantly improve the guesses we make about the best nutrition for us as individuals, compared to just knowing our medical risk factors and knowing our rough somatotype.

This book takes off from the author's previous writings about how blood type can predict a cluster of traits that include individual response to nutrients, and modifies it into a 6 part theory of types. The 6 part model is given an evolutionary rationale and the author links metabolic differences, personality traits, and speculation about ecological niches where those traits and metabolic differences might be especially adaptive. The resulting categories are so clean and tell such a compelling sort of story that they seem to invite accusations of similarity to fortune telling. But if they're right, then this would a pretty cool system, wouldn't it? At least in theory.

• The Hunter: Tall, thin, and intense, with an overabundance of adrenaline and a fierce, nervous energy that winds down with age, the Hunter was originally the success story of the human species. Vulnerable to systemic burnout when overstressed, the Hunter’s modern challenge is to conserve energy for the long haul.

• The Gatherer: Full-figured, even when not overweight, the Gatherer struggles with body image in a culture where thin is “in.” An unsuccessful crash dieter with a host of metabolic challenges, the Gatherer becomes a glowing example of health when properly nourished.

• The Teacher: Strong, sinewy, and stable, with great chemical synchronicity and stamina, the Teacher is built for longevity—given the right diet and lifestyle. This is the genotype of balance, blessed with a tremendous capacity for growth and fulfillment.

• The Explorer: Muscular and adventurous, the Explorer is a biological problem solver, with an impressive ability to adapt to environmental changes, and a better than average capacity for gene repair. The Explorer’s vulnerability to hormonal imbalances and chemical sensitivities can be overcome with a balanced diet and lifestyle.

• The Warrior: Long, lean, and healthy in youth, the Warrior is subject to a bodily rebellion in midlife.With the optimal diet and lifestyle, the Warrior can overcome the quick-aging metabolic genes and experience a second, “silver,” age of health.

• The Nomad: A GenoType of extremes, with a great sensitivity to environmental conditions—especially changes in altitude and barometric pressure, the Nomad is vulnerable to neuromuscular and immune problems. Yet a well-conditioned Nomad has the enviable gift of controlling caloric intake and aging gracefully.

If the model is right, it is a pretty remarkable and perhaps revolutionary approach to making better guesses at how to improve our own nutrition based on our biochemical individuality. The evidence for its plausibility seems reasonable, but there is still little evidence validating the particular categories here.

Full review on Amazon here.

The argument for a SANE diet

This is the second in a series of commentaries on the ideas promoted in Jonathan Bailor's book "The Smarter Science of Slim."

This one presents my own spin on his argument for a SANE diet as an effective long term strategy for counteracting the trend toward obesity and obesity-related chronic illness.

In addition to recommendations for brief, infrequent, high intensity exercise to counter insulin resistance and reset metabolism, Bailor promotes what he calls a SANE diet. This is an acronym for satiety, "aggression", nutrition, and efficiency. Rather than duplicate his explanation, I'll just summarize by saying this refers to the quality of food rather than the quanitity, and takes a number of different quality factors into consideration. This note is intended to present my version of the argument for a mixed collection of quality factors in an anti-obesity strategy.

1. Human biology tends to regulate its body weight in a very narrow range for long periods under a wide range of natural and ancestral environments.

I came to understand this well over a decade ago and in my opinion the evidence base for it has grown rather than being seriously questioned in any significant way. This model does have a potentially misleading aspect to it in practice. Many people seem to assume that a body weight set point means different people are predisposed to different body weights. That is only partly true. The set point model does not assume a fixed value, it assumes that we tend to regulate our weight within a narrow range for long periods of time. It is obvious that the body fat set point changes over time, otherwise creeping obesity simply wouldn't happen. The issue is not whether the set point can change, but whether (and how) we change it in the other direction. The forces on it appear to be asymmetric.

My 1998 article on body fat regulation, showing the weak understanding I had at that point of the specifics but I think accurately depicting the concept and showing its historical origins and scientific rationale.

There have also been a number of helpful popular press articles expanding on the dynamic set point concept and its relation to weight loss plateau.

Obesity expert Arya Sharma discusses the set point concept and a unique theory of how it works.

2. Increasingly throughout the contemporary world, a combination of global economic drivers and local environments has created conditions that defeat the biology of weight regulation and cause body weight to creep upwards, resulting in an "obesity epidemic."

A recent Lancet special issue on obesity gave a summary of the current perspective on factors in obesity.


3. Clinical studies suggest a role for dietary glycaemic index (GI) in bodyweight regulation and diabetes risk.

There are a number of lines of evidence leading to the conclusion that glycemic index plays a central role in weight regulation through glucose homeostasis. I singled out this article because it also describes why some of the implications of that conclusion are controversial.

4. The effect of lowering GI is a dynamic change to metabolism, not a static linear dropping of weight because the body adapts to the loss of energy in order to maintain its set point. Static models of calories in vs. calories out do not take the effects of metabolism into consideration and therefore do not accurately predict weight loss.

Even though glycemic load is an important factor in obesity, we can't just cut out starches and sugars without making any other change and expect to keep losing weight. We will tend to compensate in other ways because of the significance of metabolism in weight regulation. As a result it is now considered essential to consider metabolism in predictions of weight loss rather than just calorie balance.

5. Solely manipulating GI in isolation has a diminishing return over time in weight loss because the reduction of calorie intake tends to slow metabolism and because of changes in potentially confounding dietary factors such as fiber content, protein content, palatability, and energy density.

This is a continuation of the previous claim, based on the evidence gathered from trying to control weight solely by reducing glycemic load.

6. CONCLUSION: An individual nutrition strategy that replaces high GI foods by taking energy density, palatability, fiber, protein, energy density, ad other factors into account can more successfully use a reduction in glycemic index to control body fat by improving fat metabolism, slowing or stopping the accumulation of insulin resistance, and reversing the upward creeping of the body fat set point..

None of that is particularly surprising scientifically as far as I know. The set point model of weight control has been standard in both human and animal research for a long time, it is not at all controversial among obesity and physiology researchers. You simply have to take metabolism into account when considering how the body absorbs, utilizes, and catabolizes nutrients and energy. You can't just assume that everything we take in minus some estimate of what we are "burning" is going to be an accurate model of body weight fluctuation.

The part where there is still room for argument is the specific causal sequence, and that has some additional implications. There's an old conundrum in psychology research: "do we cry because we are sad, or are we sad because we cry?" The answer seems intuitively obvious but it turns out that both causal models are partly right because there is a feedback look from our behavior to the way we feel.

There is a similar conundrum in obesity research. Do we get fat because we eat too much and don't move enough, or do we eat more and move less because of the effects of obesity. Again it turns out that both capture part of what is really going on. We do gain weight when we eat more and move less, at least in the short term before our metabolism starts to regulate our weight back to normal. But the thing that causes our weight to more permanently creep upward is the changes to our metabolism over time. And those hormonal and cellular changes make us less good at burning fat and better at absorbing it, changing our body fat set point over time.

Todd I. Stark
Nov 20,2011

Dietary fads, fictions, factions, and facts

This is my first post in a series of planned commentaries based on my reading of Jonathan Bailor's highly recommended book, "The Smarter Science of Slim."

This first commentary is my perspective on the history of the issues and how I perceive they have been resolved scientifically so far.

The Low Carb Revolution

Years ago, Dr. Atkins shocked popular culture and infuriated proponents of low fat diets with the claim that people could lose weight and reduce their risk of heart disease by minimizing the mainstay of the typical diet, carbohydrates, replacing them with proteins and fats.

This was a controversial claim not only because low fat diets were popular at the time but also because in emphasizing a low carbohydrate diet, Atkins was claiming that we could be healthy with minimal amounts of important dietary elements like fiber and the nutrition we get from vegetables. He was opposed by the people who promoted an ever higher proportion of vegetables in our diet and who encouraged us to minimize saturated fats, which were long widely thought to increase the risk of heart disease and obesity. At the same time, a lot of people argued that obesity can and should be sensibly addressed with the reasonable commonsense approach of eating a little less and moving a little more: get active and cut back a little on calories each day.

Two Battles Emerge

This turned into two polarized contests of opinion in popular culture.

First, there was a conflict between the folks who think quality of food is important in weight control and those who think that emphasis is misplaced, that we should just measure calories in and calories out. The implication of the food quality view: eating more of some things and less of others should affect our weight over time without making constant efforts to otherwise monitor and control our intake by counting calories. The implication of the calorie balance model: just eat a few calories less and move around a little bit more to burn a few more calories and you will lose weight consistently.

Second, among those who consider the quality of food to be important, there was the battle of the low-carb lobby vs. the vegetable lobby. Should you eliminate carbohydrates because of their effect on metabolism, or should you eliminate saturated fats because "fats make us fat" and raise blood cholesterol?

As is often the case in polarized positions it seems from our perspective today that both sides (in both contests) got some things right and both sides got some things wrong.

Calorie Balance vs. Metabolism: The Result

The standard and consensus view of obesity among researchers is that passive overeating is the best overall description of the cause. It is widely accepted, though not universally, that the amount of caloric energy we take in plays a causal role in obesity. Further the standard and consensus view is that this results from a combination of global economic factors and local environmental factors (the "obesogenic environment").

It is also a standard and consensus view among obesity researchers and physiologists that body weight is regulated by metabolic factors (via chemical messengers) that are not not related to the number of calories we take in and the number of calories estimated for the activity we perform.

What the calorie balance model captures: our biology doesn't violate the laws of conservation of mass and energy. Eating less and catabolizing body mass for energy does cause us to lose body mass. Eating more and demanding less energy utilization does cause us to gain body mass.

What the metabolism model captures: The calorie balance model clearly predicts that our body weight should consistently fluctuate according to the overall energy balance between what we take in and our activity. This is demonstrably not the case both from common experience and controlled studies. The prediction of the calorie balance model only apply for a short time and then our body changes internally to absorb food differently and to use it differently for energy. Our appetite and subjective energy levels are also changed. This is collectively expressed as the body regulating its mass in various ways around a relatively stable point.

The common experience of a "weight loss plateau" coincides with the concept of a "weight set point" around which our body maintains itself in a narrow range over time.

When we simply eat less, we lose weight for a little while and then the loss slows down and eventually stops and tends to reverse itself into return to baseline weight and sometimes even gain above that original level.

Further, the signficance of metabolism implies that quantity of energy taken in and demanded is only part of the causal model. Metabolism is affected differently by the specific foods we eat over time and the specific kinds of activities we engage in, not just or even primarily the total amount of energy involved. The metabolism model better captures the importance of food quality than does the calorie balance model.

Low Carb vs. Veggies: The Result

Given the consensus view that metabolism is an important factor in weight control and that the kinds of food we eat are an important factor in metabolism, we arrive at the second battle, over what it means to eat higher quality food more conducive to health and fighting obesity.

What the low carb lobby got right and the vegetable lobby got wrong: saturated fat isn't itself an important cause of heart disease and obesity. You can consume reasonable amounts of fat, including saturated fat from animal sources, and still have a very healthy diet.

What the low carb lobby got wrong and the vegetable lobby got right: The low carb lobby deals with the problems of insulin response in a gross and overly restrictive way by choosing proteins and fats over carbohydrates in general rather than balancing them and eliminating high glycemic carbohydrates. This makes levels of fiber too low and eliminates important sources of nutrients and often crowds out vegetable nutrition sources using animal ones. This is probably healthier than the typical high glycemic diet, but not nearly optimal. Minimizing carbohydrates is a bad strategy because we get many nutrients from carbohydrates, especially vegetables.

What both the low carb lobby and the vegetable lobby both got right from the start: Sugars and starches as a mainstay of our diet are killing many people. Insulin resistance is a precursor to type 2 diabetes and is associated with and exacerbated by a high glycemic diet as well as by obesity. Starches are high glycemic.

The low carb lobby recognizes the role of high glycemic foods in obesity and chronic illness but they generallize it too far to all carbohydrates as an overly restrictive and overly simplified strategy.

The vegetable lobby is too concerned with the role of animal fat in nutrition and focuses on that rather than the more important distinction of higher glycemic vs. lower glycemic carbohydrates and a more balanced diet.

Saturated fat doesn't itself cause heart disease or obesity. Combined with a high glycemic diet, saturated fat exacerbates the problems caused by constant massive triggering of the insulin response because of a combination of factors:

(1) fats plus sugars tend to increase appetite dramatically,

(2) fats have a high energy density compared to their nutrition,

(3) saturated fats in particular have no protective effect for heart disease compared to omega-3 from polyunsaturated fats.

People tend to lower their risk of heart disease when they switch from saturated to unsaturated fats. This is one of the strongest arguments for eliminating saturated fat from our diet. However it turns out that this benefit is probably not because saturated fats cause heart disease. It is more likely because unsaturated fats have a protective effect compared to saturated fats.

Lowering glycemic load allows us to use nutrients more effectively, to metabolize all fats more effectively, and tends to reduce the risk of heart disease and obesity far more than reducing saturated fats.

So what works?

What dietary strategy best lowers our risk of chronic illnesses and obesity? Do they all fail equally? Do different people respond differently to different regimens?

It turns out that individuality is a real factor, both biochemical and psychological, and learning to make the principles work in your own life and for your own biology is critical. Still, there are also some general principles that we can rely on as a very good start.

The low carb strategy often works for as long as people can manage to maintain it and is healthier than the typical diet but it is not optimal for health and most people find it hard to sustain and eventually have health problems or fail to sustain it. It is not balanced nutritionally, it is too severe in limiting valuable and healthy vegetable sources of nutrition.

A vegetarian strategy often works at least for a while and is also healthier than the typical diet but it is also difficult for most people to maintain and as a general strategy it is too severe in limiting valuable and healthy animal sources of protein and what for many people are satisfying sources of texture and flavor in animal fats.

Taking what they both get right and eliminating what each gets wrong, we end up with:

1. Avoid high glycemic carbohydrates as far as possible or only under very specific conditions.

As a mainstay of our diet are a primary cause of both obesity and chronic illnesses via their effect on our response to insulin and our ability to absorb and metabolize nutrients. In other words, these commonly accepted processed products of agriculture cause a "clogging" of our metabolism over time that defeats our evolved ability to regulate our own metabolism. High glycemic carbohydrates are not essential for health and we tend to become dependent on them in various ways so minimizing them is an important first step to better health. There are specific strategies you can use to minimize their negative effects if you want to use them for athletic reasons or because you can't bear to eliminate them from your life entirely, but you have to be wary of how often you do this and the timing of it.

2. Balance the remaining nutrients between proteins, low-glycemic carbohydrates, and fats.

Compared to high glycemic foods, these categories all contain many healthy and valuable sources of nutrition.

Why a balance? In addition to just having a variety of nutrients of different kinds as well as a variety of tastes and textures to make eating more satisfying, there are also some specific nutritional reasons.

A diet of too much fat and too little protein and carbohydrate fails because fat contains relatively little nutrition compared to its energy density and tends to crowd out our intake of fiber important to digestive health as well as various nutrients. A diet of too much fat also fails because we have to eat a lot of it to make us feel satisfied in lieu of protein, fiber and water because of its low nutritional density.

A diet of too much protein fails because it avoids fiber and avoids important sources of vitamins and minerals and because it requires us to limit our protein sources very severely. It is also unhealthy to take in extreme amounts of protein. You can live on a high protein diet for a while if it isn't too high, but it isn't optimal. Temporary protein-sparing fasts are effective and popular among some athletes.

A diet of too much low-glycemic carbohydrates (taken to an extreme this would basically be a vegetarian diet that not only minimizes starches and sugars, but also seeds, nuts, and soy) is not really something anyone seriously recommends or practices as far as I know, but if they did it would fail because it would not have adequate protein sources to sustain health and would be relatively unsatisfying. Even strict vegans eat vegetable protein and fat sources, they just avoid animal sources. A diet with a huge amount of low-glycemic carbohydrates is perfectly healthy and sustainable so long as it also includes a reasonable amount of plant fat and protein sources.

The lesson here is just to mostly eat natural foods while avoiding high glycemic carbohydrates rather than striving for extremes of individual components. Avoid high glycemic foods and otherwise eat to satisfy your hunger. The specifics of satisfying our hunger are important however, balance is important in order to get a good combination of fiber, protein, water, nutrition, taste, texture, and other factors to satisfy us.

3. Take advantage of the "Paleo" principle but don't be limited entirely by it

I think the principle behind "Paleo" diets is basically correct, that our metabolism seems to be tuned by evolution to process natural animal and plant sources of nutriton that we could hunt or gather, and has not adapted yet to our dietary preponderance of processed agricultural products. This is another way of saying that high glycemic carbohydrates (the main category of highly processed and cultivated foods in our diet: the wheat, corn, potato, and rice products) are a very bad thing to emphasize all of the time.

In general the principles behind paleo eating agree with those proposed here, however they also focus on a rationale that can be misleading, which is why I consider it a fad even though it gets things mostly right. Trying to guess what our ancestors might have eaten and in what amounts is not nearly as useful a way to figure out what is healthy as just applying general principles and nutritional research results. By all means it makes sense to look to Paleo recipes and products as candidates for making it easier to maintain good nutrition. Also it is convenient and useful to imagine whether something could be hunted or gathered to know whether it is a nutritional source likely to be compatible with our metabolism.

Just don't limit yourself to the principle that we can only eat what our ancestors ate or elaborate arguments about the Pleistocene lifestyle. You have more options which you can intelligently apply to use modern foods and still be healthy.

4. Don't expect restricting calories to help you in the long run if you are eating poorly.

If you stop eating competely, you eventually die because your body eats its own tissue to try to meet its energy requirements. So when we talk about calorie restriction we aren't talking about not eating. We are talking about changing the timing of when we eat and eating less in a given period relative to what we would normally eat. We might eat less in a given period than we otherwise would, or we might eat nothing for a specific period of time. There are many strategies for calorie restriction. Some of them have a place in athletic performance and some of them can also have a place in a health regimen, but we have to be careful about how we go about it and how much we rely on it.

Sometimes the most reasonable approach is not the best. Portion control and light or moderate exercise seem perfectly reasonable and have been long recommended as a treatment or prevention for obesity. But for the vast majority of people they don't work for this purpose. Our metabolism and appetite conspire to maintain a stable weight against our efforts to make small reasonable changes in how much we eat and how much we move. We have to more directly influence our appetite and metabolism to deal with obesity. Simply eating less of the same things we are eating does not neccessarily help because it does not address the metabolic causes of our problems.

Fasting, as opposed to portion control, is a more potentially valuable and effective health strategy. This is because it can help you learn how to better control cravings, and can help you get your body fat lower than you could reasonably get with frequent eating and for longer periods. Some evidence suggests that fasting itself might trigger healthy changes in the body, although these are hard to distinguish from just the benefits of controlling body fat and eating more healthy overall. Fasting is much more difficult to do effectively, much less sustainable, and much less effective and healthy when combined with a high glycemic diet.

Your best bet is probably to eliminate high glycemic carbohydrates and get the hang of eating well first, then try fasting as an additional strategy if you want to, to make it even more effective and sustainable.

5. Don't just load up on "low fat" foods. But do emphasize lean protein sources over fatty ones.

By the best evidence available now, it seems that reducing fat by itself plays virtually no role in controlling obesity or chronic illness. Nutritionally, fat should be considered "neutral" or "filler" compared to good nutritional sources like non-starchy vegetables and good protein sources like seafood, lean meats, and egg whites. The problem with a "low fat" strategy is that if you go out of your way to avoid fats, you will also tend to replace the missing energy with starches and sugars.

If you could eliminate fats without replacing them with high glycemic foods, this strategy would probably work a lot better. Lean protein sources are an important part of modern nutrition, not because fat is bad, but because protein is so valuable. Fatty protein sources are really fats, not proteins, so they should be considered filler in your diet, rather than assuming they are good sources of protein. You have to eat an unreasonable amount of fatty protein sources to get enough protein to be valuable for satiety and nutrition.

6. If you find yourself eating something high-glycemic, at least mitigate its effects with fiber and protein and avoid fat under those conditions.

The effect of food on our insulin levels is the primarily culprit in obesity and many chronic diseases. However this glycemic effect is not just a result of what individual foods we eat, it is result of their combined effect on the amount of glucose circulating in our blood. There is no way to simply counter-act the glycemic effect short of avoiding high glycemic foods entirely but you can reduce it by lowering the combined glycemic effect by increasing the amount of fiber and protein you eat along with high glycemic foods. If you are going to indulge in high glycemic food, you can reduce the damage it causes by avoid simultaneous eating of fats (which boost appetite strikingly when combined with starches and sugars) and eat lean protein and good fiber sources with the high glycemic foods instead.

Note: This article was inspired by Jonathan Bailor's highly recommended book, "The Smarter Science of Slim."

Todd I. Stark
Nov 20,2011

Book Review: The Smarter Science of Slim by Jonathan Bailor

My review of Jonathan Bailor's book, "The Smarter Science of Slim."

Highly recommended, he does a great job integrating a lot of research faithfully into an accessible picture and offers practical advice consistent with it.

I also mention some things I would have written a little differently, like expanding the discussion of exercise a little beyond just eccentric high resistance and popular but less well studied strategies based on nutrient timing, and perhaps incorporating some discussion of self-experimentation in that light.

My book review on Amazon.

http://www.amazon.com/review/R3PZVSW9HQ89D8/ref=cm_cr_pr_perm?ie=UTF8&ASIN=0983520801&nodeID=&tag=&linkCode=

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)